feat: 港股策略研究体系——253只港股通宇宙回填+v7.1港股基线(61.5%胜率26笔)+h1.0过拟合教训+h1.1三强信号版+引擎/ API/UI分市场开关(全部/A股/港股)
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@@ -549,13 +549,14 @@ def api_research_backtest():
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from datetime import datetime, timedelta
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version = request.args.get('strategy', 'v4.1')
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period = request.args.get('period', '6m')
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market = request.args.get('market', 'all') # all | a | hk
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capital = float(request.args.get('capital', 1000000))
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end_date = '2026-07-24' # 数据完整截止日
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days = {'1m': 30, '6m': 185, '1y': 365, '2y': 730}.get(period, 185)
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start_date = (datetime.strptime(end_date, '%Y-%m-%d') - timedelta(days=days)).strftime('%Y-%m-%d')
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try:
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from strategy_lab import run_backtest, analyze_trade_list, save_analysis
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result = run_backtest(version, start_date, end_date, capital)
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result = run_backtest(version, start_date, end_date, capital, universe=market)
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analysis = analyze_trade_list(result.get('trades', []), version)
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save_analysis(version, analysis)
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result['insights'] = analysis.get('insights', [])
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+9
-3
@@ -1934,6 +1934,8 @@ function renderResearch() {
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'<select id="btPeriod" class="bg-slate-800 text-sm rounded-lg px-2 py-1 border border-slate-700">' +
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'<option value="1m">近 1 个月</option><option value="6m">近 6 个月</option>' +
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'<option value="1y">近 1 年</option><option value="2y" selected>近 2 年</option></select>' +
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'<select id="btMarket" class="bg-slate-800 text-sm rounded-lg px-2 py-1 border border-slate-700" onchange="loadStrategyList()">' +
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'<option value="all">全部市场</option><option value="a">A股</option><option value="hk">港股</option></select>' +
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'<span class="text-xs text-slate-500">每版策略基于上一版归因分析迭代 · 点击行展开明细</span></div>' +
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'<div id="strategyList"><div class="text-slate-500 text-sm">加载中...</div></div>' +
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'<div id="strategyDetail" class="mt-4"></div>';
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@@ -1946,7 +1948,10 @@ async function loadStrategyList() {
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const resp = await fetch('/api/research/strategies');
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const data = await resp.json();
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if (data.error) { listEl.innerHTML = '<div class="text-red-400">' + data.error + '</div>'; return; }
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renderStrategyTable(data.strategies || []);
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const mkt = document.getElementById('btMarket')?.value || 'all';
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let strats = data.strategies || [];
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if (mkt !== 'all') strats = strats.filter(s => (s.market || 'all') === mkt || (s.market || 'all') === 'all');
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renderStrategyTable(strats);
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} catch(e) {
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listEl.innerHTML = '<div class="text-red-400">加载失败: ' + e.message + '</div>';
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}
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@@ -2035,7 +2040,7 @@ function renderStrategyTable(strategies) {
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const retCls = pf.total_return_pct == null ? '' : (pf.total_return_pct >= 0 ? 'text-green-400' : 'text-red-400');
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const rowCls = 'border-b border-slate-800/50 hover:bg-slate-800/30 cursor-pointer' + (isCurrent ? ' bg-emerald-900/20 border-l-2 border-l-emerald-400' : '');
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html += '<tr class="' + rowCls + '" onclick="showStrategyDetail(\'' + s.version + '\')">' +
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'<td class="px-2 py-1.5 font-mono font-bold ' + (isCurrent ? 'text-emerald-400' : 'text-blue-400') + '">' + s.version + (isCurrent ? ' <span class="text-[10px] bg-emerald-500/20 text-emerald-300 px-1 rounded">当前</span>' : '') + '</td>' +
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'<td class="px-2 py-1.5 font-mono font-bold ' + (isCurrent ? 'text-emerald-400' : 'text-blue-400') + '">' + s.version + (isCurrent ? ' <span class="text-[10px] bg-emerald-500/20 text-emerald-300 px-1 rounded">当前</span>' : '') + ((s.market && s.market !== 'all') ? ' <span class="text-[10px] bg-cyan-500/20 text-cyan-300 px-1 rounded">' + (s.market === 'hk' ? '港' : 'A') + '</span>' : '') + '</td>' +
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'<td class="px-2 py-1.5" title="' + (s.hypothesis || s.summary || '').replace(/"/g, '"') + '">' + (s.name || '') + (smallSample ? ' <span class="text-amber-500" title="样本<40笔">⚠️</span>' : '') + '</td>' +
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cell('composite', s._composite, v => v, 'font-bold text-amber-300') +
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cell('universality_score', (st.universality || {}).score, v => v + '<span class="text-slate-500">/' + (st.universality || {}).months + '月</span>') +
