from __future__ import annotations

from pathlib import Path

import pandas as pd


def _load_csv(base_dir: Path, name: str) -> pd.DataFrame:
    return pd.read_csv(base_dir / name, encoding="utf-8-sig")


def _profit_factor(series: pd.Series) -> float:
    gross_profit = series[series > 0].sum()
    gross_loss = -series[series < 0].sum()
    if gross_loss == 0:
        return float("inf") if gross_profit > 0 else 0.0
    return float(gross_profit / gross_loss)


def _max_drawdown(series: pd.Series) -> float:
    if series.empty:
        return float("nan")
    equity = (1.0 + series).cumprod()
    running_max = equity.cummax()
    drawdown = equity / running_max - 1.0
    return float(drawdown.min())


def _segment_stats(df: pd.DataFrame) -> dict[str, float | int]:
    if df.empty:
        return {
            "trades": 0,
            "win_rate": float("nan"),
            "avg_return": float("nan"),
            "median_return": float("nan"),
            "profit_factor": float("nan"),
            "compounded_return": float("nan"),
            "max_drawdown": float("nan"),
        }
    returns = df["return_pct"].astype(float)
    return {
        "trades": int(len(df)),
        "win_rate": float((returns > 0).mean()),
        "avg_return": float(returns.mean()),
        "median_return": float(returns.median()),
        "profit_factor": _profit_factor(returns),
        "compounded_return": float((1.0 + returns).prod() - 1.0),
        "max_drawdown": _max_drawdown(returns),
    }


def _format_pct(value: float) -> str:
    if pd.isna(value):
        return "NA"
    if value == float("inf"):
        return "inf"
    return f"{value:.2%}"


def _format_num(value: float) -> str:
    if pd.isna(value):
        return "NA"
    if value == float("inf"):
        return "inf"
    return f"{value:.2f}"


def _build_walk_forward(trades: pd.DataFrame) -> pd.DataFrame:
    years = sorted(int(year) for year in trades["sell_year"].unique())
    rows: list[dict[str, object]] = []

    for idx, test_year in enumerate(years):
        if idx >= 1:
            train_years = years[:idx]
            train_df = trades[trades["sell_year"].isin(train_years)]
            test_df = trades[trades["sell_year"] == test_year]
            train_stats = _segment_stats(train_df)
            test_stats = _segment_stats(test_df)
            rows.append(
                {
                    "scheme": "anchored_expanding",
                    "train_start_year": train_years[0],
                    "train_end_year": train_years[-1],
                    "test_year": test_year,
                    **{f"train_{k}": v for k, v in train_stats.items()},
                    **{f"test_{k}": v for k, v in test_stats.items()},
                }
            )

        if idx >= 3:
            train_years = years[idx - 3 : idx]
            train_df = trades[trades["sell_year"].isin(train_years)]
            test_df = trades[trades["sell_year"] == test_year]
            train_stats = _segment_stats(train_df)
            test_stats = _segment_stats(test_df)
            rows.append(
                {
                    "scheme": "rolling_3y",
                    "train_start_year": train_years[0],
                    "train_end_year": train_years[-1],
                    "test_year": test_year,
                    **{f"train_{k}": v for k, v in train_stats.items()},
                    **{f"test_{k}": v for k, v in test_stats.items()},
                }
            )
    return pd.DataFrame(rows)


def _build_family_stability(trades: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame]:
    df = trades.copy()
    df["entry_family"] = df["buy_reason"].astype(str).str.split(":").str[0]

    family_year = (
        df.groupby(["entry_family", "sell_year"], dropna=False)
        .apply(
            lambda g: pd.Series(
                {
                    "trades": int(len(g)),
                    "win_rate": float((g["return_pct"] > 0).mean()),
                    "avg_return": float(g["return_pct"].mean()),
                    "profit_factor": _profit_factor(g["return_pct"]),
                    "compounded_return": float((1.0 + g["return_pct"]).prod() - 1.0),
                }
            )
        )
        .reset_index()
    )

    eligible_families = (
        df.groupby("entry_family")
        .size()
        .reset_index(name="total_trades")
        .query("total_trades >= 3")["entry_family"]
        .tolist()
    )
    family_year = family_year[family_year["entry_family"].isin(eligible_families)].copy()

    family_summary = (
        family_year.groupby("entry_family", dropna=False)
        .apply(
            lambda g: pd.Series(
                {
                    "years_active": int(len(g)),
                    "total_trades": int(g["trades"].sum()),
                    "positive_years": int((g["avg_return"] > 0).sum()),
                    "negative_years": int((g["avg_return"] < 0).sum()),
                    "avg_yearly_avg_return": float(g["avg_return"].mean()),
                    "min_yearly_avg_return": float(g["avg_return"].min()),
                    "max_yearly_avg_return": float(g["avg_return"].max()),
                }
            )
        )
        .reset_index()
        .sort_values(["avg_yearly_avg_return", "total_trades"], ascending=[False, False])
    )

