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https://github.com/xavierchuan/FX-ML-Trading-Engine.git
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xiaochuan
51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
# compute_indicators.py
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import os
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import pandas as pd
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from loguru import logger
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BASE_DIR = os.path.dirname(os.path.dirname(__file__))
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RAW_DATA_DIR = os.path.join(BASE_DIR, "data", "raw")
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DERIVED_DATA_DIR = os.path.join(BASE_DIR, "data", "derived")
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os.makedirs(DERIVED_DATA_DIR, exist_ok=True)
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def compute_indicators(df: pd.DataFrame) -> pd.DataFrame:
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# SMA
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df['SMA_20'] = df['close'].rolling(window=20).mean()
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# Bollinger Bands
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df['BB_MID'] = df['close'].rolling(window=20).mean()
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df['BB_STD'] = df['close'].rolling(window=20).std()
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df['BB_UPPER'] = df['BB_MID'] + 2 * df['BB_STD']
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df['BB_LOWER'] = df['BB_MID'] - 2 * df['BB_STD']
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# MACD
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ema12 = df['close'].ewm(span=12, adjust=False).mean()
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ema26 = df['close'].ewm(span=26, adjust=False).mean()
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df['MACD'] = ema12 - ema26
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df['MACD_signal'] = df['MACD'].ewm(span=9, adjust=False).mean()
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# RSI
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def compute_rsi(series, period=14):
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delta = series.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=period).mean()
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avg_loss = loss.rolling(window=period).mean()
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rs = avg_gain / avg_loss
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return 100 - (100 / (1 + rs))
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df['RSI_14'] = compute_rsi(df['close'])
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return df
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if __name__ == "__main__":
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input_path = os.path.join(RAW_DATA_DIR, "EURUSD_H1.csv")
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output_path = os.path.join(DERIVED_DATA_DIR, "EURUSD_H1_with_indicators.csv")
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df = pd.read_csv(input_path, parse_dates=["time"])
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df = compute_indicators(df)
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df.to_csv(output_path, index=False)
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logger.info(f"✅ Saved with indicators to {output_path}")
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