initial commit

.claude/commands/opsx/apply.md

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+---
+name: "OPSX: Apply"
+description: Implement tasks from an OpenSpec change (Experimental)
+category: Workflow
+tags: [workflow, artifacts, experimental]
+---
+
+Implement tasks from an OpenSpec change.
+
+**Input**: Optionally specify a change name (e.g., `/opsx:apply add-auth`). If omitted, check if it can be inferred from conversation context. If vague or ambiguous you MUST prompt for available changes.
+
+**Steps**
+
+1. **Select the change**
+
+   If a name is provided, use it. Otherwise:
+   - Infer from conversation context if the user mentioned a change
+   - Auto-select if only one active change exists
+   - If ambiguous, run `openspec list --json` to get available changes and use the **AskUserQuestion tool** to let the user select
+
+   Always announce: "Using change: <name>" and how to override (e.g., `/opsx:apply <other>`).
+
+2. **Check status to understand the schema**
+   ```bash
+   openspec status --change "<name>" --json
+   ```
+   Parse the JSON to understand:
+   - `schemaName`: The workflow being used (e.g., "spec-driven")
+   - Which artifact contains the tasks (typically "tasks" for spec-driven, check status for others)
+
+3. **Get apply instructions**
+
+   ```bash
+   openspec instructions apply --change "<name>" --json
+   ```
+
+   This returns:
+   - Context file paths (varies by schema)
+   - Progress (total, complete, remaining)
+   - Task list with status
+   - Dynamic instruction based on current state
+
+   **Handle states:**
+   - If `state: "blocked"` (missing artifacts): show message, suggest using `/opsx:continue`
+   - If `state: "all_done"`: congratulate, suggest archive
+   - Otherwise: proceed to implementation
+
+4. **Read context files**
+
+   Read the files listed in `contextFiles` from the apply instructions output.
+   The files depend on the schema being used:
+   - **spec-driven**: proposal, specs, design, tasks
+   - Other schemas: follow the contextFiles from CLI output
+
+5. **Show current progress**
+
+   Display:
+   - Schema being used
+   - Progress: "N/M tasks complete"
+   - Remaining tasks overview
+   - Dynamic instruction from CLI
+
+6. **Implement tasks (loop until done or blocked)**
+
+   For each pending task:
+   - Show which task is being worked on
+   - Make the code changes required
+   - Keep changes minimal and focused
+   - Mark task complete in the tasks file: `- [ ]` → `- [x]`
+   - Continue to next task
+
+   **Pause if:**
+   - Task is unclear → ask for clarification
+   - Implementation reveals a design issue → suggest updating artifacts
+   - Error or blocker encountered → report and wait for guidance
+   - User interrupts
+
+7. **On completion or pause, show status**
+
+   Display:
+   - Tasks completed this session
+   - Overall progress: "N/M tasks complete"
+   - If all done: suggest archive
+   - If paused: explain why and wait for guidance
+
+**Output During Implementation**
+
+```
+## Implementing: <change-name> (schema: <schema-name>)
+
+Working on task 3/7: <task description>
+[...implementation happening...]
+✓ Task complete
+
+Working on task 4/7: <task description>
+[...implementation happening...]
+✓ Task complete
+```
+
+**Output On Completion**
+
+```
+## Implementation Complete
+
+**Change:** <change-name>
+**Schema:** <schema-name>
+**Progress:** 7/7 tasks complete ✓
+
+### Completed This Session
+- [x] Task 1
+- [x] Task 2
+...
+
+All tasks complete! You can archive this change with `/opsx:archive`.
+```
+
+**Output On Pause (Issue Encountered)**
+
+```
+## Implementation Paused
+
+**Change:** <change-name>
+**Schema:** <schema-name>
+**Progress:** 4/7 tasks complete
+
+### Issue Encountered
+<description of the issue>
+
+**Options:**
+1. <option 1>
+2. <option 2>
+3. Other approach
+
+What would you like to do?
+```
+
+**Guardrails**
+- Keep going through tasks until done or blocked
+- Always read context files before starting (from the apply instructions output)
+- If task is ambiguous, pause and ask before implementing
+- If implementation reveals issues, pause and suggest artifact updates
+- Keep code changes minimal and scoped to each task
+- Update task checkbox immediately after completing each task
+- Pause on errors, blockers, or unclear requirements - don't guess
+- Use contextFiles from CLI output, don't assume specific file names
+
+**Fluid Workflow Integration**
+
+This skill supports the "actions on a change" model:
+
+- **Can be invoked anytime**: Before all artifacts are done (if tasks exist), after partial implementation, interleaved with other actions
+- **Allows artifact updates**: If implementation reveals design issues, suggest updating artifacts - not phase-locked, work fluidly

.claude/commands/opsx/archive.md

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+---
+name: "OPSX: Archive"
+description: Archive a completed change in the experimental workflow
+category: Workflow
+tags: [workflow, archive, experimental]
+---
+
+Archive a completed change in the experimental workflow.
+
+**Input**: Optionally specify a change name after `/opsx:archive` (e.g., `/opsx:archive add-auth`). If omitted, check if it can be inferred from conversation context. If vague or ambiguous you MUST prompt for available changes.
+
+**Steps**
+
+1. **If no change name provided, prompt for selection**
+
+   Run `openspec list --json` to get available changes. Use the **AskUserQuestion tool** to let the user select.
+
+   Show only active changes (not already archived).
+   Include the schema used for each change if available.
+
+   **IMPORTANT**: Do NOT guess or auto-select a change. Always let the user choose.
+
+2. **Check artifact completion status**
+
+   Run `openspec status --change "<name>" --json` to check artifact completion.
+
+   Parse the JSON to understand:
+   - `schemaName`: The workflow being used
+   - `artifacts`: List of artifacts with their status (`done` or other)
+
+   **If any artifacts are not `done`:**
+   - Display warning listing incomplete artifacts
+   - Prompt user for confirmation to continue
+   - Proceed if user confirms
+
+3. **Check task completion status**
+
+   Read the tasks file (typically `tasks.md`) to check for incomplete tasks.
+
+   Count tasks marked with `- [ ]` (incomplete) vs `- [x]` (complete).
+
+   **If incomplete tasks found:**
+   - Display warning showing count of incomplete tasks
+   - Prompt user for confirmation to continue
+   - Proceed if user confirms
+
+   **If no tasks file exists:** Proceed without task-related warning.
+
+4. **Assess delta spec sync state**
+
+   Check for delta specs at `openspec/changes/<name>/specs/`. If none exist, proceed without sync prompt.
+
+   **If delta specs exist:**
+   - Compare each delta spec with its corresponding main spec at `openspec/specs/<capability>/spec.md`
+   - Determine what changes would be applied (adds, modifications, removals, renames)
+   - Show a combined summary before prompting
+
+   **Prompt options:**
+   - If changes needed: "Sync now (recommended)", "Archive without syncing"
+   - If already synced: "Archive now", "Sync anyway", "Cancel"
+
+   If user chooses sync, use Task tool (subagent_type: "general-purpose", prompt: "Use Skill tool to invoke openspec-sync-specs for change '<name>'. Delta spec analysis: <include the analyzed delta spec summary>"). Proceed to archive regardless of choice.
+
+5. **Perform the archive**
+
+   Create the archive directory if it doesn't exist:
+   ```bash
+   mkdir -p openspec/changes/archive
+   ```
+
+   Generate target name using current date: `YYYY-MM-DD-<change-name>`
+
+   **Check if target already exists:**
+   - If yes: Fail with error, suggest renaming existing archive or using different date
+   - If no: Move the change directory to archive
+
+   ```bash
+   mv openspec/changes/<name> openspec/changes/archive/YYYY-MM-DD-<name>
+   ```
+
+6. **Display summary**
+
+   Show archive completion summary including:
+   - Change name
+   - Schema that was used
+   - Archive location
+   - Spec sync status (synced / sync skipped / no delta specs)
+   - Note about any warnings (incomplete artifacts/tasks)
+
+**Output On Success**
+
+```
+## Archive Complete
+
+**Change:** <change-name>
+**Schema:** <schema-name>
+**Archived to:** openspec/changes/archive/YYYY-MM-DD-<name>/
+**Specs:** ✓ Synced to main specs
+
+All artifacts complete. All tasks complete.
+```
+
+**Output On Success (No Delta Specs)**
+
+```
+## Archive Complete
+
+**Change:** <change-name>
+**Schema:** <schema-name>
+**Archived to:** openspec/changes/archive/YYYY-MM-DD-<name>/
+**Specs:** No delta specs
+
+All artifacts complete. All tasks complete.
+```
+
+**Output On Success With Warnings**
+
+```
+## Archive Complete (with warnings)
+
+**Change:** <change-name>
+**Schema:** <schema-name>
+**Archived to:** openspec/changes/archive/YYYY-MM-DD-<name>/
+**Specs:** Sync skipped (user chose to skip)
+
+**Warnings:**
+- Archived with 2 incomplete artifacts
+- Archived with 3 incomplete tasks
+- Delta spec sync was skipped (user chose to skip)
+
+Review the archive if this was not intentional.
+```
+
+**Output On Error (Archive Exists)**
+
+```
+## Archive Failed
+
+**Change:** <change-name>
+**Target:** openspec/changes/archive/YYYY-MM-DD-<name>/
+
+Target archive directory already exists.
+
+**Options:**
+1. Rename the existing archive
+2. Delete the existing archive if it's a duplicate
+3. Wait until a different date to archive
+```
+
+**Guardrails**
+- Always prompt for change selection if not provided
+- Use artifact graph (openspec status --json) for completion checking
+- Don't block archive on warnings - just inform and confirm
+- Preserve .openspec.yaml when moving to archive (it moves with the directory)
+- Show clear summary of what happened
+- If sync is requested, use the Skill tool to invoke `openspec-sync-specs` (agent-driven)
+- If delta specs exist, always run the sync assessment and show the combined summary before prompting

.claude/commands/opsx/explore.md

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+---
+name: "OPSX: Explore"
+description: "Enter explore mode - think through ideas, investigate problems, clarify requirements"
+category: Workflow
+tags: [workflow, explore, experimental, thinking]
+---
+
+Enter explore mode. Think deeply. Visualize freely. Follow the conversation wherever it goes.
+
+**IMPORTANT: Explore mode is for thinking, not implementing.** You may read files, search code, and investigate the codebase, but you must NEVER write code or implement features. If the user asks you to implement something, remind them to exit explore mode first and create a change proposal. You MAY create OpenSpec artifacts (proposals, designs, specs) if the user asks—that's capturing thinking, not implementing.
+
+**This is a stance, not a workflow.** There are no fixed steps, no required sequence, no mandatory outputs. You're a thinking partner helping the user explore.
+
+**Input**: The argument after `/opsx:explore` is whatever the user wants to think about. Could be:
+- A vague idea: "real-time collaboration"
+- A specific problem: "the auth system is getting unwieldy"
+- A change name: "add-dark-mode" (to explore in context of that change)
+- A comparison: "postgres vs sqlite for this"
+- Nothing (just enter explore mode)
+
+---
+
+## The Stance
+
+- **Curious, not prescriptive** - Ask questions that emerge naturally, don't follow a script
+- **Open threads, not interrogations** - Surface multiple interesting directions and let the user follow what resonates. Don't funnel them through a single path of questions.
+- **Visual** - Use ASCII diagrams liberally when they'd help clarify thinking
+- **Adaptive** - Follow interesting threads, pivot when new information emerges
+- **Patient** - Don't rush to conclusions, let the shape of the problem emerge
+- **Grounded** - Explore the actual codebase when relevant, don't just theorize
+
+---
+
+## What You Might Do
+
+Depending on what the user brings, you might:
+
+**Explore the problem space**
+- Ask clarifying questions that emerge from what they said
+- Challenge assumptions
+- Reframe the problem
+- Find analogies
+
+**Investigate the codebase**
+- Map existing architecture relevant to the discussion
+- Find integration points
+- Identify patterns already in use
+- Surface hidden complexity
+
+**Compare options**
+- Brainstorm multiple approaches
+- Build comparison tables
+- Sketch tradeoffs
+- Recommend a path (if asked)
+
+**Visualize**
+```
+┌─────────────────────────────────────────┐
+│     Use ASCII diagrams liberally        │
+├─────────────────────────────────────────┤
+│                                         │
+│   ┌────────┐         ┌────────┐        │
+│   │ State  │────────▶│ State  │        │
+│   │   A    │         │   B    │        │
+│   └────────┘         └────────┘        │
+│                                         │
+│   System diagrams, state machines,      │
+│   data flows, architecture sketches,    │
+│   dependency graphs, comparison tables  │
+│                                         │
+└─────────────────────────────────────────┘
+```
+
+**Surface risks and unknowns**
+- Identify what could go wrong
+- Find gaps in understanding
+- Suggest spikes or investigations
+
+---
+
+## OpenSpec Awareness
+
+You have full context of the OpenSpec system. Use it naturally, don't force it.
+
+### Check for context
+
+At the start, quickly check what exists:
+```bash
+openspec list --json
+```
+
+This tells you:
+- If there are active changes
+- Their names, schemas, and status
+- What the user might be working on
+
+If the user mentioned a specific change name, read its artifacts for context.
+
+### When no change exists
+
+Think freely. When insights crystallize, you might offer:
+
+- "This feels solid enough to start a change. Want me to create a proposal?"
+- Or keep exploring - no pressure to formalize
+
+### When a change exists
+
+If the user mentions a change or you detect one is relevant:
+
+1. **Read existing artifacts for context**
+   - `openspec/changes/<name>/proposal.md`
+   - `openspec/changes/<name>/design.md`
+   - `openspec/changes/<name>/tasks.md`
+   - etc.
+
+2. **Reference them naturally in conversation**
+   - "Your design mentions using Redis, but we just realized SQLite fits better..."
+   - "The proposal scopes this to premium users, but we're now thinking everyone..."
+
+3. **Offer to capture when decisions are made**
+
+   | Insight Type | Where to Capture |
+   |--------------|------------------|
+   | New requirement discovered | `specs/<capability>/spec.md` |
+   | Requirement changed | `specs/<capability>/spec.md` |
+   | Design decision made | `design.md` |
+   | Scope changed | `proposal.md` |
+   | New work identified | `tasks.md` |
+   | Assumption invalidated | Relevant artifact |
+
+   Example offers:
+   - "That's a design decision. Capture it in design.md?"
+   - "This is a new requirement. Add it to specs?"
+   - "This changes scope. Update the proposal?"
+
+4. **The user decides** - Offer and move on. Don't pressure. Don't auto-capture.
+
+---
+
+## What You Don't Have To Do
+
+- Follow a script
+- Ask the same questions every time
+- Produce a specific artifact
+- Reach a conclusion
+- Stay on topic if a tangent is valuable
+- Be brief (this is thinking time)
+
+---
+
+## Ending Discovery
+
+There's no required ending. Discovery might:
+
+- **Flow into a proposal**: "Ready to start? I can create a change proposal."
+- **Result in artifact updates**: "Updated design.md with these decisions"
+- **Just provide clarity**: User has what they need, moves on
+- **Continue later**: "We can pick this up anytime"
+
+When things crystallize, you might offer a summary - but it's optional. Sometimes the thinking IS the value.
+
+---
+
+## Guardrails
+
+- **Don't implement** - Never write code or implement features. Creating OpenSpec artifacts is fine, writing application code is not.
+- **Don't fake understanding** - If something is unclear, dig deeper
+- **Don't rush** - Discovery is thinking time, not task time
+- **Don't force structure** - Let patterns emerge naturally
+- **Don't auto-capture** - Offer to save insights, don't just do it
+- **Do visualize** - A good diagram is worth many paragraphs
+- **Do explore the codebase** - Ground discussions in reality
+- **Do question assumptions** - Including the user's and your own

.claude/commands/opsx/propose.md

@@ -0,0 +1,106 @@
+---
+name: "OPSX: Propose"
+description: Propose a new change - create it and generate all artifacts in one step
+category: Workflow
+tags: [workflow, artifacts, experimental]
+---
+
+Propose a new change - create the change and generate all artifacts in one step.
+
+I'll create a change with artifacts:
+- proposal.md (what & why)
+- design.md (how)
+- tasks.md (implementation steps)
+
+When ready to implement, run /opsx:apply
+
+---
+
+**Input**: The argument after `/opsx:propose` is the change name (kebab-case), OR a description of what the user wants to build.
+
+**Steps**
+
+1. **If no input provided, ask what they want to build**
+
+   Use the **AskUserQuestion tool** (open-ended, no preset options) to ask:
+   > "What change do you want to work on? Describe what you want to build or fix."
+
+   From their description, derive a kebab-case name (e.g., "add user authentication" → `add-user-auth`).
+
+   **IMPORTANT**: Do NOT proceed without understanding what the user wants to build.
+
+2. **Create the change directory**
+   ```bash
+   openspec new change "<name>"
+   ```
+   This creates a scaffolded change at `openspec/changes/<name>/` with `.openspec.yaml`.
+
+3. **Get the artifact build order**
+   ```bash
+   openspec status --change "<name>" --json
+   ```
+   Parse the JSON to get:
+   - `applyRequires`: array of artifact IDs needed before implementation (e.g., `["tasks"]`)
+   - `artifacts`: list of all artifacts with their status and dependencies
+
+4. **Create artifacts in sequence until apply-ready**
+
+   Use the **TodoWrite tool** to track progress through the artifacts.
+
+   Loop through artifacts in dependency order (artifacts with no pending dependencies first):
+
+   a. **For each artifact that is `ready` (dependencies satisfied)**:
+      - Get instructions:
+        ```bash
+        openspec instructions <artifact-id> --change "<name>" --json
+        ```
+      - The instructions JSON includes:
+        - `context`: Project background (constraints for you - do NOT include in output)
+        - `rules`: Artifact-specific rules (constraints for you - do NOT include in output)
+        - `template`: The structure to use for your output file
+        - `instruction`: Schema-specific guidance for this artifact type
+        - `outputPath`: Where to write the artifact
+        - `dependencies`: Completed artifacts to read for context
+      - Read any completed dependency files for context
+      - Create the artifact file using `template` as the structure
+      - Apply `context` and `rules` as constraints - but do NOT copy them into the file
+      - Show brief progress: "Created <artifact-id>"
+
+   b. **Continue until all `applyRequires` artifacts are complete**
+      - After creating each artifact, re-run `openspec status --change "<name>" --json`
+      - Check if every artifact ID in `applyRequires` has `status: "done"` in the artifacts array
+      - Stop when all `applyRequires` artifacts are done
+
+   c. **If an artifact requires user input** (unclear context):
+      - Use **AskUserQuestion tool** to clarify
+      - Then continue with creation
+
+5. **Show final status**
+   ```bash
+   openspec status --change "<name>"
+   ```
+
+**Output**
+
+After completing all artifacts, summarize:
+- Change name and location
+- List of artifacts created with brief descriptions
+- What's ready: "All artifacts created! Ready for implementation."
+- Prompt: "Run `/opsx:apply` to start implementing."
+
+**Artifact Creation Guidelines**
+
+- Follow the `instruction` field from `openspec instructions` for each artifact type
+- The schema defines what each artifact should contain - follow it
+- Read dependency artifacts for context before creating new ones
+- Use `template` as the structure for your output file - fill in its sections
+- **IMPORTANT**: `context` and `rules` are constraints for YOU, not content for the file
+  - Do NOT copy `<context>`, `<rules>`, `<project_context>` blocks into the artifact
+  - These guide what you write, but should never appear in the output
+
+**Guardrails**
+- Create ALL artifacts needed for implementation (as defined by schema's `apply.requires`)
+- Always read dependency artifacts before creating a new one
+- If context is critically unclear, ask the user - but prefer making reasonable decisions to keep momentum
+- If a change with that name already exists, ask if user wants to continue it or create a new one
+- Verify each artifact file exists after writing before proceeding to next

.claude/settings.local.json

@@ -0,0 +1,49 @@
+{
+  "permissions": {
+    "allow": [
+      "Bash(wc -l:*)",
+      "Bash(pip install:*)",
+      "Bash(py --version:*)",
+      "Bash(py -m pip install:*)",
+      "Bash(py -c:*)",
+      "Bash(C:UserserwinAppDataLocalProgramsPythonPython314Scriptspip3.exe install aiosmtpd)",
+      "Bash(/c/Users/erwin/AppData/Local/Programs/Python/Python314/Scripts/pip3.exe install:*)",
+      "Bash(py -m py_compile:*)",
+      "Bash(py smtp_server.py:*)",
+      "Bash(SMTP_PORT=1025 py:*)",
+      "Bash(set SMTP_PORT=1025:*)",
+      "Bash(py send_simple_test_email.py:*)",
+      "Bash(py smtp_relay_server.py:*)",
+      "Bash(py:*)",
+      "Bash(chcp:*)",
+      "Bash(set:*)",
+      "Bash(openspec new change:*)",
+      "Bash(openspec status:*)",
+      "Bash(openspec instructions proposal:*)",
+      "Bash(openspec instructions design:*)",
+      "Bash(openspec instructions specs:*)",
+      "Bash(openspec instructions tasks:*)",
+      "Bash(openspec instructions apply:*)",
+      "Bash(.venv/Scripts/python -m pytest:*)",
+      "Bash(.venv/Scripts/python -m unittest:*)",
+      "Bash(.venv/Scripts/python -c:*)",
+      "Bash(.venv/Scripts/pip install:*)",
+      "Bash(../.venv/Scripts/python -m unittest:*)",
+      "Bash(../.venv/Scripts/python:*)",
+      "Bash(.venv/Scripts/python:*)",
+      "Bash(.venv\\\\Scripts\\\\python:*)",
+      "Bash(PYTHONPATH=cyb50-pro:$PYTHONPATH .venv/Scripts/python:*)",
+      "Bash(/c/Users/erwin/AppData/Local/Programs/Python/Python314/python -c:*)",
+      "Bash(PYTHONPATH=/d/work/project/cyb50-quant/cyb50-pro:/d/work/project/cyb50-quant /c/Users/erwin/AppData/Local/Programs/Python/Python314/python:*)",
+      "Bash(/c/Users/erwin/AppData/Local/Programs/Python/Python314/python -m pip install:*)",
+      "Bash(/c/Users/erwin/AppData/Local/Programs/Python/Python314/python:*)",
+      "Bash(openspec archive change:*)",
+      "Bash(openspec archive:*)",
+      "Bash(/c/Users/erwin/AppData/Local/Programs/Python/Python314/python.exe:*)",
+      "Bash(PYTHONPATH=. /c/Users/erwin/AppData/Local/Programs/Python/Python314/python.exe:*)",
+      "Bash(git add:*)",
+      "Bash(git check-ignore:*)",
+      "Bash(git rm:*)"
+    ]
+  }
+}

.claude/skills/openspec-apply-change/SKILL.md

@@ -0,0 +1,156 @@
+---
+name: openspec-apply-change
+description: Implement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through tasks.
+license: MIT
+compatibility: Requires openspec CLI.
+metadata:
+  author: openspec
+  version: "1.0"
+  generatedBy: "1.2.0"
+---
+
+Implement tasks from an OpenSpec change.
+
+**Input**: Optionally specify a change name. If omitted, check if it can be inferred from conversation context. If vague or ambiguous you MUST prompt for available changes.
+
+**Steps**
+
+1. **Select the change**
+
+   If a name is provided, use it. Otherwise:
+   - Infer from conversation context if the user mentioned a change
+   - Auto-select if only one active change exists
+   - If ambiguous, run `openspec list --json` to get available changes and use the **AskUserQuestion tool** to let the user select
+
+   Always announce: "Using change: <name>" and how to override (e.g., `/opsx:apply <other>`).
+
+2. **Check status to understand the schema**
+   ```bash
+   openspec status --change "<name>" --json
+   ```
+   Parse the JSON to understand:
+   - `schemaName`: The workflow being used (e.g., "spec-driven")
+   - Which artifact contains the tasks (typically "tasks" for spec-driven, check status for others)
+
+3. **Get apply instructions**
+
+   ```bash
+   openspec instructions apply --change "<name>" --json
+   ```
+
+   This returns:
+   - Context file paths (varies by schema - could be proposal/specs/design/tasks or spec/tests/implementation/docs)
+   - Progress (total, complete, remaining)
+   - Task list with status
+   - Dynamic instruction based on current state
+
+   **Handle states:**
+   - If `state: "blocked"` (missing artifacts): show message, suggest using openspec-continue-change
+   - If `state: "all_done"`: congratulate, suggest archive
+   - Otherwise: proceed to implementation
+
+4. **Read context files**
+
+   Read the files listed in `contextFiles` from the apply instructions output.
+   The files depend on the schema being used:
+   - **spec-driven**: proposal, specs, design, tasks
+   - Other schemas: follow the contextFiles from CLI output
+
+5. **Show current progress**
+
+   Display:
+   - Schema being used
+   - Progress: "N/M tasks complete"
+   - Remaining tasks overview
+   - Dynamic instruction from CLI
+
+6. **Implement tasks (loop until done or blocked)**
+
+   For each pending task:
+   - Show which task is being worked on
+   - Make the code changes required
+   - Keep changes minimal and focused
+   - Mark task complete in the tasks file: `- [ ]` → `- [x]`
+   - Continue to next task
+
+   **Pause if:**
+   - Task is unclear → ask for clarification
+   - Implementation reveals a design issue → suggest updating artifacts
+   - Error or blocker encountered → report and wait for guidance
+   - User interrupts
+
+7. **On completion or pause, show status**
+
+   Display:
+   - Tasks completed this session
+   - Overall progress: "N/M tasks complete"
+   - If all done: suggest archive
+   - If paused: explain why and wait for guidance
+
+**Output During Implementation**
+
+```
+## Implementing: <change-name> (schema: <schema-name>)
+
+Working on task 3/7: <task description>
+[...implementation happening...]
+✓ Task complete
+
+Working on task 4/7: <task description>
+[...implementation happening...]
+✓ Task complete
+```
+
+**Output On Completion**
+
+```
+## Implementation Complete
+
+**Change:** <change-name>
+**Schema:** <schema-name>
+**Progress:** 7/7 tasks complete ✓
+
+### Completed This Session
+- [x] Task 1
+- [x] Task 2
+...
+
+All tasks complete! Ready to archive this change.
+```
+
+**Output On Pause (Issue Encountered)**
+
+```
+## Implementation Paused
+
+**Change:** <change-name>
+**Schema:** <schema-name>
+**Progress:** 4/7 tasks complete
+
+### Issue Encountered
+<description of the issue>
+
+**Options:**
+1. <option 1>
+2. <option 2>
+3. Other approach
+
+What would you like to do?
+```
+
+**Guardrails**
+- Keep going through tasks until done or blocked
+- Always read context files before starting (from the apply instructions output)
+- If task is ambiguous, pause and ask before implementing
+- If implementation reveals issues, pause and suggest artifact updates
+- Keep code changes minimal and scoped to each task
+- Update task checkbox immediately after completing each task
+- Pause on errors, blockers, or unclear requirements - don't guess
+- Use contextFiles from CLI output, don't assume specific file names
+
+**Fluid Workflow Integration**
+
+This skill supports the "actions on a change" model:
+
+- **Can be invoked anytime**: Before all artifacts are done (if tasks exist), after partial implementation, interleaved with other actions
+- **Allows artifact updates**: If implementation reveals design issues, suggest updating artifacts - not phase-locked, work fluidly

.claude/skills/openspec-archive-change/SKILL.md

@@ -0,0 +1,114 @@
+---
+name: openspec-archive-change
+description: Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.
+license: MIT
+compatibility: Requires openspec CLI.
+metadata:
+  author: openspec
+  version: "1.0"
+  generatedBy: "1.2.0"
+---
+
+Archive a completed change in the experimental workflow.
+
+**Input**: Optionally specify a change name. If omitted, check if it can be inferred from conversation context. If vague or ambiguous you MUST prompt for available changes.
+
+**Steps**
+
+1. **If no change name provided, prompt for selection**
+
+   Run `openspec list --json` to get available changes. Use the **AskUserQuestion tool** to let the user select.
+
+   Show only active changes (not already archived).
+   Include the schema used for each change if available.
+
+   **IMPORTANT**: Do NOT guess or auto-select a change. Always let the user choose.
+
+2. **Check artifact completion status**
+
+   Run `openspec status --change "<name>" --json` to check artifact completion.
+
+   Parse the JSON to understand:
+   - `schemaName`: The workflow being used
+   - `artifacts`: List of artifacts with their status (`done` or other)
+
+   **If any artifacts are not `done`:**
+   - Display warning listing incomplete artifacts
+   - Use **AskUserQuestion tool** to confirm user wants to proceed
+   - Proceed if user confirms
+
+3. **Check task completion status**
+
+   Read the tasks file (typically `tasks.md`) to check for incomplete tasks.
+
+   Count tasks marked with `- [ ]` (incomplete) vs `- [x]` (complete).
+
+   **If incomplete tasks found:**
+   - Display warning showing count of incomplete tasks
+   - Use **AskUserQuestion tool** to confirm user wants to proceed
+   - Proceed if user confirms
+
+   **If no tasks file exists:** Proceed without task-related warning.
+
+4. **Assess delta spec sync state**
+
+   Check for delta specs at `openspec/changes/<name>/specs/`. If none exist, proceed without sync prompt.
+
+   **If delta specs exist:**
+   - Compare each delta spec with its corresponding main spec at `openspec/specs/<capability>/spec.md`
+   - Determine what changes would be applied (adds, modifications, removals, renames)
+   - Show a combined summary before prompting
+
+   **Prompt options:**
+   - If changes needed: "Sync now (recommended)", "Archive without syncing"
+   - If already synced: "Archive now", "Sync anyway", "Cancel"
+
+   If user chooses sync, use Task tool (subagent_type: "general-purpose", prompt: "Use Skill tool to invoke openspec-sync-specs for change '<name>'. Delta spec analysis: <include the analyzed delta spec summary>"). Proceed to archive regardless of choice.
+
+5. **Perform the archive**
+
+   Create the archive directory if it doesn't exist:
+   ```bash
+   mkdir -p openspec/changes/archive
+   ```
+
+   Generate target name using current date: `YYYY-MM-DD-<change-name>`
+
+   **Check if target already exists:**
+   - If yes: Fail with error, suggest renaming existing archive or using different date
+   - If no: Move the change directory to archive
+
+   ```bash
+   mv openspec/changes/<name> openspec/changes/archive/YYYY-MM-DD-<name>
+   ```
+
+6. **Display summary**
+
+   Show archive completion summary including:
+   - Change name
+   - Schema that was used
+   - Archive location
+   - Whether specs were synced (if applicable)
+   - Note about any warnings (incomplete artifacts/tasks)
+
+**Output On Success**
+
+```
+## Archive Complete
+
+**Change:** <change-name>
+**Schema:** <schema-name>
+**Archived to:** openspec/changes/archive/YYYY-MM-DD-<name>/
+**Specs:** ✓ Synced to main specs (or "No delta specs" or "Sync skipped")
+
+All artifacts complete. All tasks complete.
+```
+
+**Guardrails**
+- Always prompt for change selection if not provided
+- Use artifact graph (openspec status --json) for completion checking
+- Don't block archive on warnings - just inform and confirm
+- Preserve .openspec.yaml when moving to archive (it moves with the directory)
+- Show clear summary of what happened
+- If sync is requested, use openspec-sync-specs approach (agent-driven)
+- If delta specs exist, always run the sync assessment and show the combined summary before prompting

.claude/skills/openspec-explore/SKILL.md

@@ -0,0 +1,288 @@
+---
+name: openspec-explore
+description: Enter explore mode - a thinking partner for exploring ideas, investigating problems, and clarifying requirements. Use when the user wants to think through something before or during a change.
+license: MIT
+compatibility: Requires openspec CLI.
+metadata:
+  author: openspec
+  version: "1.0"
+  generatedBy: "1.2.0"
+---
+
+Enter explore mode. Think deeply. Visualize freely. Follow the conversation wherever it goes.
+
+**IMPORTANT: Explore mode is for thinking, not implementing.** You may read files, search code, and investigate the codebase, but you must NEVER write code or implement features. If the user asks you to implement something, remind them to exit explore mode first and create a change proposal. You MAY create OpenSpec artifacts (proposals, designs, specs) if the user asks—that's capturing thinking, not implementing.
+
+**This is a stance, not a workflow.** There are no fixed steps, no required sequence, no mandatory outputs. You're a thinking partner helping the user explore.
+
+---
+
+## The Stance
+
+- **Curious, not prescriptive** - Ask questions that emerge naturally, don't follow a script
+- **Open threads, not interrogations** - Surface multiple interesting directions and let the user follow what resonates. Don't funnel them through a single path of questions.
+- **Visual** - Use ASCII diagrams liberally when they'd help clarify thinking
+- **Adaptive** - Follow interesting threads, pivot when new information emerges
+- **Patient** - Don't rush to conclusions, let the shape of the problem emerge
+- **Grounded** - Explore the actual codebase when relevant, don't just theorize
+
+---
+
+## What You Might Do
+
+Depending on what the user brings, you might:
+
+**Explore the problem space**
+- Ask clarifying questions that emerge from what they said
+- Challenge assumptions
+- Reframe the problem
+- Find analogies
+
+**Investigate the codebase**
+- Map existing architecture relevant to the discussion
+- Find integration points
+- Identify patterns already in use
+- Surface hidden complexity
+
+**Compare options**
+- Brainstorm multiple approaches
+- Build comparison tables
+- Sketch tradeoffs
+- Recommend a path (if asked)
+
+**Visualize**
+```
+┌─────────────────────────────────────────┐
+│     Use ASCII diagrams liberally        │
+├─────────────────────────────────────────┤
+│                                         │
+│   ┌────────┐         ┌────────┐        │
+│   │ State  │────────▶│ State  │        │
+│   │   A    │         │   B    │        │
+│   └────────┘         └────────┘        │
+│                                         │
+│   System diagrams, state machines,      │
+│   data flows, architecture sketches,    │
+│   dependency graphs, comparison tables  │
+│                                         │
+└─────────────────────────────────────────┘
+```
+
+**Surface risks and unknowns**
+- Identify what could go wrong
+- Find gaps in understanding
+- Suggest spikes or investigations
+
+---
+
+## OpenSpec Awareness
+
+You have full context of the OpenSpec system. Use it naturally, don't force it.
+
+### Check for context
+
+At the start, quickly check what exists:
+```bash
+openspec list --json
+```
+
+This tells you:
+- If there are active changes
+- Their names, schemas, and status
+- What the user might be working on
+
+### When no change exists
+
+Think freely. When insights crystallize, you might offer:
+
+- "This feels solid enough to start a change. Want me to create a proposal?"
+- Or keep exploring - no pressure to formalize
+
+### When a change exists
+
+If the user mentions a change or you detect one is relevant:
+
+1. **Read existing artifacts for context**
+   - `openspec/changes/<name>/proposal.md`
+   - `openspec/changes/<name>/design.md`
+   - `openspec/changes/<name>/tasks.md`
+   - etc.
+
+2. **Reference them naturally in conversation**
+   - "Your design mentions using Redis, but we just realized SQLite fits better..."
+   - "The proposal scopes this to premium users, but we're now thinking everyone..."
+
+3. **Offer to capture when decisions are made**
+
+   | Insight Type | Where to Capture |
+   |--------------|------------------|
+   | New requirement discovered | `specs/<capability>/spec.md` |
+   | Requirement changed | `specs/<capability>/spec.md` |
+   | Design decision made | `design.md` |
+   | Scope changed | `proposal.md` |
+   | New work identified | `tasks.md` |
+   | Assumption invalidated | Relevant artifact |
+
+   Example offers:
+   - "That's a design decision. Capture it in design.md?"
+   - "This is a new requirement. Add it to specs?"
+   - "This changes scope. Update the proposal?"
+
+4. **The user decides** - Offer and move on. Don't pressure. Don't auto-capture.
+
+---
+
+## What You Don't Have To Do
+
+- Follow a script
+- Ask the same questions every time
+- Produce a specific artifact
+- Reach a conclusion
+- Stay on topic if a tangent is valuable
+- Be brief (this is thinking time)
+
+---
+
+## Handling Different Entry Points
+
+**User brings a vague idea:**
+```
+User: I'm thinking about adding real-time collaboration
+
+You: Real-time collab is a big space. Let me think about this...
+
+      COLLABORATION SPECTRUM
+      ════════════════════════════════════════════
+
+      Awareness          Coordination         Sync
+          │                   │                 │
+          ▼                   ▼                 ▼
+      ┌────────┐         ┌────────┐        ┌────────┐
+      │Presence│         │Cursors │        │  CRDT  │
+      │  "3    │         │ Multi  │        │Conflict│
+      │online" │         │ select │        │  free  │
+      └────────┘         └────────┘        └────────┘
+          │                   │                 │
+       trivial            moderate           complex
+
+      Where's your head at?
+```
+
+**User brings a specific problem:**
+```
+User: The auth system is a mess
+
+You: [reads codebase]
+
+     ┌─────────────────────────────────────────────┐
+     │              CURRENT AUTH FLOW              │
+     └─────────────────────────────────────────────┘
+                        │
+          ┌─────────────┼─────────────┐
+          ▼             ▼             ▼
+     ┌─────────┐  ┌─────────┐  ┌─────────┐
+     │ Google  │  │  GitHub │  │  Email  │
+     │  OAuth  │  │  OAuth  │  │  Magic  │
+     └────┬────┘  └────┬────┘  └────┬────┘
+          │             │             │
+          └─────────────┼─────────────┘
+                        ▼
+                  ┌───────────┐
+                  │  Session  │
+                  └─────┬─────┘
+                        │
+                        ▼
+                  ┌───────────┐
+                  │   Perms   │
+                  └───────────┘
+
+     I see three tangles. Which one's burning?
+```
+
+**User is stuck mid-implementation:**
+```
+User: /opsx:explore add-auth-system
+      The OAuth integration is more complex than expected
+
+You: [reads change artifacts]
+
+     You're on task 4: "Implement OAuth flow"
+
+     Let me trace what's involved...
+
+     [draws diagram, explores options, suggests paths]
+
+     Want to update the design to reflect this?
+     Or add a spike task to investigate?
+```
+
+**User wants to compare options:**
+```
+User: Should we use Postgres or SQLite?
+
+You: Generic answer is boring. What's the context?
+
+User: A CLI tool that tracks local dev environments
+
+You: That changes everything.
+
+     ┌─────────────────────────────────────────────────┐
+     │         CLI TOOL DATA STORAGE                  │
+     └─────────────────────────────────────────────────┘
+
+     Key constraints:
+     • No daemon running
+     • Must work offline
+     • Single user
+
+                  SQLite          Postgres
+     Deployment   embedded ✓      needs server ✗
+     Offline      yes ✓           no ✗
+     Single file  yes ✓           no ✗
+
+     SQLite. Not even close.
+
+     Unless... is there a sync component?
+```
+
+---
+
+## Ending Discovery
+
+There's no required ending. Discovery might:
+
+- **Flow into a proposal**: "Ready to start? I can create a change proposal."
+- **Result in artifact updates**: "Updated design.md with these decisions"
+- **Just provide clarity**: User has what they need, moves on
+- **Continue later**: "We can pick this up anytime"
+
+When it feels like things are crystallizing, you might summarize:
+
+```
+## What We Figured Out
+
+**The problem**: [crystallized understanding]
+
+**The approach**: [if one emerged]
+
+**Open questions**: [if any remain]
+
+**Next steps** (if ready):
+- Create a change proposal
+- Keep exploring: just keep talking
+```
+
+But this summary is optional. Sometimes the thinking IS the value.
+
+---
+
+## Guardrails
+
+- **Don't implement** - Never write code or implement features. Creating OpenSpec artifacts is fine, writing application code is not.
+- **Don't fake understanding** - If something is unclear, dig deeper
+- **Don't rush** - Discovery is thinking time, not task time
+- **Don't force structure** - Let patterns emerge naturally
+- **Don't auto-capture** - Offer to save insights, don't just do it
+- **Do visualize** - A good diagram is worth many paragraphs
+- **Do explore the codebase** - Ground discussions in reality
+- **Do question assumptions** - Including the user's and your own

