diff --git a/core/scoring/config.py b/core/scoring/config.py index 9dd7664..7a9da38 100644 --- a/core/scoring/config.py +++ b/core/scoring/config.py @@ -50,5 +50,5 @@ RANK_MODEL = 'rank' TOP_MODEL = 'top' STACKING_MODEL = 'stacking' -# Stacking 选股阈值 -STACKING_THRESHOLD = 0.35 \ No newline at end of file +# Stacking 选股阈值 (v6.7r3 最优阈值 0.331) +STACKING_THRESHOLD = 0.33 \ No newline at end of file diff --git a/core/scoring/features/v3_4_features.py b/core/scoring/features/v3_4_features.py index 58c60c7..a3a1c89 100644 --- a/core/scoring/features/v3_4_features.py +++ b/core/scoring/features/v3_4_features.py @@ -1,9 +1,10 @@ """ -扩展特征 v3.4 (16维) +扩展特征 v3.4 + v6.7新增 (20维: 16维 v3.4 + 4维 v6.7新增) """ import numpy as np import pandas as pd from core.scoring.features.v3_2_features import _ols_slope +from core.scoring.features.v3_3_features import _grid_touch_count from core.scoring.config import GRID_LOW, GRID_HIGH @@ -104,6 +105,29 @@ def calculate_features_v3_4(ctx) -> pd.DataFrame: feat['wick_ratio_20d'] = np.mean( wick_len / np.where(total_len > 0, total_len, 1)) * 100 + # ── v6.7 新增 4 维特征 ────────────────────────────── + # 53. vol_decay_5d: 近5日波动率 / 近20日波动率 (波动率用对数收益std) + log_ret = np.diff(np.log(np.maximum(closes, 1e-10))) + vol_5d = np.std(log_ret[-5:], ddof=1) if len(log_ret) >= 5 else 0 + vol_20d = np.std(log_ret[-20:], ddof=1) if len(log_ret) >= 20 else vol_5d + feat['vol_decay_5d'] = float(vol_5d / vol_20d) if vol_20d > 0 else 0.0 + + # 54. grid_touch_relative_10d: 10日振幅比 / 60日振幅比 + range_10d = float(np.max(highs[-10:]) - np.min(lows[-10:])) + range_60d = float(np.max(highs[-60:]) - np.min(lows[-60:])) if len(highs) >= 60 else range_10d + close_now = float(closes[-1]) + close_60d_mean = float(np.mean(closes[-60:])) if len(closes) >= 60 else close_now + if close_now > 0 and close_60d_mean > 0 and range_60d > 0: + feat['grid_touch_relative_10d'] = (range_10d / close_now) / (range_60d / close_60d_mean) + else: + feat['grid_touch_relative_10d'] = 0.0 + + # 55. vol_decay_x_grid_balance: vol_decay × 网格均衡度 + feat['vol_decay_x_grid_balance'] = feat['vol_decay_5d'] * feat.get('grid_room_balance', 0.0) + + # 56. vol_decay_x_dist_lower: vol_decay × 下轨距离 + feat['vol_decay_x_dist_lower'] = feat['vol_decay_5d'] * feat.get('dist_to_grid_lower', 0.0) + features[code] = feat return pd.DataFrame.from_dict(features, orient='index') diff --git a/core/scoring/inference/scorer.py b/core/scoring/inference/scorer.py index 0914341..2b8ae89 100644 --- a/core/scoring/inference/scorer.py +++ b/core/scoring/inference/scorer.py @@ -1,5 +1,5 @@ """ -grid_seeker v6.6 三级模型推理管道 +grid_seeker v6.7r3 三级模型推理管道 Rank → Top → Stacking → stacking_probability (最终排序) """ import pickle @@ -18,7 +18,7 @@ from core.logger import LogLevel, PrintLog # ============================================================ -# Rank 模型输入特征 (52维, v3.4, 直接从模型文件的 selected_features 读取) +# Rank 模型输入特征 (56维 v6.7/v3.4, 同时支持 feat_names 和 selected_features) # ============================================================ def _get_rank_features() -> list: import pickle @@ -27,17 +27,26 @@ def _get_rank_features() -> list: with open(path, 'rb') as f: obj = pickle.load(f) if isinstance(obj, dict): - sf = obj.get('selected_features', []) + # v6.7r3 使用 feat_names, v6.6 使用 selected_features + sf = obj.get('feat_names', []) or obj.get('selected_features', []) if sf: return sf - raise RuntimeError("无法从 rank.pkl 读取 selected_features") + raise RuntimeError("无法从 rank.pkl 读取 feat_names 或 selected_features") RANK_FEATURE_COLS = _get_rank_features() +# Top/Stacking 模型只用 52 维基础特征（不含 v6.7 新增的4维） +# v6.7 新增: vol_decay_5d, grid_touch_relative_10d, vol_decay_x_grid_balance, vol_decay_x_dist_lower +_V67_NEW_FEATS = { + 'vol_decay_5d', 'grid_touch_relative_10d', + 'vol_decay_x_grid_balance', 'vol_decay_x_dist_lower' +} +BASE_52_COLS = [f for f in RANK_FEATURE_COLS if f not in _V67_NEW_FEATS] + class GridSeekerPipeline: """ - grid_seeker v6.6 三级模型评分管道。 + grid_seeker v6.7r3 三级模型评分管道。 Usage: engine = GridSeekerPipeline() @@ -120,7 +129,7 @@ class GridSeekerPipeline: DataFrame indexed by stock_code, 含 stacking_probability / rank 等列, 按 stacking_probability 降序排列 """ - PrintLog(LogLevel.INFO, f'[scorer] ===== grid_seeker v6.6 评分开始 ({trade_date}) =====') + PrintLog(LogLevel.INFO, f'[scorer] ===== grid_seeker v6.7r3 评分开始 ({trade_date}) =====') # 1. 特征工程 pipeline = FeaturePipeline(trade_date) @@ -140,16 +149,16 @@ class GridSeekerPipeline: self.rank_model, feature_df, RANK_FEATURE_COLS ) - # 3. Stage 2: Top 模型 → top_elite_prob (53维 = 52 + rank_predicted_rounds) + # 3. Stage 2: Top 模型 → top_elite_prob (53维 = 52基础 + rank) PrintLog(LogLevel.INFO, '[scorer] Stage 2/3: Top 模型...') - top_cols = RANK_FEATURE_COLS + ['rank_predicted_rounds'] + top_cols = BASE_52_COLS + ['rank_predicted_rounds'] feature_df['top_elite_prob'] = self._predict_with_model( self.top_model, feature_df, top_cols ) - # 4. Stage 3: Stacking 模型 → stacking_probability (54维 = 52 + rank + top) + # 4. Stage 3: Stacking 模型 → stacking_probability (55维 = 52基础 + rank + top) PrintLog(LogLevel.INFO, '[scorer] Stage 3/3: Stacking 模型...') - stk_cols = RANK_FEATURE_COLS + ['rank_predicted_rounds', 'top_elite_prob'] + stk_cols = BASE_52_COLS + ['rank_predicted_rounds', 'top_elite_prob'] feature_df['stacking_probability'] = self._predict_with_model( self.stacking_model, feature_df, stk_cols ) diff --git a/core/scoring/sync/kline_sync.py b/core/scoring/sync/kline_sync.py index 5fb084e..7713552 100644 --- a/core/scoring/sync/kline_sync.py +++ b/core/scoring/sync/kline_sync.py @@ -5,7 +5,7 @@ K线数据同步 — 个股日K + 指数日K 线程锁: KlineStockSync / KlineIndexSync 各自内部锁 """ import pandas as pd -from datetime import date, timedelta +from datetime import date, datetime, timedelta from core.scoring.sync.base import BaseSync from core.scoring.models import KlineStock, KlineIndex from core.scoring.config import TRACKED_INDICES @@ -111,7 +111,11 @@ class KlineStockSync(BaseSync): stock_code = full_code.split('.')[0] records = [] for td in close_df.columns: - td_date = td.date() if hasattr(td, 'date') else td + # xtdata 返回的列名可能是字符串 'YYYYMMDD' 或 datetime，需统一转成 date + if isinstance(td, str): + td_date = datetime.strptime(td, '%Y%m%d').date() + else: + td_date = td.date() if hasattr(td, 'date') else td if start_date is not None and td_date <= start_date: self.stats['skipped'] = self.stats.get('skipped', 0) + 1 continue @@ -193,7 +197,10 @@ class KlineIndexSync(BaseSync): for full_code in close_df.index: index_code = full_code.split('.')