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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 |