2204 lines
106 KiB
Python
2204 lines
106 KiB
Python
from __future__ import annotations
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import copy
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import json
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import math
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import statistics
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from collections import defaultdict
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from datetime import datetime, timedelta
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from typing import Any
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from backend.bootstrap.config import display_compact_date as _display_date
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from backend.data.numbers import finite_number as _number
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from backend.data.providers.tushare_client import TushareClient, TushareError
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from backend.features.screener.strategies import ADVANCED_CURATED_STRATEGIES
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from database import ReviewDatabase
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from backend.features.sentiment.engine import build_sentiment_history, latest_contiguous_history
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REGIMES = {
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"ice": "冰点",
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"repair": "修复",
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"fermentation": "发酵",
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"climax": "高潮",
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"divergence": "分化",
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"retreat": "退潮",
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}
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FACTOR_FIELDS = {
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"close": "收盘价",
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"pct_chg": "当日涨幅",
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"return_5d": "5日涨幅",
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"return_10d": "10日涨幅",
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"return_20d": "20日涨幅",
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"return_60d": "60日涨幅",
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"return_5d_rank": "5日涨幅排名",
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"momentum_60_5": "中期动量",
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"momentum_60_5_rank": "中期动量排名",
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"above_ma20": "站上20日线",
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"rsi_6": "RSI(6)",
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"ma60_slope": "60日线斜率",
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"ma20_slope_5d": "20日线5日斜率",
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"ma_bull_alignment": "均线多头排列",
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"drawdown_from_high_250": "距250日高点回撤",
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"donchian_breakout_pct": "唐奇安突破幅度",
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"range_20d": "20日振幅",
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"rs_high_120": "RS线120日新高",
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"excess_return_60d": "60日超额收益",
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"weekly_trend_signal": "周线趋势信号",
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"daily_buy_trigger": "日线买点",
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"weekly_amount_trend": "周成交趋势",
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"volume_ratio_5d": "5日量比",
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"turnover_5d": "5日累计换手",
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"volatility_10d": "10日波动率",
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"amount_billion": "成交额",
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"turnover_rate": "换手率",
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"circ_mv_billion": "流通市值",
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"net_flow_million": "主力净流入",
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"large_flow_million": "大单净流入",
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"net_flow_5d_million": "5日主力净流入",
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"flow_to_circ_mv_5d": "5日净流入占流通市值",
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"sector_strength": "板块强度",
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"sector_return_5d": "行业5日涨幅",
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"sector_return_20d": "行业20日涨幅",
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"sector_momentum_rank": "行业20日动量排名",
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"sector_stock_momentum_rank": "行业内个股动量排名",
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"sector_net_flow_5d_million": "行业5日主力净流入",
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"sector_flow_rank": "行业资金流排名",
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"sector_prosperity_rank": "行业景气度排名",
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"sector_trend_rank": "行业趋势排名",
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"sector_crowding_rank": "行业拥挤度排名",
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"sector_composite_score": "行业三维综合分",
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"sector_limit_count": "板块涨停数",
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"sector_up_count": "板块强势股数",
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"relative_strength": "相对强度",
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"limit_streak": "连板高度",
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"auction_change": "竞价涨幅",
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"auction_amount_million": "竞价成交额",
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"auction_turnover_rate": "竞价换手率",
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"auction_volume_ratio": "竞价量比",
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"total_mv_billion": "总市值",
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"pe_ttm": "市盈率TTM",
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"pb": "市净率",
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"ps_ttm": "市销率TTM",
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"dividend_yield_ttm": "股息率TTM",
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"dividend_years": "近年持续分红",
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"roe": "净资产收益率",
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"roa": "总资产收益率",
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"roic": "投入资本回报率",
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"gross_margin": "销售毛利率",
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"netprofit_yoy": "净利润同比",
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"revenue_yoy": "营业收入同比",
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"ocf_to_opincome": "经营现金流质量",
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"earnings_surprise_pct": "业绩超预期幅度",
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"earnings_days_since_announce": "业绩公告后天数",
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"earnings_event_quality": "业绩事件质量",
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"popularity_score": "人气榜热度",
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"popularity_rank_change": "人气排名跃升",
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"popularity_dual_source": "双榜共识",
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"institution_net_buy_million": "机构席位净买入",
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"institution_seat_count": "机构席位数",
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"style_size_fit": "大小盘风格匹配",
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"style_growth_fit": "成长价值风格匹配",
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"style_fit_score": "当前风格匹配度",
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"factor_value_score": "价值因子分",
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"factor_growth_score": "成长因子分",
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"factor_quality_score": "质量因子分",
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"factor_momentum_score": "动量因子分",
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"factor_sentiment_score": "交易情绪因子分",
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"multi_factor_composite": "动态多因子综合分",
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"relative_position_60": "60日相对位置",
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"max_abs_change_15d": "15日最大波动",
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"close_to_high_15d": "距15日高点",
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"close_to_high_60d": "距60日高点",
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"no_limit_30d": "近30日无涨停",
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"had_limit_80d": "近80日曾涨停",
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"previous_first_limit": "昨日首板",
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"previous_limit_signal": "昨日涨停或触板",
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"previous_limit_streak": "昨日连板高度",
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"previous_amount_billion": "昨日成交额",
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"is_limit_up_today": "当日涨停",
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"is_limit_down_today": "当日跌停",
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"sector_breadth_ma20": "行业20日线宽度",
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"no_limit_down_20d": "近20日无跌停",
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"financial_risk": "财务风险标记",
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"is_market_height": "当前市场最高板",
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"new_space_board": "新晋空间板",
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"max_continuous_board_10d": "近10日最高连板",
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"dragon_first_yin": "龙头首阴",
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"yin_day_pct": "首阴跌幅",
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"vol_vs_previous": "较前日量能",
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"broken_reversal": "断板反包",
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"days_since_broken": "断板后天数",
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"close_above_broken_high": "收复断板高点",
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"vol_vs_broken_day": "较断板日量能",
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"recent_limit_up_5d": "近5日涨停次数",
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"intraday_min_pct": "盘中最大跌幅",
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"lower_shadow_ratio": "下影线实体比",
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}
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FACTOR_GROUPS = {
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"行情动量": [
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"close", "pct_chg", "return_5d", "return_10d", "return_20d", "return_60d",
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"return_5d_rank", "momentum_60_5", "momentum_60_5_rank", "above_ma20",
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"rsi_6", "ma60_slope", "ma20_slope_5d", "ma_bull_alignment",
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"drawdown_from_high_250", "donchian_breakout_pct", "range_20d",
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"rs_high_120", "excess_return_60d", "weekly_trend_signal",
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"daily_buy_trigger", "weekly_amount_trend", "relative_strength",
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"relative_position_60", "close_to_high_15d", "close_to_high_60d",
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],
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"量价交易": [
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"volume_ratio_5d", "turnover_5d", "volatility_10d", "amount_billion", "turnover_rate",
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"net_flow_million", "large_flow_million", "net_flow_5d_million",
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"flow_to_circ_mv_5d", "previous_amount_billion",
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"intraday_min_pct", "lower_shadow_ratio", "vol_vs_previous", "vol_vs_broken_day",
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],
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"板块结构": [
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"sector_strength", "sector_return_5d", "sector_return_20d", "sector_momentum_rank",
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"sector_stock_momentum_rank", "sector_net_flow_5d_million", "sector_flow_rank",
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"sector_prosperity_rank", "sector_trend_rank", "sector_crowding_rank",
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"sector_composite_score",
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"sector_limit_count", "sector_up_count", "sector_breadth_ma20",
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"limit_streak", "previous_limit_streak", "previous_first_limit", "previous_limit_signal",
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"is_limit_up_today", "is_limit_down_today",
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"no_limit_30d", "had_limit_80d", "max_abs_change_15d", "no_limit_down_20d",
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"is_market_height", "new_space_board", "max_continuous_board_10d",
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"dragon_first_yin", "yin_day_pct", "broken_reversal", "days_since_broken",
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"close_above_broken_high", "recent_limit_up_5d",
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],
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"竞价因子": [
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"auction_change", "auction_amount_million", "auction_turnover_rate", "auction_volume_ratio",
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],
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"估值规模": [
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"circ_mv_billion", "total_mv_billion", "pe_ttm", "pb", "ps_ttm",
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"dividend_yield_ttm", "dividend_years",
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],
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"财务质量": [
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"roe", "roa", "roic", "gross_margin", "netprofit_yoy", "revenue_yoy",
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"ocf_to_opincome", "financial_risk",
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"earnings_surprise_pct", "earnings_days_since_announce", "earnings_event_quality",
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],
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"特色数据": [
