""" 港股 AI 综合评分系统 v2 - 回测框架 三维度加权评分：技术面(50%) + 基本面(30%) + 舆情(20%) """ import yfinance as yf import pandas as pd import numpy as np import time import warnings warnings.filterwarnings('ignore') # ── 参数 ────────────────────────────────────────────────────────────── STOCKS = { "平安好医生": "1833.HK", "叮当健康": "9886.HK", "中原建业": "9982.HK", } PERIOD = "2y" INITIAL_CAPITAL = 10000 # HKD # 三维度权重 W_TECH = 0.50 W_FUNDAMENTAL = 0.30 W_SENTIMENT = 0.20 # ── 基本面快照（手动录入，按季度/年更新）──────────────────────────── # 格式：每条记录 {"from": "YYYY-MM-DD", "score": float, "note": str} # 分数区间 -10 ~ +10 FUNDAMENTAL_TIMELINE = { "平安好医生": [ {"from": "2024-01-01", "score": -3.0, "note": "持续亏损，估值偏高"}, {"from": "2024-08-01", "score": -1.0, "note": "2024中报净利润转正，估值仍高"}, {"from": "2025-01-01", "score": 0.0, "note": "盈利改善，医险协同深化"}, {"from": "2025-08-01", "score": 1.0, "note": "营收+13.6%，经调整净利润+45.7%"}, ], "叮当健康": [ {"from": "2024-01-01", "score": -3.0, "note": "连续亏损"}, {"from": "2024-06-01", "score": -2.0, "note": "亏损收窄中"}, {"from": "2025-01-01", "score": -1.0, "note": "亏损继续收窄，毛利率提升"}, {"from": "2025-09-01", "score": 1.0, "note": "2025全年调整后盈利1070万，拐点初现"}, ], "中原建业": [ {"from": "2024-01-01", "score": -3.0, "note": "地产下行，代建收入减少"}, {"from": "2024-06-01", "score": -4.0, "note": "停牌风险，执行董事辞任"}, {"from": "2025-01-01", "score": -4.0, "note": "盈警：净利润同比-28~32%"}, {"from": "2025-10-01", "score": -5.0, "note": "地产持续低迷，无机构覆盖"}, ], } # ── 舆情快照（手动录入）──────────────────────────────────────────── SENTIMENT_TIMELINE = { "平安好医生": [ {"from": "2024-01-01", "score": -1.0, "note": "行业承压"}, {"from": "2024-10-01", "score": 1.0, "note": "互联网医疗政策边际改善"}, {"from": "2025-01-01", "score": 2.0, "note": "大摩买入评级，目标价19.65"}, {"from": "2026-01-01", "score": 3.0, "note": "主力资金持续净流入，连锁药房扩展"}, ], "叮当健康": [ {"from": "2024-01-01", "score": -2.0, "note": "市场悲观，连亏"}, {"from": "2024-08-01", "score": -1.0, "note": "关注度低"}, {"from": "2025-04-01", "score": 1.0, "note": "互联网首诊试点，4月新规利好"}, {"from": "2025-10-01", "score": 2.0, "note": "雪球社区关注回升，创新药布局"}, ], "中原建业": [ {"from": "2024-01-01", "score": -2.0, "note": "地产悲观情绪"}, {"from": "2024-06-01", "score": -3.0, "note": "管理层动荡，停牌"}, {"from": "2025-01-01", "score": -3.0, "note": "无投行覆盖，成交极低"}, {"from": "2025-10-01", "score": -4.0, "note": "发盈警，市场信心极低"}, ], } # ── 工具函数 ────────────────────────────────────────────────────────── def get_snapshot_score(timeline, date): """根据日期获取对应时间段的快照分数""" score = timeline[0]["score"] for entry in timeline: if str(date.date()) >= entry["from"]: score = entry["score"] else: break return score def calc_rsi(series, period=14): delta = series.diff() gain = delta.clip(lower=0) loss = -delta.clip(upper=0) avg_gain = gain.ewm(com=period - 1, min_periods=period).mean() avg_loss = loss.ewm(com=period - 1, min_periods=period).mean() rs = avg_gain / avg_loss return 100 - (100 / (1 + rs)) def