94 lines
3.3 KiB
Python
94 lines
3.3 KiB
Python
import numpy as np
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import pandas as pd
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import yfinance as yf
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stock_code = 'MSFT'
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def fetch_stock_data(stock_code, start_date='2024-01-01', end_date='2025-01-01'):
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# 拉取数据
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df = yf.download(stock_code, start=start_date, end=end_date)
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df['5d_change'] = df['Close'].pct_change(5) * 100
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df['20d_change'] = df['Close'].pct_change(20) * 100
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df['60d_change'] = df['Close'].pct_change(60) * 100
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# 将数据保存为 CSV 文件
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df.to_csv(f'{stock_code}.csv')
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return df
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def calculate_rsi(data, window=14):
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delta = data['Close'].diff()
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gain = np.where(delta > 0, delta, 0).ravel()
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loss = np.where(delta < 0, -delta, 0).ravel()
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avg_gain = pd.Series(gain).rolling(window=window).mean()
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avg_loss = pd.Series(loss).rolling(window=window).mean()
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rs = avg_gain / avg_loss
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rsi = 100 - (100 / (1 + rs))
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data['RSI'] = rsi
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return data
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def calculate_rsi2(data, window=14):
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delta = data['Close'].diff()
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gain = delta.clip(lower=0)
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loss = -delta.clip(upper=0)
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avg_gain = gain.rolling(window=window).mean()
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avg_loss = loss.rolling(window=window).mean()
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rs = avg_gain / avg_loss
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rsi = 100 - (100 / (1 + rs))
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data['RSI'] = rsi
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return data
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'''
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df = fetch_stock_data(stock_code)
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df = calculate_rsi2(df)
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# 输出RSI值为NaN的行
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nan_rsi_rows = df[df['RSI'].isna()]
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print("RSI值为NaN的行：")
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print(nan_rsi_rows['RSI'])
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# 统计非NaN的行数
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non_nan_count = df['RSI'].count()
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print(f"RSI值非NaN的行数：{non_nan_count}")
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'''
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def calculate_macd(data, short_window=12, long_window=26, signal_window=9):
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data['EMA12'] = data['Close'].ewm(span=short_window, adjust=False).mean()
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data['EMA26'] = data['Close'].ewm(span=long_window, adjust=False).mean()
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data['MACD'] = data['EMA12'] - data['EMA26']
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data['Signal_Line'] = data['MACD'].ewm(span=signal_window, adjust=False).mean()
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return data
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def strategy(data):
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# 策略条件：超跌 + RSI低于30 + MACD金叉
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buy_signals = (data['5d_change'] < -10) & (data['20d_change'] < -20) & (data['RSI'] < 30) & (data['MACD'] > data['Signal_Line'])
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buy_signals2 = (data['5d_change'] < -10) & (data['20d_change'] < -20)
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buy_signals3 = (data['5d_change'] < -10) & (data['RSI'] < 30) & (data['MACD'] > data['Signal_Line'])
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buy_signals4 = (data['5d_change'] < -10) & (data['RSI'] < 30)
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sell_signals = (data['Close'].pct_change() < -0.05) | (data['Close'].pct_change() > 0.1)
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data['Buy_Signal'] = buy_signals4
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data['Sell_Signal'] = sell_signals
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return data
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# 主函数
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def run_strategy(stock_code):
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df = fetch_stock_data(stock_code)
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df = calculate_rsi2(df)
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df = calculate_macd(df)
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df = strategy(df)
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buy_signal_df = df[df['Buy_Signal']]
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print(buy_signal_df[['Close', '5d_change', 'RSI', 'MACD', 'Signal_Line', 'Buy_Signal', 'Sell_Signal']])
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sell_signal_df = df[df['Sell_Signal']]
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print(sell_signal_df[['Close', '5d_change', 'RSI', 'MACD', 'Signal_Line', 'Buy_Signal', 'Sell_Signal']])
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#signal_df = df[(df['Buy_Signal']) | (df['Sell_Signal'])]
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#print(signal_df[['Close', '5d_change', 'RSI', 'MACD', 'Signal_Line', 'Buy_Signal', 'Sell_Signal']])
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#print(df[['Close', '5d_change', 'RSI', 'MACD', 'Signal_Line', 'Buy_Signal', 'Sell_Signal']].tail(20))
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# 示例：运行策略
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run_strategy('KC') |