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"popularity_score", "popularity_rank_change", "popularity_dual_source",
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"institution_net_buy_million", "institution_seat_count",
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"style_size_fit", "style_growth_fit", "style_fit_score",
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"factor_value_score", "factor_growth_score", "factor_quality_score",
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"factor_momentum_score", "factor_sentiment_score", "multi_factor_composite",
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],
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}
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ALLOWED_OPERATORS = {">", ">=", "<", "<=", "==", "!=", "between", "in"}
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BUILTIN_STRATEGIES = [
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{
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"name": "冰点抗跌先手",
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"description": "寻找冰点中保持相对强度、低波动且有板块承接的个股，允许无结果。",
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"regimes": ["ice"],
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"formula": {
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"universe": {"exclude_st": True, "listed_days_min": 120},
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"filters": [
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{"field": "pct_chg", "op": "between", "value": [-3, 7]},
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{"field": "return_5d", "op": ">=", "value": -5},
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{"field": "amount_billion", "op": ">=", "value": 1},
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{"field": "volatility_10d", "op": "<=", "value": 7},
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],
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"score": [
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{"field": "relative_strength", "weight": 0.30, "direction": "desc"},
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{"field": "sector_strength", "weight": 0.25, "direction": "desc"},
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{"field": "volume_ratio_5d", "weight": 0.20, "direction": "desc"},
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{"field": "volatility_10d", "weight": 0.15, "direction": "asc"},
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{"field": "amount_billion", "weight": 0.10, "direction": "desc"},
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],
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"limit": 12,
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"min_score": 0.58,
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},
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},
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{
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"name": "修复先锋",
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"description": "筛选率先站回趋势、温和放量并获得板块共振的修复前排。",
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"regimes": ["repair"],
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"formula": {
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"universe": {"exclude_st": True, "listed_days_min": 120},
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"filters": [
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{"field": "pct_chg", "op": "between", "value": [1, 9.7]},
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{"field": "return_5d", "op": ">", "value": 0},
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{"field": "above_ma20", "op": "==", "value": 1},
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{"field": "volume_ratio_5d", "op": ">=", "value": 1.05},
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],
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"score": [
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{"field": "sector_strength", "weight": 0.28, "direction": "desc"},
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{"field": "relative_strength", "weight": 0.24, "direction": "desc"},
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{"field": "volume_ratio_5d", "weight": 0.18, "direction": "desc"},
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{"field": "net_flow_million", "weight": 0.16, "direction": "desc"},
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{"field": "amount_billion", "weight": 0.14, "direction": "desc"},
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],
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"limit": 15,
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"min_score": 0.54,
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},
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},
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{
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"name": "主线发酵跟随",
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"description": "在主线扩散期寻找趋势、成交承载和板块涨停梯队共同增强的个股。",
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"regimes": ["fermentation"],
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"formula": {
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"universe": {"exclude_st": True, "listed_days_min": 120},
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"filters": [
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{"field": "pct_chg", "op": "between", "value": [0, 9.8]},
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{"field": "return_5d", "op": ">=", "value": 3},
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{"field": "above_ma20", "op": "==", "value": 1},
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{"field": "amount_billion", "op": ">=", "value": 2},
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],
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"score": [
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{"field": "sector_limit_count", "weight": 0.25, "direction": "desc"},
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{"field": "sector_strength", "weight": 0.24, "direction": "desc"},
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{"field": "return_10d", "weight": 0.20, "direction": "desc"},
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{"field": "amount_billion", "weight": 0.16, "direction": "desc"},
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{"field": "large_flow_million", "weight": 0.15, "direction": "desc"},
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],
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"limit": 15,
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"min_score": 0.55,
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},
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},
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{
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"name": "高潮核心去后排",
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"description": "高潮阶段只保留容量、趋势和辨识度较高的核心，降低后排跟风权重。",
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"regimes": ["climax"],
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"formula": {
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"universe": {"exclude_st": True, "listed_days_min": 120},
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"filters": [
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{"field": "pct_chg", "op": "between", "value": [-2, 7]},
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{"field": "return_10d", "op": ">=", "value": 5},
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{"field": "above_ma20", "op": "==", "value": 1},
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{"field": "amount_billion", "op": ">=", "value": 5},
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],
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"score": [
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{"field": "amount_billion", "weight": 0.28, "direction": "desc"},
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{"field": "sector_strength", "weight": 0.22, "direction": "desc"},
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{"field": "relative_strength", "weight": 0.20, "direction": "desc"},
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{"field": "volatility_10d", "weight": 0.15, "direction": "asc"},
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{"field": "limit_streak", "weight": 0.15, "direction": "desc"},
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],
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"limit": 10,
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"min_score": 0.62,
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},
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},
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{
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"name": "分化承接回流",
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"description": "寻找分化中仍有趋势承接、板块强度和资金回流的核心候选。",
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"regimes": ["divergence"],
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"formula": {
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"universe": {"exclude_st": True, "listed_days_min": 120},
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"filters": [
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{"field": "pct_chg", "op": "between", "value": [-3, 7]},
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{"field": "return_5d", "op": ">", "value": 0},
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{"field": "above_ma20", "op": "==", "value": 1},
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{"field": "volume_ratio_5d", "op": "between", "value": [0.7, 3.5]},
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],
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"score": [
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||
{"field": "relative_strength", "weight": 0.28, "direction": "desc"},
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||
{"field": "sector_strength", "weight": 0.24, "direction": "desc"},
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||
{"field": "net_flow_million", "weight": 0.20, "direction": "desc"},
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||
{"field": "volatility_10d", "weight": 0.16, "direction": "asc"},
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||
{"field": "amount_billion", "weight": 0.12, "direction": "desc"},
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||
],
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||
"limit": 12,
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||
"min_score": 0.57,
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||
},
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||
},
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{
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||
"name": "退潮防守观察",
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"description": "退潮期采用高门槛防守筛选，结果为空代表当前不宜主动出击。",
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||
"regimes": ["retreat"],
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||
"formula": {
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||
"universe": {"exclude_st": True, "listed_days_min": 180},
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||
"filters": [
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||
{"field": "pct_chg", "op": "between", "value": [-2, 4]},
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||
{"field": "return_5d", "op": ">=", "value": -2},
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||
{"field": "above_ma20", "op": "==", "value": 1},
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||
{"field": "volatility_10d", "op": "<=", "value": 4.5},
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||
{"field": "amount_billion", "op": ">=", "value": 2},
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],
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||
"score": [
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||
{"field": "volatility_10d", "weight": 0.30, "direction": "asc"},
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||
{"field": "relative_strength", "weight": 0.25, "direction": "desc"},
|
||
{"field": "amount_billion", "weight": 0.20, "direction": "desc"},
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{"field": "sector_strength", "weight": 0.15, "direction": "desc"},
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||
{"field": "net_flow_million", "weight": 0.10, "direction": "desc"},
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||
],
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"limit": 8,
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||
"min_score": 0.68,
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},
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},
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{
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||
"name": "竞价强势确认",
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||
"description": "用竞价涨幅、成交承载和量比确认修复或发酵阶段的主动进攻标的。",
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||
"regimes": ["repair", "fermentation", "divergence"],
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||
"formula": {
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||
"universe": {"exclude_st": True, "listed_days_min": 120},
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||
"filters": [
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||
{"field": "auction_change", "op": "between", "value": [1, 7]},
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||
{"field": "auction_amount_million", "op": ">=", "value": 3},
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||
{"field": "auction_volume_ratio", "op": ">=", "value": 0.8},
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||
{"field": "amount_billion", "op": ">=", "value": 1},
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],
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||
"score": [
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||
{"field": "auction_amount_million", "weight": 0.26, "direction": "desc"},
|
||
{"field": "auction_volume_ratio", "weight": 0.22, "direction": "desc"},
|
||
{"field": "auction_change", "weight": 0.18, "direction": "desc"},
|
||
{"field": "sector_strength", "weight": 0.18, "direction": "desc"},
|
||
{"field": "relative_strength", "weight": 0.16, "direction": "desc"},
|
||
],
|
||
"limit": 15,
|
||
"min_score": 0.56,
|
||
},
|
||
},
|
||
]
|
||
|
||
for _strategy in BUILTIN_STRATEGIES:
|
||
_strategy["formula"].setdefault("meta", {
|
||
"library": "smart", "category": "周期策略", "quality": "系统",
|
||
"frequency": "每日", "risk": "随市场阶段", "data_group": "行情因子",
|
||
})
|
||
|
||
|
||
CURATED_STRATEGIES = [
|
||
{
|
||
"name": "连续分红质量",
|
||
"description": "寻找持续派息、盈利质量稳定且波动可控的长期现金回报型公司。",
|
||
"regimes": list(REGIMES),
|
||
"formula": {
|
||
"meta": {"library": "curated", "category": "红利价值", "quality": "A", "frequency": "月度", "risk": "中低", "data_group": "估值与财务"},
