Backtesting Brilliance: Leveraging Analytics for Comparing Buy & Hold Vs. Trading Strategies based on Technical Indicators

Abstract

Purpose

This study performs backtesting of the technical trading strategies on MAANG stocks using the analytics platform. In addition, specific ML models are used on Sensex prices to understand whether stock movements are well explained by the trading signals. The ability to forecast stock market movements has the potential to attract investors to invest in the financial rewards attached to them.

Methodology

We have used the quantmod and PerformanceAnalytics packages of R to test the trading strategies of technical indicators. The stock prediction model is tested using TA-Lib which is again an open-source library in Python.

Findings

We have found the backtesting results of trading strategies consisting of technical indicators and compared them with the risk-return tradeoff of the buy-hold strategy. In addition, the ML models for stock price predictions provided some interesting results.

Practical implications

This study contributes to the literature on the application of analytics to trading strategies. The past performance of stocks based on these strategies is beneficial for traders and investors to investigate different strategies and technical signals.

Originality and Future Scope

The combination of indicators used in the study to backtest and model building was novel and unique compared to most similar studies. For further improvement, this study can be extended to include more technical indicators and stocks to build a robust prediction model for stock prices.