Sizing Strategies for Algorithmic Trading in Volatile Markets: A Study of Backtesting and Risk Mitigation Analysis

TL;DR

This paper evaluates various sizing strategies for algorithmic trading during volatile markets, using backtesting and risk mitigation techniques to reduce Value at Risk during crisis events.

Contribution

It introduces a comparative analysis of sizing models and their effectiveness in controlling risk during high volatility market conditions.

Findings

Sizing models can significantly reduce VaR during crises

Backtesting shows improved risk control with specific sizing strategies

Analysis includes stocks with AR, ARIMA, LSTM, GARCH models

Abstract

Backtest is a way of financial risk evaluation which helps to analyze how our trading algorithm would work in markets with past time frame. The high volatility situation has always been a critical situation which creates challenges for algorithmic traders. The paper investigates different models of sizing in financial trading and backtest to high volatility situations to understand how sizing models can lower the models of VaR during crisis events. Hence it tries to show that how crisis events with high volatility can be controlled using short and long positional size. The paper also investigates stocks with AR, ARIMA, LSTM, GARCH with ETF data.