diff --git a/server.py b/server.py index 2ad2dbed..26f70fde 100644 --- a/server.py +++ b/server.py @@ -533,28 +533,66 @@ def get_tracking(): -@app.route("/api/research/backtest") -def get_research_backtest(): - import sqlite3 - from datetime import datetime, timedelta - period = request.args.get("period", "1y") - capital = float(request.args.get("capital", 1000000)) - end_date = datetime.now().strftime("%Y-%m-%d") - if period == "6m": - start_date = (datetime.now() - timedelta(days=180)).strftime('%Y-%m-%d') - elif period == "1y": - start_date = (datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d') - else: - start_date = (datetime.now() - timedelta(days=730)).strftime('%Y-%m-%d') +@app.route("/api/research/strategies") +def api_research_strategies(): + """策略版本列表（含回测结果摘要）""" try: - from backtest_framework import run_strategy_research - result = run_strategy_research(start_date, end_date, capital) - from flask import jsonify - return jsonify(result) + from strategy_lab import list_strategies + return jsonify({'strategies': list_strategies()}) except Exception as e: - from flask import jsonify return jsonify({'error': str(e)}), 500 + +@app.route("/api/research/backtest") +def api_research_backtest(): + """运行指定策略版本的回测""" + from datetime import datetime, timedelta + version = request.args.get('strategy', 'v4.1') + period = request.args.get('period', '6m') + capital = float(request.args.get('capital', 1000000)) + end_date = '2026-07-24' # 数据完整截止日 + days = {'1m': 30, '6m': 185, '1y': 365, '2y': 730}.get(period, 185) + start_date = (datetime.strptime(end_date, '%Y-%m-%d') - timedelta(days=days)).strftime('%Y-%m-%d') + try: + from strategy_lab import run_backtest, analyze_trade_list, save_analysis + result = run_backtest(version, start_date, end_date, capital) + analysis = analyze_trade_list(result.get('trades', []), version) + save_analysis(version, analysis) + result['insights'] = analysis.get('insights', []) + return jsonify(result) + except ValueError as e: + return jsonify({'error': str(e)}), 404 + except Exception as e: + return jsonify({'error': str(e)}), 500 + + +@app.route("/api/research/analysis") +def api_research_analysis(): + """指定策略版本的因子归因分析""" + version = request.args.get('strategy', 'v4.1') + try: + from strategy_lab import analyze_trades + return jsonify(analyze_trades(version)) + except Exception as e: + return jsonify({'error': str(e)}), 500 + + +@app.route("/api/research/trades") +def api_research_trades(): + """指定策略版本的交易明细""" + import sqlite3 as _sq, json as _json + version = request.args.get('strategy', 'v4.1') + conn = _sq.connect("/home/hmo/MoFin/data/mofin.db") + row = conn.execute( + "SELECT results_json FROM strategy_research WHERE version=? ORDER BY id DESC LIMIT 1", + (version,)).fetchone() + conn.close() + if not row: + return jsonify({'error': f'无 {version} 回测结果'}), 404 + res = _json.loads(row[0]) + return jsonify({'strategy': version, 'trades': res.get('trades', [])}) + + @app.route("/api/overview") def api_overview(): """概览数据""" diff --git a/static/index.html b/static/index.html index 9e01d814..da149050 100644 --- a/static/index.html +++ b/static/index.html @@ -1929,84 +1929,156 @@ function inMd(t) { function renderResearch() { const el = document.getElementById('tab-research'); if (!el) return; - el.innerHTML = '

📋 策略研究 — 全流程回测

' + - '' + - '
' + - '
点击「运行」开始回测验证
'; - window.btEl = el; + el.innerHTML = '
' + + '

📋 策略研究 — 版本迭代对比

' + + '' + + '每版策略基于上一版归因分析迭代 · 点击行展开明细
' + + '
加载中...
' + + '
'; + loadStrategyList(); } -async function runBacktest() { - const period = document.getElementById('btPeriod')?.value || '6m'; - const resultsEl = document.getElementById('btResults'); - if (!resultsEl) return; - resultsEl.innerHTML = '
⏳ 回测运行中（约 15-30 秒）...
