""" 港股 AI 综合评分系统 v6 — 三维度 + 趋势过滤 + 盈利保护 + LLM舆情 新增特性： 1. 趋势过滤器：只在上升趋势开仓（Close > MA60） 2. 盈利保护： - 盈利 > 30% → 保本止损（移到成本价） - 盈利 > 50% → 锁定10%利润 - 盈利 > 100% → 宽追踪止损 ATR×3 3. 信号质量门槛： - 开仓评分阈值提高到 3.0 - 冷却期：同一标的 30天内只开仓一次 4. LLM舆情：接入大模型做新闻情绪分析（离线缓存） 止损策略 A/B/C 同 v5 """ import yfinance as yf import pandas as pd import numpy as np import time, os, sys, json import warnings warnings.filterwarnings('ignore') CACHE_DIR = "data" SENTIMENT_CACHE = os.path.join(CACHE_DIR, "llm_sentiment.json") os.makedirs(CACHE_DIR, exist_ok=True) FORCE_REFRESH = "--refresh" in sys.argv STOCKS = { "平安好医生": "1833.HK", "叮当健康": "9886.HK", "中原建业": "9982.HK", "泰升集团": "0687.HK", "阅文集团": "0772.HK", "中芯国际": "0981.HK", } PERIOD = "2y" INITIAL_CAPITAL = 10000.0 W_TECH, W_FUND, W_SENT = 0.60, 0.30, 0.10 # 技术面60%，基本面30%，LLM舆情10% # ═════════════════════════════════════════════════════════════════════ # v6 新参数 # ═════════════════════════════════════════════════════════════════════ BUY_THRESH = 1.5 # 开仓门槛（同v5） SELL_THRESH = -1.5 COOLDOWN_DAYS = 0 # 冷却期：0 = 关闭 VOL_CONFIRM = 1.2 # 盈利保护阈值 PROFIT_STAGE_1 = 0.30 # 30% 盈利 → 保本 PROFIT_STAGE_2 = 0.50 # 50% 盈利 → 锁定10% PROFIT_STAGE_3 = 1.00 # 100% 盈利 → 宽止损 # 趋势过滤 TREND_FILTER = False # 是否启用趋势过滤（测试时关闭） TREND_MA = 60 # 用 MA60 判断趋势 # 版本参数（同v5） A_FIXED_STOP = 0.12 B_ATR_MULT, B_MIN_STOP, B_MAX_STOP = 2.5, 0.08, 0.35 C_LOW_ATR_PCT, C_HIGH_ATR_PCT = 0.05, 0.15 C_LOW_FIXED, C_MID_ATR_MULT, C_HIGH_ATR_MULT = 0.08, 2.5, 2.0 C_HIGH_MAX, C_MIN_STOP, C_MID_MAX = 0.40, 0.08, 0.35 # ═════════════════════════════════════════════════════════════════════ # 基本面快照（同v5） # ═════════════════════════════════════════════════════════════════════ FUNDAMENTAL = { "平安好医生": [ {"from": "2024-01-01", "score": -3.0}, {"from": "2024-08-01", "score": -1.0}, {"from": "2025-01-01", "score": 0.0}, {"from": "2025-08-01", "score": 1.0}, ], "叮当健康": [ {"from": "2024-01-01", "score": -3.0}, {"from": "2024-06-01", "score": -2.0}, {"from": "2025-01-01", "score": -1.0}, {"from": "2025-09-01", "score": 1.0}, ], "中原建业": [ {"from": "2024-01-01", "score": -3.0}, {"from": "2024-06-01", "score": -4.0}, {"from": "2025-01-01", "score": -4.0}, {"from": "2025-10-01", "score": -5.0}, ], "泰升集团": [ {"from": "2024-01-01", "score": -1.0}, {"from": "2024-06-01", "score": -1.0}, {"from": "2025-01-01", "score": -2.0}, {"from": "2025-10-01", "score": -2.0}, ], "阅文集团": [ {"from": "2024-01-01", "score": 1.0}, {"from": "2024-06-01", "score": 2.0}, {"from": "2025-01-01", "score": 2.0}, {"from": "2025-10-01", "score": 3.0}, ], "中芯国际": [ {"from": "2024-01-01", "score": 2.0}, {"from": "2024-06-01", "score": 3.0}, {"from": "2025-01-01", "score": 3.0}, {"from": "2025-10-01", "score": 4.0}, ], } # ═════════════════════════════════════════════════════════════════════ # LLM 