Performance Analysis of Bollinger Bands and Long Short-Term Memory(LSTM) models based Strategies on NIFTY50 Companies

Hedge funds and financial institutions strive to build consistently profitable automated trading strategies which can provide higher returns than stock market index baselines. This work aims to beat the stock market baselines with higher returns by building a system capable of evaluating the performance of different automated trading strategies on various different metrics. An automated strategy is a set of rules according to which a computer makes buy/sell decisions in the stock market. This work leverages predictions generated by two strategies namely Bollinger Bands and Long Short-Term Memory to aid in decision making. The LSTM strategy makes use of predictions from 250 LSTM neural networks (5 models per company), while the Bollinger Bands strategy uses close price, simple moving averages and standard deviations to make buy or sell decisions. The strategies’ performance has been evaluated against historic data (backtesting) and continuous real time data (paper trading) of the stocks in the NIFTY50 index being sourced from the stock market. Upon analysis of the backtested data, it was observed that various strategy configurations have beaten the market baselines over different time periods. The results obtained by this work show that the custom strategies proposed have beaten the market baselines in 35.93% (a third) of all time periods back tested. Thus, investing in the proposed custom strategies produces higher returns than investing in the stock market index for the same time periods.