344 lines
14 KiB
Python
344 lines
14 KiB
Python
from core.trade.orb_trade import ORBStrategy
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from config import US_STOCK_MONITOR_CONFIG, OKX_MONITOR_CONFIG
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import core.logger as logging
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from datetime import datetime
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from openpyxl import Workbook
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from openpyxl.drawing.image import Image
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import openpyxl
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import pandas as pd
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import os
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logger = logging.logger
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def main():
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is_us_stock_list = [True, False]
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bar = "5m"
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direction_list = [None, "Long", "Short"]
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by_sar_list = [False, True]
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start_date = "2024-01-01"
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end_date = datetime.now().strftime("%Y-%m-%d")
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profit_target_multiple = 10
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initial_capital = 25000
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max_leverage = 4
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risk_per_trade = 0.01
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commission_per_share = 0.0005
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trades_df_list = []
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trades_summary_df_list = []
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symbol_data_cache = []
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for is_us_stock in is_us_stock_list:
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for direction in direction_list:
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for by_sar in by_sar_list:
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if is_us_stock:
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symbols = US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"symbols", ["QQQ"]
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)
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else:
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symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"symbols", ["BTC-USDT"]
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)
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for symbol in symbols:
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logger.info(
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f"开始回测 {symbol}, 交易周期:{bar}, 开始日期:{start_date}, 结束日期:{end_date}, 是否是美股:{is_us_stock}, 交易方向:{direction}, 是否使用SAR:{by_sar}"
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)
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symbol_bar_data = None
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found_symbol_bar_data = False
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for symbol_data_dict in symbol_data_cache:
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if (
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symbol_data_dict["symbol"] == symbol
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and symbol_data_dict["bar"] == bar
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):
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symbol_bar_data = symbol_data_dict["data"]
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found_symbol_bar_data = True
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break
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orb_strategy = ORBStrategy(
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symbol=symbol,
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bar=bar,
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start_date=start_date,
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end_date=end_date,
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is_us_stock=is_us_stock,
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direction=direction,
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by_sar=by_sar,
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profit_target_multiple=profit_target_multiple,
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initial_capital=initial_capital,
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max_leverage=max_leverage,
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risk_per_trade=risk_per_trade,
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commission_per_share=commission_per_share,
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symbol_bar_data=symbol_bar_data,
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)
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symbol_bar_data, trades_df, trades_summary_df = orb_strategy.run()
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if symbol_bar_data is None or len(symbol_bar_data) == 0:
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continue
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if not found_symbol_bar_data:
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symbol_data_cache.append(
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{"symbol": symbol, "bar": bar, "data": symbol_bar_data}
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)
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if trades_summary_df is None or len(trades_summary_df) == 0:
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continue
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trades_summary_df_list.append(trades_summary_df)
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trades_df_list.append(trades_df)
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total_trades_df = pd.concat(trades_df_list)
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total_trades_summary_df = pd.concat(trades_summary_df_list)
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statitics_dict = statistics_summary(total_trades_summary_df)
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output_excel_folder = r"./output/trade_sandbox/orb_strategy/excel/summary/"
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os.makedirs(output_excel_folder, exist_ok=True)
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now_str = datetime.now().strftime("%Y%m%d%H%M%S")
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excel_file_name = f"orb_strategy_summary_{now_str}.xlsx"
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output_file_path = os.path.join(output_excel_folder, excel_file_name)
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with pd.ExcelWriter(output_file_path) as writer:
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total_trades_df.to_excel(writer, sheet_name="交易详情", index=False)
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total_trades_summary_df.to_excel(writer, sheet_name="交易总结", index=False)
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statitics_dict["statistics_summary_df"].to_excel(
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writer, sheet_name="统计总结", index=False
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)
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statitics_dict["max_total_return_record_df"].to_excel(
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writer, sheet_name="最大总收益率记录", index=False
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)
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statitics_dict["max_total_return_record_df_grouped_count"].to_excel(
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writer, sheet_name="最大总收益率记录_方向和根据SAR的组合", index=False
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)
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statitics_dict["max_total_return_record_df_direction_count"].to_excel(
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writer, sheet_name="最大总收益率记录_方向", index=False
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)
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statitics_dict["max_total_return_record_df_sar_count"].to_excel(
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writer, sheet_name="最大总收益率记录_根据SAR", index=False
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)
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chart_path = r"./output/trade_sandbox/orb_strategy/chart/"
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os.makedirs(chart_path, exist_ok=True)
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copy_chart_to_excel(chart_path, output_file_path)