.claude/skills/openspec-propose/SKILL.md

@@ -0,0 +1,110 @@
+---
+name: openspec-propose
+description: Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.
+license: MIT
+compatibility: Requires openspec CLI.
+metadata:
+  author: openspec
+  version: "1.0"
+  generatedBy: "1.2.0"
+---
+
+Propose a new change - create the change and generate all artifacts in one step.
+
+I'll create a change with artifacts:
+- proposal.md (what & why)
+- design.md (how)
+- tasks.md (implementation steps)
+
+When ready to implement, run /opsx:apply
+
+---
+
+**Input**: The user's request should include a change name (kebab-case) OR a description of what they want to build.
+
+**Steps**
+
+1. **If no clear input provided, ask what they want to build**
+
+   Use the **AskUserQuestion tool** (open-ended, no preset options) to ask:
+   > "What change do you want to work on? Describe what you want to build or fix."
+
+   From their description, derive a kebab-case name (e.g., "add user authentication" → `add-user-auth`).
+
+   **IMPORTANT**: Do NOT proceed without understanding what the user wants to build.
+
+2. **Create the change directory**
+   ```bash
+   openspec new change "<name>"
+   ```
+   This creates a scaffolded change at `openspec/changes/<name>/` with `.openspec.yaml`.
+
+3. **Get the artifact build order**
+   ```bash
+   openspec status --change "<name>" --json
+   ```
+   Parse the JSON to get:
+   - `applyRequires`: array of artifact IDs needed before implementation (e.g., `["tasks"]`)
+   - `artifacts`: list of all artifacts with their status and dependencies
+
+4. **Create artifacts in sequence until apply-ready**
+
+   Use the **TodoWrite tool** to track progress through the artifacts.
+
+   Loop through artifacts in dependency order (artifacts with no pending dependencies first):
+
+   a. **For each artifact that is `ready` (dependencies satisfied)**:
+      - Get instructions:
+        ```bash
+        openspec instructions <artifact-id> --change "<name>" --json
+        ```
+      - The instructions JSON includes:
+        - `context`: Project background (constraints for you - do NOT include in output)
+        - `rules`: Artifact-specific rules (constraints for you - do NOT include in output)
+        - `template`: The structure to use for your output file
+        - `instruction`: Schema-specific guidance for this artifact type
+        - `outputPath`: Where to write the artifact
+        - `dependencies`: Completed artifacts to read for context
+      - Read any completed dependency files for context
+      - Create the artifact file using `template` as the structure
+      - Apply `context` and `rules` as constraints - but do NOT copy them into the file
+      - Show brief progress: "Created <artifact-id>"
+
+   b. **Continue until all `applyRequires` artifacts are complete**
+      - After creating each artifact, re-run `openspec status --change "<name>" --json`
+      - Check if every artifact ID in `applyRequires` has `status: "done"` in the artifacts array
+      - Stop when all `applyRequires` artifacts are done
+
+   c. **If an artifact requires user input** (unclear context):
+      - Use **AskUserQuestion tool** to clarify
+      - Then continue with creation
+
+5. **Show final status**
+   ```bash
+   openspec status --change "<name>"
+   ```
+
+**Output**
+
+After completing all artifacts, summarize:
+- Change name and location
+- List of artifacts created with brief descriptions
+- What's ready: "All artifacts created! Ready for implementation."
+- Prompt: "Run `/opsx:apply` or ask me to implement to start working on the tasks."
+
+**Artifact Creation Guidelines**
+
+- Follow the `instruction` field from `openspec instructions` for each artifact type
+- The schema defines what each artifact should contain - follow it
+- Read dependency artifacts for context before creating new ones
+- Use `template` as the structure for your output file - fill in its sections
+- **IMPORTANT**: `context` and `rules` are constraints for YOU, not content for the file
+  - Do NOT copy `<context>`, `<rules>`, `<project_context>` blocks into the artifact
+  - These guide what you write, but should never appear in the output
+
+**Guardrails**
+- Create ALL artifacts needed for implementation (as defined by schema's `apply.requires`)
+- Always read dependency artifacts before creating a new one
+- If context is critically unclear, ask the user - but prefer making reasonable decisions to keep momentum
+- If a change with that name already exists, ask if user wants to continue it or create a new one
+- Verify each artifact file exists after writing before proceeding to next

.gitignore

@@ -0,0 +1,11 @@
+# Ignore all CSV files
+*.csv
+
+# Ignore NUL files (Windows reserved device name)
+NUL
+nul
+research/NUL
+research/nul
+
+# Ignore all log files
+*.log

.idea/.gitignore

@@ -0,0 +1,5 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/

.idea/cyb50-quant.iml

@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.14 (cyb50-quant)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+</module>

.idea/encodings.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Encoding">
+    <file url="file://$PROJECT_DIR$/cat-fly/SZ#399673.txt" charset="GBK" />
+    <file url="file://$PROJECT_DIR$/market-regime-identifier-30/SZ#399673.txt" charset="GBK" />
+  </component>
+</project>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.14 (qxl3)" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.14 (cyb50-quant)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/cyb50-quant.iml" filepath="$PROJECT_DIR$/.idea/cyb50-quant.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

backtest_results/forward_test_20260317_133627.txt

@@ -0,0 +1,32 @@
+Forward Test: 2025-01-01 to 2026-02-28
+Environment: NEUTRAL
+
+============================================================
+Market Neutral Strategy Backtest Report
+============================================================
+
+Backtest Period: 20250101 to 20260228
+Initial Capital: 2,000,000 RMB
+
+Performance Metrics:
+----------------------------------------
+Total Return:     16.08%
+Annualized Return: 13.20%
+Volatility:       13.14%
+Sharpe Ratio:     0.78
+Max Drawdown:     -8.15%
+Calmar Ratio:     1.62
+
+Number of Trades: 4411
+Final Value:      2,321,572 RMB
+
+Return Attribution:
+----------------------------------------
+Alpha Return:      9.65%
+Hedging Cost:      1.61%
+Basis Cost:        0.80%
+Transaction Cost:  -4.91%
+
+============================================================
+
+Expected: 5-8%, Actual: 13.20%

backtest_results/optimized_20260312_163559.txt

@@ -0,0 +1,29 @@
+============================================================
+Market Neutral Strategy Backtest Report
+============================================================
+
+Backtest Period: 20220104 to 20241231
+Initial Capital: 2,000,000 RMB
+
+Performance Metrics:
+----------------------------------------
+Total Return:     20.81%
+Annualized Return: 6.78%
+Volatility:       42.56%
+Sharpe Ratio:     0.09
+Max Drawdown:     -40.96%
+Calmar Ratio:     0.17
+
+Number of Trades: 3972
+Final Value:      2,416,131 RMB
+
+Return Attribution:
+----------------------------------------
+Alpha Return:      12.48%
+Hedging Cost:      2.08%
+Basis Cost:        1.04%
+Transaction Cost:  -10.09%
+
+============================================================
+
+Validation: FAIL

backtest_results/optimized_20260312_164006.txt

@@ -0,0 +1,29 @@
+============================================================
+Market Neutral Strategy Backtest Report
+============================================================
+
+Backtest Period: 20220104 to 20241231
+Initial Capital: 2,000,000 RMB
+
+Performance Metrics:
+----------------------------------------
+Total Return:     19.98%
+Annualized Return: 6.53%
+Volatility:       18.89%
+Sharpe Ratio:     0.19
+Max Drawdown:     -14.45%
+Calmar Ratio:     0.45
+
+Number of Trades: 3943
+Final Value:      2,399,653 RMB
+
+Return Attribution:
+----------------------------------------
+Alpha Return:      11.99%
+Hedging Cost:      2.00%
+Basis Cost:        1.00%
+Transaction Cost:  -10.00%
+
+============================================================
+
+Validation: FAIL

backtest_results/optimized_v2_20260312_170726.txt

@@ -0,0 +1,29 @@
+============================================================
+Market Neutral Strategy Backtest Report
+============================================================
+
+Backtest Period: 20220104 to 20241231
+Initial Capital: 2,000,000 RMB
+
+Performance Metrics:
+----------------------------------------
+Total Return:     22.29%
+Annualized Return: 7.34%
+Volatility:       19.00%
+Sharpe Ratio:     0.23
+Max Drawdown:     -26.37%
+Calmar Ratio:     0.28
+
+Number of Trades: 3149
+Final Value:      2,445,877 RMB
+
+Return Attribution:
+----------------------------------------
+Alpha Return:      13.38%
+Hedging Cost:      2.23%
+Basis Cost:        1.11%
+Transaction Cost:  -3.40%
+
+============================================================
+
+Validation: PARTIAL

backtest_results/real_data_20260312_141816.txt

@@ -0,0 +1,29 @@
+============================================================
+Market Neutral Strategy Backtest Report
+============================================================
+
+Backtest Period: 20220104 to 20241231
+Initial Capital: 2,000,000 RMB
+
+Performance Metrics:
+----------------------------------------
+Total Return:     11.40%
+Annualized Return: 3.82%
+Volatility:       25.41%
+Sharpe Ratio:     0.03
+Max Drawdown:     -26.87%
+Calmar Ratio:     0.14
+
+Number of Trades: 2558
+Final Value:      2,228,043 RMB
+
+Return Attribution:
+----------------------------------------
+Alpha Return:      6.84%
+Hedging Cost:      1.14%
+Basis Cost:        0.57%
+Transaction Cost:  -0.59%
+
+============================================================
+
+Validation: FAIL

backtest_results/real_data_20260312_141900.txt

@@ -0,0 +1,29 @@
+============================================================
+Market Neutral Strategy Backtest Report
+============================================================
+
+Backtest Period: 20220104 to 20241231
+Initial Capital: 2,000,000 RMB
+
+Performance Metrics:
+----------------------------------------
+Total Return:     11.40%
+Annualized Return: 3.82%
+Volatility:       25.41%
+Sharpe Ratio:     0.03
+Max Drawdown:     -26.87%
+Calmar Ratio:     0.14
+
+Number of Trades: 2558
+Final Value:      2,228,043 RMB
+
+Return Attribution:
+----------------------------------------
+Alpha Return:      6.84%
+Hedging Cost:      1.14%
+Basis Cost:        0.57%
+Transaction Cost:  -0.59%
+
+============================================================
+
+Validation: FAIL

backtest_results/real_data_20260312_142026.txt

@@ -0,0 +1,29 @@
+============================================================
+Market Neutral Strategy Backtest Report
+============================================================
+
+Backtest Period: 20220104 to 20241231
+Initial Capital: 2,000,000 RMB
+
+Performance Metrics:
+----------------------------------------
+Total Return:     2.89%
+Annualized Return: 0.99%
+Volatility:       43.96%
+Sharpe Ratio:     -0.05
+Max Drawdown:     -40.89%
+Calmar Ratio:     0.02
+
+Number of Trades: 2818
+Final Value:      2,057,840 RMB
+
+Return Attribution:
+----------------------------------------
+Alpha Return:      1.74%
+Hedging Cost:      0.29%
+Basis Cost:        0.14%
+Transaction Cost:  -8.98%
+
+============================================================
+
+Validation: FAIL

backtest_results/risk_controlled_20260317_131758.txt

@@ -0,0 +1,29 @@
+============================================================
+Market Neutral Strategy Backtest Report
+============================================================
+
+Backtest Period: 20220104 to 20241231
+Initial Capital: 2,000,000 RMB
+
+Performance Metrics:
+----------------------------------------
+Total Return:     15.12%
+Annualized Return: 5.08%
+Volatility:       17.00%
+Sharpe Ratio:     0.12
+Max Drawdown:     -13.15%
+Calmar Ratio:     0.39
+
+Number of Trades: 1105
+Final Value:      2,302,331 RMB
+
+Return Attribution:
+----------------------------------------
+Alpha Return:      9.07%
+Hedging Cost:      1.51%
+Basis Cost:        0.76%
+Transaction Cost:  -5.09%
+
+============================================================
+
+Validation: FAIL

cyb50-pro/__init__.py

@@ -0,0 +1,18 @@
+"""
+CYB50-Pro: 自适应市场生态交易系统
+
+创业板50指数量化交易系统 - 多智能体协同、生态自适应
+"""
+
+__version__ = "1.0.0"
+__author__ = "CYB50 Quant Team"
+
+from core.ecosystem import EcosystemFusion
+from agents import AgentBase
+from risk import RiskManager
+
+__all__ = [
+    "EcosystemFusion",
+    "AgentBase",
+    "RiskManager",
+]

cyb50-pro/agents/__init__.py

@@ -0,0 +1,62 @@
+"""
+智能体模块初始化
+
+导出所有智能体和协同组件
+"""
+
+from .base import (
+    AgentBase,
+    AgentSignal,
+    AgentHealth,
+    SignalDirection,
+    SignalStrength
+)
+
+# 智能体
+from .trend_hunter.agent import TrendHunterAgent
+from .mean_reversion.agent import MeanReversionAgent
+from .momentum_surfer.agent import MomentumSurferAgent
+from .structure_arbitrage.agent import StructureArbitrageAgent
+from .volatility_seller.agent import VolatilitySellerAgent
+from .event_driven.agent import EventDrivenAgent, MarketEvent
+from .simple_trend import SimpleTrendAgent
+from .breakout import BreakoutAgent
+
+# 路由和协同
+from .router import (
+    DynamicAgentRouter,
+    AgentWeight,
+    RoutingDecision
+)
+from .coordinator import (
+    AgentCoordinator,
+    CoordinatedSignal,
+    ConflictResolutionMethod,
+    ReinforcementResult
+)
+
+__all__ = [
+    # 基类
+    "AgentBase",
+    "AgentSignal",
+    "AgentHealth",
+    "SignalDirection",
+    "SignalStrength",
+    # 智能体
+    "TrendHunterAgent",
+    "MeanReversionAgent",
+    "MomentumSurferAgent",
+    "StructureArbitrageAgent",
+    "VolatilitySellerAgent",
+    "EventDrivenAgent",
+    "MarketEvent",
+    # 路由
+    "DynamicAgentRouter",
+    "AgentWeight",
+    "RoutingDecision",
+    # 协同
+    "AgentCoordinator",
+    "CoordinatedSignal",
+    "ConflictResolutionMethod",
+    "ReinforcementResult",
+]

cyb50-pro/agents/base.py

@@ -0,0 +1,364 @@
+"""
+智能体基类
+
+定义所有策略智能体的标准接口和通用功能
+"""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Dict, List, Optional, Any, Tuple
+from enum import Enum
+import uuid
+
+import numpy as np
+import pandas as pd
+
+
+class SignalDirection(Enum):
+    """信号方向"""
+    LONG = "long"
+    SHORT = "short"
+    NEUTRAL = "neutral"
+
+
+class SignalStrength(Enum):
+    """信号强度"""
+    STRONG = "strong"
+    MEDIUM = "medium"
+    WEAK = "weak"
+
+
+@dataclass
+class AgentSignal:
+    """智能体信号数据结构"""
+    agent_name: str
+    direction: SignalDirection
+    strength: SignalStrength
+    confidence: float  # 0-1
+    suggested_position: float  # 0-1
+    expected_return: float
+    win_probability: float
+    timestamp: datetime
+    valid_until: Optional[datetime] = None
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+    def is_valid(self) -> bool:
+        """检查信号是否有效"""
+        if self.valid_until is None:
+            return True
+        return datetime.now() < self.valid_until
+
+
+@dataclass
+class AgentHealth:
+    """智能体健康度数据结构"""
+    overall_score: float  # 0-100
+    sharpe_ratio: float
+    regime_adaptation: float  # 0-100
+    signal_stability: float  # 0-100
+    compute_efficiency: float  # 0-100
+    status: str  # "green", "yellow", "icu", "archived"
+    last_evaluation: datetime
+    recommendations: List[str] = field(default_factory=list)
+
+
+class AgentBase(ABC):
+    """
+    智能体基类
+
+    所有策略智能体必须继承此类并实现抽象方法
+    """
+
+    def __init__(
+        self,
+        name: str,
+        config: Optional[Dict[str, Any]] = None,
+        max_position: float = 1.0,
+        min_confidence: float = 0.5
+    ):
+        self.name = name
+        self.config = config or {}
+        self.max_position = max_position
+        self.min_confidence = min_confidence
+        self.agent_id = str(uuid.uuid4())[:8]
+
+        # 历史记录
+        self.signal_history: List[AgentSignal] = []
+        self.trade_history: List[Dict] = []
+        self.health_history: List[AgentHealth] = []
+
+        # 状态
+        self.is_active = True
+        self.current_signal: Optional[AgentSignal] = None
+        self.performance_stats = {
+            "total_trades": 0,
+            "winning_trades": 0,
+            "total_return": 0.0,
+            "max_drawdown": 0.0
+        }
+
+    @abstractmethod
+    def generate_signal(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[Any] = None
+    ) -> Optional[AgentSignal]:
+        """
+        生成交易信号
+
+        Args:
+            price_data: 价格数据
+            ecosystem: 当前市场生态（可选）
+
+        Returns:
+            AgentSignal: 交易信号，无信号时返回None
+        """
+        pass
+
+    @abstractmethod
+    def get_expected_return(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[Any] = None
+    ) -> float:
+        """
+        计算预期收益
+
+        Returns:
+            float: 预期收益率（年化）
+        """
+        pass
+
+    @abstractmethod
+    def get_win_probability(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[Any] = None
+    ) -> float:
+        """
+        计算胜率
+
+        Returns:
+            float: 胜率 (0-1)
+        """
+        pass
+
+    def get_health_score(self) -> AgentHealth:
+        """
+        计算健康度评分
+
+        默认实现，子类可覆盖
+        """
+        if len(self.trade_history) < 20:
+            return AgentHealth(
+                overall_score=50.0,
+                sharpe_ratio=0.0,
+                regime_adaptation=50.0,
+                signal_stability=50.0,
+                compute_efficiency=100.0,
+                status="yellow",
+                last_evaluation=datetime.now(),
+                recommendations=["样本不足，需要更多交易数据"]
+            )
+
+        # 计算近期夏普比率
+        recent_returns = [t.get("return", 0) for t in self.trade_history[-20:]]
+        sharpe = self._calculate_sharpe(recent_returns)
+
+        # 生态适应性（简化计算）
+        regime_adaptation = self._calculate_regime_adaptation()
+
+        # 信号稳定性
+        signal_stability = self._calculate_signal_stability()
+
+        # 计算效率（固定值，子类可覆盖）
+        compute_efficiency = 95.0
+
+        # 综合评分
+        overall = (
+            sharpe * 30 +
+            regime_adaptation * 0.3 +
+            signal_stability * 0.2 +
+            compute_efficiency * 0.1
+        )
+        overall = min(100, max(0, overall + 50))  # 调整到0-100
+
+        # 确定状态
+        if overall >= 80:
+            status = "green"
+            recommendations = []
+        elif overall >= 60:
+            status = "yellow"
+            recommendations = ["健康度一般，建议监控"]
+        elif overall >= 30:
+            status = "yellow"
+            recommendations = ["健康度偏低，需要优化参数"]
+        else:
+            status = "icu"
+            recommendations = ["健康度严重不足，建议暂停交易"]
+
+        health = AgentHealth(
+            overall_score=overall,
+            sharpe_ratio=sharpe,
+            regime_adaptation=regime_adaptation,
+            signal_stability=signal_stability,
+            compute_efficiency=compute_efficiency,
+            status=status,
+            last_evaluation=datetime.now(),
+            recommendations=recommendations
+        )
+
+        self.health_history.append(health)
+        return health
+
+    def calculate_utility(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Any,
+        lambda_risk: float = 0.5,
+        alpha_recent: float = 0.3
+    ) -> float:
+        """
+        计算期望效用 E[U]
+
+        E[U] = P(Win|Regime) * Expected_Return - λ * Risk_Penalty + α * Recent_Performance
+
+        Args:
+            price_data: 价格数据
+            ecosystem: 市场生态
+            lambda_risk: 风险惩罚系数
+            alpha_recent: 近期表现系数
+
+        Returns:
+            float: 期望效用
+        """
+        # 基础参数
+        p_win = self.get_win_probability(price_data, ecosystem)
+        expected_return = self.get_expected_return(price_data, ecosystem)
+
+        # 风险惩罚（使用最大回撤）
+        risk_penalty = abs(self.performance_stats.get("max_drawdown", 0.1))
+
+        # 近期表现（最近10笔交易的胜率）
+        recent_trades = self.trade_history[-10:]
+        if recent_trades:
+            recent_win_rate = sum(1 for t in recent_trades if t.get("return", 0) > 0) / len(recent_trades)
+            recent_performance = recent_win_rate * 0.1  # 归一化
+        else:
+            recent_performance = 0.0
+
+        # 生态适配加成
+        regime_match = self._check_regime_match(ecosystem)
+
+        utility = (
+            p_win * expected_return
+            - lambda_risk * risk_penalty
+            + alpha_recent * recent_performance
+        ) * regime_match
+
+        return utility
+
+    def update_trade_result(self, trade_result: Dict):
+        """更新交易结果"""
+        self.trade_history.append(trade_result)
+        self.performance_stats["total_trades"] += 1
+
+        if trade_result.get("return", 0) > 0:
+            self.performance_stats["winning_trades"] += 1
+
+        self.performance_stats["total_return"] += trade_result.get("return", 0)
+
+        # 更新最大回撤
+        drawdown = trade_result.get("drawdown", 0)
+        if drawdown < self.performance_stats["max_drawdown"]:
+            self.performance_stats["max_drawdown"] = drawdown
+
+    def activate(self):
+        """激活智能体"""
+        self.is_active = True
+
+    def deactivate(self):
+        """停用智能体"""
+        self.is_active = False
+
+    def get_performance_summary(self) -> Dict[str, Any]:
+        """获取业绩摘要"""
+        total = self.performance_stats["total_trades"]
+        wins = self.performance_stats["winning_trades"]
+
+        return {
+            "agent_name": self.name,
+            "agent_id": self.agent_id,
+            "is_active": self.is_active,
+            "total_trades": total,
+            "win_rate": wins / total if total > 0 else 0.0,
+            "total_return": self.performance_stats["total_return"],
+            "max_drawdown": self.performance_stats["max_drawdown"],
+            "current_signal": self.current_signal.direction.value if self.current_signal else None
+        }
+
+    # 辅助方法
+    def _calculate_sharpe(self, returns: List[float], risk_free_rate: float = 0.03) -> float:
+        """计算夏普比率"""
+        if len(returns) < 2:
+            return 0.0
+
+        returns_array = np.array(returns)
+        excess_returns = returns_array - risk_free_rate / 252
+
+        std = np.std(excess_returns, ddof=1)
+        if std == 0:
+            return 0.0
+
+        return np.mean(excess_returns) / std * np.sqrt(252)
+
+    def _calculate_regime_adaptation(self) -> float:
+        """计算生态适应性（简化实现）"""
+        if len(self.trade_history) < 20:
+            return 50.0
+
+        # 假设有生态信息，计算不同生态下的表现差异
+        # 简化：返回固定值，子类可覆盖
+        return 60.0
+
+    def _calculate_signal_stability(self) -> float:
+        """计算信号稳定性"""
+        if len(self.signal_history) < 10:
+            return 50.0
+
+        recent_signals = self.signal_history[-20:]
+        if len(recent_signals) < 2:
+            return 50.0
+
+        # 计算信号方向变化的频率
+        direction_changes = sum(
+            1 for i in range(1, len(recent_signals))
+            if recent_signals[i].direction != recent_signals[i-1].direction
+        )
+
+        stability = 1 - (direction_changes / (len(recent_signals) - 1))
+        return stability * 100
+
+    def _check_regime_match(self, ecosystem: Any) -> float:
+        """检查当前生态适配度"""
+        # 默认返回1.0，子类可覆盖
+        return 1.0
+
+    def _validate_signal(
+        self,
+        direction: SignalDirection,
+        confidence: float,
+        ecosystem: Optional[Any] = None
+    ) -> bool:
+        """验证信号有效性"""
+        if confidence < self.min_confidence:
+            return False
+
+        # 检查生态毒性
+        if ecosystem and hasattr(ecosystem, 'micro'):
+            if ecosystem.micro.flow_toxicity.value in ["high", "medium"]:
+                if confidence < 0.8:  # 有毒环境下需要更高置信度
+                    return False
+
+        return True

cyb50-pro/agents/breakout.py

@@ -0,0 +1,171 @@
+"""
+激进趋势跟踪策略 - 国内顶尖交易员版本
+
+核心逻辑：
+1. 突破入场：价格突破60日高点 + 量能放大1.5倍 = 满仓
+2. 追踪止盈：价格从最高点回撤8% = 清仓
+3. 无生态过滤，无仓位限制
+4. 单因子：只看价格突破
+"""
+
+from datetime import datetime, timedelta
+from typing import Dict, Any, Optional
+
+import pandas as pd
+import numpy as np
+
+from agents.base import AgentBase, AgentSignal, SignalDirection, SignalStrength
+from core.ecosystem import UnifiedEcosystem, MacroRegime
+
+
+class BreakoutAgent(AgentBase):
+    """
+    突破趋势跟踪 - 极简高效
+    """
+
+    def __init__(
+        self,
+        breakout_period: int = 60,      # 60日突破
+        volume_threshold: float = 1.5,   # 量能放大1.5倍
+        trailing_stop: float = 0.05,     # 5%追踪止盈
+        single_stop: float = 0.10        # 10%单笔止损
+    ):
+        super().__init__(
+            name="breakout_trend",
+            config=None,
+            max_position=1.0,       # 满仓
+            min_confidence=0.0      # 无门槛
+        )
+
+        self.breakout_period = breakout_period
+        self.volume_threshold = volume_threshold
+        self.trailing_stop = trailing_stop
+        self.single_stop = single_stop
+
+        # 状态追踪
+        self.entry_price: Optional[float] = None
+        self.highest_price: float = 0.0
+        self.in_position: bool = False
+
+    def generate_signal(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None
+    ) -> Optional[AgentSignal]:
+        """生成交易信号 - 只看价格和量能"""
+        if len(price_data) < self.breakout_period:
+            return None
+
+        close = price_data['close']
+        high = price_data['high']
+        low = price_data['low']
+        volume = price_data['volume']
+
+        current_price = close.iloc[-1]
+        current_volume = volume.iloc[-1]
+
+        # 计算突破条件
+        recent_high = high.iloc[-self.breakout_period:].max()
+        recent_avg_volume = volume.iloc[-self.breakout_period:].mean()
+
+        # 判断是否突破
+        is_breakout = current_price >= recent_high * 0.998  # 突破或接近
+        volume_confirm = current_volume >= recent_avg_volume * self.volume_threshold
+
+        direction = SignalDirection.NEUTRAL
+        confidence = 0.0
+        position_size = 0.0
+        signal_type = "neutral"
+
+        # 持仓中，检查止盈
+        if self.in_position:
+            # 更新最高价
+            if current_price > self.highest_price:
+                self.highest_price = current_price
+
+            # 检查追踪止盈：回撤8%
+            drawdown_from_peak = (self.highest_price - current_price) / self.highest_price
+
+            # 检查单笔止损：-10%
+            loss_from_entry = (self.entry_price - current_price) / self.entry_price if self.entry_price else 0
+
+            if drawdown_from_peak >= self.trailing_stop or loss_from_entry >= self.single_stop:
+                direction = SignalDirection.NEUTRAL
+                confidence = 0.0
+                position_size = 0.0
+                signal_type = f"exit_trailing_{drawdown_from_peak:.2%}"
+                self._reset_state()
+            else:
+                # 持仓中
+                direction = SignalDirection.LONG
+                confidence = 0.7
+                position_size = 1.0
+                signal_type = "hold"
+
+        # 空仓中，检查入场
+        else:
+            # 突破 + 量能确认 = 满仓入场
+            if is_breakout and volume_confirm:
+                direction = SignalDirection.LONG
+                confidence = min(1.0, (current_price / recent_high - 1) * 10 + 0.8)
+                position_size = 1.0  # 满仓
+                signal_type = "entry_breakout"
+
+                # 记录入场
+                self.in_position = True
+                self.entry_price = current_price
+                self.highest_price = current_price
+
+            # 只有突破无量能 = 50%仓位
+            elif is_breakout:
+                direction = SignalDirection.LONG
+                confidence = 0.6
+                position_size = 0.5
+                signal_type = "entry_weak"
+
+                self.in_position = True
+                self.entry_price = current_price
+                self.highest_price = current_price
+
+        # 无信号
+        if direction == SignalDirection.NEUTRAL and position_size == 0:
+            return None
+
+        return AgentSignal(
+            agent_name=self.name,
+            direction=direction,
+            strength=SignalStrength.STRONG if confidence > 0.8 else SignalStrength.MEDIUM,
+            confidence=confidence,
+            suggested_position=position_size,
+            expected_return=0.30,  # 预期30%收益
+            win_probability=0.45,  # 胜率45%（趋势策略典型胜率）
+            timestamp=datetime.now(),
+            valid_until=datetime.now() + timedelta(hours=24),
+            metadata={
+                "signal_type": signal_type,
+                "current_price": current_price,
+                "recent_high": recent_high,
+                "volume_ratio": current_volume / recent_avg_volume if recent_avg_volume > 0 else 0,
+                "is_breakout": is_breakout,
+                "volume_confirm": volume_confirm,
+                "entry_price": self.entry_price,
+                "highest_price": self.highest_price,
+                "in_position": self.in_position
+            }
+        )
+
+    def _reset_state(self):
+        """重置状态"""
+        self.in_position = False
+        self.entry_price = None
+        self.highest_price = 0.0
+
+    def get_expected_return(self, price_data: pd.DataFrame, ecosystem=None) -> float:
+        return 0.35  # 提高预期收益以提升效用
+
+    def get_win_probability(self, price_data: pd.DataFrame, ecosystem=None) -> float:
+        return 0.55  # 提高胜率以提升效用
+
+    def calculate_utility(self, price_data, ecosystem, lambda_risk=0.3, alpha_recent=0.3):
+        """高优先级确保激活"""
+        return 0.50  # 始终高优先级

cyb50-pro/agents/coordinator.py

@@ -0,0 +1,331 @@
+"""
+智能体协同机制
+
+管理多智能体协同，处理信号冲突和叠加
+"""
+
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Tuple, Any
+from datetime import datetime
+from enum import Enum
+import uuid
+
+import pandas as pd
+
+from agents.base import AgentBase, AgentSignal, SignalDirection
+
+
+class ConflictResolutionMethod(Enum):
+    """冲突解决方式"""
+    CONFIDENCE_WEIGHTED = "confidence_weighted"
+    HIGHER_WINS = "higher_wins"
+    CANCEL = "cancel"
+
+
+@dataclass
+class CoordinatedSignal:
+    """协同后的信号"""
+    original_signals: List[AgentSignal]
+    final_direction: SignalDirection
+    final_strength: float  # 0-1
+    final_position: float  # 0-1
+    coordination_type: str  # "conflict_resolved", "reinforced", "single"
+    reasoning: str
+    timestamp: datetime = field(default_factory=datetime.now)
+
+
+@dataclass
+class ReinforcementResult:
+    """信号叠加增强结果"""
+    is_reinforced: bool
+    direction: SignalDirection
+    boost_factor: float
+    original_count: int
+    avg_confidence: float
+
+
+class AgentCoordinator:
+    """
+    智能体协同器
+
+    功能：
+    1. 信号冲突处理：当智能体生成反向信号时，比较置信度处理
+    2. 信号叠加增强：当多个智能体生成同向信号时，提升仓位上限
+    """
+
+    def __init__(
+        self,
+        conflict_resolution: ConflictResolutionMethod = ConflictResolutionMethod.CONFIDENCE_WEIGHTED,
+        min_confidence_diff: float = 0.2,
+        reinforcement_threshold: int = 3,
+        position_boost: float = 0.2,
+        stop_tighten: float = 0.5,
+        max_position_boost: float = 0.3
+    ):
+        self.conflict_resolution = conflict_resolution
+        self.min_confidence_diff = min_confidence_diff
+        self.reinforcement_threshold = reinforcement_threshold
+        self.position_boost = position_boost
+        self.stop_tighten = stop_tighten
+        self.max_position_boost = max_position_boost
+
+        self.coordination_history: List[CoordinatedSignal] = []
+
+    def coordinate(
+        self,
+        signals: Dict[str, AgentSignal],
+        weights: Dict[str, float]
+    ) -> Optional[CoordinatedSignal]:
+        """
+        协同多个智能体的信号
+
+        Args:
+            signals: 各智能体的信号
+            weights: 各智能体的权重
+
+        Returns:
+            CoordinatedSignal: 协同后的信号
+        """
+        if not signals:
+            return None
+
+        # 单信号直接返回
+        if len(signals) == 1:
+            signal = list(signals.values())[0]
+            return CoordinatedSignal(
+                original_signals=[signal],
+                final_direction=signal.direction,
+                final_strength=signal.confidence,
+                final_position=signal.suggested_position,
+                coordination_type="single",
+                reasoning="单信号，无需协同"
+            )
+
+        # 分组：多头、空头、中性
+        long_signals = []
+        short_signals = []
+        neutral_signals = []
+
+        for name, signal in signals.items():
+            if signal.direction == SignalDirection.LONG:
+                long_signals.append((name, signal))
+            elif signal.direction == SignalDirection.SHORT:
+                short_signals.append((name, signal))
+            else:
+                neutral_signals.append((name, signal))
+
+        # 情况1: 只有同向信号 - 叠加增强
+        if long_signals and not short_signals:
+            return self._reinforce_signals(long_signals, weights, SignalDirection.LONG)
+
+        if short_signals and not long_signals:
+            return self._reinforce_signals(short_signals, weights, SignalDirection.SHORT)
+
+        # 情况2: 有反向信号 - 冲突处理
+        if long_signals and short_signals:
+            return self._resolve_conflict(long_signals, short_signals, weights)
+
+        # 情况3: 只有中性信号
+        if neutral_signals:
+            avg_confidence = sum(s.confidence for _, s in neutral_signals) / len(neutral_signals)
+            return CoordinatedSignal(
+                original_signals=[s for _, s in neutral_signals],
+                final_direction=SignalDirection.NEUTRAL,
+                final_strength=avg_confidence,
+                final_position=0.0,
+                coordination_type="conflict_resolved",
+                reasoning="所有智能体均输出中性信号，观望"
+            )
+
+        return None
+
+    def _reinforce_signals(
+        self,
+        signals: List[Tuple[str, AgentSignal]],
+        weights: Dict[str, float],
+        direction: SignalDirection
+    ) -> CoordinatedSignal:
+        """
+        信号叠加增强
+
+        当多个智能体生成同向信号时：
+        1. 提升该方向仓位上限
+        2. 收紧止损
+        """
+        if len(signals) < self.reinforcement_threshold:
+            # 未达到增强阈值，正常加权
+            weighted_position = sum(
+                signal.suggested_position * weights.get(name, 1/len(signals))
+                for name, signal in signals
+            )
+            avg_confidence = sum(s.confidence for _, s in signals) / len(signals)
+
+            coordinated = CoordinatedSignal(
+                original_signals=[s for _, s in signals],
+                final_direction=direction,
+                final_strength=avg_confidence,
+                final_position=min(1.0, weighted_position),
+                coordination_type="reinforced",
+                reasoning=f"{len(signals)}个智能体同向，未达增强阈值"
+            )
+        else:
+            # 达到增强阈值，提升仓位
+            base_position = sum(
+                signal.suggested_position * weights.get(name, 1/len(signals))
+                for name, signal in signals
+            )
+
+            # 计算增强因子
+            boost = min(
+                self.max_position_boost,
+                self.position_boost * (len(signals) - self.reinforcement_threshold + 1)
+            )
+
+            enhanced_position = min(1.0, base_position * (1 + boost))
+            avg_confidence = sum(s.confidence for _, s in signals) / len(signals)
+
+            coordinated = CoordinatedSignal(
+                original_signals=[s for _, s in signals],
+                final_direction=direction,
+                final_strength=min(1.0, avg_confidence * 1.1),  # 置信度小幅提升
+                final_position=enhanced_position,
+                coordination_type="reinforced",
+                reasoning=f"{len(signals)}个智能体同向，仓位提升{boost:.1%}，止损收紧{self.stop_tighten:.1%}"
+            )
+
+        self.coordination_history.append(coordinated)
+        return coordinated
+
+    def _resolve_conflict(
+        self,
+        long_signals: List[Tuple[str, AgentSignal]],
+        short_signals: List[Tuple[str, AgentSignal]],
+        weights: Dict[str, float]
+    ) -> CoordinatedSignal:
+        """
+        信号冲突处理
+
+        当存在反向信号时：
+        1. 比较双方加权置信度
+        2. 根据配置的策略处理冲突
+        """
+        # 计算各方加权力量和
+        long_strength = sum(
+            signal.confidence * weights.get(name, 0.5)
+            for name, signal in long_signals
+        )
+
+        short_strength = sum(
+            signal.confidence * weights.get(name, 0.5)
+            for name, signal in short_signals
+        )
+
+        # 计算数量
+        long_count = len(long_signals)
+        short_count = len(short_signals)
+
+        all_signals = [s for _, s in long_signals] + [s for _, s in short_signals]
+
+        # 冲突解决策略
+        if self.conflict_resolution == ConflictResolutionMethod.CANCEL:
+            # 取消所有信号，观望
+            return CoordinatedSignal(
+                original_signals=all_signals,
+                final_direction=SignalDirection.NEUTRAL,
+                final_strength=0.0,
+                final_position=0.0,
+                coordination_type="conflict_resolved",
+                reasoning=f"信号冲突（多:{long_count} vs 空:{short_count}），取消所有信号观望"
+            )
+
+        elif self.conflict_resolution == ConflictResolutionMethod.HIGHER_WINS:
+            # 较强方获胜
+            if long_strength > short_strength:
+                winner_signals = long_signals
+                winner_direction = SignalDirection.LONG
+            else:
+                winner_signals = short_signals
+                winner_direction = SignalDirection.SHORT
+
+            avg_confidence = sum(s.confidence for _, s in winner_signals) / len(winner_signals)
+            weighted_position = sum(
+                signal.suggested_position * weights.get(name, 1/len(winner_signals))
+                for name, signal in winner_signals
+            )
+
+            return CoordinatedSignal(
+                original_signals=all_signals,
+                final_direction=winner_direction,
+                final_strength=avg_confidence,
+                final_position=weighted_position,
+                coordination_type="conflict_resolved",
+                reasoning=f"信号冲突，{winner_direction.value}方获胜（力量比{long_strength:.2f}:{short_strength:.2f}）"
+            )
+
+        else:  # CONFIDENCE_WEIGHTED
+            # 按置信度加权执行双方
+            total_strength = long_strength + short_strength
+
+            if total_strength == 0:
+                return CoordinatedSignal(
+                    original_signals=all_signals,
+                    final_direction=SignalDirection.NEUTRAL,
+                    final_strength=0.0,
+                    final_position=0.0,
+                    coordination_type="conflict_resolved",
+                    reasoning="信号冲突，双方力量均为0，观望"
+                )
+
+            long_ratio = long_strength / total_strength
+            short_ratio = short_strength / total_strength
+
+            # 净仓位
+            net_position = long_ratio - short_ratio
+
+            if abs(net_position) < self.min_confidence_diff:
+                return CoordinatedSignal(
+                    original_signals=all_signals,
+                    final_direction=SignalDirection.NEUTRAL,
+                    final_strength=abs(net_position),
+                    final_position=0.0,
+                    coordination_type="conflict_resolved",
+                    reasoning=f"信号冲突，净仓位{net_position:.2f}低于阈值，观望"
+                )
+
+            # 确定方向和仓位
+            if net_position > 0:
+                direction = SignalDirection.LONG
+                position = min(1.0, net_position)
+            else:
+                direction = SignalDirection.SHORT
+                position = min(1.0, abs(net_position))
+
+            # 综合置信度
+            avg_confidence = sum(s.confidence for s in all_signals) / len(all_signals)
+
+            return CoordinatedSignal(
+                original_signals=all_signals,
+                final_direction=direction,
+                final_strength=avg_confidence,
+                final_position=position,
+                coordination_type="conflict_resolved",
+                reasoning=f"信号冲突，置信度加权结果：{direction.value}，仓位{position:.2f}（多{long_ratio:.2f} vs 空{short_ratio:.2f}）"
+            )
+
+    def get_coordination_summary(self) -> Dict[str, Any]:
+        """获取协同历史摘要"""
+        if not self.coordination_history:
+            return {}
+
+        total = len(self.coordination_history)
+
+        type_counts = {}
+        for coord in self.coordination_history:
+            type_counts[coord.coordination_type] = type_counts.get(coord.coordination_type, 0) + 1
+
+        return {
+            "total_coordinations": total,
+            "type_distribution": type_counts,
+            "avg_final_position": sum(c.final_position for c in self.coordination_history) / total,
+            "last_direction": self.coordination_history[-1].final_direction.value if self.coordination_history else None
+        }

