hmo
b9c68a83a7
- 新增 strategy_research_methodology.md（由果及因/12维/铁律/支撑压力规范） - 新增 predictive_oversold_strategy.md（v5定稿，年化18.57%） - 新增 deployment-plan-predictive-oversold.md（整合部署计划） - 归档 docs/research/（63份研究过程文档）+ scripts/research/（19个研究脚本） - 更新 docs/README.md 文档中心（策略研究章节）
65 lines
2.9 KiB
Python
65 lines
2.9 KiB
Python
#!/usr/bin/env python3
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"""step29_compress_predictive.py — 压缩预测信号到稀缺（数据定阈值，验证预测力保持）
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在四因子基础上加更多预测因子压缩信号量：
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- 行业状态（sec_above/sec_ret20）
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- 个股技术（bias60深度/量能/距低点）
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- 资金流
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逐层验证：信号量下降 且 大涨率/avg60 保持或提升
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"""
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import numpy as np
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import pandas as pd
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print("=== 加载 ===", flush=True)
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panel = pd.read_pickle("/tmp/panel_12d.pkl")
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panel = panel.sort_values(["code", "date"]).reset_index(drop=True)
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panel["fwd_ret60"] = panel.groupby("code")["close"].transform(lambda x: x.shift(-60)/x - 1) * 100
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panel["is_big"] = (panel["fwd_ret60"] >= 50).astype(int)
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print("面板:", len(panel), flush=True)
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base = panel.dropna(subset=["fwd_ret60"])
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print("基线: 大涨率={:.2f}% avg60={:.2f}%".format(
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base["is_big"].mean()*100, base["fwd_ret60"].mean()), flush=True)
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def validate(cond, label, min_n=50):
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s = base[cond]
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if len(s) < min_n:
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print("{}: n={} 样本不足".format(label, len(s)), flush=True)
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return None
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rate = s["is_big"].mean()*100
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avg = s["fwd_ret60"].mean()
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wr = (s["fwd_ret60"]>0).mean()*100
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cand = s[["code", "date"]].sort_values(["code", "date"])
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cand["prev"] = cand.groupby("code")["date"].shift(1)
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cand["gap"] = (pd.to_datetime(cand["date"]) - pd.to_datetime(cand["prev"])).dt.days
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cand = cand[(cand["prev"].isna()) | (cand["gap"] > 30)]
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cand["year"] = cand["date"].str[:4]
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n_dedup = len(cand)
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monthly = n_dedup / max(len(cand["year"].unique()),1) / 12
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print("{}: n={} 大涨率={:.2f}% avg60={:.2f}% wr={:.1f}% | 去重{} 月均{:.1f}".format(
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label, len(s), rate, avg, wr, n_dedup, monthly), flush=True)
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return {"n": n_dedup, "monthly": monthly, "rate": rate, "avg": avg, "wr": wr}
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# 基础四因子
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c4 = (panel["mkt_rsi"] < 41) & (panel["mcap_q"] < 0.2) & (panel["pe_q"] < 0.2) & (panel["news3"] >= 1)
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print("\n=== 四因子基准 ===", flush=True)
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validate(c4, "四因子(弱市+小市值+低PE+新闻)")
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print("\n=== 加行业状态 ===", flush=True)
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validate(c4 & (panel["sec_above"] == 0), "+行业MA20下")
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validate(c4 & (panel["sec_ret20"] < 0), "+行业20日跌")
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validate(c4 & (panel["sec_ret20"] < -5), "+行业20日深跌")
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print("\n=== 加个股技术 ===", flush=True)
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validate(c4 & (panel["bias60"] < -10), "+bias60<-10(深跌)")
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validate(c4 & (panel["bias60"] < -15), "+bias60<-15")
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validate(c4 & (panel["bias60"] < -20), "+bias60<-20")
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validate(c4 & (panel["vol_ratio"] < 1.0), "+缩量")
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validate(c4 & (panel["dist_lo20"] < 5), "+距前低<5%")
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print("\n=== 组合最优方向 ===", flush=True)
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# 行业弱 + 深跌
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validate(c4 & (panel["sec_ret20"] < -5) & (panel["bias60"] < -15), "四因子+行业深跌+bias60<-15")
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validate(c4 & (panel["sec_ret20"] < -5) & (panel["bias60"] < -10) & (panel["dist_lo20"] < 8), "四因子+行业深跌+bias60<-10+距低点<8")
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print("\n=== 完成 ===", flush=True)
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