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logger.info(f"交易总结已输出到{output_file_path}")
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def statistics_summary(trades_summary_df: pd.DataFrame):
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statistics_summary_list = []
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summary = {}
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# 1. 统计总收益率% > 0 的占比
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total_return_gt_0 = trades_summary_df[trades_summary_df["总收益率%"] > 0].shape[0]
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total_return_gt_0_ratio = round((total_return_gt_0 / trades_summary_df.shape[0]) * 100, 2)
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summary["总收益率%>0占比"] = total_return_gt_0_ratio
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logger.info(f"总收益率% > 0 的占比：{total_return_gt_0_ratio:.2f}%")
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# 2. 统计总收益率% > 自然收益率% 的占比
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total_return_gt_natural_return = trades_summary_df[
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trades_summary_df["总收益率%"] > trades_summary_df["自然收益率%"]
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].shape[0]
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total_return_gt_natural_return_ratio = (
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round((total_return_gt_natural_return / trades_summary_df.shape[0]) * 100, 2)
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)
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summary["总收益率%>自然收益率%占比"] = total_return_gt_natural_return_ratio
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logger.info(
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f"总收益率% > 自然收益率% 的占比：{total_return_gt_natural_return_ratio:.2f}%"
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)
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statistics_summary_list.append(summary)
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statistics_summary_df = pd.DataFrame(statistics_summary_list)
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symbol_list = trades_summary_df["股票代码"].unique()
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max_total_return_record_list = []
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for symbol in symbol_list:
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trades_summary_df_copy = trades_summary_df.copy()
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symbol_trades_summary_df = trades_summary_df_copy[
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trades_summary_df_copy["股票代码"] == symbol
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]
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symbol_trades_summary_df.reset_index(drop=True, inplace=True)
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if symbol_trades_summary_df.empty:
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continue
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# 过滤掉NaN，避免idxmax报错
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valid_df = symbol_trades_summary_df[
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symbol_trades_summary_df["总收益率%"].notna()
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]
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if valid_df.empty:
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continue
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# 获得总收益率%最大的记录
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max_idx = valid_df["总收益率%"].idxmax()
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max_total_return_record = symbol_trades_summary_df.loc[max_idx]
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summary = {}
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summary["股票代码"] = symbol
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summary["方向"] = max_total_return_record["方向"]
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summary["根据SAR"] = max_total_return_record["根据SAR"]
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summary["总收益率%"] = max_total_return_record["总收益率%"]
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summary["自然收益率%"] = max_total_return_record["自然收益率%"]
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max_total_return_record_list.append(summary)
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max_total_return_record_df = pd.DataFrame(max_total_return_record_list)
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# 统计max_total_return_record_df中方向和根据SAR的组合（使用size更稳健，支持空分组与缺失值）
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# 强制将分组键转为可哈希的标量类型，避免单元格为Series/列表导致的unhashable错误
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if len(max_total_return_record_df) > 0:
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def _to_hashable_scalar(v):
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# 标量或None直接返回
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if isinstance(v, (str, int, float, bool)) or v is None:
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return v
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try:
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import numpy as _np
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if _np.isscalar(v):
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return v
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except Exception:
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pass
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# 其它（如Series、list、dict、ndarray等）转字符串
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return str(v)
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for key_col in ["方向", "根据SAR"]:
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if key_col in max_total_return_record_df.columns:
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max_total_return_record_df[key_col] = max_total_return_record_df[
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key_col
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].apply(_to_hashable_scalar)
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# 分组统计
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max_total_return_record_df_grouped_count = (
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max_total_return_record_df.groupby(["方向", "根据SAR"], dropna=False)
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.size()
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.reset_index(name="数量")
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)
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max_total_return_record_df_grouped_count.sort_values(
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by="数量", ascending=False, inplace=True
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)
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max_total_return_record_df_grouped_count.reset_index(drop=True, inplace=True)
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# 统计方向的记录数目
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max_total_return_record_df_direction_count = (
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max_total_return_record_df.groupby(["方向"], dropna=False)
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.size()
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.reset_index(name="数量")
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)
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max_total_return_record_df_direction_count.sort_values(
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by="数量", ascending=False, inplace=True
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)
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max_total_return_record_df_direction_count.reset_index(drop=True, inplace=True)
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# 统计根据SAR的记录数目
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max_total_return_record_df_sar_count = (
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max_total_return_record_df.groupby(["根据SAR"], dropna=False)
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.size()
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.reset_index(name="数量")
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)
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max_total_return_record_df_sar_count.sort_values(
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by="数量", ascending=False, inplace=True
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)
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max_total_return_record_df_sar_count.reset_index(drop=True, inplace=True)
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else:
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# 构造空结果，保证下游写入Excel不报错
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max_total_return_record_df_grouped_count = pd.DataFrame(