'; +async function loadStrategyList() { + const listEl = document.getElementById('strategyList'); try { - const resp = await fetch('/api/research/backtest?period=' + period); + const resp = await fetch('/api/research/strategies'); const data = await resp.json(); - if (data.error) { resultsEl.innerHTML = '
错误: ' + data.error + '
'; return; } - displayBacktestResults(data); + if (data.error) { listEl.innerHTML = '
' + data.error + '
'; return; } + renderStrategyTable(data.strategies || []); } catch(e) { - resultsEl.innerHTML = '
请求失败: ' + e.message + '
'; + listEl.innerHTML = '
加载失败: ' + e.message + '
'; } } -function displayBacktestResults(data) { - const el = document.getElementById('btResults'); - if (!el) return; - const s = data.summary || {}; - const winRate = s.win_rate || 0; - const winRateClass = winRate >= 50 ? 'text-green-400' : winRate >= 40 ? 'text-yellow-400' : 'text-red-400'; - const sharpeClass = (s.sharpe_ratio || 0) >= 1 ? 'text-green-400' : (s.sharpe_ratio || 0) >= 0 ? 'text-yellow-400' : 'text-red-400'; - - let html = '
' + - '
测试周期
' + (data.period || '') + '
' + - '
初始资金
¥' + (s.capital_end ? (data.capital || 1000000).toLocaleString() : '100万') + '
' + - '
最终资金
¥' + (s.capital_end ? s.capital_end.toLocaleString() : '-') + '
' + - '
总交易
' + (s.total_trades || 0) + ' 笔
' + - '
胜率
' + s.win_rate + '%
' + - '
平均收益
' + (s.avg_profit_pct || 0) + '%
' + - '
夏普比率
' + (s.sharpe_ratio || 0).toFixed(2) + '
' + - '
最大回撤
' + (s.max_drawdown_pct || 0).toFixed(1) + '%
' + - '
' + - '
' + - '
✅ 盈利: ' + (s.wins || 0) + ' 均+' + (s.avg_win_pct || 0).toFixed(1) + '%
' + - '
❌ 亏损: ' + (s.losses || 0) + ' 均' + (s.avg_loss_pct || 0).toFixed(1) + '%
' + - '
📊 盈亏比: ' + (s.profit_factor || 0).toFixed(2) + '
' + - '
📈 筛选: ' + (data.total_stocks_screened || 0) + ' 只
' + - '
'; - - // Trade list - const trades = data.trades || []; - html += '
交易明细（前 50 笔）
' + - '
' + - '' + - '' + - ''; - for (const t of trades) { - const pnlClass = (t.profit_pct || 0) >= 0 ? 'text-green-400' : 'text-red-400'; - const reasonMap = { 'target': '🎯 止盈', 'stop': '🛑 止损', 'keep': '⏳ 持仓中' }; - html += '' + - '' + - '' + - '' + - '' + - '' + - '' + - '' + - '' + - ''; +function renderStrategyTable(strategies) { + const el = document.getElementById('strategyList'); + if (!strategies.length) { el.innerHTML = '
暂无策略版本
'; return; } + // 找每列最优值用于高亮 + const best = { win_rate: -999, sharpe_ratio: -999, profit_factor: -999, avg_profit_pct: -999, max_drawdown_pct: 999 }; + for (const s of strategies) { + const st = s.summary_stats || {}; + if (st.win_rate != null) best.win_rate = Math.max(best.win_rate, st.win_rate); + if (st.sharpe_ratio != null) best.sharpe_ratio = Math.max(best.sharpe_ratio, st.sharpe_ratio); + if (st.profit_factor != null) best.profit_factor = Math.max(best.profit_factor, st.profit_factor); + if (st.avg_profit_pct != null) best.avg_profit_pct = Math.max(best.avg_profit_pct, st.avg_profit_pct); + if (st.max_drawdown_pct != null) best.max_drawdown_pct = Math.min(best.max_drawdown_pct, st.max_drawdown_pct); } - html += '
代码名称买入日买入价卖出价收益结果持仓评分
' + (t.code || '') + '' + (t.name || '') + '' + (t.entry_date || '') + '' + (t.entry_price || 0).toFixed(2) + '' + (t.exit_price || 0).toFixed(2) + '' + (t.profit_pct || 0).toFixed(1) + '%' + (reasonMap[t.exit_reason] || t.exit_reason || '') + '' + (t.hold_days || 0) + 'd' + (t.score || 0) + '
'; - + const hl = (val, bestVal, invert) => { + if (val == null) return ''; + const isBest = invert ? (val === bestVal && bestVal !== 999) : (val === bestVal && bestVal !== -999); + return isBest ? 'text-green-400 font-bold' : ''; + }; + let html = '' + + '' + + '' + + '' + + '' + + '' + + ''; + for (const s of strategies) { + const st = s.summary_stats || {}; + const hasResult = st.total_trades != null; + const smallSample = hasResult && st.total_trades < 40; + html += '' + + '' + + '' + + '' + + '' + + '' + + '' + + '' + + '' + + '' + + ''; + } + html += '
版本名称改进假设交易数胜率平均收益夏普盈亏比最大回撤操作
' + s.version + '' + (s.name || '') + '' + (s.hypothesis || s.summary || '') + '' + (hasResult ? st.total_trades + (smallSample ? '⚠️' : '') : '—') + '' + (st.win_rate != null ? st.win_rate + '%' : '—') + '' + (st.avg_profit_pct != null ? st.avg_profit_pct + '%' : '—') + '' + (st.sharpe_ratio != null ? st.sharpe_ratio : '—') + '' + (st.profit_factor != null ? st.profit_factor : '—') + '' + (st.max_drawdown_pct != null ? st.max_drawdown_pct + '%' : '—') + '' + + '' + + '
' + + '
⚠️ = 样本量<40笔，统计意义有限 · 绿色 = 该列最优 · 回测期间: 2026-01-21 ~ 2026-07-24
'; el.innerHTML = html; } +async function runStrategyBacktest(version) { + const period = document.getElementById('btPeriod')?.value || '6m'; + const detail = document.getElementById('strategyDetail'); + detail.innerHTML = '
⏳ 正在回测 ' + version + '（约15-30秒）...
'; + try { + const resp = await fetch('/api/research/backtest?strategy=' + version + '&period=' + period); + const data = await resp.json(); + if (data.error) { detail.innerHTML = '
' + data.error + '
'; return; } + await loadStrategyList(); + showStrategyResult(data); + } catch(e) { + detail.innerHTML = '
失败: ' + e.message + '
'; + } +} + +async function showStrategyDetail(version) { + const detail = document.getElementById('strategyDetail'); + detail.innerHTML = '
加载 ' + version + ' 明细...
