vue 和 main 合并

ak_share.py

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+# =========================================
+# 工业级AI选股系统（最终整合版）
+# 指数择时 + 动态仓位 + 回测 + 调参
+# =========================================
+
+import sqlite3
+import akshare as ak
+import pandas as pd
+
+DB_PATH = "stock_data.db"
+
+# =========================================
+# 全局参数配置
+# =========================================
+CONFIG = {
+
+    # ===== 数据 =====
+    "START_DATE": "20230101",
+    "END_DATE": "20240401",
+
+    # ===== 均线 =====
+    "MA_SHORT": 5,
+    "MA_MID": 20,
+    "MA_LONG": 60,
+
+    # ===== 特征 =====
+    "RETURN_WINDOW": 3,
+    "VOL_WINDOW": 5,
+    "VOLATILITY_WINDOW": 10,
+
+    # ===== 权重 =====
+    "W_MOMENTUM": 0.4,
+    "W_TREND": 0.3,
+    "W_VOLUME": 0.2,
+    "W_VOLATILITY": -0.1,
+
+    # ===== 策略 =====
+    "RETURN_THRESH": 0.02,
+    "VOL_THRESH": 1.2,
+
+    "HOLD_DAYS": 3,
+    "STOP_LOSS": -0.05,
+    "TAKE_PROFIT": 0.1,
+
+    # ===== 成本 =====
+    "FEE": 0.001,
+    "SLIPPAGE": 0.001,
+
+    # ===== Walk Forward =====
+    "TRAIN_WINDOW": 60,
+    "TEST_WINDOW": 20,
+
+    # ===== 指数 =====
+    "INDEX_LIST": {
+        "hs300": "000300",
+        "zz500": "000905",
+        "cyb": "399006",
+    }
+}
+
+# =========================
+# 数据库
+# =========================
+def init_db():
+    conn = sqlite3.connect(DB_PATH)
+    conn.execute("""
+    CREATE TABLE IF NOT EXISTS stock_daily (
+        code TEXT,
+        date TEXT,
+        open REAL,
+        close REAL,
+        high REAL,
+        low REAL,
+        volume REAL,
+        PRIMARY KEY (code, date)
+    )
+    """)
+    conn.close()
+
+
+def load_from_db(code):
+    conn = sqlite3.connect(DB_PATH)
+    df = pd.read_sql_query(
+        f"SELECT * FROM stock_daily WHERE code='{code}'", conn
+    )
+    conn.close()
+    return df.sort_values("date")
+
+
+def save_to_db(df, code):
+    conn = sqlite3.connect(DB_PATH)
+    df['code'] = code
+    df.to_sql("stock_daily", conn, if_exists="append", index=False)
+    conn.close()
+
+# =========================
+# 数据获取
+# =========================
+def fetch_data(code):
+    try:
+        df = ak.stock_zh_a_hist(
+            symbol=code,
+            period="daily",
+            start_date=CONFIG["START_DATE"],
+            end_date=CONFIG["END_DATE"],
+            adjust="qfq"
+        )
+    except:
+        df = ak.stock_zh_a_daily(symbol=code)
+
+    if df is None or df.empty:
+        return None
+
+    df = df.rename(columns={
+        "日期": "date",
+        "开盘": "open",
+        "收盘": "close",
+        "最高": "high",
+        "最低": "low",
+        "成交量": "volume"
+    })
+
+    if "date" not in df.columns:
+        return None
+
+    return df[["date", "open", "close", "high", "low", "volume"]]
+
+
+def get_data(code):
+    try:
+        df = load_from_db(code)
+        if len(df) > 30:
+            return df
+    except:
+        pass
+
+    df = fetch_data(code)
+    if df is not None:
+        save_to_db(df, code)
+    return df
+
+# =========================
+# 特征工程
+# =========================
+def add_features(df):
+
+    df['return_n'] = df['close'].pct_change(CONFIG["RETURN_WINDOW"])
+
+    df['ma_short'] = df['close'].rolling(CONFIG["MA_SHORT"]).mean()
+    df['ma_mid'] = df['close'].rolling(CONFIG["MA_MID"]).mean()
+    df['ma_long'] = df['close'].rolling(CONFIG["MA_LONG"]).mean()
+
+    df['vol_ratio'] = df['volume'] / df['volume'].rolling(CONFIG["VOL_WINDOW"]).mean()
+    df['volatility'] = df['close'].pct_change().rolling(CONFIG["VOLATILITY_WINDOW"]).std()
