feat(stats): 增加全局交易统计功能
在后端新增了全局交易统计功能，包括按交易对和按小时的聚合统计，支持生成软黑名单和时段过滤建议。前端组件更新以展示基于最近N天交易统计的过滤结果，旨在提升交易策略的灵活性和风险控制能力。
This commit is contained in:
薇薇安
parent
076b597fb4
commit
861f1dc548
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@ -1527,6 +1527,60 @@ class TradeStats:
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logger.debug(f"[TradeStats] get_hourly_stats 失败: {e}")
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return []
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@staticmethod
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def get_global_symbol_stats(days: int = 7):
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"""
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查询最近 N 天【跨账号聚合】的按交易对统计（无 account_id 维度），
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用于全局白名单/黑名单评估。「大家一视同仁」。
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"""
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try:
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rows = db.execute_query(
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"""SELECT
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symbol,
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SUM(trade_count) AS trade_count,
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SUM(win_count) AS win_count,
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SUM(loss_count) AS loss_count,
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SUM(gross_pnl) AS gross_pnl,
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SUM(net_pnl) AS net_pnl,
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SUM(total_commission) AS total_commission
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FROM trade_stats_daily
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WHERE trade_date >= DATE_SUB(CURDATE(), INTERVAL %s DAY)
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GROUP BY symbol
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ORDER BY net_pnl DESC""",
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(max(1, int(days)),),
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)
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return [dict(r) for r in rows] if rows else []
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except Exception as e:
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logger.debug(f"[TradeStats] get_global_symbol_stats 失败: {e}")
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return []
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@staticmethod
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def get_global_hourly_stats(days: int = 7):
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"""
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查询最近 N 天【跨账号聚合】的按小时统计（无 account_id 维度），
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用于全局时段表现评估。
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"""
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try:
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rows = db.execute_query(
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"""SELECT
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hour,
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SUM(trade_count) AS trade_count,
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SUM(win_count) AS win_count,
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SUM(loss_count) AS loss_count,
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SUM(gross_pnl) AS gross_pnl,
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SUM(net_pnl) AS net_pnl,
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SUM(total_commission) AS total_commission
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FROM trade_stats_time_bucket
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WHERE trade_date >= DATE_SUB(CURDATE(), INTERVAL %s DAY)
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GROUP BY hour
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ORDER BY hour ASC""",
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(max(1, int(days)),),
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)
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return [dict(r) for r in rows] if rows else []
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except Exception as e:
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logger.debug(f"[TradeStats] get_global_hourly_stats 失败: {e}")
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return []
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class AccountSnapshot:
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"""账户快照模型"""
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@ -568,6 +568,9 @@ const GlobalConfig = () => {
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BLOCK_LONG_WHEN_4H_DOWN: { value: false, type: 'boolean', category: 'strategy', description: '4H 趋势下跌时禁止开多。bear / conservative 方案下自动为 true。' },
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BLOCK_SHORT_WHEN_4H_UP: { value: true, type: 'boolean', category: 'strategy', description: '4H 趋势上涨时禁止开空。默认 true，避免逆势做空导致止损。' },
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AUTO_MARKET_SCHEME_ENABLED: { value: false, type: 'boolean', category: 'strategy', description: '开启后，crontab 定时运行 update_market_scheme.py --apply 时自动更新 MARKET_SCHEME（根据 BTC 行情识别牛/熊/正常）。' },
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// 基于7天统计自动生成的全局过滤结果（仅展示，自动更新）
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STATS_SYMBOL_FILTERS: { value: {}, type: 'json', category: 'stats', description: '基于最近 N 天交易统计自动生成的 symbol 白/黑名单（软降权），由脚本 aggregate_trade_stats.py 自动更新，请勿手动修改。' },
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STATS_HOUR_FILTERS: { value: {}, type: 'json', category: 'stats', description: '基于最近 N 天交易统计自动生成的按小时过滤建议，用于差时段减仓/提门槛，由脚本 aggregate_trade_stats.py 自动更新，请勿手动修改。' },
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}
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const loadConfigs = async () => {
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@ -27,9 +27,134 @@ def main():
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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 database.models import TradeStats
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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
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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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# 软黑名单：近期净亏 + 有一定笔数 -> 降权（不一刀切禁止）
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if net_pnl < blacklist_pnl_threshold:
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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": "reduced", # 软黑名单：仅降权+提门槛
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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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@ -771,7 +771,7 @@ class RiskManager:
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# 计算最终的名义价值与保证金
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final_notional_value = quantity * current_price
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final_margin = final_notional_value / actual_leverage if actual_leverage > 0 else final_notional_value
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# 仓位放大系数（盈利时适当放大仓位，1.0=正常，1.2=+20%，上限2.0，仍受 MAX_POSITION_PERCENT 约束）
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position_scale = config.TRADING_CONFIG.get('POSITION_SCALE_FACTOR', 1.0)
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try:
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@ -779,8 +779,47 @@ class RiskManager:
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position_scale = max(1.0, min(position_scale, 2.0))
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except (TypeError, ValueError):
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position_scale = 1.0