'; + try { + const [anaResp, tradesResp] = await Promise.all([ + fetch('/api/research/analysis?strategy=' + version), + fetch('/api/research/trades?strategy=' + version) + ]); + const ana = await anaResp.json(); + const tradesData = await tradesResp.json(); + showStrategyResult({ strategy: version, trades: tradesData.trades || [], insights: (ana.insights || []), analysis: ana }); + } catch(e) { + detail.innerHTML = '
失败: ' + e.message + '
'; + } +} + +function showStrategyResult(data) { + const el = document.getElementById('strategyDetail'); + const s = data.summary || {}; + let html = '
' + + '

' + (data.strategy || '') + ' ' + (data.strategy_name || '') + '

' + + '' + (data.period || '') + '
'; + if (s.total_trades != null) { + html += '
' + + '
交易
' + s.total_trades + '
' + + '
胜率
' + s.win_rate + '%
' + + '
平均收益
' + s.avg_profit_pct + '%
' + + '
夏普
' + s.sharpe_ratio + '
' + + '
盈亏比
' + (s.profit_factor || '—') + '
' + + '
回撤
' + s.max_drawdown_pct + '%
'; + } + // 归因洞察 + if (data.insights && data.insights.length) { + html += '
🔬 因子归因洞察（下一版迭代依据）
' + + '
' + data.insights.map(i => '
' + i + '
').join('') + '
'; + } + // 交易明细 + const trades = (data.trades || []).slice(0, 50); + if (trades.length) { + html += '
交易明细（前50笔）
' + + '
' + + '' + + '' + + ''; + const reasonMap = { target: '🎯止盈', stop: '🛑止损', keep: '⏳到期' }; + for (const t of trades) { + const pc = (t.profit_pct || 0) >= 0 ? 'text-green-400' : 'text-red-400'; + html += '' + + '' + + '' + + '' + + '' + + '' + + '' + + '' + + ''; + } + html += '
代码名称入场买入卖出收益结果持仓评分
' + t.code + '' + (t.name || '') + '' + (t.entry_date || '') + '' + (t.entry_price || 0).toFixed(2) + '' + (t.exit_price || 0).toFixed(2) + '' + (t.profit_pct || 0).toFixed(1) + '%' + (reasonMap[t.exit_reason] || t.exit_reason) + '' + (t.hold_days || 0) + 'd' + (t.score || 0) + '
'; + } + html += '
'; + el.innerHTML = html; +} + + \ No newline at end of file diff --git a/strategy_lab.py b/strategy_lab.py new file mode 100644 index 00000000..f45b72eb --- /dev/null +++ b/strategy_lab.py @@ -0,0 +1,628 @@ +#!/usr/bin/env python3 +"""MoFin 策略实验室 v2 — 多版本策略回测 + 12维上下文 + 因子归因 + 维度: 个股技术(水平+趋势变化) / 大盘状态 / 行业强度 + 每个策略版本 = 命名配置 + 元数据(名称/假设/父版本)""" +import sqlite3, json, math, os +from datetime import datetime, timedelta + +DB_PATH = "/home/hmo/MoFin/data/mofin.db" + +import sys +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) +from backtest_framework import prepare_bars, compute_single_score, compute_kelly + +# ══════════════════════════════════════════════════════ +# 策略版本注册表 +# ══════════════════════════════════════════════════════ +STRATEGIES = { + "v1.0": { + "version": "v1.0", + "name": "多因子基线", + "summary": "五因子评分≥45 + 动量≥8，10%止盈 / 2×ATR止损，半Kelly", + "hypothesis": "基线版本：验证多因子评分体系的基础有效性", + "parent": None, + "created": "2026-07-28", + "config": { + "entry": {"min_score": 45, "min_momentum": 8, "filters": {}}, + "exit": {"tp_pct": 0.10, "sl_atr": 2.0, "max_hold_days": 20}, + "sizing": {"kelly": True, "kelly_fraction": 0.5}, + "eval_step": 5, + }, + }, + "v2.0": { + "version": "v2.0", + "name": "趋势动能过滤", + "summary": "v1 + MACD柱>0 + ROC>2 + ADX≥20 + ATR%≥2.8 过滤弱势入场", + "hypothesis": "v1归因：MACD>0.69胜率54%vs33%，ROC>11胜率57%vs35%，ADX>43胜率50%，ATR%>4.3胜率49%vs31%。过滤无趋势/无动能/死鱼股", + "parent": "v1.0", + "created": "2026-07-28", + "config": { + "entry": {"min_score": 45, "min_momentum": 8, + "filters": {"adx_min": 20, "atr_pct_min": 2.8, "roc_min": 2, "macd_hist_min": 0}}, + "exit": {"tp_pct": 0.10, "sl_atr": 2.0, "max_hold_days": 20}, + "sizing": {"kelly": True, "kelly_fraction": 0.5}, + "eval_step": 5, + }, + }, + "v3.0": { + "version": "v3.0", + "name": "强动量+优盈亏比", + "summary": "v2 + ROC≥8 + 距MA20≥4% + 量比1.0~1.8；止盈15%/止损1.5×ATR（RR→2.2:1）", + "hypothesis": "v2归因：ROC>17.5胜率58.5%，距MA20>12.9胜率56.5%，量比1.12~1.45胜率55.1%。