calc_macd(series, fast=12, slow=26, signal=9): ema_fast = series.ewm(span=fast, adjust=False).mean() ema_slow = series.ewm(span=slow, adjust=False).mean() macd = ema_fast - ema_slow signal_line = macd.ewm(span=signal, adjust=False).mean() return macd, signal_line, macd - signal_line def score_technical(row): """技术面评分 -10 ~ +10""" score = 0 # RSI if row["RSI"] < 30: score += 3 elif row["RSI"] < 45: score += 1 elif row["RSI"] > 70: score -= 3 elif row["RSI"] > 55: score -= 1 # MACD 金叉/死叉 if row["MACD_hist"] > 0 and row["MACD_hist_prev"] <= 0: score += 3 elif row["MACD_hist"] < 0 and row["MACD_hist_prev"] >= 0: score -= 3 elif row["MACD_hist"] > 0: score += 1 else: score -= 1 # 均线排列 if row["MA5"] > row["MA20"] > row["MA60"]: score += 2 elif row["MA5"] < row["MA20"] < row["MA60"]: score -= 2 # MA20 突破/跌破 if row["Close"] > row["MA20"] and row["Close_prev"] <= row["MA20_prev"]: score += 1 elif row["Close"] < row["MA20"] and row["Close_prev"] >= row["MA20_prev"]: score -= 1 return float(np.clip(score, -10, 10)) # ── 回测主函数 ──────────────────────────────────────────────────────── def backtest(name, ticker): print(f"\n{'='*65}") print(f" 回测: {name} ({ticker})") print(f"{'='*65}") df = yf.download(ticker, period=PERIOD, auto_adjust=True, progress=False) if df.empty or len(df) < 60: print(" ⚠️ 数据不足，跳过") return None if isinstance(df.columns, pd.MultiIndex): df.columns = df.columns.droplevel(1) close = df["Close"] df["RSI"] = calc_rsi(close) macd, sig_line, hist = calc_macd(close) df["MACD_hist"] = hist df["MACD_hist_prev"] = hist.shift(1) for p in [5, 20, 60]: df[f"MA{p}"] = close.rolling(p).mean() df["MA20_prev"] = df["MA20"].shift(1) df["Close_prev"] = close.shift(1) df = df.dropna() # 加入三维度评分 tech_scores = [] fund_scores = [] sent_scores = [] total_scores = [] for date, row in df.iterrows(): t = score_technical(row) f = get_snapshot_score(FUNDAMENTAL_TIMELINE[name], date) s = get_snapshot_score(SENTIMENT_TIMELINE[name], date) combined = W_TECH * t + W_FUNDAMENTAL * f + W_SENTIMENT * s tech_scores.append(t) fund_scores.append(f) sent_scores.append(s) total_scores.append(combined) df["Tech"] = tech_scores df["Fund"] = fund_scores df["Sent"] = sent_scores df["Score"] = total_scores # 买卖阈值：综合评分 >= 1.5 买入；<= -1.5 卖出 BUY_THRESH = 1.5 SELL_THRESH = -1.5 df["Signal"] = 0 df.loc[df["Score"] >= BUY_THRESH, "Signal"] = 1 df.loc[df["Score"] <= SELL_THRESH, "Signal"] = -1 # ── 模拟交易 ── capital = float(INITIAL_CAPITAL) position = 0 entry_price = 0.0 trades = [] for date, row in df.iterrows(): price = float(row["Close"]) sig = int(row["Signal"]) if sig == 1 and position == 0 and capital > price: shares = int(capital / price) position = shares entry_price = price capital -= shares * price trades.append({ "日期": date.date(), "操作": "买入", "价格": round(price, 4), "股数": shares, "综合分": round(float(row["Score"]), 2), "技术": round(float(row["Tech"]), 