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columns=["方向", "根据SAR", "数量"]
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)
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max_total_return_record_df_direction_count = pd.DataFrame(
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columns=["方向", "数量"]
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)
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max_total_return_record_df_sar_count = pd.DataFrame(columns=["根据SAR", "数量"])
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result = {
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"statistics_summary_df": statistics_summary_df,
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"max_total_return_record_df": max_total_return_record_df,
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"max_total_return_record_df_grouped_count": max_total_return_record_df_grouped_count,
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"max_total_return_record_df_direction_count": max_total_return_record_df_direction_count,
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"max_total_return_record_df_sar_count": max_total_return_record_df_sar_count,
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}
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return result
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def copy_chart_to_excel(chart_path: str, excel_file_path: str):
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f"""
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将chart图片复制到excel中
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算法：
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1. 读取chart_path
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2. chart文件名开头是symbol，结尾是.png
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3. 每个symbol创建一个Excel Sheet，Sheet名称为symbol_chart
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4. 将chart图片插入到Sheet中
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5. 要求每张图片大小为800x400
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6. 要求两张图片左右并列显示
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7. 要求上下图片间距为20px
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"""
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# 收集所有图片
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if not os.path.isdir(chart_path):
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return
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chart_files = [f for f in os.listdir(chart_path) if f.lower().endswith(".png")]
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if len(chart_files) == 0:
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return
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# 汇总需要处理的symbol列表（去重）
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symbols = set(US_STOCK_MONITOR_CONFIG.get("volume_monitor", {}).get("symbols", ["QQQ"]))
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symbols.update(OKX_MONITOR_CONFIG.get("volume_monitor", {}).get("symbols", ["BTC-USDT"]))
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symbols = list(symbols)
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symbols.sort()
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# 每个symbol创建一个sheet并插图
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for symbol in symbols:
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logger.info(f"开始保存{symbol}的图表")
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symbol_files = [f for f in chart_files if f.startswith(symbol)]
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if len(symbol_files) == 0:
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continue
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# 排序以稳定显示顺序
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symbol_files.sort()
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copy_chart_to_excel_sheet(chart_path, symbol_files, excel_file_path, symbol)
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def copy_chart_to_excel_sheet(
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chart_path: str, chart_files: list, excel_file_path: str, symbol: str
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):
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"""
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将chart图片复制到excel中
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算法：
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1. 读取chart_files
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2. 创建一个Excel Sheet，Sheet名称为{symbol}_chart
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3. 将chart_files中的图片插入到Sheet中
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4. 要求每张图片大小为800x400
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5. 要求两张图片左右并列显示, 如6张图片则图片行数为3，列数为2
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6. 要求上下图片间距为20px
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"""
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# 打开已经存在的Excel文件
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wb = openpyxl.load_workbook(excel_file_path)
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# 如果sheet已存在，先删除，避免重复插入
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sheet_name = f"{symbol}_chart"
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if sheet_name in wb.sheetnames:
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del wb[sheet_name]
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ws = wb.create_sheet(title=sheet_name)
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# 两列布局：左列A，右列L；行间距通过起始行步进控制
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left_col = "A"
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right_col = "L"
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row_step = 26 # 行步进，控制上下间距
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for idx, chart_file in enumerate(chart_files):
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try:
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img_path = os.path.join(chart_path, chart_file)
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img = Image(img_path)
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# 设置图片尺寸 800x400 像素
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img.width = 800
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img.height = 400
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row_block = idx // 2
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col_block = idx % 2
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anchor_col = left_col if col_block == 0 else right_col
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anchor_cell = f"{anchor_col}{1 + row_block * row_step}"
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ws.add_image(img, anchor_cell)
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except Exception:
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continue
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wb.save(excel_file_path)
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logger.info(f"{symbol}的图表已输出到{excel_file_path}")
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def test():
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orb_strategy = ORBStrategy(
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symbol="BTC-USDT",
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bar="5m",
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start_date="2024-01-01",
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end_date="2025-09-02",
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is_us_stock=False,
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direction=None,
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by_sar=True,
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profit_target_multiple=10,
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initial_capital=25000,
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max_leverage=4,
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risk_per_trade=0.01,
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commission_per_share=0.0005,
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)
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orb_strategy.run()
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if __name__ == "__main__":
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# main()
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chart_path = r"./output/trade_sandbox/orb_strategy/chart/"
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excel_file_path = r"./output/trade_sandbox/orb_strategy/excel/summary/orb_strategy_summary_20250902174203.xlsx"
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copy_chart_to_excel(chart_path, excel_file_path)
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# test()
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