Volatility-Based Trading Signal Algorithm: This algorithm leverages multiple technical indicators to evaluate market conditions and generate a consolidated trading signal ranging from -3 to +3. Each indicator contributes to the overall score based on its specific criteria, helping traders to identify optimal entry and exit points.

volatilitybased.py

import pandas as pd
import numpy as np
import ta
from ta.volatility import BollingerBands
from ta.momentum import RSIIndicator, StochasticOscillator
from ta.trend import MACD, EMAIndicator
from ta.volume import OnBalanceVolumeIndicator

def calculate_signal(df):
    # Calculate Bollinger Bands
    bb_indicator = BollingerBands(close=df['close'], window=20, window_dev=2)
    df['bb_upper'] = bb_indicator.bollinger_hband()
    df['bb_lower'] = bb_indicator.bollinger_lband()
    
    # Calculate RSI
    rsi_indicator = RSIIndicator(close=df['close'], window=14)
    df['rsi'] = rsi_indicator.rsi()
    
    # Calculate Moving Averages
    short_ema = EMAIndicator(close=df['close'], window=12)
    long_ema = EMAIndicator(close=df['close'], window=26)
    df['short_ema'] = short_ema.ema_indicator()
    df['long_ema'] = long_ema.ema_indicator()
    
    # Calculate MACD
    macd_indicator = MACD(close=df['close'], window_slow=26, window_fast=12, window_sign=9)
    df['macd'] = macd_indicator.macd()
    df['macd_signal'] = macd_indicator.macd_signal()
    
    # Calculate Stochastic Oscillator
    stoch_indicator = StochasticOscillator(high=df['high'], low=df['low'], close=df['close'], window=14, smooth_window=3)
    df['%K'] = stoch_indicator.stoch_k()
    df['%D'] = stoch_indicator.stoch_d()
    
    # Calculate VWAP
    df['vwap'] = (df['close'] * df['volume']).cumsum() / df['volume'].cumsum()
    
    # Calculate Parabolic SAR
    df['sar'] = ta.trend.stc(high=df['high'], low=df['low'], close=df['close'], window_slow=50, window_fast=13)
    
    # Calculate OBV
    obv_indicator = OnBalanceVolumeIndicator(close=df['close'], volume=df['volume'])
    df['obv'] = obv_indicator.on_balance_volume()
    
    # Initialize signals
    df['signal'] = 0
    
    # Bollinger Bands signal
    df.loc[df['close'] > df['bb_upper'], 'signal'] += 1
    df.loc[df['close'] < df['bb_lower'], 'signal'] -= 1
    
    # RSI signal
    df.loc[df['rsi'] > 70, 'signal'] -= 1
    df.loc[df['rsi'] < 30, 'signal'] += 1
    
    # EMA signal
    df.loc[df['short_ema'] > df['long_ema'], 'signal'] += 1
    df.loc[df['short_ema'] < df['long_ema'], 'signal'] -= 1
    
    # MACD signal
    df.loc[df['macd'] > df['macd_signal'], 'signal'] += 1
    df.loc[df['macd'] < df['macd_signal'], 'signal'] -= 1
    
    # Stochastic Oscillator signal
    df.loc[(df['%K'] > df['%D']) & (df['%K'] < 20), 'signal'] += 1
    df.loc[(df['%K'] < df['%D']) & (df['%K'] > 80), 'signal'] -= 1
    
    # VWAP signal
    df.loc[df['close'] > df['vwap'], 'signal'] += 1
    df.loc[df['close'] < df['vwap'], 'signal'] -= 1
    
    # Parabolic SAR signal
    df.loc[df['sar'] < df['close'], 'signal'] += 1
    df.loc[df['sar'] > df['close'], 'signal'] -= 1
    
    # OBV signal
    df['obv_diff'] = df['obv'].diff()
    df.loc[df['obv_diff'] > 0, 'signal'] += 1
    df.loc[df['obv_diff'] < 0, 'signal'] -= 1
    
    # Clamp signal
    df['signal'] = df['signal'].clip(-3, 3)
    
    return df

# Example usage
# Assuming you have a DataFrame `df` with columns: 'date', 'open', 'high', 'low', 'close', 'volume'
df = pd.read_csv('historical_data.csv')
df = calculate_signal(df)
print(df[['date', 'close', 'signal']])