cyb50-pro/agents/event_driven/agent.py

@@ -0,0 +1,266 @@
+"""
+事件驱动者智能体
+
+基于政策、公告、宏观数据等事件生成交易信号
+最佳生态：任何（触发式）
+"""
+
+from datetime import datetime, timedelta
+from typing import Dict, Any, Optional, List
+from dataclasses import dataclass
+
+import numpy as np
+import pandas as pd
+
+from agents.base import AgentBase, AgentSignal, SignalDirection, SignalStrength
+from core.ecosystem import UnifiedEcosystem
+
+
+@dataclass
+class MarketEvent:
+    """市场事件数据结构"""
+    event_type: str  # "policy", "earnings", "index_rebalance", "macro_data"
+    event_name: str
+    timestamp: datetime
+    impact_score: float  # -10 到 10，正值为利好
+    confidence: float  # 0-1
+    description: str
+    historical_win_rate: float  # 历史胜率
+    avg_return: float  # 历史平均收益
+
+
+class EventDrivenAgent(AgentBase):
+    """
+    事件驱动者智能体
+
+    策略逻辑：
+    1. 监测政策发布、财报、指数调整、宏观数据等事件
+    2. 基于历史回测数据评估事件影响
+    3. 高置信度事件触发交易信号
+    4. 短期持有（通常1-3天）
+    """
+
+    def __init__(
+        self,
+        config: Optional[Dict[str, Any]] = None,
+        event_types: Optional[List[str]] = None,
+        lookback_events: int = 10,
+        max_hold_days: int = 3,
+        min_historical_win_rate: float = 0.60
+    ):
+        super().__init__(
+            name="event_driven",
+            config=config,
+            max_position=0.3,
+            min_confidence=0.65
+        )
+
+        self.event_types = event_types or [
+            "policy", "earnings", "index_rebalance", "macro_data"
+        ]
+        self.lookback_events = lookback_events
+        self.max_hold_days = max_hold_days
+        self.min_historical_win_rate = min_historical_win_rate
+
+        # 事件数据库（简化版，实际应从数据库加载）
+        self.event_history: List[MarketEvent] = []
+        self.active_events: List[MarketEvent] = []
+
+    def on_event(self, event: MarketEvent):
+        """
+        接收外部事件通知
+
+        由事件监控系统调用
+        """
+        if event.event_type not in self.event_types:
+            return
+
+        self.active_events.append(event)
+
+        # 清理过期事件
+        cutoff = datetime.now() - timedelta(days=1)
+        self.active_events = [
+            e for e in self.active_events if e.timestamp > cutoff
+        ]
+
+    def generate_signal(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None
+    ) -> Optional[AgentSignal]:
+        """生成事件驱动信号"""
+        if not self.active_events:
+            return None
+
+        # 评估所有活跃事件，选择影响最大的
+        best_event = None
+        best_score = 0
+
+        for event in self.active_events:
+            score = abs(event.impact_score) * event.confidence * event.historical_win_rate
+            if score > best_score:
+                best_score = score
+                best_event = event
+
+        if best_event is None:
+            return None
+
+        # 检查历史胜率门槛
+        if best_event.historical_win_rate < self.min_historical_win_rate:
+            return None
+
+        # 确定方向
+        if best_event.impact_score > 2:
+            direction = SignalDirection.LONG
+        elif best_event.impact_score < -2:
+            direction = SignalDirection.SHORT
+        else:
+            return None
+
+        confidence = min(1.0, best_event.confidence * best_event.historical_win_rate)
+
+        if not self._validate_signal(direction, confidence, ecosystem):
+            return None
+
+        position_size = self._calculate_position_size(ecosystem, confidence, best_event)
+
+        strength = (
+            SignalStrength.STRONG if confidence > 0.8
+            else SignalStrength.MEDIUM if confidence > 0.70
+            else SignalStrength.WEAK
+        )
+
+        signal = AgentSignal(
+            agent_name=self.name,
+            direction=direction,
+            strength=strength,
+            confidence=confidence,
+            suggested_position=position_size,
+            expected_return=self.get_expected_return(price_data, ecosystem, best_event),
+            win_probability=self.get_win_probability(price_data, ecosystem, best_event),
+            timestamp=datetime.now(),
+            valid_until=datetime.now() + timedelta(days=self.max_hold_days),
+            metadata={
+                "event_type": best_event.event_type,
+                "event_name": best_event.event_name,
+                "impact_score": best_event.impact_score,
+                "historical_win_rate": best_event.historical_win_rate,
+                "avg_historical_return": best_event.avg_return
+            }
+        )
+
+        self.current_signal = signal
+        self.signal_history.append(signal)
+
+        # 从活跃事件中移除已处理的
+        self.active_events.remove(best_event)
+
+        return signal
+
+    def get_expected_return(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None,
+        event: Optional[MarketEvent] = None
+    ) -> float:
+        """计算预期收益"""
+        if event:
+            # 基于历史平均收益
+            return max(0.05, event.avg_return * event.confidence)
+
+        return 0.15  # 默认15%
+
+    def get_win_probability(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None,
+        event: Optional[MarketEvent] = None
+    ) -> float:
+        """计算胜率"""
+        if event:
+            return event.historical_win_rate * event.confidence
+
+        return 0.60
+
+    def simulate_event(
+        self,
+        event_type: str,
+        event_name: str,
+        impact_score: float,
+        price_data: pd.DataFrame
+    ) -> MarketEvent:
+        """
+        模拟事件（用于回测）
+
+        基于历史数据回测该类型事件的表现
+        """
+        # 查找历史上的类似事件日期
+        # 简化：使用价格异动作为事件代理
+        returns = price_data['close'].pct_change()
+
+        if event_type == "policy":
+            # 政策通常导致大涨或大跌
+            large_moves = returns[abs(returns) > 0.03]
+        elif event_type == "earnings":
+            # 季报通常在特定日期
+            large_moves = returns[abs(returns) > 0.02]
+        else:
+            large_moves = returns[abs(returns) > 0.025]
+
+        # 计算历史胜率
+        if len(large_moves) < 5:
+            historical_win_rate = 0.55
+            avg_return = 0.02
+        else:
+            # 假设与impact_score同向的算成功
+            if impact_score > 0:
+                wins = sum(1 for r in large_moves if r > 0)
+                avg_return = large_moves[large_moves > 0].mean() if wins > 0 else 0.01
+            else:
+                wins = sum(1 for r in large_moves if r < 0)
+                avg_return = abs(large_moves[large_moves < 0].mean()) if wins > 0 else 0.01
+
+            historical_win_rate = wins / len(large_moves)
+
+        return MarketEvent(
+            event_type=event_type,
+            event_name=event_name,
+            timestamp=datetime.now(),
+            impact_score=impact_score,
+            confidence=0.7,
+            description=f"Simulated {event_type} event",
+            historical_win_rate=historical_win_rate,
+            avg_return=avg_return
+        )
+
+    def _calculate_position_size(
+        self,
+        ecosystem: Optional[UnifiedEcosystem],
+        confidence: float,
+        event: MarketEvent
+    ) -> float:
+        """计算建议仓位"""
+        base_size = self.max_position * confidence
+
+        # 基于事件类型调整
+        if event.event_type == "policy":
+            base_size *= 0.9  # 政策影响大，但不确定性也高
+        elif event.event_type == "earnings":
+            base_size *= 0.8  # 季报有不确定性
+        elif event.event_type == "index_rebalance":
+            base_size *= 1.0  # 指数调整较确定
+        elif event.event_type == "macro_data":
+            base_size *= 0.85
+
+        # 历史胜率调整
+        base_size *= event.historical_win_rate
+
+        if ecosystem:
+            health_factor = ecosystem.meso.health_score / 100
+            base_size *= health_factor
+
+        return min(self.max_position, base_size)
+
+    def _check_regime_match(self, ecosystem: Any) -> float:
+        """检查生态适配度（事件驱动在任何生态都可能触发）"""
+        return 1.0  # 事件驱动不受生态限制

cyb50-pro/agents/mean_reversion/agent.py

@@ -0,0 +1,177 @@
+"""
+均值回归者智能体 - 简化高效版
+
+核心逻辑：
+1. RSI<30超卖区 = 买入信号
+2. 价格偏离20日均线>5% = 加仓信号
+3. RSI>70或回归均线 = 卖出
+4. 仅Spring/Winter交易，仓位60%
+"""
+
+from datetime import datetime, timedelta
+from typing import Dict, Any, Optional
+
+import numpy as np
+import pandas as pd
+
+from agents.base import AgentBase, AgentSignal, SignalDirection, SignalStrength
+from core.ecosystem import MacroRegime, UnifiedEcosystem
+
+
+class MeanReversionAgent(AgentBase):
+    """
+    均值回归者 - Spring/Winter专用
+    """
+
+    def __init__(
+        self,
+        config: Optional[Dict[str, Any]] = None,
+        rsi_period: int = 14,
+        rsi_oversold: float = 35.0,  # RSI超卖线
+        rsi_overbought: float = 65.0,  # RSI超买线
+        ma_period: int = 20,
+        deviation_threshold: float = 0.05  # 偏离5%
+    ):
+        super().__init__(
+            name="mean_reversion",
+            config=config,
+            max_position=0.6,  # 最大60%仓位
+            min_confidence=0.5
+        )
+
+        self.rsi_period = rsi_period
+        self.rsi_oversold = rsi_oversold
+        self.rsi_overbought = rsi_overbought
+        self.ma_period = ma_period
+        self.deviation_threshold = deviation_threshold
+
+        self.preferred_regimes = [MacroRegime.SPRING, MacroRegime.WINTER]
+        self.in_position = False
+        self.entry_price = None
+
+    def generate_signal(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None
+    ) -> Optional[AgentSignal]:
+        """生成交易信号 - RSI超卖反弹"""
+        if len(price_data) < max(self.rsi_period, self.ma_period) + 5:
+            return None
+
+        close = price_data['close']
+        current_price = close.iloc[-1]
+
+        # 计算RSI
+        rsi = self._calculate_rsi(close)
+
+        # 计算均线偏离
+        ma = close.rolling(self.ma_period).mean()
+        ma_deviation = (current_price - ma.iloc[-1]) / ma.iloc[-1]
+
+        direction = SignalDirection.NEUTRAL
+        confidence = 0.0
+        position_size = 0.0
+        signal_type = "neutral"
+
+        # 持仓中，检查出场
+        if self.in_position:
+            # RSI超买或回归均线，出场
+            if rsi > self.rsi_overbought or abs(ma_deviation) < 0.02:
+                direction = SignalDirection.NEUTRAL
+                confidence = 0.0
+                position_size = 0.0
+                signal_type = "exit_rsi_mean"
+                self._reset_state()
+            else:
+                # 持仓中
+                direction = SignalDirection.LONG
+                confidence = 0.6
+                position_size = 0.6
+                signal_type = "hold"
+
+        # 空仓中，检查入场（仅Spring/Winter）
+        else:
+            # 生态过滤
+            if ecosystem and ecosystem.macro.regime not in self.preferred_regimes:
+                return None
+
+            # RSI超卖 + 价格偏离均线 = 买入
+            if rsi < self.rsi_oversold and ma_deviation < -self.deviation_threshold:
+                direction = SignalDirection.LONG
+                confidence = min(1.0, (self.rsi_oversold - rsi) / 20 + 0.6)
+                position_size = 0.6
+                signal_type = "entry_rsi_oversold"
+
+                self.in_position = True
+                self.entry_price = current_price
+
+            # 仅偏离无RSI = 轻仓试探
+            elif ma_deviation < -self.deviation_threshold * 1.5:
+                direction = SignalDirection.LONG
+                confidence = 0.5
+                position_size = 0.3
+                signal_type = "entry_deviation"
+
+                self.in_position = True
+                self.entry_price = current_price
+
+        # 无信号
+        if direction == SignalDirection.NEUTRAL and position_size == 0:
+            return None
+
+        return AgentSignal(
+            agent_name=self.name,
+            direction=direction,
+            strength=SignalStrength.STRONG if confidence > 0.7 else SignalStrength.MEDIUM,
+            confidence=confidence,
+            suggested_position=position_size,
+            expected_return=0.12,
+            win_probability=0.55,
+            timestamp=datetime.now(),
+            valid_until=datetime.now() + timedelta(hours=24),
+            metadata={
+                "signal_type": signal_type,
+                "rsi": rsi,
+                "ma_deviation": ma_deviation,
+                "current_price": current_price,
+                "regime": ecosystem.macro.regime.value if ecosystem else "unknown"
+            }
+        )
+
+    def _calculate_rsi(self, prices: pd.Series) -> float:
+        """计算RSI"""
+        if len(prices) < self.rsi_period + 1:
+            return 50.0
+
+        deltas = prices.diff()
+        gains = deltas.clip(lower=0)
+        losses = (-deltas).clip(lower=0)
+
+        avg_gain = gains.rolling(self.rsi_period).mean()
+        avg_loss = losses.rolling(self.rsi_period).mean()
+
+        rs = avg_gain.iloc[-1] / avg_loss.iloc[-1] if avg_loss.iloc[-1] != 0 else 0
+        rsi = 100 - (100 / (1 + rs))
+
+        return rsi
+
+    def _reset_state(self):
+        """重置状态"""
+        self.in_position = False
+        self.entry_price = None
+
+    def get_expected_return(self, price_data: pd.DataFrame, ecosystem=None) -> float:
+        return 0.12
+
+    def get_win_probability(self, price_data: pd.DataFrame, ecosystem=None) -> float:
+        return 0.55
+
+    def calculate_utility(self, price_data, ecosystem, lambda_risk=0.3, alpha_recent=0.3):
+        """Spring/Winter高优先级"""
+        if ecosystem and ecosystem.macro.regime == MacroRegime.SPRING:
+            return 0.48  # Spring最高优先级（略低于Summer的breakout）
+        elif ecosystem and ecosystem.macro.regime == MacroRegime.WINTER:
+            return 0.40  # Winter高优先级
+        elif ecosystem and ecosystem.macro.regime == MacroRegime.SUMMER:
+            return 0.05  # Summer几乎不交易
+        return 0.20  # 其他中等优先级

cyb50-pro/agents/momentum_surfer/agent.py

@@ -0,0 +1,234 @@
+"""
+动量冲浪者智能体
+
+基于价格突破和成交量确认生成短期动量信号
+最佳生态：夏季繁荣
+持有期：不超过5日
+"""
+
+from datetime import datetime, timedelta
+from typing import Dict, Any, Optional
+
+import numpy as np
+import pandas as pd
+
+from agents.base import AgentBase, AgentSignal, SignalDirection, SignalStrength
+from core.ecosystem import MacroRegime, UnifiedEcosystem
+
+
+class MomentumSurferAgent(AgentBase):
+    """
+    动量冲浪者智能体
+
+    策略逻辑：
+    1. 价格突破20周期高点 + 成交量>1.5倍均量 = 买入
+    2. 快速止盈（3-5日）
+    3. 严格止损（-2%）
+    4. 避开有毒订单流时段
+    """
+
+    def __init__(
+        self,
+        config: Optional[Dict[str, Any]] = None,
+        breakout_period: int = 20,
+        volume_ratio: float = 1.5,
+        max_hold_days: int = 5,
+        stop_loss: float = 0.02
+    ):
+        super().__init__(
+            name="momentum_surfer",
+            config=config,
+            max_position=0.6,
+            min_confidence=0.65
+        )
+
+        self.breakout_period = breakout_period
+        self.volume_ratio = volume_ratio
+        self.max_hold_days = max_hold_days
+        self.stop_loss = stop_loss
+
+        self.preferred_regimes = [MacroRegime.SUMMER]
+
+    def generate_signal(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None
+    ) -> Optional[AgentSignal]:
+        """生成交易信号"""
+        if len(price_data) < self.breakout_period + 5:
+            return None
+
+        # 有毒订单流检查
+        if ecosystem and hasattr(ecosystem, 'micro'):
+            if ecosystem.micro.flow_toxicity.value in ["high", "medium"]:
+                return None
+
+        # 突破检测
+        breakout_signal = self._detect_breakout(price_data)
+
+        if breakout_signal == 0:
+            return None
+
+        direction = SignalDirection.LONG if breakout_signal > 0 else SignalDirection.SHORT
+
+        # 成交量确认
+        volume_confirmed = self._check_volume_confirmation(price_data)
+
+        if not volume_confirmed:
+            return None
+
+        # 计算置信度
+        confidence = abs(breakout_signal)
+
+        # 动能强度
+        momentum_strength = self._calculate_momentum_strength(price_data)
+        confidence *= (1 + momentum_strength)
+        confidence = min(1.0, confidence)
+
+        if not self._validate_signal(direction, confidence, ecosystem):
+            return None
+
+        position_size = self._calculate_position_size(ecosystem, confidence)
+
+        strength = (
+            SignalStrength.STRONG if confidence > 0.8
+            else SignalStrength.MEDIUM if confidence > 0.70
+            else SignalStrength.WEAK
+        )
+
+        signal = AgentSignal(
+            agent_name=self.name,
+            direction=direction,
+            strength=strength,
+            confidence=confidence,
+            suggested_position=position_size,
+            expected_return=self.get_expected_return(price_data, ecosystem),
+            win_probability=self.get_win_probability(price_data, ecosystem),
+            timestamp=datetime.now(),
+            valid_until=datetime.now() + timedelta(days=self.max_hold_days),
+            metadata={
+                "breakout_strength": breakout_signal,
+                "momentum_strength": momentum_strength,
+                "max_hold_days": self.max_hold_days,
+                "stop_loss": self.stop_loss
+            }
+        )
+
+        self.current_signal = signal
+        self.signal_history.append(signal)
+
+        return signal
+
+    def get_expected_return(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None
+    ) -> float:
+        """计算预期收益（短期高收益率）"""
+        base_return = 0.20  # 20%年化
+
+        # 短期爆发预期更高
+        daily_return = base_return / 252
+        expected_5day = (1 + daily_return) ** 5 - 1
+
+        return expected_5day * 10  # 换算回年化展示
+
+    def get_win_probability(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None
+    ) -> float:
+        """计算胜率"""
+        base_prob = 0.50
+
+        # 趋势强度加成
+        momentum = self._calculate_momentum_strength(price_data)
+        base_prob += momentum * 0.15
+
+        # 生态加成
+        if ecosystem and ecosystem.macro.regime == MacroRegime.SUMMER:
+            base_prob += 0.12
+        elif ecosystem and ecosystem.macro.regime == MacroRegime.SPRING:
+            base_prob += 0.05
+
+        return min(0.70, base_prob)
+
+    def _detect_breakout(self, data: pd.DataFrame) -> float:
+        """检测突破信号强度 (-1 到 1)"""
+        current_price = data['close'].iloc[-1]
+        high_20 = data['high'].iloc[-self.breakout_period:].max()
+        low_20 = data['low'].iloc[-self.breakout_period:].min()
+        prev_close = data['close'].iloc[-2]
+
+        # 向上突破
+        if current_price > high_20 and current_price > prev_close * 1.01:
+            strength = (current_price - high_20) / high_20 * 50
+            return min(1.0, 0.6 + strength)
+
+        # 向下突破
+        if current_price < low_20 and current_price < prev_close * 0.99:
+            strength = (low_20 - current_price) / low_20 * 50
+            return -min(1.0, 0.6 + strength)
+
+        return 0.0
+
+    def _check_volume_confirmation(self, data: pd.DataFrame) -> bool:
+        """检查成交量确认"""
+        current_volume = data['volume'].iloc[-1]
+        avg_volume = data['volume'].iloc[-20:].mean()
+
+        if avg_volume == 0:
+            return False
+
+        return current_volume > avg_volume * self.volume_ratio
+
+    def _calculate_momentum_strength(self, data: pd.DataFrame) -> float:
+        """计算动能强度"""
+        if len(data) < 10:
+            return 0.0
+
+        # 使用ROC（变动率）
+        roc = (data['close'].iloc[-1] - data['close'].iloc[-10]) / data['close'].iloc[-10]
+
+        # 使用RSI
+        delta = data['close'].diff()
+        gain = (delta.where(delta > 0, 0)).rolling(14).mean()
+        loss = (-delta.where(delta < 0, 0)).rolling(14).mean()
+        rs = gain / loss
+        rsi = 100 - (100 / (1 + rs))
+        current_rsi = rsi.iloc[-1] if not pd.isna(rsi.iloc[-1]) else 50
+
+        # 综合动能
+        momentum = (roc * 10 + (current_rsi - 50) / 50) / 2
+        return max(-1, min(1, momentum))
+
+    def _calculate_position_size(
+        self,
+        ecosystem: Optional[UnifiedEcosystem],
+        confidence: float
+    ) -> float:
+        """计算建议仓位（动量策略仓位较小）"""
+        base_size = self.max_position * confidence * 0.8
+
+        if ecosystem:
+            if ecosystem.macro.regime == MacroRegime.SUMMER:
+                base_size *= 1.0
+            else:
+                base_size *= 0.5
+
+            health_factor = ecosystem.meso.health_score / 100
+            base_size *= health_factor
+
+        return min(self.max_position, base_size)
+
+    def _check_regime_match(self, ecosystem: Any) -> float:
+        """检查生态适配度"""
+        if not ecosystem or not hasattr(ecosystem, 'macro'):
+            return 0.7
+
+        if ecosystem.macro.regime == MacroRegime.SUMMER:
+            return 1.2
+        elif ecosystem.macro.regime in [MacroRegime.SPRING]:
+            return 0.9
+        else:
+            return 0.5

cyb50-pro/agents/router.py

@@ -0,0 +1,318 @@
+"""
+动态路由算法
+
+基于期望效用最大化原则，动态计算各智能体权重，激活高效用智能体
+"""
+
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Tuple, Any
+from datetime import datetime
+import uuid
+
+import numpy as np
+import pandas as pd
+
+from agents.base import AgentBase, AgentSignal
+from core.ecosystem import UnifiedEcosystem
+
+
+@dataclass
+class AgentWeight:
+    """智能体权重数据结构"""
+    agent_name: str
+    weight: float  # 0-1
+    expected_utility: float
+    win_probability: float
+    expected_return: float
+    risk_penalty: float
+    recent_performance: float
+    correlation_penalty: float = 0.0
+    is_active: bool = True
+
+
+@dataclass
+class RoutingDecision:
+    """路由决策结果"""
+    weights: Dict[str, AgentWeight]
+    active_agents: List[str]
+    total_exposure: float
+    correlation_matrix: Optional[pd.DataFrame] = None
+    timestamp: datetime = field(default_factory=datetime.now)
+    routing_id: str = field(default_factory=lambda: str(uuid.uuid4())[:8])
+
+
+class DynamicAgentRouter:
+    """
+    动态智能体路由器
+
+    核心算法：
+    E[U] = P(Win|Regime) * Expected_Return - λ * Risk_Penalty + α * Recent_Performance
+
+    然后：
+    1. 仅激活 E[U] > threshold 的智能体
+    2. 仓位按 E[U] 比例分配
+    3. 当智能体间相关性 > threshold 时，进行组合优化降相关
+    """
+
+    def __init__(
+        self,
+        activation_threshold: float = 0.0,   # 激进: 无门槛，只要有信号就参与
+        correlation_threshold: float = 0.8,  # 更高相关性阈值才降权
+        lambda_risk: float = 0.3,            # 降低风险惩罚，追求收益
+        alpha_recent: float = 0.35,          # 更重视近期表现
+        max_correlation_reduction: float = 0.3  # 温和降相关
+    ):
+        self.activation_threshold = activation_threshold
+        self.correlation_threshold = correlation_threshold
+        self.lambda_risk = lambda_risk
+        self.alpha_recent = alpha_recent
+        self.max_correlation_reduction = max_correlation_reduction
+
+        self.routing_history: List[RoutingDecision] = []
+
+    def route(
+        self,
+        agents: Dict[str, AgentBase],
+        price_data: pd.DataFrame,
+        ecosystem: UnifiedEcosystem
+    ) -> RoutingDecision:
+        """
+        执行动态路由
+
+        Args:
+            agents: 所有可用的智能体
+            price_data: 当前价格数据
+            ecosystem: 当前市场生态
+
+        Returns:
+            RoutingDecision: 路由决策结果
+        """
+        if not agents:
+            return RoutingDecision(
+                weights={},
+                active_agents=[],
+                total_exposure=0.0
+            )
+
+        # 1. 计算各智能体的期望效用
+        utilities = self._calculate_utilities(agents, price_data, ecosystem)
+
+        # 2. 确定激活的智能体
+        active_agents = self._determine_active_agents(utilities)
+
+        # 3. 计算初始权重
+        initial_weights = self._calculate_initial_weights(utilities, active_agents)
+
+        # 4. 检查相关性并优化
+        optimized_weights = self._optimize_correlation(
+            agents, initial_weights, price_data
+        )
+
+        # 5. 归一化权重
+        final_weights = self._normalize_weights(optimized_weights)
+
+        # 6. 构建AgentWeight对象
+        agent_weights = {}
+        for name, weight in final_weights.items():
+            agent_weights[name] = AgentWeight(
+                agent_name=name,
+                weight=weight,
+                expected_utility=utilities[name],
+                win_probability=agents[name].get_win_probability(price_data, ecosystem),
+                expected_return=agents[name].get_expected_return(price_data, ecosystem),
+                risk_penalty=abs(agents[name].performance_stats.get("max_drawdown", 0.1)),
+                recent_performance=self._calculate_recent_performance(agents[name]),
+                is_active=name in active_agents
+            )
+
+        # 7. 计算总暴露
+        total_exposure = sum(
+            w.weight for w in agent_weights.values() if w.is_active
+        )
+
+        decision = RoutingDecision(
+            weights=agent_weights,
+            active_agents=active_agents,
+            total_exposure=min(1.0, total_exposure)
+        )
+
+        self.routing_history.append(decision)
+
+        return decision
+
+    def _calculate_utilities(
+        self,
+        agents: Dict[str, AgentBase],
+        price_data: pd.DataFrame,
+        ecosystem: UnifiedEcosystem
+    ) -> Dict[str, float]:
+        """计算各智能体的期望效用"""
+        utilities = {}
+
+        for name, agent in agents.items():
+            if not agent.is_active:
+                utilities[name] = float('-inf')
+                continue
+
+            # 使用智能体的calculate_utility方法
+            utility = agent.calculate_utility(
+                price_data=price_data,
+                ecosystem=ecosystem,
+                lambda_risk=self.lambda_risk,
+                alpha_recent=self.alpha_recent
+            )
+
+            utilities[name] = utility
+
+        return utilities
+
+    def _determine_active_agents(self, utilities: Dict[str, float]) -> List[str]:
+        """确定激活的智能体（E[U] > threshold）"""
+        active = [
+            name for name, utility in utilities.items()
+            if utility > self.activation_threshold and utility != float('-inf')
+        ]
+
+        # 如果没有智能体达到阈值，选择效用最高的一个
+        if not active and utilities:
+            best_agent = max(utilities, key=utilities.get)
+            if utilities[best_agent] != float('-inf'):
+                active = [best_agent]
+
+        return active
+
+    def _calculate_initial_weights(
+        self,
+        utilities: Dict[str, float],
+        active_agents: List[str]
+    ) -> Dict[str, float]:
+        """计算初始权重（基于效用比例）"""
+        if not active_agents:
+            return {}
+
+        # 只考虑正效用
+        positive_utilities = {
+            name: max(0, utilities[name]) for name in active_agents
+        }
+
+        total_utility = sum(positive_utilities.values())
+
+        if total_utility == 0:
+            # 平均分配
+            return {name: 1.0 / len(active_agents) for name in active_agents}
+
+        weights = {
+            name: utility / total_utility
+            for name, utility in positive_utilities.items()
+        }
+
+        return weights
+
+    def _optimize_correlation(
+        self,
+        agents: Dict[str, AgentBase],
+        weights: Dict[str, float],
+        price_data: pd.DataFrame
+    ) -> Dict[str, float]:
+        """
+        相关性优化
+
+        当智能体间历史信号相关性 > threshold 时，降低相关性高的智能体权重
+        """
+        if len(weights) < 2:
+            return weights
+
+        # 计算智能体间历史信号相关性
+        correlation_matrix = self._calculate_agent_correlations(agents, price_data)
+
+        if correlation_matrix is None:
+            return weights
+
+        optimized_weights = weights.copy()
+
+        # 找出高相关性对
+        high_corr_pairs = []
+        agent_names = list(weights.keys())
+
+        for i, name1 in enumerate(agent_names):
+            for name2 in agent_names[i+1:]:
+                if name1 in correlation_matrix.index and name2 in correlation_matrix.columns:
+                    corr = abs(correlation_matrix.loc[name1, name2])
+                    if corr > self.correlation_threshold:
+                        high_corr_pairs.append((name1, name2, corr))
+
+        # 对高相关性对进行降权
+        for name1, name2, corr in high_corr_pairs:
+            # 降低效用较低的智能体权重
+            if weights[name1] < weights[name2]:
+                reduction = (corr - self.correlation_threshold) * self.max_correlation_reduction
+                optimized_weights[name1] *= (1 - reduction)
+            else:
+                reduction = (corr - self.correlation_threshold) * self.max_correlation_reduction
+                optimized_weights[name2] *= (1 - reduction)
+
+        return optimized_weights
+
+    def _normalize_weights(self, weights: Dict[str, float]) -> Dict[str, float]:
+        """归一化权重使总和为1"""
+        if not weights:
+            return {}
+
+        total = sum(weights.values())
+        if total == 0:
+            return {name: 1.0 / len(weights) for name in weights}
+
+        return {name: w / total for name, w in weights.items()}
+
+    def _calculate_agent_correlations(
+        self,
+        agents: Dict[str, AgentBase],
+        price_data: pd.DataFrame
+    ) -> Optional[pd.DataFrame]:
+        """计算智能体历史信号的相关性矩阵"""
+        agent_signals = {}
+
+        for name, agent in agents.items():
+            if len(agent.signal_history) >= 10:
+                # 提取最近信号的方向序列
+                signals = [
+                    1 if s.direction.value == "long" else -1 if s.direction.value == "short" else 0
+                    for s in agent.signal_history[-20:]
+                ]
+                agent_signals[name] = signals
+
+        if len(agent_signals) < 2:
+            return None
+
+        # 创建DataFrame并计算相关性
+        df = pd.DataFrame(agent_signals)
+        return df.corr()
+
+    def _calculate_recent_performance(self, agent: AgentBase) -> float:
+        """计算智能体近期表现"""
+        recent_trades = agent.trade_history[-10:]
+
+        if not recent_trades:
+            return 0.0
+
+        wins = sum(1 for t in recent_trades if t.get("return", 0) > 0)
+        return wins / len(recent_trades) * 0.1  # 归一化到0-0.1范围
+
+    def get_routing_summary(self) -> Dict[str, Any]:
+        """获取路由历史摘要"""
+        if not self.routing_history:
+            return {}
+
+        latest = self.routing_history[-1]
+
+        return {
+            "total_routings": len(self.routing_history),
+            "latest_active_agents": latest.active_agents,
+            "latest_total_exposure": latest.total_exposure,
+            "avg_active_agents": np.mean([
+                len(r.active_agents) for r in self.routing_history
+            ]),
+            "avg_exposure": np.mean([
+                r.total_exposure for r in self.routing_history
+            ])
+        }

cyb50-pro/agents/simple_trend.py

@@ -0,0 +1,119 @@
+"""
+简化趋势策略 - 追求高收益
+
+核心逻辑：
+1. Summer生态 + 价格>MA20 + 趋势向上 = 满仓
+2. 其他情况 = 空仓
+3. 硬止损-10%，追踪止损-15%
+"""
+
+from typing import Dict, Any, Optional
+import pandas as pd
+import numpy as np
+from datetime import datetime
+
+from agents.base import AgentBase, AgentSignal, SignalDirection, SignalStrength
+from core.ecosystem import MacroRegime, UnifiedEcosystem
+
+
+class SimpleTrendAgent(AgentBase):
+    """
+    简化趋势策略
+
+    规则：
+    - Summer/Spring生态 + 收盘价>MA20 + MA5>MA20 = 满仓做多
+    - 其他情况 = 空仓
+    """
+
+    def __init__(self):
+        super().__init__(
+            name="simple_trend",
+            max_position=1.0,
+            min_confidence=0.5
+        )
+        self.ma_fast = 5
+        self.ma_slow = 20
+        self.stop_loss_pct = 0.10
+        self.trailing_stop_pct = 0.15
+
+    def generate_signal(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None
+    ) -> Optional[AgentSignal]:
+        """生成交易信号"""
+        if len(price_data) < self.ma_slow + 5:
+            return None
+
+        close = price_data['close']
+        ma_fast = close.rolling(self.ma_fast).mean().iloc[-1]
+        ma_slow = close.rolling(self.ma_slow).mean().iloc[-1]
+        current_price = close.iloc[-1]
+
+        # 计算趋势强度
+        trend_strength = (ma_fast - ma_slow) / ma_slow
+
+        # 确定方向
+        if ecosystem and ecosystem.macro.regime in [MacroRegime.SUMMER, MacroRegime.SPRING]:
+            if current_price > ma_slow and ma_fast > ma_slow and trend_strength > 0.005:
+                # 强做多信号
+                direction = SignalDirection.LONG
+                confidence = min(1.0, 0.6 + trend_strength * 10)
+                position_size = 1.0  # 满仓
+            elif current_price < ma_slow * 0.95:
+                # 跌破支撑，平仓
+                direction = SignalDirection.NEUTRAL
+                confidence = 0.5
+                position_size = 0.0
+            else:
+                return None
+        else:
+            # 非 Summer/Spring，观望
+            direction = SignalDirection.NEUTRAL
+            confidence = 0.5
+            position_size = 0.0
+
+        # 根据中观健康度调整
+        if ecosystem and direction == SignalDirection.LONG:
+            health = ecosystem.meso.health_score / 100
+            position_size *= health
+            confidence *= health
+
+        if direction == SignalDirection.NEUTRAL and position_size == 0:
+            return AgentSignal(
+                agent_name=self.name,
+                direction=direction,
+                strength=SignalStrength.WEAK,
+                confidence=confidence,
+                suggested_position=0.0,
+                expected_return=0.0,
+                win_probability=0.5,
+                timestamp=datetime.now(),
+                valid_until=datetime.now(),
+                metadata={"reason": "outside_preferred_regime_or_no_trend"}
+            )
+
+        return AgentSignal(
+            agent_name=self.name,
+            direction=direction,
+            strength=SignalStrength.STRONG if confidence > 0.7 else SignalStrength.MEDIUM,
+            confidence=confidence,
+            suggested_position=position_size,
+            expected_return=0.25,  # 预期25%年化
+            win_probability=0.6,
+            timestamp=datetime.now(),
+            valid_until=datetime.now(),
+            metadata={
+                "price": current_price,
+                "ma_fast": ma_fast,
+                "ma_slow": ma_slow,
+                "trend_strength": trend_strength,
+                "regime": ecosystem.macro.regime.value if ecosystem else "unknown"
+            }
+        )
+
+    def get_expected_return(self, price_data: pd.DataFrame, ecosystem=None) -> float:
+        return 0.25
+
+    def get_win_probability(self, price_data: pd.DataFrame, ecosystem=None) -> float:
+        return 0.6

cyb50-pro/agents/structure_arbitrage/agent.py

@@ -0,0 +1,242 @@
+"""
+结构套利者智能体
+
+基于期现背离和成分股配对交易生成套利信号
+最佳生态：秋季分化
+"""
+
+from datetime import datetime, timedelta
+from typing import Dict, Any, Optional, List, Tuple
+
+import numpy as np
+import pandas as pd
+
+from agents.base import AgentBase, AgentSignal, SignalDirection, SignalStrength
+from core.ecosystem import MacroRegime, UnifiedEcosystem
+
+
+class StructureArbitrageAgent(AgentBase):
+    """
+    结构套利者智能体
+
+    策略逻辑：
+    1. 创业板50指数与成分股加权价格偏离>0.5%时触发
+    2. 做多低估端，做空高估端
+    3. 利用板块轮动和分化获利
+    """
+
+    def __init__(
+        self,
+        config: Optional[Dict[str, Any]] = None,
+        basis_threshold: float = 0.005,
+        pair_correlation_min: float = 0.8,
+        pair_zscore_threshold: float = 2.0
+    ):
+        super().__init__(
+            name="structure_arbitrage",
+            config=config,
+            max_position=0.5,
+            min_confidence=0.60
+        )
+
+        self.basis_threshold = basis_threshold
+        self.pair_correlation_min = pair_correlation_min
+        self.pair_zscore_threshold = pair_zscore_threshold
+
+        self.preferred_regimes = [MacroRegime.AUTUMN]
+
+    def generate_signal(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None,
+        constituent_data: Optional[pd.DataFrame] = None
+    ) -> Optional[AgentSignal]:
+        """生成套利信号"""
+        if len(price_data) < 20:
+            return None
+
+        # 期现背离检测
+        basis_signal = self._detect_basis_deviation(price_data, constituent_data)
+
+        # 配对交易信号
+        pair_signal = self._detect_pair_opportunity(price_data, constituent_data)
+
+        # 选择更强的信号
+        if abs(basis_signal) > abs(pair_signal):
+            signal_type = "basis"
+            signal_strength = basis_signal
+        else:
+            signal_type = "pair"
+            signal_strength = pair_signal
+
+        if abs(signal_strength) < self.basis_threshold:
+            return None
+
+        direction = SignalDirection.LONG if signal_strength > 0 else SignalDirection.SHORT
+        confidence = min(1.0, abs(signal_strength) / self.basis_threshold * 0.5 + 0.5)
+
+        if not self._validate_signal(direction, confidence, ecosystem):
+            return None
+
+        position_size = self._calculate_position_size(ecosystem, confidence)
+
+        strength = (
+            SignalStrength.STRONG if confidence > 0.8
+            else SignalStrength.MEDIUM if confidence > 0.65
+            else SignalStrength.WEAK
+        )
+
+        signal = AgentSignal(
+            agent_name=self.name,
+            direction=direction,
+            strength=strength,
+            confidence=confidence,
+            suggested_position=position_size,
+            expected_return=self.get_expected_return(price_data, ecosystem),
+            win_probability=self.get_win_probability(price_data, ecosystem),
+            timestamp=datetime.now(),
+            valid_until=datetime.now() + timedelta(days=3),
+            metadata={
+                "signal_type": signal_type,
+                "deviation": signal_strength,
+                "is_hedged": True
+            }
+        )
+
+        self.current_signal = signal
+        self.signal_history.append(signal)
+
+        return signal
+
+    def get_expected_return(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None
+    ) -> float:
+        """计算预期收益（套利收益通常较低但稳定）"""
+        base_return = 0.08  # 8%年化，套利策略收益稳定但不高
+
+        if ecosystem and ecosystem.macro.regime == MacroRegime.AUTUMN:
+            base_return = 0.15  # 秋季分化大，套利机会多
+
+        return base_return
+
+    def get_win_probability(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None
+    ) -> float:
+        """计算胜率（套利通常胜率较高）"""
+        base_prob = 0.65
+
+        if ecosystem and ecosystem.macro.regime in self.preferred_regimes:
+            base_prob += 0.10
+
+        return min(0.80, base_prob)
+
+    def _detect_basis_deviation(
+        self,
+        index_data: pd.DataFrame,
+        constituent_data: Optional[pd.DataFrame]
+    ) -> float:
+        """检测期现（指数-成分股）背离"""
+        if constituent_data is None or constituent_data.empty:
+            return 0.0
+
+        # 计算成分股加权价格
+        weighted_price = constituent_data.mean(axis=1)  # 简化：等权平均
+
+        index_price = index_data['close']
+
+        # 对齐数据
+        common_dates = index_price.index.intersection(weighted_price.index)
+        if len(common_dates) < 20:
+            return 0.0
+
+        index_aligned = index_price.loc[common_dates]
+        weighted_aligned = weighted_price.loc[common_dates]
+
+        # 标准化后比较
+        index_norm = index_aligned / index_aligned.iloc[-20] - 1
+        weighted_norm = weighted_aligned / weighted_aligned.iloc[-20] - 1
+
+        # 当前偏离
+        deviation = index_norm.iloc[-1] - weighted_norm.iloc[-1]
+
+        return deviation
+
+    def _detect_pair_opportunity(
+        self,
+        price_data: pd.DataFrame,
+        constituent_data: Optional[pd.DataFrame]
+    ) -> float:
+        """检测配对交易机会"""
+        if constituent_data is None or len(constituent_data.columns) < 2:
+            return 0.0
+
+        # 简化：寻找相关性高的股票对
+        returns = constituent_data.pct_change().dropna()
+
+        if len(returns) < 20:
+            return 0.0
+
+        # 找相关性最高的股票对
+        corr_matrix = returns.iloc[-60:].corr()
+
+        max_corr = 0
+        best_pair = None
+        for i in range(len(corr_matrix.columns)):
+            for j in range(i+1, len(corr_matrix.columns)):
+                corr = corr_matrix.iloc[i, j]
+                if corr > max_corr:
+                    max_corr = corr
+                    best_pair = (corr_matrix.columns[i], corr_matrix.columns[j])
+
+        if max_corr < self.pair_correlation_min or best_pair is None:
+            return 0.0
+
+        # 计算Z-score
+        stock1 = constituent_data[best_pair[0]]
+        stock2 = constituent_data[best_pair[1]]
+
+        spread = np.log(stock1) - np.log(stock2)
+        spread_mean = spread.iloc[-60:].mean()
+        spread_std = spread.iloc[-60:].std()
+
+        if spread_std == 0:
+            return 0.0
+
+        zscore = (spread.iloc[-1] - spread_mean) / spread_std
+
+        return zscore / self.pair_zscore_threshold * 0.5
+
+    def _calculate_position_size(
+        self,
+        ecosystem: Optional[UnifiedEcosystem],
+        confidence: float
+    ) -> float:
+        """计算建议仓位（套利策略仓位较小）"""
+        base_size = self.max_position * confidence * 0.6
+
+        if ecosystem:
+            if ecosystem.macro.regime == MacroRegime.AUTUMN:
+                base_size *= 1.0
+            else:
+                base_size *= 0.5
+
+            health_factor = ecosystem.meso.health_score / 100
+            base_size *= health_factor
+
+        return min(self.max_position, base_size)
+
+    def _check_regime_match(self, ecosystem: Any) -> float:
+        """检查生态适配度"""
+        if not ecosystem or not hasattr(ecosystem, 'macro'):
+            return 0.7
+
+        if ecosystem.macro.regime == MacroRegime.AUTUMN:
+            return 1.2
+        elif ecosystem.macro.regime == MacroRegime.SUMMER:
+            return 0.6  # 夏季趋势强，套利空间小
+        else:
+            return 0.8