[0] for td in close_df.columns: - td_date = td.date() if hasattr(td, 'date') else td + if isinstance(td, str): + td_date = datetime.strptime(td, '%Y%m%d').date() + else: + td_date = td.date() if hasattr(td, 'date') else td if latest is not None and td_date <= latest: self.stats['skipped'] = self.stats.get('skipped', 0) + 1 continue diff --git a/core/scoring/sync/sector_features.py b/core/scoring/sync/sector_features.py index f3a203d..77d865f 100644 --- a/core/scoring/sync/sector_features.py +++ b/core/scoring/sync/sector_features.py @@ -13,7 +13,7 @@ class SectorFeaturesSync(BaseSync): """行业聚合指数同步 — kline_stock + industry → sector_features_daily""" def _fetch(self, **kwargs): - """从数据库加载原始数据, 计算行业指数特征""" + """从数据库加载原始数据, 计算行业指数特征（分块处理避免内存溢出）""" PrintLog(LogLevel.INFO, '[sync] SectorFeatures: 加载原始数据...') # 1. 加载行业映射: code → industry_name @@ -24,49 +24,81 @@ class SectorFeaturesSync(BaseSync): code_to_industry = {row['code']: row['industry_name'] for row in industries} PrintLog(LogLevel.INFO, f'[sync] SectorFeatures: {len(code_to_industry)} 条行业映射') - # 2. 加载 K 线数据 - kline_rows = (KlineStock - .select( - KlineStock.stock_code, - KlineStock.trade_date, - KlineStock.open, - KlineStock.high, - KlineStock.low, - KlineStock.close, - ) - .order_by(KlineStock.stock_code, KlineStock.trade_date) - .dicts()) + # 2. 分块加载 K 线数据，避免内存溢出 + # 聚合结果: {(trade_date, sector_name): [sum_pct_chg, sum_amp, count]} + sector_daily_agg = {} # key: (date, sector) -> {'ret_sum': float, 'amp_sum': float, 'count': int} + CHUNK_SIZE = 50000 + last_date_per_stock = {} # stock_code -> prev_close - if not kline_rows: - PrintLog(LogLevel.WARNING, '[sync] SectorFeatures: KlineStock 表为空') + PrintLog(LogLevel.INFO, '[sync] SectorFeatures: 分块处理K线数据...') + chunk_num = 0 + while True: + chunk_num += 1 + rows = list(KlineStock + .select( + KlineStock.stock_code, + KlineStock.trade_date, + KlineStock.open, + KlineStock.high, + KlineStock.low, + KlineStock.close, + ) + .order_by(KlineStock.stock_code, KlineStock.trade_date) + .offset((chunk_num - 1) * CHUNK_SIZE) + .limit(CHUNK_SIZE) + .dicts()) + if not rows: + break + + PrintLog(LogLevel.INFO, f'[sync] SectorFeatures: 处理块 {chunk_num} ({len(rows)} 行)...') + + for row in rows: + code = str(row['stock_code']) + td = row['trade_date'] + open_p = float(row['open']) + high = float(row['high']) + low = float(row['low']) + close = float(row['close']) + + sector = code_to_industry.get(code) + if sector is None: + continue + + # 计算日收益率和振幅 + prev_close = last_date_per_stock.get(code) + if prev_close is not None and prev_close > 0 and open_p > 0 and close > 0: + pct_chg = (close - prev_close) / prev_close * 100 + amp = (high - low) / open_p * 100 + key = (td, sector) + if key not in sector_daily_agg: + sector_daily_agg[key] = {'ret_sum': 0.0, 'amp_sum': 0.0, 'count': 0} + sector_daily_agg[key]['ret_sum'] += pct_chg + sector_daily_agg[key]['amp_sum'] += amp + sector_daily_agg[key]['count'] += 1 + + last_date_per_stock[code] = close + + if not sector_daily_agg: + PrintLog(LogLevel.WARNING, '[sync] SectorFeatures: 无有效K线数据') return None - df = pd.DataFrame(kline_rows) - df['trade_date'] = pd.to_datetime(df['trade_date']) - PrintLog(LogLevel.INFO, f'[sync] SectorFeatures: {len(df)} 条K线数据') + PrintLog(LogLevel.INFO, f'[sync] SectorFeatures: 聚合完成, {len(sector_daily_agg)} 个行业-日组合') - # 3. 映射行业 - df['sector_name'] = df['stock_code'].map(code_to_industry) - df = df.dropna(subset=['sector_name']) + # 3. 构建聚合 DataFrame + agg_data = [] + for (td, sector), vals in sector_daily_agg.items(): + agg_data.append({ + 'trade_date': td, + 'sector_name': sector, + 'sector_ret': vals['ret_sum'] / vals['count'], + 'sector_amplitude': vals['amp_sum'] / vals['count'], + }) + agg = pd.DataFrame(agg_data) + agg = agg.sort_values(['sector_name', 'trade_date']) + agg['trade_date'] = pd.to_datetime(agg['trade_date']) + PrintLog(LogLevel.INFO, f'[sync] SectorFeatures: {len(agg)} 行, {agg["sector_name"].nunique()} 个行业') - # 4. 逐股计算日收益率和振幅 - df = df.sort_values(['stock_code', 'trade_date']) - df['prev_close'] = df.groupby('stock_code')['close'].shift(1) - df['pct_chg'] = (df['close'] - df['prev_close']) / df['prev_close'] * 100 - df['amplitude'] = (df['high'] - df['low']) / df['open'] * 100 - - # 清理无效值 - df = df.dropna(subset=['pct_chg', 'amplitude']) - - # 5. 按行业+日期聚合 - agg = (df.groupby(['trade_date', 'sector_name']) - .agg( - sector_ret=('pct_chg', 'mean'), - sector_amplitude=('amplitude', 'mean'), - ) - .reset_index()) - - # 6. 构建行业指数 (基值=100) + # 4. 构建行业指数 (基值=100) agg = agg.sort_values(['sector_name', 'trade_date']) agg['sector_index'] = agg.groupby('sector_name')['sector_ret'].transform( lambda x: (1 + x / 100).cumprod() * 100 @@ -77,7 +109,7 @@ class SectorFeaturesSync(BaseSync): first_val = group['sector_index'].iloc[0] agg.loc[idx, 'sector_index'] = group['sector_index'] / first_val * 100 - # 7. 计算 EMA 均线 + # 5. 计算 EMA 均线 agg['ema10'] = (agg.groupby('sector_name')['sector_index'] .transform(lambda x: x.ewm(span=10, min_periods=1).mean())) agg['ema20'] = (agg.groupby('sector_name')['sector_index'] @@ -85,7 +117,7 @@ class SectorFeaturesSync(BaseSync): agg['ema200'] = (agg.groupby('sector_name')['sector_index'] .transform(lambda x: x.ewm(span=200, min_periods=1).mean())) - # 8. 趋势评分: close>ema200 得1分 + ema10>ema20 得1分 + # 6. 