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Intraday Volatility Jump Mean-Reversion (JMR) Trading Strategy for BTC-USD in Python
Bias-Free Profitability of Volatility Jumps with Overnight Gaps Using 1-Minute OHLC Candle Data from Bitstamp
Alexzap14 min read·Just now--
Keywords: Python, BTC, high-frequency trading, mean-reversion, volatility jumps, bias, risk.
“I’ve always viewed high-frequency trading as a tax on the rest of us.” — David. F. Swensen.
The goal of this post is to implement and backtest a high-frequency trading (HFT) strategy using historical 1-min BTC-USD OHLC data from Bitstamp [1].
Cryptocurrencies are known for their high volatility, meaning their prices can move up or down rapidly by significant amounts. This volatility creates opportunities for traders to buy at favorable prices and sell a few hours later once the value increases by a certain percentage. By repeating this process consistently, it is possible to generate meaningful daily returns. Even during broader downtrends, short-term intraday movements often present high-margin opportunities that can still be exploited for profit [2].
Conversely, mean reversion refers to the tendency of an asset to move back toward its recent average or fair value following significant price swings.