cyb50-pro/agents/trend_hunter/agent.py

@@ -0,0 +1,213 @@
+"""
+趋势猎手智能体 - 稳健趋势跟踪
+
+核心逻辑：
+1. 双均线金叉入场
+2. ADX>20确认趋势
+3. 价格>MA20确认方向
+4. 生态过滤：只在Summer/Spring交易
+"""
+
+from datetime import datetime, timedelta
+from typing import Dict, Any, Optional
+
+import numpy as np
+import pandas as pd
+
+from agents.base import AgentBase, AgentSignal, SignalDirection, SignalStrength
+from core.ecosystem import MacroRegime, UnifiedEcosystem
+
+
+class TrendHunterAgent(AgentBase):
+    """
+    趋势猎手 - 稳健版本
+    """
+
+    def __init__(
+        self,
+        config: Optional[Dict[str, Any]] = None,
+        ma_fast: int = 5,              # 5日均线
+        ma_slow: int = 20,             # 20日均线
+        adx_period: int = 14,
+        adx_threshold: float = 20.0,   # ADX门槛
+    ):
+        super().__init__(
+            name="trend_hunter",
+            config=config,
+            max_position=1.0,
+            min_confidence=0.55
+        )
+
+        self.ma_fast = ma_fast
+        self.ma_slow = ma_slow
+        self.adx_period = adx_period
+        self.adx_threshold = adx_threshold
+        self.preferred_regimes = [MacroRegime.SUMMER, MacroRegime.SPRING]
+
+    def generate_signal(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None
+    ) -> Optional[AgentSignal]:
+        """生成交易信号"""
+        if len(price_data) < self.ma_slow + 10:
+            return None
+
+        close = price_data['close']
+        high = price_data['high']
+        low = price_data['low']
+
+        # 计算指标
+        ma_fast = close.rolling(self.ma_fast).mean()
+        ma_slow = close.rolling(self.ma_slow).mean()
+        adx = self._calculate_adx(price_data)
+
+        current_price = close.iloc[-1]
+        current_ma_fast = ma_fast.iloc[-1]
+        current_ma_slow = ma_slow.iloc[-1]
+        prev_ma_fast = ma_fast.iloc[-2]
+        prev_ma_slow = ma_slow.iloc[-2]
+
+        # 金叉/死叉判断
+        golden_cross = (prev_ma_fast <= prev_ma_slow) and (current_ma_fast > current_ma_slow)
+        death_cross = (prev_ma_fast >= prev_ma_slow) and (current_ma_fast < current_ma_slow)
+
+        # 趋势强度
+        ma_diff = (current_ma_fast - current_ma_slow) / current_ma_slow
+
+        direction = SignalDirection.NEUTRAL
+        confidence = 0.0
+        position_size = 0.0
+
+        # 只在Summer/Spring交易
+        if ecosystem and ecosystem.macro.regime in self.preferred_regimes:
+            health = ecosystem.meso.health_score / 100
+
+            # 入场：简化条件 - 只要在均线上方 + 健康度>50
+            if current_price > current_ma_slow and current_ma_fast > current_ma_slow and health > 0.5:
+                # 金叉时高仓位，否则维持仓位
+                if golden_cross:
+                    direction = SignalDirection.LONG
+                    confidence = min(1.0, 0.7 * health)
+                    position_size = confidence
+                else:
+                    direction = SignalDirection.LONG
+                    confidence = 0.5 * health
+                    position_size = confidence * 0.6
+
+            # 出场：死叉或跌破慢线
+            elif death_cross or current_price < current_ma_slow * 0.98:  # 允许2%回撤
+                direction = SignalDirection.NEUTRAL
+                confidence = 0.0
+                position_size = 0.0
+
+        # 非目标生态，观望
+        else:
+            direction = SignalDirection.NEUTRAL
+            confidence = 0.0
+            position_size = 0.0
+
+        # 无信号时返回None
+        if direction == SignalDirection.NEUTRAL and position_size == 0:
+            return None
+
+        # 确定强度
+        if confidence > 0.75:
+            strength = SignalStrength.STRONG
+        elif confidence > 0.6:
+            strength = SignalStrength.MEDIUM
+        else:
+            strength = SignalStrength.WEAK
+
+        signal = AgentSignal(
+            agent_name=self.name,
+            direction=direction,
+            strength=strength,
+            confidence=confidence,
+            suggested_position=position_size,
+            expected_return=self.get_expected_return(price_data, ecosystem),
+            win_probability=self.get_win_probability(price_data, ecosystem),
+            timestamp=datetime.now(),
+            valid_until=datetime.now() + timedelta(hours=24),
+            metadata={
+                "current_price": current_price,
+                "ma_fast": current_ma_fast,
+                "ma_slow": current_ma_slow,
+                "adx": adx,
+                "golden_cross": golden_cross,
+                "death_cross": death_cross,
+                "ma_diff": ma_diff,
+                "health": ecosystem.meso.health_score if ecosystem else 0
+            }
+        )
+
+        self.current_signal = signal
+        self.signal_history.append(signal)
+
+        return signal
+
+    def _calculate_adx(self, data: pd.DataFrame) -> float:
+        """计算ADX指标"""
+        if len(data) < self.adx_period * 2:
+            return 20.0
+
+        high = data['high']
+        low = data['low']
+        close = data['close']
+
+        # +DM和-DM
+        plus_dm = high.diff()
+        minus_dm = -low.diff()
+
+        plus_dm = plus_dm.clip(lower=0)
+        minus_dm = minus_dm.clip(lower=0)
+
+        # TR
+        tr1 = high - low
+        tr2 = (high - close.shift(1)).abs()
+        tr3 = (low - close.shift(1)).abs()
+        tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+
+        # ATR
+        atr = tr.rolling(self.adx_period).mean()
+
+        # +DI和-DI
+        plus_di = 100 * (plus_dm.rolling(self.adx_period).mean() / atr)
+        minus_di = 100 * (minus_dm.rolling(self.adx_period).mean() / atr)
+
+        # DX和ADX
+        dx = 100 * (plus_di - minus_di).abs() / (plus_di + minus_di)
+        adx = dx.rolling(self.adx_period).mean()
+
+        return adx.iloc[-1] if not pd.isna(adx.iloc[-1]) else 20.0
+
+    def get_expected_return(self, price_data: pd.DataFrame, ecosystem=None) -> float:
+        """预期收益"""
+        if ecosystem and ecosystem.macro.regime == MacroRegime.SUMMER:
+            return 0.35
+        elif ecosystem and ecosystem.macro.regime == MacroRegime.SPRING:
+            return 0.25
+        return 0.15
+
+    def get_win_probability(self, price_data: pd.DataFrame, ecosystem=None) -> float:
+        """胜率估算"""
+        base_prob = 0.55
+
+        if ecosystem:
+            if ecosystem.macro.regime == MacroRegime.SUMMER:
+                base_prob += 0.12
+            elif ecosystem.macro.regime == MacroRegime.SPRING:
+                base_prob += 0.08
+
+            health = ecosystem.meso.health_score / 100
+            base_prob += (health - 0.5) * 0.1
+
+        return min(0.80, base_prob)
+
+    def _calculate_position_size(
+        self,
+        ecosystem: Optional[UnifiedEcosystem],
+        confidence: float
+    ) -> float:
+        """计算仓位"""
+        return self.max_position * confidence

cyb50-pro/agents/volatility_seller/agent.py

@@ -0,0 +1,246 @@
+"""
+波动率卖家智能体
+
+基于IV Rank分析和卖方策略生成信号
+最佳生态：夏季繁荣、春季复苏（避开秋季）
+注意：需要期权数据支持，当前为预留接口
+"""
+
+from datetime import datetime, timedelta
+from typing import Dict, Any, Optional
+
+import numpy as np
+import pandas as pd
+
+from agents.base import AgentBase, AgentSignal, SignalDirection, SignalStrength
+from core.ecosystem import MacroRegime, UnifiedEcosystem
+
+
+class VolatilitySellerAgent(AgentBase):
+    """
+    波动率卖家智能体
+
+    策略逻辑：
+    1. 当隐含波动率（IV Rank）> 80%时，做空波动率
+    2. 使用卖方策略：卖出宽跨式（Strangle）或铁鹰（Iron Condor）
+    3. 避开秋季（高波动不确定期）
+    4. 收取权利金，利用波动率均值回归
+
+    注意：此策略需要期权数据支持
+    """
+
+    def __init__(
+        self,
+        config: Optional[Dict[str, Any]] = None,
+        iv_rank_high: float = 80,
+        iv_rank_low: float = 20,
+        strategy_type: str = "strangle",  # "straddle", "strangle", "iron_condor"
+        delta_target: float = 0.30,
+        dte_target: int = 30  # 到期日目标（天）
+    ):
+        super().__init__(
+            name="volatility_seller",
+            config=config,
+            max_position=0.4,
+            min_confidence=0.70
+        )
+
+        self.iv_rank_high = iv_rank_high
+        self.iv_rank_low = iv_rank_low
+        self.strategy_type = strategy_type
+        self.delta_target = delta_target
+        self.dte_target = dte_target
+
+        self.preferred_regimes = [MacroRegime.SUMMER, MacroRegime.SPRING]
+        self.avoid_regimes = [MacroRegime.AUTUMN]
+
+        # 检查是否有期权数据
+        self.has_option_data = False
+
+    def generate_signal(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None,
+        option_data: Optional[Dict] = None
+    ) -> Optional[AgentSignal]:
+        """生成卖方信号"""
+        if len(price_data) < 60:
+            return None
+
+        # 检查是否在避免的生态
+        if ecosystem and hasattr(ecosystem, 'macro'):
+            if ecosystem.macro.regime in self.avoid_regimes:
+                return None
+
+        # 计算IV Rank（使用历史波动率作为代理）
+        iv_rank = self._calculate_iv_rank(price_data)
+
+        # 检查是否满足做空波动率条件
+        if iv_rank < self.iv_rank_high:
+            return None
+
+        # 检查趋势（避免在强趋势市场做卖方）
+        trend_strength = self._calculate_trend_strength(price_data)
+        if trend_strength > 0.7:  # 强趋势市场风险高
+            return None
+
+        direction = SignalDirection.NEUTRAL  # 卖方策略方向中性
+        confidence = min(1.0, (iv_rank - self.iv_rank_high) / 20 + 0.5)
+
+        if not self._validate_signal(direction, confidence, ecosystem):
+            return None
+
+        position_size = self._calculate_position_size(ecosystem, confidence, iv_rank)
+
+        strength = (
+            SignalStrength.STRONG if confidence > 0.85
+            else SignalStrength.MEDIUM if confidence > 0.75
+            else SignalStrength.WEAK
+        )
+
+        signal = AgentSignal(
+            agent_name=self.name,
+            direction=direction,
+            strength=strength,
+            confidence=confidence,
+            suggested_position=position_size,
+            expected_return=self.get_expected_return(price_data, ecosystem),
+            win_probability=self.get_win_probability(price_data, ecosystem),
+            timestamp=datetime.now(),
+            valid_until=datetime.now() + timedelta(days=self.dte_target),
+            metadata={
+                "iv_rank": iv_rank,
+                "strategy_type": self.strategy_type,
+                "delta_target": self.delta_target,
+                "dte_target": self.dte_target,
+                "trend_strength": trend_strength,
+                "note": "Option strategy - requires options data"
+            }
+        )
+
+        self.current_signal = signal
+        self.signal_history.append(signal)
+
+        return signal
+
+    def get_expected_return(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None
+    ) -> float:
+        """计算预期收益（卖方策略收益稳定但有限）"""
+        # 卖方策略通常每月收取1-3%权利金
+        monthly_premium = 0.02
+        annual_return = (1 + monthly_premium) ** 12 - 1
+
+        return annual_return
+
+    def get_win_probability(
+        self,
+        price_data: pd.DataFrame,
+        ecosystem: Optional[UnifiedEcosystem] = None
+    ) -> float:
+        """计算胜率（卖方策略胜率通常较高，70-80%）"""
+        base_prob = 0.70
+
+        iv_rank = self._calculate_iv_rank(price_data)
+        if iv_rank > 85:
+            base_prob += 0.10
+        elif iv_rank > 80:
+            base_prob += 0.05
+
+        # 避开秋季降低胜率预期
+        if ecosystem and ecosystem.macro.regime == MacroRegime.AUTUMN:
+            base_prob -= 0.15
+
+        return min(0.85, base_prob)
+
+    def _calculate_iv_rank(self, data: pd.DataFrame) -> float:
+        """
+        计算IV Rank
+
+        使用历史波动率作为隐含波动率的代理
+        IV Rank = (当前IV - 52周最低IV) / (52周最高IV - 52周最低IV)
+        """
+        if len(data) < 252:
+            lookback = len(data) // 2
+        else:
+            lookback = 252
+
+        # 计算历史波动率
+        returns = data['close'].pct_change().dropna()
+        current_vol = returns.iloc[-20:].std() * np.sqrt(252)
+
+        # 滚动计算历史波动率
+        rolling_vol = returns.rolling(20).std() * np.sqrt(252)
+        historical_vol = rolling_vol.iloc[-lookback:]
+
+        vol_min = historical_vol.min()
+        vol_max = historical_vol.max()
+
+        if vol_max == vol_min:
+            return 50.0
+
+        iv_rank = (current_vol - vol_min) / (vol_max - vol_min) * 100
+        return iv_rank
+
+    def _calculate_trend_strength(self, data: pd.DataFrame) -> float:
+        """计算趋势强度（用于避开关强趋势市场）"""
+        if len(data) < 20:
+            return 0.0
+
+        # 使用ADX作为趋势强度代理
+        high = data['high']
+        low = data['low']
+        close = data['close']
+
+        plus_dm = high.diff().clip(lower=0)
+        minus_dm = (-low.diff()).clip(lower=0)
+
+        tr = pd.concat([high - low, (high - close.shift(1)).abs(), (low - close.shift(1)).abs()], axis=1).max(axis=1)
+        atr = tr.rolling(14).mean()
+
+        plus_di = 100 * (plus_dm.rolling(14).mean() / atr)
+        minus_di = 100 * (minus_dm.rolling(14).mean() / atr)
+
+        dx = 100 * (plus_di - minus_di).abs() / (plus_di + minus_di)
+        adx = dx.rolling(14).mean()
+
+        current_adx = adx.iloc[-1] if not pd.isna(adx.iloc[-1]) else 20
+
+        return min(1.0, current_adx / 50)
+
+    def _calculate_position_size(
+        self,
+        ecosystem: Optional[UnifiedEcosystem],
+        confidence: float,
+        iv_rank: float
+    ) -> float:
+        """计算建议仓位（卖方策略仓位保守）"""
+        base_size = self.max_position * confidence * 0.5
+
+        # IV越高，仓位越小（风险控制）
+        if iv_rank > 90:
+            base_size *= 0.7
+        elif iv_rank > 85:
+            base_size *= 0.85
+
+        if ecosystem:
+            if ecosystem.macro.regime in self.preferred_regimes:
+                base_size *= 1.0
+            else:
+                base_size *= 0.6
+
+        return min(self.max_position, base_size)
+
+    def _check_regime_match(self, ecosystem: Any) -> float:
+        """检查生态适配度"""
+        if not ecosystem or not hasattr(ecosystem, 'macro'):
+            return 0.7
+
+        if ecosystem.macro.regime in self.avoid_regimes:
+            return 0.3
+        elif ecosystem.macro.regime in self.preferred_regimes:
+            return 1.1
+        else:
+            return 0.8

cyb50-pro/backtest/__init__.py

@@ -0,0 +1,13 @@
+"""
+回测模块
+
+提供全链路回测和绩效归因
+"""
+
+from .engine import CYB50ProBacktester, BacktestResult, BacktestTrade
+
+__all__ = [
+    "CYB50ProBacktester",
+    "BacktestResult",
+    "BacktestTrade",
+]

cyb50-pro/backtest/engine.py

@@ -0,0 +1,543 @@
+"""
+CYB50-Pro 回测引擎
+
+支持全链路回测和绩效归因
+"""
+
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional, Tuple, Any
+import json
+
+import numpy as np
+import pandas as pd
+
+from core.engine import CYB50ProEngine
+from core.ecosystem import UnifiedEcosystem
+from utils.logger import get_logger, EventType
+
+
+@dataclass
+class BacktestTrade:
+    """回测交易记录"""
+    entry_time: datetime
+    exit_time: Optional[datetime]
+    direction: str
+    entry_price: float
+    exit_price: Optional[float]
+    position_size: float
+    pnl: float
+    pnl_pct: float
+    agent_signals: Dict[str, Any]
+    ecosystem_state: Dict[str, Any]
+
+
+@dataclass
+class BacktestResult:
+    """回测结果"""
+    # 基础信息（必须参数在前）
+    start_date: datetime
+    end_date: datetime
+    initial_capital: float
+    final_capital: float
+
+    # 收益指标
+    total_return: float
+    annual_return: float
+
+    # 风险指标
+    max_drawdown: float
+    max_drawdown_duration: int
+    volatility: float
+    sharpe_ratio: float
+    sortino_ratio: float
+
+    # 交易统计
+    total_trades: int
+    winning_trades: int
+    losing_trades: int
+    win_rate: float
+    avg_profit: float
+    avg_loss: float
+    profit_factor: float
+
+    # 归因分析
+    regime_performance: Dict[str, Dict]
+    agent_contributions: Dict[str, float]
+
+    # 硬止损信息
+    hard_stop_triggered: bool = False
+    hard_stop_date: Optional[datetime] = None
+
+    # 原始数据（有默认值的参数在后）
+    daily_returns: pd.Series = field(default_factory=pd.Series)
+    equity_curve: pd.Series = field(default_factory=pd.Series)
+    trades: List[BacktestTrade] = field(default_factory=list)
+
+
+class CYB50ProBacktester:
+    """
+    CYB50-Pro 回测器
+
+    功能：
+    1. 加载历史数据（2018-2025）
+    2. 逐日/逐30分钟运行策略
+    3. 记录交易和权益变化
+    4. 计算绩效指标
+    5. 归因分析
+    """
+
+    def __init__(
+        self,
+        initial_capital: float = 1_000_000,
+        commission_rate: float = 0.0005,  # 万5佣金
+        slippage: float = 0.001,  # 0.1%滑点
+        log_level: str = "INFO",
+        catastrophic_drawdown: float = 0.15,  # -15%熔断（更严格）
+        position_stop: float = 0.10  # -10%单笔止损
+    ):
+        self.initial_capital = initial_capital
+        self.commission_rate = commission_rate
+        self.slippage = slippage
+        self.logger = get_logger(log_level=log_level)
+
+        # 熔断参数
+        self.catastrophic_drawdown = catastrophic_drawdown
+        self.position_stop = position_stop
+
+        # 初始化引擎
+        self.engine = CYB50ProEngine()
+
+        # 回测状态
+        self.current_capital = initial_capital
+        self.current_position = 0.0  # 0-1
+        self.equity_curve: List[Tuple[datetime, float]] = []
+        self.trades: List[BacktestTrade] = []
+        self.current_trade: Optional[BacktestTrade] = None
+
+        # 追踪峰值用于熔断判断
+        self.peak_equity = initial_capital
+        self.entry_equity = initial_capital  # 记录入场时的权益
+        self.trade_peak_equity = initial_capital  # 单笔交易峰值
+
+    def run_backtest(
+        self,
+        price_data: pd.DataFrame,
+        start_date: Optional[str] = None,
+        end_date: Optional[str] = None
+    ) -> BacktestResult:
+        """
+        运行回测
+
+        Args:
+            price_data: 历史价格数据
+            start_date: 开始日期 (YYYY-MM-DD)
+            end_date: 结束日期 (YYYY-MM-DD)
+
+        Returns:
+            BacktestResult: 回测结果
+        """
+        # 数据预处理
+        data = price_data.copy()
+        if start_date:
+            data = data[data.index >= start_date]
+        if end_date:
+            data = data[data.index <= end_date]
+
+        self.logger.log_system(
+            EventType.SYSTEM_START,
+            f"Starting backtest from {data.index[0]} to {data.index[-1]}",
+            level="info"
+        )
+
+        # 初始化
+        self.current_capital = self.initial_capital
+        self.equity_curve = [(data.index[0], self.initial_capital)]
+
+        # 滚动回测
+        lookback = 60  # 需要60日历史数据
+
+        for i in range(lookback, len(data)):
+            current_time = data.index[i]
+            current_price = data['close'].iloc[i]
+
+            # 获取历史数据窗口
+            hist_data = data.iloc[i-lookback:i+1]
+
+            # 运行引擎
+            try:
+                result = self.engine.run_cycle(
+                    price_data=hist_data,
+                    account_value=self.current_capital,
+                    tick_data=None
+                )
+
+                # 处理信号
+                self._process_signal(
+                    result=result,
+                    current_time=current_time,
+                    current_price=current_price
+                )
+
+                # 更新权益并检查熔断
+                self._update_equity(current_time, current_price)
+
+            except Exception as e:
+                self.logger.log_system(
+                    EventType.SYSTEM_ERROR,
+                    f"Error at {current_time}: {str(e)}",
+                    level="error"
+                )
+
+        # 平仓最后一笔交易
+        if self.current_trade:
+            self._close_trade(
+                data.index[-1],
+                data['close'].iloc[-1],
+                "backtest_end"
+            )
+
+        # 计算绩效指标
+        return self._calculate_metrics(data)
+
+    def _process_signal(
+        self,
+        result: Dict,
+        current_time: datetime,
+        current_price: float
+    ):
+        """处理引擎信号"""
+        if not result.get("executed"):
+            return
+
+        target_position = result.get("final_position", 0)
+
+        # 检查是否需要调仓
+        if abs(target_position - self.current_position) < 0.05:
+            return  # 变化小于5%，不调仓
+
+        # 先平仓
+        if self.current_position > 0 and self.current_trade:
+            self._close_trade(current_time, current_price, "signal_change")
+
+        # 再开仓
+        if target_position > 0:
+            self._open_trade(
+                current_time,
+                current_price,
+                result.get("coordinated", {}).get("direction", "long"),
+                target_position,
+                result
+            )
+
+    def _open_trade(
+        self,
+        time: datetime,
+        price: float,
+        direction: str,
+        position_size: float,
+        engine_result: Dict
+    ):
+        """开仓"""
+        # 考虑滑点
+        if direction == "long":
+            exec_price = price * (1 + self.slippage)
+        else:
+            exec_price = price * (1 - self.slippage)
+
+        # 记录交易
+        self.current_trade = BacktestTrade(
+            entry_time=time,
+            exit_time=None,
+            direction=direction,
+            entry_price=exec_price,
+            exit_price=None,
+            position_size=position_size,
+            pnl=0.0,
+            pnl_pct=0.0,
+            agent_signals=engine_result.get("signals", {}),
+            ecosystem_state=engine_result.get("ecosystem", {})
+        )
+
+        self.current_position = position_size
+        self.entry_equity = self.current_capital  # 记录入场权益
+        self.trade_peak_equity = self.current_capital  # 重置交易峰值
+
+        # 扣除佣金
+        trade_value = self.current_capital * position_size
+        commission = trade_value * self.commission_rate
+        self.current_capital -= commission
+
+    def _close_trade(
+        self,
+        time: datetime,
+        price: float,
+        reason: str
+    ):
+        """平仓"""
+        if not self.current_trade:
+            return
+
+        # 考虑滑点
+        if self.current_trade.direction == "long":
+            exec_price = price * (1 - self.slippage)
+        else:
+            exec_price = price * (1 + self.slippage)
+
+        # 计算盈亏
+        if self.current_trade.direction == "long":
+            pnl_pct = (exec_price - self.current_trade.entry_price) / self.current_trade.entry_price
+        else:
+            pnl_pct = (self.current_trade.entry_price - exec_price) / self.current_trade.entry_price
+
+        pnl = self.current_capital * self.current_position * pnl_pct
+
+        # 扣除佣金
+        trade_value = self.current_capital * self.current_position
+        commission = trade_value * self.commission_rate
+        self.current_capital -= commission
+
+        # 更新资金
+        self.current_capital += pnl
+
+        # 记录交易
+        self.current_trade.exit_time = time
+        self.current_trade.exit_price = exec_price
+        self.current_trade.pnl = pnl
+        self.current_trade.pnl_pct = pnl_pct
+
+        self.trades.append(self.current_trade)
+        self.current_trade = None
+        self.current_position = 0.0
+
+    def _update_equity(self, time: datetime, price: float):
+        """更新权益曲线，仅检查-20%熔断"""
+        if self.current_position > 0 and self.current_trade:
+            # 计算浮动盈亏
+            if self.current_trade.direction == "long":
+                unrealized_pct = (price - self.current_trade.entry_price) / self.current_trade.entry_price
+            else:
+                unrealized_pct = (self.current_trade.entry_price - price) / self.current_trade.entry_price
+
+            unrealized_pnl = self.current_capital * self.current_position * unrealized_pct
+            total_equity = self.current_capital + unrealized_pnl
+        else:
+            total_equity = self.current_capital
+
+        self.equity_curve.append((time, total_equity))
+
+        # 仅检查-20%熔断
+        self._check_catastrophic_stop(time, price, total_equity)
+
+    def _check_catastrophic_stop(self, time: datetime, price: float, total_equity: float):
+        """
+        检查熔断和追踪止盈（路径一优化版）
+        """
+        # 更新峰值权益
+        if total_equity > self.peak_equity:
+            self.peak_equity = total_equity
+
+        # 计算回撤
+        if self.peak_equity > 0:
+            drawdown = (total_equity - self.peak_equity) / self.peak_equity
+        else:
+            drawdown = 0.0
+
+        # 1. 追踪止盈：从峰值回撤5%即出场
+        if self.current_position > 0:
+            if total_equity > self.trade_peak_equity:
+                self.trade_peak_equity = total_equity
+
+            trailing_drawdown = (total_equity - self.trade_peak_equity) / self.trade_peak_equity
+            if trailing_drawdown <= -0.05:  # 5%追踪止盈
+                self.logger.log_system(
+                    EventType.RISK_WARNING,
+                    f"TRAILING STOP: Drawdown from peak {trailing_drawdown:.2%}. Closing trade.",
+                    level="warning"
+                )
+                self._close_trade(time, price, f"trailing_stop_{trailing_drawdown:.2%}")
+                self.peak_equity = total_equity
+                return
+
+        # 2. -15%熔断
+        if drawdown <= -self.catastrophic_drawdown:
+            self.logger.log_system(
+                EventType.RISK_WARNING,
+                f"CATASTROPHIC STOP: Drawdown {drawdown:.2%}. Liquidating all positions.",
+                level="critical"
+            )
+            if self.current_position > 0:
+                self._close_trade(time, price, f"catastrophic_stop_{drawdown:.2%}")
+            self.peak_equity = total_equity
+
+    def _calculate_metrics(self, price_data: pd.DataFrame) -> BacktestResult:
+        """计算回测绩效指标"""
+        # 构建权益曲线
+        equity_df = pd.DataFrame(
+            self.equity_curve,
+            columns=['timestamp', 'equity']
+        ).set_index('timestamp')
+
+        # 计算日收益率
+        daily_equity = equity_df['equity'].resample('D').last().dropna()
+        daily_returns = daily_equity.pct_change().dropna()
+
+        # 基础指标
+        total_return = (daily_equity.iloc[-1] - self.initial_capital) / self.initial_capital
+
+        # 年化收益
+        n_years = (daily_equity.index[-1] - daily_equity.index[0]).days / 365.25
+        annual_return = (1 + total_return) ** (1 / n_years) - 1 if n_years > 0 else 0
+
+        # 最大回撤
+        rolling_max = daily_equity.cummax()
+        drawdown = (daily_equity - rolling_max) / rolling_max
+        max_drawdown = drawdown.min()
+
+        # 回撤持续时间
+        is_drawdown = drawdown < 0
+        drawdown_starts = is_drawdown.ne(is_drawdown.shift()).cumsum()
+        drawdown_durations = is_drawdown.groupby(drawdown_starts).sum()
+        max_drawdown_duration = drawdown_durations.max() if len(drawdown_durations) > 0 else 0
+
+        # 波动率和夏普
+        volatility = daily_returns.std() * np.sqrt(252)
+        sharpe_ratio = (annual_return - 0.03) / volatility if volatility > 0 else 0
+
+        # Sortino比率
+        downside_returns = daily_returns[daily_returns < 0]
+        downside_std = downside_returns.std() * np.sqrt(252)
+        sortino_ratio = (annual_return - 0.03) / downside_std if downside_std > 0 else 0
+
+        # 交易统计
+        total_trades = len(self.trades)
+        winning_trades = sum(1 for t in self.trades if t.pnl > 0)
+        losing_trades = total_trades - winning_trades
+        win_rate = winning_trades / total_trades if total_trades > 0 else 0
+
+        profits = [t.pnl for t in self.trades if t.pnl > 0]
+        losses = [t.pnl for t in self.trades if t.pnl < 0]
+
+        avg_profit = np.mean(profits) if profits else 0
+        avg_loss = np.mean(losses) if losses else 0
+
+        profit_factor = abs(sum(profits) / sum(losses)) if losses and sum(losses) != 0 else float('inf')
+
+        # 归因分析
+        regime_perf = self._analyze_regime_performance()
+        agent_contrib = self._analyze_agent_contributions()
+
+        return BacktestResult(
+            start_date=daily_equity.index[0],
+            end_date=daily_equity.index[-1],
+            initial_capital=self.initial_capital,
+            final_capital=daily_equity.iloc[-1],
+            total_return=total_return,
+            annual_return=annual_return,
+            daily_returns=daily_returns,
+            max_drawdown=max_drawdown,
+            max_drawdown_duration=int(max_drawdown_duration),
+            volatility=volatility,
+            sharpe_ratio=sharpe_ratio,
+            sortino_ratio=sortino_ratio,
+            total_trades=total_trades,
+            winning_trades=winning_trades,
+            losing_trades=losing_trades,
+            win_rate=win_rate,
+            avg_profit=avg_profit,
+            avg_loss=avg_loss,
+            profit_factor=profit_factor,
+            regime_performance=regime_perf,
+            agent_contributions=agent_contrib,
+            hard_stop_triggered=False,
+            hard_stop_date=None,
+            equity_curve=daily_equity,
+            trades=self.trades
+        )
+
+    def _analyze_regime_performance(self) -> Dict[str, Dict]:
+        """分析不同生态下的表现"""
+        regime_stats = {}
+
+        for trade in self.trades:
+            regime = trade.ecosystem_state.get('macro', {}).get('regime', 'unknown')
+
+            if regime not in regime_stats:
+                regime_stats[regime] = {
+                    'trades': 0,
+                    'wins': 0,
+                    'total_pnl': 0,
+                    'avg_pnl': 0
+                }
+
+            regime_stats[regime]['trades'] += 1
+            regime_stats[regime]['wins'] += 1 if trade.pnl > 0 else 0
+            regime_stats[regime]['total_pnl'] += trade.pnl
+
+        # 计算平均值
+        for regime in regime_stats:
+            stats = regime_stats[regime]
+            stats['win_rate'] = stats['wins'] / stats['trades'] if stats['trades'] > 0 else 0
+            stats['avg_pnl'] = stats['total_pnl'] / stats['trades'] if stats['trades'] > 0 else 0
+
+        return regime_stats
+
+    def _analyze_agent_contributions(self) -> Dict[str, float]:
+        """分析各智能体贡献"""
+        agent_pnl = {}
+
+        for trade in self.trades:
+            for agent_name, signal in trade.agent_signals.items():
+                if agent_name not in agent_pnl:
+                    agent_pnl[agent_name] = 0
+                # 简单归因：按信号置信度加权分配盈亏
+                confidence = signal.get('confidence', 0.5)
+                agent_pnl[agent_name] += trade.pnl * confidence
+
+        return agent_pnl
+
+    def generate_report(self, result: BacktestResult, output_path: str):
+        """生成回测报告"""
+        report = {
+            "summary": {
+                "测试区间": f"{result.start_date.strftime('%Y-%m-%d')} to {result.end_date.strftime('%Y-%m-%d')}",
+                "初始资金": f"{result.initial_capital:,.0f}",
+                "最终资金": f"{result.final_capital:,.0f}",
+                "总收益率": f"{result.total_return:.2%}",
+                "年化收益率": f"{result.annual_return:.2%}",
+                "最大回撤": f"{result.max_drawdown:.2%}",
+                "夏普比率": f"{result.sharpe_ratio:.2f}",
+                "Sortino比率": f"{result.sortino_ratio:.2f}"
+            },
+            "trading_stats": {
+                "总交易次数": result.total_trades,
+                "盈利次数": result.winning_trades,
+                "亏损次数": result.losing_trades,
+                "胜率": f"{result.win_rate:.2%}",
+                "平均盈利": f"{result.avg_profit:,.2f}",
+                "平均亏损": f"{result.avg_loss:,.2f}",
+                "盈亏比": f"{result.profit_factor:.2f}"
+            },
+            "regime_analysis": result.regime_performance,
+            "agent_contributions": result.agent_contributions
+        }
+
+        # 目标达成检查
+        targets = {
+            "年化收益 25-35%": "✓" if 0.25 <= result.annual_return <= 0.35 else "✗",
+            "最大回撤 <12%": "✓" if result.max_drawdown > -0.12 else "✗",
+            "夏普比率 >1.5": "✓" if result.sharpe_ratio > 1.5 else "✗"
+        }
+        report["target_validation"] = targets
+
+        # 保存JSON
+        with open(output_path, 'w', encoding='utf-8') as f:
+            json.dump(report, f, ensure_ascii=False, indent=2)
+
+        self.logger.log_system(
+            EventType.SYSTEM_STOP,
+            f"Backtest report saved to {output_path}",
+            level="info"
+        )
+
+        return report

cyb50-pro/config/agents.yaml

@@ -0,0 +1,143 @@
+# 多智能体策略矩阵配置
+
+# 智能体通用配置
+base:
+  # 健康度计算权重
+  health_weights:
+    sharpe: 0.40           # 近期夏普比率
+    regime_adaptation: 0.30 # 生态适应性
+    signal_stability: 0.20  # 信号稳定性
+    compute_efficiency: 0.10 # 计算效率
+
+  # 健康度分级
+  health_levels:
+    green: 80
+    yellow: 60
+    icu: 30
+
+  # 生命周期管理
+  lifecycle:
+    min_trades_for_evaluation: 20    # 最少交易次数评估
+    icu_duration_days: 60            # ICU观察期
+    archive_min_trades: 500          # 归档最少交易次数
+    archive_max_sharpe: 0.5          # 归档夏普阈值
+
+# 动态路由配置
+routing:
+  # 期望效用计算参数
+  utility:
+    lambda_risk: 0.5        # 风险惩罚系数
+    alpha_recent: 0.3       # 近期表现系数
+    activation_threshold: 0.0  # 激活阈值
+
+  # 相关性降权
+  correlation:
+    threshold: 0.7          # 相关性阈值
+    reduction_factor: 0.5   # 降权因子
+
+# 各智能体配置
+agents:
+  # 1. 趋势猎手
+  trend_hunter:
+    enabled: true
+    weight_max: 1.0
+    # 信号生成参数
+    ma_short: 5
+    ma_long: 20
+    adx_period: 14
+    adx_threshold: 25
+    rsrs_threshold: 0.7
+    # 最佳生态
+    preferred_regimes: ["summer", "spring"]
+    # 仓位计算
+    position_factor: 1.0
+
+  # 2. 均值回归者
+  mean_reversion:
+    enabled: true
+    weight_max: 0.8
+    # 布林带参数
+    bb_period: 20
+    bb_std: 2.0
+    # 卡尔曼滤波参数
+    kalman_transition: 1.0
+    kalman_observation: 1.0
+    # 最佳生态
+    preferred_regimes: ["winter", "spring"]
+    # 偏离度阈值
+    deviation_threshold: 0.02
+    position_factor: 0.8
+
+  # 3. 动量冲浪者
+  momentum_surfer:
+    enabled: true
+    weight_max: 0.6
+    # 突破参数
+    breakout_period: 20
+    volume_ratio: 1.5
+    # 持有期限制
+    max_hold_days: 5
+    # 最佳生态
+    preferred_regimes: ["summer"]
+    # 过滤条件
+    avoid_toxic_flow: true
+    position_factor: 0.6
+
+  # 4. 结构套利者
+  structure_arbitrage:
+    enabled: true
+    weight_max: 0.5
+    # 期现背离阈值
+    basis_threshold: 0.005
+    # 配对交易参数
+    pair_correlation_min: 0.8
+    pair_zscore_threshold: 2.0
+    # 最佳生态
+    preferred_regimes: ["autumn"]
+    position_factor: 0.5
+
+  # 5. 波动率卖家
+  volatility_seller:
+    enabled: false  # 需要期权支持，默认关闭
+    weight_max: 0.4
+    # IV Rank阈值
+    iv_rank_high: 80
+    iv_rank_low: 20
+    # 策略类型: "straddle", "strangle", "iron_condor"
+    strategy_type: "strangle"
+    # 最佳生态
+    preferred_regimes: ["summer", "spring"]
+    avoid_regimes: ["autumn"]
+    position_factor: 0.4
+
+  # 6. 事件驱动者
+  event_driven:
+    enabled: true
+    weight_max: 0.3
+    # 事件类型
+    event_types:
+      - policy        # 政策发布
+      - earnings      # 财报
+      - index_rebalance  # 指数调整
+      - macro_data    # 宏观数据
+    # 历史回测窗口
+    lookback_events: 10
+    # 最佳生态
+    preferred_regimes: ["all"]
+    position_factor: 0.3
+    # 持有期
+    max_hold_days: 3
+
+# 智能体协同配置
+coordination:
+  # 信号冲突处理
+  conflict_resolution:
+    method: "confidence_weighted"  # "confidence_weighted", "higher_wins", "cancel"
+    min_confidence_diff: 0.2       # 最小置信度差才执行
+
+  # 信号叠加增强
+  reinforcement:
+    enabled: true
+    same_direction_agents: 3       # 同向智能体数量阈值
+    position_boost: 0.2            # 仓位提升幅度
+    stop_tighten: 0.5              # 止损收紧幅度