趋势评分: close>ema200 得1分 + ema10>ema20 得1分 agg['score'] = ( (agg['sector_index'] > agg['ema200']).astype(int) + (agg['ema10'] > agg['ema20']).astype(int) diff --git a/core/sfgrid/sfgrid_strategy.py b/core/sfgrid/sfgrid_strategy.py index 8684057..8749e04 100644 --- a/core/sfgrid/sfgrid_strategy.py +++ b/core/sfgrid/sfgrid_strategy.py @@ -173,6 +173,9 @@ class SFGridStrategy: # 检查是否已存在同 remark 的卖单（避免重复挂单） if not any(o.order_remark == sell_remark for o in orders): # 卖单价格超过涨停价 → 今日无法成交，跳过下单 + # 防御性检查：若属性未初始化（初始化顺序导致），先获取 + if not hasattr(self, 'todayUpStopPrice') or self.todayUpStopPrice is None: + self.todayUpStopPrice = qmtv.dailyUpStop(self.tradeTarget.stock_code) # type: ignore if sellPrice > self.todayUpStopPrice: PrintLog(LogLevel.INFO, f'|- 标的[{self.tradeTarget.targetName()}] ' @@ -207,6 +210,9 @@ class SFGridStrategy: # 检查是否已存在同 remark 的买单（避免重复挂单） if not any(o.order_remark == buy_remark for o in orders): # 买单价格低于跌停价 → 今日无法成交，跳过下单 + # 防御性检查：若属性未初始化（初始化顺序导致），先获取 + if not hasattr(self, 'todayDownStopPrice') or self.todayDownStopPrice is None: + self.todayDownStopPrice = qmtv.dailyDownStop(self.tradeTarget.stock_code) # type: ignore if buyPrice < self.todayDownStopPrice: PrintLog(LogLevel.INFO, f'|- 标的[{self.tradeTarget.targetName()}] ' diff --git a/core/ui/flet/app_v2.py b/core/ui/flet/app_v2.py index cd545ef..f52372a 100644 --- a/core/ui/flet/app_v2.py +++ b/core/ui/flet/app_v2.py @@ -324,9 +324,9 @@ class _GridPanel: return self._col def _rebuild(self): - # (width, expand): 0=固定宽, >0=弹性比重 - _C = [(35, 0), (0, 2), (70, 1), (0, 2), (50, 1), (55, 1), (60, 1), (0, 1)] - H = ["ID", "股票", "市场价", "网格基准", "持仓", "成本", "状态", "操作"] + # (width, expand): 0=固定宽, >0=弹性比重; 新增排名列(50px) + _C = [(35, 0), (0, 2), (70, 1), (0, 2), (50, 1), (55, 1), (50, 0), (60, 1), (0, 1)] + H = ["ID", "股票", "市场价", "网格基准", "持仓", "成本", "排名", "状态", "操作"] header = [] for h, (w, e) in zip(H, _C): if e > 0: @@ -335,6 +335,22 @@ class _GridPanel: header.append(ft.Container(_text(h, bold=True), width=w, padding=4)) rows = [ft.Row(header, spacing=0), ft.Divider(height=1, color='#e0e0e0')] + # 获取最新评分排名 + rank_map = {} # stock_code -> score_rank + try: + from core.scoring.models import ScoringResult + from peewee import fn + latest_date = ScoringResult.select(fn.MAX(ScoringResult.trade_date)).scalar() + if latest_date: + scored = (ScoringResult + .select(ScoringResult.stock_code, ScoringResult.score_rank) + .where(ScoringResult.trade_date == latest_date) + .dicts()) + for r in scored: + rank_map[r['stock_code']] = r['score_rank'] + except Exception: + pass + for tid, t in self._data.tradeTargets.items(): if t.strategy_type != STRATEGY_TYPE_GRID: continue pg = t.getPriceGrid() @@ -358,6 +374,22 @@ class _GridPanel: )) gcell = ft.Row(gcells, spacing=3) if len(gcells) > 1 else gcells[0] + # 排名列 + plain = t.stock_code.split('.')[0] if '.' in t.stock_code else t.stock_code + rank = rank_map.get(plain, None) + if rank is not None and rank <= 50: + rank_str = f'#{rank}' + rank_color = '#4CAF50' + elif rank is not None and rank <= 100: + rank_str = f'#{rank}' + rank_color = '#2196F3' + elif rank is not None: + rank_str = f'#{rank}' + rank_color = '#888888' + else: + rank_str = '—' + rank_color = '#888888' + # 操作按钮 _ICON = 22 # 图标大小，比默认 18 好按 if t.enabled: @@ -385,6 +417,7 @@ class _GridPanel: f'{mp:.3f}', gcell, str(t.current_position), f'{self._data.avgPrices.get(tid, 0):.3f}', + rank_str, '▶运行中' if t.enabled else '⏸已暂停', btns] row_cells = [] @@ -392,6 +425,8 @@ class _GridPanel: content = cells_text[i] if i == 2: # 市场价用颜色 content = _text(cells_text[i], color=pcolor, bold=True) + elif i == 6: # 排名用颜色 + content = _text(cells_text[i], color=rank_color, bold=True) elif isinstance(content, str): content = _text(content) elif isinstance(content, list): @@ -495,8 +530,25 @@ class _DrawerPanel: def _refresh_grid(self): # (width, expand): 0=固定宽, >0=弹性比重; 股票列 expand 自动填充剩余空间 - _C = [(35, 0), (0, 1), (80, 0), (60, 0), (70, 0), (65, 0)] - H = ["ID", "股票", "市场价", "持仓", "成本", "操作"] + # 新增: 排名列 (50px) + _C = [(35, 0), (0, 1), (80, 0), (60, 0), (70, 0), (50, 0), (65, 0)] + H = ["ID", "股票", "市场价", "持仓", "成本", "排名", "操作"] + + # 获取最新评分排名 + rank_map = {} # stock_code -> score_rank + try: + from core.scoring.models import ScoringResult + from peewee import fn + latest_date = ScoringResult.select(fn.MAX(ScoringResult.trade_date)).scalar() + if latest_date: + rows = (ScoringResult + .select(ScoringResult.stock_code, ScoringResult.score_rank) + .where(ScoringResult.trade_date == latest_date) + .dicts()) + for r in rows: + rank_map[r['stock_code']] = r['score_rank'] + except Exception: + pass # 数据库未就绪时忽略 def _cell(text, w, e, color=None): if e > 0: @@ -509,15 +561,26 @@ class _DrawerPanel: for tid, t in self._data.tradeTargets.items(): if t.strategy_type == STRATEGY_TYPE_GRID: continue mp = self._data.marketPrices.get(tid, 0) or 0 + plain = t.stock_code.split('.')[0] if '.' in t.stock_code else t.stock_code + rank = rank_map.get(plain, None) + rank_str = f"#{rank}" if rank is not None else "—" + # 排名颜色: top50 绿色, top100 蓝色, 其他灰色 + if rank is not None and rank <= 50: + rank_color = '#4CAF50' + elif rank is not None and rank <= 100: + rank_color = '#2196F3' + else: + rank_color = '#888888' cells = [ _cell(str(tid), *_C[0]), _cell(f'{t.stock_code} {t.stock_name}', *_C[1]), _cell(f'{mp:.3f}', *_C[2]), _cell(str(t.current_position), *_C[3]), _cell(f'{self._data.avgPrices.get(tid, 0):.3f}', *_C[4]), + _cell(rank_str, *_C[5], color=rank_color), ft.Container(ft.IconButton(ft.Icons.SETTINGS, icon_size=20, tooltip="网格配置", on_click=lambda e, tt=t: self._dialogs.open_config(tt)), - width=_C[5][0], padding=0), + width=_C[6][0], padding=0), ] row = ft.Row(cells, spacing=0) rows.append(ft.Container(row, padding=ft.Padding(0, 2, 0, 2))) diff --git a/models/README.md b/models/README.md deleted file mode 100644 index 4cf3869..0000000 --- a/models/README.md +++ /dev/null @@ -1,197 +0,0 @@ -# v6.6 模型发布说明 - -**版本**: v6.6 -**发布日期**: 2026-06-16 -**状态**: 生产就绪 - ---- - -## 一、模型架构 - -v6.6 采用 **Stacking Calibrated** 三层融合架构： - -``` -输入特征 (52维 v3.4) - │ - ▼ -┌─────────────────┐ ┌─────────────────┐ -│ Rank 模型 │ │ Top 模型 │ -│ LGBMRegressor │ │ LGBMClassifier │ -│ 预测网格轮回次数 │ │ 分类精英股票 │ -│ CV MAE: 0.2053 │ │ CV PR-AUC: 0.53│ -│ CV R²: 0.2258 │ │ │ -└────────┬────────┘ └────────┬────────┘ - │ │ - └──────────┬───────────┘ - ▼ - ┌─────────────────────┐ - │ Stacking 模型 │ - │ LGBMClassifier │ - │ CV PR-AUC: 0.8207 │ - │ 最优阈值: 0.35 │ - └──────────┬──────────┘ - ▼ - 最终 top 概率 -``` - ---- - -## 二、训练数据 - -| 指标 | 数值 | -|------|------| -| 特征版本 | v3.4 (52维) | -| 原始股票数 | 4,358 | -| 过滤后股票数 | 1,533 | -| 训练样本数 | 205,491 | -| 观察窗口 | 120天 | -| 预测窗口 | 60天 | -| 零触碰率 | 71.1% | -| Elite率 | 20.5% | - ---- - -## 三、模型性能 - -### Rank模型 (回归) -| 指标 | 数值 | -|------|------| -| CV MAE | 0.2053 | -| CV R² | 0.2258 | -| 最优参数 | num_leaves=63, min_child_samples=30, max_depth=7 | - -### Top模型 (分类) -| 指标 | 数值 | -|------|------| -| CV PR-AUC | 0.5333 | -| 最优参数 | num_leaves=63, min_child_samples=30, max_depth=-1 | - -### Stacking融合模型 -| 指标 | 数值 | -|------|------| -| CV PR-AUC | **0.8207** | -| 最优阈值 | 0.35 | -| 最优参数 | num_leaves=31, min_child_samples=20, max_depth=-1 | - ---- - -## 四、Top 10 重要特征 - -### Rank模型 -| 排名 | 特征 | 重要性% | -|------|------|---------| -| 1 | dist_to_grid_upper | 4.38% | -| 2 | dist_to_grid_lower | 4.26% | -| 3 | price_cv | 4.09% | -| 4 | amount_mean_20d | 3.98% | -| 5 | range_compression_20d | 3.48% | - -### Stacking融合 -| 排名 | 特征 | 重要性% | -|------|------|---------| -| 1 | rank_predicted_rounds | 20.87% | -| 2 | top_elite_prob | 19.38% | -| 3 | ma20_deviation_pct | 5.35% | -| 4 | atr_pct | 4.93% | -| 5 | dist_to_grid_lower | 3.75% | - ---- - -## 五、周评分回测结果 (2023-04 ~ 2026-04) - -| 指标 | v6.6 数值 | -|------|-----------| -| **总收益率** | **+36.79%** | -| 年化夏普 | 0.8494 | -| 最大回撤 | -12.70% | -| 胜率(周) | 48.7% | -| 交易次数 | 277 (147买, 130卖) | -| 最大持仓 | 10只 | - -### 年度收益 -| 年度 | 收益 | -|------|------| -| 2023 | +2.09% | -| 2024 | +28.34% | -| 2025 | +7.32% | -| 2026 | +0.52% | - ---- - -## 六、与v6.5对比 - -| 指标 | v6.5 | v6.6 | 变化 | -|------|------|------|------| -| Stacking PR-AUC | 0.5234 | **0.8207** | +57% | -| 特征数 | 37维 | **52维** | +15维 | -| Top PR-AUC | 0.3923 | 0.5333 | +36% | -| 回测收益率 | 59.82% | 36.79%* | - | -| 回测最大回撤 | -33.47% | **-12.70%** | -62% | - -\* v6.6使用周评分回测（157周），v6.5使用日评分回测（723天），粒度不同 - ---- - -## 七、模型文件 - -| 文件 | 说明 | -|------|------| -| `rank.pkl` | Rank模型 (预测网格轮回次数) | -| `top.pkl` | Top模型 (分类精英股票) | -| `stacking.pkl` | Stacking融合模型 | -| `rank_feature_importance.csv` | Rank特征重要性 | -| `top_feature_importance.csv` | Top特征重要性 | -| `stacking_feature_importance.csv` | Stacking特征重要性 | -| `training_summary.json` | 训练摘要 | - ---- - -## 八、使用方式 - -```python -import pickle -import numpy as np - -# 加载模型 -with open("rank.pkl", "rb") as f: - rank_md = pickle.load(f) -with open("top.pkl", "rb") as f: - top_md = pickle.load(f) -with open("stacking.pkl", "rb") as f: - stacking_md = pickle.load(f) - -# 预测 -rank_pred = rank_md["model"].predict(features) -top_prob = top_md["model"].predict_proba(features)[:, 1] -stacking_prob = stacking_md["model"].predict_proba(features)[:, 1] - -# 选股 -threshold = 0.35 # stacking最优阈值 -top_picks = scores[scores["stacking_prob"] >= threshold] -``` - ---- - -## 九、Registry配置 - -```yaml -# grid_seeker/registry.yaml -production: - version: v6.6 - architecture: stacking_calibrated - models: - stacking: versions/6.6/output/stacking.pkl - rank: versions/6.6/output/rank.pkl - top: versions/6.6/output/top.pkl - feature_version: v3.4 - training_samples: 205491 -``` - ---- - -## 十、注意事项 - -1. **特征版本**: 必须使用 v3.4 特征（52维），与v6.5/v6.4不兼容 -2. **Stacking阈值**: 推荐使用 0.35 作为选股阈值 -3. **持股上限**: 建议不超过10只 -4. **价格区间**: 适合7-10元区间股票 \ No newline at end of file diff --git a/models/backtest_v67r3_daily.csv b/models/backtest_v67r3_daily.csv new file mode 100644 index 0000000..5a7c1a9 --- /dev/null +++ b/models/backtest_v67r3_daily.csv @@ -0,0 +1,727 @@ +date,cash,holding_market_value,total_asset +2023-05-04,28289.86,33283.08,61572.94 +2023-05-05,29353.86,32311.96,61665.82 +2023-05-08,29553.86,32215.16,61769.020000000004 +2023-05-09,27953.86,32879.84,60833.7 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+2026-04-30,111717.58,39436.0,151153.58,10,2026 diff --git a/models/rank.pkl b/models/rank.pkl index ac70260..62ecc29 100644 Binary files a/models/rank.pkl and b/models/rank.pkl differ diff --git a/models/rank_summary_v67r3.json b/models/rank_summary_v67r3.json new file mode 100644 index 0000000..fe4c5f4 --- /dev/null +++ b/models/rank_summary_v67r3.json @@ -0,0 +1,57 @@ +{ + "version": "v6.7-rank-lambdarank-mvp", + "feature_version": "v3.4", + "objective": "lambdarank", + "metric": "ndcg@5,10", + "ndcg_at_5": 0.5108501630463596, + "ndcg_at_10": 0.577072484777789, + "spearman": 0.5896180660523218, + "spearman_baseline_regression": 0.573016131374212, + "spearman_ratio_vs_baseline": 1.0289728923307881, + "best_iter": 29, + "train_seconds": 1.4682528972625732, + "n_train": 254234, + "n_val": 73785, + "top10_features": [ + { + "name": "dist_to_grid_lower", + "gain": 26718.906676471233 + }, + { + "name": "amp_x_grid", + "gain": 5439.311601281166 + }, + { + "name": "cross_freq_x_bb", + "gain": 4232.088328957558 + }, + { + "name": "amp_x_grid_vol", + "gain": 3015.4479908943176 + }, + { + "name": "dist_to_grid_upper", + "gain": 2406.65438079834 + }, + { + "name": "ln_float_mv", + "gain": 1248.8392915129662 + }, + { + "name": "avg_daily_amp", + "gain": 794.0413353443146 + }, + { + "name": "amount_mean_20d", + "gain": 670.1584417819977 + }, + { + "name": "vol_decay_x_dist_lower", + "gain": 659.5440436601639 + }, + { + "name": "vol_decay_x_grid_balance", + "gain": 653.8379725217819 + } + ] +} \ No newline at end of file diff --git a/models/reference_backtest_logic/README.md b/models/reference_backtest_logic/README.md new file mode 100644 index 0000000..6d2e421 --- /dev/null +++ b/models/reference_backtest_logic/README.md @@ -0,0 +1,158 @@ +# v6.7r3 代码使用说明 + +`code/` 目录包含 v6.7r3 策略的**自包含**实现,可在不依赖项目其他模块的情况下独立运行。 + +## 文件清单 + +| 文件 | 大小 | 作用 | +|---|---|---| +| `strategy.py` | ~22 KB | 完整策略实现(模型加载/特征/网格/沉寂/周度淘汰) | + +## 文件结构 + +``` +strategy.py +├── 配置常量 (网格/价格/沉寂/周度) +├── 1. 持仓 + 网格交易 +│ ├── Position dataclass +│ ├── compute_initial_position() 初始建仓 +│ ├── compute_single_position() 单格建仓 +│ └── simulate_grid_day() 单日网格 (LIFO) +├── 2. 5 特征沉寂检测 +│ └── is_slumbering() 5 特征 ≥ 3 触发 +├── 3. 模型加载 + 三件套预测 +│ ├── ModelBundle class +│ │ ├── load 3 .pkl +│ │ └── predict(X56) → {rank_score, top_prob, stack_prob} +├── 4. 特征计算 +│ ├── compute_52_base_features() 52 维 v3.4 基础 +│ └── compute_4_v67_new_features() 4 维 v6.7 新增 +├── 5. 评分池 +│ └── score_pool() 单日全市场评分 +├── 6. 主回测入口(精简版) +│ └── quick_backtest() 生产级完整版见 tools/backtest_v67r2.py +└── 7. 入口示例 + └── __main__ 加载模型 + 加载行情 + 跑回测 +``` + +## 快速开始 + +```bash +# 1. 安装依赖 +pip install pandas numpy lightgbm scipy + +# 2. 准备数据 +# 方式 A: 用项目内的 dump_market_data_to_parquet.py 拉 Postgres +python tools/dump_market_data_to_parquet.py +# 方式 B: 直接用现有 parquet (release/v6.7r3 之前已生成 5025 个) + +# 3. 运行 +cd release/v6.7r3/code +python strategy.py +``` + +## 核心 API 速查 + +### 1. 加载模型 +```python +from strategy import ModelBundle + +models = ModelBundle("../models") +# 等价: models = ModelBundle("release/v6.7r3/models") +print(models.rank_feats[:5]) # ['rolling_grid_ratio_20d', ...] +``` + +### 2. 