|
||
"universe": {"exclude_st": True, "listed_days_min": 1095},
|
||
"filters": [
|
||
{"field": "dividend_years", "op": ">=", "value": 4},
|
||
{"field": "dividend_yield_ttm", "op": ">=", "value": 2},
|
||
{"field": "roe", "op": ">=", "value": 6},
|
||
{"field": "pb", "op": "between", "value": [0.1, 4]},
|
||
],
|
||
"score": [
|
||
{"field": "dividend_yield_ttm", "weight": 0.30, "direction": "desc"},
|
||
{"field": "roe", "weight": 0.24, "direction": "desc"},
|
||
{"field": "ocf_to_opincome", "weight": 0.18, "direction": "desc"},
|
||
{"field": "volatility_10d", "weight": 0.16, "direction": "asc"},
|
||
{"field": "total_mv_billion", "weight": 0.12, "direction": "desc"},
|
||
], "limit": 20, "min_score": 0.52,
|
||
},
|
||
},
|
||
{
|
||
"name": "ROIC质量低波",
|
||
"description": "以投入资本回报、毛利率和估值为核心，寻找低波动的高质量公司。",
|
||
"regimes": ["ice", "repair", "divergence", "retreat"],
|
||
"formula": {
|
||
"meta": {"library": "curated", "category": "质量价值", "quality": "A-", "frequency": "月度", "risk": "中低", "data_group": "估值与财务"},
|
||
"universe": {"exclude_st": True, "listed_days_min": 730},
|
||
"filters": [
|
||
{"field": "roic", "op": ">=", "value": 6},
|
||
{"field": "gross_margin", "op": ">=", "value": 15},
|
||
{"field": "pe_ttm", "op": "between", "value": [1, 45]},
|
||
{"field": "amount_billion", "op": ">=", "value": 1},
|
||
],
|
||
"score": [
|
||
{"field": "roic", "weight": 0.28, "direction": "desc"},
|
||
{"field": "gross_margin", "weight": 0.22, "direction": "desc"},
|
||
{"field": "ps_ttm", "weight": 0.18, "direction": "asc"},
|
||
{"field": "volatility_10d", "weight": 0.18, "direction": "asc"},
|
||
{"field": "total_mv_billion", "weight": 0.14, "direction": "desc"},
|
||
], "limit": 20, "min_score": 0.54,
|
||
},
|
||
},
|
||
{
|
||
"name": "低估值现金流白马",
|
||
"description": "筛选估值克制、经营现金流健康、资产回报稳定的大中型公司。",
|
||
"regimes": ["ice", "repair", "divergence", "retreat"],
|
||
"formula": {
|
||
"meta": {"library": "curated", "category": "现金流价值", "quality": "A-", "frequency": "月度", "risk": "中低", "data_group": "估值与财务"},
|
||
"universe": {"exclude_st": True, "listed_days_min": 730},
|
||
"filters": [
|
||
{"field": "pb", "op": "between", "value": [0.1, 1.8]},
|
||
{"field": "roa", "op": ">=", "value": 3},
|
||
{"field": "ocf_to_opincome", "op": ">", "value": 0},
|
||
{"field": "netprofit_yoy", "op": ">=", "value": -15},
|
||
{"field": "total_mv_billion", "op": ">=", "value": 100},
|
||
],
|
||
"score": [
|
||
{"field": "roa", "weight": 0.26, "direction": "desc"},
|
||
{"field": "ocf_to_opincome", "weight": 0.24, "direction": "desc"},
|
||
{"field": "pb", "weight": 0.20, "direction": "asc"},
|
||
{"field": "total_mv_billion", "weight": 0.16, "direction": "desc"},
|
||
{"field": "volatility_10d", "weight": 0.14, "direction": "asc"},
|
||
], "limit": 20, "min_score": 0.53,
|
||
},
|
||
},
|
||
{
|
||
"name": "高增长合理估值",
|
||
"description": "在收入和利润同步增长的公司中，优先选择估值合理、趋势得到确认的标的。",
|
||
"regimes": ["repair", "fermentation", "divergence"],
|
||
"formula": {
|
||
"meta": {"library": "curated", "category": "成长质量", "quality": "B+", "frequency": "月度", "risk": "中", "data_group": "估值与财务"},
|
||
"universe": {"exclude_st": True, "listed_days_min": 365},
|
||
"filters": [
|
||
{"field": "pe_ttm", "op": "between", "value": [1, 35]},
|
||
{"field": "revenue_yoy", "op": ">=", "value": 10},
|
||
{"field": "netprofit_yoy", "op": ">=", "value": 15},
|
||
{"field": "roe", "op": ">=", "value": 5},
|
||
{"field": "amount_billion", "op": ">=", "value": 1},
|
||
],
|
||
"score": [
|
||
{"field": "netprofit_yoy", "weight": 0.27, "direction": "desc"},
|
||
{"field": "revenue_yoy", "weight": 0.23, "direction": "desc"},
|
||
{"field": "roe", "weight": 0.20, "direction": "desc"},
|
||
{"field": "pe_ttm", "weight": 0.16, "direction": "asc"},
|
||
{"field": "relative_strength", "weight": 0.14, "direction": "desc"},
|
||
], "limit": 20, "min_score": 0.55,
|
||
},
|
||
},
|
||
{
|
||
"name": "行业宽度主线",
|
||
"description": "从行业站上20日线的覆盖率和板块强度出发，筛选主线中的强势个股。",
|
||
"regimes": ["repair", "fermentation", "climax", "divergence"],
|
||
"formula": {
|
||
"meta": {"library": "curated", "category": "行业轮动", "quality": "B+", "frequency": "每周", "risk": "中", "data_group": "行情与行业"},
|
||
"universe": {"exclude_st": True, "listed_days_min": 180},
|
||
"filters": [
|
||
{"field": "sector_breadth_ma20", "op": ">=", "value": 55},
|
||
{"field": "sector_strength", "op": ">=", "value": 55},
|
||
{"field": "above_ma20", "op": "==", "value": 1},
|
||
{"field": "amount_billion", "op": ">=", "value": 2},
|
||
],
|
||
"score": [
|
||
{"field": "sector_breadth_ma20", "weight": 0.28, "direction": "desc"},
|
||
{"field": "sector_strength", "weight": 0.24, "direction": "desc"},
|
||
{"field": "relative_strength", "weight": 0.20, "direction": "desc"},
|
||
{"field": "sector_limit_count", "weight": 0.16, "direction": "desc"},
|
||
{"field": "amount_billion", "weight": 0.12, "direction": "desc"},
|
||
], "limit": 20, "min_score": 0.56,
|
||
},
|
||
},
|
||
{
|
||
"name": "首板低开",
|
||
"description": "昨日首板且位置不高，次日竞价温和低开并具备成交承载时进入候选。",
|
||
"regimes": ["ice", "repair", "divergence"],
|
||
"formula": {
|
||
"meta": {"library": "curated", "category": "短线竞价", "quality": "B+", "frequency": "每日9:25", "risk": "高", "data_group": "行情与竞价"},
|
||
"universe": {"exclude_st": True, "listed_days_min": 250},
|
||
"filters": [
|
||
{"field": "previous_first_limit", "op": "==", "value": 1},
|
||
{"field": "auction_change", "op": "between", "value": [-4.5, -2.5]},
|
||
{"field": "relative_position_60", "op": "<=", "value": 0.55},
|
||
{"field": "previous_amount_billion", "op": ">=", "value": 1},
|
||
],
|
||
"score": [
|
||
{"field": "auction_amount_million", "weight": 0.28, "direction": "desc"},
|
||
{"field": "previous_amount_billion", "weight": 0.24, "direction": "desc"},
|
||
{"field": "relative_position_60", "weight": 0.20, "direction": "asc"},
|
||
{"field": "sector_strength", "weight": 0.16, "direction": "desc"},
|
||
{"field": "auction_volume_ratio", "weight": 0.12, "direction": "desc"},
|
||
], "limit": 12, "min_score": 0.50,
|
||
},
|
||
},
|
||
{
|
||
"name": "小碎步临界突破",
|
||
"description": "寻找近期窄幅爬升、接近阶段高点且具备历史活跃记忆的突破候选。",
|
||
"regimes": ["repair", "fermentation", "divergence"],
|
||
"formula": {
|
||
"meta": {"library": "curated", "category": "形态突破", "quality": "B+", "frequency": "每日", "risk": "中高", "data_group": "历史行情"},
|
||
"universe": {"exclude_st": True, "listed_days_min": 250},
|
||
"filters": [
|
||
{"field": "no_limit_30d", "op": "==", "value": 1},
|
||
{"field": "had_limit_80d", "op": "==", "value": 1},
|
||
{"field": "max_abs_change_15d", "op": "<=", "value": 3},
|
||
{"field": "close_to_high_15d", "op": ">=", "value": 0.98},
|
||
{"field": "close_to_high_60d", "op": ">=", "value": 0.90},
|
||
],
|
||
"score": [
|
||
{"field": "close_to_high_15d", "weight": 0.26, "direction": "desc"},
|
||
{"field": "volume_ratio_5d", "weight": 0.22, "direction": "desc"},
|
||
{"field": "relative_strength", "weight": 0.20, "direction": "desc"},
|
||
{"field": "max_abs_change_15d", "weight": 0.18, "direction": "asc"},
|
||
{"field": "circ_mv_billion", "weight": 0.14, "direction": "asc"},
|
||
], "limit": 15, "min_score": 0.54,
|
||
},
|
||
},
|
||
{
|
||
"name": "连板龙头",
|
||
"description": "从昨日连板梯队中按高度、板块热度和成交承载筛选辨识度前排。",
|
||
"regimes": ["fermentation", "climax", "divergence"],
|
||
"formula": {
|
||
"meta": {"library": "curated", "category": "连板接力", "quality": "B", "frequency": "每日", "risk": "很高", "data_group": "涨停结构"},
|
||
"universe": {"exclude_st": True, "listed_days_min": 120},
|
||
"filters": [
|
||
{"field": "previous_limit_streak", "op": ">=", "value": 2},
|
||
{"field": "previous_amount_billion", "op": ">=", "value": 1},
|
||
],
|
||
"score": [
|
||
{"field": "previous_limit_streak", "weight": 0.34, "direction": "desc"},
|
||
{"field": "sector_limit_count", "weight": 0.24, "direction": "desc"},
|
||
{"field": "previous_amount_billion", "weight": 0.18, "direction": "desc"},
|
||
{"field": "turnover_rate", "weight": 0.14, "direction": "desc"},
|
||
{"field": "sector_strength", "weight": 0.10, "direction": "desc"},
|
||
], "limit": 10, "min_score": 0.50,
|
||
},
|
||
},
|
||
{
|
||
"name": "微盘三正",
|
||
"description": "以正估值、正盈利和正经营现金流约束微盘暴露，保留明确风险提示。",
|
||
"regimes": ["repair", "fermentation"],
|
||
"formula": {
|
||
"meta": {"library": "curated", "category": "小盘质量", "quality": "B", "frequency": "每周", "risk": "高", "data_group": "估值与财务"},
|
||
"universe": {"exclude_st": True, "listed_days_min": 365},
|
||
"filters": [
|
||
{"field": "pb", "op": ">", "value": 0},
|
||
{"field": "roe", "op": ">", "value": 0},
|
||
{"field": "ocf_to_opincome", "op": ">", "value": 0},
|
||
{"field": "circ_mv_billion", "op": "between", "value": [5, 100]},
|
||
{"field": "amount_billion", "op": ">=", "value": 0.5},
|
||
],
|
||
"score": [
|
||
{"field": "circ_mv_billion", "weight": 0.32, "direction": "asc"},
|
||
{"field": "roe", "weight": 0.24, "direction": "desc"},
|
||
{"field": "ocf_to_opincome", "weight": 0.20, "direction": "desc"},
|
||
{"field": "turnover_rate", "weight": 0.14, "direction": "desc"},
|
||
{"field": "relative_strength", "weight": 0.10, "direction": "desc"},
|
||
], "limit": 20, "min_score": 0.52,
|
||
},
|
||
},
|
||
{
|
||
"name": "首板高开弱转强",
|
||
"description": "昨日涨停或触板后，使用9:25最终竞价涨幅、量比和板块承接确认强度。",
|
||
"regimes": ["repair", "fermentation", "divergence"],
|
||
"formula": {
|
||
"meta": {"library": "curated", "category": "短线竞价", "quality": "B-", "frequency": "每日9:25", "risk": "高", "data_group": "行情与竞价"},
|
||
"universe": {"exclude_st": True, "listed_days_min": 120},
|
||
"filters": [
|
||
{"field": "previous_limit_signal", "op": "==", "value": 1},
|
||
{"field": "auction_change", "op": "between", "value": [1, 6]},
|
||
{"field": "auction_volume_ratio", "op": ">=", "value": 0.8},
|
||
{"field": "previous_amount_billion", "op": "between", "value": [3, 25]},
|
||
],
|
||
"score": [
|
||
{"field": "auction_amount_million", "weight": 0.28, "direction": "desc"},
|
||
{"field": "auction_volume_ratio", "weight": 0.24, "direction": "desc"},
|
||
{"field": "auction_change", "weight": 0.18, "direction": "desc"},
|
||
{"field": "sector_strength", "weight": 0.17, "direction": "desc"},
|
||
{"field": "relative_strength", "weight": 0.13, "direction": "desc"},
|
||
], "limit": 15, "min_score": 0.52,
|
||
},
|
||
},
|
||
]
|
||
|
||
CURATED_STRATEGIES.extend(ADVANCED_CURATED_STRATEGIES)
|
||
|
||
STRATEGY_ENVIRONMENT_NOTES = {
|
||
"连续分红质量": (
|
||
"防守市、低利率环境与中长期配置窗口",
|
||
"风险偏好快速上升时，稳健资产的价格弹性通常落后",
|
||
),
|
||
"ROIC质量低波": (
|
||
"震荡偏弱、重视盈利质量与回撤控制的市场",
|
||
"主题快速扩散或高弹性行情中，低波筛选可能错过进攻方向",
|
||
),
|
||
"低估值现金流白马": (
|
||
"估值修复、价值回归及防守配置阶段",
|
||
"低估值可能来自基本面持续走弱，需警惕价值陷阱",
|
||
),
|
||
"高增长合理估值": (
|
||
"业绩驱动、成长风格占优且趋势获得确认的阶段",
|
||
"增长预期下修或估值快速收缩时，回撤可能明显放大",
|
||
),
|
||
"行业宽度主线": (
|
||
"主线清晰、行业内部多数个股同步走强的行情",
|
||
"板块快速轮动时，宽度信号容易在确认后迅速衰减",
|
||
),
|
||
"首板低开": (
|
||
"情绪修复期的分歧转一致与首板次日承接",
|
||
"退潮加速或低开缺少量能承接时，弱势可能继续扩大",
|
||
),
|
||
"小碎步临界突破": (
|
||
"趋势蓄势、波动收敛后临近突破的结构市",
|
||
"无量突破或指数剧烈震荡时，容易形成冲高回落",
|
||
),
|
||
"连板龙头": (
|
||
"高度拓展、题材梯队完整且接力情绪活跃的阶段",
|
||
"亏钱效应扩散或高位股集中退潮时，接力风险很高",
|
||
),
|
||
"微盘三正": (
|
||
"小盘风格活跃、流动性宽松且风险偏好较高的行情",
|
||
"风格切向大盘或微盘流动性收缩时，组合波动会显著上升",
|
||
),
|
||
"首板高开弱转强": (
|
||
"竞价承接明确、短线情绪修复或主线发酵阶段",
|
||
"高开缺乏板块共振时，竞价强势可能转为盘中兑现",
|
||
),
|
||
"中期动量·强者恒强": (
|
||
"趋势延续、主升段及强弱分化清晰的行情",
|
||
"无趋势震荡或快速轮动中，动量信号容易反复失效",
|
||
),
|
||
"强者回调": (
|
||
"主升趋势未破、强势股完成良性回踩的窗口",
|
||
"趋势已反转时，回调信号可能演变为下跌中继",
|
||
),
|
||
"超跌反转": (
|
||
"急跌后恐慌释放充分、市场进入修复预期的阶段",
|
||
"单边下跌初段容易过早介入，超跌不等于止跌",
|
||
),
|
||
"相对强度新高": (
|
||
"指数偏弱但结构性主线明确，或机构抱团强化的行情",
|
||
"基准快速补涨或强势方向瓦解时，相对优势可能迅速消失",
|
||
),
|
||
"均线多头排列": (
|
||
"中期趋势向上、回撤有序的趋势市与主升段",
|
||
"高位趋势末端或宽幅震荡中，均线信号通常反应滞后",
|
||
),
|
||
"唐奇安通道突破": (
|
||
"整理末端、放量突破并启动新趋势的行情",
|
||
"无量突破和宽幅震荡环境中，假突破出现概率较高",
|
||
),
|
||
"周线趋势·日线买点": (
|
||
"中期趋势稳定、日线回踩或再启动的多周期共振阶段",
|
||
"周线拐点尚未确认时，日线信号可能只是短暂反抽",
|
||
),
|
||
"空间板": (
|
||
"市场高度持续拓展、板块梯队完整的强接力环境",
|
||
"高度压缩或亏钱效应扩散时，最高板的补跌风险极高",
|
||
),
|
||
"龙头首阴": (
|
||
"主线龙头仍有辨识度、首次分歧后存在回流预期的阶段",
|
||
"题材退潮或龙头地位被替代后，首阴可能只是下跌起点",
|
||
),
|
||
"断板反包": (
|
||
"强势题材分歧后快速修复、核心股重新获得资金承接时",
|
||
"板块强度不足或反包缩量时，形态持续性通常较弱",
|
||
),
|
||
"核按钮反核": (
|
||
"恐慌释放后出现明确承接、短线情绪转暖的窗口",
|
||
"系统性退潮中深水拉回可能只是日内脉冲，隔日风险较高",
|
||
),
|
||
"行业动量轮动": (
|
||
"主线相对清晰、行业趋势能够延续两周以上的结构市",
|
||
"行业轮动速度过快或前三名差距很小时，动量优势容易迅速衰减",
|
||
),
|
||
"主力资金行业流入": (
|
||
"板块轮动初期、资金先于价格形成连续净流入的阶段",
|
||
"资金流口径可能受大宗交易和短期对倒影响，单日突增不代表趋势",
|
||
),
|
||
"景气-趋势-拥挤三维行业打分": (
|
||
"行业景气与价格趋势同向、但交易拥挤尚未达到极端的结构市",
|
||
"财务披露存在滞后，行业快速反转时三维综合分可能反应偏慢",
|
||
),
|
||
"大小盘/成长价值风格切换(元策略)": (
|
||
"大小盘或成长价值风格形成持续相对强弱的阶段",
|
||
"风格快速往返切换时，近20日相对表现容易产生滞后信号",
|
||
),
|
||
"业绩超预期漂移(SUE/PEAD)": (
|
||
"业绩披露窗口中，快报相对预告继续上修且价格尚未充分兑现时",
|
||
"预告与快报口径可能不同，公告后高开兑现会削弱漂移效应",
|
||
),
|
||
"多因子综合打分(IC动态加权)": (
|
||
"因子表现具备一定延续性、市场并非由单一极端主题主导时",
|
||
"近期有效因子可能快速失效，动态权重不能消除风格突变风险",
|
||
),
|
||
"热度突增潜伏(另类数据)": (
|
||
"人气快速抬升但股价尚未明显启动的题材萌芽与扩散初期",
|
||
"榜单热度可能由短期讨论驱动，缺少价格确认时误报率较高",
|
||
),
|
||
"机构榜溢价": (
|
||
"机构专用席位在相对低位形成明确净买入、且成交承载正常时",
|
||
"高位机构榜可能对应兑现或对倒，席位净买入不等于持续锁仓",
|
||
),
|
||
}
|
||
|
||
for strategy in CURATED_STRATEGIES:
|
||
suitable_environment, failure_risk = STRATEGY_ENVIRONMENT_NOTES[strategy["name"]]
|
||
strategy["formula"]["meta"].update(
|
||
{
|
||
"suitable_environment": suitable_environment,
|
||
"failure_risk": failure_risk,
|
||
}
|
||
)
|
||
|
||
BUILTIN_STRATEGIES.extend(CURATED_STRATEGIES)
|
||
|
||
|
||
def _quarter_periods(trade_date: str, count: int) -> list[str]:
|
||
current = datetime.strptime(trade_date, "%Y%m%d")
|
||
quarter_ends = ((3, 31), (6, 30), (9, 30), (12, 31))
|
||
periods = []
|
||
year = current.year
|
||
while len(periods) < count:
|
||
for month, day in reversed(quarter_ends):
|
||
value = datetime(year, month, day)
|
||
if value <= current:
|
||
periods.append(value.strftime("%Y%m%d"))
|
||
if len(periods) == count:
|
||
break
|
||
year -= 1
|
||
return sorted(periods)
|
||
|
||
|
||
def _earnings_event_rows(
|
||
forecasts: list[dict[str, Any]], expresses: list[dict[str, Any]], trade_date: str,
|
||
) -> list[dict[str, Any]]:
|
||
forecast_map: dict[tuple[str, str], dict[str, Any]] = {}
|
||
for row in forecasts:
|
||
key = (str(row.get("ts_code") or ""), str(row.get("end_date") or ""))
|
||
ann_date = str(row.get("ann_date") or "")
|
||
if not all(key) or not ann_date or ann_date > trade_date:
|
||
continue
|
||
previous = forecast_map.get(key)
|
||
if previous is None or ann_date > str(previous.get("ann_date") or ""):
|
||
forecast_map[key] = row
|
||
result = []
|
||
for row in expresses:
|
||
ts_code = str(row.get("ts_code") or "")
|
||
end_date = str(row.get("end_date") or "")
|
||
ann_date = str(row.get("ann_date") or "")
|
||
forecast = forecast_map.get((ts_code, end_date))
|
||
if not forecast or not ts_code or not end_date or not ann_date or ann_date > trade_date:
|
||
continue
|
||
lower = _optional_number(forecast.get("net_profit_min"))
|
||
upper = _optional_number(forecast.get("net_profit_max"))
|
||
forecast_profit = statistics.fmean(
|
||
value for value in (lower, upper) if value is not None
|
||
) if lower is not None or upper is not None else None
|
||
actual_profit = _optional_number(row.get("n_income"))
|
||
if forecast_profit in (None, 0) or actual_profit is None:
|
||
continue
|
||
# forecast is reported in ten-thousand yuan while express uses yuan.
|
||
if abs(actual_profit) > max(abs(forecast_profit), 1) * 100:
|
||
actual_profit /= 10000
|
||
surprise_pct = (actual_profit / forecast_profit - 1) * 100
|
||
result.append(
|
||
{
|
||
"end_date": end_date,
|
||
"ann_date": ann_date,
|
||
"ts_code": ts_code,
|
||
"forecast_profit": forecast_profit,
|
||
"actual_profit": actual_profit,
|
||
"surprise_pct": surprise_pct,
|
||
"revenue_yoy": _optional_number(row.get("yoy_sales")),
|
||
"netprofit_yoy": _optional_number(row.get("yoy_net_profit")),
|
||
"source": "forecast+express",
|
||
}
|
||
)
|
||
return result
|
||
|
||
|
||
def _popularity_factor_rows(
|
||
trade_date: str,
|
||
ths_rows: list[dict[str, Any]],
|
||
dc_rows: list[dict[str, Any]],
|
||
previous_ths: list[dict[str, Any]],
|
||
previous_dc: list[dict[str, Any]],
|
||
) -> list[dict[str, Any]]:
|
||
def ranks(rows: list[dict[str, Any]], data_type: str) -> dict[str, int]:
|
||
result = {}
|
||
for row in rows:
|
||
if data_type and str(row.get("data_type") or "") != data_type:
|
||
continue
|
||
ts_code = str(row.get("ts_code") or "")
|
||
rank = int(_number(row.get("rank")))
|
||
if ts_code and rank > 0:
|
||
result[ts_code] = rank
|
||
return result
|
||
|
||
ths = ranks(ths_rows, "热股")
|
||
dc = ranks(dc_rows, "A股市场")
|
||
previous_ths_map = ranks(previous_ths, "热股")
|
||
previous_dc_map = ranks(previous_dc, "A股市场")
|
||
result = []
|
||
for ts_code in set(ths) | set(dc):
|
||
ths_rank = ths.get(ts_code)
|
||
dc_rank = dc.get(ts_code)
|
||
current_best = min(value for value in (ths_rank, dc_rank) if value is not None)
|
||
previous_candidates = [
|
||
value for value in (previous_ths_map.get(ts_code), previous_dc_map.get(ts_code))
|
||
if value is not None
|
||
]
|
||
previous_best = min(previous_candidates) if previous_candidates else None
|
||
score = (101 - (ths_rank or 101)) * 0.5 + (201 - (dc_rank or 201)) * 0.25
|
||
result.append(
|
||
{
|
||
"trade_date": trade_date,
|
||
"ts_code": ts_code,
|
||
"ths_rank": ths_rank,
|
||
"dc_rank": dc_rank,
|
||
"combined_score": round(score, 2),
|
||
"rank_change": (
|
||
previous_best - current_best
|
||
if previous_best is not None
|
||
else min(30, max(0, 31 - current_best))
|
||
if previous_ths_map or previous_dc_map else 0
|
||
),
|
||
"dual_source": bool(ths_rank and dc_rank),
|
||
}
|
||
)
|
||
return result
|
||
|
||
|
||
class FactorDataService:
|
||
def __init__(self, database: ReviewDatabase, client: TushareClient) -> None:
|
||
self.database = database
|
||
self.client = client
|
||
|
||
def sync(self, requested_date: str, lookback: int = 45) -> dict[str, Any]:
|
||
lookback = max(25, min(260, int(lookback)))
|
||
trade_date, _ = self.client.resolve_trade_context(requested_date)
|
||
end = datetime.strptime(trade_date, "%Y%m%d")
|
||
start = (end - timedelta(days=max(100, lookback * 2 + 20))).strftime("%Y%m%d")
|
||
calendar = self.client.query(
|
||
"trade_cal",
|
||
{"exchange": "SSE", "start_date": start, "end_date": trade_date, "is_open": 1},
|
||
"cal_date,is_open",
|
||
)
|
||
dates = sorted(row["cal_date"] for row in calendar if row.get("is_open") == 1)[-lookback:]
|
||
existing = set(self.database.factor_dates(trade_date, lookback + 10))
|
||
dates_to_fetch = [value for value in dates if value not in existing or value == trade_date]
|
||
auction_source_dates = dates[-min(80, len(dates)):]
|
||
existing_auction = set(self.database.auction_factor_dates(trade_date, 90))
|
||
auction_dates_to_fetch = [
|
||
value for value in auction_source_dates
|
||
if value not in existing_auction or value == trade_date
|
||
]
|
||
long_calendar = self.client.query(
|
||
"trade_cal",
|
||
{
|
||
"exchange": "SSE",
|
||
"start_date": datetime(end.year - 5, 1, 1).strftime("%Y%m%d"),
|
||
"end_date": trade_date,
|
||
"is_open": 1,
|
||
},
|
||
"cal_date,is_open",
|
||
)
|
||
last_open_by_year: dict[str, str] = {}
|
||
last_open_by_month: dict[str, str] = {}
|
||
for row in long_calendar:
|
||
if row.get("is_open") == 1 and row.get("cal_date"):
|
||
value = str(row["cal_date"])
|
||
last_open_by_year[value[:4]] = max(last_open_by_year.get(value[:4], ""), value)
|
||
last_open_by_month[value[:6]] = max(last_open_by_month.get(value[:6], ""), value)
|
||
valuation_dates = set(dates[-min(80, len(dates)):])
|
||
valuation_dates.update(last_open_by_year.values())
|
||
valuation_dates.update(last_open_by_month.values())
|
||
existing_indicators = set(self.database.daily_indicator_dates(trade_date, 500))
|
||
indicator_dates_to_fetch = sorted(
|
||
value for value in valuation_dates if value not in existing_indicators or value == trade_date
|
||
)
|
||
|
||
master = self.client.query(
|
||
"stock_basic",
|
||
{"list_status": "L"},
|
||
"ts_code,name,industry,market,list_date",
|
||
)
|
||
master_count = self.database.upsert_stock_master(master)
|
||
bar_count = 0
|
||
for current_date in dates_to_fetch:
|
||
rows = self.client.query(
|
||
"daily",
|
||
{"trade_date": current_date},
|
||
"ts_code,trade_date,open,high,low,close,pct_chg,vol,amount",
|
||
)
|
||
bar_count += self.database.upsert_daily_bars(rows)
|
||
|
||
indicator_count = 0
|
||
for current_date in indicator_dates_to_fetch:
|
||
indicators = self.client.query(
|
||
"daily_basic",
|
||
{"trade_date": current_date},
|
||
"ts_code,trade_date,turnover_rate,volume_ratio,total_mv,circ_mv,"
|
||
"pe_ttm,pb,ps_ttm,dv_ttm",
|
||
)
|
||
indicator_count += self.database.upsert_daily_indicators(indicators)
|
||
|
||
notices = []
|
||
benchmark_count = 0
|
||
try:
|
||
benchmark_rows = self.client.query(
|
||
"index_daily",
|
||
{"ts_code": "000300.SH", "start_date": dates[0], "end_date": trade_date},
|
||
"ts_code,trade_date,close,pct_chg",
|
||
)
|
||
benchmark_count = self.database.upsert_benchmark_bars(benchmark_rows)
|
||
except TushareError as exc:
|
||
notices.append(f"沪深300基准暂不可用：{exc}")
|
||
fundamental_count = 0
|
||
existing_periods = set(self.database.fundamental_periods())
|
||
for period in _quarter_periods(trade_date, 9):
|
||
if period in existing_periods and period < trade_date[:4] + "0101":
|
||
continue
|
||
try:
|
||
rows = self.client.query(
|
||
"fina_indicator_vip",
|
||
{"period": period},
|
||
"ts_code,ann_date,end_date,roe,roa,roic,grossprofit_margin,"
|
||
"netprofit_yoy,or_yoy,ocf_to_opincome",
|
||
)
|
||
except TushareError as exc:
|
||
notices.append(f"财务质量接口不可用：{exc}")
|
||
break
|
||
published = [
|
||
row for row in rows
|
||
if not row.get("ann_date") or str(row.get("ann_date")) <= trade_date
|
||
]
|
||
published.sort(key=lambda row: str(row.get("ann_date") or ""))
|
||
fundamental_count += self.database.upsert_fundamental_indicators(published)
|
||
auction_count = 0
|
||
auction_dates = 0
|
||
for current_date in auction_dates_to_fetch:
|
||
try:
|
||
auction_rows = self.client.query(
|
||
"stk_auction",
|
||
{"trade_date": current_date},
|
||
"ts_code,trade_date,vol,price,amount,pre_close,turnover_rate,volume_ratio,float_share",
|
||
)
|
||
if auction_rows:
|
||
auction_count += self.database.upsert_auction_factors(auction_rows)
|
||
auction_dates += 1
|
||
except TushareError as exc:
|
||
notices.append(f"竞价因子接口不可用：{exc}")
|
||
break
|
||
moneyflow_count = 0
|
||
moneyflow_dates = 0
|
||
for current_date in dates[-min(5, len(dates)):]:
|
||
try:
|
||
moneyflow = self.client.query(
|
||
"moneyflow",
|
||
{"trade_date": current_date},
|
||
"ts_code,trade_date,buy_sm_amount,sell_sm_amount,buy_md_amount,sell_md_amount,"
|
||
"buy_lg_amount,sell_lg_amount,buy_elg_amount,sell_elg_amount,net_mf_amount",
|
||
)
|
||
moneyflow_count += self.database.upsert_moneyflow(moneyflow)
|
||
if moneyflow:
|
||
moneyflow_dates += 1
|
||
except TushareError as exc:
|
||
notices.append(f"资金流接口不可用：{exc}")
|
||
break
|
||
|
||
earnings_count = 0
|
||
forecasts: list[dict[str, Any]] = []
|
||
expresses: list[dict[str, Any]] = []
|
||
for period in _quarter_periods(trade_date, 5):
|
||
try:
|
||
forecast_rows = self.client.query(
|
||
"forecast_vip",
|
||
{"period": period},
|
||
"ts_code,ann_date,end_date,net_profit_min,net_profit_max,last_parent_net,p_change_min,p_change_max",
|
||
)
|
||
express_rows = self.client.query(
|
||
"express_vip",
|
||
{"period": period},
|
||
"ts_code,ann_date,end_date,n_income,yoy_net_profit,yoy_sales",
|
||
)
|
||
except TushareError as exc:
|
||
notices.append(f"业绩事件接口不可用：{exc}")
|
||
break
|
||
forecasts.extend(forecast_rows)
|
||
expresses.extend(express_rows)
|
||
if forecasts and expresses:
|
||
earnings_count = self.database.upsert_earnings_events(
|
||
_earnings_event_rows(forecasts, expresses, trade_date)
|
||
)
|
||
|
||
popularity_count = 0
|
||
previous_trade_date = dates[-2] if len(dates) >= 2 else ""
|
||
try:
|
||
ths_rows = self.client.query("ths_hot", {"trade_date": trade_date})
|
||
dc_rows = self.client.query("dc_hot", {"trade_date": trade_date})
|
||
previous_ths = (
|
||
self.client.query("ths_hot", {"trade_date": previous_trade_date})
|
||
if previous_trade_date else []
|
||
)
|
||
previous_dc = (
|
||
self.client.query("dc_hot", {"trade_date": previous_trade_date})
|
||
if previous_trade_date else []
|
||
)
|
||
popularity_count = self.database.upsert_popularity_factors(
|
||
_popularity_factor_rows(
|
||
trade_date, ths_rows, dc_rows, previous_ths, previous_dc
|
||
)
|
||
)
|
||
except TushareError as exc:
|
||
notices.append(f"人气榜因子不可用：{exc}")
|
||
|
||
institution_count = 0
|
||
try:
|
||
institution_rows = self.client.query(
|
||
"top_inst",
|
||
{"trade_date": trade_date},
|
||
"trade_date,ts_code,exalter,buy,sell,net_buy,side,reason",
|
||
)
|
||
institution_count = self.database.upsert_lhb_institutions(institution_rows)
|
||
except TushareError as exc:
|
||
notices.append(f"机构席位明细不可用：{exc}")
|
||
|
||
return {
|
||
"trade_date": trade_date,
|
||
"calendar_dates": len(dates),
|
||
"fetched_dates": len(dates_to_fetch),
|
||
"stocks": master_count,
|
||
"bars": bar_count,
|
||
"benchmark_bars": benchmark_count,
|
||
"indicators": indicator_count,
|
||
"indicator_dates": len(indicator_dates_to_fetch),
|
||
"fundamentals": fundamental_count,
|
||
"moneyflow": moneyflow_count,
|
||
"moneyflow_dates": moneyflow_dates,
|
||
"auction_rows": auction_count,
|
||
"auction_dates": auction_dates,
|
||
"earnings_events": earnings_count,
|
||
"popularity_rows": popularity_count,
|
||
"institution_rows": institution_count,
|
||
"notice": "；".join(notices),
|
||
}
|
||
|
||
|
||
class ScreenerEngine:
|
||
def __init__(self, database: ReviewDatabase) -> None:
|
||
self.database = database
|
||
self._backtest_factor_cache: dict[tuple[str, int], list[dict[str, Any]]] = {}
|
||
|
||
def ensure_builtin_strategies(self) -> None:
|
||
existing = {
|
||
item["name"]: item
|
||
for item in self.database.list_screener_strategies()
|
||
if item["builtin"]
|
||
}
|
||
for strategy in BUILTIN_STRATEGIES:
|
||
current = existing.get(strategy["name"])
|
||
self.database.save_screener_strategy(
|
||
None, **strategy, builtin=True,
|
||
strategy_id=int(current["id"]) if current else None,
|
||
)
|
||
|
||
def detect_regime(self, trade_date: str) -> dict[str, Any]:
|
||
series = latest_contiguous_history(
|
||
build_sentiment_history(self.database.list_snapshot_payloads(trade_date, 260))
|
||
)
|
||
if not series:
|
||
return {
|
||
"id": "repair", "label": REGIMES["repair"], "confidence": 25,
|
||
"reason": "复盘快照不足，暂按中性修复处理。", "evidence": [], "history": [],
|
||
}
|
||
current = series[-1]
|
||
previous = series[-2] if len(series) > 1 else current
|
||
score = _number(current.get("score"))
|
||
previous_score = _number(previous.get("score"))
|
||
delta = score - previous_score
|
||
seal_rate = _number(current.get("seal_rate"))
|
||
limit_up = _number(current.get("limit_up_count"))
|
||
broken = _number(current.get("broken_count"))
|
||
regime = next(
|
||
(key for key, label in REGIMES.items() if label == current.get("phase")),
|
||
"divergence",
|
||
)
|
||
confidence = min(92, 45 + len(series[-8:]) * 5 + min(abs(delta), 12))