cyb50-pro/config/ecosystem.yaml

@@ -0,0 +1,93 @@
+# 四维市场生态识别配置
+
+# 宏观生态识别
+macro:
+  # 季节识别阈值
+  seasons:
+    spring:
+      volatility_rebound: 0.20  # 波动率从低位回升阈值
+      volume_ratio: 1.2         # 成交量5日/20日均值比
+      dispersion_percentile: [30, 60]  # 板块离散度历史分位
+    summer:
+      adx_threshold: 25         # ADX阈值
+      adx_duration: 5           # 持续天数
+      volatility_percentile: [40, 70]
+      dispersion_max: 40
+    autumn:
+      dispersion_percentile: 70
+      rotation_high: true       # 轮动指数高位
+      volatility_increase: 0.15
+    winter:
+      volume_percentile: 50     # 成交量低于历史中位
+      volatility_compression: 20
+      adx_flat: 20              # ADX低于此值视为无趋势
+      flat_duration: 10
+
+  # 计算周期
+  lookback_days: 252  # 历史数据回看天数
+
+# 中观生态识别
+meso:
+  # 结构健康度权重
+  health_weights:
+    price_impact: 0.30      # 价格冲击系数
+    order_flow: 0.25        # 订单流平衡
+    liquidity_depth: 0.20   # 流动性深度
+    volatility_efficiency: 0.15  # 波动率效率
+    info_response: 0.10     # 信息冲击响应
+
+  # 健康度分级
+  levels:
+    high: 70
+    medium: 40
+    low: 0
+
+  # 计算周期
+  lookback_days: 60
+
+# 微观生态识别
+micro:
+  # HMM配置
+  hmm:
+    n_components: 3         # 状态数：震荡/趋势/反转
+    covariance_type: "full"
+    n_iter: 100
+
+  # 有毒订单流检测
+  toxic_flow:
+    price_reversal: 0.003   # 大单成交后价格反向运动阈值
+    depth_ratio: 0.8        # 买卖盘深度比阈值
+    duration_minutes: 10    # 持续时间
+
+  # 主力资金识别
+  smart_money:
+    min_order_value: 1000000  # 大单阈值（元）
+    accumulation_period: 5    # 建仓识别周期（分钟）
+
+  # 计算周期
+  timeframe: "30min"
+
+# 瞬时生态识别
+instant:
+  # 买卖盘不平衡
+  imbalance:
+    ratio_threshold: 2.0    # 买卖盘量比阈值
+    duration_minutes: 3     # 持续时间
+
+  # 跳动率突变
+  tick_rate:
+    change_threshold: 0.5   # 较前5分钟均值变化阈值
+
+  # 大单流向
+  block_trade:
+    min_value: 500000       # 大单阈值（元）
+    window_minutes: 1       # 统计窗口
+
+# 生态融合
+fusion:
+  confidence_threshold: 0.6  # 置信度阈值
+  update_frequency:
+    macro: "1d"      # 日度更新
+    meso: "1d"       # 日度更新
+    micro: "30min"   # 30分钟更新
+    instant: "1min"  # 分钟级更新

cyb50-pro/config/optimized.yaml

@@ -0,0 +1,41 @@
+# 优化后的策略配置
+# 目标: 年化25-35%, 回撤<12%, 夏普>1.5
+
+risk:
+  # 硬止损
+  max_drawdown_stop: 0.11      # -11% 清仓 (接近-12%目标)
+  max_daily_loss: 0.04          # -4% 单日减仓
+  hard_stop_cooldown: 45        # 45天后重置
+
+  # 仓位管理
+  position_sizing:
+    kelly_fraction: 0.35        # 提高凯利比例
+    max_position: 0.7           # 提高最大仓位
+    regime_multipliers:
+      SPRING: 0.9
+      SUMMER: 1.1               # 夏季允许超配
+      AUTUMN: 0.4
+      WINTER: 0.1
+
+agents:
+  trend_hunter:
+    ma_short: 8                 # 更敏感的均线
+    ma_long: 25
+    adx_threshold: 18           # 降低ADX门槛
+    rsrs_threshold: 0.55        # 降低RSRS门槛
+    max_position: 0.9
+
+  mean_reversion:
+    bollinger_period: 20
+    bollinger_std: 2.0
+    max_position: 0.6
+
+  momentum_surfer:
+    breakout_period: 20
+    volume_confirm: true
+    max_position: 0.7
+
+routing:
+  activation_threshold: 0.02    # 降低激活门槛
+  lambda_risk: 0.5              # 平衡风险
+  alpha_recent: 0.25            # 适度考虑近期表现

cyb50-pro/config/risk.yaml

@@ -0,0 +1,146 @@
+# 博弈式风险管理体系配置
+
+# 反脆弱仓位管理
+position_sizing:
+  # 基础凯利公式
+  kelly:
+    fraction: 0.5           # 半凯利（保守）
+    max_position: 1.0       # 最大仓位
+    min_position: 0.0       # 最小仓位
+
+  # 凸性调节
+  convexity:
+    enabled: true
+    gamma: 0.2              # 凸性系数
+    positive_boost: 0.1     # 正凸性提升
+    negative_penalty: 0.15  # 负凸性惩罚
+
+  # 生态适应系数
+  regime_adjustment:
+    spring: 1.0
+    summer: 1.2
+    autumn: 0.8
+    winter: 0.5
+
+  # 单品种限制
+  single_symbol:
+    max_weight: 0.30        # 最大30%
+
+# 风险预算系统
+risk_budget:
+  # 预算分配
+  allocation:
+    monthly_pct: 0.05       # 月度风险预算（账户价值5%）
+    cooling_period_days: 3  # 冷却期（交易日）
+
+  # 触发阈值
+  thresholds:
+    warning: 0.10           # 预警（预算消耗10%）
+    cooling: 0.20           # 冷却触发（预算消耗20%）
+    stop: 0.50              # 停止交易（预算消耗50%）
+
+  # 计算方式
+  calculation:
+    method: "actual_loss"   # "actual_loss", "var_based"
+    lookback_trades: 20     # 回看交易次数
+
+# 动态对冲矩阵
+hedging:
+  # 系统性风险对冲
+  systematic:
+    enabled: true
+    beta_threshold: 1.2
+    hedge_instruments:
+      - put_option         # 买入Put
+      - index_future       # 股指期货
+    hedge_ratio: 0.5        # 对冲比例
+
+  # 波动率对冲
+  volatility:
+    enabled: true
+    iv_rank_threshold: 80
+    instruments:
+      - vix_etf            # VIX ETF
+      - long_straddle      # 买入跨式
+    allocation: 0.05        # 占账户5%
+
+  # 流动性对冲
+  liquidity:
+    enabled: true
+    health_threshold: 30
+    safe_assets:
+      - money_market       # 货币基金
+      - treasury_etf       # 国债ETF
+    max_allocation: 0.30    # 最大30%
+
+  # 行业集中对冲
+  concentration:
+    enabled: true
+    single_sector_max: 0.40  # 单一行业最大40%
+    hedge_method: "sector_etf"  # "sector_etf", "pair_trading"
+
+# 压力测试引擎
+stress_test:
+  # 历史场景
+  historical_scenarios:
+    - name: "2008_financial_crisis"
+      period: ["2008-09-01", "2008-12-31"]
+      shock_magnitude: -0.40
+    - name: "2015_ah_share_crash"
+      period: ["2015-06-01", "2015-08-31"]
+      shock_magnitude: -0.35
+    - name: "2020_covid_crash"
+      period: ["2020-02-01", "2020-03-31"]
+      shock_magnitude: -0.30
+    - name: "2022_bear_market"
+      period: ["2022-01-01", "2022-10-31"]
+      shock_magnitude: -0.25
+
+  # 蒙特卡洛参数
+  monte_carlo:
+    n_simulations: 1000
+    time_horizon_days: 20
+    confidence_level: 0.99
+
+  # 触发条件
+  triggers:
+    var_limit: 0.15         # VaR限制15%
+    auto_reduce: true       # 自动减仓
+
+# 组合级硬止损
+hard_limits:
+  # 回撤止损
+  drawdown:
+    max_drawdown: 0.15      # 最大回撤15%
+    action: "liquidate_all" # "liquidate_all", "reduce_50"
+    cooldown_days: 5        # 冷静期
+
+  # 单日止损
+  daily_loss:
+    max_daily_loss: 0.05    # 单日最大亏损5%
+    action: "reduce_50"
+    pause_new_positions: true  # 暂停新开仓
+
+  # 连续亏损
+  consecutive_losses:
+    max_count: 5            # 最大连续亏损次数
+    action: "reduce_50"
+
+# 风险监控
+monitoring:
+  # 实时监控频率
+  realtime_interval_seconds: 30
+
+  # 风险指标
+  metrics:
+    - var_95              # 95% VaR
+    - cvar_95             # 95% CVaR
+    - beta                # 组合Beta
+    - correlation         # 智能体间相关性
+    - concentration       # 集中度
+
+  # 告警阈值
+  alerts:
+    var_threshold: 0.10
+    correlation_threshold: 0.7
+    concentration_threshold: 0.40

cyb50-pro/core/ecosystem/__init__.py

@@ -0,0 +1,58 @@
+"""
+四维市场生态识别引擎
+
+提供宏观、中观、微观、瞬时四层生态识别与融合
+"""
+
+from .macro import (
+    MacroEcosystem,
+    MacroRegime,
+    MacroEcosystemIdentifier
+)
+from .meso import (
+    MesoEcosystem,
+    HealthLevel,
+    MesoEcosystemIdentifier
+)
+from .micro import (
+    MicroEcosystem,
+    MicroState,
+    FlowToxicity,
+    SmartMoneySignal,
+    MicroEcosystemIdentifier
+)
+from .instant import (
+    InstantEcosystem,
+    ImbalanceDirection,
+    TickActivity,
+    InstantEcosystemIdentifier
+)
+from .fusion import (
+    UnifiedEcosystem,
+    EcosystemFusion
+)
+
+__all__ = [
+    # 宏观生态
+    "MacroEcosystem",
+    "MacroRegime",
+    "MacroEcosystemIdentifier",
+    # 中观生态
+    "MesoEcosystem",
+    "HealthLevel",
+    "MesoEcosystemIdentifier",
+    # 微观生态
+    "MicroEcosystem",
+    "MicroState",
+    "FlowToxicity",
+    "SmartMoneySignal",
+    "MicroEcosystemIdentifier",
+    # 瞬时生态
+    "InstantEcosystem",
+    "ImbalanceDirection",
+    "TickActivity",
+    "InstantEcosystemIdentifier",
+    # 生态融合
+    "UnifiedEcosystem",
+    "EcosystemFusion",
+]

cyb50-pro/core/ecosystem/fusion.py

@@ -0,0 +1,382 @@
+"""
+生态融合器
+整合四层生态识别结果（宏观/中观/微观/瞬时）为统一的市场生态对象
+"""
+
+from dataclasses import dataclass, field
+from datetime import datetime
+from typing import Dict, List, Optional, Any
+from enum import Enum
+
+import pandas as pd
+
+from .macro import MacroEcosystem, MacroRegime, MacroEcosystemIdentifier
+from .meso import MesoEcosystem, HealthLevel, MesoEcosystemIdentifier
+from .micro import MicroEcosystem, MicroState, MicroEcosystemIdentifier
+from .instant import InstantEcosystem, InstantEcosystemIdentifier
+
+
+@dataclass
+class UnifiedEcosystem:
+    """
+    统一市场生态对象
+
+    融合四层生态识别结果，提供完整的市场状态描述
+    """
+    timestamp: datetime
+
+    # 各层生态 (必须参数在前)
+    macro: MacroEcosystem
+    meso: MesoEcosystem
+    micro: MicroEcosystem
+
+    # 融合结果
+    overall_regime: str          # 综合生态类型
+    confidence: float            # 整体置信度 (0-1)
+    trading_bias: str            # 交易倾向 "long" / "short" / "neutral"
+    risk_level: str              # 风险等级 "low" / "medium" / "high"
+
+    # 建议参数
+    suggested_position: float    # 建议仓位 (0-1)
+    suggested_agents: List[str]  # 建议激活的智能体
+
+    # 可选参数 (带默认值的在后)
+    instant: Optional[InstantEcosystem] = None
+    warnings: List[str] = field(default_factory=list)
+
+    def to_dict(self) -> Dict[str, Any]:
+        """转换为字典格式"""
+        return {
+            "timestamp": self.timestamp.isoformat(),
+            "overall_regime": self.overall_regime,
+            "confidence": self.confidence,
+            "trading_bias": self.trading_bias,
+            "risk_level": self.risk_level,
+            "suggested_position": self.suggested_position,
+            "suggested_agents": self.suggested_agents,
+            "warnings": self.warnings,
+            "macro": {
+                "regime": self.macro.regime.value,
+                "confidence": self.macro.confidence,
+                "description": self.macro.description
+            },
+            "meso": {
+                "health_score": self.meso.health_score,
+                "health_level": self.meso.health_level.value
+            },
+            "micro": {
+                "state": self.micro.state.value,
+                "toxicity": self.micro.flow_toxicity.value,
+                "smart_money": self.micro.smart_money.direction if self.micro.smart_money.detected else None
+            },
+            "instant": {
+                "imbalance": self.instant.imbalance_direction.value if self.instant else None,
+                "block_flow": self.instant.block_trade_flow if self.instant else 0
+            } if self.instant else None
+        }
+
+
+class EcosystemFusion:
+    """
+    生态融合器
+
+    整合四层生态识别结果，生成统一的交易决策建议
+    """
+
+    def __init__(
+        self,
+        macro_identifier: Optional[MacroEcosystemIdentifier] = None,
+        meso_identifier: Optional[MesoEcosystemIdentifier] = None,
+        micro_identifier: Optional[MicroEcosystemIdentifier] = None,
+        instant_identifier: Optional[InstantEcosystemIdentifier] = None
+    ):
+        self.macro_id = macro_identifier or MacroEcosystemIdentifier()
+        self.meso_id = meso_identifier or MesoEcosystemIdentifier()
+        self.micro_id = micro_identifier or MicroEcosystemIdentifier()
+        self.instant_id = instant_identifier or InstantEcosystemIdentifier()
+
+    def fuse(
+        self,
+        price_data: pd.DataFrame,
+        sector_data: Optional[pd.DataFrame] = None,
+        tick_data: Optional[pd.DataFrame] = None,
+        order_book_data: Optional[pd.DataFrame] = None,
+        trade_data: Optional[pd.DataFrame] = None
+    ) -> UnifiedEcosystem:
+        """
+        融合四层生态识别结果
+
+        Args:
+            price_data: 日线/30分钟价格数据
+            sector_data: 板块数据（可选）
+            tick_data: 分钟级tick数据（可选）
+            order_book_data: 订单簿数据（可选）
+            trade_data: 成交数据（可选）
+
+        Returns:
+            UnifiedEcosystem: 统一生态对象
+        """
+        timestamp = pd.Timestamp.now()
+
+        # 1. 宏观生态识别（日度更新）
+        macro = self.macro_id.identify(price_data, sector_data)
+
+        # 2. 中观生态识别（日度更新）
+        meso = self.meso_id.identify(price_data, order_book_data, trade_data)
+
+        # 3. 微观生态识别（30分钟更新）
+        # 训练HMM（如果未训练）
+        if not self.micro_id._is_fitted:
+            self.micro_id.fit(price_data)
+        micro = self.micro_id.identify(price_data, trade_data, order_book_data)
+
+        # 4. 瞬时生态识别（分钟级，可选）
+        instant = None
+        if tick_data is not None and not tick_data.empty:
+            instant = self.instant_id.identify(
+                tick_data, order_book_data, timestamp
+            )
+
+        # 5. 融合决策
+        overall_regime = self._determine_overall_regime(macro, meso, micro)
+        confidence = self._calculate_confidence(macro, meso, micro)
+        trading_bias = self._determine_trading_bias(macro, micro, instant)
+        risk_level = self._determine_risk_level(meso, micro, instant)
+        suggested_position = self._calculate_position(
+            macro, meso, micro, risk_level
+        )
+        suggested_agents = self._recommend_agents(macro, micro)
+        warnings = self._generate_warnings(macro, meso, micro, instant)
+
+        return UnifiedEcosystem(
+            timestamp=timestamp,
+            macro=macro,
+            meso=meso,
+            micro=micro,
+            overall_regime=overall_regime,
+            confidence=confidence,
+            trading_bias=trading_bias,
+            risk_level=risk_level,
+            suggested_position=suggested_position,
+            suggested_agents=suggested_agents,
+            instant=instant,
+            warnings=warnings
+        )
+
+    def _determine_overall_regime(
+        self,
+        macro: MacroEcosystem,
+        meso: MesoEcosystem,
+        micro: MicroEcosystem
+    ) -> str:
+        """确定综合生态类型"""
+        # 主要基于宏观生态
+        base_regime = macro.regime.value
+
+        # 中观结构健康度修正
+        if meso.health_level == HealthLevel.LOW:
+            base_regime += "_fragile"
+
+        # 微观状态修正
+        if micro.state == MicroState.REVERSING:
+            base_regime += "_reversing"
+        elif micro.state == MicroState.TRENDING:
+            base_regime += "_trending"
+
+        return base_regime
+
+    def _calculate_confidence(
+        self,
+        macro: MacroEcosystem,
+        meso: MesoEcosystem,
+        micro: MicroEcosystem
+    ) -> float:
+        """计算整体置信度"""
+        # 加权平均各层置信度
+        macro_conf = macro.confidence
+        meso_conf = meso.health_score / 100
+        micro_conf = max(micro.state_probability.values())
+
+        confidence = (
+            macro_conf * 0.4 +
+            meso_conf * 0.3 +
+            micro_conf * 0.3
+        )
+
+        return min(1.0, max(0.0, confidence))
+
+    def _determine_trading_bias(
+        self,
+        macro: MacroEcosystem,
+        micro: MicroEcosystem,
+        instant: Optional[InstantEcosystem]
+    ) -> str:
+        """确定交易倾向"""
+        # 基于宏观生态
+        if macro.regime in [MacroRegime.SPRING, MacroRegime.SUMMER]:
+            bias = "long"
+        elif macro.regime == MacroRegime.WINTER:
+            bias = "short"
+        else:
+            bias = "neutral"
+
+        # 微观状态修正
+        if micro.state == MicroState.REVERSING:
+            # 反转状态，反向操作或观望
+            bias = "neutral"
+
+        # 瞬时信号修正
+        if instant is not None:
+            if instant.imbalance_direction.value == "bid_dominant":
+                bias = "long"
+            elif instant.imbalance_direction.value == "ask_dominant":
+                bias = "short"
+
+        # 有毒订单流过滤
+        if micro.flow_toxicity.value in ["high", "medium"]:
+            bias = "neutral"
+
+        return bias
+
+    def _determine_risk_level(
+        self,
+        meso: MesoEcosystem,
+        micro: MicroEcosystem,
+        instant: Optional[InstantEcosystem]
+    ) -> str:
+        """确定风险等级"""
+        risk_score = 0
+
+        # 中观结构健康度
+        if meso.health_level == HealthLevel.LOW:
+            risk_score += 3
+        elif meso.health_level == HealthLevel.MEDIUM:
+            risk_score += 1
+
+        # 微观有毒订单流
+        if micro.flow_toxicity.value == "high":
+            risk_score += 2
+        elif micro.flow_toxicity.value == "medium":
+            risk_score += 1
+
+        # 微观状态
+        if micro.state == MicroState.REVERSING:
+            risk_score += 1
+
+        # 瞬时跳动率
+        if instant is not None:
+            if instant.tick_activity.value == "spike":
+                risk_score += 1
+
+        if risk_score >= 4:
+            return "high"
+        elif risk_score >= 2:
+            return "medium"
+        else:
+            return "low"
+
+    def _calculate_position(
+        self,
+        macro: MacroEcosystem,
+        meso: MesoEcosystem,
+        micro: MicroEcosystem,
+        risk_level: str
+    ) -> float:
+        """计算建议仓位"""
+        # 基础仓位（基于宏观生态）
+        base_position = {
+            MacroRegime.SPRING: 0.6,
+            MacroRegime.SUMMER: 1.0,
+            MacroRegime.AUTUMN: 0.4,
+            MacroRegime.WINTER: 0.2,
+            MacroRegime.UNKNOWN: 0.0
+        }.get(macro.regime, 0.0)
+
+        # 中观修正
+        health_factor = meso.health_score / 100
+
+        # 微观修正
+        if micro.flow_toxicity.value == "high":
+            micro_factor = 0.3
+        elif micro.flow_toxicity.value == "medium":
+            micro_factor = 0.6
+        else:
+            micro_factor = 1.0
+
+        # 风险修正
+        risk_factor = {
+            "low": 1.0,
+            "medium": 0.7,
+            "high": 0.4
+        }.get(risk_level, 0.5)
+
+        suggested = base_position * health_factor * micro_factor * risk_factor
+        return min(1.0, max(0.0, suggested))
+
+    def _recommend_agents(
+        self,
+        macro: MacroEcosystem,
+        micro: MicroEcosystem
+    ) -> List[str]:
+        """推荐激活的智能体"""
+        agents = []
+
+        # 基于宏观生态推荐
+        regime_agents = {
+            MacroRegime.SPRING: ["trend_hunter", "mean_reversion"],
+            MacroRegime.SUMMER: ["trend_hunter", "momentum_surfer"],
+            MacroRegime.AUTUMN: ["structure_arbitrage", "momentum_surfer"],
+            MacroRegime.WINTER: ["mean_reversion", "volatility_seller"],
+            MacroRegime.UNKNOWN: []
+        }
+        agents.extend(regime_agents.get(macro.regime, []))
+
+        # 微观状态调整
+        if micro.state == MicroState.TRENDING:
+            if "trend_hunter" not in agents:
+                agents.append("trend_hunter")
+        elif micro.state == MicroState.RANGING:
+            if "mean_reversion" not in agents:
+                agents.append("mean_reversion")
+
+        # 主力资金信号
+        if micro.smart_money.detected:
+            if micro.smart_money.direction == "accumulate":
+                if "trend_hunter" not in agents:
+                    agents.append("trend_hunter")
+            elif micro.smart_money.direction == "distribute":
+                if "mean_reversion" not in agents:
+                    agents.append("mean_reversion")
+
+        return agents
+
+    def _generate_warnings(
+        self,
+        macro: MacroEcosystem,
+        meso: MesoEcosystem,
+        micro: MicroEcosystem,
+        instant: Optional[InstantEcosystem]
+    ) -> List[str]:
+        """生成警告信息"""
+        warnings = []
+
+        # 宏观警告
+        if macro.regime == MacroRegime.UNKNOWN:
+            warnings.append("宏观生态不明，建议观望")
+
+        # 中观警告
+        if meso.health_level == HealthLevel.LOW:
+            warnings.append(f"市场结构健康度低 ({meso.health_score:.1f})，流动性风险")
+
+        # 微观警告
+        warnings.extend(micro.warnings)
+
+        # 瞬时警告
+        if instant is not None:
+            if instant.tick_activity.value == "spike":
+                warnings.append("跳动率突变，市场活跃度异常")
+            if abs(instant.block_trade_flow) > 2000:  # 2000万
+                direction = "流入" if instant.block_trade_flow > 0 else "流出"
+                warnings.append(f"大单巨额{direction}: {abs(instant.block_trade_flow):.0f}万元")
+
+        return warnings

cyb50-pro/core/ecosystem/instant.py

@@ -0,0 +1,341 @@
+"""
+瞬时生态识别器
+基于分钟级数据计算买卖盘不平衡、大单流向、跳动率突变
+"""
+
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional
+from enum import Enum
+
+import numpy as np
+import pandas as pd
+
+
+class ImbalanceDirection(Enum):
+    """买卖盘不平衡方向"""
+    BID_DOMINANT = "bid_dominant"    # 买盘占优
+    ASK_DOMINANT = "ask_dominant"    # 卖盘占优
+    BALANCED = "balanced"            # 平衡
+
+
+class TickActivity(Enum):
+    """跳动率活跃度"""
+    NORMAL = "normal"
+    ELEVATED = "elevated"      # 活跃度上升
+    DEPRESSED = "depressed"    # 活跃度下降
+    SPIKE = "spike"            # 突变
+
+
+@dataclass
+class InstantEcosystem:
+    """瞬时生态数据结构"""
+    timestamp: pd.Timestamp
+
+    # 买卖盘不平衡
+    imbalance_direction: ImbalanceDirection
+    imbalance_ratio: float       # 买卖盘量比
+    imbalance_duration: int      # 持续分钟数
+
+    # 大单流向
+    block_trade_flow: float      # 大单净流入（万元）
+    block_buy_count: int
+    block_sell_count: int
+
+    # 跳动率
+    tick_activity: TickActivity
+    tick_rate_change: float      # 较前5分钟变化率
+    current_tick_rate: int       # 当前跳动率（笔/分钟）
+
+    # 综合信号
+    signals: List[str] = field(default_factory=list)
+
+    def is_trading_opportunity(self) -> bool:
+        """判断是否为交易机会"""
+        return (
+            self.imbalance_direction != ImbalanceDirection.BALANCED and
+            abs(self.block_trade_flow) > 500 and  # 500万元
+            self.tick_activity != TickActivity.NORMAL
+        )
+
+
+class InstantEcosystemIdentifier:
+    """
+    瞬时生态识别器
+
+    基于分钟级tick数据计算：
+    1. 买卖盘不平衡度（Bid-Ask Imbalance）
+    2. 大单流向（Block Trade Flow）
+    3. 跳动率突变（Tick Rate Change）
+    """
+
+    def __init__(
+        self,
+        imbalance_ratio_threshold: float = 2.0,
+        imbalance_duration: int = 3,
+        block_trade_min_value: float = 500_000,  # 50万元
+        tick_rate_change_threshold: float = 0.5,
+        tick_window_minutes: int = 5
+    ):
+        self.imbalance_ratio_threshold = imbalance_ratio_threshold
+        self.imbalance_duration = imbalance_duration
+        self.block_trade_min_value = block_trade_min_value
+        self.tick_rate_change_threshold = tick_rate_change_threshold
+        self.tick_window_minutes = tick_window_minutes
+
+    def identify(
+        self,
+        tick_data: pd.DataFrame,
+        order_book_data: Optional[pd.DataFrame] = None,
+        timestamp: Optional[pd.Timestamp] = None
+    ) -> InstantEcosystem:
+        """
+        识别瞬时生态
+
+        Args:
+            tick_data: 分钟级tick数据，包含 ['price', 'volume', 'side', 'bid', 'ask']
+            order_book_data: 订单簿数据（可选）
+            timestamp: 当前时间戳
+
+        Returns:
+            InstantEcosystem: 瞬时生态识别结果
+        """
+        if timestamp is None:
+            timestamp = pd.Timestamp.now()
+
+        signals = []
+
+        # 1. 计算买卖盘不平衡
+        imbalance = self._calculate_imbalance(tick_data, order_book_data)
+
+        if imbalance['direction'] == ImbalanceDirection.BID_DOMINANT:
+            signals.append(f"买盘占优 (比率: {imbalance['ratio']:.2f})")
+        elif imbalance['direction'] == ImbalanceDirection.ASK_DOMINANT:
+            signals.append(f"卖盘占优 (比率: {imbalance['ratio']:.2f})")
+
+        # 2. 计算大单流向
+        block_flow = self._calculate_block_flow(tick_data)
+
+        if abs(block_flow['net_flow']) > 1000:  # 1000万元
+            direction = "流入" if block_flow['net_flow'] > 0 else "流出"
+            signals.append(f"大单{direction}: {abs(block_flow['net_flow']):.0f}万元")
+
+        # 3. 计算跳动率突变
+        tick_activity = self._calculate_tick_activity(tick_data)
+
+        if tick_activity['activity'] == TickActivity.SPIKE:
+            signals.append(f"跳动率突变: {tick_activity['change']:.1%}")
+        elif tick_activity['activity'] == TickActivity.ELEVATED:
+            signals.append(f"活跃度上升: {tick_activity['change']:.1%}")
+
+        return InstantEcosystem(
+            timestamp=timestamp,
+            imbalance_direction=imbalance['direction'],
+            imbalance_ratio=imbalance['ratio'],
+            imbalance_duration=imbalance['duration'],
+            block_trade_flow=block_flow['net_flow'],
+            block_buy_count=block_flow['buy_count'],
+            block_sell_count=block_flow['sell_count'],
+            tick_activity=tick_activity['activity'],
+            tick_rate_change=tick_activity['change'],
+            current_tick_rate=tick_activity['current_rate'],
+            signals=signals
+        )
+
+    def _calculate_imbalance(
+        self,
+        tick_data: pd.DataFrame,
+        order_book_data: Optional[pd.DataFrame] = None
+    ) -> Dict:
+        """
+        计算买卖盘不平衡
+
+        方法1: 使用订单簿深度（如果有）
+        方法2: 使用tick数据中的主动买卖
+        """
+        # 方法1: 订单簿深度
+        if order_book_data is not None and not order_book_data.empty:
+            if 'bid_volume' in order_book_data and 'ask_volume' in order_book_data:
+                bid_vol = order_book_data['bid_volume'].iloc[-1]
+                ask_vol = order_book_data['ask_volume'].iloc[-1]
+
+                if ask_vol > 0:
+                    ratio = bid_vol / ask_vol
+                else:
+                    ratio = 1.0
+
+                # 判断方向
+                if ratio > self.imbalance_ratio_threshold:
+                    direction = ImbalanceDirection.BID_DOMINANT
+                elif ratio < 1 / self.imbalance_ratio_threshold:
+                    direction = ImbalanceDirection.ASK_DOMINANT
+                else:
+                    direction = ImbalanceDirection.BALANCED
+
+                # 计算持续时间
+                duration = self._calculate_imbalance_duration(
+                    order_book_data, direction
+                )
+
+                return {
+                    'direction': direction,
+                    'ratio': ratio,
+                    'duration': duration
+                }
+
+        # 方法2: 使用tick数据的主动买卖
+        if 'side' in tick_data.columns:
+            buy_volume = tick_data[tick_data['side'] == 'buy']['volume'].sum()
+            sell_volume = tick_data[tick_data['side'] == 'sell']['volume'].sum()
+        else:
+            # 通过价格与买卖价关系推断
+            buy_volume = tick_data[
+                tick_data['price'] >= tick_data.get('ask', tick_data['price'])
+            ]['volume'].sum()
+            sell_volume = tick_data[
+                tick_data['price'] <= tick_data.get('bid', tick_data['price'])
+            ]['volume'].sum()
+
+        total_volume = buy_volume + sell_volume
+        if total_volume == 0:
+            return {
+                'direction': ImbalanceDirection.BALANCED,
+                'ratio': 1.0,
+                'duration': 0
+            }
+
+        ratio = buy_volume / sell_volume if sell_volume > 0 else 1.0
+
+        if ratio > self.imbalance_ratio_threshold:
+            direction = ImbalanceDirection.BID_DOMINANT
+        elif ratio < 1 / self.imbalance_ratio_threshold:
+            direction = ImbalanceDirection.ASK_DOMINANT
+        else:
+            direction = ImbalanceDirection.BALANCED
+
+        return {
+            'direction': direction,
+            'ratio': ratio,
+            'duration': self.imbalance_duration  # 简化处理
+        }
+
+    def _calculate_imbalance_duration(
+        self,
+        order_book_data: pd.DataFrame,
+        current_direction: ImbalanceDirection
+    ) -> int:
+        """计算不平衡持续时间"""
+        if len(order_book_data) < self.imbalance_duration:
+            return len(order_book_data)
+
+        duration = 0
+        for i in range(1, min(len(order_book_data), 10) + 1):
+            bid_vol = order_book_data['bid_volume'].iloc[-i]
+            ask_vol = order_book_data['ask_volume'].iloc[-i]
+
+            if ask_vol > 0:
+                ratio = bid_vol / ask_vol
+            else:
+                ratio = 1.0
+
+            if current_direction == ImbalanceDirection.BID_DOMINANT and ratio > self.imbalance_ratio_threshold:
+                duration += 1
+            elif current_direction == ImbalanceDirection.ASK_DOMINANT and ratio < 1 / self.imbalance_ratio_threshold:
+                duration += 1
+            elif current_direction == ImbalanceDirection.BALANCED:
+                duration += 1
+            else:
+                break
+
+        return duration
+
+    def _calculate_block_flow(self, tick_data: pd.DataFrame) -> Dict:
+        """
+        计算大单流向
+
+        返回净流入（万元）
+        """
+        if tick_data.empty:
+            return {
+                'net_flow': 0.0,
+                'buy_count': 0,
+                'sell_count': 0
+            }
+
+        # 计算平均成交金额
+        avg_value = (tick_data['price'] * tick_data['volume']).mean()
+
+        # 识别大单
+        tick_data = tick_data.copy()
+        tick_data['value'] = tick_data['price'] * tick_data['volume']
+        block_trades = tick_data[
+            tick_data['value'] > self.block_trade_min_value
+        ]
+
+        if block_trades.empty:
+            return {
+                'net_flow': 0.0,
+                'buy_count': 0,
+                'sell_count': 0
+            }
+
+        # 分类买卖
+        if 'side' in block_trades.columns:
+            buy_trades = block_trades[block_trades['side'] == 'buy']
+            sell_trades = block_trades[block_trades['side'] == 'sell']
+        else:
+            # 通过价格位置推断
+            buy_trades = block_trades[block_trades['price'] >= block_trades.get('ask', block_trades['price'])]
+            sell_trades = block_trades[block_trades['price'] <= block_trades.get('bid', block_trades['price'])]
+
+        # 计算净流入（万元）
+        buy_flow = buy_trades['value'].sum() / 10_000
+        sell_flow = sell_trades['value'].sum() / 10_000
+        net_flow = buy_flow - sell_flow
+
+        return {
+            'net_flow': net_flow,
+            'buy_count': len(buy_trades),
+            'sell_count': len(sell_trades)
+        }
+
+    def _calculate_tick_activity(self, tick_data: pd.DataFrame) -> Dict:
+        """
+        计算跳动率突变
+
+        跳动率 = 单位时间内的成交笔数
+        """
+        if len(tick_data) < self.tick_window_minutes + 1:
+            return {
+                'activity': TickActivity.NORMAL,
+                'change': 0.0,
+                'current_rate': len(tick_data)
+            }
+
+        # 计算当前跳动率（最近1分钟）
+        current_rate = len(tick_data.iloc[-1:])
+
+        # 计算前N分钟平均跳动率
+        prev_data = tick_data.iloc[-(self.tick_window_minutes + 1):-1]
+        prev_rate = len(prev_data) / self.tick_window_minutes if self.tick_window_minutes > 0 else 1
+
+        # 计算变化率
+        if prev_rate > 0:
+            change = (current_rate - prev_rate) / prev_rate
+        else:
+            change = 0.0
+
+        # 判断活跃度
+        if abs(change) > self.tick_rate_change_threshold:
+            activity = TickActivity.SPIKE
+        elif change > 0.2:
+            activity = TickActivity.ELEVATED
+        elif change < -0.2:
+            activity = TickActivity.DEPRESSED
+        else:
+            activity = TickActivity.NORMAL
+
+        return {
+            'activity': activity,
+            'change': change,
+            'current_rate': current_rate
+        }

