利用MACD和OBV构建的智能交易策略提升投资者交易效率python

以下是一个基于MACD和oBV组合交易策略的Python代码示例：

import pandas as pdimport numpy as npimport talib# 计算MACD指标def calculate_macd(data):    close_price = data['close']    macd, signal, hist = talib.MACD(close_price, fastperiod=12, slowperiod=26, signalperiod=9)    return macd, signal, hist# 计算oBV指标def calculate_obv(data):    close_price = data['close']    volume = data['volume']    obv = talib.OBV(close_price, volume)    return obv# 定义交易策略def trading_strategy(data):    # 计算MACD指标    macd, signal, hist = calculate_macd(data)        # 计算oBV指标    obv = calculate_obv(data)        # 生成交易信号    trading_signal = np.zeros(len(data))    for i in range(1, len(data)):        if macd[i] > signal[i] and macd[i-1] < signal[i-1] and obv[i] > obv[i-1]:            # MACD上穿信号且oBV上升，买入            trading_signal[i] = 1        elif macd[i] < signal[i] and macd[i-1] > signal[i-1] and obv[i] < obv[i-1]:            # MACD下穿信号且oBV下降，卖出            trading_signal[i] = -1                return trading_signal# 示例：读取股票数据并运行交易策略data = pd.read_csv('stock_data.csv')  # 读取股票数据文件# 运行交易策略signals = trading_strategy(data)# 输出交易信号for i, signal in enumerate(signals):    if signal == 1:        print(f"买入信号 - 日期: {data['date'][i]}, 价格: {data['close'][i]}")    elif signal == -1:        print(f"卖出信号 - 日期: {data['date'][i]}, 价格: {data['close'][i]}")

请注意，以上代码仅为示例，实际应用时需要根据具体需求进行适当的修改和优化。此外，代码中使用了talib库来计算MACD和oBV指标，你需要确保已将该库正确安装。