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@@ -2063,7 +2068,8 @@ async function runStrategyBacktest(version) {
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const detail = document.getElementById('strategyDetail');
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detail.innerHTML = '<div class="text-blue-400 animate-pulse text-sm">⏳ 正在回测 ' + version + '（约15-30秒）...</div>';
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try {
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const resp = await fetch('/api/research/backtest?strategy=' + version + '&period=' + period);
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const mkt = document.getElementById('btMarket')?.value || 'all';
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const resp = await fetch('/api/research/backtest?strategy=' + version + '&period=' + period + '&market=' + mkt);
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const data = await resp.json();
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if (data.error) { detail.innerHTML = '<div class="text-red-400">' + data.error + '</div>'; return; }
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await loadStrategyList();
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+49
-9
@@ -258,6 +258,30 @@ STRATEGIES.update({
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"v9归因反用：v7.1交易中weekly_up=False胜率78.4% vs True 60.9%——周线级回调中的日线动量回归正是本策略的核心边缘",
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entry_overrides={"vol_ratio_min": 0.9, "vol_ratio_max": 2.0, "sector_slope_max": 1.0,
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"hl_only": True, "rsi_delta_min": 6, "weekly_down_only": True}),
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# ── 港股专用版本（港股通宇宙归因推导，2026-07-29）──
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"h1.0": {
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"version": "h1.0",
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"name": "港股v1-高波动甜区",
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"summary": "v7.1港股化：ATR≥2.8无上限+量比≥1.2无上限+MACD柱0~0.3+周线必须向上+恒指回调情景",
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"hypothesis": "港股8619笔归因：ATR4.88~28胜率59%持续上涨(无过热惩罚)，量比>1.86胜率57%(天量=强势)，MACD柱0~0.03最佳，weekly_up=True+9pp(与A股相反须周线完好)，恒指斜率回调买与A股同构",
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"parent": "v7.1",
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"created": "2026-07-29",
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"config": {
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"entry": {"min_score": 45, "min_momentum": 8,
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"filters": {"adx_min": 20, "atr_pct_min": 2.8,
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"roc_min": 8,
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"macd_hist_min": 0, "macd_hist_max": 0.3,
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"dist_ma20_min": 4,
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"vol_ratio_min": 1.2,
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"ma20_slope_max": 1.5,
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"mkt_above_ma20": True, "mkt_slope_max": -0.05,
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"hh_only": True, "hl_only": True,
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"rsi_delta_min": 6, "weekly_up": True}},
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"exit": {"tp_pct": 0.15, "sl_atr": 1.5, "max_hold_days": 20},
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"sizing": {"kelly": True, "kelly_fraction": 0.5},
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"eval_step": 1,
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},
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},
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})
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@@ -575,7 +599,7 @@ def calc_factors(bars, idx):
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# ══════════════════════════════════════════════════════
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# 回测引擎（配置驱动 + 12维上下文记录）
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# ══════════════════════════════════════════════════════
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def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=True):
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def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=True, universe='all'):
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strat = get_strategy(strategy_version)
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cfg = strat['config']
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entry_cfg, exit_cfg = cfg['entry'], cfg['exit']
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@@ -597,6 +621,12 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T
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""", (start_date, end_date)).fetchall()
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conn.close()
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# 市场过滤：hk=仅港股, a=仅A股, all=全部
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if universe == 'hk':
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stocks = [(c, n) for c, n in stocks if is_hk_code(c)]
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elif universe == 'a':
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stocks = [(c, n) for c, n in stocks if not is_hk_code(c)]
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trades = []
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screened = scored_n = 0
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@@ -832,6 +862,7 @@ def run_backtest(strategy_version, start_date, end_date, capital=1000000, save=T
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result = {
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'strategy': strat['version'],
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'strategy_name': strat['name'],
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'market': universe,
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'period': f"{start_date} ~ {end_date}",