+
+    df['trend'] = df['ma_mid'] > df['ma_long']
+
+    return df.dropna()
+
+
+def score(df):
+    df['score'] = (
+            CONFIG["W_MOMENTUM"] * df['return_n'] +
+            CONFIG["W_TREND"] * ((df['ma_short'] - df['ma_mid']) / df['ma_mid']) +
+            CONFIG["W_VOLUME"] * df['vol_ratio'] +
+            CONFIG["W_VOLATILITY"] * df['volatility']
+    )
+    return df
+
+# =========================
+# 指数模块
+# =========================
+def get_index_data(code):
+    try:
+        df = ak.index_zh_a_hist(
+            symbol=code,
+            period="daily",
+            start_date=CONFIG["START_DATE"],
+            end_date=CONFIG["END_DATE"]
+        )
+    except:
+        return None
+
+    if df is None or df.empty:
+        return None
+
+    df = df.rename(columns={"日期": "date", "收盘": "close"})
+    return df[['date', 'close']]
+
+
+def index_trend(df):
+    ma20 = df['close'].rolling(CONFIG["MA_MID"]).mean()
+    ma60 = df['close'].rolling(CONFIG["MA_LONG"]).mean()
+    return ma20.iloc[-1] > ma60.iloc[-1]
+
+
+def market_score(index_data):
+    score = 0
+
+    if index_trend(index_data["hs300"]):
+        score += 2
+    else:
+        score -= 2
+
+    if index_trend(index_data["zz500"]):
+        score += 2
+
+    if index_trend(index_data["cyb"]):
+        score += 1
+
+    return score
+
+
+def market_strategy(score):
+    if score >= 4:
+        return 0.7
+    elif score >= 2:
+        return 0.4
+    elif score >= 0:
+        return 0.2
+    else:
+        return 0
+
+# =========================
+# 交易函数
+# =========================
+def trade_return(prices):
+    if len(prices) == 0:
+        return 0
+
+    entry = prices[0] * (1 + CONFIG["SLIPPAGE"])
+    ret = 0
+
+    for p in prices:
+        exit_price = p * (1 - CONFIG["SLIPPAGE"])
+        r = (exit_price - entry) / entry
+
+        if r <= CONFIG["STOP_LOSS"]:
+            ret = CONFIG["STOP_LOSS"]
+            break
+        elif r >= CONFIG["TAKE_PROFIT"]:
+            ret = CONFIG["TAKE_PROFIT"]
+            break
+        else:
+            ret = r
+
+    return ret - CONFIG["FEE"] * 2
+
+# =========================
+# 回测
+# =========================
+def backtest_real(df_all, params, position_ratio, init_cash=100000):
+
+    cash = init_cash
+    equity = []
+
+    dates = sorted(df_all['date'].unique())
+
+    for i in range(len(dates) - params['hold_days']):
+
+        today = dates[i]
+
+        df_today = df_all[df_all['date'] <= today]
+        latest = df_today.groupby('code').tail(1)
+
+        picks = latest[
+            (latest['return_n'] > params['return_thresh']) &
+            (latest['vol_ratio'] > params['vol_thresh']) &
+            (latest['trend'])
+            ].sort_values('score', ascending=False).head(3)
+
+        if len(picks) == 0 or position_ratio == 0:
+            equity.append(cash)
+            continue
+
+        position = cash * position_ratio
+        per_stock = position / len(picks)
+
+        profit = 0
+
+        for _, row in picks.iterrows():
+            code = row['code']
+
+            prices = df_all[
+                         (df_all['code'] == code) &
+                         (df_all['date'] >= today)
+                         ].sort_values('date')['close'].values[:params['hold_days']]
+
+            r = trade_return(prices)
+            profit += per_stock * r
+
+        cash += profit
+        equity.append(cash)
+
+    return equity
+
+# =========================
+# 调参
+# =========================
