55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
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"""
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Indicator calculation utilities for backtesting.
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These functions calculate indicators directly from price data,
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without requiring MT5 indicator handles.
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"""
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import numpy as np
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import pandas as pd
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def calculate_rsi(prices: pd.Series, period: int = 14) -> pd.Series:
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"""Calculate RSI indicator."""
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delta = prices.diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
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rs = gain / loss
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rsi = 100 - (100 / (1 + rs))
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return rsi
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def calculate_ema(prices: pd.Series, period: int = 50) -> pd.Series:
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"""Calculate EMA indicator."""
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return prices.ewm(span=period, adjust=False).mean()
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def calculate_sma(prices: pd.Series, period: int = 50) -> pd.Series:
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"""Calculate SMA indicator."""
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return prices.rolling(window=period).mean()
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def calculate_atr(df: pd.DataFrame, period: int = 14) -> pd.Series:
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"""Calculate ATR indicator."""
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high_low = df['high'] - df['low']
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high_close = np.abs(df['high'] - df['close'].shift())
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low_close = np.abs(df['low'] - df['close'].shift())
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tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
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atr = tr.rolling(window=period).mean()
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return atr
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def calculate_macd(prices: pd.Series, fast: int = 12, slow: int = 26, signal: int = 9) -> pd.DataFrame:
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"""Calculate MACD indicator."""
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ema_fast = prices.ewm(span=fast, adjust=False).mean()
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ema_slow = prices.ewm(span=slow, adjust=False).mean()
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macd = ema_fast - ema_slow
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signal_line = macd.ewm(span=signal, adjust=False).mean()
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histogram = macd - signal_line
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return pd.DataFrame({
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'macd': macd,
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'signal': signal_line,
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'histogram': histogram
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})
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