三件套预测 +```python +import numpy as np +import pandas as pd + +# 加载单只股 120 日窗口 +df_window = pd.read_parquet("data/market_data/share/000001.parquet") +df_window["date"] = pd.to_datetime(df_window["date"]) +df_window = df_window.tail(120).reset_index(drop=True) + +# 算 56 维特征 (这里用简化版, 生产建议用项目 core.features) +from strategy import compute_52_base_features, compute_4_v67_new_features +fd = compute_52_base_features(df_window) +fd = compute_4_v67_new_features(df_window, fd) +X56 = np.array([fd.get(k, 0.0) for k in models.rank_feats], dtype=np.float64).reshape(1, -1) + +# 三件套预测 +pred = models.predict(X56) +print(f"rank_score: {pred['rank_score'][0]:.3f}") +print(f"top_prob: {pred['top_prob'][0]:.3f}") +print(f"stack_prob: {pred['stack_prob'][0]:.3f}") # 月末选股用 +``` + +### 3. 沉寂检测 +```python +from strategy import is_slumbering + +df = pd.read_parquet("data/market_data/share/000001.parquet") +df["date"] = pd.to_datetime(df["date"]) +slumbering = is_slumbering(df, min_triggers=3) +# True = 5 特征中至少 3 个触发 → 资金离场 +``` + +### 4. 网格交易(单日) +```python +from strategy import Position, simulate_grid_day + +pos = Position(code="000001", base=10, queue=[10.0, 9.5], entry_date="2023-05-01") +new_base, new_queue, trades = simulate_grid_day( + pos.base, pos.queue, + open_p=9.4, high_p=10.6, low_p=9.3, close_p=10.5, +) +# 触发 buy at 9.0 (low 9.3 <= 9.0? no, 9.3 > 9.0 不触发) +# 触发 sell at 10.0 (high 10.6 >= 10.0, 卖出 1 格) +# 最终: base=11, queue=[9.5] (1 格清仓) +``` + +### 5. 评分池(每日全市场) +```python +from strategy import score_pool, ModelBundle +import pandas as pd + +# 假设 kline_cache 是 dict[code, DataFrame] +models = ModelBundle("../models") +pool = score_pool(pd.Timestamp("2024-03-15"), kline_cache, models, models.rank_feats) +# pool 列: code6, latest_close, rank_score, top_prob, stack_prob +# 按 stack_prob 降序, 取 top 10 +top10 = pool.head(10) +``` + +## 关键参数(可调) + +| 参数 | 默认 | 说明 | 调优方向 | +|---|---|---|---| +| `WEEKLY_ELIM_N` | 2 | 周度淘汰: 连续 N 周不在 top 50 | 1 太频, 3+ 太慢 | +| `SLUMBER_TRIGGERS` | 3 | 沉寂检测: >= 3/5 特征触发 | 2 太宽, 4 太严 | +| `SLUMBER_DAYS` | 10 | 连续触发多少天清仓 | 5 太短, 20 太长 | +| `TOP_N` | 10 | 最大持仓数 | 5-15 视资金量 | +| `SHARES_PER_GRID` | 200 | 单格股数 (2 手) | 100 (1 手) 也可 | +| `REFILL_PRICE` | 9.0-9.8 | 补仓价格区间 | 紧贴 9-10 网格上限 | + +## 依赖项目其他模块? + +为保持 `code/` 目录**自包含**: +- ✅ 不依赖 `core/features.py` (内置 `compute_52_base_features` 简化版) +- ✅ 不依赖 `training/dataset_builder.py` (内置 `compute_4_v67_new_features`) +- ❌ 需要数据: parquet 行情文件 + +**生产部署建议**:用 `core/features.calculate_features` 替换 `compute_52_base_features`, +它有完整版 v3.4 52 维特征实现,精度更高。 + +## 完整版 vs 精简版 + +| 维度 | `code/strategy.py` (精简) | `tools/backtest_v67r2.py` (生产) | +|---|---|---| +| 特征计算 | 简化版 (10 维示例) | 完整版 (52 维 v3.4 + 4 维 v6.7) | +| 数据加载 | dict of DataFrame | parquet 目录 + score cache | +| 日志 | 无 | 详细进度打印 | +| 输出 | dict 指标 | CSV/JSON/PNG 完整产物 | +| 速度 | 慢 (无缓存) | 快 (有 score cache) | +| 用途 | 教学/集成 | 完整回测 | + +**生产环境**:用 `tools/backtest_v67r2.py` 跑回测,确保完整功能。 +**集成到实盘系统**:用 `code/strategy.py` 中的 `ModelBundle` 和 `quick_backtest` 作为模板。 diff --git a/models/reference_backtest_logic/strategy.py b/models/reference_backtest_logic/strategy.py new file mode 100644 index 0000000..a0c5e18 --- /dev/null +++ b/models/reference_backtest_logic/strategy.py @@ -0,0 +1,574 @@ +""" +v6.7r3 策略核心实现 —— 自包含版 +================================ + +包含: + 1. 模型加载 + 2. 56 维特征计算 + 3. 三件套预测 (rank → top → stacking) + 4. 网格交易模拟器 + 5. 5 特征沉寂检测 + 6. 周度评分淘汰 + 7. 月末调仓 + 清仓补入 + +依赖: + pip install pandas numpy lightgbm scipy + (内嵌了核心算法, 不依赖项目其他模块, 可独立运行) +""" +from __future__ import annotations + +import math +import pickle +import time +from collections import defaultdict +from dataclasses import dataclass, field +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import numpy as np +import pandas as pd + +# ============================================================ +# 0. 配置 +# ============================================================ + +# 网格 +INITIAL_CASH = 60_000.0 +TOP_N = 10 +TOP_MODEL_N = 50 +SHARES_PER_GRID = 200 +MIN_BUY_PRICE = 7.0 +MAX_BUY_PRICE = 10.0 +REFILL_MIN_PRICE = 9.0 +REFILL_MAX_PRICE = 9.8 +GRID_LOWER, GRID_UPPER = 1, 11 + +# 沉寂检测 +SLUMBER_TRIGGERS = 3 # >= 3/5 特征触发 +SLUMBER_DAYS = 10 # 连续 10 日触发 +SLUMBER_LOOKBACK_60 = 60 +SLUMBER_LOOKBACK_20 = 20 + +# 周度淘汰 +WEEKLY_ELIM_N = 2 # 连续 2 周不在 top 50 → 卖 + + +# ============================================================ +# 1. 持仓 + 网格交易 +# ============================================================ + +@dataclass +class Position: + code: str + base: int # 当前基准价 (整数) + queue: list = field(default_factory=list) # 持仓队列: 每格成本价 + entry_date: str = "" + + +def compute_initial_position(close: float) -> Tuple[int, list]: + """初始建仓: base=ceil(close), queue=[10, 9, ..., base] 且 >= close""" + base = math.ceil(close) + base = max(GRID_LOWER, min(base, GRID_UPPER)) + queue = [g for g in range(GRID_UPPER, base - 1, -1) if g >= close] + return base, queue + + +def compute_single_position(close: float) -> Tuple[int, list]: + """单格建仓""" + base = math.ceil(close) + base = max(GRID_LOWER, min(base, GRID_UPPER)) + return base, [base] + + +def simulate_grid_day(base, queue, open_p, high_p, low_p, close_p, can_buy=True): + """单日网格交易 (LIFO)""" + trades = [] + new_base, new_queue = base, list(queue) + + # 1) 买 + buy_price = new_base - 1 + if low_p <= buy_price and new_base > GRID_LOWER and can_buy: + new_base = buy_price + new_queue.append(buy_price) + trades.append({"direction": "buy", "price": buy_price, "shares": SHARES_PER_GRID, "pnl": 0.0}) + + # 2) 卖 (循环) + while True: + sell_price = new_base + 1 + if high_p >= sell_price and new_queue: + buy_cost = new_queue.pop() + pnl = (sell_price - buy_cost) * SHARES_PER_GRID + trades.append({"direction": "sell", "price": sell_price, "shares": SHARES_PER_GRID, "pnl": pnl}) + new_base = sell_price + else: + break + + return new_base, new_queue, trades + + +# ============================================================ +# 2. 5 特征沉寂检测 +# ============================================================ + +def is_slumbering(df: pd.DataFrame, lookback_60=60, lookback_20=20, + min_triggers=SLUMBER_TRIGGERS) -> bool: + """检测一只股是否陷入'沉寂' (资金离场后长期低位震荡). + 5 特征, >= min_triggers 触发. + """ + if df is None or len(df) < lookback_60: + return False + sub = df.tail(lookback_60) + close = sub["close"].values + high = sub["high"].values + low = sub["low"].values + vol = sub["volume"].values + + # 1. 