+def simple_grid(df):
+    best_score = -999
+    best_params = None
+
+    for r in [0.01, 0.02]:
+        for v in [1.1, 1.2]:
+
+            params = {
+                "return_thresh": r,
+                "vol_thresh": v,
+                "hold_days": CONFIG["HOLD_DAYS"]
+            }
+
+            equity = backtest_real(df, params, 0.4)
+
+            if len(equity) < 10:
+                continue
+
+            ret = (equity[-1] - equity[0]) / equity[0]
+            dd = calc_max_drawdown(equity)
+
+            score = ret - dd
+
+            if score > best_score:
+                best_score = score
+                best_params = params
+
+    return best_params
+
+# =========================
+# 评估
+# =========================
+def calc_max_drawdown(equity):
+    peak = equity[0]
+    max_dd = 0
+
+    for x in equity:
+        if x > peak:
+            peak = x
+        dd = (peak - x) / peak
+        max_dd = max(max_dd, dd)
+
+    return max_dd
+
+# =========================
+# Walk Forward（带指数）
+# =========================
+def walk_forward(df_all, index_data):
+
+    dates = sorted(df_all['date'].unique())
+
+    window = CONFIG["TRAIN_WINDOW"]
+    step = CONFIG["TEST_WINDOW"]
+
+    final_equity = []
+
+    for start in range(0, len(dates) - window - step, step):
+
+        train_dates = dates[start:start+window]
+        test_dates = dates[start+window:start+window+step]
+
+        train_df = df_all[df_all['date'].isin(train_dates)]
+        test_df = df_all[df_all['date'].isin(test_dates)]
+
+        best_params = simple_grid(train_df)
+
+        # ===== 指数决定仓位 =====
+        score = market_score(index_data)
+        position_ratio = market_strategy(score)
+
+        if position_ratio == 0:
+            continue
+
+        equity = backtest_real(test_df, best_params, position_ratio)
+
+        if len(final_equity) == 0:
+            final_equity = equity
+        else:
+            base = final_equity[-1]
+            equity = [base + (x - equity[0]) for x in equity]
+            final_equity.extend(equity)
+
+    return final_equity
+
+# =========================
+# 主程序
+# =========================
+def main():
+
+    init_db()
+
+    codes = ["000001", "000002", "600519", "600036", "601318"]
+
+    all_data = []
+
+    for code in codes:
+        df = get_data(code)
+        if df is None:
+            continue
+
+        df['code'] = code
+        df = add_features(df)
+        df = score(df)
+        all_data.append(df)
+
+    df_all = pd.concat(all_data)
+
+    # ===== 指数一次性获取 =====
+    index_data = {}
+    for name, code in CONFIG["INDEX_LIST"].items():
+        df = get_index_data(code)
+        if df is not None:
+            index_data[name] = df
+
+    equity = walk_forward(df_all, index_data)
+
+    print("\n===== 实盘级回测 =====")
+    print("最终资金:", round(equity[-1], 2))
+
+    ret = (equity[-1] - equity[0]) / equity[0]
+    dd = calc_max_drawdown(equity)
+
+    print("总收益:", round(ret, 2))
+    print("最大回撤:", round(dd, 2))
+
+
+if __name__ == "__main__":
+    main()

data_engine.py

@@ -0,0 +1,26 @@
+import sqlite3
+import pandas as pd
+import os
+
+DB_PATH = "stock_data.db"
+
+
+def init_db():
+    conn = sqlite3.connect(DB_PATH)
+    cursor = conn.cursor()
+
+    cursor.execute("""
+    CREATE TABLE IF NOT EXISTS stock_daily (
+        code TEXT,
+        date TEXT,
+        open REAL,
+        close REAL,
+        high REAL,
+        low REAL,
+        volume REAL,
+        PRIMARY KEY (code, date)
+    )
+    """)
+
+    conn.commit()
+    conn.close()