且v2平均亏损-9.14%≈止盈10%，RR仅1.1:1是盈亏比恶化主因→收紧止损放大止盈", + "parent": "v2.0", + "created": "2026-07-28", + "config": { + "entry": {"min_score": 45, "min_momentum": 8, + "filters": {"adx_min": 20, "atr_pct_min": 2.8, "roc_min": 8, + "macd_hist_min": 0, "dist_ma20_min": 4, + "vol_ratio_min": 1.0, "vol_ratio_max": 1.8}}, + "exit": {"tp_pct": 0.15, "sl_atr": 1.5, "max_hold_days": 20}, + "sizing": {"kelly": True, "kelly_fraction": 0.5}, + "eval_step": 5, + }, + }, + "v4.0": { + "version": "v4.0", + "name": "大盘回调+趋势结构", + "summary": "v3 + 大盘须在MA20上且MA20斜率<-0.05(上升中回调) + 个股更高高点结构 + ROC 10~25 + MACD柱<1.3(避追高)", + "hypothesis": "v3归因：大盘MA20斜率-1.76~-0.56时胜率58.8%vs平坡20.6%(差38pp最强信号)；大盘在MA20上胜率42%vs34%；hh结构+15pp；ROC甜区12.9~16.2胜率61%；MACD柱>1.33胜率仅28%(追高必死)；个股MA20斜率<1.5胜率56%vs≥1.5约35%(强势回调买)", + "parent": "v3.0", + "created": "2026-07-28", + "config": { + "entry": {"min_score": 45, "min_momentum": 8, + "filters": {"adx_min": 20, "atr_pct_min": 3.5, "atr_pct_max": 5.5, + "roc_min": 10, "roc_max": 25, + "macd_hist_min": 0.25, "macd_hist_max": 1.3, + "dist_ma20_min": 4, + "vol_ratio_min": 1.2, "vol_ratio_max": 1.5, + "ma20_slope_max": 1.5, + "mkt_above_ma20": True, "mkt_slope_max": -0.05, + "hh_only": True}}, + "exit": {"tp_pct": 0.15, "sl_atr": 1.5, "max_hold_days": 20}, + "sizing": {"kelly": True, "kelly_fraction": 0.5}, + "eval_step": 5, + }, + }, + "v4.1": { + "version": "v4.1", + "name": "大盘回调·宽松带", + "summary": "v4.0放宽：ROC 8~25 + MACD柱0~1.5 + ATR 2.8~6.0 + 量比1.0~1.8；保留大盘MA20上+斜率<0 + hh结构", + "hypothesis": "v4.0仅5笔交易=过滤器叠加过拟合(分桶样本仅36笔/桶)。保留归因最强的市场状态+趋势结构信号，放宽窄幅过滤器换取统计样本量", + "parent": "v4.0", + "created": "2026-07-28", + "config": { + "entry": {"min_score": 45, "min_momentum": 8, + "filters": {"adx_min": 20, "atr_pct_min": 2.8, "atr_pct_max": 6.0, + "roc_min": 8, "roc_max": 25, + "macd_hist_min": 0, "macd_hist_max": 1.5, + "dist_ma20_min": 4, + "vol_ratio_min": 1.0, "vol_ratio_max": 1.8, + "ma20_slope_max": 1.5, + "mkt_above_ma20": True, "mkt_slope_max": 0, + "hh_only": True}}, + "exit": {"tp_pct": 0.15, "sl_atr": 1.5, "max_hold_days": 20}, + "sizing": {"kelly": True, "kelly_fraction": 0.5}, + "eval_step": 5, + }, + }, +} + + +def get_strategy(version): + if version not in STRATEGIES: + raise ValueError(f"未知策略版本: {version}，可用: {list(STRATEGIES.keys())}") + return STRATEGIES[version] + + +# ══════════════════════════════════════════════════════ +# 大盘 / 行业上下文 +# ══════════════════════════════════════════════════════ +_MKT_CTX = {} +_SECTOR_CTX = {} +_STOCK_SECTOR = {} + +def prepare_market_context(start_date, end_date): + """大盘指数(sh000001)每日状态: 是否在MA20上、MA20斜率、ROC""" + global _MKT_CTX + _MKT_CTX = {} + bars = prepare_bars('sh000001', start_date, end_date) + if not bars: + return + for i, b in enumerate(bars): + slope = None + if i >= 5: + m0, m1 = bars[i-5].get('ma20'), b.get('ma20') + if m0 and m1: + slope = round((m1 - m0) / m0 * 100, 3) + ma20 = b.get('ma20') or 0 + _MKT_CTX[b['date']] = { + 'above_ma20': (b.get('close') or 0) > ma20 if ma20 > 0 else None, + 'ma20_slope': slope, + 'roc': b.get('roc'), + } + +def prepare_sector_context(start_date, end_date): + """每日行业强度: 平均涨幅/净流入/当日排名 + 个股→行业映射""" + global _SECTOR_CTX, _STOCK_SECTOR + _SECTOR_CTX, _STOCK_SECTOR = {}, {} + conn = sqlite3.connect(DB_PATH) + try: + rows = conn.execute(""" + SELECT substr(m.timestamp,1,10) as d, s.name, + AVG(s.change_pct), SUM(s.net_inflow) + FROM sector_snapshots s JOIN market_snapshots m ON s.snapshot_id = m.id + WHERE m.timestamp >= ? AND m.timestamp <= ? + GROUP BY d, s.name + """, (start_date, end_date + ' 23:59')).fetchall() + # 优先 THS 源命名（与 sector_snapshots 同体系），证监会分类作兜底 + _STOCK_SECTOR = {} + for code, sec, src in conn.execute( + "SELECT code, sector_name, source FROM stock_sectors").fetchall(): + if src == 'ths' or code not in _STOCK_SECTOR: + _STOCK_SECTOR[code] = sec + finally: + conn.close() + for d, name, chg, inflow in rows: + _SECTOR_CTX.setdefault(d, {})[name] = {'change': round(chg or 0, 2), 'inflow': round(inflow or 0, 1)} + for d in _SECTOR_CTX: + ranked = sorted(_SECTOR_CTX[d].items(), key=lambda x: -(x[1]['change'])) + total = len(ranked) + for rank, (name, v) in enumerate(ranked): + v['rank_pct'] = round(rank / total, 3) if total else None # 0=最强 + +def mkt_ctx(date): + return _MKT_CTX.get(date, {}) + +def sector_ctx(code, date): + sec = _STOCK_SECTOR.get(code) + if not