1), "基本面": round(float(row["Fund"]), 1), "舆情": round(float(row["Sent"]), 1), }) elif sig == -1 and position > 0: revenue = position * price pnl = revenue - position * entry_price pnl_pct = pnl / (position * entry_price) * 100 capital += revenue trades.append({ "日期": date.date(), "操作": "卖出", "价格": round(price, 4), "股数": position, "综合分": round(float(row["Score"]), 2), "技术": round(float(row["Tech"]), 1), "基本面": round(float(row["Fund"]), 1), "舆情": round(float(row["Sent"]), 1), "盈亏HKD": round(pnl, 2), "盈亏%": f"{pnl_pct:+.1f}%", }) position = 0 entry_price = 0.0 last_price = float(df["Close"].iloc[-1]) if position > 0: unrealized = position * (last_price - entry_price) capital_total = capital + position * last_price trades.append({ "日期": "持仓中", "操作": "未平仓", "价格": round(last_price, 4), "股数": position, "未实现盈亏HKD": round(unrealized, 2), }) else: capital_total = capital strategy_return = (capital_total - INITIAL_CAPITAL) / INITIAL_CAPITAL * 100 buy_hold_return = (last_price / float(df["Close"].iloc[0]) - 1) * 100 # ── 输出 ── print(f"\n 📊 交易记录:") tdf = pd.DataFrame(trades) if not tdf.empty: print(tdf.to_string(index=False)) else: print(" 无交易信号") # 评分分布统计 print(f"\n 📉 评分分布（综合）:") bins = [-10, -3, -1.5, 0, 1.5, 3, 10] labels = ["强卖[-10,-3]","卖[-3,-1.5]","中性[-1.5,0]","中性[0,1.5]","买[1.5,3]","强买[3,10]"] score_ser = df["Score"] for label, cnt in zip(labels, np.histogram(score_ser, bins=bins)[0]): bar = "█" * int(cnt / max(1, len(df)) * 40) print(f" {label:>18}: {bar} ({cnt}天)") print(f"\n 📈 回测结果汇总:") print(f" 初始资金: HKD {INITIAL_CAPITAL:>10,.0f}") print(f" 最终资金: HKD {capital_total:>10,.2f}") print(f" 策略总收益: {strategy_return:>+10.1f}%") print(f" 买入持有收益: {buy_hold_return:>+10.1f}% （同期）") print(f" 超额收益(α): {strategy_return - buy_hold_return:>+10.1f}%") print(f" 触发交易次数: {len([t for t in trades if t.get('操作') in ['买入','卖出']]):>10}") return { "name": name, "strategy": strategy_return, "buy_hold": buy_hold_return, "alpha": strategy_return - buy_hold_return, "trades": len([t for t in trades if t.get("操作") in ["买入", "卖出"]]), } # ── 主入口 ──────────────────────────────────────────────────────────── if __name__ == "__main__": print("\n🔬 港股 AI 综合评分系统 v2 — 历史回测") print(f" 权重: 技术面 {W_TECH*100:.0f}% | 基本面 {W_FUNDAMENTAL*100:.0f}% | 舆情 {W_SENTIMENT*100:.0f}%") print(f" 买入阈值: ≥1.5 | 卖出阈值: ≤-1.5 | 数据周期: {PERIOD}\n") results = [] for i, (name, ticker) in enumerate(STOCKS.items()): if i > 0: time.sleep(3) # 避免 yfinance 限速 r = backtest(name, ticker) if r: results.append(r) if results: print(f"\n{'='*65}") print(" 📋 三股票综合汇总") print(f"{'='*65}") print(f" {'股票':<12} {'策略收益':>10} {'买持收益':>10} {'超额收益α':>10} {'交易次数':>8}") print(f" {'-'*54}") for r in results: flag = "✅" if r["alpha"] > 0 else "❌" print(f" {r['name']:<12} {r['strategy']:>+9.1f}% {r['buy_hold']:>+9.1f}% {r['alpha']:>+9.1f}% {r['trades']:>6} {flag}") print()