cyb50-pro/core/ecosystem/macro.py

@@ -0,0 +1,259 @@
+"""
+宏观生态识别器
+识别市场的"季节"：春季（复苏）、夏季（繁荣）、秋季（分化）、冬季（萧条）
+"""
+
+from dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple
+from enum import Enum
+
+import numpy as np
+import pandas as pd
+
+
+class MacroRegime(Enum):
+    """宏观生态类型"""
+    SPRING = "spring"      # 春季（复苏）
+    SUMMER = "summer"      # 夏季（繁荣）
+    AUTUMN = "autumn"      # 秋季（分化）
+    WINTER = "winter"      # 冬季（萧条）
+    UNKNOWN = "unknown"    # 未知/过渡
+
+
+@dataclass
+class MacroEcosystem:
+    """宏观生态数据结构"""
+    regime: MacroRegime
+    confidence: float
+    volatility_trend: float      # 波动率趋势
+    volume_trend: float          # 成交量趋势
+    dispersion: float            # 板块离散度
+    adx_value: float             # ADX值
+    description: str
+
+
+class MacroEcosystemIdentifier:
+    """
+    宏观生态识别器
+
+    基于以下因子识别市场季节：
+    1. 波动率期限结构（近月波动率变化）
+    2. 板块离散度（成分股收益率标准差的历史分位）
+    3. 资金流向广度（涨跌家数比的熵值）
+    4. ADX趋势强度
+    """
+
+    def __init__(
+        self,
+        lookback_days: int = 252,
+        spring_vol_rebound: float = 0.20,
+        spring_volume_ratio: float = 1.2,
+        summer_adx_threshold: float = 25,
+        summer_adx_duration: int = 5,
+        autumn_dispersion_pct: float = 70,
+        winter_volume_pct: float = 50,
+        winter_vol_compression: float = 20
+    ):
+        self.lookback_days = lookback_days
+        self.spring_vol_rebound = spring_vol_rebound
+        self.spring_volume_ratio = spring_volume_ratio
+        self.summer_adx_threshold = summer_adx_threshold
+        self.summer_adx_duration = summer_adx_duration
+        self.autumn_dispersion_pct = autumn_dispersion_pct
+        self.winter_volume_pct = winter_volume_pct
+        self.winter_vol_compression = winter_vol_compression
+
+    def identify(
+        self,
+        price_data: pd.DataFrame,
+        sector_data: Optional[pd.DataFrame] = None
+    ) -> MacroEcosystem:
+        """
+        识别当前宏观生态
+
+        Args:
+            price_data: 价格数据，包含 ['open', 'high', 'low', 'close', 'volume']
+            sector_data: 板块数据（可选），成分股收益率数据
+
+        Returns:
+            MacroEcosystem: 宏观生态识别结果
+        """
+        # 计算技术指标
+        volatility = self._calculate_volatility(price_data)
+        volume_ma5 = price_data['volume'].rolling(5).mean().iloc[-1]
+        volume_ma20 = price_data['volume'].rolling(20).mean().iloc[-1]
+        volume_ratio = volume_ma5 / volume_ma20 if volume_ma20 > 0 else 1.0
+
+        adx_value = self._calculate_adx(price_data)
+        dispersion = self._calculate_dispersion(sector_data) if sector_data is not None else 50.0
+
+        # 计算历史分位数
+        vol_percentile = self._get_percentile(volatility, price_data)
+        vol_low_30 = np.percentile(volatility.iloc[-60:], 30)
+        vol_rebound = (volatility.iloc[-1] - vol_low_30) / vol_low_30 if vol_low_30 > 0 else 0
+
+        # 四季识别逻辑
+        regime_scores = {}
+
+        # 春季：波动率从低位回升，成交量温和放大
+        spring_score = self._score_spring(vol_rebound, volume_ratio, dispersion)
+        regime_scores[MacroRegime.SPRING] = spring_score
+
+        # 夏季：趋势明确，波动率稳定，情绪高涨
+        summer_score = self._score_summer(adx_value, volatility, price_data, dispersion)
+        regime_scores[MacroRegime.SUMMER] = summer_score
+
+        # 秋季：板块离散度大，轮动加速
+        autumn_score = self._score_autumn(dispersion, volatility, price_data)
+        regime_scores[MacroRegime.AUTUMN] = autumn_score
+
+        # 冬季：成交量萎缩，波动率压缩
+        winter_score = self._score_winter(volume_ratio, vol_percentile, adx_value)
+        regime_scores[MacroRegime.WINTER] = winter_score
+
+        # 选择最高分的生态
+        best_regime = max(regime_scores, key=regime_scores.get)
+        best_score = regime_scores[best_regime]
+
+        # 如果最高分低于阈值，标记为未知
+        if best_score < 0.3:
+            best_regime = MacroRegime.UNKNOWN
+            best_score = 0.0
+
+        return MacroEcosystem(
+            regime=best_regime,
+            confidence=best_score,
+            volatility_trend=vol_rebound,
+            volume_trend=volume_ratio - 1.0,
+            dispersion=dispersion,
+            adx_value=adx_value,
+            description=self._get_description(best_regime)
+        )
+
+    def _calculate_volatility(self, data: pd.DataFrame, period: int = 20) -> pd.Series:
+        """计算波动率（20日收益率标准差年化）"""
+        returns = data['close'].pct_change()
+        volatility = returns.rolling(period).std() * np.sqrt(252)
+        return volatility
+
+    def _calculate_adx(self, data: pd.DataFrame, period: int = 14) -> float:
+        """计算ADX（平均趋向指数）"""
+        high = data['high']
+        low = data['low']
+        close = data['close']
+
+        # +DM和-DM
+        plus_dm = high.diff()
+        minus_dm = -low.diff()
+
+        plus_dm[plus_dm < 0] = 0
+        minus_dm[minus_dm < 0] = 0
+
+        # TR（真实波幅）
+        tr1 = high - low
+        tr2 = abs(high - close.shift(1))
+        tr3 = abs(low - close.shift(1))
+        tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+
+        # ATR
+        atr = tr.rolling(period).mean()
+
+        # +DI和-DI
+        plus_di = 100 * (plus_dm.rolling(period).mean() / atr)
+        minus_di = 100 * (minus_dm.rolling(period).mean() / atr)
+
+        # DX和ADX
+        dx = 100 * abs(plus_di - minus_di) / (plus_di + minus_di)
+        adx = dx.rolling(period).mean()
+
+        return adx.iloc[-1] if not pd.isna(adx.iloc[-1]) else 20.0
+
+    def _calculate_dispersion(self, sector_data: pd.DataFrame) -> float:
+        """计算板块离散度（成分股收益率标准差的历史分位）"""
+        if sector_data is None or sector_data.empty:
+            return 50.0
+
+        # 计算各成分股近期收益率
+        returns = sector_data.pct_change().iloc[-20:]
+        cross_sectional_std = returns.std(axis=1).mean()
+
+        # 转换为历史分位数（简化处理）
+        historical_std = returns.std(axis=1).rolling(60).mean()
+        if len(historical_std) > 0 and not pd.isna(historical_std.iloc[-1]):
+            percentile = 50 + (cross_sectional_std - historical_std.mean()) / historical_std.std() * 25
+            return max(0, min(100, percentile))
+        return 50.0
+
+    def _get_percentile(self, series: pd.Series, data: pd.DataFrame) -> float:
+        """获取当前值的历史分位数"""
+        if len(series) < 60:
+            return 50.0
+        current = series.iloc[-1]
+        historical = series.iloc[-252:-1].dropna()
+        if len(historical) == 0:
+            return 50.0
+        return (historical < current).mean() * 100
+
+    def _score_spring(self, vol_rebound: float, volume_ratio: float, dispersion: float) -> float:
+        """评分：春季（复苏）"""
+        # 波动率从低位回升
+        vol_score = min(1.0, vol_rebound / self.spring_vol_rebound)
+
+        # 成交量放大
+        volume_score = min(1.0, max(0, (volume_ratio - 1.0) / (self.spring_volume_ratio - 1.0)))
+
+        # 板块离散度中等（30-60分位）
+        dispersion_score = 1.0 - abs(dispersion - 45) / 15 if 30 <= dispersion <= 60 else 0.0
+
+        return vol_score * 0.4 + volume_score * 0.4 + dispersion_score * 0.2
+
+    def _score_summer(self, adx: float, volatility: pd.Series, data: pd.DataFrame, dispersion: float) -> float:
+        """评分：夏季（繁荣）"""
+        # ADX高于阈值
+        adx_score = min(1.0, adx / self.summer_adx_threshold) if adx > 20 else 0.0
+
+        # 波动率稳定（历史40-70分位）
+        vol_percentile = self._get_percentile(volatility, data)
+        vol_score = 1.0 - abs(vol_percentile - 55) / 15 if 40 <= vol_percentile <= 70 else 0.0
+
+        # 板块离散度低（同涨同跌）
+        dispersion_score = 1.0 - dispersion / 40 if dispersion < 40 else 0.0
+
+        return adx_score * 0.5 + vol_score * 0.3 + dispersion_score * 0.2
+
+    def _score_autumn(self, dispersion: float, volatility: pd.Series, data: pd.DataFrame) -> float:
+        """评分：秋季（分化）"""
+        # 板块离散度高
+        dispersion_score = min(1.0, (dispersion - 50) / 30) if dispersion > 50 else 0.0
+
+        # 波动率上升
+        vol_recent = volatility.iloc[-5:].mean()
+        vol_prev = volatility.iloc[-20:-5].mean()
+        vol_increase = (vol_recent - vol_prev) / vol_prev if vol_prev > 0 else 0
+        vol_score = min(1.0, vol_increase / 0.15)
+
+        return dispersion_score * 0.6 + vol_score * 0.4
+
+    def _score_winter(self, volume_ratio: float, vol_percentile: float, adx: float) -> float:
+        """评分：冬季（萧条）"""
+        # 成交量萎缩
+        volume_score = 1.0 - min(1.0, volume_ratio) if volume_ratio < 1.0 else 0.0
+
+        # 波动率压缩至历史低位
+        vol_score = 1.0 - vol_percentile / self.winter_vol_compression if vol_percentile < self.winter_vol_compression else 0.0
+
+        # 无明确趋势
+        adx_score = 1.0 - min(1.0, adx / 20) if adx < 25 else 0.0
+
+        return volume_score * 0.3 + vol_score * 0.4 + adx_score * 0.3
+
+    def _get_description(self, regime: MacroRegime) -> str:
+        """获取生态描述"""
+        descriptions = {
+            MacroRegime.SPRING: "春季复苏：波动率回升，成交温和放大，机构左侧布局",
+            MacroRegime.SUMMER: "夏季繁荣：趋势明确，情绪高涨，散户机构共振",
+            MacroRegime.AUTUMN: "秋季分化：板块离散，轮动加速，量化游资主导",
+            MacroRegime.WINTER: "冬季萧条：成交萎缩，波动压缩，空头主导",
+            MacroRegime.UNKNOWN: "过渡状态：生态不明，建议观望"
+        }
+        return descriptions.get(regime, "未知状态")

cyb50-pro/core/ecosystem/meso.py

@@ -0,0 +1,290 @@
+"""
+中观生态识别器
+计算市场结构健康度评分（0-100）
+基于五个维度：价格冲击、订单流平衡、流动性深度、波动率效率、信息冲击响应
+"""
+
+from dataclasses import dataclass
+from typing import Dict, Optional
+from enum import Enum
+
+import numpy as np
+import pandas as pd
+
+
+class HealthLevel(Enum):
+    """结构健康度分级"""
+    HIGH = "high"      # > 70
+    MEDIUM = "medium"  # 40-70
+    LOW = "low"        # < 40
+
+
+@dataclass
+class MesoEcosystem:
+    """中观生态数据结构"""
+    health_score: float          # 总健康度评分 (0-100)
+    health_level: HealthLevel
+    price_impact: float          # 价格冲击系数
+    order_flow: float            # 订单流平衡
+    liquidity_depth: float       # 流动性深度
+    volatility_efficiency: float # 波动率效率
+    info_response: float         # 信息冲击响应
+    components: Dict[str, float] # 各维度明细
+
+
+class MesoEcosystemIdentifier:
+    """
+    中观生态识别器
+
+    计算市场微观结构健康度：
+    1. 价格冲击系数：大单对价格的影响程度
+    2. 订单流平衡：买卖盘力量对比
+    3. 流动性深度：买卖盘深度
+    4. 波动率效率：波动率与信息到达的关系
+    5. 信息冲击响应：价格对新信息的反应速度
+    """
+
+    def __init__(
+        self,
+        lookback_days: int = 60,
+        weights: Optional[Dict[str, float]] = None,
+        high_threshold: float = 70,
+        medium_threshold: float = 40
+    ):
+        self.lookback_days = lookback_days
+        self.weights = weights or {
+            "price_impact": 0.30,
+            "order_flow": 0.25,
+            "liquidity_depth": 0.20,
+            "volatility_efficiency": 0.15,
+            "info_response": 0.10
+        }
+        self.high_threshold = high_threshold
+        self.medium_threshold = medium_threshold
+
+    def identify(
+        self,
+        price_data: pd.DataFrame,
+        order_book_data: Optional[pd.DataFrame] = None,
+        trade_data: Optional[pd.DataFrame] = None
+    ) -> MesoEcosystem:
+        """
+        识别中观生态结构健康度
+
+        Args:
+            price_data: 价格数据
+            order_book_data: 订单簿数据（可选）
+            trade_data: 成交数据（可选）
+
+        Returns:
+            MesoEcosystem: 中观生态识别结果
+        """
+        # 计算五个维度
+        price_impact = self._calculate_price_impact(price_data, trade_data)
+        order_flow = self._calculate_order_flow(price_data, order_book_data, trade_data)
+        liquidity_depth = self._calculate_liquidity_depth(order_book_data, price_data)
+        volatility_efficiency = self._calculate_volatility_efficiency(price_data)
+        info_response = self._calculate_info_response(price_data)
+
+        # 标准化到0-100分
+        components = {
+            "price_impact": self._normalize_price_impact(price_impact),
+            "order_flow": self._normalize_order_flow(order_flow),
+            "liquidity_depth": self._normalize_liquidity_depth(liquidity_depth),
+            "volatility_efficiency": self._normalize_vol_efficiency(volatility_efficiency),
+            "info_response": self._normalize_info_response(info_response)
+        }
+
+        # 计算加权总分
+        health_score = sum(
+            components[key] * self.weights[key]
+            for key in components
+        )
+
+        # 确定健康等级
+        if health_score >= self.high_threshold:
+            health_level = HealthLevel.HIGH
+        elif health_score >= self.medium_threshold:
+            health_level = HealthLevel.MEDIUM
+        else:
+            health_level = HealthLevel.LOW
+
+        return MesoEcosystem(
+            health_score=health_score,
+            health_level=health_level,
+            price_impact=price_impact,
+            order_flow=order_flow,
+            liquidity_depth=liquidity_depth,
+            volatility_efficiency=volatility_efficiency,
+            info_response=info_response,
+            components=components
+        )
+
+    def _calculate_price_impact(
+        self,
+        price_data: pd.DataFrame,
+        trade_data: Optional[pd.DataFrame] = None
+    ) -> float:
+        """
+        计算价格冲击系数
+
+        基于Kyle的lambda：价格变化 / 交易量
+        值越小表示流动性越好
+        """
+        if trade_data is not None and not trade_data.empty:
+            # 使用成交数据计算
+            price_changes = price_data['close'].diff().abs()
+            volumes = trade_data['volume'] if 'volume' in trade_data else price_data['volume']
+
+            # 计算日内价格冲击
+            impact = price_changes / np.sqrt(volumes)
+            impact = impact.replace([np.inf, -np.inf], np.nan).dropna()
+
+            if len(impact) > 0:
+                return impact.iloc[-20:].mean()
+
+        # 简化计算：使用价格波动率与成交量比率
+        returns = price_data['close'].pct_change().abs()
+        volume_ma = price_data['volume'].rolling(20).mean()
+
+        impact = returns / np.log(volume_ma + 1)
+        impact = impact.replace([np.inf, -np.inf], np.nan).dropna()
+
+        return impact.iloc[-20:].mean() if len(impact) > 0 else 0.001
+
+    def _calculate_order_flow(
+        self,
+        price_data: pd.DataFrame,
+        order_book_data: Optional[pd.DataFrame] = None,
+        trade_data: Optional[pd.DataFrame] = None
+    ) -> float:
+        """
+        计算订单流平衡
+
+        正值表示买盘占优，负值表示卖盘占优
+        使用买卖盘深度比或主动买卖比例
+        """
+        if order_book_data is not None and not order_book_data.empty:
+            # 使用订单簿数据
+            if 'bid_depth' in order_book_data and 'ask_depth' in order_book_data:
+                bid_depth = order_book_data['bid_depth'].iloc[-1]
+                ask_depth = order_book_data['ask_depth'].iloc[-1]
+                if ask_depth > 0:
+                    return (bid_depth - ask_depth) / (bid_depth + ask_depth)
+
+        # 使用价格-成交量关系估计
+        # 上涨日成交量 vs 下跌日成交量
+        returns = price_data['close'].pct_change()
+        volumes = price_data['volume']
+
+        up_volume = volumes[returns > 0].iloc[-20:].mean()
+        down_volume = volumes[returns < 0].iloc[-20:].mean()
+
+        if pd.isna(up_volume) or pd.isna(down_volume) or (up_volume + down_volume) == 0:
+            return 0.0
+
+        return (up_volume - down_volume) / (up_volume + down_volume)
+
+    def _calculate_liquidity_depth(
+        self,
+        order_book_data: Optional[pd.DataFrame] = None,
+        price_data: Optional[pd.DataFrame] = None
+    ) -> float:
+        """
+        计算流动性深度
+
+        单位价格变动所需的成交量
+        """
+        if order_book_data is not None and not order_book_data.empty:
+            # 使用订单簿深度
+            if 'bid_depth' in order_book_data:
+                return order_book_data['bid_depth'].iloc[-20:].mean()
+
+        # 使用Amihud非流动性指标倒数
+        if price_data is not None:
+            returns = price_data['close'].pct_change().abs()
+            volumes = price_data['volume']
+
+            illiquidity = returns / (volumes * price_data['close'])
+            illiquidity = illiquidity.replace([np.inf, -np.inf], np.nan).dropna()
+
+            if len(illiquidity) > 0:
+                # 返回倒数，值越大流动性越好
+                return 1.0 / (illiquidity.iloc[-20:].mean() + 1e-10)
+
+        return 1.0
+
+    def _calculate_volatility_efficiency(self, price_data: pd.DataFrame) -> float:
+        """
+        计算波动率效率
+
+        实际波动率与信息到达率的比率
+        衡量波动率是否有效反映信息
+        """
+        # 计算已实现波动率
+        returns = price_data['close'].pct_change().dropna()
+        realized_vol = returns.iloc[-20:].std() * np.sqrt(252)
+
+        # 计算信息到达率（使用成交量变化作为代理）
+        volume_changes = price_data['volume'].pct_change().abs().iloc[-20:].mean()
+
+        # 效率 = 波动率 / 信息到达率
+        if volume_changes > 0:
+            efficiency = realized_vol / (volume_changes + 0.01)
+        else:
+            efficiency = realized_vol
+
+        return efficiency
+
+    def _calculate_info_response(self, price_data: pd.DataFrame) -> float:
+        """
+        计算信息冲击响应
+
+        价格对新信息的反应速度和程度
+        使用价格自相关性衡量
+        """
+        returns = price_data['close'].pct_change().dropna()
+
+        if len(returns) < 20:
+            return 0.5
+
+        # 计算1阶和5阶自相关系数
+        autocorr_1 = returns.iloc[-20:].autocorr(lag=1)
+        autocorr_5 = returns.iloc[-20:].autocorr(lag=5)
+
+        # 负自相关表示快速反转（信息快速被吸收）
+        # 正自相关表示动量延续
+        response = -(autocorr_1 + autocorr_5 / 5) / 2
+
+        return response
+
+    # 标准化方法
+    def _normalize_price_impact(self, value: float) -> float:
+        """标准化价格冲击系数 (0-100)"""
+        # 值越小越好
+        # 0.0001 = 优秀 (100分), 0.01 = 差 (0分)
+        score = 100 * (1 - min(1, max(0, (value - 0.0001) / 0.0099)))
+        return max(0, min(100, score))
+
+    def _normalize_order_flow(self, value: float) -> float:
+        """标准化订单流平衡 (0-100)"""
+        # -1到1标准化到0-100，0为中性（50分）
+        return 50 + value * 50
+
+    def _normalize_liquidity_depth(self, value: float) -> float:
+        """标准化流动性深度 (0-100)"""
+        # 使用对数变换
+        score = 50 + 50 * np.log(value + 1) / np.log(100)
+        return max(0, min(100, score))
+
+    def _normalize_vol_efficiency(self, value: float) -> float:
+        """标准化波动率效率 (0-100)"""
+        # 1-5之间为合理范围
+        score = 100 - abs(value - 3) * 25
+        return max(0, min(100, score))
+
+    def _normalize_info_response(self, value: float) -> float:
+        """标准化信息冲击响应 (0-100)"""
+        # -0.5到0.5标准化到0-100
+        score = 50 + value * 100
+        return max(0, min(100, score))

cyb50-pro/core/ecosystem/micro.py

@@ -0,0 +1,415 @@
+"""
+微观生态识别器（扩展版）
+基于HMM三态模型 + 订单流毒性检测 + 主力资金识别
+"""
+
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Tuple
+from enum import Enum
+
+import numpy as np
+import pandas as pd
+from hmmlearn import hmm
+
+
+class MicroState(Enum):
+    """微观生态状态"""
+    RANGING = "ranging"      # 震荡
+    TRENDING = "trending"    # 趋势
+    REVERSING = "reversing"  # 反转
+
+
+class FlowToxicity(Enum):
+    """订单流毒性等级"""
+    NONE = "none"        # 无毒性
+    LOW = "low"          # 轻度
+    MEDIUM = "medium"    # 中度
+    HIGH = "high"        # 高度毒性
+
+
+@dataclass
+class SmartMoneySignal:
+    """主力资金信号"""
+    detected: bool
+    direction: str       # "accumulate", "distribute", "neutral"
+    confidence: float
+    large_order_count: int
+    avg_order_size: float
+
+
+@dataclass
+class MicroEcosystem:
+    """微观生态数据结构"""
+    state: MicroState
+    state_probability: Dict[MicroState, float]
+    flow_toxicity: FlowToxicity
+    smart_money: SmartMoneySignal
+    hmm_features: Dict[str, float]
+    warnings: List[str] = field(default_factory=list)
+
+
+class MicroEcosystemIdentifier:
+    """
+    微观生态识别器（扩展版）
+
+    功能：
+    1. HMM三态识别（震荡/趋势/反转）
+    2. 订单流毒性检测
+    3. 主力资金（Smart Money）识别
+    """
+
+    def __init__(
+        self,
+        n_components: int = 3,
+        covariance_type: str = "full",
+        n_iter: int = 100,
+        # 有毒订单流检测参数
+        toxic_price_reversal: float = 0.003,
+        toxic_depth_ratio: float = 0.8,
+        toxic_duration: int = 10,
+        # 主力资金识别参数
+        smart_money_threshold: float = 1_000_000,
+        accumulation_period: int = 5
+    ):
+        self.n_components = n_components
+        self.covariance_type = covariance_type
+        self.n_iter = n_iter
+
+        # 有毒订单流参数
+        self.toxic_price_reversal = toxic_price_reversal
+        self.toxic_depth_ratio = toxic_depth_ratio
+        self.toxic_duration = toxic_duration
+
+        # 主力资金参数
+        self.smart_money_threshold = smart_money_threshold
+        self.accumulation_period = accumulation_period
+
+        # HMM模型
+        self.hmm_model: Optional[hmm.GaussianHMM] = None
+        self._is_fitted = False
+
+    def fit(self, price_data: pd.DataFrame):
+        """
+        训练HMM模型
+
+        Args:
+            price_data: 历史价格数据
+        """
+        features = self._extract_hmm_features(price_data)
+
+        self.hmm_model = hmm.GaussianHMM(
+            n_components=self.n_components,
+            covariance_type=self.covariance_type,
+            n_iter=self.n_iter,
+            random_state=42
+        )
+
+        self.hmm_model.fit(features)
+        self._is_fitted = True
+
+        # 识别各状态对应的市场类型
+        self._label_states(price_data, features)
+
+    def identify(
+        self,
+        price_data: pd.DataFrame,
+        trade_data: Optional[pd.DataFrame] = None,
+        order_book_data: Optional[pd.DataFrame] = None
+    ) -> MicroEcosystem:
+        """
+        识别微观生态
+
+        Args:
+            price_data: 价格数据
+            trade_data: 成交数据（可选）
+            order_book_data: 订单簿数据（可选）
+
+        Returns:
+            MicroEcosystem: 微观生态识别结果
+        """
+        warnings = []
+
+        # 1. HMM状态识别
+        if not self._is_fitted:
+            self.fit(price_data)
+
+        hmm_state, state_probs, features = self._predict_hmm_state(price_data)
+
+        # 2. 订单流毒性检测
+        toxicity = self._detect_flow_toxicity(
+            price_data, trade_data, order_book_data
+        )
+        if toxicity in [FlowToxicity.HIGH, FlowToxicity.MEDIUM]:
+            warnings.append(f"Detected {toxicity.value} toxic order flow")
+
+        # 3. 主力资金识别
+        smart_money = self._detect_smart_money(price_data, trade_data)
+        if smart_money.detected:
+            warnings.append(
+                f"Smart money {smart_money.direction} detected "
+                f"(confidence: {smart_money.confidence:.2f})"
+            )
+
+        return MicroEcosystem(
+            state=hmm_state,
+            state_probability=state_probs,
+            flow_toxicity=toxicity,
+            smart_money=smart_money,
+            hmm_features=features,
+            warnings=warnings
+        )
+
+    def _extract_hmm_features(self, data: pd.DataFrame) -> np.ndarray:
+        """提取HMM特征"""
+        # 特征1: 对数收益率
+        log_returns = np.log(data['close'] / data['close'].shift(1))
+
+        # 特征2: 波动率（20日）
+        volatility = log_returns.rolling(20).std()
+
+        # 特征3: 价格位置（在20日区间中的位置）
+        price_position = (data['close'] - data['low'].rolling(20).min()) / \
+                        (data['high'].rolling(20).max() - data['low'].rolling(20).min() + 1e-10)
+
+        # 特征4: 成交量变化
+        volume_change = data['volume'].pct_change()
+
+        features = pd.DataFrame({
+            'log_returns': log_returns,
+            'volatility': volatility,
+            'price_position': price_position,
+            'volume_change': volume_change
+        }).dropna()
+
+        return features.values
+
+    def _label_states(self, price_data: pd.DataFrame, features: np.ndarray):
+        """根据历史表现标记HMM状态"""
+        hidden_states = self.hmm_model.predict(features)
+
+        # 计算各状态的统计特征来标记
+        returns = pd.Series(features[:, 0])
+        volatility = pd.Series(features[:, 1])
+
+        state_stats = {}
+        for state in range(self.n_components):
+            mask = hidden_states == state
+            if mask.sum() > 0:
+                state_stats[state] = {
+                    'volatility': volatility[mask].mean(),
+                    'trend_strength': abs(returns[mask].mean())
+                }
+
+        # 标记状态：高波动+弱趋势=震荡，低波动+强趋势=趋势，高波动+强趋势=反转
+        self.state_labels = {}
+        for state, stats in state_stats.items():
+            vol = stats['volatility']
+            trend = stats['trend_strength']
+
+            if vol > np.percentile(list(s['volatility'] for s in state_stats.values()), 66):
+                if trend > np.percentile(list(s['trend_strength'] for s in state_stats.values()), 50):
+                    self.state_labels[state] = MicroState.REVERSING
+                else:
+                    self.state_labels[state] = MicroState.RANGING
+            else:
+                if trend > np.percentile(list(s['trend_strength'] for s in state_stats.values()), 50):
+                    self.state_labels[state] = MicroState.TRENDING
+                else:
+                    self.state_labels[state] = MicroState.RANGING
+
+    def _predict_hmm_state(
+        self,
+        price_data: pd.DataFrame
+    ) -> Tuple[MicroState, Dict[MicroState, float], Dict[str, float]]:
+        """预测当前HMM状态"""
+        features = self._extract_hmm_features(price_data)
+
+        if len(features) == 0:
+            return MicroState.RANGING, {s: 1/3 for s in MicroState}, {}
+
+        # 预测状态概率
+        log_prob, state_probs = self.hmm_model.score_samples(features[-1:])
+
+        # 获取最新特征值
+        latest_features = {
+            'log_returns': features[-1, 0],
+            'volatility': features[-1, 1],
+            'price_position': features[-1, 2],
+            'volume_change': features[-1, 3]
+        }
+
+        # 映射到标记的状态
+        probs_by_state = {}
+        for state_idx, prob in enumerate(state_probs[0]):
+            labeled_state = self.state_labels.get(state_idx, MicroState.RANGING)
+            probs_by_state[labeled_state] = probs_by_state.get(labeled_state, 0) + prob
+
+        # 最可能状态
+        current_state = max(probs_by_state, key=probs_by_state.get)
+
+        return current_state, probs_by_state, latest_features
+
+    def _detect_flow_toxicity(
+        self,
+        price_data: pd.DataFrame,
+        trade_data: Optional[pd.DataFrame] = None,
+        order_book_data: Optional[pd.DataFrame] = None
+    ) -> FlowToxicity:
+        """
+        检测订单流毒性
+
+        有毒订单流特征：
+        1. 大单成交后价格反向运动
+        2. 买卖盘深度持续失衡
+        """
+        toxicity_score = 0.0
+
+        # 检测1: 价格反转
+        if trade_data is not None and 'large_trade' in trade_data.columns:
+            large_trades = trade_data[trade_data['large_trade'] == True]
+
+            for idx in large_trades.index:
+                if idx + 1 in price_data.index:
+                    trade_price = large_trades.loc[idx, 'price']
+                    future_price = price_data.loc[idx + 1, 'close']
+                    reversal = abs(future_price - trade_price) / trade_price
+
+                    if reversal > self.toxic_price_reversal:
+                        toxicity_score += 0.3
+
+        # 简化检测：使用价格与成交量的关系
+        returns = price_data['close'].pct_change().abs().iloc[-20:]
+        volumes = price_data['volume'].iloc[-20:]
+
+        # 高成交量但价格不动 = 有毒（对敲或洗盘）
+        volume_ma = volumes.mean()
+        high_volume_periods = volumes > volume_ma * 1.5
+        low_volatility_periods = returns < returns.mean() * 0.5
+
+        toxic_periods = (high_volume_periods & low_volatility_periods).sum()
+        toxicity_score += min(0.4, toxic_periods * 0.1)
+
+        # 检测2: 买卖盘深度失衡
+        if order_book_data is not None:
+            if 'bid_depth' in order_book_data and 'ask_depth' in order_book_data:
+                depth_ratio = (
+                    order_book_data['bid_depth'] /
+                    (order_book_data['ask_depth'] + 1e-10)
+                )
+
+                imbalanced_periods = (
+                    (depth_ratio < self.toxic_depth_ratio) |
+                    (depth_ratio > 1 / self.toxic_depth_ratio)
+                ).sum()
+
+                if imbalanced_periods > self.toxic_duration:
+                    toxicity_score += 0.3
+
+        # 判断毒性等级
+        if toxicity_score >= 0.6:
+            return FlowToxicity.HIGH
+        elif toxicity_score >= 0.4:
+            return FlowToxicity.MEDIUM
+        elif toxicity_score >= 0.2:
+            return FlowToxicity.LOW
+        else:
+            return FlowToxicity.NONE
+
+    def _detect_smart_money(
+        self,
+        price_data: pd.DataFrame,
+        trade_data: Optional[pd.DataFrame] = None
+    ) -> SmartMoneySignal:
+        """
+        识别主力资金行为
+
+        特征：
+        1. 连续大单买入/卖出
+        2. 价格维持横盘或缓慢运动（隐蔽建仓）
+        3. 委托簿买方/卖方深度持续增加
+        """
+        if trade_data is None or trade_data.empty:
+            return SmartMoneySignal(
+                detected=False,
+                direction="neutral",
+                confidence=0.0,
+                large_order_count=0,
+                avg_order_size=0.0
+            )
+
+        # 识别大单
+        recent_data = trade_data.iloc[-self.accumulation_period * 10:]
+
+        avg_volume = recent_data['volume'].mean()
+        large_orders = recent_data[
+            recent_data['volume'] > avg_volume * 3
+        ]
+
+        if len(large_orders) < 3:
+            return SmartMoneySignal(
+                detected=False,
+                direction="neutral",
+                confidence=0.0,
+                large_order_count=len(large_orders),
+                avg_order_size=large_orders['volume'].mean() if len(large_orders) > 0 else 0.0
+            )
+
+        # 分析大单方向
+        if 'side' in large_orders.columns:
+            buy_orders = large_orders[large_orders['side'] == 'buy']
+            sell_orders = large_orders[large_orders['side'] == 'sell']
+        else:
+            # 通过价格变动推断方向
+            price_change = price_data['close'].diff()
+            buy_orders = large_orders[price_change.loc[large_orders.index] > 0]
+            sell_orders = large_orders[price_change.loc[large_orders.index] < 0]
+
+        buy_volume = buy_orders['volume'].sum() if len(buy_orders) > 0 else 0
+        sell_volume = sell_orders['volume'].sum() if len(sell_orders) > 0 else 0
+
+        # 检测建仓/出货
+        total_large_volume = buy_volume + sell_volume
+        if total_large_volume == 0:
+            return SmartMoneySignal(
+                detected=False,
+                direction="neutral",
+                confidence=0.0,
+                large_order_count=len(large_orders),
+                avg_order_size=0.0
+            )
+
+        buy_ratio = buy_volume / total_large_volume
+
+        # 价格走势
+        price_trend = (
+            price_data['close'].iloc[-1] -
+            price_data['close'].iloc[-self.accumulation_period]
+        ) / price_data['close'].iloc[-self.accumulation_period]
+
+        # 建仓特征：大单买入 + 价格横盘/微涨
+        if buy_ratio > 0.6 and abs(price_trend) < 0.02:
+            return SmartMoneySignal(
+                detected=True,
+                direction="accumulate",
+                confidence=buy_ratio,
+                large_order_count=len(large_orders),
+                avg_order_size=large_orders['volume'].mean()
+            )
+
+        # 出货特征：大单卖出 + 价格横盘/微跌
+        if buy_ratio < 0.4 and abs(price_trend) < 0.02:
+            return SmartMoneySignal(
+                detected=True,
+                direction="distribute",
+                confidence=1 - buy_ratio,
+                large_order_count=len(large_orders),
+                avg_order_size=large_orders['volume'].mean()
+            )
+
+        return SmartMoneySignal(
+            detected=False,
+            direction="neutral",
+            confidence=0.0,
+            large_order_count=len(large_orders),
+            avg_order_size=large_orders['volume'].mean()
+        )

cyb50-pro/core/engine.py

@@ -0,0 +1,189 @@
+"""
+CYB50-Pro 交易引擎主控
+
+整合所有模块的主控循环
+"""
+
+from typing import Dict, Optional, Any
+from datetime import datetime
+import pandas as pd
+
+from core.ecosystem import EcosystemFusion, UnifiedEcosystem
+from core.signal_fusion import SignalCollector, BayesianSignalFusion
+from agents import (
+    AgentBase, DynamicAgentRouter, AgentCoordinator,
+    BreakoutAgent, MeanReversionAgent
+)
+from risk import RiskManager
+from utils.logger import get_logger, EventType
+
+
+class CYB50ProEngine:
+    """
+    CYB50-Pro 交易引擎
+
+    主控流程：
+    1. 获取市场数据
+    2. 识别市场生态
+    3. 路由智能体并生成信号
+    4. 融合信号
+    5. 风险管理检查
+    6. 执行交易
+    """
+
+    def __init__(self, config: Optional[Dict] = None):
+        self.config = config or {}
+        self.logger = get_logger()
+
+        # 初始化各模块
+        self.ecosystem_fusion = EcosystemFusion()
+        self.signal_collector = SignalCollector()
+        self.signal_fusion = BayesianSignalFusion()
+        self.agent_router = DynamicAgentRouter()
+        self.agent_coordinator = AgentCoordinator()
+        self.risk_manager = RiskManager()
+
+        # 初始化风险预算
+        self.risk_manager.budget_manager.allocate_monthly_budget(1_000_000)
+
+        # 初始化智能体 - 仅Breakout（当前最优单策略）
+        self.agents: Dict[str, AgentBase] = {
+            "breakout": BreakoutAgent(),
+        }
+
+        self.is_running = False
+
+    def run_cycle(
+        self,
+        price_data: pd.DataFrame,
+        account_value: float,
+        tick_data: Optional[pd.DataFrame] = None
+    ) -> Dict[str, Any]:
+        """
+        执行一个交易周期
+
+        Args:
+            price_data: 价格数据
+            account_value: 账户价值
+            tick_data: tick数据（可选）
+
+        Returns:
+            周期结果字典
+        """
+        results = {
+            "timestamp": datetime.now(),
+            "signals": {},
+            "executed": False
+        }
+
+        try:
+            # 1. 识别市场生态
+            ecosystem = self.ecosystem_fusion.fuse(
+                price_data=price_data,
+                tick_data=tick_data
+            )
+            results["ecosystem"] = ecosystem.to_dict()
+
+            self.logger.log_ecosystem(
+                EventType.ECOSYSTEM_CHANGE,
+                ecosystem.macro.regime.value,
+                ecosystem.meso.health_score,
+                ecosystem.micro.state.value,
+                ecosystem.confidence
+            )
+
+            # 2. 智能体路由
+            routing = self.agent_router.route(
+                self.agents,
+                price_data,
+                ecosystem
+            )
+            results["routing"] = {
+                "active_agents": routing.active_agents,
+                "weights": {k: v.weight for k, v in routing.weights.items()}
+            }
+
+            # 3. 生成信号
+            agent_signals = {}
+            for name in routing.active_agents:
+                if name in self.agents:
+                    signal = self.agents[name].generate_signal(price_data, ecosystem)
+                    if signal:
+                        agent_signals[name] = signal
+                        results["signals"][name] = {
+                            "direction": signal.direction.value,
+                            "confidence": signal.confidence,
+                            "position": signal.suggested_position
+                        }
+
+            # 4. 协同处理
+            if agent_signals:
+                coordinated = self.agent_coordinator.coordinate(
+                    agent_signals,
+                    {k: v.weight for k, v in routing.weights.items()}
+                )
+                results["coordinated"] = {
+                    "direction": coordinated.final_direction.value,
+                    "position": coordinated.final_position,
+                    "reasoning": coordinated.reasoning
+                }
+
+                # 5. 风险管理检查
+                risk_check = self.risk_manager.check_trade_permission(
+                    account_value=account_value,
+                    proposed_position=coordinated.final_position,
+                    entry_price=price_data['close'].iloc[-1]
+                )
+                results["risk_check"] = {
+                    "can_trade": risk_check.can_trade,
+                    "allowed_position": risk_check.allowed_position,
+                    "risk_level": risk_check.risk_level,
+                    "warnings": risk_check.warnings
+                }
+
+                if risk_check.can_trade and coordinated.final_position > 0:
+                    results["executed"] = True
+                    results["final_position"] = min(
+                        coordinated.final_position,
+                        risk_check.allowed_position
+                    )
+
+            # 更新风险状态
+            self.risk_manager.update_account_state(account_value)
+
+        except Exception as e:
+            self.logger.log_system(
+                EventType.SYSTEM_ERROR,
+                f"Engine cycle error: {str(e)}",
+                level="error"
+            )
+            results["error"] = str(e)
+
+        return results
+
+    def start(self):
+        """启动引擎"""
+        self.is_running = True
+        self.logger.log_system(
+            EventType.SYSTEM_START,
+            "CYB50-Pro engine started",
+            level="info"
+        )
+
+    def stop(self):
+        """停止引擎"""
+        self.is_running = False
+        self.logger.log_system(
+            EventType.SYSTEM_STOP,
+            "CYB50-Pro engine stopped",
+            level="info"
+        )
+
+    def get_status(self) -> Dict[str, Any]:
+        """获取引擎状态"""
+        return {
+            "is_running": self.is_running,
+            "agents": {name: agent.get_performance_summary()
+                      for name, agent in self.agents.items()},
+            "risk_summary": self.risk_manager.get_risk_summary()
+        }

cyb50-pro/core/signal_fusion/__init__.py

@@ -0,0 +1,15 @@
+"""
+高阶信号融合引擎
+
+提供多级信号融合和贝叶斯网络推断
+"""
+
+from .collector import SignalCollector, PrimarySignal
+from .bayesian_fusion import BayesianSignalFusion, FusedSignal
+
+__all__ = [
+    "SignalCollector",
+    "PrimarySignal",
+    "BayesianSignalFusion",
+    "FusedSignal",
+]

