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

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  • S. M. Masrur Ahmed

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.

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  • S. M. Masrur Ahmed, 2023. "Sizing Strategies for Algorithmic Trading in Volatile Markets: A Study of Backtesting and Risk Mitigation Analysis," Papers 2309.09094, arXiv.org, revised Sep 2023.
    • Handle: RePEc:arx:papers:2309.09094
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    References listed on IDEAS

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    1. Henry Allen Latane, 1959. "Criteria for Choice Among Risky Ventures," Journal of Political Economy, University of Chicago Press, vol. 67(2), pages 144-144.
    2. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, September.
    3. F R Johnston & J E Boyland & M Meadows & E Shale, 1999. "Some properties of a simple moving average when applied to forecasting a time series," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(12), pages 1267-1271, December.
    4. Denis Pelletier & Wei Wei, 2016. "The Geometric-VaR Backtesting Method," Journal of Financial Econometrics, Oxford University Press, vol. 14(4), pages 725-745.
    5. Giovanni Barone‐Adesi & Kostas Giannopoulos & Les Vosper, 1999. "VaR without correlations for portfolios of derivative securities," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 19(5), pages 583-602, August.
    6. Piyapas Tharavanij & Vasan Siraprapasiri & Kittichai Rajchamaha, 2017. "Profitability of Candlestick Charting Patterns in the Stock Exchange of Thailand," SAGE Open, , vol. 7(4), pages 21582440177, October.
    7. Thor Pajhede, 2015. "Backtesting Value-at-Risk: A Generalized Markov Framework," Discussion Papers 15-18, University of Copenhagen. Department of Economics.
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