sec: + return {} + return _SECTOR_CTX.get(date, {}).get(sec, {}) + + +# ══════════════════════════════════════════════════════ +# 入场过滤器 +# ══════════════════════════════════════════════════════ +def pass_filters(factors, filters): + if not filters: + return True + def chk(key, vmin=None, vmax=None): + v = factors.get(key) + if vmin is not None and (v is None or v < vmin): + return False + if vmax is not None and v is not None and v > vmax: + return False + return True + if not chk('rsi', filters.get('rsi_min'), filters.get('rsi_max')): return False + if not chk('adx', filters.get('adx_min'), filters.get('adx_max')): return False + if not chk('dist_ma20', filters.get('dist_ma20_min'), filters.get('dist_ma20_max')): return False + if not chk('vol_ratio', filters.get('vol_ratio_min'), filters.get('vol_ratio_max')): return False + if not chk('roc', filters.get('roc_min'), filters.get('roc_max')): return False + if not chk('atr_pct', filters.get('atr_pct_min'), filters.get('atr_pct_max')): return False + if not chk('macd_hist', filters.get('macd_hist_min'), filters.get('macd_hist_max')): return False + # 趋势变化 + if not chk('ma20_slope', filters.get('ma20_slope_min'), filters.get('ma20_slope_max')): return False + if not chk('macd_hist_delta', filters.get('macd_hist_delta_min'), filters.get('macd_hist_delta_max')): return False + if filters.get('adx_rising') and not factors.get('adx_rising'): return False + if filters.get('trend_only') and not factors.get('trend_aligned'): return False + if filters.get('hh_only') and not factors.get('hh_structure'): return False + if filters.get('no_new_high') and factors.get('near_high_20d'): return False + # 大盘 + if filters.get('mkt_above_ma20') and factors.get('mkt_above_ma20') is not True: return False + if not chk('mkt_slope', filters.get('mkt_slope_min'), filters.get('mkt_slope_max')): return False + # 行业 + if not chk('sector_change', filters.get('sector_change_min'), filters.get('sector_change_max')): return False + if not chk('sector_rank_pct', None, filters.get('sector_rank_pct_max')): return False + return True + + +def calc_factors(bars, idx): + """个股因子: 水平值 + 趋势变化""" + b = bars[idx] + prev5 = bars[max(0, idx-5)] + close = b.get('close') or 0 + ma20 = b.get('ma20') or 0 + atr = b.get('atr') or 0 + vol = b.get('volume') or 0 + pvol = prev5.get('volume') or 0 + window = bars[max(0, idx-19):idx+1] + high20 = max((x.get('high') or 0) for x in window) if window else 0 + ma5, ma10 = b.get('ma5') or 0, b.get('ma10') or 0 + f = { + 'rsi': b.get('rsi'), + 'adx': b.get('adx'), + 'macd_hist': b.get('macd_hist'), + 'roc': b.get('roc'), + 'atr_pct': round(atr / close * 100, 2) if close > 0 and atr else None, + 'dist_ma20': round((close - ma20) / ma20 * 100, 2) if ma20 > 0 else None, + 'vol_ratio': round(vol / pvol, 2) if pvol > 0 else None, + 'obv_delta': (b.get('obv') or 0) - (prev5.get('obv') or 0), + 'trend_aligned': ma5 > ma10 > ma20 > 0, + 'near_high_20d': close >= high20 * 0.98 if high20 > 0 else False, + } + # 趋势变化因子（不能孤立看点值，要看方向和变化） + if idx >= 5: + b5 = bars[idx-5] + m0, m1 = b5.get('ma20'), b.get('ma20') + f['ma20_slope'] = round((m1 - m0) / m0 * 100, 3) if m0 and m1 else None + h0, h1 = b5.get('macd_hist'), b.get('macd_hist') + f['macd_hist_delta'] = round(h1 - h0, 3) if h0 is not None and h1 is not None else None + a0, a1 = b5.get('adx'), b.get('adx') + f['adx_rising'] = (a1 > a0) if a0 is not None and a1 is not None else None + r0, r1 = b5.get('rsi'), b.get('rsi') + f['rsi_delta'] = round(r1 - r0, 2) if r0 is not None and r1 is not None else None + if idx >= 10: + h5 = max(x.get('high') or 0 for x in bars[idx-4:idx+1]) + h10 = max(x.get('high') or 0 for x in bars[idx-9:idx-4]) + l5 = min(x.get('low') or 1e9 for x in bars[idx-4:idx+1]) + l10 = min(x.get('low') or 1e9 for x in bars[idx-9:idx-4]) + f['hh_structure'] = h5 > h10 # 更高的高点 = 上升结构 + f['hl_structure'] = l5 > l10 # 更高的低点 = 上升结构 + return f + + +# ══════════════════════════════════════════════════════ +# 回测引擎（配置驱动 + 12维上下文记录） +# ══════════════════════════════════════════════════════ +def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=True): + strat = get_strategy(strategy_version) + cfg = strat['config'] + entry_cfg, exit_cfg = cfg['entry'], cfg['exit'] + filters = entry_cfg.get('filters', {}) + step = cfg.get('eval_step', 5) + + prepare_market_context(start_date, end_date) + prepare_sector_context(start_date, end_date) + + conn = sqlite3.connect(DB_PATH) + stocks = conn.execute(""" + SELECT DISTINCT sd.code, COALESCE(s.name, sd.code) as name + FROM stock_daily sd LEFT JOIN stocks s ON sd.code = s.code + WHERE sd.date>=? AND sd.date<=? + """, (start_date, end_date)).fetchall() + conn.close() + + trades = [] + screened = scored_n = 0 + + for code, name in stocks: + screened += 1 + bars = prepare_bars(code, start_date, end_date) + if not bars or len(bars) < 25: + continue + + i = 20 + while i < len(bars): + window = bars[:i+1] + sc = compute_single_score(window) + if sc is None: + i += step + continue + total_score, comp = sc + scored_n += 1 + + last = bars[i] + close = last.get('close') or 0 + + if total_score >= entry_cfg['min_score'] and comp['momentum'] >= entry_cfg['min_momentum']: + factors = calc_factors(bars, i) + # 附加大盘/行业上下文 + date = last.get('date') + mk = mkt_ctx(date) + sc_ctx = sector_ctx(code, date) + factors['mkt_above_ma20'] = mk.get('above_ma20') + factors['mkt_slope'] = mk.get('ma20_slope') + factors['mkt_roc'] = mk.get('roc') + factors['sector_change'] = sc_ctx.get('change') + factors['sector_rank_pct'] = sc_ctx.get('rank_pct') + factors['sector_inflow'] = sc_ctx.get('inflow') + + if pass_filters(factors, filters): + atr_val = last.get('atr') or 0 + if exit_cfg.get('tp_pct'): + target = close * (1 + exit_cfg['tp_pct']) + elif exit_cfg.get('tp_atr') and atr_val > 0: + target = close + atr_val * exit_cfg['tp_atr'] + else: + target = close * 1.10 + if exit_cfg.get('sl_atr') and atr_val > 0: + stop = close - atr_val * exit_cfg['sl_atr'] + elif exit_cfg.get('sl_pct'): + stop = close * (1 - exit_cfg['sl_pct']) + else: + stop = close * 0.93 + + kelly = 0 + if cfg['sizing'].get('kelly'): + kelly = compute_kelly(total_score, (target-close)/close if close>0 else 0.1, + (close-stop)/close if close>0 else 0.07) + + max_hold = exit_cfg.get('max_hold_days', 20) + future = bars[i+1:i+1+max_hold] + exit_price = exit_reason = None + hold_days = 0 + for k, fb in enumerate(future): + fh, fl, fc = fb.get('high') or 0, fb.get('low') or 0, fb.get('close') or 0 + if fh >= target: + exit_price, exit_reason, hold_days = target, 'target', k+1 + break + elif fl <= stop: + exit_price, exit_reason, hold_days = fc, 'stop', k+1 + break + if exit_price is None: + exit_price = future[-1].get('close') if future else close + exit_reason, hold_days = 'keep', len(future) + + pnl = (exit_price - close) / close * 100 if close > 0 else 0 + trades.append({ + 'code': code, 'name': name, + 'entry_date': date, + 'entry_price': round(close, 2), + 'exit_price': round(exit_price, 2), + 'profit_pct': round(pnl, 2), + 'exit_reason': exit_reason, + 'hold_days': hold_days, + 'score': total_score, + 'score_comp': comp, + 'kelly': round(kelly, 3), + 'stop_loss': round(stop, 2), + 'target': round(target, 2), + 'factors': {k: (round(v, 3) if isinstance(v, float) else v) + for k, v in factors.items()}, + }) + i += step + + summary = calc_summary(trades, capital) + result = { + 'strategy': strat['version'], + 'strategy_name': strat['name'], + 'period': f"{start_date} ~ {end_date}", + 'capital': capital, + 'total_stocks_screened': screened, + 'scored_events': scored_n, + 'trades': trades, + 'summary': summary, + } + if save: + save_result(strat, result) + return result + + +def calc_summary(trades, capital): + if not trades: + return {} + profits = [t['profit_pct'] for t in trades] + wins = [t for t in trades if t['profit_pct'] > 0] + losses = [t for t in trades if t['profit_pct'] <= 0] + win_rate = len(wins) / len(trades) * 100 + avg_p = sum(profits) / len(profits) + avg_w = sum(t['profit_pct'] for t in wins) / len(wins) if wins else 0 + avg_l = sum(t['profit_pct'] for t in losses) / len(losses) if losses else 