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if position_scale > 1.0:
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quantity = quantity * position_scale
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# 基于 7 天统计的 symbol / 小时软黑名单：差标的 & 差时段进一步缩小仓位
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stats_scale = 1.0
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try:
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from datetime import datetime, timezone, timedelta
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from config_manager import GlobalStrategyConfigManager
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mgr = GlobalStrategyConfigManager()
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stats_symbol = mgr.get("STATS_SYMBOL_FILTERS", {}) or {}
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stats_hour = mgr.get("STATS_HOUR_FILTERS", {}) or {}
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bl = stats_symbol.get("blacklist") or []
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if symbol and isinstance(bl, list):
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if any((item.get("symbol") or "").upper() == symbol.upper() for item in bl):
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# 软黑名单：默认 0.5 倍仓位
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stats_scale *= 0.5
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# 小时过滤
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try:
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bj = timezone(timedelta(hours=8))
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h = datetime.now(bj).hour
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hours = stats_hour.get("hours") or []
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for item in hours:
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if int(item.get("hour", -1)) == int(h):
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pf = item.get("position_factor")
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try:
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pf_val = float(pf)
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except (TypeError, ValueError):
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pf_val = 1.0
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# 仅在 <1 时缩小仓位；>1 的“好时段”放大留给 POSITION_SCALE_FACTOR 处理
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if pf_val > 0 and pf_val < 1.0:
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stats_scale *= pf_val
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break
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except Exception:
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pass
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except Exception:
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stats_scale = 1.0
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total_scale = position_scale * stats_scale
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if total_scale != 1.0:
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quantity = quantity * total_scale
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final_notional_value = quantity * current_price
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final_margin = final_notional_value / actual_leverage if actual_leverage > 0 else final_notional_value
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max_margin_cap = available_balance * config.get_effective_config('MAX_POSITION_PERCENT', 0.20)
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@ -789,7 +828,7 @@ class RiskManager:
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final_notional_value = final_margin * actual_leverage
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quantity = final_notional_value / current_price if current_price and current_price > 0 else quantity
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logger.info(f" 仓位放大后超过单笔上限，已截断至 MAX_POSITION_PERCENT 对应保证金")
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logger.info(f" 仓位放大系数: {position_scale:.2f} -> 最终数量: {quantity:.4f}")
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logger.info(f" 仓位缩放系数: POSITION_SCALE={position_scale:.2f}, STATS_SCALE={stats_scale:.2f} -> 最终数量: {quantity:.4f}")
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# 添加最小名义价值检查（0.2 USDT），避免下无意义的小单子
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MIN_NOTIONAL_VALUE = 0.2 # 最小名义价值0.2 USDT
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@ -688,7 +688,41 @@ class TradingStrategy:
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reasons.append("❌ 逆4H趋势，拒绝交易")
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# 判断是否应该交易（信号强度 >= 有效 MIN_SIGNAL_STRENGTH 才交易；低波动期自动提高门槛）
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min_signal_strength = config.get_effective_config('MIN_SIGNAL_STRENGTH', 7)
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base_min_signal_strength = config.get_effective_config('MIN_SIGNAL_STRENGTH', 7)
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# 基于全局 7 天统计的 symbol / 小时过滤：差标的、差时段提高信号门槛
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extra_signal_boost = 0
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try:
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from datetime import datetime, timezone, timedelta
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from config_manager import GlobalStrategyConfigManager
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mgr = GlobalStrategyConfigManager()
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stats_symbol = mgr.get("STATS_SYMBOL_FILTERS", {}) or {}
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stats_hour = mgr.get("STATS_HOUR_FILTERS", {}) or {}
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# symbol 软黑名单：命中则在基础门槛上 +1
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bl = stats_symbol.get("blacklist") or []
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if symbol and isinstance(bl, list):
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if any((item.get("symbol") or "").upper() == symbol.upper() for item in bl):
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extra_signal_boost += 1
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# 按小时过滤：bad 小时再 +1
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try:
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bj = timezone(timedelta(hours=8))
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h = datetime.now(bj).hour
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hours = stats_hour.get("hours") or []
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for item in hours:
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if int(item.get("hour", -1)) == int(h):
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if (item.get("bucket") or "").lower() == "bad":
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extra_signal_boost += int(item.get("signal_boost") or 1)
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break
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except Exception:
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pass
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except Exception:
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base_min_signal_strength = config.get_effective_config('MIN_SIGNAL_STRENGTH', 7)
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extra_signal_boost = 0
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min_signal_strength = max(0, min(10, int(base_min_signal_strength) + int(extra_signal_boost)))
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# 强度上限归一到 0-10，避免出现 12/10 这种误导显示
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try:
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signal_strength = max(0, min(int(signal_strength), 10))
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