    return family_year, family_summary


def main() -> None:
    base_dir = Path(__file__).resolve().parent
    trades = _load_csv(base_dir, "dragon_strategy_trades.csv").copy()
    trades["sell_dt"] = pd.to_datetime(trades["sell_date"])
    trades["sell_year"] = trades["sell_dt"].dt.year.astype(int)

    walk_forward = _build_walk_forward(trades)
    family_year, family_summary = _build_family_stability(trades)

    walk_forward.to_csv(base_dir / "dragon_walk_forward_summary.csv", index=False, encoding="utf-8-sig")
    family_year.to_csv(base_dir / "dragon_walk_forward_family_year.csv", index=False, encoding="utf-8-sig")
    family_summary.to_csv(base_dir / "dragon_walk_forward_family_stability.csv", index=False, encoding="utf-8-sig")

    anchored = walk_forward[walk_forward["scheme"] == "anchored_expanding"].copy()
    rolling = walk_forward[walk_forward["scheme"] == "rolling_3y"].copy()

    lines = [
        "# Dragon Walk-Forward Validation",
        "",
        "- Method: fixed current baseline rules, no refit, evaluate temporal stability by yearly out-of-sample slices.",
        "- Goal: verify whether the workbook-preserving baseline still behaves coherently outside any single full-sample summary.",
        "",
        "## Anchored Expanding Windows",
    ]
    for _, row in anchored.iterrows():
        lines.append(
            f"- train `{int(row['train_start_year'])}-{int(row['train_end_year'])}` -> test `{int(row['test_year'])}`: "
            f"test trades `{int(row['test_trades'])}`, test avg_return `{_format_pct(float(row['test_avg_return']))}`, "
            f"test profit_factor `{_format_num(float(row['test_profit_factor']))}`, "
            f"test compounded_return `{_format_pct(float(row['test_compounded_return']))}`, "
            f"test max_drawdown `{_format_pct(float(row['test_max_drawdown']))}`"
        )

    lines.extend(["", "## Rolling 3Y Windows"])
    for _, row in rolling.iterrows():
        lines.append(
            f"- train `{int(row['train_start_year'])}-{int(row['train_end_year'])}` -> test `{int(row['test_year'])}`: "
            f"test trades `{int(row['test_trades'])}`, test avg_return `{_format_pct(float(row['test_avg_return']))}`, "
            f"test profit_factor `{_format_num(float(row['test_profit_factor']))}`, "
            f"test compounded_return `{_format_pct(float(row['test_compounded_return']))}`, "
            f"test max_drawdown `{_format_pct(float(row['test_max_drawdown']))}`"
        )

    lines.extend(["", "## Entry-Family Stability"])
    for _, row in family_summary.head(8).iterrows():
        lines.append(
            f"- `{row['entry_family']}`: years_active `{int(row['years_active'])}`, total_trades `{int(row['total_trades'])}`, "
            f"positive_years `{int(row['positive_years'])}`, negative_years `{int(row['negative_years'])}`, "
            f"avg_yearly_avg_return `{_format_pct(float(row['avg_yearly_avg_return']))}`, "
            f"min_yearly_avg_return `{_format_pct(float(row['min_yearly_avg_return']))}`"
        )

    weakest = family_summary.sort_values(["avg_yearly_avg_return", "min_yearly_avg_return"]).head(5)
    lines.extend(["", "## Weak Entry-Family Stability"])
    for _, row in weakest.iterrows():
        lines.append(
            f"- `{row['entry_family']}`: years_active `{int(row['years_active'])}`, total_trades `{int(row['total_trades'])}`, "
            f"positive_years `{int(row['positive_years'])}`, negative_years `{int(row['negative_years'])}`, "
            f"avg_yearly_avg_return `{_format_pct(float(row['avg_yearly_avg_return']))}`, "
            f"min_yearly_avg_return `{_format_pct(float(row['min_yearly_avg_return']))}`"
        )

    positive_anchored = int((anchored["test_avg_return"] > 0).sum()) if not anchored.empty else 0
    negative_anchored = int((anchored["test_avg_return"] < 0).sum()) if not anchored.empty else 0
    positive_rolling = int((rolling["test_avg_return"] > 0).sum()) if not rolling.empty else 0
    negative_rolling = int((rolling["test_avg_return"] < 0).sum()) if not rolling.empty else 0

    lines.extend(
        [
            "",
            "## Quant Judgment",
            f"- Anchored walk-forward windows: positive years `{positive_anchored}`, negative years `{negative_anchored}`.",
            f"- Rolling 3Y windows: positive years `{positive_rolling}`, negative years `{negative_rolling}`.",
            "- This is a stability audit, not a parameter-search walk-forward. The strategy was held fixed throughout.",
            "- Families with repeated negative yearly averages are research candidates; families with broad multi-year persistence are baseline keepers.",
        ]
    )

    (base_dir / "dragon_walk_forward_report.md").write_text("\n".join(lines) + "\n", encoding="utf-8")


if __name__ == "__main__":
    main()