0 + mean_r = avg_p / 100 + std_r = math.sqrt(sum((p/100 - mean_r)**2 for p in profits) / (len(profits)-1)) if len(profits) > 1 else 0 + sharpe = mean_r / std_r * math.sqrt(252) if std_r > 0 else 0 + curve = [capital] + for t in trades: + curve.append(curve[-1] * (1 + t['profit_pct']/100)) + peak = capital + max_dd = 0 + for c in curve: + peak = max(peak, c) + max_dd = max(max_dd, (peak - c) / peak * 100) + return { + 'total_trades': len(trades), + 'win_rate': round(win_rate, 1), + 'avg_profit_pct': round(avg_p, 2), + 'avg_win_pct': round(avg_w, 2), + 'avg_loss_pct': round(avg_l, 2), + 'sharpe_ratio': round(sharpe, 2), + 'max_drawdown_pct': round(max_dd, 2), + 'profit_factor': round(abs(avg_w/avg_l), 2) if avg_l != 0 else None, + 'wins': len(wins), 'losses': len(losses), + 'capital_end': round(curve[-1], 2), + } + + +# ══════════════════════════════════════════════════════ +# 因子归因分析（连续分桶 + 布尔分组） +# ══════════════════════════════════════════════════════ +ANALYZE_FACTORS = ['rsi', 'adx', 'macd_hist', 'roc', 'atr_pct', 'dist_ma20', 'vol_ratio', + 'ma20_slope', 'macd_hist_delta', 'rsi_delta', 'mkt_slope', 'mkt_roc', + 'sector_change', 'sector_rank_pct', 'score'] +BOOL_FACTORS = ['trend_aligned', 'hh_structure', 'hl_structure', 'adx_rising', + 'mkt_above_ma20', 'near_high_20d'] + +def analyze_trades(strategy_version): + conn = sqlite3.connect(DB_PATH) + row = conn.execute( + "SELECT results_json FROM strategy_research WHERE version=? ORDER BY id DESC LIMIT 1", + (strategy_version,)).fetchone() + conn.close() + if not row: + return {'error': f'无 {strategy_version} 的回测结果，请先运行回测'} + result = json.loads(row[0]) + return analyze_trade_list(result.get('trades', []), strategy_version) + + +def analyze_trade_list(trades, label=''): + if not trades: + return {'error': '无交易数据'} + wins = [t for t in trades if t['profit_pct'] > 0] + report = { + 'label': label, + 'total': len(trades), 'wins': len(wins), 'losses': len(trades) - len(wins), + 'factors': {}, 'bool_factors': {}, 'exit_reasons': {}, 'hold_analysis': {}, 'insights': [], + } + + # 连续因子: 五分桶胜率 + for f in ANALYZE_FACTORS: + pairs = [(t['factors'].get(f), t['profit_pct'] > 0) + for t in trades if t.get('factors', {}).get(f) is not None] + if len(pairs) < 30: + continue + vals = sorted(pairs, key=lambda x: x[0]) + w_vals = [v for v, w in pairs if w] + l_vals = [v for v, w in pairs if not w] + buckets = [] + n = len(vals) + for bi in range(5): + seg = vals[int(n*bi/5):int(n*(bi+1)/5)] + if seg: + wr = sum(1 for _, w in seg if w) / len(seg) * 100 + buckets.append({'range': f"{seg[0][0]:.2f}~{seg[-1][0]:.2f}", + 'win_rate': round(wr, 1), 'count': len(seg)}) + report['factors'][f] = { + 'winner_mean': round(sum(w_vals)/len(w_vals), 3) if w_vals else None, + 'loser_mean': round(sum(l_vals)/len(l_vals), 3) if l_vals else None, + 'buckets': buckets, + } + + # 布尔因子: True/False 分组胜率 + for f in BOOL_FACTORS: + pairs = [(t['factors'].get(f), t['profit_pct'] > 0) + for t in trades if t.get('factors', {}).get(f) is not None] + if len(pairs) < 30: + continue + t_grp = [w for v, w in pairs if v] + f_grp = [w for v, w in pairs if not v] + if t_grp and f_grp: + report['bool_factors'][f] = { + 'true_win_rate': round(sum(t_grp)/len(t_grp)*100, 1), 'true_count': len(t_grp), + 'false_win_rate': round(sum(f_grp)/len(f_grp)*100, 1), 'false_count': len(f_grp), + } + + # 出场方式 + for t in trades: + r = t['exit_reason'] + report['exit_reasons'].setdefault(r, {'count': 0, 'total_pnl': 0, 'avg_hold': 0}) + d = report['exit_reasons'][r] + d['count'] += 1 + d['total_pnl'] += t['profit_pct'] + d['avg_hold'] += t['hold_days'] + for r, d in report['exit_reasons'].items(): + d['avg_pnl'] = round(d['total_pnl'] / d['count'], 2) + d['avg_hold'] = round(d['avg_hold'] / d['count'], 1) + d['total_pnl'] = round(d['total_pnl'], 1) + + # 持仓天数 + hold_buckets = {} + for t in trades: + hb = '1-3天' if t['hold_days'] <= 3 else ('4-7天' if t['hold_days'] <= 7 else ('8-14天' if t['hold_days'] <= 14 else '15天+')) + hold_buckets.setdefault(hb, {'count': 0, 'wins': 0}) + hold_buckets[hb]['count'] += 1 + if t['profit_pct'] > 0: + hold_buckets[hb]['wins'] += 1 + for hb, d in hold_buckets.items(): + d['win_rate'] = round(d['wins'] / d['count'] * 100, 1) + report['hold_analysis'] = hold_buckets + + # 自动洞察 + ins = [] + for f, d in report['factors'].items(): + if len(d['buckets']) >= 4: + wrs = [b['win_rate'] for b in d['buckets']] + spread = max(wrs) - min(wrs) + if spread >= 12: + best = d['buckets'][wrs.index(max(wrs))] + worst = d['buckets'][wrs.index(min(wrs))] + ins.append(f"📌 {f} 区分度{spread:.0f}pp: [{best['range']}]胜率{best['win_rate']}% vs [{worst['range']}]胜率{worst['win_rate']}%") + for f, d in report['bool_factors'].items(): + diff = d['true_win_rate'] - d['false_win_rate'] + if abs(diff) >= 8: + arrow = '✅' if diff > 0 else '❌' + ins.append(f"{arrow} {f}=True 胜率{d['true_win_rate']}% vs False {d['false_win_rate']}% (差{abs(diff):.0f}pp)") + er = report['exit_reasons'] + if 'stop' in er and er['stop']['count'] > er.get('target', {}).get('count', 0) * 2: + ins.append(f"⚠️ 止损({er['stop']['count']})远多于止盈({er.get('target',{}).get('count',0)}): 入场追高或止损过紧") + report['insights'] = ins + return report + + +# ══════════════════════════════════════════════════════ +# 持久化 +# ══════════════════════════════════════════════════════ +def init_table(): + conn = sqlite3.connect(DB_PATH) + conn.execute(""" + CREATE TABLE IF NOT EXISTS strategy_research ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + version TEXT, name TEXT, summary TEXT, hypothesis TEXT, parent TEXT, + config_json TEXT, results_json TEXT, analysis_json TEXT, + period TEXT, created_at TEXT + ) + """) + conn.commit() + conn.close() + + +def save_result(strat, result): + init_table() + conn = sqlite3.connect(DB_PATH) + conn.execute(""" + INSERT INTO strategy_research (version, name, summary, hypothesis, parent, + config_json, results_json, period, created_at) + VALUES (?,?,?,?,?,?,?,?,?) + """, (strat['version'], strat['name'], strat['summary'], strat['hypothesis'], + strat.get('parent'), json.dumps(strat['config'], ensure_ascii=False), + json.dumps(result, ensure_ascii=False), result['period'], + datetime.now().strftime('%Y-%m-%d %H:%M:%S'))) + conn.commit() + conn.close() + + +def save_analysis(version, analysis): + init_table() + conn = sqlite3.connect(DB_PATH) + conn.execute(""" + UPDATE strategy_research SET analysis_json=? + WHERE id = (SELECT id FROM strategy_research WHERE version=? ORDER BY id DESC LIMIT 1) + """, (json.dumps(analysis, ensure_ascii=False), version)) + conn.commit() + conn.close() + + +def list_strategies(): + init_table() + conn = sqlite3.connect(DB_PATH) + conn.row_factory = sqlite3.Row + rows = conn.execute(""" + SELECT sr.* FROM strategy_research sr + INNER JOIN (SELECT version, MAX(id) as max_id FROM strategy_research GROUP BY version) latest + ON sr.id = latest.max_id + ORDER BY sr.version + """).fetchall() + conn.close() + out = [] + for r in rows: + d = dict(r) + res = json.loads(d['results_json']) if d.get('results_json') else {} + ana = json.loads(d['analysis_json']) if d.get('analysis_json') else None + d['summary_stats'] = res.get('summary', {}) + d['insights'] = (ana or {}).get('insights', []) + d['trades_count'] = len(res.get('trades', [])) + del d['results_json'] + del d['analysis_json'] + out.append(d) + existing = {d['version'] for d in out} + for v, s in STRATEGIES.items(): + if v not in existing: + out.append({ + 'version': v, 'name': s['name'], 'summary': s['summary'], + 'hypothesis': s['hypothesis'], 'parent': s.get('parent'), + 'config_json': json.dumps(s['config'], ensure_ascii=False), + 'summary_stats': {}, 'insights': [], 'created_at': s.get('created'), + }) + out.sort(key=lambda x: x['version']) + return out + + +if __name__ == '__main__': + import sys + ver = sys.argv[1] if len(sys.argv) > 1 else 'v3.0' + end = '2026-07-24' + start = '2026-01-21' + r = run_backtest(ver, start, end) + print(json.dumps(r['summary'], indent=2, ensure_ascii=False)) + a = analyze_trade_list(r['trades'], ver) + save_analysis(ver, a) + print(json.dumps(a.get('insights', []), indent=2, ensure_ascii=False))