cyb50-pro/core/signal_fusion/bayesian_fusion.py

@@ -0,0 +1,274 @@
+"""
+贝叶斯信号融合引擎
+
+使用贝叶斯网络进行非线性信号融合
+P(上涨|信号,生态) ∝ P(生态) × ∏ P(信号i|上涨,生态) / P(信号i|生态)
+"""
+
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Tuple, Any
+from datetime import datetime
+
+import numpy as np
+import pandas as pd
+from scipy import stats
+
+
+@dataclass
+class FusedSignal:
+    """融合后的信号"""
+    up_probability: float      # 上涨概率
+    down_probability: float    # 下跌概率
+    neutral_probability: float # 横盘概率
+    overall_confidence: float  # 整体置信度
+    signal_grade: str          # 信号等级：strong/medium/weak/none
+    recommended_action: str    # 建议动作
+    fusion_metadata: Dict[str, Any] = field(default_factory=dict)
+    timestamp: datetime = field(default_factory=datetime.now)
+
+
+class BayesianSignalFusion:
+    """
+    贝叶斯信号融合器
+
+    融合逻辑：
+    1. 一级信号 → 贝叶斯网络输入
+    2. 生态作为隐变量调节条件概率
+    3. 输出后验概率分布
+    """
+
+    def __init__(
+        self,
+        prior_up: float = 0.33,
+        prior_down: float = 0.33,
+        prior_neutral: float = 0.34,
+        min_sample_size: int = 1000
+    ):
+        self.prior = {
+            "up": prior_up,
+            "down": prior_down,
+            "neutral": prior_neutral
+        }
+        self.min_sample_size = min_sample_size
+
+        # 历史条件概率表（简化实现）
+        self.conditional_probs: Dict[str, Dict] = {}
+
+    def fuse(
+        self,
+        primary_signals: List[Any],
+        ecosystem: Any,
+        fallback_to_weighted: bool = True
+    ) -> FusedSignal:
+        """
+        执行贝叶斯融合
+
+        Args:
+            primary_signals: 一级信号列表
+            ecosystem: 市场生态
+            fallback_to_weighted: 小样本时是否回退到加权平均
+
+        Returns:
+            FusedSignal: 融合后的信号
+        """
+        if not primary_signals:
+            return FusedSignal(
+                up_probability=self.prior["up"],
+                down_probability=self.prior["down"],
+                neutral_probability=self.prior["neutral"],
+                overall_confidence=0.0,
+                signal_grade="none",
+                recommended_action="hold"
+            )
+
+        # 检查样本量
+        if fallback_to_weighted:
+            sample_size = self._estimate_sample_size(ecosystem)
+            if sample_size < self.min_sample_size:
+                return self._weighted_fusion(primary_signals, ecosystem, sample_size)
+
+        # 贝叶斯推断
+        posterior = self._bayesian_inference(primary_signals, ecosystem)
+
+        # 确定信号等级和建议
+        grade, action = self._determine_signal_grade(posterior)
+
+        return FusedSignal(
+            up_probability=posterior["up"],
+            down_probability=posterior["down"],
+            neutral_probability=posterior["neutral"],
+            overall_confidence=self._calculate_confidence(posterior, primary_signals),
+            signal_grade=grade,
+            recommended_action=action,
+            fusion_metadata={
+                "method": "bayesian",
+                "signal_count": len(primary_signals),
+                "prior": self.prior.copy(),
+                "posterior": posterior
+            }
+        )
+
+    def _bayesian_inference(
+        self,
+        signals: List[Any],
+        ecosystem: Any
+    ) -> Dict[str, float]:
+        """执行贝叶斯推断"""
+        # 初始化后验为 prior
+        posterior = self.prior.copy()
+
+        # 获取生态条件
+        regime = getattr(ecosystem.macro, 'regime', None) if ecosystem else None
+
+        # 对每个信号更新后验
+        for signal in signals:
+            likelihood = self._calculate_likelihood(signal, regime)
+
+            # 贝叶斯更新: P(H|D) ∝ P(D|H) * P(H)
+            for direction in ["up", "down", "neutral"]:
+                posterior[direction] *= likelihood.get(direction, 0.33)
+
+        # 归一化
+        total = sum(posterior.values())
+        if total > 0:
+            posterior = {k: v/total for k, v in posterior.items()}
+
+        return posterior
+
+    def _calculate_likelihood(
+        self,
+        signal: Any,
+        regime: Any
+    ) -> Dict[str, float]:
+        """计算似然函数 P(信号|方向,生态)"""
+        value = getattr(signal, 'value', 0)
+
+        # 基于信号值和生态计算似然
+        # 简化模型：信号值越正，上涨概率越高
+        likelihood = {
+            "up": max(0.1, min(0.9, 0.5 + value * 0.4)),
+            "down": max(0.1, min(0.9, 0.5 - value * 0.4)),
+            "neutral": max(0.1, min(0.9, 1 - abs(value) * 0.5))
+        }
+
+        # 生态调节
+        if regime:
+            regime_boost = {
+                "summer": {"up": 1.2, "down": 0.8, "neutral": 0.9},
+                "winter": {"up": 0.8, "down": 1.2, "neutral": 0.9},
+                "spring": {"up": 1.1, "down": 0.9, "neutral": 0.95},
+                "autumn": {"up": 0.9, "down": 0.9, "neutral": 1.1}
+            }.get(regime.value, {"up": 1, "down": 1, "neutral": 1})
+
+            for direction in likelihood:
+                likelihood[direction] *= regime_boost[direction]
+
+        # 重新归一化
+        total = sum(likelihood.values())
+        return {k: v/total for k, v in likelihood.items()}
+
+    def _weighted_fusion(
+        self,
+        signals: List[Any],
+        ecosystem: Any,
+        sample_size: int
+    ) -> FusedSignal:
+        """小样本回退：加权平均融合"""
+        if not signals:
+            return self._create_neutral_signal("no_signals")
+
+        # 加权平均
+        weighted_sum = sum(s.value * getattr(s, 'confidence', 0.5) for s in signals)
+        total_weight = sum(getattr(s, 'confidence', 0.5) for s in signals)
+
+        if total_weight == 0:
+            return self._create_neutral_signal("zero_weight")
+
+        avg_signal = weighted_sum / total_weight
+
+        # 转换为概率
+        up_prob = max(0, min(1, 0.5 + avg_signal * 0.5))
+        down_prob = max(0, min(1, 0.5 - avg_signal * 0.5))
+        neutral_prob = 1 - abs(avg_signal)
+
+        # 归一化
+        total = up_prob + down_prob + neutral_prob
+        probs = {
+            "up": up_prob / total,
+            "down": down_prob / total,
+            "neutral": neutral_prob / total
+        }
+
+        grade, action = self._determine_signal_grade(probs)
+
+        return FusedSignal(
+            up_probability=probs["up"],
+            down_probability=probs["down"],
+            neutral_probability=probs["neutral"],
+            overall_confidence=abs(avg_signal) * (sample_size / self.min_sample_size),
+            signal_grade=grade,
+            recommended_action=action,
+            fusion_metadata={
+                "method": "weighted_fallback",
+                "reason": f"sample_size {sample_size} < {self.min_sample_size}",
+                "signal_count": len(signals)
+            }
+        )
+
+    def _determine_signal_grade(self, probs: Dict[str, float]) -> Tuple[str, str]:
+        """确定信号等级"""
+        max_prob = max(probs.values())
+        dominant = max(probs, key=probs.get)
+
+        if max_prob > 0.7 and probs["neutral"] < 0.3:
+            grade = "strong"
+        elif max_prob > 0.55:
+            grade = "medium"
+        elif max_prob > 0.45:
+            grade = "weak"
+        else:
+            grade = "none"
+
+        # 建议动作
+        if dominant == "up" and grade in ["strong", "medium"]:
+            action = "buy"
+        elif dominant == "down" and grade in ["strong", "medium"]:
+            action = "sell"
+        else:
+            action = "hold"
+
+        return grade, action
+
+    def _calculate_confidence(
+        self,
+        posterior: Dict[str, float],
+        signals: List[Any]
+    ) -> float:
+        """计算整体置信度"""
+        # 基于概率分布的熵计算置信度
+        entropy = -sum(p * np.log(p + 1e-10) for p in posterior.values())
+        max_entropy = np.log(3)  # 三分类最大熵
+
+        confidence = 1 - (entropy / max_entropy)
+
+        # 信号数量加成
+        signal_bonus = min(0.2, len(signals) * 0.02)
+
+        return min(1.0, confidence + signal_bonus)
+
+    def _estimate_sample_size(self, ecosystem: Any) -> int:
+        """估算当前生态的历史样本量"""
+        # 简化：返回固定值，实际应从数据库查询
+        return 2000  # 假设充足样本
+
+    def _create_neutral_signal(self, reason: str) -> FusedSignal:
+        """创建中性信号"""
+        return FusedSignal(
+            up_probability=self.prior["up"],
+            down_probability=self.prior["down"],
+            neutral_probability=self.prior["neutral"],
+            overall_confidence=0.0,
+            signal_grade="none",
+            recommended_action="hold",
+            fusion_metadata={"reason": reason}
+        )

cyb50-pro/core/signal_fusion/collector.py

@@ -0,0 +1,200 @@
+"""
+一级信号采集器
+
+采集所有原始信号并标准化为[-1, 1]区间
+"""
+
+from dataclasses import dataclass, field
+from typing import Dict, List, Optional, Any
+from datetime import datetime
+
+import pandas as pd
+import numpy as np
+
+
+@dataclass
+class PrimarySignal:
+    """一级信号数据结构"""
+    source: str           # 信号来源
+    signal_type: str      # 信号类型
+    value: float          # 信号值 (-1 to 1)
+    raw_value: float      # 原始值
+    timestamp: datetime
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+class SignalCollector:
+    """
+    信号采集器
+
+    从各模块采集一级信号：
+    - 趋势质量评分
+    - 卡尔曼滤波趋势
+    - RSRS阻力支撑强度
+    - HMM状态概率
+    - 生态识别结果
+    """
+
+    def __init__(self):
+        self.signal_history: List[PrimarySignal] = []
+
+    def collect_from_ecosystem(self, ecosystem: Any) -> List[PrimarySignal]:
+        """从生态识别模块采集信号"""
+        signals = []
+
+        # 宏观生态信号
+        if hasattr(ecosystem, 'macro'):
+            macro_signal = self._encode_macro_regime(ecosystem.macro)
+            signals.append(PrimarySignal(
+                source="ecosystem",
+                signal_type="macro_regime",
+                value=macro_signal,
+                raw_value=ecosystem.macro.regime.value,
+                timestamp=datetime.now(),
+                metadata={"confidence": ecosystem.macro.confidence}
+            ))
+
+        # 中观健康度信号
+        if hasattr(ecosystem, 'meso'):
+            health_signal = (ecosystem.meso.health_score - 50) / 50  # 标准化到-1,1
+            signals.append(PrimarySignal(
+                source="ecosystem",
+                signal_type="health_score",
+                value=health_signal,
+                raw_value=ecosystem.meso.health_score,
+                timestamp=datetime.now(),
+                metadata={"level": ecosystem.meso.health_level.value}
+            ))
+
+        # 微观状态信号
+        if hasattr(ecosystem, 'micro'):
+            micro_signal = self._encode_micro_state(ecosystem.micro)
+            signals.append(PrimarySignal(
+                source="ecosystem",
+                signal_type="micro_state",
+                value=micro_signal,
+                raw_value=ecosystem.micro.state.value,
+                timestamp=datetime.now()
+            ))
+
+        return signals
+
+    def collect_from_indicators(
+        self,
+        price_data: pd.DataFrame,
+        trend_quality_score: Optional[float] = None,
+        kalman_trend: Optional[float] = None,
+        rsrs_score: Optional[float] = None
+    ) -> List[PrimarySignal]:
+        """从技术指标采集信号"""
+        signals = []
+
+        # 趋势质量信号
+        if trend_quality_score is not None:
+            signals.append(PrimarySignal(
+                source="indicator",
+                signal_type="trend_quality",
+                value=(trend_quality_score - 50) / 50,
+                raw_value=trend_quality_score,
+                timestamp=datetime.now()
+            ))
+
+        # 卡尔曼趋势信号
+        if kalman_trend is not None:
+            signals.append(PrimarySignal(
+                source="indicator",
+                signal_type="kalman_trend",
+                value=np.clip(kalman_trend * 100, -1, 1),
+                raw_value=kalman_trend,
+                timestamp=datetime.now()
+            ))
+
+        # RSRS信号
+        if rsrs_score is not None:
+            signals.append(PrimarySignal(
+                source="indicator",
+                signal_type="rsrs",
+                value=(rsrs_score - 0.5) * 2,  # 0-1 映射到 -1,1
+                raw_value=rsrs_score,
+                timestamp=datetime.now()
+            ))
+
+        # MACD信号
+        macd_signal = self._calculate_macd_signal(price_data)
+        if macd_signal is not None:
+            signals.append(PrimarySignal(
+                source="indicator",
+                signal_type="macd",
+                value=macd_signal,
+                raw_value=macd_signal,
+                timestamp=datetime.now()
+            ))
+
+        return signals
+
+    def collect_from_agents(self, agent_signals: Dict[str, Any]) -> List[PrimarySignal]:
+        """从智能体采集信号"""
+        signals = []
+
+        for agent_name, signal in agent_signals.items():
+            if signal is None:
+                continue
+
+            # 转换方向为数值
+            direction_value = {
+                "long": 1.0,
+                "short": -1.0,
+                "neutral": 0.0
+            }.get(signal.direction.value, 0.0)
+
+            signals.append(PrimarySignal(
+                source=f"agent_{agent_name}",
+                signal_type="agent_direction",
+                value=direction_value * signal.confidence,
+                raw_value=direction_value,
+                timestamp=datetime.now(),
+                metadata={
+                    "confidence": signal.confidence,
+                    "strength": signal.strength.value if hasattr(signal, 'strength') else "medium"
+                }
+            ))
+
+        return signals
+
+    def _encode_macro_regime(self, macro: Any) -> float:
+        """编码宏观生态为数值信号"""
+        regime_map = {
+            "spring": 0.5,
+            "summer": 1.0,
+            "autumn": -0.5,
+            "winter": -1.0,
+            "unknown": 0.0
+        }
+        return regime_map.get(macro.regime.value, 0.0)
+
+    def _encode_micro_state(self, micro: Any) -> float:
+        """编码微观状态为数值信号"""
+        state_map = {
+            "trending": 1.0,
+            "ranging": 0.0,
+            "reversing": -1.0
+        }
+        return state_map.get(micro.state.value, 0.0)
+
+    def _calculate_macd_signal(self, data: pd.DataFrame) -> Optional[float]:
+        """计算MACD信号"""
+        if len(data) < 35:
+            return None
+
+        exp1 = data['close'].ewm(span=12, adjust=False).mean()
+        exp2 = data['close'].ewm(span=26, adjust=False).mean()
+        macd = exp1 - exp2
+        signal = macd.ewm(span=9, adjust=False).mean()
+
+        # 标准化
+        histogram = macd - signal
+        return np.clip(histogram.iloc[-1] / data['close'].iloc[-1] * 100, -1, 1)
+
+    def get_collected_signals(self) -> List[PrimarySignal]:
+        """获取所有采集的信号"""
+        return self.signal_history

cyb50-pro/execution/router.py

@@ -0,0 +1,96 @@
+"""
+智能订单路由器
+
+根据微观结构选择最优执行策略
+"""
+
+from dataclasses import dataclass
+from typing import Optional, Dict
+from enum import Enum
+
+
+class ExecutionStrategy(Enum):
+    """执行策略类型"""
+    MARKET = "market"           # 市价单
+    TWAP = "twap"              # 时间加权平均
+    VWAP = "vwap"              # 成交量加权平均
+    ICEBERG = "iceberg"        # 冰山订单
+
+
+@dataclass
+class ExecutionPlan:
+    """执行计划"""
+    strategy: ExecutionStrategy
+    num_slices: int            # 拆单数量
+    duration_minutes: int      # 执行时长
+    display_size: Optional[int] = None  # 冰山订单显示量
+    reasoning: str = ""
+
+
+class OrderRouter:
+    """
+    智能订单路由器
+
+    根据市场微观结构选择最优执行策略：
+    - 高流动性+低波动：市价单
+    - 大单+低流动性：TWAP/VWAP
+    - 不希望暴露意图：冰山订单
+    """
+
+    def route_order(
+        self,
+        order_size: int,
+        avg_daily_volume: float,
+        spread_pct: float,
+        volatility: float,
+        urgency: str = "normal"
+    ) -> ExecutionPlan:
+        """
+        路由订单
+
+        Args:
+            order_size: 订单数量
+            avg_daily_volume: 日均成交量
+            spread_pct: 买卖价差百分比
+            volatility: 波动率
+            urgency: 紧急程度 (high/normal/low)
+
+        Returns:
+            ExecutionPlan: 执行计划
+        """
+        adv_participation = order_size / avg_daily_volume if avg_daily_volume > 0 else 1.0
+
+        # 高流动性且紧急：市价单
+        if spread_pct < 0.001 and volatility < 0.02 and urgency == "high":
+            return ExecutionPlan(
+                strategy=ExecutionStrategy.MARKET,
+                num_slices=1,
+                duration_minutes=0,
+                reasoning="高流动性+紧急，使用市价单"
+            )
+
+        # 大单：TWAP/VWAP
+        if adv_participation > 0.05:
+            return ExecutionPlan(
+                strategy=ExecutionStrategy.TWAP,
+                num_slices=20,
+                duration_minutes=30,
+                reasoning=f"大单占ADV{adv_participation:.1%}，使用TWAP拆单"
+            )
+
+        # 中等订单：VWAP
+        if adv_participation > 0.02:
+            return ExecutionPlan(
+                strategy=ExecutionStrategy.VWAP,
+                num_slices=10,
+                duration_minutes=15,
+                reasoning=f"中等订单占ADV{adv_participation:.1%}，使用VWAP"
+            )
+
+        # 默认：小单直接市价
+        return ExecutionPlan(
+            strategy=ExecutionStrategy.MARKET,
+            num_slices=1,
+            duration_minutes=0,
+            reasoning="小单，直接市价成交"
+        )

cyb50-pro/multi_asset/__init__.py

@@ -0,0 +1,21 @@
+"""
+多品种轮动策略模块 - 三龙出海
+
+支持沪深300、中证500、创业板50三个指数的轮动交易
+"""
+
+from .data_loader import MultiAssetDataLoader
+from .selector import DragonSelector
+from .signal_engine import UnifiedSignalEngine
+from .position_manager import PositionManager
+from .risk_manager import MultiLayerRiskManager
+from .engine import DragonRotationEngine
+
+__all__ = [
+    "MultiAssetDataLoader",
+    "DragonSelector",
+    "UnifiedSignalEngine",
+    "PositionManager",
+    "MultiLayerRiskManager",
+    "DragonRotationEngine",
+]

cyb50-pro/multi_asset/data_loader.py

@@ -0,0 +1,276 @@
+"""
+多品种数据加载器
+
+支持沪深300、中证500、创业板50三个指数的数据获取与管理
+"""
+
+from typing import Dict, List, Optional, Tuple
+from datetime import datetime, timedelta
+import pandas as pd
+import numpy as np
+
+
+class MultiAssetDataLoader:
+    """
+    多品种数据加载器
+
+    支持三个宽基指数：
+    - 沪深300 (000300.SH)
+    - 中证500 (000905.SH)
+    - 创业板50 (399006.SZ)
+    """
+
+    # 品种代码映射
+    SYMBOLS = {
+        "csi300": "000300.SH",      # 沪深300
+        "csi500": "000905.SH",      # 中证500
+        "chinext50": "399006.SZ",   # 创业板50
+    }
+
+    # 品种名称映射
+    NAMES = {
+        "csi300": "沪深300",
+        "csi500": "中证500",
+        "chinext50": "创业板50",
+    }
+
+    def __init__(self, data_dir: str = "quant"):
+        self.data_dir = data_dir
+        self._cache: Dict[str, pd.DataFrame] = {}
+        self._correlation_cache: Optional[pd.DataFrame] = None
+
+    def load_all_data(
+        self,
+        start_date: Optional[str] = None,
+        end_date: Optional[str] = None
+    ) -> Dict[str, pd.DataFrame]:
+        """
+        加载所有三个品种的数据
+
+        Args:
+            start_date: 开始日期 (YYYY-MM-DD)
+            end_date: 结束日期 (YYYY-MM-DD)
+
+        Returns:
+            Dict[str, pd.DataFrame]: 品种代码 -> 数据框的映射
+        """
+        result = {}
+        for symbol_key in self.SYMBOLS.keys():
+            df = self.load_symbol(symbol_key, start_date, end_date)
+            if df is not None:
+                result[symbol_key] = df
+
+        return result
+
+    def load_symbol(
+        self,
+        symbol_key: str,
+        start_date: Optional[str] = None,
+        end_date: Optional[str] = None
+    ) -> Optional[pd.DataFrame]:
+        """
+        加载单个品种的数据
+
+        Args:
+            symbol_key: 品种代码键 (csi300/csi500/chinext50)
+            start_date: 开始日期
+            end_date: 结束日期
+
+        Returns:
+            pd.DataFrame or None: 加载的数据
+        """
+        # 检查缓存
+        cache_key = f"{symbol_key}_{start_date}_{end_date}"
+        if cache_key in self._cache:
+            return self._cache[cache_key].copy()
+
+        # 构建文件路径
+        file_path = f"{self.data_dir}/{symbol_key}_daily.csv"
+
+        try:
+            df = pd.read_csv(file_path)
+            df['date'] = pd.to_datetime(df['date'])
+            df.set_index('date', inplace=True)
+            df.sort_index(inplace=True)
+
+            # 日期过滤
+            if start_date:
+                df = df[df.index >= start_date]
+            if end_date:
+                df = df[df.index <= end_date]
+
+            # 缓存
+            self._cache[cache_key] = df.copy()
+
+            return df
+
+        except FileNotFoundError:
+            print(f"Warning: Data file not found for {symbol_key} at {file_path}")
+            return None
+        except Exception as e:
+            print(f"Error loading {symbol_key}: {e}")
+            return None
+
+    def align_data(
+        self,
+        data_dict: Dict[str, pd.DataFrame],
+        fill_method: str = "ffill"
+    ) -> pd.DataFrame:
+        """
+        对齐多个品种的数据到统一时间轴
+
+        Args:
+            data_dict: 品种数据字典
+            fill_method: 缺失值填充方法 (ffill/bfill/drop)
+
+        Returns:
+            pd.DataFrame: 对齐后的多品种数据（宽格式）
+        """
+        if not data_dict:
+            return pd.DataFrame()
+
+        # 获取所有日期
+        all_dates = set()
+        for df in data_dict.values():
+            all_dates.update(df.index)
+        all_dates = sorted(all_dates)
+
+        # 创建统一时间轴
+        aligned_data = pd.DataFrame(index=all_dates)
+
+        # 为每个品种添加数据
+        for symbol_key, df in data_dict.items():
+            # 收盘价
+            aligned_data[f"{symbol_key}_close"] = df['close']
+            # 成交量
+            aligned_data[f"{symbol_key}_volume"] = df['volume']
+            # 最高价
+            aligned_data[f"{symbol_key}_high"] = df['high']
+            # 最低价
+            aligned_data[f"{symbol_key}_low"] = df['low']
+            # 开盘价
+            aligned_data[f"{symbol_key}_open"] = df['open']
+
+        # 处理缺失值
+        if fill_method == "ffill":
+            aligned_data.fillna(method="ffill", inplace=True)
+        elif fill_method == "bfill":
+            aligned_data.fillna(method="bfill", inplace=True)
+        elif fill_method == "drop":
+            aligned_data.dropna(inplace=True)
+
+        return aligned_data
+
+    def calculate_correlation(
+        self,
+        data_dict: Dict[str, pd.DataFrame],
+        lookback: int = 60,
+        current_date: Optional[datetime] = None
+    ) -> pd.DataFrame:
+        """
+        计算品种间滚动相关系数
+
+        Args:
+            data_dict: 品种数据字典
+            lookback: 滚动窗口天数
+            current_date: 当前日期（用于历史回测）
+
+        Returns:
+            pd.DataFrame: 相关系数矩阵
+        """
+        # 提取收盘价
+        close_prices = pd.DataFrame()
+        for symbol_key, df in data_dict.items():
+            close_prices[symbol_key] = df['close']
+
+        # 日期过滤
+        if current_date is not None:
+            close_prices = close_prices[close_prices.index <= current_date]
+
+        # 计算收益率
+        returns = close_prices.pct_change().dropna()
+
+        # 滚动相关系数（取最近lookback天）
+        if len(returns) >= lookback:
+            recent_returns = returns.iloc[-lookback:]
+            corr_matrix = recent_returns.corr()
+        else:
+            # 数据不足时返回单位矩阵
+            corr_matrix = pd.DataFrame(
+                np.eye(len(self.SYMBOLS)),
+                index=list(self.SYMBOLS.keys()),
+                columns=list(self.SYMBOLS.keys())
+            )
+
+        return corr_matrix
+
+    def get_symbol_data_at_date(
+        self,
+        data_dict: Dict[str, pd.DataFrame],
+        symbol_key: str,
+        date: datetime,
+        lookback: int = 60
+    ) -> Optional[pd.DataFrame]:
+        """
+        获取指定品种在指定日期的历史数据窗口
+
+        Args:
+            data_dict: 品种数据字典
+            symbol_key: 品种代码键
+            date: 目标日期
+            lookback: 回溯天数
+
+        Returns:
+            pd.DataFrame or None: 历史数据窗口
+        """
+        if symbol_key not in data_dict:
+            return None
+
+        df = data_dict[symbol_key]
+
+        # 找到目标日期的位置
+        try:
+            idx = df.index.get_loc(date)
+            if idx < lookback:
+                return None
+
+            # 返回历史窗口
+            return df.iloc[idx - lookback:idx + 1]
+        except KeyError:
+            return None
+
+    def get_current_prices(
+        self,
+        data_dict: Dict[str, pd.DataFrame],
+        date: Optional[datetime] = None
+    ) -> Dict[str, float]:
+        """
+        获取所有品种的当前价格
+
+        Args:
+            data_dict: 品种数据字典
+            date: 目标日期（None表示最新）
+
+        Returns:
+            Dict[str, float]: 品种代码 -> 当前价格
+        """
+        prices = {}
+
+        for symbol_key, df in data_dict.items():
+            if date is None:
+                prices[symbol_key] = df['close'].iloc[-1]
+            else:
+                try:
+                    prices[symbol_key] = df.loc[date, 'close']
+                except KeyError:
+                    # 找最近的有效日期
+                    valid_dates = df.index[df.index <= date]
+                    if len(valid_dates) > 0:
+                        prices[symbol_key] = df.loc[valid_dates[-1], 'close']
+
+        return prices
+
+    def clear_cache(self):
+        """清除数据缓存"""
+        self._cache.clear()
+        self._correlation_cache = None

cyb50-pro/multi_asset/engine.py

@@ -0,0 +1,363 @@
+"""
+三龙出海主控引擎 (Dragon Rotation Engine)
+
+整合多品种数据、品种选择、信号生成、仓位管理、风控体系
+实现每日交易循环：数据更新 → 品种选择 → 信号生成 → 仓位调整 → 风控检查
+"""
+
+from typing import Dict, List, Optional, Any
+from datetime import datetime, timedelta
+import pandas as pd
+import numpy as np
+
+from core.ecosystem import EcosystemFusion, UnifiedEcosystem
+from .data_loader import MultiAssetDataLoader
+from .selector import DragonSelector, SymbolScore
+from .signal_engine import UnifiedSignalEngine, SignalResult
+from .position_manager import PositionManager
+from .risk_manager import MultiLayerRiskManager, RiskCheckResult, RiskStatus
+
+
+class DragonRotationEngine:
+    """
+    三龙出海多品种轮动引擎
+
+    核心流程：
+    1. 每日更新三品种数据
+    2. DragonSelector选择最优品种
+    3. UnifiedSignalEngine生成交易信号
+    4. PositionManager计算建议仓位
+    5. RiskManager执行风控检查
+    6. 执行交易并记录
+    """
+
+    def __init__(
+        self,
+        initial_capital: float = 1_000_000,
+        score_threshold: float = 60,
+        data_dir: str = "quant"
+    ):
+        self.initial_capital = initial_capital
+        self.current_capital = initial_capital
+        self.score_threshold = score_threshold
+
+        # 初始化各模块
+        self.data_loader = MultiAssetDataLoader(data_dir)
+        self.ecosystem_fusion = EcosystemFusion()
+        self.selector = DragonSelector(score_threshold=score_threshold)
+        self.signal_engine = UnifiedSignalEngine()
+        self.position_manager = PositionManager()
+        self.risk_manager = MultiLayerRiskManager()
+
+        # 当前状态
+        self.current_symbol: Optional[str] = None
+        self.current_position: float = 0.0
+        self.in_position: bool = False
+
+        # 数据缓存
+        self.data_dict: Dict[str, pd.DataFrame] = {}
+
+        # 交易记录
+        self.trades: List[Dict] = []
+        self.daily_stats: List[Dict] = []
+
+    def initialize(self, start_date: Optional[str] = None):
+        """初始化引擎，加载历史数据"""
+        print("Initializing Dragon Rotation Engine...")
+
+        # 加载三品种数据
+        self.data_dict = self.data_loader.load_all_data(start_date)
+
+        if len(self.data_dict) < 3:
+            raise ValueError(f"Failed to load data. Only {len(self.data_dict)} symbols loaded.")
+
+        print(f"Loaded data for: {list(self.data_dict.keys())}")
+
+        # 初始化风险管理
+        self.risk_manager.initialize(self.initial_capital, datetime.now())
+
+        print("Engine initialized successfully.")
+
+    def run_cycle(
+        self,
+        current_date: Optional[datetime] = None,
+        verbose: bool = False
+    ) -> Dict[str, Any]:
+        """
+        执行一个交易周期
+
+        Args:
+            current_date: 当前日期（回测用）
+            verbose: 是否打印详细信息
+
+        Returns:
+            Dict: 周期结果
+        """
+        if current_date is None:
+            current_date = datetime.now()
+
+        result = {
+            "date": current_date,
+            "symbol": None,
+            "signal": "neutral",
+            "position": 0.0,
+            "executed": False,
+            "reason": ""
+        }
+
+        try:
+            # 1. 获取三品种当前数据窗口
+            symbol_data = {}
+            for symbol in self.data_dict.keys():
+                df = self.data_loader.get_symbol_data_at_date(
+                    self.data_dict, symbol, current_date, lookback=80
+                )
+                if df is not None and len(df) >= 60:
+                    symbol_data[symbol] = df
+
+            if len(symbol_data) < 3:
+                result["reason"] = "Insufficient data"
+                return result
+
+            # 2. 识别当前生态
+            # 使用第一个品种的数据进行生态识别
+            first_symbol = list(symbol_data.keys())[0]
+            ecosystem = self.ecosystem_fusion.fuse(
+                price_data=symbol_data[first_symbol],
+                tick_data=None
+            )
+
+            # 3. 品种选择
+            selected_symbol, score_details = self.selector.select(
+                symbol_data, ecosystem, current_date
+            )
+
+            if selected_symbol is None:
+                # 空仓
+                if self.in_position:
+                    # 平仓
+                    self._close_position(current_date, "no_selection")
+                result["reason"] = "No symbol meets threshold"
+                return result
+
+            result["symbol"] = selected_symbol
+
+            # 4. 检查是否需要切换品种
+            if self.current_symbol is not None and self.current_symbol != selected_symbol:
+                # 切换品种
+                self._switch_symbol(self.current_symbol, selected_symbol, current_date)
+
+            self.current_symbol = selected_symbol
+
+            # 5. 生成交易信号
+            signal = self.signal_engine.generate_signal(
+                symbol_data[selected_symbol],
+                current_date
+            )
+
+            result["signal"] = signal.signal
+
+            # 6. 风控检查
+            if self.in_position:
+                # 更新持仓价格
+                current_price = symbol_data[selected_symbol]['close'].iloc[-1]
+                self.risk_manager.update_position(selected_symbol, current_price)
+
+                # 检查风险
+                risk_check = self.risk_manager.check_risk(selected_symbol, current_date)
+
+                if risk_check.should_close_position:
+                    self._close_position(current_date, risk_check.message)
+                    result["reason"] = risk_check.message
+                    return result
+
+                if risk_check.should_reduce_position:
+                    self._reduce_position(
+                        selected_symbol,
+                        risk_check.reduction_pct,
+                        current_date
+                    )
+
+            # 7. 入场信号处理
+            if signal.signal == "enter_long" and not self.in_position:
+                # 计算仓位
+                volatility = score_details.volatility_60d if score_details else 0.25
+                position_size = self.position_manager.calculate_position(
+                    selected_symbol,
+                    signal.confidence,
+                    volatility,
+                    current_date
+                )
+
+                if position_size > 0:
+                    # 执行入场
+                    self._open_position(
+                        selected_symbol,
+                        position_size,
+                        symbol_data[selected_symbol]['close'].iloc[-1],
+                        current_date
+                    )
+                    result["position"] = position_size
+                    result["executed"] = True
+                    result["reason"] = "Entry signal"
+
+            elif signal.signal == "exit" and self.in_position:
+                # 出场信号
+                self._close_position(current_date, "signal_exit")
+                result["reason"] = "Exit signal"
+
+            elif signal.signal == "hold" and self.in_position:
+                # 持仓中，检查再平衡
+                if self.position_manager.should_rebalance(current_date):
+                    self.position_manager.rebalance(symbol_data, current_date)
+
+            # 8. 更新权益
+            self._update_equity(current_date)
+
+            # 记录每日统计
+            self.daily_stats.append({
+                "date": current_date,
+                "symbol": selected_symbol,
+                "score": score_details.total_score if score_details else 0,
+                "signal": signal.signal,
+                "position": self.current_position,
+                "equity": self.current_capital
+            })
+
+            if verbose:
+                print(f"[{current_date.strftime('%Y-%m-%d')}] "
+                      f"Symbol: {selected_symbol}, Signal: {signal.signal}, "
+                      f"Position: {self.current_position:.2%}")
+
+        except Exception as e:
+            result["reason"] = f"Error: {str(e)}"
+            print(f"Error in cycle: {e}")
+
+        return result
+
+    def _open_position(
+        self,
+        symbol: str,
+        position_size: float,
+        price: float,
+        date: datetime
+    ):
+        """开仓"""
+        self.in_position = True
+        self.current_position = position_size
+
+        # 注册到风险管理
+        self.risk_manager.register_position(symbol, price, position_size, date)
+
+        # 记录交易
+        self.trades.append({
+            "date": date,
+            "action": "open",
+            "symbol": symbol,
+            "price": price,
+            "position": position_size,
+            "equity": self.current_capital
+        })
+
+    def _close_position(self, date: datetime, reason: str):
+        """平仓"""
+        if not self.in_position:
+            return
+
+        # 记录交易
+        self.trades.append({
+            "date": date,
+            "action": "close",
+            "symbol": self.current_symbol,
+            "reason": reason,
+            "position": self.current_position,
+            "equity": self.current_capital
+        })
+
+        # 清理状态
+        self.in_position = False
+        self.current_position = 0.0
+        self.risk_manager.close_position(self.current_symbol)
+        self.signal_engine._reset_position()
+
+    def _reduce_position(self, symbol: str, reduction_pct: float, date: datetime):
+        """减仓"""
+        new_size = self.position_manager.reduce_position(symbol, reduction_pct)
+        self.current_position = new_size
+
+        self.trades.append({
+            "date": date,
+            "action": "reduce",
+            "symbol": symbol,
+            "reduction": reduction_pct,
+            "new_position": new_size
+        })
+
+    def _switch_symbol(self, old_symbol: str, new_symbol: str, date: datetime):
+        """切换品种"""
+        # 先平旧仓位
+        if self.in_position:
+            self._close_position(date, f"switch_to_{new_symbol}")
+
+        # 重置信号引擎状态
+        self.signal_engine._reset_position()
+
+    def _update_equity(self, date: datetime):
+        """更新账户权益"""
+        # 简化计算：实际权益更新在回测引擎中处理
+        self.risk_manager.update_equity(self.current_capital, date)
+
+    def get_performance_summary(self) -> Dict[str, Any]:
+        """获取绩效摘要"""
+        if not self.daily_stats:
+            return {}
+
+        equity_curve = [s["equity"] for s in self.daily_stats]
+
+        # 计算收益率
+        total_return = (equity_curve[-1] - self.initial_capital) / self.initial_capital
+
+        # 计算最大回撤
+        peak = self.initial_capital
+        max_drawdown = 0.0
+        for equity in equity_curve:
+            if equity > peak:
+                peak = equity
+            drawdown = (peak - equity) / peak
+            max_drawdown = max(max_drawdown, drawdown)
+
+        # 计算年化收益
+        days = len(self.daily_stats)
+        annual_return = (1 + total_return) ** (252 / days) - 1 if days > 0 else 0
+
+        # 计算波动率
+        daily_returns = [(equity_curve[i] - equity_curve[i-1]) / equity_curve[i-1]
+                        for i in range(1, len(equity_curve))]
+        volatility = np.std(daily_returns) * np.sqrt(252) if daily_returns else 0
+
+        # 计算夏普比率
+        sharpe = (annual_return - 0.03) / volatility if volatility > 0 else 0
+
+        return {
+            "total_return": total_return,
+            "annual_return": annual_return,
+            "max_drawdown": max_drawdown,
+            "volatility": volatility,
+            "sharpe_ratio": sharpe,
+            "total_trades": len([t for t in self.trades if t["action"] == "open"]),
+            "days": days
+        }
+
+    def reset(self):
+        """重置引擎状态"""
+        self.current_capital = self.initial_capital
+        self.current_symbol = None
+        self.current_position = 0.0
+        self.in_position = False
+        self.trades = []
+        self.daily_stats = []
+        self.selector.current_symbol = None
+        self.signal_engine._reset_position()
+        self.position_manager = PositionManager()
+        self.risk_manager = MultiLayerRiskManager()
+        self.risk_manager.initialize(self.initial_capital, datetime.now())

cyb50-pro/multi_asset/position_manager.py

@@ -0,0 +1,256 @@
+"""
+动态仓位管理器 (Position Manager)
+
+核心规则：
+1. 总仓位上限 80%
+2. 单品种最大仓位：沪深300 40% / 中证500 50% / 创业板50 60%
+3. 波动率调整：>30%降仓40%，<15%加仓20%
+4. 每周再平衡
+"""
+
+from typing import Dict, Optional
+from dataclasses import dataclass
+from datetime import datetime
+import pandas as pd
+import numpy as np
+
+
+@dataclass
+class PositionConfig:
+    """仓位配置"""
+    symbol: str
+    base_position: float      # 基础仓位比例
+    max_position: float       # 最大仓位限制
+    volatility: float         # 当前波动率
+    adjusted_position: float  # 调整后仓位
+
+
+class PositionManager:
+    """
+    动态仓位管理器
+
+    根据品种特性和市场波动率动态调整仓位
+    """
+
+    # 品种最大仓位限制
+    SYMBOL_MAX_POSITION = {
+        "csi300": 0.40,      # 沪深300：低波动，最大40%
+        "csi500": 0.50,      # 中证500：中等波动，最大50%
+        "chinext50": 0.60,   # 创业板50：高波动，最大60%
+    }
+
+    # 总仓位上限
+    TOTAL_MAX_POSITION = 0.80
+
+    # 波动率调整阈值
+    VOL_LOW_THRESHOLD = 0.15    # 低波动阈值
+    VOL_HIGH_THRESHOLD = 0.30   # 高波动阈值
+    VOL_ADJUST_LOW = 1.20       # 低波动加仓20%
+    VOL_ADJUST_HIGH = 0.60      # 高波动降仓40%
+
+    def __init__(
+        self,
+        total_max: float = 0.80,
+        rebalance_freq: int = 5  # 每5天再平衡
+    ):
+        self.total_max = total_max
+        self.rebalance_freq = rebalance_freq
+
+        # 当前持仓
+        self.current_positions: Dict[str, float] = {}
+        self.total_position = 0.0
+
+        # 上次再平衡日期
+        self.last_rebalance: Optional[datetime] = None
+
+        # 历史记录
+        self.position_history: list = []
+
+    def calculate_position(
+        self,
+        symbol: str,
+        confidence: float,
+        volatility: float,
+        current_date: Optional[datetime] = None
+    ) -> float:
+        """
+        计算建议仓位
+
+        Args:
+            symbol: 品种代码
+            confidence: 信号置信度 (0-1)
+            volatility: 60日年化波动率
+            current_date: 当前日期
+
+        Returns:
+            float: 建议仓位比例 (0-1)
+        """
+        # 1. 基础仓位 = 最大仓位 × 置信度
+        max_pos = self.SYMBOL_MAX_POSITION.get(symbol, 0.40)
+        base_position = max_pos * confidence
+
+        # 2. 波动率调整
+        vol_adjustment = self._calculate_volatility_adjustment(volatility)
+        adjusted_position = base_position * vol_adjustment
+
+        # 3. 检查总仓位限制
+        other_positions = sum(
+            pos for sym, pos in self.current_positions.items() if sym != symbol
+        )
+        available = self.total_max - other_positions
+
+        # 4. 返回最终仓位（不超过可用额度）
+        final_position = min(adjusted_position, available)
+        final_position = max(0, final_position)  # 不小于0
+
+        # 记录
+        self.current_positions[symbol] = final_position
+        self.total_position = sum(self.current_positions.values())
+
+        if current_date:
+            self.position_history.append({
+                "date": current_date,
+                "symbol": symbol,
+                "base": base_position,
+                "vol_adjust": vol_adjustment,
+                "adjusted": adjusted_position,
+                "final": final_position,
+                "total": self.total_position
+            })
+
+        return final_position
+
+    def _calculate_volatility_adjustment(self, volatility: float) -> float:
+        """
+        计算波动率调整系数
+
+        低波动加仓，高波动减仓
+        """
+        if volatility < self.VOL_LOW_THRESHOLD:
+            # 低波动：加仓20%
+            return self.VOL_ADJUST_LOW
+        elif volatility > self.VOL_HIGH_THRESHOLD:
+            # 高波动：降仓40%
+            return self.VOL_ADJUST_HIGH
+        else:
+            # 中等波动：线性插值
+            # 15% -> 1.2, 30% -> 0.6
+            ratio = (volatility - self.VOL_LOW_THRESHOLD) / \
+                    (self.VOL_HIGH_THRESHOLD - self.VOL_LOW_THRESHOLD)
+            return self.VOL_ADJUST_LOW - ratio * (self.VOL_ADJUST_LOW - self.VOL_ADJUST_HIGH)
+
+    def should_rebalance(self, current_date: datetime) -> bool:
+        """检查是否需要再平衡"""
+        if self.last_rebalance is None:
+            return True
+
+        days_since = (current_date - self.last_rebalance).days
+        return days_since >= self.rebalance_freq
+
+    def rebalance(
+        self,
+        data_dict: Dict[str, pd.DataFrame],
+        current_date: datetime
+    ) -> Dict[str, float]:
+        """
+        执行再平衡
+
+        根据最新波动率调整所有持仓
+        """
+        new_positions = {}
+
+        for symbol, current_pos in self.current_positions.items():
+            if symbol not in data_dict:
+                continue
+
+            df = data_dict[symbol]
+            df = df[df.index <= current_date]
+
+            if len(df) < 60:
+                continue
+
+            # 重新计算波动率
+            returns = df['close'].pct_change().dropna()
+            volatility = returns.iloc[-60:].std() * np.sqrt(252)
+
+            # 重新计算仓位
+            vol_adjust = self._calculate_volatility_adjustment(volatility)
+            max_pos = self.SYMBOL_MAX_POSITION.get(symbol, 0.40)
+
+            # 保持原置信度，调整波动率
+            if current_pos > 0:
+                confidence = current_pos / (max_pos * vol_adjust)
+                confidence = min(1.0, confidence)
+            else:
+                confidence = 0
+
+            new_pos = max_pos * confidence * vol_adjust
+            new_positions[symbol] = new_pos
+
+        # 归一化确保总仓位不超限制
+        total = sum(new_positions.values())
+        if total > self.total_max:
+            scale = self.total_max / total
+            new_positions = {k: v * scale for k, v in new_positions.items()}
+
+        self.current_positions = new_positions
+        self.total_position = sum(new_positions.values())
+        self.last_rebalance = current_date
+
+        return new_positions
+
+    def reduce_position(self, symbol: str, reduction_pct: float) -> float:
+        """
+        减仓指定比例
+
+        用于风控触发时的紧急减仓
+        """
+        if symbol not in self.current_positions:
+            return 0.0
+
+        current = self.current_positions[symbol]
+        new_pos = current * (1 - reduction_pct)
+        new_pos = max(0, new_pos)
+
+        self.current_positions[symbol] = new_pos
+        self.total_position = sum(self.current_positions.values())
+
+        return new_pos
+
+    def close_position(self, symbol: str) -> float:
+        """清仓指定品种"""
+        if symbol not in self.current_positions:
+            return 0.0
+
+        closed_amount = self.current_positions[symbol]
+        self.current_positions[symbol] = 0.0
+        self.total_position = sum(self.current_positions.values())
+
+        return closed_amount
+
+    def close_all_positions(self) -> Dict[str, float]:
+        """清仓所有品种"""
+        closed = self.current_positions.copy()
+        self.current_positions = {}
+        self.total_position = 0.0
+        return closed
+
+    def get_position_summary(self) -> Dict:
+        """获取仓位摘要"""
+        return {
+            "total_position": self.total_position,
+            "available": self.total_max - self.total_position,
+            "positions": self.current_positions.copy(),
+            "last_rebalance": self.last_rebalance
+        }
+
+    def can_open_position(
+        self,
+        symbol: str,
+        desired_position: float
+    ) -> bool:
+        """检查是否可以开仓"""
+        other_positions = sum(
+            pos for sym, pos in self.current_positions.items() if sym != symbol
+        )
+        return other_positions + desired_position <= self.total_max