|
||
evidence = [
|
||
f"情绪温度 {score:.0f}，较前一交易日 {delta:+.0f}，{current.get('direction') or '持平'}",
|
||
f"封板率 {seal_rate:.1f}%",
|
||
f"涨停 {limit_up:.0f} 家，炸板 {broken:.0f} 家",
|
||
]
|
||
return {
|
||
"id": regime,
|
||
"label": REGIMES[regime],
|
||
"confidence": round(confidence),
|
||
"reason": _regime_reason(regime),
|
||
"evidence": evidence,
|
||
"history": [
|
||
{"trade_date": item["trade_date"], "score": _number(item.get("score"))}
|
||
for item in series[-8:]
|
||
],
|
||
}
|
||
|
||
def factor_health(self, trade_date: str) -> dict[str, Any]:
|
||
return self.database.factor_health_summary(trade_date)
|
||
|
||
def validate_formula(self, formula: dict[str, Any]) -> dict[str, Any]:
|
||
if not isinstance(formula, dict):
|
||
raise ValueError("选股公式必须是 JSON 对象。")
|
||
result = copy.deepcopy(formula)
|
||
universe = result.setdefault("universe", {})
|
||
universe["exclude_st"] = bool(universe.get("exclude_st", True))
|
||
universe["listed_days_min"] = max(0, min(5000, int(universe.get("listed_days_min", 120))))
|
||
filters = result.setdefault("filters", [])
|
||
if not isinstance(filters, list) or len(filters) > 20:
|
||
raise ValueError("筛选条件必须是列表，且不能超过 20 条。")
|
||
for condition in filters:
|
||
field = condition.get("field")
|
||
operator = condition.get("op")
|
||
if field not in FACTOR_FIELDS:
|
||
raise ValueError(f"不支持的选股因子：{field}")
|
||
if operator not in ALLOWED_OPERATORS:
|
||
raise ValueError(f"不支持的运算符：{operator}")
|
||
if "value" not in condition:
|
||
raise ValueError(f"因子 {field} 缺少比较值。")
|
||
scores = result.setdefault("score", [])
|
||
if not isinstance(scores, list) or not scores or len(scores) > 12:
|
||
raise ValueError("评分因子应为 1 至 12 条。")
|
||
for item in scores:
|
||
if item.get("field") not in FACTOR_FIELDS:
|
||
raise ValueError(f"不支持的评分因子：{item.get('field')}")
|
||
item["weight"] = float(item.get("weight", 0))
|
||
if item["weight"] <= 0 or item["weight"] > 1:
|
||
raise ValueError("评分权重必须大于 0 且不超过 1。")
|
||
if item.get("direction", "desc") not in {"asc", "desc"}:
|
||
raise ValueError("评分方向只能是 asc 或 desc。")
|
||
item["direction"] = item.get("direction", "desc")
|
||
result["limit"] = max(1, min(50, int(result.get("limit", 15))))
|
||
result["min_score"] = max(0, min(1, float(result.get("min_score", 0))))
|
||
return result
|
||
|
||
def screen(
|
||
self, user_id: int, trade_date: str, formula: dict[str, Any], regime: str,
|
||
strategy_name: str, run_backtest: bool = True,
|
||
realtime_snapshot: dict[str, Any] | None = None,
|
||
mode: str = "smart",
|
||
prepared_factors: list[dict[str, Any]] | None = None,
|
||
prepared_date: str = "",
|
||
) -> dict[str, Any]:
|
||
mode = mode if mode in {"smart", "curated", "quant"} else "smart"
|
||
formula = self.validate_formula(formula)
|
||
if prepared_factors is None:
|
||
history_days = int((formula.get("meta") or {}).get("history_days") or 80)
|
||
factors, actual_date = self.build_factors(
|
||
trade_date, realtime_snapshot, history_days
|
||
)
|
||
else:
|
||
factors = prepared_factors
|
||
actual_date = prepared_date or trade_date
|
||
candidates = self.apply_formula(factors, formula, regime)
|
||
backtest = self.backtest(actual_date, formula) if run_backtest else None
|
||
required_fields = sorted({
|
||
str(item.get("field") or "")
|
||
for item in list(formula.get("filters") or []) + list(formula.get("score") or [])
|
||
if item.get("field")
|
||
})
|
||
complete_rows = sum(
|
||
1 for row in factors
|
||
if all(row.get(field) is not None for field in required_fields)
|
||
)
|
||
coverage = round(complete_rows / len(factors) * 100, 1) if factors else 0.0
|
||
health_status = "normal" if candidates else "no_signal"
|
||
if backtest and backtest["samples"] >= 20:
|
||
for candidate in candidates:
|
||
estimate = backtest["win_rate"] * 0.65 + candidate["score"] * 100 * 0.35
|
||
candidate["historical_probability"] = round(min(95, max(5, estimate)), 1)
|
||
candidate["probability_samples"] = backtest["samples"]
|
||
else:
|
||
for candidate in candidates:
|
||
candidate["historical_probability"] = None
|
||
candidate["probability_samples"] = backtest["samples"] if backtest else 0
|
||
result = {
|
||
"meta": {
|
||
"trade_date": _display_date(actual_date),
|
||
"regime": regime,
|
||
"regime_label": REGIMES.get(regime, regime),
|
||
"strategy_name": strategy_name,
|
||
"mode": mode,
|
||
"library_version": int(
|
||
(formula.get("meta") or {}).get("library_version") or 0
|
||
),
|
||
"universe_count": len(factors),
|
||
"candidate_count": len(candidates),
|
||
"updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
|
||
"health": {
|
||
"status": health_status,
|
||
"required_field_count": len(required_fields),
|
||
"complete_rows": complete_rows,
|
||
"universe_rows": len(factors),
|
||
"coverage": coverage,
|
||
"signal_count": len(candidates),
|
||
},
|
||
"selection_source": (
|
||
"tushare_rt_k+history" if realtime_snapshot else "historical_eod"
|
||
),
|
||
"realtime": bool(realtime_snapshot),
|
||
"history_cutoff": (
|
||
str(realtime_snapshot.get("previous_trade_date") or "")
|
||
if realtime_snapshot else actual_date
|
||
),
|
||
"factor_freshness": {
|
||
"realtime": [
|
||
"价格", "涨跌幅", "成交量", "成交额", "换手率",
|
||
"均线位置", "5/10日动量", "板块强度", "开盘竞价",
|
||
] if realtime_snapshot else [],
|
||
"historical": ["历史波动率", "流通市值", "资金流", "竞价因子", "回测"],
|
||
},
|
||
},
|
||
"formula": formula,
|
||
"candidates": candidates,
|
||
"backtest": backtest,
|
||
"disclaimer": (
|
||
"候选仅由策略条件与当日数据计算；历史统计不代表未来收益。"
|
||
if mode == "curated"
|
||
else "概率为历史条件估计，不代表未来收益；退潮或样本不足时允许无候选。"
|
||
),
|
||
}
|
||
run_id = self.database.save_screener_run(
|
||
user_id, actual_date, regime, strategy_name, formula, result, mode
|
||
)
|
||
result["meta"]["run_id"] = run_id
|
||
return result
|
||
|
||
def build_factors(
|
||
self,
|
||
trade_date: str,
|
||
realtime_snapshot: dict[str, Any] | None = None,
|
||
history_days: int = 80,
|
||
) -> tuple[list[dict[str, Any]], str]:
|
||
history_days = max(21, min(260, int(history_days)))
|
||
data = self.database.load_factor_data(trade_date, history_days)
|
||
dates = [value for value in data["dates"] if value <= trade_date]
|
||
if len(dates) < 21:
|
||
raise ValueError("历史行情不足 21 个交易日，请先同步因子数据。")
|
||
history_date = dates[-1]
|
||
realtime_map = {
|
||
str(row.get("ts_code") or ""): row
|
||
for row in (realtime_snapshot or {}).get("rows") or []
|
||
}
|
||
realtime_date = str((realtime_snapshot or {}).get("trade_date") or "")
|
||
use_realtime = bool(realtime_map and realtime_date == trade_date and history_date < trade_date)
|
||
actual_date = trade_date if use_realtime else history_date
|
||
master = {row["ts_code"]: row for row in data["master"]}
|
||
indicators = {row["ts_code"]: row for row in data["indicators"]}
|
||
fundamentals = {row["ts_code"]: row for row in data.get("fundamentals", [])}
|
||
indicator_history: dict[str, list[dict[str, Any]]] = defaultdict(list)
|
||
for row in data.get("indicator_history", []):
|
||
indicator_history[str(row.get("ts_code") or "")].append(row)
|
||
indicator_series: dict[str, list[dict[str, Any]]] = defaultdict(list)
|
||
for row in data.get("indicator_series", []):
|
||
indicator_series[str(row.get("ts_code") or "")].append(row)
|
||
benchmark_by_date = {
|
||
str(row.get("trade_date") or ""): _number(row.get("close"))
|
||
for row in data.get("benchmarks", [])
|
||
if _number(row.get("close")) > 0
|
||
}
|
||
moneyflow = {row["ts_code"]: row for row in data["moneyflow"]}
|
||
moneyflow_history: dict[str, list[dict[str, Any]]] = defaultdict(list)
|
||
for row in data.get("moneyflow_history", []):
|
||
moneyflow_history[str(row.get("ts_code") or "")].append(row)
|
||
auction = {
|
||
row["ts_code"]: row
|
||
for row in data.get("auction", [])
|
||
if str(row.get("trade_date") or "") == actual_date
|
||
}
|
||
earnings_events: dict[str, dict[str, Any]] = {}
|
||
for row in data.get("earnings_events", []):
|
||
ts_code = str(row.get("ts_code") or "")
|
||
ann_date = str(row.get("ann_date") or "")
|
||
if ann_date <= actual_date and (
|
||
ts_code not in earnings_events
|
||
or ann_date > str(earnings_events[ts_code].get("ann_date") or "")
|
||
):
|
||
earnings_events[ts_code] = row
|
||
popularity = {
|
||
str(row.get("ts_code") or ""): row
|
||
for row in data.get("popularity", [])
|
||
}
|
||
institutions = {
|
||
str(row.get("ts_code") or ""): row
|
||
for row in data.get("institutions", [])
|
||
}
|
||
grouped: dict[str, list[dict[str, Any]]] = defaultdict(list)
|
||
for row in data["bars"]:
|
||
if row["trade_date"] <= history_date:
|
||
grouped[row["ts_code"]].append(row)
|
||
|
||
snapshot = self.database.get_snapshot(actual_date) or {}
|
||
limit_map: dict[str, tuple[str, int]] = {}
|
||
for key, status in (("limits", "涨停"), ("broken", "炸板"), ("down_limits", "跌停")):
|
||
for row in snapshot.get(key) or []:
|
||
limit_map[str(row.get("code"))] = (status, int(row.get("streak") or 0))
|
||
|
||
factors = []
|
||
current_day = datetime.strptime(actual_date, "%Y%m%d")
|
||
for ts_code, bars in grouped.items():
|
||
bars.sort(key=lambda item: item["trade_date"])
|
||
if len(bars) < 21 or bars[-1]["trade_date"] != history_date:
|
||
continue
|
||
info = master.get(ts_code)
|
||
if not info:
|
||
continue
|
||
historical_closes = [_number(item["close"]) for item in bars]
|
||
historical_volumes = [_number(item["vol"]) for item in bars]
|
||
realtime = realtime_map.get(ts_code) if use_realtime else None
|
||
current = realtime or bars[-1]
|
||
closes = historical_closes + ([_number(realtime["close"])] if realtime else [])
|
||
volumes = historical_volumes + ([_number(realtime["vol"])] if realtime else [])
|
||
if closes[-1] <= 0:
|
||
continue
|
||
returns_10 = [_number(item["pct_chg"]) for item in bars[-10:]]
|
||
if realtime:
|
||
returns_10 = returns_10[-9:] + [_number(realtime.get("pct_chg"))]
|
||
previous_volume = statistics.fmean(volumes[-6:-1]) if any(volumes[-6:-1]) else 0
|
||
indicator = indicators.get(ts_code, {})
|
||
fundamental = fundamentals.get(ts_code, {})
|
||
flow = moneyflow.get(ts_code, {})
|
||
flow_history = moneyflow_history.get(ts_code, [])
|
||
auction_row = auction.get(ts_code, {})
|
||
list_date = str(info.get("list_date") or "")
|
||
try:
|
||
listed_days = (current_day - datetime.strptime(list_date, "%Y%m%d")).days
|
||
except ValueError:
|
||
listed_days = 9999
|
||
code = str(info.get("code") or ts_code.split(".")[0])
|
||
status, streak = limit_map.get(code, ("", 0))
|
||
name = str(info.get("name") or "--")
|
||
shape_rows = bars + ([realtime] if realtime else [])
|
||
shape_close = [_number(item.get("close")) for item in shape_rows]
|
||
shape_high = [_number(item.get("high") or item.get("close")) for item in shape_rows]
|
||
shape_low = [_number(item.get("low") or item.get("close")) for item in shape_rows]
|
||
shape_changes = [_number(item.get("pct_chg")) for item in shape_rows]
|
||
position_rows = shape_rows[-60:]
|
||
position_high = max((_number(item.get("high") or item.get("close")) for item in position_rows), default=0)
|
||
position_low = min((_number(item.get("low") or item.get("close")) for item in position_rows), default=0)
|
||
relative_position = (
|
||
(closes[-1] - position_low) / (position_high - position_low)
|
||
if position_high > position_low else 0.5
|
||
)
|
||
previous_index = len(bars) - 1 if realtime else len(bars) - 2
|
||
previous_bar = bars[previous_index] if previous_index >= 0 else {}
|
||
previous_limit = _is_limit_bar(bars, previous_index, code, name)
|
||
previous_touched = _touched_limit_bar(bars, previous_index, code, name)
|
||
recent_prior_signal = any(
|
||
_is_limit_bar(bars, index, code, name)
|
||
or _touched_limit_bar(bars, index, code, name)
|
||
for index in range(max(0, previous_index - 2), previous_index)
|
||
)
|
||
previous_streak = 0
|
||
streak_index = previous_index
|
||
while streak_index >= 0 and _is_limit_bar(bars, streak_index, code, name):
|
||
previous_streak += 1
|
||
streak_index -= 1
|
||
limit_flags = [
|
||
