patterns.py

import numpy as np
import pandas as pd

def identify_patterns(df):
    """Identify candlestick patterns in the data using basic calculations"""
    patterns = pd.DataFrame(index=df.index)

    # Calculate basic candlestick properties
    body = df['Close'] - df['Open']
    body_abs = abs(body)
    upper_shadow = df['High'] - df[['Open', 'Close']].max(axis=1)
    lower_shadow = df[['Open', 'Close']].min(axis=1) - df['Low']

    # 1. Hammer Pattern
    patterns['HAMMER'] = np.where(
        (lower_shadow > 2 * body_abs) &  # Long lower shadow
        (upper_shadow <= 0.1 * body_abs) &  # Minimal upper shadow
        (body > 0),  # Bullish close
        1, 0
    )

    # 2. Inverted Hammer Pattern
    patterns['INVERTED_HAMMER'] = np.where(
        (upper_shadow > 2 * body_abs) &  # Long upper shadow
        (lower_shadow <= 0.1 * body_abs) &  # Minimal lower shadow
        (body > 0),  # Bullish close
        1, 0
    )

    # 3. Piercing Line Pattern
    patterns['PIERCING_LINE'] = np.where(
        (body.shift(1) < 0) &  # Previous candle bearish
        (body > 0) &  # Current candle bullish
        (df['Open'] < df['Close'].shift(1)) &  # Opens below previous close
        (df['Close'] > (df['Open'].shift(1) + df['Close'].shift(1)) / 2),  # Closes above midpoint
        1, 0
    )

    # 4. Bullish Engulfing Pattern
    patterns['BULLISH_ENGULFING'] = np.where(
        (body.shift(1) < 0) &  # Previous candle bearish
        (body > 0) &  # Current candle bullish
        (df['Open'] < df['Close'].shift(1)) &  # Opens below previous close
        (df['Close'] > df['Open'].shift(1)),  # Closes above previous open
        1, 0
    )

    # 5. Morning Star Pattern
    patterns['MORNING_STAR'] = np.where(
        (body.shift(2) < 0) &  # First candle bearish
        (abs(body.shift(1)) < abs(body.shift(2)) * 0.3) &  # Second candle small
        (body > 0) &  # Third candle bullish
        (df['Close'] > df['Close'].shift(2) * 0.5),  # Closes above midpoint
        1, 0
    )

    # 6. Three White Soldiers
    patterns['THREE_WHITE_SOLDIERS'] = np.where(
        (body > 0) &  # Current candle bullish
        (body.shift(1) > 0) &  # Previous candle bullish
        (body.shift(2) > 0) &  # Two candles ago bullish
        (df['Close'] > df['Close'].shift(1)) &  # Each closes higher
        (df['Close'].shift(1) > df['Close'].shift(2)),
        1, 0
    )

    # 7. Bullish Harami
    patterns['BULLISH_HARAMI'] = np.where(
        (body.shift(1) < 0) &  # Previous candle bearish
        (body > 0) &  # Current candle bullish
        (df['Open'] > df['Close'].shift(1)) &  # Opens inside previous body
        (df['Close'] < df['Open'].shift(1)),  # Closes inside previous body
        1, 0
    )

    # 8. Hanging Man
    patterns['HANGING_MAN'] = np.where(
        (lower_shadow > 2 * body_abs) &  # Long lower shadow
        (upper_shadow <= 0.1 * body_abs) &  # Minimal upper shadow
        (body < 0),  # Bearish close
        1, 0
    )

    # 9. Dark Cloud Cover
    patterns['DARK_CLOUD_COVER'] = np.where(
        (body.shift(1) > 0) &  # Previous candle bullish
        (body < 0) &  # Current candle bearish
        (df['Open'] > df['High'].shift(1)) &  # Opens above previous high
        (df['Close'] < (df['Open'].shift(1) + df['Close'].shift(1)) / 2),  # Closes below midpoint
        1, 0
    )

    # 10. Bearish Engulfing
    patterns['BEARISH_ENGULFING'] = np.where(
        (body.shift(1) > 0) &  # Previous candle bullish
        (body < 0) &  # Current candle bearish
        (df['Open'] > df['Close'].shift(1)) &  # Opens above previous close
        (df['Close'] < df['Open'].shift(1)),  # Closes below previous open
        1, 0
    )

    # 11. Evening Star
    patterns['EVENING_STAR'] = np.where(
        (body.shift(2) > 0) &  # First candle bullish
        (abs(body.shift(1)) < abs(body.shift(2)) * 0.3) &  # Second candle small
        (body < 0) &  # Third candle bearish
        (df['Close'] < df['Close'].shift(2) * 0.5),  # Closes below midpoint
        1, 0
    )

    # 12. Three Black Crows
    patterns['THREE_BLACK_CROWS'] = np.where(
        (body < 0) &  # Current candle bearish
        (body.shift(1) < 0) &  # Previous candle bearish
        (body.shift(2) < 0) &  # Two candles ago bearish
        (df['Close'] < df['Close'].shift(1)) &  # Each closes lower
        (df['Close'].shift(1) < df['Close'].shift(2)),
        1, 0
    )

    # 13. Shooting Star
    patterns['SHOOTING_STAR'] = np.where(
        (upper_shadow > 2 * body_abs) &  # Long upper shadow
        (lower_shadow <= 0.1 * body_abs) &  # Minimal lower shadow
        (body < 0),  # Bearish close
        1, 0
    )

    # 14. Doji Patterns
    patterns['DOJI'] = np.where(
        abs(body) <= 0.1 * (df['High'] - df['Low']),  # Very small body
        1, 0
    )

    # 15. Dragonfly Doji
    patterns['DRAGONFLY_DOJI'] = np.where(
        (abs(body) <= 0.1 * (df['High'] - df['Low'])) &  # Doji body
        (upper_shadow <= 0.1 * (df['High'] - df['Low'])) &  # Minimal upper shadow
        (lower_shadow >= 0.7 * (df['High'] - df['Low'])),  # Long lower shadow
        1, 0
    )

    # 16. Gravestone Doji
    patterns['GRAVESTONE_DOJI'] = np.where(
        (abs(body) <= 0.1 * (df['High'] - df['Low'])) &  # Doji body
        (lower_shadow <= 0.1 * (df['High'] - df['Low'])) &  # Minimal lower shadow
        (upper_shadow >= 0.7 * (df['High'] - df['Low'])),  # Long upper shadow
        1, 0
    )

    return patterns

def calculate_technical_indicators(df):
    """Calculate technical indicators for analysis"""
    # RSI
    delta = df['Close'].diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
    rs = gain / loss
    df['RSI'] = 100 - (100 / (1 + rs))

    # MACD
    exp1 = df['Close'].ewm(span=12, adjust=False).mean()
    exp2 = df['Close'].ewm(span=26, adjust=False).mean()
    df['MACD'] = exp1 - exp2
    df['MACD_Signal'] = df['MACD'].ewm(span=9, adjust=False).mean()
    df['MACD_Hist'] = df['MACD'] - df['MACD_Signal']

    # Moving Averages
    df['SMA_20'] = df['Close'].rolling(window=20).mean()
    df['SMA_50'] = df['Close'].rolling(window=50).mean()

    return df