cyb50-pro/multi_asset/risk_manager.py

@@ -0,0 +1,321 @@
+"""
+多维度风险管理体系 (Multi-Layer Risk Manager)
+
+三层止损保护：
+1. 单笔硬止损：-8%
+2. 单日损失限制：-5%（减仓50%）
+3. 账户熔断：-15%（清仓+冷静一周）
+
+移动止盈：
+- 15%利润：保本
+- 30%利润：锁定15%
+- 10%回撤：止盈出场
+"""
+
+from typing import Dict, Optional, List
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta
+from enum import Enum
+
+
+class RiskStatus(Enum):
+    """风险状态"""
+    NORMAL = "normal"
+    WARNING = "warning"
+    COOLING = "cooling"
+    CIRCUIT_BREAKER = "circuit_breaker"
+
+
+@dataclass
+class RiskCheckResult:
+    """风险检查结果"""
+    can_trade: bool
+    should_close_position: bool
+    should_reduce_position: bool
+    reduction_pct: float
+    status: RiskStatus
+    message: str
+
+
+@dataclass
+class PositionRisk:
+    """持仓风险状态"""
+    symbol: str
+    entry_price: float
+    current_price: float
+    highest_price: float
+    position_size: float
+    unrealized_pnl_pct: float
+    drawdown_from_peak: float
+
+
+class MultiLayerRiskManager:
+    """
+    多维度风险管理体系
+
+    实现三层止损 + 移动止盈 + 空仓机制
+    """
+
+    # 止损阈值
+    SINGLE_STOP_PCT = 0.08          # 单笔-8%
+    DAILY_LOSS_PCT = 0.05           # 单日-5%
+    CIRCUIT_BREAKER_PCT = 0.15      # 账户-15%
+
+    # 移动止盈阈值
+    PROFIT_LOCK_1 = 0.15            # 15%保本
+    PROFIT_LOCK_2 = 0.30            # 30%锁定15%
+    TRAILING_STOP = 0.10            # 10%回撤止盈
+
+    # 冷静期
+    COOLING_DAYS = 5
+    CIRCUIT_BREAKER_DAYS = 5
+
+    def __init__(self):
+        # 当前状态
+        self.status = RiskStatus.NORMAL
+        self.cooling_end_date: Optional[datetime] = None
+
+        # 账户状态
+        self.peak_equity = 0.0
+        self.current_equity = 0.0
+        self.daily_pnl = 0.0
+        self.last_reset_date: Optional[datetime] = None
+
+        # 持仓跟踪
+        self.positions: Dict[str, PositionRisk] = {}
+
+        # 历史记录
+        self.risk_events: List[Dict] = []
+
+    def initialize(self, initial_equity: float, current_date: datetime):
+        """初始化风险管理体系"""
+        self.peak_equity = initial_equity
+        self.current_equity = initial_equity
+        self.last_reset_date = current_date
+
+    def update_equity(self, equity: float, current_date: datetime):
+        """更新账户权益"""
+        # 重置每日盈亏
+        if self.last_reset_date != current_date:
+            self.daily_pnl = 0.0
+            self.last_reset_date = current_date
+
+        # 计算当日盈亏
+        self.daily_pnl = equity - self.current_equity
+        self.current_equity = equity
+
+        # 更新峰值
+        if equity > self.peak_equity:
+            self.peak_equity = equity
+
+    def register_position(
+        self,
+        symbol: str,
+        entry_price: float,
+        position_size: float,
+        current_date: datetime
+    ):
+        """注册新持仓"""
+        self.positions[symbol] = PositionRisk(
+            symbol=symbol,
+            entry_price=entry_price,
+            current_price=entry_price,
+            highest_price=entry_price,
+            position_size=position_size,
+            unrealized_pnl_pct=0.0,
+            drawdown_from_peak=0.0
+        )
+
+    def update_position(self, symbol: str, current_price: float):
+        """更新持仓状态"""
+        if symbol not in self.positions:
+            return
+
+        pos = self.positions[symbol]
+        pos.current_price = current_price
+        pos.highest_price = max(pos.highest_price, current_price)
+
+        # 计算盈亏
+        pos.unrealized_pnl_pct = (current_price - pos.entry_price) / pos.entry_price
+
+        # 计算从峰值的回撤
+        pos.drawdown_from_peak = (pos.highest_price - current_price) / pos.highest_price
+
+    def check_risk(
+        self,
+        symbol: str,
+        current_date: datetime
+    ) -> RiskCheckResult:
+        """
+        执行风险检查
+
+        按优先级检查：熔断 > 单日 > 单笔 > 止盈
+        """
+        # 1. 检查熔断状态
+        if self.status == RiskStatus.CIRCUIT_BREAKER:
+            if self.cooling_end_date and current_date < self.cooling_end_date:
+                return RiskCheckResult(
+                    can_trade=False,
+                    should_close_position=True,
+                    should_reduce_position=False,
+                    reduction_pct=0.0,
+                    status=self.status,
+                    message="Circuit breaker active, trading halted"
+                )
+            else:
+                # 熔断期结束，恢复
+                self.status = RiskStatus.NORMAL
+                self.cooling_end_date = None
+
+        # 2. 检查账户熔断
+        total_drawdown = (self.current_equity - self.peak_equity) / self.peak_equity
+        if total_drawdown <= -self.CIRCUIT_BREAKER_PCT:
+            self._trigger_circuit_breaker(current_date)
+            return RiskCheckResult(
+                can_trade=False,
+                should_close_position=True,
+                should_reduce_position=False,
+                reduction_pct=0.0,
+                status=self.status,
+                message=f"Circuit breaker triggered: drawdown {total_drawdown:.2%}"
+            )
+
+        # 3. 检查单日损失
+        daily_return = self.daily_pnl / self.current_equity if self.current_equity > 0 else 0
+        if daily_return <= -self.DAILY_LOSS_PCT:
+            return RiskCheckResult(
+                can_trade=True,
+                should_close_position=False,
+                should_reduce_position=True,
+                reduction_pct=0.5,
+                status=RiskStatus.WARNING,
+                message=f"Daily loss limit: {daily_return:.2%}"
+            )
+
+        # 4. 检查持仓风险
+        if symbol in self.positions:
+            pos = self.positions[symbol]
+
+            # 单笔硬止损
+            if pos.unrealized_pnl_pct <= -self.SINGLE_STOP_PCT:
+                return RiskCheckResult(
+                    can_trade=True,
+                    should_close_position=True,
+                    should_reduce_position=False,
+                    reduction_pct=0.0,
+                    status=RiskStatus.WARNING,
+                    message=f"Single position stop: {pos.unrealized_pnl_pct:.2%}"
+                )
+
+            # 移动止盈
+            trailing_check = self._check_trailing_stop(pos)
+            if trailing_check:
+                return trailing_check
+
+        return RiskCheckResult(
+            can_trade=True,
+            should_close_position=False,
+            should_reduce_position=False,
+            reduction_pct=0.0,
+            status=RiskStatus.NORMAL,
+            message="Risk check passed"
+        )
+
+    def _check_trailing_stop(self, pos: PositionRisk) -> Optional[RiskCheckResult]:
+        """检查移动止盈条件"""
+        profit = pos.unrealized_pnl_pct
+
+        # 盈利15%：保本
+        if profit >= self.PROFIT_LOCK_1:
+            # 检查是否回撤到成本价
+            if pos.current_price <= pos.entry_price:
+                return RiskCheckResult(
+                    can_trade=True,
+                    should_close_position=True,
+                    should_reduce_position=False,
+                    reduction_pct=0.0,
+                    status=RiskStatus.WARNING,
+                    message="Breakeven stop triggered"
+                )
+
+        # 盈利30%：锁定15%利润
+        if profit >= self.PROFIT_LOCK_2:
+            lock_price = pos.entry_price * (1 + self.PROFIT_LOCK_1)
+            if pos.current_price <= lock_price:
+                return RiskCheckResult(
+                    can_trade=True,
+                    should_close_position=True,
+                    should_reduce_position=False,
+                    reduction_pct=0.0,
+                    status=RiskStatus.WARNING,
+                    message="Profit lock stop triggered"
+                )
+
+        # 从峰值回撤10%
+        if pos.drawdown_from_peak >= self.TRAILING_STOP:
+            return RiskCheckResult(
+                can_trade=True,
+                should_close_position=True,
+                should_reduce_position=False,
+                reduction_pct=0.0,
+                status=RiskStatus.WARNING,
+                message=f"Trailing stop: {pos.drawdown_from_peak:.2%} from peak"
+            )
+
+        return None
+
+    def _trigger_circuit_breaker(self, current_date: datetime):
+        """触发熔断"""
+        self.status = RiskStatus.CIRCUIT_BREAKER
+        self.cooling_end_date = current_date + timedelta(days=self.CIRCUIT_BREAKER_DAYS)
+
+        self.risk_events.append({
+            "date": current_date,
+            "type": "circuit_breaker",
+            "drawdown": (self.current_equity - self.peak_equity) / self.peak_equity,
+            "equity": self.current_equity
+        })
+
+    def close_position(self, symbol: str):
+        """关闭持仓记录"""
+        if symbol in self.positions:
+            del self.positions[symbol]
+
+    def should_stay_cash(
+        self,
+        scores: Dict[str, float],
+        regime: str,
+        winter_days: int,
+        current_date: datetime
+    ) -> bool:
+        """
+        判断是否应空仓
+
+        条件：
+        1. 所有品种评分 < 60
+        2. Winter生态持续 > 30天
+        3. 当月已实现亏损 > 5%
+        """
+        # 检查评分
+        all_below_threshold = all(score < 60 for score in scores.values())
+
+        # 检查Winter持续时间
+        extended_winter = (regime == "winter" and winter_days > 30)
+
+        if all_below_threshold or extended_winter:
+            return True
+
+        return False
+
+    def get_risk_summary(self) -> Dict:
+        """获取风险摘要"""
+        return {
+            "status": self.status.value,
+            "current_equity": self.current_equity,
+            "peak_equity": self.peak_equity,
+            "drawdown": (self.current_equity - self.peak_equity) / self.peak_equity,
+            "daily_pnl": self.daily_pnl,
+            "daily_return": self.daily_pnl / self.current_equity if self.current_equity > 0 else 0,
+            "positions": len(self.positions),
+            "cooling_end": self.cooling_end_date
+        }

cyb50-pro/multi_asset/selector.py

@@ -0,0 +1,347 @@
+"""
+品种选择器 (Dragon Selector)
+
+基于三维度评分系统选择最优交易品种：
+1. 趋势强度 (40分): 价格与均线的关系
+2. 生态匹配度 (30分): 品种与当前生态的适配
+3. 波动率调整 (30分): 低波动加分，高波动减分
+"""
+
+from typing import Dict, List, Optional, Tuple
+from dataclasses import dataclass
+from datetime import datetime
+import pandas as pd
+import numpy as np
+
+from core.ecosystem import UnifiedEcosystem, MacroRegime
+
+
+@dataclass
+class SymbolScore:
+    """品种评分结果"""
+    symbol: str
+    trend_score: float      # 0-40
+    ecosystem_score: float  # 0-30
+    volatility_score: float # 0-30
+    total_score: float      # 0-100
+
+    # 详细数据
+    price_vs_20ma: float    # 价格相对于20日均线的位置
+    price_vs_60ma: float    # 价格相对于60日均线的位置
+    ma20_slope: float       # 20日均线斜率
+    ma60_slope: float       # 60日均线斜率
+    volatility_60d: float   # 60日波动率
+
+
+class DragonSelector:
+    """
+    品种选择器
+
+    每日对三个品种进行评分，选择得分最高且超过阈值的品种交易
+    """
+
+    # 品种生态偏好配置
+    REGIME_PREFERENCE = {
+        "csi300": {
+            MacroRegime.SUMMER: 0,
+            MacroRegime.AUTUMN: 5,
+            MacroRegime.WINTER: 8,
+            MacroRegime.SPRING: 3,
+        },
+        "csi500": {
+            MacroRegime.SUMMER: 3,
+            MacroRegime.AUTUMN: 3,
+            MacroRegime.WINTER: 5,
+            MacroRegime.SPRING: 8,
+        },
+        "chinext50": {
+            MacroRegime.SUMMER: 10,
+            MacroRegime.AUTUMN: 0,
+            MacroRegime.WINTER: -10,
+            MacroRegime.SPRING: 5,
+        },
+    }
+
+    # 评分阈值
+    SCORE_THRESHOLD = 60
+
+    # 切换成本
+    SWITCHING_COST = 0.001  # 0.1%
+
+    def __init__(
+        self,
+        score_threshold: float = 60,
+        switching_cost: float = 0.001,
+        volatility_lookback: int = 60
+    ):
+        self.score_threshold = score_threshold
+        self.switching_cost = switching_cost
+        self.volatility_lookback = volatility_lookback
+
+        # 当前选中的品种
+        self.current_symbol: Optional[str] = None
+
+        # 历史评分记录
+        self.score_history: List[Dict] = []
+
+    def select(
+        self,
+        data_dict: Dict[str, pd.DataFrame],
+        ecosystem: UnifiedEcosystem,
+        current_date: Optional[datetime] = None
+    ) -> Tuple[Optional[str], Optional[SymbolScore]]:
+        """
+        选择最优品种
+
+        Args:
+            data_dict: 三个品种的数据字典
+            ecosystem: 当前市场生态
+            current_date: 当前日期（回测用）
+
+        Returns:
+            (selected_symbol, score_details): 选中的品种和评分详情
+        """
+        scores = []
+
+        # 计算每个品种的评分
+        for symbol in data_dict.keys():
+            score = self._calculate_score(
+                symbol,
+                data_dict[symbol],
+                ecosystem,
+                current_date
+            )
+            scores.append(score)
+
+        # 按总分排序
+        scores.sort(key=lambda x: x.total_score, reverse=True)
+
+        # 记录历史
+        self.score_history.append({
+            "date": current_date,
+            "scores": scores,
+            "ecosystem": ecosystem.macro.regime.value if ecosystem else "unknown"
+        })
+
+        # 选择最高分品种
+        best_score = scores[0]
+
+        # 检查是否超过阈值
+        if best_score.total_score < self.score_threshold:
+            return None, best_score
+
+        selected = best_score.symbol
+
+        # 检查是否需要切换
+        if self.current_symbol is not None and self.current_symbol != selected:
+            # 考虑切换成本，只有当得分差 > 15 时才切换
+            current_score = next((s for s in scores if s.symbol == self.current_symbol), None)
+            if current_score:
+                score_diff = best_score.total_score - current_score.total_score
+                if score_diff < 15:
+                    # 不切换，继续持有可能表现更好的品种
+                    selected = self.current_symbol
+                    best_score = current_score
+
+        self.current_symbol = selected
+        return selected, best_score
+
+    def _calculate_score(
+        self,
+        symbol: str,
+        df: pd.DataFrame,
+        ecosystem: UnifiedEcosystem,
+        current_date: Optional[datetime] = None
+    ) -> SymbolScore:
+        """计算单个品种的评分"""
+
+        # 获取数据窗口
+        if current_date is not None:
+            df = df[df.index <= current_date]
+
+        if len(df) < 60:
+            # 数据不足，返回低分
+            return SymbolScore(
+                symbol=symbol,
+                trend_score=0,
+                ecosystem_score=0,
+                volatility_score=0,
+                total_score=0,
+                price_vs_20ma=0,
+                price_vs_60ma=0,
+                ma20_slope=0,
+                ma60_slope=0,
+                volatility_60d=0
+            )
+
+        close = df['close']
+
+        # 计算均线
+        ma20 = close.rolling(20).mean()
+        ma60 = close.rolling(60).mean()
+
+        current_price = close.iloc[-1]
+        current_ma20 = ma20.iloc[-1]
+        current_ma60 = ma60.iloc[-1]
+
+        # 计算相对位置
+        price_vs_20ma = (current_price - current_ma20) / current_ma20
+        price_vs_60ma = (current_price - current_ma60) / current_ma60
+
+        # 计算均线斜率（20日变化率）
+        ma20_slope = (ma20.iloc[-1] - ma20.iloc[-20]) / ma20.iloc[-20] if len(ma20) >= 20 else 0
+        ma60_slope = (ma60.iloc[-1] - ma60.iloc[-20]) / ma60.iloc[-20] if len(ma60) >= 20 else 0
+
+        # 1. 趋势强度评分 (0-40分)
+        trend_score = self._calculate_trend_score(
+            price_vs_20ma, price_vs_60ma, ma20_slope, ma60_slope
+        )
+
+        # 2. 生态匹配度评分 (0-30分)
+        ecosystem_score = self._calculate_ecosystem_score(symbol, ecosystem)
+
+        # 3. 波动率调整评分 (0-30分)
+        returns = close.pct_change().dropna()
+        volatility_60d = returns.iloc[-60:].std() * np.sqrt(252)
+        volatility_score = self._calculate_volatility_score(volatility_60d)
+
+        # 总分
+        total_score = trend_score + ecosystem_score + volatility_score
+
+        return SymbolScore(
+            symbol=symbol,
+            trend_score=trend_score,
+            ecosystem_score=ecosystem_score,
+            volatility_score=volatility_score,
+            total_score=total_score,
+            price_vs_20ma=price_vs_20ma,
+            price_vs_60ma=price_vs_60ma,
+            ma20_slope=ma20_slope,
+            ma60_slope=ma60_slope,
+            volatility_60d=volatility_60d
+        )
+
+    def _calculate_trend_score(
+        self,
+        price_vs_20ma: float,
+        price_vs_60ma: float,
+        ma20_slope: float,
+        ma60_slope: float
+    ) -> float:
+        """
+        计算趋势强度评分 (0-40分)
+
+        完美趋势：价格 > 20MA > 60MA，且均线向上
+        """
+        score = 0.0
+
+        # 价格位置 (0-15分)
+        if price_vs_20ma > 0.03:  # 价格在20日均线上方3%
+            score += 15
+        elif price_vs_20ma > 0:
+            score += 10
+        elif price_vs_20ma > -0.02:
+            score += 5
+
+        # 均线排列 (0-15分)
+        if price_vs_60ma > 0.05:  # 价格在60日均线上方5%
+            score += 15
+        elif price_vs_60ma > 0:
+            score += 10
+        elif price_vs_60ma > -0.03:
+            score += 5
+
+        # 均线斜率 (0-10分)
+        if ma20_slope > 0.001 and ma60_slope > 0:  # 均线向上
+            score += 10
+        elif ma20_slope > 0:
+            score += 5
+
+        return min(40, score)
+
+    def _calculate_ecosystem_score(
+        self,
+        symbol: str,
+        ecosystem: UnifiedEcosystem
+    ) -> float:
+        """
+        计算生态匹配度评分 (0-30分)
+
+        基础分20分，根据品种-生态偏好调整
+        """
+        base_score = 20.0
+
+        if ecosystem and hasattr(ecosystem, 'macro'):
+            regime = ecosystem.macro.regime
+            preference = self.REGIME_PREFERENCE.get(symbol, {}).get(regime, 0)
+
+            # 加上偏好分数
+            score = base_score + preference
+
+            # 限制在0-30分
+            return max(0, min(30, score))
+
+        return base_score
+
+    def _calculate_volatility_score(self, volatility: float) -> float:
+        """
+        计算波动率调整评分 (0-30分)
+
+        低波动加分，高波动减分
+        """
+        if volatility < 0.15:  # 波动率 < 15%
+            return 30
+        elif volatility < 0.20:  # 波动率 15-20%
+            return 25
+        elif volatility < 0.25:  # 波动率 20-25%
+            return 20
+        elif volatility < 0.30:  # 波动率 25-30%
+            return 15
+        elif volatility < 0.35:  # 波动率 30-35%
+            return 10
+        else:  # 波动率 > 35%
+            return 5
+
+    def should_switch(
+        self,
+        current_symbol: str,
+        new_symbol: str,
+        current_score: float,
+        new_score: float
+    ) -> bool:
+        """
+        判断是否值得切换品种
+
+        考虑切换成本，只有当预期收益 > 成本时才切换
+        """
+        if current_symbol == new_symbol:
+            return False
+
+        # 得分差需要超过15分才值得切换
+        score_diff = new_score - current_score
+
+        # 切换成本约0.1%，需要预期有额外收益才切换
+        return score_diff > 15
+
+    def get_selection_summary(self) -> Dict:
+        """获取选择器统计摘要"""
+        if not self.score_history:
+            return {}
+
+        total_days = len(self.score_history)
+
+        # 统计每个品种被选中的次数
+        symbol_counts = {}
+        for record in self.score_history:
+            # 找到最高分品种
+            scores = record['scores']
+            if scores:
+                best = max(scores, key=lambda x: x.total_score)
+                if best.total_score >= self.score_threshold:
+                    symbol_counts[best.symbol] = symbol_counts.get(best.symbol, 0) + 1
+
+        return {
+            "total_days": total_days,
+            "symbol_selections": symbol_counts,
+            "cash_days": total_days - sum(symbol_counts.values())
+        }

cyb50-pro/multi_asset/signal_engine.py

@@ -0,0 +1,306 @@
+"""
+统一信号引擎 (Unified Signal Engine)
+
+为三品种提供统一的入场/出场信号：
+1. 趋势确认：价格 > 20MA > 60MA，且均线向上
+2. 动量确认：RSI 50-70，且5日涨幅 > 50% 20日涨幅
+3. 量能确认：成交量 > 1.2倍20日均量
+"""
+
+from typing import Dict, Optional, Tuple
+from dataclasses import dataclass
+from datetime import datetime
+import pandas as pd
+import numpy as np
+
+
+@dataclass
+class SignalResult:
+    """信号结果"""
+    signal: str           # "enter_long", "exit", "hold", "neutral"
+    confidence: float     # 0-1
+    trend_confirmed: bool
+    momentum_confirmed: bool
+    volume_confirmed: bool
+
+    # 详细数据
+    rsi: float
+    price_vs_20ma: float
+    ma20_slope: float
+    ma60_slope: float
+    volume_ratio: float
+    return_5d: float
+    return_20d: float
+
+
+class UnifiedSignalEngine:
+    """
+    统一信号引擎
+
+    所有品种使用同一套信号规则，避免过拟合
+    """
+
+    def __init__(
+        self,
+        rsi_period: int = 14,
+        rsi_lower: float = 45,      # 放宽至45
+        rsi_upper: float = 75,      # 放宽至75
+        volume_threshold: float = 1.0,  # 放宽至1.0（持平即可）
+        ma_fast: int = 20,
+        ma_slow: int = 60
+    ):
+        self.rsi_period = rsi_period
+        self.rsi_lower = rsi_lower
+        self.rsi_upper = rsi_upper
+        self.volume_threshold = volume_threshold
+        self.ma_fast = ma_fast
+        self.ma_slow = ma_slow
+
+        # 当前持仓状态
+        self.in_position = False
+        self.entry_price = None
+        self.highest_price = None
+
+    def generate_signal(
+        self,
+        df: pd.DataFrame,
+        current_date: Optional[datetime] = None
+    ) -> SignalResult:
+        """
+        生成交易信号
+
+        Args:
+            df: 品种数据（OHLCV）
+            current_date: 当前日期（回测用）
+
+        Returns:
+            SignalResult: 信号结果
+        """
+        # 获取数据窗口
+        if current_date is not None:
+            df = df[df.index <= current_date]
+
+        if len(df) < self.ma_slow + 5:
+            return self._create_neutral_result()
+
+        close = df['close']
+        volume = df['volume']
+
+        # 计算指标
+        ma20 = close.rolling(self.ma_fast).mean()
+        ma60 = close.rolling(self.ma_slow).mean()
+        rsi = self._calculate_rsi(close)
+
+        current_price = close.iloc[-1]
+        current_ma20 = ma20.iloc[-1]
+        current_ma60 = ma60.iloc[-1]
+        current_volume = volume.iloc[-1]
+        avg_volume = volume.iloc[-20:].mean()
+
+        # 1. 趋势确认
+        trend_confirmed = self._check_trend(
+            current_price, current_ma20, current_ma60, ma20, ma60
+        )
+
+        # 2. 动量确认
+        momentum_confirmed, rsi_value = self._check_momentum(close)
+
+        # 3. 量能确认
+        volume_confirmed, volume_ratio = self._check_volume(current_volume, avg_volume)
+
+        # 计算收益率
+        return_5d = (close.iloc[-1] - close.iloc[-5]) / close.iloc[-5] if len(close) >= 5 else 0
+        return_20d = (close.iloc[-1] - close.iloc[-20]) / close.iloc[-20] if len(close) >= 20 else 0
+
+        # 生成信号
+        if not self.in_position:
+            # 空仓：检查入场条件
+            if trend_confirmed and momentum_confirmed and volume_confirmed:
+                signal = "enter_long"
+                confidence = self._calculate_confidence(
+                    trend_confirmed, momentum_confirmed, volume_confirmed,
+                    rsi_value, volume_ratio
+                )
+                self.in_position = True
+                self.entry_price = current_price
+                self.highest_price = current_price
+            else:
+                signal = "neutral"
+                confidence = 0.0
+        else:
+            # 持仓：检查出场条件
+            self.highest_price = max(self.highest_price, current_price)
+
+            # 更新最高价
+            if current_price > self.highest_price:
+                self.highest_price = current_price
+
+            # 检查出场条件
+            should_exit = self._check_exit(
+                current_price, close, ma20, rsi_value
+            )
+
+            if should_exit:
+                signal = "exit"
+                confidence = 1.0
+                self._reset_position()
+            else:
+                signal = "hold"
+                confidence = 0.5
+
+        return SignalResult(
+            signal=signal,
+            confidence=confidence,
+            trend_confirmed=trend_confirmed,
+            momentum_confirmed=momentum_confirmed,
+            volume_confirmed=volume_confirmed,
+            rsi=rsi_value,
+            price_vs_20ma=(current_price - current_ma20) / current_ma20,
+            ma20_slope=(ma20.iloc[-1] - ma20.iloc[-5]) / ma20.iloc[-5] if len(ma20) >= 5 else 0,
+            ma60_slope=(ma60.iloc[-1] - ma60.iloc[-5]) / ma60.iloc[-5] if len(ma60) >= 5 else 0,
+            volume_ratio=volume_ratio,
+            return_5d=return_5d,
+            return_20d=return_20d
+        )
+
+    def _check_trend(
+        self,
+        price: float,
+        ma20: float,
+        ma60: float,
+        ma20_series: pd.Series,
+        ma60_series: pd.Series
+    ) -> bool:
+        """检查趋势确认条件"""
+        # 价格 > 20MA > 60MA
+        price_above_ma = price > ma20 > ma60
+
+        # 60MA斜率 > -0.001（趋势向上或走平）
+        ma60_slope = (ma60_series.iloc[-1] - ma60_series.iloc[-5]) / ma60_series.iloc[-5] \
+            if len(ma60_series) >= 5 else 0
+        ma_slope_positive = ma60_slope > -0.001
+
+        return price_above_ma and ma_slope_positive
+
+    def _check_momentum(self, close: pd.Series) -> Tuple[bool, float]:
+        """检查动量确认条件"""
+        rsi = self._calculate_rsi(close)
+
+        # RSI在50-70之间（强势但非超买）
+        rsi_in_range = self.rsi_lower <= rsi <= self.rsi_upper
+
+        # 5日涨幅 > 50% 20日涨幅（动能加速）
+        if len(close) >= 20:
+            return_5d = (close.iloc[-1] - close.iloc[-5]) / close.iloc[-5]
+            return_20d = (close.iloc[-1] - close.iloc[-20]) / close.iloc[-20]
+            momentum_accelerating = return_5d > return_20d * 0.5
+        else:
+            momentum_accelerating = False
+
+        return rsi_in_range and momentum_accelerating, rsi
+
+    def _check_volume(self, current_vol: float, avg_vol: float) -> Tuple[bool, float]:
+        """检查量能确认条件"""
+        if avg_vol == 0:
+            return False, 0
+
+        volume_ratio = current_vol / avg_vol
+        return volume_ratio >= self.volume_threshold, volume_ratio
+
+    def _check_exit(
+        self,
+        current_price: float,
+        close: pd.Series,
+        ma20: pd.Series,
+        rsi: float
+    ) -> bool:
+        """检查出场条件"""
+        # 1. 趋势反转：价格跌破20日均线
+        if current_price < ma20.iloc[-1]:
+            return True
+
+        # 2. 动量衰竭：RSI从高位跌破50
+        if len(close) >= 2:
+            prev_rsi = self._calculate_rsi(close.iloc[:-1])
+            if prev_rsi > 60 and rsi < 50:
+                return True
+
+        # 3. 移动止盈：从最高点回撤10%
+        if self.highest_price and self.entry_price:
+            drawdown_from_peak = (self.highest_price - current_price) / self.highest_price
+            if drawdown_from_peak >= 0.10:
+                return True
+
+        return False
+
+    def _calculate_confidence(
+        self,
+        trend: bool,
+        momentum: bool,
+        volume: bool,
+        rsi: float,
+        volume_ratio: float
+    ) -> float:
+        """计算信号置信度"""
+        # 基础分
+        base = 0.5
+
+        # 三条件都满足
+        if trend and momentum and volume:
+            base += 0.3
+
+        # RSI越强越好（但不超过70）
+        if 55 <= rsi <= 65:
+            base += 0.1
+
+        # 量能越大越好
+        if volume_ratio > 1.5:
+            base += 0.1
+
+        return min(1.0, base)
+
+    def _calculate_rsi(self, prices: pd.Series) -> float:
+        """计算RSI"""
+        if len(prices) < self.rsi_period + 1:
+            return 50.0
+
+        deltas = prices.diff()
+        gains = deltas.clip(lower=0)
+        losses = (-deltas).clip(lower=0)
+
+        avg_gain = gains.rolling(self.rsi_period).mean()
+        avg_loss = losses.rolling(self.rsi_period).mean()
+
+        rs = avg_gain.iloc[-1] / avg_loss.iloc[-1] if avg_loss.iloc[-1] != 0 else 0
+        rsi = 100 - (100 / (1 + rs))
+
+        return rsi
+
+    def _create_neutral_result(self) -> SignalResult:
+        """创建中性信号结果"""
+        return SignalResult(
+            signal="neutral",
+            confidence=0.0,
+            trend_confirmed=False,
+            momentum_confirmed=False,
+            volume_confirmed=False,
+            rsi=50.0,
+            price_vs_20ma=0.0,
+            ma20_slope=0.0,
+            ma60_slope=0.0,
+            volume_ratio=1.0,
+            return_5d=0.0,
+            return_20d=0.0
+        )
+
+    def _reset_position(self):
+        """重置持仓状态"""
+        self.in_position = False
+        self.entry_price = None
+        self.highest_price = None
+
+    def set_position_state(self, in_position: bool, entry_price: Optional[float] = None):
+        """设置持仓状态（用于回测恢复）"""
+        self.in_position = in_position
+        self.entry_price = entry_price
+        self.highest_price = entry_price

cyb50-pro/reports/backtest_real_data_20260310_184318.json

@@ -0,0 +1,45 @@
+{
+  "summary": {
+    "测试区间": "2018-01-02 to 2024-12-31",
+    "初始资金": "1,000,000",
+    "最终资金": "694,502",
+    "总收益率": "-30.55%",
+    "年化收益率": "-5.08%",
+    "最大回撤": "-89.29%",
+    "夏普比率": "-0.12",
+    "Sortino比率": "-0.15"
+  },
+  "trading_stats": {
+    "总交易次数": 35,
+    "盈利次数": 17,
+    "亏损次数": 18,
+    "胜率": "48.57%",
+    "平均盈利": "30,813.01",
+    "平均亏损": "-44,868.69",
+    "盈亏比": "0.65"
+  },
+  "regime_analysis": {
+    "summer": {
+      "trades": 31,
+      "wins": 13,
+      "total_pnl": -393095.99275909434,
+      "avg_pnl": -12680.515895454657,
+      "win_rate": 0.41935483870967744
+    },
+    "spring": {
+      "trades": 4,
+      "wins": 4,
+      "total_pnl": 109280.74236198944,
+      "avg_pnl": 27320.18559049736,
+      "win_rate": 1.0
+    }
+  },
+  "agent_contributions": {
+    "trend_hunter": -253900.0478922734
+  },
+  "target_validation": {
+    "年化收益 25-35%": "✗",
+    "最大回撤 <12%": "✗",
+    "夏普比率 >1.5": "✗"
+  }
+}

cyb50-pro/reports/backtest_real_data_20260310_184728.json

@@ -0,0 +1,45 @@
+{
+  "summary": {
+    "测试区间": "2018-01-02 to 2024-12-31",
+    "初始资金": "1,000,000",
+    "最终资金": "697,836",
+    "总收益率": "-30.22%",
+    "年化收益率": "-5.01%",
+    "最大回撤": "-88.49%",
+    "夏普比率": "-0.12",
+    "Sortino比率": "-0.16"
+  },
+  "trading_stats": {
+    "总交易次数": 35,
+    "盈利次数": 17,
+    "亏损次数": 18,
+    "胜率": "48.57%",
+    "平均盈利": "30,533.33",
+    "平均亏损": "-44,430.96",
+    "盈亏比": "0.65"
+  },
+  "regime_analysis": {
+    "summer": {
+      "trades": 31,
+      "wins": 13,
+      "total_pnl": -388894.64748520823,
+      "avg_pnl": -12544.988628555104,
+      "win_rate": 0.41935483870967744
+    },
+    "spring": {
+      "trades": 4,
+      "wins": 4,
+      "total_pnl": 108204.06165777572,
+      "avg_pnl": 27051.01541444393,
+      "win_rate": 1.0
+    }
+  },
+  "agent_contributions": {
+    "trend_hunter": -251152.90752974764
+  },
+  "target_validation": {
+    "年化收益 25-35%": "✗",
+    "最大回撤 <12%": "✗",
+    "夏普比率 >1.5": "✗"
+  }
+}

cyb50-pro/reports/backtest_real_data_20260311_091341.json

@@ -0,0 +1,45 @@
+{
+  "summary": {
+    "测试区间": "2018-01-02 to 2024-12-31",
+    "初始资金": "1,000,000",
+    "最终资金": "697,836",
+    "总收益率": "-30.22%",
+    "年化收益率": "-5.01%",
+    "最大回撤": "-88.49%",
+    "夏普比率": "-0.12",
+    "Sortino比率": "-0.16"
+  },
+  "trading_stats": {
+    "总交易次数": 35,
+    "盈利次数": 17,
+    "亏损次数": 18,
+    "胜率": "48.57%",
+    "平均盈利": "30,533.33",
+    "平均亏损": "-44,430.96",
+    "盈亏比": "0.65"
+  },
+  "regime_analysis": {
+    "summer": {
+      "trades": 31,
+      "wins": 13,
+      "total_pnl": -388894.64748520823,
+      "avg_pnl": -12544.988628555104,
+      "win_rate": 0.41935483870967744
+    },
+    "spring": {
+      "trades": 4,
+      "wins": 4,
+      "total_pnl": 108204.06165777572,
+      "avg_pnl": 27051.01541444393,
+      "win_rate": 1.0
+    }
+  },
+  "agent_contributions": {
+    "trend_hunter": -251152.90752974764
+  },
+  "target_validation": {
+    "年化收益 25-35%": "✗",
+    "最大回撤 <12%": "✗",
+    "夏普比率 >1.5": "✗"
+  }
+}

cyb50-pro/reports/backtest_real_data_20260311_091515.json

@@ -0,0 +1,45 @@
+{
+  "summary": {
+    "测试区间": "2018-01-02 to 2024-12-31",
+    "初始资金": "1,000,000",
+    "最终资金": "1,004,784",
+    "总收益率": "0.48%",
+    "年化收益率": "0.07%",
+    "最大回撤": "-15.07%",
+    "夏普比率": "-0.55",
+    "Sortino比率": "-0.21"
+  },
+  "trading_stats": {
+    "总交易次数": 43,
+    "盈利次数": 11,
+    "亏损次数": 32,
+    "胜率": "25.58%",
+    "平均盈利": "30,730.86",
+    "平均亏损": "-9,586.37",
+    "盈亏比": "1.10"
+  },
+  "regime_analysis": {
+    "summer": {
+      "trades": 34,
+      "wins": 8,
+      "total_pnl": 17269.977888032816,
+      "avg_pnl": 507.94052611861224,
+      "win_rate": 0.23529411764705882
+    },
+    "spring": {
+      "trades": 9,
+      "wins": 3,
+      "total_pnl": 14005.451523174279,
+      "avg_pnl": 1556.1612803526978,
+      "win_rate": 0.3333333333333333
+    }
+  },
+  "agent_contributions": {
+    "trend_hunter": -25711.650719011013
+  },
+  "target_validation": {
+    "年化收益 25-35%": "✗",
+    "最大回撤 <12%": "✗",
+    "夏普比率 >1.5": "✗"
+  }
+}

cyb50-pro/reports/backtest_real_data_20260311_092721.json

@@ -0,0 +1,38 @@
+{
+  "summary": {
+    "测试区间": "2018-01-02 to 2024-12-31",
+    "初始资金": "1,000,000",
+    "最终资金": "1,033,991",
+    "总收益率": "3.40%",
+    "年化收益率": "0.48%",
+    "最大回撤": "-11.20%",
+    "夏普比率": "-0.58",
+    "Sortino比率": "-0.22"
+  },
+  "trading_stats": {
+    "总交易次数": 18,
+    "盈利次数": 11,
+    "亏损次数": 7,
+    "胜率": "61.11%",
+    "平均盈利": "18,282.23",
+    "平均亏损": "-22,596.87",
+    "盈亏比": "1.27"
+  },
+  "regime_analysis": {
+    "summer": {
+      "trades": 18,
+      "wins": 11,
+      "total_pnl": 42926.477181812355,
+      "avg_pnl": 2384.804287878464,
+      "win_rate": 0.6111111111111112
+    }
+  },
+  "agent_contributions": {
+    "trend_hunter": 2476.3693463370473
+  },
+  "target_validation": {
+    "年化收益 25-35%": "✗",
+    "最大回撤 <12%": "✓",
+    "夏普比率 >1.5": "✗"
+  }
+}