_is_limit_bar(shape_rows, index, code, name)
|
||
for index in range(len(shape_rows))
|
||
]
|
||
annual_dividend_rows = indicator_history.get(ts_code, [])
|
||
dividend_years = sum(
|
||
1 for item in annual_dividend_rows if _optional_number(item.get("dv_ttm")) not in (None, 0)
|
||
)
|
||
current_streak = _ending_streak(limit_flags)
|
||
prior_streak = _ending_streak(limit_flags, len(limit_flags) - 2)
|
||
streak = max(streak, current_streak)
|
||
return_60d = (
|
||
(closes[-1] / closes[-61] - 1) * 100 if len(closes) >= 61 and closes[-61] else 0
|
||
)
|
||
momentum_60_5 = (
|
||
(closes[-6] / closes[-61] - 1) * 100 if len(closes) >= 61 and closes[-61] else 0
|
||
)
|
||
ma20 = statistics.fmean(closes[-20:])
|
||
ma60 = statistics.fmean(closes[-60:]) if len(closes) >= 60 else ma20
|
||
prior_ma20 = statistics.fmean(closes[-25:-5]) if len(closes) >= 25 else ma20
|
||
prior_ma60 = statistics.fmean(closes[-65:-5]) if len(closes) >= 65 else ma60
|
||
ma20_slope = (ma20 / prior_ma20 - 1) * 100 if prior_ma20 else 0
|
||
ma60_slope = (ma60 / prior_ma60 - 1) * 100 if prior_ma60 else 0
|
||
ma_values = [statistics.fmean(closes[-window:]) for window in (5, 10, 20, 60)]
|
||
high_250 = max(shape_high[-250:]) if len(shape_high) >= 250 else max(shape_high)
|
||
drawdown_250 = (1 - closes[-1] / high_250) * 100 if high_250 else 100
|
||
prior_high_20 = max(shape_high[-21:-1]) if len(shape_high) >= 21 else 0
|
||
breakout_pct = (closes[-1] / prior_high_20 - 1) * 100 if prior_high_20 else 0
|
||
prior_lows_20 = shape_low[-21:-1]
|
||
range_20d = (
|
||
(prior_high_20 / min(prior_lows_20) - 1) * 100
|
||
if prior_lows_20 and min(prior_lows_20) > 0 else 100
|
||
)
|
||
turnover_rows = sorted(
|
||
indicator_series.get(ts_code, []), key=lambda item: str(item.get("trade_date") or "")
|
||
)
|
||
turnover_values = [_number(item.get("turnover_rate")) for item in turnover_rows[-5:]]
|
||
if realtime and _number(realtime.get("turnover_rate")):
|
||
turnover_values = turnover_values[-4:] + [_number(realtime.get("turnover_rate"))]
|
||
turnover_5d = sum(turnover_values)
|
||
rs_values = [
|
||
_number(item.get("close")) / benchmark_by_date[str(item.get("trade_date"))]
|
||
for item in shape_rows[-120:]
|
||
if benchmark_by_date.get(str(item.get("trade_date"))) and _number(item.get("close")) > 0
|
||
]
|
||
benchmark_60 = [
|
||
benchmark_by_date.get(str(item.get("trade_date")))
|
||
for item in shape_rows[-61:]
|
||
if benchmark_by_date.get(str(item.get("trade_date")))
|
||
]
|
||
benchmark_return_60 = (
|
||
(benchmark_60[-1] / benchmark_60[0] - 1) * 100
|
||
if len(benchmark_60) >= 61 and benchmark_60[0] else 0
|
||
)
|
||
weekly_closes, weekly_amounts = _weekly_series(shape_rows)
|
||
weekly_dif, weekly_dea = _macd_last(weekly_closes)
|
||
daily_dif, daily_dea = _macd_series(closes)
|
||
daily_cross = (
|
||
len(daily_dif) >= 2 and daily_dif[-1] > daily_dea[-1]
|
||
and daily_dif[-2] <= daily_dea[-2]
|
||
)
|
||
current_open = _number(current.get("open"))
|
||
daily_pullback = closes[-1] >= ma20 and current_open <= ma20 * 1.02 and closes[-1] > current_open
|
||
previous_close = closes[-2] if len(closes) >= 2 else closes[-1]
|
||
intraday_min = (
|
||
(_number(current.get("low")) / previous_close - 1) * 100 if previous_close else 0
|
||
)
|
||
body = abs(closes[-1] - current_open)
|
||
lower_shadow = max(0.0, min(current_open, closes[-1]) - _number(current.get("low")))
|
||
lower_shadow_ratio = lower_shadow / body if body > 0 else (10.0 if lower_shadow > 0 else 0.0)
|
||
previous_volume_value = volumes[-2] if len(volumes) >= 2 else 0
|
||
vol_vs_previous = volumes[-1] / previous_volume_value if previous_volume_value else 0
|
||
broken = _broken_reversal_metrics(shape_rows, limit_flags, code, name)
|
||
netprofit_yoy = _optional_number(fundamental.get("netprofit_yoy"))
|
||
earnings_event = earnings_events.get(ts_code, {})
|
||
announcement_date = str(earnings_event.get("ann_date") or "")
|
||
earnings_days = (
|
||
sum(1 for value in dates if announcement_date < value <= actual_date)
|
||
if announcement_date and announcement_date <= actual_date
|
||
else None
|
||
)
|
||
announcement_bar = next(
|
||
(item for item in shape_rows if str(item.get("trade_date") or "") == announcement_date),
|
||
None,
|
||
)
|
||
announcement_bad = False
|
||
if announcement_bar is not None:
|
||
bar_index = shape_rows.index(announcement_bar)
|
||
prior_volumes = [
|
||
_number(item.get("vol")) for item in shape_rows[max(0, bar_index - 5):bar_index]
|
||
if _number(item.get("vol")) > 0
|
||
]
|
||
volume_baseline = statistics.fmean(prior_volumes) if prior_volumes else 0
|
||
announcement_bad = (
|
||
_number(announcement_bar.get("close")) < _number(announcement_bar.get("open"))
|
||
and _number(announcement_bar.get("pct_chg")) < 0
|
||
and volume_baseline > 0
|
||
and _number(announcement_bar.get("vol")) / volume_baseline >= 1.8
|
||
)
|
||
popularity_row = popularity.get(ts_code)
|
||
institution_row = institutions.get(ts_code)
|
||
factors.append(
|
||
{
|
||
"code": code,
|
||
"ts_code": ts_code,
|
||
"name": name,
|
||
"sector": info.get("industry") or "其他",
|
||
"market": info.get("market") or "--",
|
||
"listed_days": listed_days,
|
||
"close": round(closes[-1], 2),
|
||
"price": round(closes[-1], 2),
|
||
"pct_chg": round(_number(current["pct_chg"]), 2),
|
||
"return_5d": round((closes[-1] / closes[-6] - 1) * 100, 2),
|
||
"return_10d": round((closes[-1] / closes[-11] - 1) * 100, 2),
|
||
"return_20d": round((closes[-1] / closes[-21] - 1) * 100, 2),
|
||
"return_60d": round(return_60d, 2),
|
||
"momentum_60_5": round(momentum_60_5, 2),
|
||
"above_ma20": int(closes[-1] > ma20),
|
||
"rsi_6": round(_rsi(closes, 6), 2),
|
||
"ma60_slope": round(ma60_slope, 3),
|
||
"ma20_slope_5d": round(ma20_slope, 3),
|
||
"ma_bull_alignment": int(ma_values[0] > ma_values[1] > ma_values[2] > ma_values[3]),
|
||
"drawdown_from_high_250": round(drawdown_250, 2),
|
||
"donchian_breakout_pct": round(breakout_pct, 2),
|
||
"range_20d": round(range_20d, 2),
|
||
"rs_high_120": int(len(rs_values) >= 120 and rs_values[-1] >= max(rs_values)),
|
||
"excess_return_60d": round(return_60d - benchmark_return_60, 2),
|
||
"weekly_trend_signal": int(len(weekly_closes) >= 30 and weekly_dif > 0 and weekly_dea > 0),
|
||
"daily_buy_trigger": int(daily_cross or daily_pullback),
|
||
"weekly_amount_trend": int(
|
||
len(weekly_amounts) >= 5
|
||
and weekly_amounts[-1] >= statistics.fmean(weekly_amounts[-5:-1])
|
||
),
|
||
"volume_ratio_5d": round(volumes[-1] / previous_volume, 2) if previous_volume else 0,
|
||
"turnover_5d": round(turnover_5d, 2),
|
||
"volatility_10d": round(statistics.pstdev(returns_10), 2),
|
||
"amount_billion": round(
|
||
_number(current["amount"]) / (100000000 if realtime else 100000), 2
|
||
),
|
||
"turnover_rate": round(
|
||
_number(realtime.get("turnover_rate"))
|
||
if realtime else _number(indicator.get("turnover_rate")),
|
||
2,
|
||
),
|
||
"circ_mv_billion": round(_number(indicator.get("circ_mv")) / 10000, 2),
|
||
"total_mv_billion": round(_number(indicator.get("total_mv")) / 10000, 2),
|
||
"pe_ttm": _rounded_optional(indicator.get("pe_ttm"), 2),
|
||
"pb": _rounded_optional(indicator.get("pb"), 2),
|
||
"ps_ttm": _rounded_optional(indicator.get("ps_ttm"), 2),
|
||
"dividend_yield_ttm": _rounded_optional(indicator.get("dv_ttm"), 2),
|
||
"dividend_years": dividend_years,
|
||
"roe": _rounded_optional(fundamental.get("roe"), 2),
|
||
"roa": _rounded_optional(fundamental.get("roa"), 2),
|
||
"roic": _rounded_optional(fundamental.get("roic"), 2),
|
||
"gross_margin": _rounded_optional(fundamental.get("grossprofit_margin"), 2),
|
||
"netprofit_yoy": _rounded_optional(fundamental.get("netprofit_yoy"), 2),
|
||
"revenue_yoy": _rounded_optional(fundamental.get("or_yoy"), 2),
|
||
"ocf_to_opincome": _rounded_optional(fundamental.get("ocf_to_opincome"), 2),
|
||
"earnings_surprise_pct": _rounded_optional(earnings_event.get("surprise_pct"), 2),
|
||
"earnings_days_since_announce": earnings_days,
|
||
"earnings_event_quality": int(not announcement_bad) if earnings_days is not None else None,
|
||
"popularity_score": _rounded_optional(
|
||
popularity_row.get("combined_score") if popularity_row else None, 2
|
||
),
|
||
"popularity_rank_change": (
|
||
int(popularity_row["rank_change"])
|
||
if popularity_row and popularity_row.get("rank_change") is not None else None
|
||
),
|
||
"popularity_dual_source": (
|
||
int(bool(popularity_row.get("dual_source"))) if popularity_row else None
|
||
),
|
||
"institution_net_buy_million": (
|
||
round(_number(institution_row.get("net_buy_amount")) / 1_000_000, 2)
|
||
if institution_row else None
|
||
),
|
||
"institution_seat_count": (
|
||
int(institution_row.get("seat_count") or 0) if institution_row else None
|
||
),
|
||
"net_flow_million": round(_number(flow.get("net_mf_amount")) / 100, 2),
|
||
"large_flow_million": round(_number(flow.get("large_net_amount")) / 100, 2),
|
||
"net_flow_5d_million": round(
|
||
sum(_number(item.get("net_mf_amount")) for item in flow_history) / 100,
|
||
2,
|
||
),
|
||
"flow_to_circ_mv_5d": round(
|
||
sum(_number(item.get("net_mf_amount")) for item in flow_history)
|
||
/ _number(indicator.get("circ_mv")) * 100,
|
||
4,
|
||
) if _number(indicator.get("circ_mv")) else 0,
|
||
"limit_status": status,
|
||
"limit_streak": streak,
|
||
"is_limit_up_today": int(limit_flags[-1]),
|
||
"is_limit_down_today": int(_number(current.get("pct_chg")) <= -_limit_threshold(code, name)),
|
||
"auction_change": round(_number(auction_row.get("change")), 2),
|
||
"auction_amount_million": round(_number(auction_row.get("amount")) / 1_000_000, 2),
|
||
"auction_turnover_rate": round(_number(auction_row.get("turnover_rate")), 4),
|
||
"auction_volume_ratio": round(_number(auction_row.get("volume_ratio")), 2),
|
||
"relative_position_60": round(relative_position, 4),
|
||
"max_abs_change_15d": round(max((abs(value) for value in shape_changes[-15:]), default=0), 2),
|
||
"close_to_high_15d": round(closes[-1] / max(shape_high[-15:]), 4) if shape_high[-15:] and max(shape_high[-15:]) else 0,
|
||
"close_to_high_60d": round(closes[-1] / max(shape_high[-60:]), 4) if shape_high[-60:] and max(shape_high[-60:]) else 0,
|
||
"no_limit_30d": int(not any(limit_flags[-30:])),
|
||
"had_limit_80d": int(any(limit_flags[-80:-30] if len(limit_flags) > 30 else [])),
|
||
"no_limit_down_20d": int(not any(
|
||
_number(item.get("pct_chg")) <= -_limit_threshold(code, name)
|
||
for item in shape_rows[-20:]
|
||
)),
|
||
"financial_risk": int(
|
||
"ST" in name.upper() or "退" in name
|
||
or (netprofit_yoy is not None and netprofit_yoy <= -100)
|
||
),
|
||
"prior_limit_streak": prior_streak,
|
||
"max_continuous_board_10d": _max_streak(limit_flags[-10:]),
|
||
"dragon_first_yin": int(
|
||
prior_streak >= 3 and not limit_flags[-1] and closes[-1] < current_open
|
||
),
|
||
"yin_day_pct": round(_number(current.get("pct_chg")), 2),
|
||
"vol_vs_previous": round(vol_vs_previous, 3),
|
||
"broken_reversal": broken["signal"],
|
||
"days_since_broken": broken["days"],
|
||
"close_above_broken_high": broken["recovered"],
|
||
"vol_vs_broken_day": broken["volume_ratio"],
|
||
"recent_limit_up_5d": sum(limit_flags[-5:]),
|
||
"intraday_min_pct": round(intraday_min, 2),
|
||
"lower_shadow_ratio": round(lower_shadow_ratio, 2),
|
||
"previous_first_limit": int(previous_limit and not recent_prior_signal),
|
||
"previous_limit_signal": int((previous_limit or previous_touched) and not recent_prior_signal),
|
||
"previous_limit_streak": previous_streak,
|
||
"previous_amount_billion": round(_number(previous_bar.get("amount")) / 100000, 2),
|
||
}
|
||
)
|
||
|
||
market_return = statistics.fmean(row["return_5d"] for row in factors) if factors else 0
|
||
sectors: dict[str, list[dict[str, Any]]] = defaultdict(list)
|
||
for row in factors:
|
||
sectors[row["sector"]].append(row)
|
||
sector_metrics = []
|
||
market_amount = sum(max(0.0, row["amount_billion"]) for row in factors)
|
||
