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 np## 1. 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 df`

## 2. 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 df`

## 3. 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 df`

## Example 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 df`

## Fetch 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`UpperBand`

and`LowerBand`

representing 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:

: 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.`period=20`

: 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.`std_dev=2`

### 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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