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'capital': capital,
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'total_stocks_screened': screened,
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@@ -1095,9 +1126,14 @@ def init_table():
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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version TEXT, name TEXT, summary TEXT, hypothesis TEXT, parent TEXT,
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config_json TEXT, results_json TEXT, analysis_json TEXT,
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period TEXT, created_at TEXT
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period TEXT, created_at TEXT, market TEXT DEFAULT 'all'
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)
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""")
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# 兼容老表加 market 列
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try:
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conn.execute("ALTER TABLE strategy_research ADD COLUMN market TEXT DEFAULT 'all'")
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except sqlite3.OperationalError:
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pass
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conn.commit()
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conn.close()
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@@ -1107,12 +1143,12 @@ def save_result(strat, result):
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conn = sqlite3.connect(DB_PATH)
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conn.execute("""
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INSERT INTO strategy_research (version, name, summary, hypothesis, parent,
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config_json, results_json, period, created_at)
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VALUES (?,?,?,?,?,?,?,?,?)
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config_json, results_json, period, created_at, market)
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VALUES (?,?,?,?,?,?,?,?,?,?)
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""", (strat['version'], strat['name'], strat['summary'], strat['hypothesis'],
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strat.get('parent'), json.dumps(strat['config'], ensure_ascii=False),
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json.dumps(result, ensure_ascii=False), result['period'],
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datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
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datetime.now().strftime('%Y-%m-%d %H:%M:%S'), result.get('market', 'all')))
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conn.commit()
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conn.close()
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@@ -1132,9 +1168,11 @@ def list_strategies():
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init_table()
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conn = sqlite3.connect(DB_PATH)
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conn.row_factory = sqlite3.Row
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# 每个 (version, market) 组合取最新一条
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rows = conn.execute("""
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SELECT sr.* FROM strategy_research sr
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INNER JOIN (SELECT version, MAX(id) as max_id FROM strategy_research GROUP BY version) latest
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INNER JOIN (SELECT version, COALESCE(market,'all') as mkt, MAX(id) as max_id
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FROM strategy_research GROUP BY version, COALESCE(market,'all')) latest
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ON sr.id = latest.max_id
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ORDER BY sr.version
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""").fetchall()
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@@ -1147,19 +1185,21 @@ def list_strategies():
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d['summary_stats'] = res.get('summary', {})
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d['insights'] = (ana or {}).get('insights', [])
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d['trades_count'] = len(res.get('trades', []))
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d['market'] = d.get('market') or res.get('market') or 'all'
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del d['results_json']
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del d['analysis_json']
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out.append(d)
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existing = {d['version'] for d in out}
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existing = {(d['version'], d['market']) for d in out}
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for v, s in STRATEGIES.items():
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if v not in existing:
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if (v, 'all') not in existing and not any(d['version'] == v for d in out):
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out.append({
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'version': v, 'name': s['name'], 'summary': s['summary'],
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'hypothesis': s['hypothesis'], 'parent': s.get('parent'),
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'config_json': json.dumps(s['config'], ensure_ascii=False),
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'summary_stats': {}, 'insights': [], 'created_at': s.get('created'),
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'market': 'all',
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})
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out.sort(key=lambda x: x['version'])
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out.sort(key=lambda x: (x['version'], x.get('market', 'all')))
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return out
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