for sector_name, sector_rows in sectors.items():
|
||
average_return = statistics.fmean(row["return_5d"] for row in sector_rows)
|
||
average_return_20d = statistics.fmean(row["return_20d"] for row in sector_rows)
|
||
sector_net_flow = sum(row["net_flow_5d_million"] for row in sector_rows)
|
||
limit_count = sum(row["limit_status"] == "涨停" or row["pct_chg"] >= 9.5 for row in sector_rows)
|
||
up_count = sum(row["pct_chg"] >= 5 for row in sector_rows)
|
||
breadth_ma20 = sum(row["above_ma20"] for row in sector_rows) / max(len(sector_rows), 1) * 100
|
||
sector_growth = [
|
||
statistics.fmean(values)
|
||
for row in sector_rows
|
||
if (values := [
|
||
value for value in (row.get("revenue_yoy"), row.get("netprofit_yoy"))
|
||
if value is not None
|
||
])
|
||
]
|
||
prosperity_raw = statistics.median(sector_growth) if sector_growth else -100.0
|
||
average_turnover = statistics.fmean(row["turnover_rate"] for row in sector_rows)
|
||
amount_share = (
|
||
sum(max(0.0, row["amount_billion"]) for row in sector_rows) / market_amount * 100
|
||
if market_amount else 0.0
|
||
)
|
||
crowding_raw = average_turnover + amount_share
|
||
trend_raw = average_return_20d + breadth_ma20 / 10
|
||
strength = min(100, max(0, 50 + average_return * 4 + limit_count * 3 + up_count * 0.6))
|
||
sector_metrics.append(
|
||
{
|
||
"ts_code": sector_name,
|
||
"sector_return_20d": average_return_20d,
|
||
"sector_net_flow_5d_million": sector_net_flow,
|
||
"sector_prosperity_raw": prosperity_raw,
|
||
"sector_trend_raw": trend_raw,
|
||
"sector_crowding_raw": crowding_raw,
|
||
}
|
||
)
|
||
stock_momentum_ranks = _percentile_map(sector_rows, "return_20d", "desc")
|
||
for row in sector_rows:
|
||
row["sector_strength"] = round(strength, 1)
|
||
row["sector_return_5d"] = round(average_return, 2)
|
||
row["sector_return_20d"] = round(average_return_20d, 2)
|
||
row["sector_net_flow_5d_million"] = round(sector_net_flow, 2)
|
||
row["sector_stock_momentum_rank"] = round(
|
||
stock_momentum_ranks.get(row["ts_code"], 0.0), 4
|
||
)
|
||
row["sector_limit_count"] = limit_count
|
||
row["sector_up_count"] = up_count
|
||
row["sector_breadth_ma20"] = round(breadth_ma20, 1)
|
||
row["relative_strength"] = round(row["return_5d"] - market_return, 2)
|
||
sector_momentum_ranks = _percentile_map(
|
||
sector_metrics, "sector_return_20d", "desc"
|
||
)
|
||
sector_flow_ranks = _percentile_map(
|
||
sector_metrics, "sector_net_flow_5d_million", "desc"
|
||
)
|
||
sector_prosperity_ranks = _percentile_map(
|
||
sector_metrics, "sector_prosperity_raw", "desc"
|
||
)
|
||
sector_trend_ranks = _percentile_map(
|
||
sector_metrics, "sector_trend_raw", "desc"
|
||
)
|
||
sector_crowding_ranks = _percentile_map(
|
||
sector_metrics, "sector_crowding_raw", "desc"
|
||
)
|
||
for sector_name, sector_rows in sectors.items():
|
||
prosperity_rank = sector_prosperity_ranks.get(sector_name, 0.0)
|
||
trend_rank = sector_trend_ranks.get(sector_name, 0.0)
|
||
crowding_rank = sector_crowding_ranks.get(sector_name, 0.0)
|
||
composite_score = (
|
||
prosperity_rank * 0.40 + trend_rank * 0.30 + (1 - crowding_rank) * 0.30
|
||
)
|
||
for row in sector_rows:
|
||
row["sector_momentum_rank"] = round(
|
||
sector_momentum_ranks.get(sector_name, 0.0), 4
|
||
)
|
||
row["sector_flow_rank"] = round(
|
||
sector_flow_ranks.get(sector_name, 0.0), 4
|
||
)
|
||
row["sector_prosperity_rank"] = round(prosperity_rank, 4)
|
||
row["sector_trend_rank"] = round(trend_rank, 4)
|
||
row["sector_crowding_rank"] = round(crowding_rank, 4)
|
||
row["sector_composite_score"] = round(composite_score, 4)
|
||
|
||
factor_specs = {
|
||
"factor_value_score": (("pe_ttm", "asc"), ("pb", "asc"), ("dividend_yield_ttm", "desc")),
|
||
"factor_growth_score": (("revenue_yoy", "desc"), ("netprofit_yoy", "desc")),
|
||
"factor_quality_score": (("roe", "desc"), ("roic", "desc"), ("gross_margin", "desc")),
|
||
"factor_momentum_score": (("momentum_60_5", "desc"), ("relative_strength", "desc")),
|
||
"factor_sentiment_score": (("turnover_rate", "desc"), ("volume_ratio_5d", "desc")),
|
||
}
|
||
for output_field, specs in factor_specs.items():
|
||
maps = [_available_percentile_map(factors, field, direction) for field, direction in specs]
|
||
for row in factors:
|
||
values = [mapping.get(row["ts_code"]) for mapping in maps]
|
||
available = [value for value in values if value is not None]
|
||
row[output_field] = round(statistics.fmean(available), 4) if available else None
|
||
|
||
return_rank_map = _available_percentile_map(factors, "return_20d", "desc")
|
||
factor_weights = {}
|
||
for output_field in factor_specs:
|
||
pairs = [
|
||
(row.get(output_field), return_rank_map.get(row["ts_code"]))
|
||
for row in factors
|
||
if row.get(output_field) is not None and return_rank_map.get(row["ts_code"]) is not None
|
||
]
|
||
correlation = _pearson([pair[0] for pair in pairs], [pair[1] for pair in pairs])
|
||
factor_weights[output_field] = max(0.05, correlation)
|
||
factor_weight_total = sum(factor_weights.values()) or 1
|
||
for row in factors:
|
||
weighted = [
|
||
(row.get(field), weight)
|
||
for field, weight in factor_weights.items()
|
||
if row.get(field) is not None
|
||
]
|
||
row["multi_factor_composite"] = round(
|
||
sum(value * weight for value, weight in weighted)
|
||
/ (sum(weight for _, weight in weighted) or factor_weight_total),
|
||
4,
|
||
) if weighted else None
|
||
|
||
size_ranks = _available_percentile_map(factors, "total_mv_billion", "desc")
|
||
large_rows = [row for row in factors if (size_ranks.get(row["ts_code"]) or 0) >= 0.70]
|
||
small_rows = [
|
||
row for row in factors
|
||
if size_ranks.get(row["ts_code"]) is not None
|
||
and size_ranks[row["ts_code"]] <= 0.30
|
||
]
|
||
large_return = statistics.fmean(row["return_20d"] for row in large_rows) if large_rows else 0
|
||
small_return = statistics.fmean(row["return_20d"] for row in small_rows) if small_rows else 0
|
||
prefer_large = large_return >= small_return
|
||
growth_rows = [row for row in factors if (row.get("factor_growth_score") or 0) >= 0.70]
|
||
value_rows = [row for row in factors if (row.get("factor_value_score") or 0) >= 0.70]
|
||
growth_return = statistics.fmean(row["return_20d"] for row in growth_rows) if growth_rows else 0
|
||
value_return = statistics.fmean(row["return_20d"] for row in value_rows) if value_rows else 0
|
||
prefer_growth = growth_return >= value_return
|
||
for row in factors:
|
||
size_rank = size_ranks.get(row["ts_code"])
|
||
row["style_size_fit"] = round(
|
||
size_rank if prefer_large else 1 - size_rank, 4
|
||
) if size_rank is not None else None
|
||
style_factor = "factor_growth_score" if prefer_growth else "factor_value_score"
|
||
row["style_growth_fit"] = row.get(style_factor)
|
||
style_values = [
|
||
value for value in (row.get("style_size_fit"), row.get("style_growth_fit"))
|
||
if value is not None
|
||
]
|
||
row["style_fit_score"] = round(statistics.fmean(style_values), 4) if style_values else None
|
||
momentum_ranks = _percentile_map(factors, "momentum_60_5", "desc")
|
||
return_ranks = _percentile_map(factors, "return_5d", "desc")
|
||
market_height = max((int(row.get("limit_streak") or 0) for row in factors), default=0)
|
||
prior_market_height = max((int(row.get("prior_limit_streak") or 0) for row in factors), default=0)
|
||
for row in factors:
|
||
row["momentum_60_5_rank"] = round(momentum_ranks.get(row["ts_code"], 0.0), 4)
|
||
row["return_5d_rank"] = round(return_ranks.get(row["ts_code"], 0.0), 4)
|
||
is_height = market_height >= 2 and int(row.get("limit_streak") or 0) == market_height
|
||
row["is_market_height"] = int(is_height)
|
||
row["new_space_board"] = int(
|
||
is_height
|
||
and not (
|
||
prior_market_height >= 2
|
||
and int(row.get("prior_limit_streak") or 0) == prior_market_height
|
||
)
|
||
)
|
||
return factors, actual_date
|
||
|
||
def apply_formula(
|
||
self, rows: list[dict[str, Any]], formula: dict[str, Any], regime: str
|
||
) -> list[dict[str, Any]]:
|
||
universe = formula["universe"]
|
||
eligible = []
|
||
score_fields = [item["field"] for item in formula["score"]]
|
||
for row in rows:
|
||
name = str(row.get("name") or "")
|
||
if universe.get("exclude_st") and ("ST" in name.upper() or "退" in name):
|
||
continue
|
||
if row.get("listed_days", 0) < universe.get("listed_days_min", 0):
|
||
continue
|
||
if any(row.get(field) is None for field in score_fields):
|
||
continue
|
||
if all(_matches(row.get(item["field"]), item["op"], item["value"]) for item in formula["filters"]):
|
||
eligible.append(row)
|
||
if not eligible:
|
||
return []
|
||
|
||
percentiles = {
|
||
item["field"]: _percentile_map(eligible, item["field"], item["direction"])
|
||
for item in formula["score"]
|
||
}
|
||
weight_total = sum(item["weight"] for item in formula["score"])
|
||
results = []
|
||
for row in eligible:
|
||
contributions = []
|
||
score = 0.0
|
||
for item in formula["score"]:
|
||
percentile = percentiles[item["field"]].get(row["ts_code"], 0.5)
|
||
points = percentile * item["weight"] / weight_total
|
||
score += points
|
||
contributions.append(
|
||
{
|
||
"field": item["field"],
|
||
"label": FACTOR_FIELDS[item["field"]],
|
||
"value": row.get(item["field"], 0),
|
||
"points": round(points * 100, 1),
|
||
}
|
||
)
|
||
if score < formula["min_score"]:
|
||
continue
|
||
contributions.sort(key=lambda item: item["points"], reverse=True)
|
||
item = dict(row)
|
||
item["score"] = round(score, 4)
|
||
item["score_display"] = round(score * 100, 1)
|
||
item["contributions"] = contributions
|
||
item["reason"] = "、".join(entry["label"] for entry in contributions[:3])
|
||
include_regime_risk = formula.get("meta", {}).get("library") != "curated"
|
||
item["risk_flags"] = _risk_flags(row, regime, include_regime_risk)
|
||
results.append(item)
|
||
results.sort(key=lambda item: item["score"], reverse=True)
|
||
return results[: formula["limit"]]
|
||
|
||
def backtest(self, trade_date: str, formula: dict[str, Any]) -> dict[str, Any]:
|
||
meta = formula.get("meta") or {}
|
||
history_days = max(21, min(260, int(meta.get("history_days") or 80)))
|
||
holding_days = max(1, min(30, int(meta.get("backtest_days") or 3)))
|
||
take_profit = max(0.5, min(50.0, float(meta.get("take_profit") or 3)))
|
||
stop_loss = min(-0.5, max(-50.0, float(meta.get("stop_loss") or -3)))
|
||
dates = self.database.factor_dates(trade_date, history_days + holding_days + 20)
|
||
eligible_dates = dates[:-holding_days] if len(dates) > holding_days else []
|
||
frequency = str(meta.get("frequency") or "每日")
|
||
if "月" in frequency:
|
||
grouped = {}
|
||
for value in eligible_dates:
|
||
grouped[value[:6]] = value
|
||
evaluation_dates = list(grouped.values())[-8:]
|
||
elif "双周" in frequency:
|
||
weekly_dates = []
|
||
grouped = {}
|
||
for value in eligible_dates:
|
||
parsed = datetime.strptime(value, "%Y%m%d")
|
||
grouped[parsed.strftime("%G-%V")] = value
|
||
weekly_dates = list(grouped.values())
|
||
evaluation_dates = weekly_dates[-16::2][-8:]
|
||
elif "周" in frequency:
|
||
grouped = {}
|
||
for value in eligible_dates:
|
||
parsed = datetime.strptime(value, "%Y%m%d")
|
||
grouped[parsed.strftime("%G-%V")] = value
|
||
evaluation_dates = list(grouped.values())[-8:]
|
||
else:
|
||
evaluation_dates = eligible_dates[-8:]
|
||
wins = 0
|
||
losses = 0
|
||
samples = 0
|
||
returns = []
|
||
drawdowns = []
|
||
all_data = self.database.load_factor_data(
|
||
trade_date, history_days + holding_days + 20
|
||
)
|
||
bars_by_code: dict[str, list[dict[str, Any]]] = defaultdict(list)
|
||
for row in all_data["bars"]:
|
||
bars_by_code[row["ts_code"]].append(row)
|
||
for bars in bars_by_code.values():
|
||
bars.sort(key=lambda item: item["trade_date"])
|
||
|
||
for current_date in evaluation_dates:
|
||
try:
|
||
cache_key = (current_date, history_days)
|
||
factors = self._backtest_factor_cache.get(cache_key)
|
||
if factors is None:
|
||
factors, _ = self.build_factors(
|
||
current_date, history_days=history_days
