import pandas as pd
import sys

# 读取本地 parquet 数据
df = pd.read_parquet('/home/erwin/.openclaw/workspace/cyb50-quant/index-rotation/data/features/chinext50/daily.parquet')

print(f"原始数据列: {df.columns.tolist()}")
print(f"数据条数: {len(df)}")
print(f"日期范围: {df['trade_date'].min()} 至 {df['trade_date'].max()}")

# 转换为 backtrader 格式
df_bt = df.rename(columns={
    'trade_date': 'datetime',
    'close': 'close',
}).copy()

# backtrader 需要 open/high/low/close/volume
# 如果原始数据有这些列，直接使用；如果没有，用 close 填充
if 'open' not in df_bt.columns:
    df_bt['open'] = df_bt['close']
if 'high' not in df_bt.columns:
    df_bt['high'] = df_bt['close']
if 'low' not in df_bt.columns:
    df_bt['low'] = df_bt['close']
if 'volume' not in df_bt.columns:
    df_bt['volume'] = 0

df_bt['datetime'] = pd.to_datetime(df_bt['datetime'])
df_bt.set_index('datetime', inplace=True)

# 保存为 CSV
df_bt.to_csv('/home/erwin/.openclaw/workspace/cyb50-quant/backtrader-lab/chinext50.csv')
print(f"\n数据已转换并保存到 backtrader-lab/chinext50.csv")
print(f"共 {len(df_bt)} 条记录")