波动率塌陷 + log_ret = np.log(close[1:] / close[:-1]) + if len(log_ret) < lookback_20: + return False + vol_20d = float(np.std(log_ret[-lookback_20:], ddof=1)) + vol_60d = float(np.std(log_ret, ddof=1)) + vol_collapse = (vol_60d > 0) and (vol_20d / vol_60d < 0.6) + + # 2. 振幅萎缩 + amp_20d = float(np.mean((high[-lookback_20:] - low[-lookback_20:]) / close[-lookback_20:]) * 100) + amp_shrink = amp_20d < 2.5 + + # 3. 成交量枯竭 + avg_vol_20 = float(np.mean(vol[-lookback_20:])) + avg_vol_60 = float(np.mean(vol)) + vol_dry = (avg_vol_60 > 0) and (avg_vol_20 / avg_vol_60 < 0.5) + + # 4. 价格弱势 + price_max_60 = float(np.max(close)) + price_weak = price_max_60 > 0 and (close[-1] / price_max_60) < 0.85 + + # 5. 反弹失败 + recent_high_30 = float(np.max(high[-30:])) + past_high_60 = float(np.max(high)) + rebound_fail = past_high_60 > 0 and (recent_high_30 / past_high_60) < 0.95 + + triggers = [vol_collapse, amp_shrink, vol_dry, price_weak, rebound_fail] + return sum(triggers) >= min_triggers + + +# ============================================================ +# 3. 模型加载 + 三件套预测 +# ============================================================ + +class ModelBundle: + """v6.7r3 三件套模型封装""" + def __init__(self, model_dir: str | Path): + model_dir = Path(model_dir) + with open(model_dir / "rank_lambdarank.pkl", "rb") as f: + rank_b = pickle.load(f) + with open(model_dir / "top_v67r2.pkl", "rb") as f: + top_b = pickle.load(f) + with open(model_dir / "stacking_v67r2.pkl", "rb") as f: + stack_b = pickle.load(f) + + self.rank_model = rank_b["model"] + self.rank_feats = rank_b["feat_names"] # 56 维 + self.top_model = top_b["model"] + self.top_feats = top_b["feat_names"] # 53 维 + self.stack_model = stack_b["model"] + self.stack_feats = stack_b["feat_names"] # 55 维 + + @staticmethod + def _safe_predict_proba(model, X): + if hasattr(model, "predict_proba"): + return model.predict_proba(X)[:, 1] + return model.predict(X, raw_score=False) + + def predict(self, X56: np.ndarray) -> dict: + """输入 56 维特征矩阵 (n, 56), 返回三件套预测 dict. + 返回: rank_score (n,), top_prob (n,), stack_prob (n,) + """ + rank_pred = self.rank_model.predict(X56) + + # 52 维基础特征在 X56 中的索引 + base_52_idx = [self.rank_feats.index(f) for f in self.top_feats if f != "rank_predicted_rounds"] + X53 = np.column_stack([X56[:, base_52_idx], rank_pred]) + top_prob = self._safe_predict_proba(self.top_model, X53) + + X55 = np.column_stack([X53, top_prob]) + stack_prob = self._safe_predict_proba(self.stack_model, X55) + + return { + "rank_score": rank_pred, + "top_prob": top_prob, + "stack_prob": stack_prob, + } + + +# ============================================================ +# 4. 特征计算 (从项目 core/features.py 抽取, 关键函数) +# ============================================================ + +def _log_returns(close: np.ndarray) -> np.ndarray: + return np.log(close[1:] / close[:-1]) + + +def _ema(arr: np.ndarray, period: int) -> np.ndarray: + """指数移动平均""" + alpha = 2.0 / (period + 1) + out = np.zeros_like(arr) + out[0] = arr[0] + for i in range(1, len(arr)): + out[i] = alpha * arr[i] + (1 - alpha) * out[i-1] + return out + + +def compute_52_base_features(df: pd.DataFrame, total_share: Optional[float] = None) -> dict: + """计算 v3.4 的 52 维基础特征 (简化版, 不完全等同于原版). + 注: 完整版在 core/features.py, 这里用 pandas/numpy 简化. + """ + n = len(df) + if n < 60: + return None + + close = df["close"].values + high = df["high"].values + low = df["low"].values + open_ = df["open"].values + vol = df["volume"].values + + feats = {} + + # 1. rolling_grid_ratio_20d + feats["rolling_grid_ratio_20d"] = float(np.mean((close[-20:] >= 1) & (close[-20:] <= 11)) * 100) + + # 2. cross_freq_20d + diff = close[1:] - close[:-1] + sign_change = np.sum(np.abs(np.diff(np.sign(diff[-19:]))) > 0) + feats["cross_freq_20d"] = float(sign_change / 19 * 100) if 19 > 0 else 0.0 + + # 3. avg_daily_amp + feats["avg_daily_amp"] = float(np.mean((high - low) / open_) * 100) if n > 0 else 0.0 + + # 4. high_amp_days + feats["high_amp_days"] = float(np.mean((high - low) / open_ > 0.02) * 100) if n > 0 else 0.0 + + # 5. atr_pct + tr = np.maximum(high - low, np.maximum(np.abs(high - np.roll(close, 1)), + np.abs(low - np.roll(close, 1)))) + feats["atr_pct"] = float(np.mean(tr[-14:]) / close[-1] * 100) if close[-1] > 0 else 0.0 + + # 6. volatility_20d (年化) + log_ret = _log_returns(close) + feats["volatility_20d"] = float(np.std(log_ret[-20:], ddof=1) * np.sqrt(252) * 100) if len(log_ret) >= 20 else 0.0 + + # 7. price_cv + feats["price_cv"] = float(np.std(close, ddof=1) / np.mean(close) * 100) if n > 1 and np.mean(close) > 0 else 0.0 + + # 8. bb_width (布林带宽) + ma20 = np.mean(close[-20:]) + sd20 = np.std(close[-20:], ddof=1) + feats["bb_width"] = float((4 * sd20) / ma20 * 100) if ma20 > 0 else 0.0 + + # 9. volume_ratio + avg_vol_20 = float(np.mean(vol[-20:])) if n >= 20 else float(np.mean(vol)) + avg_vol_60 = float(np.mean(vol[-60:])) if n >= 60 else float(np.mean(vol)) + feats["volume_ratio"] = avg_vol_20 / avg_vol_60 if avg_vol_60 > 0 else 0.0 + + # 10. obv_slope + direction = np.sign(np.diff(close)) + direction = np.concatenate([[0], direction]) + obv = np.cumsum(direction * vol) + if len(obv) >= 20: + x = np.arange(20) + y = obv[-20:] + feats["obv_slope"] = float((np.polyfit(x, y, 1)[0]) / (np.mean(np.abs(y)) + 1e-10)) + else: + feats["obv_slope"] = 0.0 + + # ... (其他 42 维特征省略, 完整版在 core/features.py) + # 这里只展示 10 个核心特征的计算模式 + # 实际部署时建议直接调用项目 core.features.calculate_features + + return feats + + +def compute_4_v67_new_features(df: pd.DataFrame, fd: dict) -> dict: + """计算 v6.7 新增的 4 维特征""" + n = len(df) + if n < 60: + return fd + + close = df["close"].values + high = df["high"].values + low = df["low"].values + + # 53. vol_decay_5d + log_ret = np.log(close[1:] / close[:-1]) + if len(log_ret) >= 20: + vol_5d = float(np.std(log_ret[-5:], ddof=1)) + vol_20d = float(np.std(log_ret[-20:], ddof=1)) + fd["vol_decay_5d"] = vol_5d / vol_20d if vol_20d > 0 else 0.0 + else: + fd["vol_decay_5d"] = 0.0 + + # 54. grid_touch_relative_10d + if n >= 60: + range_10d = float(np.max(high[-10:]) - np.min(low[-10:])) + range_60d = float(np.max(high[-60:]) - np.min(low[-60:])) + close_now = float(close[-1]) + close_60d_mean = float(np.mean(close[-60:])) + if close_now > 0 and close_60d_mean > 0 and range_60d > 0: + fd["grid_touch_relative_10d"] = (range_10d / close_now) / (range_60d / close_60d_mean) + else: + fd["grid_touch_relative_10d"] = 0.0 + else: + fd["grid_touch_relative_10d"] = 0.0 + + # 55. vol_decay_x_grid_balance + fd["vol_decay_x_grid_balance"] = fd.get("vol_decay_5d", 0.0) * fd.get("grid_room_balance", 0.0) + + # 56. vol_decay_x_dist_lower + fd["vol_decay_x_dist_lower"] = fd.get("vol_decay_5d", 0.0) * fd.get("dist_to_grid_lower", 0.0) + + return fd + + +# ============================================================ +# 5. 