用python写了一个自认为完美的股票交易的策略，跑不起来怎么回事

best大毛

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```python import pandas as pd import numpy as np import logging   # 设置日志级别 logging.basicConfig(level=logging.INFO)   def flexible_calculate_macd(data, short_window_range=(10, 15), long_window_range=(20, 30), signal_window_range=(5, 10)):     """计算 MACD 指标，参数范围可调整"""     if 'close' not in data.columns:         logging.warning("数据中缺少 'close' 列，无法计算 MACD。")         return data       short_window = np.random.randint(*short_window_range)     long_window = np.random.randint(*long_window_range)     signal_window = np.random.randint(*signal_window_range)       exp1 = data['close'].ewm(span=short_window, adjust=False).mean()     exp2 = data['close'].ewm(span=long_window, adjust=False).mean()     data['macd'] = exp1 - exp2     data['signal_line'] = data['macd'].ewm(span=signal_window, adjust=False).mean()     data['macd_diff'] = data['macd'] - data['signal_line']     return data   def flexible_calculate_rsi(data, period_range=(10, 20)):     """计算相对强弱指标（RSI），参数范围可调整"""     if 'close' not in data.columns:         logging.warning("数据中缺少 'close' 列，无法计算 RSI。")         return data       period = np.random.randint(*period_range)     delta = data['close'].diff()     gain = delta.where(delta > 0, 0)     loss = -delta.where(delta < 0, 0)     avg_gain = gain.ewm(alpha=1/period, adjust=False).mean()     avg_loss = loss.ewm(alpha=1/period, adjust=False).mean()     rs = avg_gain / avg_loss     data['rsi'] = 100 - (100 / (1 + rs))     return data   def flexible_calculate_bias(data, n_range=(5, 8)):     """计算乖离率（BIAS），参数范围可调整"""     if 'close' not in data.columns:         logging.warning("数据中缺少 'close' 列，无法计算 BIAS。")         return data       n = np.random.randint(*n_range)     ma = data['close'].ewm(alpha=1/n, adjust=False).mean()     data['bias'] = (data['close'] - ma) / ma * 100     return data   def flexible_calculate_williams_r(data, period_range=(10, 15)):     """计算威廉指标（%R），参数范围可调整"""     if 'high' not in data.columns or 'low' not in data.columns or 'close' not in data.columns:         logging.warning("数据缺少 'high'、'low' 或 'close' 列，无法计算威廉指标。")         return data       period = np.random.randint(*period_range)     highest_high = data['high'].rolling(window=period).max()     lowest_low = data['low'].rolling(window=period).min()     data['williams_r'] = (highest_high - data['close']) / (highest_high - lowest_low) * -100     return data   def flexible_calculate_kdj(data, n_range=(7, 12), m1_range=(2, 4), m2_range=(2, 4)):     """计算随机指标（KDJ），参数范围可调整"""     if 'high' not in data.columns or 'low' not in data.columns or 'close' not in data.columns:         logging.warning("数据缺少 'high'、'low' 或 'close' 列，无法计算 KDJ。")         return data       n = np.random.randint(*n_range)     m1 = np.random.randint(*m1_range)     m2 = np.random.randint(*m2_range)     lowest_low = data['low'].rolling(window=n).min()     highest_high = data['high'].rolling(window=n).max()     rsv = ((data['close'] - lowest_low) / (highest_high - lowest_low)) * 100     k = rsv.ewm(span=m1, adjust=False).mean()     d = k.ewm(span=m2, adjust=False).mean()     j = 3 * k - 2 * d     data['k'] = k     data['d'] = d     data['j'] = j     return data   def flexible_calculate_cci(data, period_range=(15, 25)):     """计算顺势指标（CCI），参数范围可调整"""     if 'high' not in data.columns or 'low' not in data.columns or 'close' not in data.columns:         logging.warning("数据缺少 'high'、'low' 或 'close' 列，无法计算 CCI。")         return data       period = np.random.randint(*period_range)     typical_price = (data['high'] + data['low'] + data['close']) / 3     mean_deviation = typical_price.rolling(window=period).apply(lambda x: np.abs(x - x.mean()).mean())     data['cci'] = (typical_price - typical_price.rolling(window=period).mean()) / (0.015 * mean_deviation)     return data   def flexible_calculate_adx(data, period_range=(10, 15)):     """计算平均趋向指标（ADX），参数范围可调整"""     if 'high' not in data.columns or 'low' not in data.columns or 'close' not in data.columns:         logging.warning("数据缺少 'high'、'low' 或 'close' 列，无法计算 ADX。")         