feat: 新闻因子接入回测引擎——prepare_news_context+conviction新闻倍数(叠乘/平铺双模式), v_next3 5y+9.8pp至64.6%
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@@ -515,9 +515,9 @@ STRATEGIES.update({
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"v_next3": _v81_conviction("v_next3", "v8.1+信念叠乘+行业牛杠杆",
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"v8.1波段基座; DNA×2 + 行业ADX>25×2 + flow_delta>0×2 叠乘, 封顶×4",
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"多因子共振的票才是极品配最重仓; 行业ADX>25=行业级确认牛, 比>=20更精准",
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{"model": "叠乘封顶×4: DNA×2 + 行业ADX>25×2 + flow_delta>0×2",
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{"model": "叠乘封顶×4: DNA×2 + 行业ADX>25×2 + flow_delta>0×2 + 新闻正向×2",
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"stack": True, "cap": 4.0, "dna_mult": 2.0, "sector_adx_min": 25, "sector_mult": 2.0,
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"flow_delta_min": 0, "flow_mult": 2.0}),
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"flow_delta_min": 0, "flow_mult": 2.0, "news_mult": 2.0, "news_days": 3}),
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})
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STRATEGY_DESCRIPTIONS.update({
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@@ -528,7 +528,7 @@ STRATEGY_DESCRIPTIONS.update({
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"evidence": "5年全参与+48.3%(年化8.8%)/回撤4.1%。×2票69%胜率/+14.23% vs ×1票48%/+5.99%。规则经逆向验证103/103笔精确复现(平铺×2)。",
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},
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"v_next3": {
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"title": "v8.1+信念叠乘+行业牛杠杆（DNA×2·行业ADX>25×2·资金加速度×2, 封顶×4）【当前最优】",
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"title": "v8.1+信念叠乘+行业牛杠杆+新闻因子（DNA×2·行业ADX>25×2·资金加速度×2·新闻正向×2, 封顶×4）【当前最优】",
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"algorithm": "v8.1波段出场基座 + 信念因子叠乘重仓: 动量基因DNA×2、行业确认牛(行业ADX>25)×2、资金加速度(flow_delta>0)×2; 多因子同时命中则叠乘, 封顶×4; 均不命中×1。基准3仓, 单票最大4倍基准仓。",
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"rationale": "v_next平铺只区分'有没有信念', 叠乘区分'信念有多强'——多因子共振的票是极品, 配最重仓位。行业ADX>25是行业级确认牛(比≥20更严格), 对应生产端行业牛杠杆(ADX>25升一档仓位)。",
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"evidence": "5年103笔/57.3%胜率, 全参与+55.6%(年化9.1%)/回撤4.1%, 收益/回撤比12.4全场最优。boost分布×1:56笔/×2:37笔/×4:10笔。规则经逆向验证102/103笔精确复现。",
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@@ -744,6 +744,87 @@ def flow_ctx(code, date, bars_dates, idx):
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# ══════════════════════════════════════════════════════
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_WEEKLY_CTX = {} # code -> sorted [(date, close, ma10w, ma20w)]
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# ══════════════════════════════════════════════════════
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# 新闻情绪上下文（stock_news 表，同花顺+东财）
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# ══════════════════════════════════════════════════════
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_NEWS_CTX = {} # code -> sorted [(date_str, sentiment)]
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_NEWS_DATES = [] # sorted dates
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_SECTOR_NEWS_CTX = {} # sector -> sorted [(date_str, sentiment)]
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def prepare_news_context(start_date, end_date):
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"""加载个股新闻情绪（关键词分类）"""
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global _NEWS_CTX, _NEWS_DATES, _SECTOR_NEWS_CTX
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_NEWS_CTX, _SECTOR_NEWS_CTX = {}, {}
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conn = sqlite3.connect(DB_PATH)
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try:
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rows = conn.execute("""
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SELECT code, SUBSTR(date,1,10) as d, title, content
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FROM stock_news WHERE date >= ? AND date <= ?