|
||
)
|
||
if len(self._backtest_factor_cache) >= 64:
|
||
self._backtest_factor_cache.pop(
|
||
next(iter(self._backtest_factor_cache))
|
||
)
|
||
self._backtest_factor_cache[cache_key] = factors
|
||
except ValueError:
|
||
continue
|
||
selected = self.apply_formula(factors, {**formula, "limit": min(10, formula["limit"])}, "backtest")
|
||
for candidate in selected:
|
||
bars = bars_by_code.get(candidate["ts_code"], [])
|
||
index = next((i for i, row in enumerate(bars) if row["trade_date"] == current_date), -1)
|
||
future = bars[index + 1:index + 1 + holding_days] if index >= 0 else []
|
||
if len(future) < holding_days:
|
||
continue
|
||
entry = candidate["price"]
|
||
won = False
|
||
lost = False
|
||
for day in future:
|
||
low_return = (_number(day["low"]) / entry - 1) * 100
|
||
high_return = (_number(day["high"]) / entry - 1) * 100
|
||
if low_return <= stop_loss:
|
||
lost = True
|
||
break
|
||
if high_return >= take_profit:
|
||
won = True
|
||
break
|
||
if won:
|
||
wins += 1
|
||
elif lost:
|
||
losses += 1
|
||
samples += 1
|
||
returns.append((_number(future[-1]["close"]) / entry - 1) * 100)
|
||
drawdowns.append(min((_number(day["low"]) / entry - 1) * 100 for day in future))
|
||
return {
|
||
"samples": samples,
|
||
"wins": wins,
|
||
"losses": losses,
|
||
"win_rate": round(wins / samples * 100, 1) if samples else 0,
|
||
"average_3d_return": round(statistics.fmean(returns), 2) if returns else 0,
|
||
"average_holding_return": round(statistics.fmean(returns), 2) if returns else 0,
|
||
"average_drawdown": round(statistics.fmean(drawdowns), 2) if drawdowns else 0,
|
||
"evaluation_days": len(evaluation_dates),
|
||
"frequency": frequency,
|
||
"holding_days": holding_days,
|
||
"take_profit": take_profit,
|
||
"stop_loss": stop_loss,
|
||
"definition": (
|
||
f"收盘后选股，未来{holding_days}日先触及+{take_profit:g}%且未先触及"
|
||
f"{stop_loss:g}%计为成功；同日双触发按失败处理。"
|
||
),
|
||
"approximate": True,
|
||
}
|
||
|
||
|
||
def compile_local_strategy(prompt: str, regime: str) -> dict[str, Any]:
|
||
base = next((item for item in BUILTIN_STRATEGIES if regime in item["regimes"]), BUILTIN_STRATEGIES[1])
|
||
formula = copy.deepcopy(base["formula"])
|
||
description = prompt.strip() or base["description"]
|
||
lowered = description.lower()
|
||
if "低吸" in description:
|
||
formula["filters"] = [item for item in formula["filters"] if item["field"] != "pct_chg"]
|
||
formula["filters"].append({"field": "pct_chg", "op": "between", "value": [-3, 3]})
|
||
if "放量" in description:
|
||
formula["filters"].append({"field": "volume_ratio_5d", "op": ">=", "value": 1.2})
|
||
if "强势" in description or "突破" in description:
|
||
formula["filters"].append({"field": "return_5d", "op": ">=", "value": 5})
|
||
if "低波" in description or "稳健" in description:
|
||
formula["score"].append({"field": "volatility_10d", "weight": 0.18, "direction": "asc"})
|
||
if "资金" in description or "主力" in description:
|
||
formula["score"].append({"field": "net_flow_million", "weight": 0.18, "direction": "desc"})
|
||
if "小市值" in description or "小盘" in description:
|
||
formula["score"].append({"field": "circ_mv_billion", "weight": 0.15, "direction": "asc"})
|
||
if "竞价" in description:
|
||
formula["filters"].extend(
|
||
[
|
||
{"field": "auction_change", "op": "between", "value": [0.5, 8]},
|
||
{"field": "auction_amount_million", "op": ">=", "value": 2},
|
||
]
|
||
)
|
||
formula["score"].extend(
|
||
[
|
||
{"field": "auction_volume_ratio", "weight": 0.20, "direction": "desc"},
|
||
{"field": "auction_amount_million", "weight": 0.18, "direction": "desc"},
|
||
]
|
||
)
|
||
if "少量" in description or "精选" in description:
|
||
formula["limit"] = min(formula["limit"], 8)
|
||
formula["score"] = formula["score"][:12]
|
||
return {
|
||
"name": f"{REGIMES.get(regime, regime)}自定义策略",
|
||
"description": description,
|
||
"regimes": [regime],
|
||
"formula": formula,
|
||
"compiler": "local_template",
|
||
}
|
||
|
||
|
||
def _optional_number(value: Any) -> float | None:
|
||
if value in (None, ""):
|
||
return None
|
||
try:
|
||
result = float(value)
|
||
except (TypeError, ValueError):
|
||
return None
|
||
return result if math.isfinite(result) else None
|
||
|
||
|
||
def _rounded_optional(value: Any, digits: int = 2) -> float | None:
|
||
parsed = _optional_number(value)
|
||
return round(parsed, digits) if parsed is not None else None
|
||
|
||
|
||
def _limit_threshold(code: str, name: str) -> float:
|
||
if code.startswith(("4", "8")):
|
||
return 29.0
|
||
if code.startswith(("30", "68")):
|
||
return 19.0
|
||
return 9.5
|
||
|
||
|
||
def _ending_streak(flags: list[bool], end_index: int | None = None) -> int:
|
||
if not flags:
|
||
return 0
|
||
index = len(flags) - 1 if end_index is None else min(end_index, len(flags) - 1)
|
||
streak = 0
|
||
while index >= 0 and flags[index]:
|
||
streak += 1
|
||
index -= 1
|
||
return streak
|
||
|
||
|
||
def _max_streak(flags: list[bool]) -> int:
|
||
best = current = 0
|
||
for value in flags:
|
||
current = current + 1 if value else 0
|
||
best = max(best, current)
|
||
return best
|
||
|
||
|
||
def _rsi(values: list[float], period: int = 6) -> float:
|
||
if len(values) <= period:
|
||
return 50.0
|
||
changes = [values[index] - values[index - 1] for index in range(len(values) - period, len(values))]
|
||
gains = sum(max(change, 0.0) for change in changes) / period
|
||
losses = sum(max(-change, 0.0) for change in changes) / period
|
||
if losses == 0:
|
||
return 100.0 if gains > 0 else 50.0
|
||
return 100 - 100 / (1 + gains / losses)
|
||
|
||
|
||
def _ema(values: list[float], period: int) -> list[float]:
|
||
if not values:
|
||
return []
|
||
alpha = 2 / (period + 1)
|
||
result = [values[0]]
|
||
for value in values[1:]:
|
||
result.append(value * alpha + result[-1] * (1 - alpha))
|
||
return result
|
||
|
||
|
||
def _macd_series(values: list[float]) -> tuple[list[float], list[float]]:
|
||
fast = _ema(values, 12)
|
||
slow = _ema(values, 26)
|
||
dif = [left - right for left, right in zip(fast, slow)]
|
||
return dif, _ema(dif, 9)
|
||
|
||
|
||
def _macd_last(values: list[float]) -> tuple[float, float]:
|
||
dif, dea = _macd_series(values)
|
||
return (dif[-1], dea[-1]) if dif and dea else (0.0, 0.0)
|
||
|
||
|
||
def _weekly_series(rows: list[dict[str, Any]]) -> tuple[list[float], list[float]]:
|
||
weeks: dict[str, tuple[float, float]] = {}
|
||
for row in rows:
|
||
trade_date = str(row.get("trade_date") or "")
|
||
try:
|
||
key = datetime.strptime(trade_date, "%Y%m%d").strftime("%G-%V")
|
||
except ValueError:
|
||
continue
|
||
close = _number(row.get("close"))
|
||
amount = _number(row.get("amount"))
|
||
previous = weeks.get(key, (close, 0.0))
|
||
weeks[key] = (close, previous[1] + amount)
|
||
ordered = list(weeks.values())
|
||
return [item[0] for item in ordered], [item[1] for item in ordered]
|
||
|
||
|
||
def _broken_reversal_metrics(
|
||
rows: list[dict[str, Any]], flags: list[bool], code: str, name: str,
|
||
) -> dict[str, Any]:
|
||
result = {"signal": 0, "days": 0, "recovered": 0, "volume_ratio": 0.0}
|
||
if not rows or not flags[-1]:
|
||
return result
|
||
current_close = _number(rows[-1].get("close"))
|
||
current_volume = _number(rows[-1].get("vol"))
|
||
for days in range(1, 4):
|
||
index = len(rows) - 1 - days
|
||
if index <= 0 or flags[index] or _ending_streak(flags, index - 1) < 2:
|
||
continue
|
||
broken_high = _number(rows[index].get("high"))
|
||
broken_volume = _number(rows[index].get("vol"))
|
||
recovered = int(current_close >= broken_high > 0)
|
||
volume_ratio = current_volume / broken_volume if broken_volume else 0.0
|
||
return {
|
||
"signal": int(recovered and volume_ratio >= 1),
|
||
"days": days,
|
||
"recovered": recovered,
|
||
"volume_ratio": round(volume_ratio, 3),
|
||
}
|
||
return result
|
||
|
||
|
||
def _is_limit_bar(rows: list[dict[str, Any]], index: int, code: str, name: str) -> bool:
|
||
if index < 0 or index >= len(rows):
|
||
return False
|
||
return _number(rows[index].get("pct_chg")) >= _limit_threshold(code, name)
|
||
|
||
|
||
def _touched_limit_bar(rows: list[dict[str, Any]], index: int, code: str, name: str) -> bool:
|
||
if index <= 0 or index >= len(rows):
|
||
return False
|
||
previous_close = _number(rows[index - 1].get("close"))
|
||
high = _number(rows[index].get("high"))
|
||
if previous_close <= 0 or high <= 0:
|
||
return False
|
||
touched_change = (high / previous_close - 1) * 100
|
||
return touched_change >= _limit_threshold(code, name)
|
||
|
||
|
||
def _matches(actual: Any, operator: str, expected: Any) -> bool:
|
||
if actual is None:
|
||
return False
|
||
try:
|
||
if operator == "between":
|
||
return float(expected[0]) <= float(actual) <= float(expected[1])
|
||
if operator == "in":
|
||
return actual in expected
|
||
if operator == ">":
|
||
return float(actual) > float(expected)
|
||
if operator == ">=":
|
||
return float(actual) >= float(expected)
|
||
if operator == "<":
|
||
return float(actual) < float(expected)
|
||
if operator == "<=":
|
||
return float(actual) <= float(expected)
|
||
if operator == "==":
|
||
return actual == expected or float(actual) == float(expected)
|
||
if operator == "!=":
|
||
return actual != expected
|
||
except (TypeError, ValueError, IndexError):
|
||
return False
|
||
return False
|
||
|
||
|
||
def _percentile_map(rows: list[dict[str, Any]], field: str, direction: str) -> dict[str, float]:
|
||
ordered = sorted(rows, key=lambda item: _number(item.get(field)))
|
||
denominator = max(1, len(ordered) - 1)
|
||
result = {}
|
||
for index, row in enumerate(ordered):
|
||
percentile = index / denominator
|
||
result[row["ts_code"]] = 1 - percentile if direction == "asc" else percentile
|
||
return result
|
||
|
||
|
||
def _available_percentile_map(
|
||
rows: list[dict[str, Any]], field: str, direction: str,
|
||
) -> dict[str, float | None]:
|
||
available = [row for row in rows if row.get(field) is not None]
|
||
result: dict[str, float | None] = {
|
||
str(row.get("ts_code") or ""): None for row in rows
|
||
}
|
||
if not available:
|
||
return result
|
||
ordered = sorted(available, key=lambda item: _number(item.get(field)))
|
||
denominator = max(1, len(ordered) - 1)
|
||
for index, row in enumerate(ordered):
|
||
percentile = 0.5 if len(ordered) == 1 else index / denominator
|
||
result[str(row.get("ts_code") or "")] = (
|
||
1 - percentile if direction == "asc" else percentile
|
||
)
|
||
return result
|
||
|
||
|
||
def _pearson(first: list[float], second: list[float]) -> float:
|
||
if len(first) != len(second) or len(first) < 20:
|
||
return 0.0
|
||
first_mean = statistics.fmean(first)
|
||
second_mean = statistics.fmean(second)
|
||
numerator = sum(
|
||
(left - first_mean) * (right - second_mean)
|
||
for left, right in zip(first, second)
|
||
)
|
||
left_sum = sum((value - first_mean) ** 2 for value in first)
|
||
right_sum = sum((value - second_mean) ** 2 for value in second)
|
||
denominator = math.sqrt(left_sum * right_sum)
|
||
return numerator / denominator if denominator else 0.0
|
||
|
||
|
||
def _risk_flags(
|
||
row: dict[str, Any], regime: str, include_regime_risk: bool = True
|
||
) -> list[str]:
|
||
flags = []
|
||
if row.get("pct_chg", 0) >= 9.5:
|
||
flags.append("当日接近涨停，次日存在高开与无法成交风险")
|
||
if row.get("return_10d", 0) >= 25:
|
||
flags.append("短期累计涨幅较高")
|
||
if row.get("volatility_10d", 0) >= 7:
|
||
flags.append("波动率偏高")
|
||
if row.get("amount_billion", 0) < 1:
|
||
flags.append("成交承载力偏弱")
|
||
if include_regime_risk and regime == "retreat":
|
||
flags.append("市场处于退潮阶段，策略可能选择空仓")
|
||
return flags
|
||
|
||
|
||
def _regime_reason(regime: str) -> str:
|
||
return {
|
||
"ice": "情绪和赚钱效应处于低位，重点观察率先抗跌与转折信号。",
|
||
"repair": "核心指标从低位改善，适合观察率先修复且有板块共振的方向。",
|
||
"fermentation": "赚钱效应扩散，主线和梯队持续增强。",
|
||
"climax": "情绪处于高位，后排跟风与兑现风险同时上升。",
|
||
"divergence": "指数或核心仍强，但广度、封板质量开始分化。",
|
||
"retreat": "情绪指标继续走弱，应提高筛选门槛并接受无候选结果。",
|
||
}.get(regime, "市场阶段待确认。")
|