return data       period = np.random.randint(*period_range)     plus_dm = data['high'].diff()     minus_dm = data['low'].diff()     plus_dm[plus_dm < 0] = 0     minus_dm[minus_dm > 0] = 0     true_range = pd.DataFrame({'high_low': data['high'] - data['low'],                                'high_close': np.abs(data['high'] - data['close'].shift()),                                'low_close': np.abs(data['low'] - data['close'].shift())})     true_range['tr'] = true_range.max(axis=1)     plus_di = 100 * (plus_dm.ewm(alpha=1/period, adjust=False).mean() / true_range['tr'].ewm(alpha=1/period, adjust=False).mean())     minus_di = 100 * (-minus_dm.ewm(alpha=1/period, adjust=False).mean() / true_range['tr'].ewm(alpha=1/period, adjust=False).mean())     dx = 100 * np.abs((plus_di - minus_di) / (plus_di + minus_di))     data['adx'] = dx.ewm(alpha=1/period, adjust=False).mean()     return data   def flexible_calculate_roc(data, period_range=(8, 12)):     """计算变动率指标（ROC），参数范围可调整"""     if 'close' not in data.columns:         logging.warning("数据中缺少 'close' 列，无法计算 ROC。")         return data       period = np.random.randint(*period_range)     data['roc'] = data['close'].pct_change(periods=period)     return data   def flexible_calculate_mfi(data, period_range=(10, 15)):     """计算资金流量指标（MFI），参数范围可调整"""     if 'high' not in data.columns or 'low' not in data.columns or 'close' not in data.columns or 'volume' not in data.columns:         logging.warning("数据缺少必要列，无法计算 MFI。")         return data       period = np.random.randint(*period_range)     typical_price = (data['high'] + data['low'] + data['close']) / 3     money_flow = typical_price * data['volume']     positive_flow = money_flow.where((data['close'] > data['close'].shift(1)), 0).rolling(window=period).sum()     negative_flow = money_flow.where((data['close'] < data['close'].shift(1)), 0).rolling(window=period).sum()     money_ratio = positive_flow / negative_flow     data['mfi'] = 100 - (100 / (1 + money_ratio))     return data   def flexible_calculate_aroon(data, period_range=(20, 30)):     """计算阿隆指标（Aroon），参数范围可调整"""     if 'high' not in data.columns or 'low' not in data.columns:         logging.warning("数据缺少必要列，无法计算 Aroon。")         return data       period = np.random.randint(*period_range)     def aroon_calc(col):         n = len(col)         return 100 * pd.Series(col).rolling(window=period).apply(lambda x: np.argmax(x) if len(x) > 0 else 0) / n       data['aroon_up'] = aroon_calc(data['high'])     data['aroon_down'] = aroon_calc(data['low'])     return data   def flexible_calculate_stochastic_oscillator(data, k_period_range=(10, 15), d_period_range=(3, 5)):     """计算随机震荡指标（Stochastic Oscillator），参数范围可调整"""     if 'high' not in data.columns or 'low' not in data.columns or 'close' not in data.columns:         logging.warning("数据缺少必要列，无法计算 Stochastic Oscillator。")         return data       k_period = np.random.randint(*k_period_range)     d_period = np.random.randint(*d_period_range)     lowest_low = data['low'].rolling(window=k_period).min()     highest_high = data['high'].rolling(window=k_period).max()     data['%K'] = 100 * ((data['close'] - lowest_low) / (highest_high - lowest_low))     data['%D'] = data['%K'].rolling(window=d_period).mean()     return data   def run_strategy(data, ma_window_range=(20, 40), stop_loss_pct_range=(0.03, 0.07), take_profit_pct_range=(0.1, 0.2), transaction_cost=0.001):     if 'close' not in data.columns:         logging.warning("数据中缺少 'close' 列，无法运行策略。")         return data       # 考虑中国股市涨跌停限制为 10%     data['daily_limit'] = data['close'].shift(1) * 1.1     data['lower_limit'] = data['close'].shift(1) * 0.9       # 随机选择参数，但记录下来以便后续分析和优化     ma_window = np.random.randint(*ma_window_range)     stop_loss_pct = np.random.uniform(*stop_loss_pct_range)     take_profit_pct = np.random.uniform(*take_profit_pct_range)       # 计算布林线上下轨     if len(data) < ma_window:         logging.warning(f"数据长度不足 {ma_window}，无法计算布林线。")         