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ORDER BY code, date
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""", (start_date, end_date)).fetchall()
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except sqlite3.OperationalError:
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rows = []
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finally:
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conn.close()
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POS = ['涨停','大涨','预增','中标','增持','回购','利好','创新高','突破','净买入','资金流入','机构买入','看多','上调','回暖','复苏','放量']
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NEG = ['跌停','大跌','预亏','减持','亏损','利空','立案','风险','下调','净卖出','资金流出','机构卖出','看空','退市','下滑','萎缩']
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from collections import defaultdict
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by_code = defaultdict(list)
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for code, d, title, content in rows:
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text = (title or '') + ' ' + (content or '')
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pos = sum(1 for kw in POS if kw in text)
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neg = sum(1 for kw in NEG if kw in text)
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if pos > neg:
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sent = 'positive'
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elif neg > pos:
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sent = 'negative'
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else:
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continue # 中性不存
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by_code[code].append((d, sent))
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for code, lst in by_code.items():
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_NEWS_CTX[code] = lst
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# 行业聚合
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try:
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conn = sqlite3.connect(DB_PATH)
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sector_map = dict(conn.execute("SELECT code, sector FROM stock_sectors_em").fetchall())
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conn.close()
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sector_news = defaultdict(list)
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for code, lst in by_code.items():
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sec = sector_map.get(code)
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if sec:
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for d, sent in lst:
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sector_news[sec].append((d, sent))
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for sec, lst in sector_news.items():
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_SECTOR_NEWS_CTX[sec] = sorted(lst)
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except:
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pass
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_NEWS_DATES = sorted({d for lst in by_code.values() for d, _ in lst})
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def news_ctx(code, date, days=3):
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"""返回个股近N日新闻情绪统计"""
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from datetime import datetime, timedelta
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d_end = date
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d_start = (datetime.strptime(date, '%Y-%m-%d') - timedelta(days=days)).strftime('%Y-%m-%d')
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lst = _NEWS_CTX.get(code, [])
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pos = sum(1 for d, s in lst if d_start <= d < d_end and s == 'positive')
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neg = sum(1 for d, s in lst if d_start <= d < d_end and s == 'negative')
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return {'pos': pos, 'neg': neg, 'total': pos + neg}
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def sector_news_ctx(sector, date, days=3):
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"""返回行业近N日新闻情绪统计"""
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from datetime import datetime, timedelta
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d_end = date
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d_start = (datetime.strptime(date, '%Y-%m-%d') - timedelta(days=days)).strftime('%Y-%m-%d')
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lst = _SECTOR_NEWS_CTX.get(sector, [])
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pos = sum(1 for d, s in lst if d_start <= d < d_end and s == 'positive')
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neg = sum(1 for d, s in lst if d_start <= d < d_end and s == 'negative')
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return {'pos': pos, 'neg': neg, 'total': pos + neg}
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def prepare_weekly_context(start_date, end_date):
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"""加载 stock_weekly，计算每周 MA10/MA20"""
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global _WEEKLY_CTX
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@@ -924,6 +1005,7 @@ def run_backtest(strategy_version, start_date, end_date, capital=913000, save=Tr
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prepare_sector_context(start_date, end_date)
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prepare_flow_context(fetch_start, end_date)
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prepare_weekly_context(fetch_start, end_date)
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prepare_news_context(start_date, end_date)
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conn = sqlite3.connect(DB_PATH)
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stocks = conn.execute("""
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@@ -1288,6 +1370,8 @@ def run_backtest(strategy_version, start_date, end_date, capital=913000, save=Tr
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_sec_m = conv.get('sector_mult', 2.0)
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_flo_t = conv.get('flow_delta_min', 0)
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_flo_m = conv.get('flow_mult', 2.0)
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_news_m = conv.get('news_mult', 0) # 新闻因子倍数（0=不启用）
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_news_days = conv.get('news_days', 3)
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_cap = conv.get('cap')
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_stack = conv.get('stack', True)
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for t in trades:
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@@ -1296,6 +1380,15 @@ def run_backtest(strategy_version, start_date, end_date, capital=913000, save=Tr
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_sec_hit = _sa is not None and _sa > _sec_t
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_fd = _f.get('flow_delta')
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_flo_hit = _fd is not None and _fd > _flo_t
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# 新闻因子（个股+行业）
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_news_hit = False
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if _news_m > 0:
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_code = t.get('code')
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_date = t.get('entry_date')
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_sector = _STOCK_SECTOR.get(_code, '')
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_sn = news_ctx(_code, _date, _news_days)
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_ssn = sector_news_ctx(_sector, _date, _news_days) if _sector else {'pos': 0, 'neg': 0}
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_news_hit = (_sn['pos'] + _ssn['pos']) > (_sn['neg'] + _ssn['neg']) and (_sn['pos'] + _ssn['pos']) >= 1
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if _stack:
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b = 1.0
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if t.get('dna'):
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@@ -1304,16 +1397,29 @@ def run_backtest(strategy_version, start_date, end_date, capital=913000, save=Tr
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b *= _sec_m
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if _flo_hit:
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b *= _flo_m
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if _news_hit:
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b *= _news_m
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if _cap:
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b = min(b, _cap)
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else:
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b = _dna_m if (t.get('dna') or _sec_hit or _flo_hit) else 1.0
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b = _dna_m if (t.get('dna') or _sec_hit or _flo_hit or _news_hit) else 1.0
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t['boost'] = b
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summary['conviction_model'] = conv.get('model', '')
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else:
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boost_k = cfg.get('exit', {}).get('dna_boost', 2.5)
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_news_m = cfg.get('news_mult', 0)
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_news_days = cfg.get('news_days', 3)
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for t in trades:
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t['boost'] = boost_k if t.get('dna') else 1.0
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b = boost_k if t.get('dna') else 1.0
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if _news_m > 0:
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_code = t.get('code')
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_date = t.get('entry_date')
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_sector = _STOCK_SECTOR.get(_code, '')
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_sn = news_ctx(_code, _date, _news_days)
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_ssn = sector_news_ctx(_sector, _date, _news_days) if _sector else {'pos': 0, 'neg': 0}
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if (_sn['pos'] + _ssn['pos']) > (_sn['neg'] + _ssn['neg']) and (_sn['pos'] + _ssn['pos']) >= 1:
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b *= _news_m
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t['boost'] = b
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# 集中仓位（该策略最优激进仓位）
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slots = STRATEGY_SIZING.get(strategy_version, 10)
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summary['portfolio'] = portfolio_sim(trades, capital, slots)
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