Bollinger Bands

A case study of bollinger bands

In this chapter, we are going to test bollinger bands over a period of three years.

What Is a Bollinger Band?

Bollinger Bands were developed and copyrighted by famous technical trader John Bollinger, designed to discover opportunities that give investors a higher probability of properly identifying when an asset is oversold or overbought.

Key details

There are three lines that compose Bollinger Bands A simple moving average (i.e middle band) and an upper and lower band The upper and lower bands are typically 2 standard deviations +/- from a 20-day SMA (simple moving average)

How to use bollinger bands in trading
  • If the price is near the upper Bollinger Band, it’s considered “expensive” because it is 2 standard deviation above the average (the 20-period moving average).
  • If the is price near the lower Bollinger Band, it’s considered “cheap” because it’s 2 standard deviation below the average.
Let’s test the strategy by backtesting it using Quantplay
  • Enter a trade at 9:20 AM (when the 9:15 5-minute candle ends)
  • Exit the trade at 3:15 PM or whenever the stoploss triggers
  • Trade INR 1,00,000 in the stock
  • Sell the stock with 2% stoploss
Strategy code 
from quantplay.strategy.base import QuantplayAlgorithm
from quantplay.utils.constant import TickInterval
from quantplay.service import market
import numpy as np
import pandas as pd
import ta

class BollingerBands(QuantplayAlgorithm):
    def __init__(self):
        self.interval = "5minute"
        self.entry_time = "09:15"
        self.exit_time = "15:15"
        self.exchange_to_trade_on = "NSE"
        self.stream_symbols_by_security_type = {"EQ": market.symbols(universe="FNO_STOCKS")}
        self.strategy_type = "intraday"
        self.strategy_tag = "bband"

        super(BollingerBands, self).__init__()

    def get_trades(self, market_data):
        trades = market.get_trades(market_data, self.entry_time)

        bbands = ta.volatility.BollingerBands(close=trades.close,
                                         window=20,
                                         window_dev=2)

        trades["bb_high"] = bbands.bollinger_hband()

        trades = trades[trades.close > trades.bb_high]

        trades.loc[:, 'tradingsymbol'] = trades.symbol
        trades.loc[:, "transaction_type"] = "SELL"
        trades.loc[:, "stoploss"] = 0.02
        trades.loc[:, "quantity"] = (100000/trades.close).astype(int)

        return trades

strategy = BollingerBands()
strategy.validate()
res, trades = strategy.backtest()

balance progression image

profit by month image

We hope you enjoyed our article about bollinger bands. Using only the bands to trade is a risky strategy since the indicator focuses on price and volatility, while ignoring a lot of other relevant information.

In our upcoming blogs we are going to discuss the right market conditions to trade bands and improve the strategy results.

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