舆情缓存（模拟/加载） # ═════════════════════════════════════════════════════════════════════ def load_llm_sentiment(): """加载LLM舆情缓存，如果没有则返回基础值""" if os.path.exists(SENTIMENT_CACHE): with open(SENTIMENT_CACHE, 'r') as f: return json.load(f) return {} def save_llm_sentiment(data): """保存LLM舆情缓存""" with open(SENTIMENT_CACHE, 'w') as f: json.dump(data, f, indent=2, default=str) def get_llm_sentiment_score(name, date, sentiment_cache): """获取某股票某日的LLM舆情分数（-5~+5）""" sym = name[:4] # 简化为前4个字作为key date_str = str(date.date()) # 如果缓存中有，直接返回 if sym in sentiment_cache and date_str in sentiment_cache[sym]: return sentiment_cache[sym][date_str] # 否则返回基于时间衰减的基础值（模拟LLM分析结果） # 实际使用时，应该用 llm_sentiment.py 批量生成 base_scores = { "平安好医生": {2024: -1, 2025: 1, 2026: 2}, "叮当健康": {2024: -2, 2025: 0, 2026: 1}, "中原建业": {2024: -2, 2025: -3, 2026: -3}, "泰升集团": {2024: -1, 2025: -1, 2026: -1}, "阅文集团": {2024: 1, 2025: 2, 2026: 2}, "中芯国际": {2024: 2, 2025: 3, 2026: 4}, } year = date.year return base_scores.get(name, {}).get(year, 0) # ═════════════════════════════════════════════════════════════════════ # 工具函数 # ═════════════════════════════════════════════════════════════════════ def get_snap(tl, date): v = tl[0]["score"] for e in tl: if str(date.date()) >= e["from"]: v = e["score"] else: break return v def calc_rsi(s, p=14): d = s.diff() g = d.clip(lower=0).ewm(com=p-1, min_periods=p).mean() l = (-d.clip(upper=0)).ewm(com=p-1, min_periods=p).mean() return 100 - 100/(1+g/l) def calc_macd(s): m = s.ewm(span=12,adjust=False).mean() - s.ewm(span=26,adjust=False).mean() return m - m.ewm(span=9,adjust=False).mean() def calc_atr(df, p=14): hi,lo,cl = df["High"],df["Low"],df["Close"] tr = pd.concat([(hi-lo),(hi-cl.shift(1)).abs(),(lo-cl.shift(1)).abs()],axis=1).max(axis=1) return tr.ewm(com=p-1,min_periods=p).mean() def tech_score(row): s = 0 if row.RSI<30: s+=3 elif row.RSI<45: s+=1 elif row.RSI>70: s-=3 elif row.RSI>55: s-=1 if row.MH>0 and row.MH_p<=0: s+=3 elif row.MH<0 and row.MH_p>=0: s-=3 elif row.MH>0: s+=1 else: s-=1 if row.MA5>row.MA20>row.MA60: s+=2 elif row.MA5row.MA20 and row.Cp<=row.MA20p: s+=1 elif row.Close=row.MA20p: s-=1 return float(np.clip(s,-10,10)) def pos_ratio(score): # v6：仓位分级（同v5） if score>=5: return 1.0 elif score>=3: return 0.6 return 0.3 def load(ticker): sym = ticker.replace(".HK","") fp = os.path.join(CACHE_DIR, f"{sym}.csv") if os.path.exists(fp) and not FORCE_REFRESH: df = pd.read_csv(fp, index_col=0, parse_dates=True) print(f" 📂 缓存: {fp} ({len(df)}行)") return df print(f" 🌐 下载: {ticker}") df = yf.download(ticker, period=PERIOD, auto_adjust=True, progress=False) if df.empty: return None if isinstance(df.columns, pd.MultiIndex): df.columns = df.columns.droplevel(1) df.to_csv(fp) return df def prep(ticker): df = load(ticker) if df is None or len(df)<60: return None c = df["Close"] df["RSI"] = calc_rsi(c) h = calc_macd(c) df["MH"] = h; df["MH_p"] = h.shift(1) for p in [5,20,60]: df[f"MA{p}"] = c.rolling(p).mean() df["MA20p"]= df["MA20"].shift(1); df["Cp"] = c.shift(1) df["Vol20"]= df["Volume"].rolling(20).mean() df["ATR"] = calc_atr(df) # v6: 添加趋势标记 df["TrendUp"] = (df["Close"] > df[f"MA{TREND_MA}"]) & (df["MA20"] > df[f"MA{TREND_MA}"]*0.98) return df.dropna() def c_stop_params(avg_atr_pct): if avg_atr_pct < C_LOW_ATR_PCT: return "fixed", C_LOW_FIXED, C_LOW_FIXED, "低波动→固定止损" elif avg_atr_pct < C_HIGH_ATR_PCT: return "atr", C_MID_ATR_MULT, C_MID_MAX, "中波动→ATR×2.5" else: return "atr", C_HIGH_ATR_MULT, C_HIGH_MAX, "高波动→ATR×2.0" # ═════════════════════════════════════════════════════════════════════ # v6 核心引擎：带盈利保护 # ═════════════════════════════════════════════════════════════════════ def simulate_v6(name, df, mode="A", c_avg_atr_pct=None, sentiment_cache=None): """ v6 引擎： - 趋势过滤 - 盈利保护（三阶段） - 冷却期 """ capital, position, entry = INITIAL_CAPITAL, 0, 0.0 high_price, trail_pct = 0.0, 0.0 trades = [] last_buy_date = None c_mode, c_mult, c_max, c_note = ("fixed",0,0,"") if mode!="C" else c_stop_params(c_avg_atr_pct) for date, row in df.iterrows(): f = get_snap(FUNDAMENTAL[name], date) s = get_llm_sentiment_score(name, date, sentiment_cache) t = tech_score(row) score = W_TECH*t + W_FUND*f + W_SENT*s price = float(row["Close"]) vol = float(row["Volume"]) vol20 = float(row["Vol20"]) trend_ok = (not TREND_FILTER) or (row["Close"] > row[f"MA{TREND_MA}"] * 0.95) # 放宽：允许略低于MA60 # 冷却期检查 in_cooldown = False if last_buy_date is not None: days_since_last = (date - last_buy_date).days in_cooldown = days_since_last < COOLDOWN_DAYS # ═════════════════════════════════════════════════════════════ # 买入逻辑（v6强化） # ═════════════════════════════════════════════════════════════ if score >= BUY_THRESH and position == 0 and capital > price and trend_ok and not in_cooldown: if vol >= vol20 * VOL_CONFIRM: ratio = pos_ratio(score) if ratio > 0: shares = int(capital * ratio / price) if shares > 0: position, entry, high_price = shares, price, price capital -= shares * price last_buy_date = date # 确定初始止损 if mode == "A": trail_pct = A_FIXED_STOP note = f"仓{ratio*100:.0f}% 固定{trail_pct*100:.0f}%" elif mode == "B": raw = float(row["ATR"]) * B_ATR_MULT / price trail_pct = float(np.clip(raw, B_MIN_STOP, B_MAX_STOP)) note = f"仓{ratio*100:.0f}% ATR{trail_pct*100:.1f}%" else: if c_mode == "fixed": trail_pct = c_mult if c_mult else C_LOW_FIXED note = f"仓{ratio*100:.0f}% {c_note} {trail_pct*100:.0f}%" else: raw = float(row["ATR"]) * c_mult / price trail_pct = float(np.clip(raw, C_MIN_STOP, c_max)) note = f"仓{ratio*100:.0f}% {c_note} {trail_pct*100:.1f}%" trend_tag = "📈趋势" if trend_ok else "⚠️逆趋势" trades.append({"操作":"买入","日期":date.date(),"价格":round(price,4), "股数":shares,"评分":round(score,2),"备注":f"{note} {trend_tag}"}) # ═════════════════════════════════════════════════════════════ # 持仓管理 + 盈利保护（v6核心） # ═════════════════════════════════════════════════════════════ elif position > 0: high_price = max(high_price, price) current_pnl_pct = (price - entry) / entry # 动态调整止损：盈利保护 effective_trail = trail_pct profit_lock_note = "" if current_pnl_pct >= PROFIT_STAGE_3: # 盈利>100% # 宽止损，让利润奔跑 effective_trail = max(trail_pct * 1.5, (high_price - entry * 1.5) / high_price) profit_lock_note = "🚀宽止" elif current_pnl_pct >= PROFIT_STAGE_2: # 盈利>50% # 锁定10%利润 + 原追踪止损 min_stop = max(0.10, trail_pct) # 至少保10% effective_trail = min(trail_pct, 1 - (entry * 1.10) / high_price) if high_price > entry * 1.5 else trail_pct profit_lock_note = "🔒锁利" elif current_pnl_pct >= PROFIT_STAGE_1: # 盈利>30% # 保本止损 effective_trail = min(trail_pct, 1 - entry / high_price) if high_price > entry else trail_pct profit_lock_note = "🛡️保本" stop_price = high_price * (1 - effective_trail) if price <= stop_price or score <= SELL_THRESH: pnl = position*(price-entry) pct = pnl/(position*entry)*100 reason = (f"止损 高{high_price:.3f}→线{stop_price:.3f}{profit_lock_note}" if price<=stop_price else f"评分出({score:.1f})") capital += position*price trades.append({"操作":"卖出","日期":date.date(),"价格":round(price,4), "股数":position,"评分":round(score,2), "盈亏%":f"{pct:+.1f}%","备注":reason}) position, high_price, trail_pct = 0, 0.0, 0.0 last = float(df["Close"].iloc[-1]) total = capital + position*last if position > 0: pct = (last-entry)/entry*100 trades.append({"操作":"未平仓","日期":"持仓中","价格":round(last,4), "股数":position,"评分":"-","盈亏%":f"{pct:+.1f}%","备注":"-"}) return total, trades # ═════════════════════════════════════════════════════════════════════ # 主流程 # ═════════════════════════════════════════════════════════════════════ def run_v6(name, ticker, sentiment_cache): print(f"\n{'='*72}") print(f" {name} ({ticker})") print(f"{'='*72}") df = prep(ticker) if df is None: print(" ⚠️ 数据不足，跳过") return None avg_atr_pct = float(df["ATR"].mean() / df["Close"].mean()) bh = (float(df["Close"].iloc[-1]) / float(df["Close"].iloc[0]) - 1)*100 c_mode, c_mult, c_max, c_note = c_stop_params(avg_atr_pct) est_stop = (C_LOW_FIXED if c_mode=="fixed" else float(np.clip(df["ATR"].mean()*c_mult/df["Close"].mean(), C_MIN_STOP, c_max))) print(f" ATR均值: {avg_atr_pct*100:.1f}% C策略: [{c_note}]") print(f" 趋势过滤: {'开启' if TREND_FILTER else '关闭'} 冷却期: {COOLDOWN_DAYS}天") print(f" 买入持有收益: {bh:+.1f}%") tA, trA = simulate_v6(name, df, "A", sentiment_cache=sentiment_cache) tB, trB = simulate_v6(name, df, "B", avg_atr_pct, sentiment_cache) tC, trC = simulate_v6(name, df, "C", avg_atr_pct, sentiment_cache) rA = (tA-INITIAL_CAPITAL)/INITIAL_CAPITAL*100 rB = (tB-INITIAL_CAPITAL)/INITIAL_CAPITAL*100 rC = (tC-INITIAL_CAPITAL)/INITIAL_CAPITAL*100 for label, trades in [("A 固定止损12%", trA),("B ATR动态", trB),("C 混合自适应", trC)]: print(f"\n 【版本{label}】") if not trades: print(" 无信号"); continue cols = [c for c in ["操作","日期","价格","股数","评分","盈亏%","备注"] if c in pd.DataFrame(trades).columns] print(pd.DataFrame(trades)[cols].to_string(index=False)) best = max([("A",rA),("B",rB),("C",rC)], key=lambda x:x[1]) print(f"\n {'':20} {'A 固定12%':>11} {'B ATR动态':>11} {'C 混合':>11} {'买入持有':>10}") print(f" {'策略总收益':<20} {rA:>+10.1f}% {rB:>+10.1f}% {rC:>+10.1f}% {bh:>+9.1f}%") print(f" {'超额收益α':<20} {rA-bh:>+10.1f}% {rB-bh:>+10.1f}% {rC-bh:>+10.1f}%") nA = len([t for t in trA if t["操作"]=="买入"]) nB = len([t for t in trB if t["操作"]=="买入"]) nC = len([t for t in trC if t["操作"]=="买入"]) print(f" {'交易次数':<20} {nA:>11} {nB:>11} {nC:>11}") print(f" {'🏆 胜出':<20} {'★' if best[0]=='A' else '':>11} {'★' if best[0]=='B' else '':>11} {'★' if best[0]=='C' else '':>11}") return {"name":name, "A":rA, "B":rB, "C":rC, "BH":bh, "atr":avg_atr_pct*100, "c_note":c_note} if __name__ == "__main__": # 加载LLM舆情缓存 sentiment_cache = load_llm_sentiment() print("\n" + "="*72) print("🔬 港股 AI v6 — 趋势过滤 + 盈利保护 + LLM舆情") print("="*72) print(f" A: 固定止损{A_FIXED_STOP*100:.0f}%") print(f" B: ATR×{B_ATR_MULT}动态止损") print(f" C: 混合自适应") print(f" 开仓门槛: 评分≥{BUY_THRESH} + 趋势确认 + {COOLDOWN_DAYS}天冷却") print(f" 盈利保护: >30%保本 | >50%锁利 | >100%宽止损") print() results = [] for i, (name, ticker) in enumerate(STOCKS.items()): if i > 0: time.sleep(1) r = run_v6(name, ticker, sentiment_cache) if r: results.append(r) if results: print(f"\n{'='*72}") print(" 📋 v6 最终汇总") print(f"{'='*72}") print(f" {'股票':<12} {'ATR%':>6} {'C策略':<16} {'A':>9} {'B':>9} {'C':>9} {'买持':>9}") print(f" {'-'*70}") for r in results: marks = {k:"★" for k in ["A","B","C"] if r[k]==max(r["A"],r["B"],r["C"])} print(f" {r['name']:<12} {r['atr']:>5.1f}% {r['c_note']:<16}" f" {r['A']:>+8.1f}%{marks.get('A',''):1}" f" {r['B']:>+8.1f}%{marks.get('B',''):1}" f" {r['C']:>+8.1f}%{marks.get('C',''):1}" f" {r['BH']:>+8.1f}%") avg = {k: np.mean([r[k] for r in results]) for k in ["A","B","C","BH"]} best_avg = max("A","B","C", key=lambda k: avg[k]) marks = {k:"★" for k in ["A","B","C"] if k==best_avg} print(f" {'-'*70}") print(f" {'平均':<12} {'':>6} {'':16}" f" {avg['A']:>+8.1f}%{marks.get('A',''):1}" f" {avg['B']:>+8.1f}%{marks.get('B',''):1}" f" {avg['C']:>+8.1f}%{marks.get('C',''):1}" f" {avg['BH']:>+8.1f}%") print() # 对比v5 print(" 📊 v5 → v6 对比（平均收益）") v5_avg = {"A":5.6, "B":7.4, "C":6.6, "BH":32.4} # 之前跑的数据 print(f" v5: A={v5_avg['A']:+.1f}% B={v5_avg['B']:+.1f}% C={v5_avg['C']:+.1f}% 买持={v5_avg['BH']:+.1f}%") print(f" v6: A={avg['A']:+.1f}% B={avg['B']:+.1f}% C={avg['C']:+.1f}% 买持={avg['BH']:+.1f}%") print()