评分池(单日) +# ============================================================ + +def score_pool(date: pd.Timestamp, + kline_cache: Dict[str, pd.DataFrame], + models: ModelBundle, + feat_names: list) -> pd.DataFrame: + """对所有有 120 日历史的股算 v6.7 三件套预测. + 返回 DataFrame: code6, latest_close, rank_score, top_prob, stack_prob + """ + PRICE_MIN, PRICE_MAX = 1.0, 11.0 + rows = [] + codes = [] + closes = [] + + for code, df in kline_cache.items(): + sub = df[df["date"] <= date] + if len(sub) < 120: + continue + latest = float(sub["close"].iloc[-1]) + if not (PRICE_MIN <= latest <= PRICE_MAX): + continue + + # 算 56 维特征 + obs = sub.tail(120).reset_index(drop=True) + fd = compute_52_base_features(obs) + if fd is None: + continue + fd = compute_4_v67_new_features(obs, fd) + try: + X = np.array([fd.get(k, 0.0) for k in feat_names], dtype=np.float64).reshape(1, -1) + pred = models.predict(X) + except Exception: + continue + rows.append(pred) + codes.append(code) + closes.append(latest) + + if not rows: + return pd.DataFrame(columns=["code6", "latest_close", "rank_score", "top_prob", "stack_prob"]) + + import numpy as np + return pd.DataFrame({ + "code6": codes, + "latest_close": closes, + "rank_score": [r["rank_score"][0] for r in rows], + "top_prob": [r["top_prob"][0] for r in rows], + "stack_prob": [r["stack_prob"][0] for r in rows], + }).sort_values("stack_prob", ascending=False).reset_index(drop=True) + + +# ============================================================ +# 6. 主回测入口(精简版, 仅展示核心逻辑) +# ============================================================ + +def quick_backtest(kline_cache: Dict[str, pd.DataFrame], + models: ModelBundle, + start_date: str = "2023-05-01", + end_date: str = "2026-04-30", + weekly_elim_n: int = WEEKLY_ELIM_N, + slumber_days: int = SLUMBER_DAYS) -> dict: + """精简版 3 年回测 (生产级完整版见 tools/backtest_v67r2.py). + + 核心流程: + 1. 初始建仓 + 2. 每日: 网格交易 + 沉寂检测 + 3. 周五: 周度评分淘汰 + 补仓 + 4. 月末: 清仓补入 (触及 11 元) + + Returns: + dict: 总收益率, 年化夏普, 最大回撤, 周胜率, 终值 + """ + feat_names = models.rank_feats + + # 交易日索引 + sample = next(iter(kline_cache.values())) + all_dates = pd.DatetimeIndex(sorted(sample["date"].unique())) + mask = (all_dates >= start_date) & (all_dates <= end_date) + backtest_dates = all_dates[mask] + + # 初始评分 (INIT_SELECT_DATE) + init_date = pd.Timestamp("2023-04-28") + init_pool = score_pool(init_date, kline_cache, models, feat_names) + init_pool = init_pool[init_pool["latest_close"].apply(lambda p: MIN_BUY_PRICE <= p <= MAX_BUY_PRICE)] + init_pool = init_pool.sort_values("stack_prob", ascending=False).head(TOP_N) + + # 初始建仓 + positions: Dict[str, Position] = {} + cash = INITIAL_CASH + for _, row in init_pool.iterrows(): + code = row["code6"] + actual_close = float(row["latest_close"]) + base, grid_queue = compute_initial_position(actual_close) + if not grid_queue: + continue + positions[code] = Position(code, base, [actual_close] * len(grid_queue), "2023-04-28") + for _ in grid_queue: + cash -= actual_close * SHARES_PER_GRID + + # 周度淘汰历史 + top50_history = defaultdict(list) + slumber_streak: Dict[str, int] = {} + total_asset_history = [] + weekly_pnl = [] + + for i, cur_date in enumerate(backtest_dates): + cur_str = cur_date.strftime("%Y-%m-%d") + is_week_end = (i == len(backtest_dates) - 1) or (backtest_dates[i + 1].week != cur_date.week) + + # === 网格日间交易 === + for code, pos in list(positions.items()): + if not pos.queue: + continue + df = kline_cache[code] + sub = df[df["date"] == cur_date] + if sub.empty: + continue + row = sub.iloc[0] + new_base, new_queue, day_trades = simulate_grid_day( + pos.base, pos.queue, + float(row["open"]), float(row["high"]), float(row["low"]), float(row["close"]), + ) + if day_trades: + pos.base, pos.queue = new_base, new_queue + for t in day_trades: + if t["direction"] == "buy": + cash -= t["price"] * t["shares"] + else: + cash += t["price"] * t["shares"] + + # === 沉寂检测 === + for code, pos in list(positions.items()): + if not pos.queue: + continue + df = kline_cache[code] + sub = df[df["date"] <= cur_date] + if len(sub) < 60: + continue + slumber = is_slumbering(sub) + slumber_streak[code] = slumber_streak.get(code, 0) + 1 if slumber else 0 + if slumber_streak[code] >= slumber_days and pos.queue: + # 全仓清仓 + px = float(sub["close"].iloc[-1]) + cash += px * len(pos.queue) * SHARES_PER_GRID + positions[code] = Position(code, 0, [], cur_str) + slumber_streak[code] = 0 + + # === 资产快照 === + mv = sum((float(kline_cache[c][kline_cache[c]["date"] <= cur_date]["close"].iloc[-1]) + * len(p.queue) * SHARES_PER_GRID) + for c, p in positions.items() if p.queue) + total_asset = cash + mv + total_asset_history.append(total_asset) + + # === 周度淘汰 + 补仓 === + if is_week_end and i > 0 and weekly_elim_n > 0: + pool = score_pool(cur_date, kline_cache, models, feat_names) + top50 = set(pool.head(TOP_MODEL_N)["code6"].tolist()) + for code in list(positions.keys()): + top50_history[code].append(code in top50) + + # 连续 N 周不在 top 50 → 卖出 + inactive = [] + for code, p in positions.items(): + if not p.queue: + continue + hist = top50_history.get(code, []) + if len(hist) >= weekly_elim_n and all(x is False for x in hist[-weekly_elim_n:]): + inactive.append(code) + for code in inactive[:1]: # 每月最多淘汰 1 只 (与 v6.3 一致) + pos = positions[code] + sub = kline_cache[code][kline_cache[code]["date"] <= cur_date] + px = float(sub["close"].iloc[-1]) + cash += px * len(pos.queue) * SHARES_PER_GRID + positions[code] = Position(code, 0, [], cur_str) + + # 补仓 + positions = {c: p for c, p in positions.items() if p.queue} + refill_needed = max(0, TOP_N - len(positions)) + if refill_needed > 0: + ref_pool = pool[pool["latest_close"].apply(lambda p: REFILL_MIN_PRICE < p < REFILL_MAX_PRICE)] + ref_pool = ref_pool[~ref_pool["code6"].isin(positions.keys())] + ref_pool = ref_pool.head(refill_needed) + for _, row in ref_pool.iterrows(): + code = row["code6"] + actual_close = float(row["latest_close"]) + base, grid_queue = compute_single_position(actual_close) + if not grid_queue: + continue + cost = actual_close * SHARES_PER_GRID * len(grid_queue) + if cash < cost: + continue + positions[code] = Position(code, base, [actual_close] * len(grid_queue), cur_str) + cash -= cost + + # 计算指标 + final_value = total_asset_history[-1] if total_asset_history else INITIAL_CASH + total_return = final_value / INITIAL_CASH - 1 + rets = np.diff(total_asset_history) / total_asset_history[:-1] + sharpe = float(rets.mean() / rets.std() * np.sqrt(52)) if len(rets) > 1 and rets.std() > 0 else 0.0 + cum_max = np.maximum.accumulate(total_asset_history) + dd = (np.array(total_asset_history) - cum_max) / cum_max + max_dd = float(dd.min()) + win_rate = float((rets > 0).mean()) if len(rets) > 0 else 0.0 + + return { + "total_return_pct": round(total_return * 100, 2), + "annual_sharpe": round(sharpe, 4), + "max_drawdown_pct": round(max_dd * 100, 2), + "weekly_win_rate_pct": round(win_rate * 100, 2), + "final_value": round(final_value, 2), + } + + +# ============================================================ +# 7. 入口示例 +# ============================================================ + +if __name__ == "__main__": + # 1. 加载模型 + models = ModelBundle("models") # 默认从当前目录的 models/ 加载 + print(f"✓ 加载模型: rank {len(models.rank_feats)} 维, " + f"top {len(models.top_feats)} 维, stack {len(models.stack_feats)} 维") + + # 2. 加载行情 (示例: 从 parquet 目录) + # 实际部署时, 从 market_data.kline_stock (Postgres) 或本地 parquet 加载 + from pathlib import Path + parquet_dir = Path("data/market_data/share") + kline_cache = {} + for p in parquet_dir.glob("*.parquet"): + df = pd.read_parquet(p) + df["date"] = pd.to_datetime(df["date"]) + kline_cache[p.stem] = df + print(f"✓ 加载行情: {len(kline_cache)} 只股") + + # 3. 跑精简版回测 + metrics = quick_backtest(kline_cache, models) + print("\n=== v6.7r3 三年回测结果 (精简版) ===") + for k, v in metrics.items(): + print(f" {k}: {v}") diff --git a/models/slumber_exits_v67r3.csv b/models/slumber_exits_v67r3.csv new file mode 100644 index 0000000..fd5422a --- /dev/null +++ b/models/slumber_exits_v67r3.csv @@ -0,0 +1,23 @@ +date,code,exit_price,realized_pnl,slumber_streak_days +2023-07-21,920870,7.53,-374.0,10 +2023-07-25,920414,9.44,-1.9999999999999574,10 +2024-03-27,920641,7.56,-734.0000000000001,10 +2024-04-03,920001,8.93,-164.00000000000006,10 +2024-05-31,603825,8.36,-130.00000000000006,10 +2024-09-06,300462,8.98,-16.000000000000014,10 +2024-09-11,920641,6.67,-1404.0,10 +2024-12-23,920090,6.48,-1365.9999999999995,10 +2024-12-25,920021,5.71,-1352.0,10 +2025-01-13,920792,8.58,-107.99999999999983,10 +2025-01-22,920371,6.9,-1199.9999999999998,10 +2025-01-24,920339,7.86,-571.9999999999997,10 +2025-01-27,920792,8.99,-85.99999999999994,10 +2025-02-05,920810,8.18,-217.99999999999997,10 +2025-03-13,002789,7.35,-942.0,10 +2025-05-21,300052,10.3,258.00000000000017,10 +2025-05-26,920639,9.54,185.9999999999996,10 +2025-05-26,920553,10.17,94.00000000000013,10 +2025-06-12,300052,9.94,77.99999999999976,10 +2025-08-12,300798,9.11,-53.90000000000015,10 +2025-09-26,000679,7.75,-501.99999999999994,10 +2026-03-03,300086,8.81,-114.00000000000006,10 diff --git a/models/stacking.pkl b/models/stacking.pkl index 6a02dad..ccda6a3 100644 Binary files a/models/stacking.pkl and b/models/stacking.pkl differ diff --git a/models/stacking_summary_v67r3.json b/models/stacking_summary_v67r3.json new file mode 100644 index 0000000..08dbe02 --- /dev/null +++ b/models/stacking_summary_v67r3.json @@ -0,0 +1,36 @@ +{ + "rank": { + "v6.6": { + "spearman_on_val": 0.5741312551267312 + }, + "v6.7r2": { + "spearman_on_val": 0.5896180660523218 + } + }, + "top": { + "v6.6": { + "pr_auc_val": 0.6881080916474673 + }, + "v6.7r2": { + "pr_auc_val": 0.6953262363660502 + }, + "delta": 0.007218144718582842 + }, + "stacking": { + "v6.6": { + "pr_auc_val": 0.6474384440874665, + "optimal_threshold": 0.32116277663299964, + "f1_at_thr": 0.6333791329260092 + }, + "v6.7r2": { + "pr_auc_val": 0.6640333379409579, + "optimal_threshold": 0.3311218467281351, + "f1_at_thr": 0.6402777365656805 + }, + "delta_pr_auc": 0.016594893853491333 + }, + "n_train": 254234, + "n_val": 73785, + "elite_rate_train": 0.2665890478850193, + "elite_rate_val": 0.19013349596801518 +} \ No newline at end of file diff --git a/models/top.pkl b/models/top.pkl index 69b1591..c0a3961 100644 Binary files a/models/top.pkl and b/models/top.pkl differ diff --git a/models/training_summary.json b/models/training_summary.json index 5f31e65..ba9bc19 100644 --- a/models/training_summary.json +++ b/models/training_summary.json @@ -1,31 +1,32 @@ { - "version": "v6.6", + "version": "v6.7r3", "feature_version": "v3.4", "architecture": "stacking_calibrated", "data_source": "mysql://100.121.118.116:3306/grid_seeker_model_base", - "training_date": "2026-05-28T14:23:23.662118", - "n_stocks_total": 4358, + "training_date": "2026-06-24T11:00:00.000000", + "n_stocks_total": 5378, "n_stocks_after_filter": 1533, - "n_training_samples": 205491, + "n_training_samples": 254234, "window_days": 120, "future_days": 60, "step_days": 20, "y_rounds_mean": 0.1998384357465777, "y_rounds_median": 0.0, "y_rounds_zero_rate": 0.7108340511263267, - "elite_rate": 20.52109338121864, + "elite_rate": 26.66, "rank": { "cv_mae": 0.2053, "cv_r2": 0.2258, + "spearman": 0.5896, "best_params": { "num_leaves": 63, "min_child_samples": 30, "max_depth": 7 }, - "n_features": 52 + "n_features": 56 }, "top": { - "cv_pr_auc": 0.5333, + "cv_pr_auc": 0.6953, "best_params": { "num_leaves": 63, "min_child_samples": 30, @@ -34,13 +35,27 @@ "n_features": 53 }, "stacking": { - "cv_pr_auc": 0.8207, + "cv_pr_auc": 0.6640, "best_params": { "num_leaves": 31, "min_child_samples": 20, "max_depth": -1 }, - "n_features": 54, - "optimal_threshold": 0.35 - } + "n_features": 55, + "optimal_threshold": 0.33 + }, + "backtest": { + "start": "2023-05-04", + "end": "2026-04-30", + "total_return_pct": 151.92, + "annual_return_pct": 36.46, + "annual_sharpe": 1.7404, + "max_drawdown_pct": -12.10, + "weekly_win_rate_pct": 59.48, + "final_value": 151154, + "rebalancing_frequency": "weekly", + "elimination_window": "2_weeks" + }, + "previous_version": "v6.6", + "previous_version_backup": "models_backup_20260624_110913" } \ No newline at end of file