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）
190 lines
7.6 KiB
Python
190 lines
7.6 KiB
Python
#!/usr/bin/env python3
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"""step39_drawdown_fix.py — 回撤优化验证（数据驱动）
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针对根因（信号扎堆满仓→全止损）测试：
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方案A: 单日买入限流（每日最多N个新买入）
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方案B: 连亏降仓（连续止损>=M次后仓位降到X%，恢复后再加回）
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方案C: A+B 组合
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对比: 年化/回撤/资金利用率
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"""
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import numpy as np
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import pandas as pd
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import sqlite3
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import sys
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sys.path.insert(0, "/tmp")
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from sr_calculator import SRCalculator
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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["_key"] = panel["code"] + "_" + panel["date"]
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pos_map = {k: i for i, k in enumerate(panel["_key"])}
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dates = sorted(panel["date"].unique())
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idx_df = pd.read_sql("SELECT date, close, high FROM stock_daily WHERE code='sh000001' ORDER BY date",
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sqlite3.connect("file:/home/hmo/MoFin/data/mofin.db?mode=ro", uri=True))
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idx_df["date"] = idx_df["date"].astype(str)
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idx_df["hi60"] = idx_df["high"].rolling(60).max()
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idx_df["mkt_dd60"] = (idx_df["close"] / idx_df["hi60"] - 1) * 100
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dd_map = dict(zip(idx_df["date"], idx_df["mkt_dd60"]))
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panel["mkt_dd60"] = panel["date"].map(dd_map)
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sig_cond = (
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(panel["mkt_rsi"] < 50) & (panel["mcap_q"] < 0.2) & (panel["pe_q"] < 0.2) &
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(panel["news3"] >= 1) & (panel["sec_ret20"] < 0) & (panel["bias60"] < -20) &
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(panel["mkt_dd60"] <= -5)
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)
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cand = panel[sig_cond][["code", "date"]].copy()
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cand = cand.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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print("信号:", len(cand), flush=True)
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conn = sqlite3.connect("file:/home/hmo/MoFin/data/mofin.db?mode=ro", uri=True)
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codes = cand["code"].unique().tolist()
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ph = ",".join("?" * len(codes))
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df = pd.read_sql("SELECT code, date, close, high, low FROM stock_daily WHERE code IN ({}) ORDER BY code, date".format(ph), conn, params=codes)
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df["date"] = df["date"].astype(str)
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df["code"] = df["code"].astype(str).str.zfill(6)
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df = df.sort_values(["code", "date"]).reset_index(drop=True)
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df["_key"] = df["code"] + "_" + df["date"]
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dpos = {k: i for i, k in enumerate(df["_key"])}
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closes = df["close"].values
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highs = df["high"].values
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lows = df["low"].values
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cand["kidx"] = (cand["code"] + "_" + cand["date"]).map(dpos)
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cand = cand.dropna(subset=["kidx"]).copy()
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cand["kidx"] = cand["kidx"].astype(int)
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sr = SRCalculator()
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print("=== 计算支撑压力 ===", flush=True)
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sig_list = []
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for r in cand.itertuples():
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bars_code = sr.get_bars(r.code)
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d_idx = bars_code.index[bars_code["date"] == r.date]
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if len(d_idx) == 0:
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continue
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sr_full = sr.sr_full(r.code, d_idx[0])
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pv = sr_full["pivot"]
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chip = sr_full["chip"]
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if not pv:
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continue
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sig_close = closes[r.kidx]
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support = pv["s2"]
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if chip and chip["chip_ss"] < sig_close:
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support = max(pv["s2"], chip["chip_ss"])
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resist = pv["r2"]
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if chip and chip["chip_sr"] > sig_close:
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resist = min(pv["r2"], chip["chip_sr"])
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if support >= sig_close or resist <= sig_close:
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continue
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sig_list.append({"code": r.code, "date": r.date, "kidx": r.kidx,
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"sig_close": sig_close, "support": support, "resist": resist})
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sigdf = pd.DataFrame(sig_list)
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print("有支撑压力:", len(sigdf), flush=True)
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sig_by_date = {}
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for r in sigdf.itertuples():
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sig_by_date.setdefault(r.date, []).append(r)
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def run_sim(slots=10, hold=40, pos_frac=0.15, stop_buf=0.05,
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max_daily=99, loss_reduce=0, loss_threshold=99, reduce_frac=0.5):
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"""max_daily: 单日最大买入数; loss_reduce: 连亏>=N次降仓"""
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INIT_CAP = 1_000_000
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positions = {}
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cash = INIT_CAP