return data       data['std'] = data['close'].rolling(window=ma_window).std()     data['upper_band'] = data['close'].rolling(window=ma_window).mean() + 2 * data['std']     data['lower_band'] = data['close'].rolling(window=ma_window).mean() - 2 * data['std']       # 计算各种指标     data = flexible_calculate_macd(data)     data = flexible_calculate_rsi(data)     data = flexible_calculate_bias(data)     data = flexible_calculate_williams_r(data)     data = flexible_calculate_kdj(data)     data = flexible_calculate_cci(data)     data = flexible_calculate_adx(data)     data = flexible_calculate_roc(data)     data = flexible_calculate_mfi(data)     data = flexible_calculate_aroon(data)     data = flexible_calculate_stochastic_oscillator(data)       # 处理缺失值     data = data.dropna()       # 计算简单移动平均线     data['sma'] = data['close'].rolling(window=ma_window).mean()       # 初始化交易信号和持仓状态     data['signal'] = 0     data['position'] = 0     data['entry_price'] = np.nan     data['strategy_params'] = f"MA_Window={ma_window}, StopLoss={stop_loss_pct:.2f}, TakeProfit={take_profit_pct:.2f}"       # 生成交易信号     for i in range(ma_window, len(data)):         # 判断上升趋势且多个指标同时满足，同时考虑价格未触及涨停         if data.at[i, 'adx'] > 25 and data.at[i, 'close'] > data.at[i - 1, 'sma'] and data.at[i - 1, 'position'] <= 0:             macd_condition = data.at[i, 'macd_diff'] > 0 and data.at[i, 'macd_diff'] > data.at[i - 1, 'macd_diff']             rsi_condition = data.at[i, 'rsi'] < 70 and data.at[i, 'rsi'] > data.at[i - 1, 'rsi']             bias_condition = data.at[i, 'bias'] > -5 and data.at[i, 'bias'] > data.at[i - 1, 'bias']             williams_r_condition = data.at[i, 'williams_r'] > -20 and data.at[i, 'williams_r'] > data.at[i - 1, 'williams_r']             kdj_condition = data.at[i, 'k'] > data.at[i, 'd'] and data.at[i, 'k'] > data.at[i - 1, 'k'] and data.at[i, 'd'] > data.at[i - 1, 'd']             cci_condition = data.at[i, 'cci'] > -100 and data.at[i, 'cci'] > data.at[i - 1, 'cci']             roc_condition = data.at[i, 'roc'] > 0 and data.at[i, 'roc'] > data.at[i - 1, 'roc']             mfi_condition = data.at[i, 'mfi'] > 50 and data.at[i, 'mfi'] > data.at[i - 1, 'mfi']             aroon_condition = data.at[i, 'aroon_up'] > data.at[i, 'aroon_down'] and data.at[i, 'aroon_up'] > data.at[i - 1, 'aroon_up'] and data.at[i, 'aroon_down'] < data.at[i - 1, 'aroon_down']             stochastic_condition = data.at[i, '%K'] < data.at[i, '%D'] and data.at[i, '%K'] > data.at[i - 1, '%K'] and data.at[i, '%D'] > data.at[i - 1, '%D']             if (macd_condition and rsi_condition and bias_condition and williams_r_condition and kdj_condition and cci_condition and roc_condition and mfi_condition and aroon_condition and stochastic_condition and data.at[i, 'close'] < data.at[i, 'daily_limit']):                 data.at[i, 'signal'] = 1                 data.at[i, 'position'] += 1                 data.at[i, 'entry_price'] = data.at[i, 'close']                 logging.info(f"发出买入提示：在 {data.at[i, 'date']} 可能是买入时机。参数：{data.at[i, 'strategy_params']}")         # 判断下降趋势且多个指标同时满足，同时考虑价格未触及跌停         elif data.at[i, 'adx'] > 25 and data.at[i, 'close'] < data.at[i - 1, 'sma'] and data.at[i - 1, 'position'] >= 0:             macd_condition = data.at[i, 'macd_diff'] < 0 and data.at[i, 'macd_diff'] < data.at[i - 1, 'macd_diff']             rsi_condition = data.at[i, 'rsi'] > 30 and data.at[i, 'rsi'] < data.at[i - 1, 'rsi']             bias_condition = data.at[i, 'bias'] < 5 and data.at[i, 'bias'] < data.at[i - 1, 'bias']             williams_r_condition = data.at[i, 'williams_r'] < -80 and data.at[i, 'williams_r'] < data.at[i - 1, 'williams_r']             kdj_condition = data.at[i, 'k'] < data.at[i, 'd'] and data.at[i, 'k'] < data.at[i - 1, 'k'] and data.at[i, 'd'] < data.at[i - 1, 'd']             cci_condition = data.at[i, 'cci'] < 100 and data.at[i, 'cci'] < data.at[i - 1, 'cci']             roc_condition = data.at[i, 'roc'] < 0 and data.at[i, 'roc'] < data.at[i - 1, 'roc']             mfi_condition = data.at[i, 'mfi'] < 50 and data.at[i, 'mfi'] < data.at[i - 1

bohong65

没有样例或报错日志看不出来。还有建议把策略部分单独剥离出来，不要混在回测框架里，方便以后模块化测试

devilpanama

没有main函数，入口呢

lizy169

代码不全，只看到函数，没看到执行的逻辑，所以不好建议什么

zhu2023

只靠布林带和均线就判断吗

Tianshan

devilpanama 发表于 2024-10-3 16:44
没有main函数，入口呢

让程序，找不到执行的入口

dcc123qwe

我把你的代码改好了，用图形界面了，我看了你的交易策略，其实问题还有不少，只能在趋势行情交易，还可以用用，震荡行情你要亏死。

moonalong8

你这只是把各个指标混合到一起，并不会提高胜率，只是单纯心理安慰。不由的让我想起十几年前刚刚接触股票软件的我。

sapin

Tianshan 发表于 2024-10-3 18:25
让程序，找不到执行的入口

或者说这只是更大的程序里的一个小模块,导入到别的模块里这样就不直接用了?

fishtopup

不由的让我想起十几年前刚刚接触股票软件的我。