78007040f1
Updated the trading system to differentiate between soft and hard blacklists based on recent performance metrics. The frontend now displays the blacklist status with clear visual indicators. This change aims to improve risk management and user awareness of trading conditions.
175 lines
7.2 KiB
Python
175 lines
7.2 KiB
Python
#!/usr/bin/env python3
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"""
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定时任务：将最近 N 天的交易数据聚合到 trade_stats_daily / trade_stats_time_bucket。
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优先从 binance_trades 读取（需先跑 sync_binance_orders.py），无数据时用 trades 表。
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用法:
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python scripts/aggregate_trade_stats.py # 默认 7 天、默认账号
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python scripts/aggregate_trade_stats.py -d 30 # 最近 30 天
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python scripts/aggregate_trade_stats.py -a 2 # 指定账号
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"""
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import argparse
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import sys
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from pathlib import Path
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proj = Path(__file__).resolve().parent.parent
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if (proj / "backend").exists():
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sys.path.insert(0, str(proj / "backend"))
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sys.path.insert(0, str(proj))
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def main():
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parser = argparse.ArgumentParser(description="聚合交易统计到 trade_stats_* 表")
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parser.add_argument("-a", "--account", type=int, default=None, help="账号 ID，不传则用默认")
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parser.add_argument("-d", "--days", type=int, default=7, help="聚合最近 N 天，默认 7")
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args = parser.parse_args()
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if args.days <= 0:
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print("days 须 > 0")
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sys.exit(1)
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try:
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from datetime import datetime, timezone
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from database.models import TradeStats, GlobalStrategyConfig, TradingConfig
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# 1) 先按账号聚合到 trade_stats_* 表（供仪表盘等使用）
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TradeStats.aggregate_recent_days(days=args.days, account_id=args.account)
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print(f"已聚合最近 {args.days} 天统计 (account_id={args.account or 'default'})")
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# 2) 再基于 trade_stats_* 做一次【全局】评估：按交易对 / 按小时生成软黑名单与时段过滤建议
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days = max(1, int(args.days))
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symbol_rows = TradeStats.get_global_symbol_stats(days=days)
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hour_rows = TradeStats.get_global_hourly_stats(days=days)
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# 规则参数（后续可改成全局配置，这里先写死一版直观规则）：
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min_trades_for_symbol = 5 # 至少 N 笔才纳入评估，避免样本太少
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min_trades_for_hour = 10 # 小时至少 N 笔才纳入评估
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blacklist_pnl_threshold = 0.0 # 净盈亏 < 0 视为表现不佳（软黑名单）
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hard_blacklist_pnl_threshold = -3.0 # 净亏绝对值较大时考虑升级为硬黑名单
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hard_blacklist_min_trades = 10 # 硬黑名单至少需要的样本数
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hard_blacklist_max_win_rate = 10.0 # 胜率≤10% 且净亏较大时视为严重拖累
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good_hour_pnl_threshold = 0.0 # 净盈亏 > 0 视为表现较好
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symbol_blacklist = []
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symbol_whitelist = []
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for r in symbol_rows or []:
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try:
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sym = (r.get("symbol") or "").strip()
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tc = int(r.get("trade_count") or 0)
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net_pnl = float(r.get("net_pnl") or 0.0)
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win = int(r.get("win_count") or 0)
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loss = int(r.get("loss_count") or 0)
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except Exception:
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continue
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if not sym or tc < min_trades_for_symbol:
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continue
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total = max(1, win + loss)
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win_rate = 100.0 * win / total
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# 黑名单：近期净亏 + 有一定笔数 -> 降权或升级为硬黑名单
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if net_pnl < blacklist_pnl_threshold:
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mode = "reduced"
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if (
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net_pnl <= hard_blacklist_pnl_threshold
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and tc >= hard_blacklist_min_trades
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and win_rate <= hard_blacklist_max_win_rate
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):
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mode = "hard"
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symbol_blacklist.append(
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{
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"symbol": sym,
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"trade_count": tc,
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"net_pnl": round(net_pnl, 4),
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"win_rate_pct": round(win_rate, 1),
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"mode": mode, # reduced=软黑名单（降权+提门槛）；hard=硬黑名单（自动不再开仓）
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}
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)
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# 白名单：近期净盈且胜率尚可
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elif net_pnl > 0 and win_rate >= 50.0:
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symbol_whitelist.append(
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{
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"symbol": sym,
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"trade_count": tc,
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"net_pnl": round(net_pnl, 4),
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"win_rate_pct": round(win_rate, 1),
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}
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)
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hour_filters = []
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for r in hour_rows or []:
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try:
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h = int(r.get("hour"))
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tc = int(r.get("trade_count") or 0)
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net_pnl = float(r.get("net_pnl") or 0.0)
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except Exception:
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continue
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if h < 0 or h > 23:
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continue
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if tc < min_trades_for_hour:
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bucket = "unknown"
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elif net_pnl > good_hour_pnl_threshold:
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bucket = "good"
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elif net_pnl < -good_hour_pnl_threshold:
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bucket = "bad"
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else:
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bucket = "neutral"
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# 约定：bad 时段默认 0.5 仓位系数 + 信号门槛+1；good 时段略微放宽（可视需求调整）
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if bucket == "bad":
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position_factor = 0.5
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signal_boost = 1
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elif bucket == "good":
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position_factor = 1.1
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signal_boost = 0
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else:
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position_factor = 1.0
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signal_boost = 0
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hour_filters.append(
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{
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"hour": h,
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"bucket": bucket,
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"trade_count": tc,
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"net_pnl": round(net_pnl, 4),
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"position_factor": position_factor,
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"signal_boost": signal_boost,
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}
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)
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now_iso = datetime.now(timezone.utc).isoformat()
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symbol_payload = {
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"generated_at": now_iso,
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"days": days,
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"min_trades": min_trades_for_symbol,
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"blacklist": sorted(symbol_blacklist, key=lambda x: (x["net_pnl"], -x["trade_count"])),
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"whitelist": sorted(symbol_whitelist, key=lambda x: (-x["net_pnl"], -x["trade_count"])),
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}
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hour_payload = {
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"generated_at": now_iso,
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"days": days,
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"min_trades": min_trades_for_hour,
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"hours": sorted(hour_filters, key=lambda x: x["hour"]),
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}
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# 写入全局策略配置（json），供交易进程 & 前端全局配置页读取
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GlobalStrategyConfig.set(
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"STATS_SYMBOL_FILTERS",
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symbol_payload,
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"json",
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"stats_filters",
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description="基于最近 N 天交易统计自动生成的 symbol 白/黑名单（软降权），请勿手动修改。",
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)
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GlobalStrategyConfig.set(
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"STATS_HOUR_FILTERS",
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hour_payload,
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"json",
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"stats_filters",
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description="基于最近 N 天交易统计自动生成的按小时时段过滤建议，请勿手动修改。",
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)
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print(f"已更新全局统计过滤配置：{len(symbol_blacklist)} 个黑名单 symbol，{len(symbol_whitelist)} 个白名单 symbol。")
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except Exception as e:
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print(f"聚合失败: {e}")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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