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navs = []
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streak_loss = 0
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cur_pos_frac = pos_frac
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for di, d in enumerate(dates):
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# 离场
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for code in list(positions.keys()):
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pos = positions[code]
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k = dpos.get(code + "_" + d)
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if k is None:
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continue
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hi, lo, cl = highs[k], lows[k], closes[k]
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exited = False
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if hi >= pos["tp"]:
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sell_qty = pos["qty"] // 2
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if sell_qty > 0:
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cash += sell_qty * pos["tp"]
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pos["qty"] -= sell_qty
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if pos["qty"] <= 0:
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del positions[code]
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exited = True
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streak_loss = 0 # 盈利离场，重置连亏
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if not exited and lo <= pos["stop"]:
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cash += pos["qty"] * lo
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del positions[code]
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streak_loss += 1 # 止损，连亏+1
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if loss_reduce > 0 and streak_loss >= loss_threshold:
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cur_pos_frac = pos_frac * reduce_frac # 降仓
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exited = True
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if not exited and di - pos["entry_di"] >= hold:
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cash += pos["qty"] * cl
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del positions[code]
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streak_loss = 0
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# 买入（限流）
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if d in sig_by_date:
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bought = 0
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for r in sig_by_date[d]:
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if bought >= max_daily:
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break
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if len(positions) >= slots:
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break
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if r.code in positions:
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continue
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cur_nav = cash
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for c, p in positions.items():
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k = dpos.get(c + "_" + d)
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px = closes[k] if k is not None else p["avg_cost"]
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cur_nav += p["qty"] * px
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pos_val = cur_nav * cur_pos_frac
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price = r.sig_close
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if price <= 0:
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continue
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qty = int(pos_val / price)
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if qty <= 0 or qty * price > cash:
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continue
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cash -= qty * price
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positions[r.code] = {
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"entry_di": di, "qty": qty, "avg_cost": price,
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"stop": r.support * (1 - stop_buf), "tp": r.resist,
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}
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bought += 1
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nav = cash
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for c, p in positions.items():
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k = dpos.get(c + "_" + d)
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px = closes[k] if k is not None else p["avg_cost"]
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nav += p["qty"] * px
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navs.append(nav)
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nav_arr = np.array(navs)
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final = nav_arr[-1]
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years = len(navs) / 250
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cagr = ((final / INIT_CAP) ** (1 / years) - 1) * 100 if final > 0 else -100
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cummax = np.maximum.accumulate(nav_arr)
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dd = ((nav_arr - cummax) / cummax).min() * 100
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cash_ratio = np.mean([1 - nav_arr[i]/nav_arr[i] for i in range(len(navs))]) # placeholder
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return cagr, dd, len(navs)
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# 方案A: 单日限流
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print("\n=== 方案A: 单日买入限流 ===", flush=True)
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for maxd in [1, 2, 3, 5, 99]:
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cagr, dd, _ = run_sim(max_daily=maxd)
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print("单日限{}个: 年化={:.2f}% 回撤={:.1f}%".format(maxd, cagr, dd), flush=True)
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# 方案B: 连亏降仓
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print("\n=== 方案B: 连亏降仓 ===", flush=True)
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for thr, frac in [(2, 0.5), (3, 0.5), (3, 0.7), (4, 0.5)]:
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cagr, dd, _ = run_sim(loss_reduce=1, loss_threshold=thr, reduce_frac=frac)
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print("连亏{}次降{}%仓位: 年化={:.2f}% 回撤={:.1f}%".format(thr, int(frac*100), cagr, dd), flush=True)
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# 方案C: 组合
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print("\n=== 方案C: 组合 ===", flush=True)
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for maxd, thr, frac in [(3, 3, 0.5), (2, 3, 0.5), (3, 2, 0.5)]:
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cagr, dd, _ = run_sim(max_daily=maxd, loss_reduce=1, loss_threshold=thr, reduce_frac=frac)
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print("限{}个+连亏{}降{}%: 年化={:.2f}% 回撤={:.1f}%".format(maxd, thr, int(frac*100), cagr, dd), flush=True)
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print("\n=== 完成 ===", flush=True)
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