The Bollinger Bands indicator is a powerful tool used in both mean reversion and momentum trading strategies. By measuring the volatility of the market, it creates a band around a moving average that can indicate overbought or oversold conditions, as well as the strength of a trend.
1. Mean Reversion Strategy:
- Idea: Prices that deviate far from the moving average (i.e., touch or breach the bands) will revert to the mean (the moving average).
- Strategy: If the price touches or breaks below the lower Bollinger Band, it’s a potential buy signal (oversold); if it touches or breaks above the upper band, itâs a potential sell signal (overbought).
2. Momentum Strategy:
- Idea: When prices break out of the Bollinger Bands, it may indicate a strong trend is developing.
- Strategy: A breakout above the upper band indicates bullish momentum (buy signal), and a breakout below the lower band indicates bearish momentum (sell signal).
Python Implementation of Bollinger Bands for Both Strategies:
Here’s how you can calculate and use Bollinger Bands for both Mean Reversion and Momentum Trading strategies.
import pandas as pd
import numpy as np1. Bollinger Bands Calculation
def calculate_bollinger_bands(df, period=20, std_dev=2):
df[‘MovingAverage’] = df[‘Close’].rolling(window=period).mean()
df[‘StdDev’] = df[‘Close’].rolling(window=period).std()df['UpperBand'] = df['MovingAverage'] + (std_dev * df['StdDev']) df['LowerBand'] = df['MovingAverage'] - (std_dev * df['StdDev']) return df2. Mean Reversion Strategy
def mean_reversion_strategy(df):
# Buy when price touches or falls below the lower Bollinger Band
df[‘MeanReversionBuySignal’] = np.where(df[‘Close’] <= df[‘LowerBand’], ‘Buy’, None)# Sell when price touches or rises above the upper Bollinger Band df['MeanReversionSellSignal'] = np.where(df['Close'] >= df['UpperBand'], 'Sell', None) return df3. Momentum Trading Strategy
def momentum_trading_strategy(df):
# Buy when price breaks above the upper Bollinger Band (momentum breakout)
df[‘MomentumBuySignal’] = np.where(df[‘Close’] > df[‘UpperBand’], ‘Buy’, None)# Sell when price breaks below the lower Bollinger Band (momentum breakdown) df['MomentumSellSignal'] = np.where(df['Close'] < df['LowerBand'], 'Sell', None) return dfExample to save to SQLite (same as before)
import sqlite3
def save_dataframe_to_sqlite(df, db_name=’ZScorePI.db’, table_name=’bollinger_data’):
with sqlite3.connect(db_name) as conn:
df.to_sql(table_name, conn, if_exists=’append’, index=False)Usage Example:
Assuming df is your price DataFrame with ‘Open’, ‘High’, ‘Low’, ‘Close’ columns
def integrate_bollinger_bands(df):
# Calculate Bollinger Bands
df = calculate_bollinger_bands(df)# Apply Mean Reversion Strategy df = mean_reversion_strategy(df) # Apply Momentum Trading Strategy df = momentum_trading_strategy(df) # Save to SQLite (if needed) save_dataframe_to_sqlite(df) return dfFetch your data, then pass the data
Explanation:
- Bollinger Bands: Calculated with a moving average and standard deviation over a defined period (usually 20 periods), with
UpperBandandLowerBandrepresenting volatility thresholds. - Mean Reversion Strategy: The logic identifies signals where the price touches or moves outside the lower band for a buy signal (indicating oversold conditions) and outside the upper band for a sell signal (indicating overbought conditions).
- Momentum Strategy: The logic here captures when the price breaks out of the Bollinger Bandsâabove the upper band for a buy signal (indicating bullish momentum) and below the lower band for a sell signal (indicating bearish momentum).
- Saving to SQLite: The resulting DataFrame is stored in an SQLite database.
Parameters You Can Adjust:
period=20: This is the number of periods over which the moving average and standard deviation are calculated. You can experiment with different values depending on your strategy.std_dev=2: This is the number of standard deviations that defines the width of the Bollinger Bands. Standard practice uses 2 standard deviations, but you may adjust this to increase or decrease the sensitivity.
Integration:
- You can further customize this strategy to incorporate it into your existing trading algorithms by integrating it with other signals (such as RSI or SuperTrend) to confirm trades.
- Add filtering or more complex logic to refine your buy/sell conditions.
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