Rolling Crypto Perpetual Screening with Dual Moving Average Backtests
Summary
This article presents a rolling framework for selecting crypto perpetual contracts and assigning each selected token its own dual moving average parameters. It first takes the highest-volume USDT perpetual symbols, then backtests multiple fast and slow moving average combinations for each candidate. A heuristic composite score combines win rate, profit factor, drawdown control, and a bonus for elevated current volatility. Each token advances with its best historical parameter score, and the top-scoring tokens form a whitelist for live crossover signals.
The live framework adds trailing profit protection and adjusts position size according to a Bitcoin volatility regime, with shorting disabled in extreme conditions. The article explains its evidence as historical backtests and describes the framework as an empirical experiment; it does not establish future profitability. It explicitly identifies major weaknesses: the full history is used without an out-of-sample holdout, there is no deployment validation gate, and choosing both parameters and tokens by historical scores compounds overfitting risk. The proposed trend-persistence assumption is not rigorously tested, while score weights and risk thresholds are heuristic.
Key ideas
- The candidate universe is narrowed to high-volume USDT perpetual contracts before strategy evaluation.
- Each candidate is tested across multiple dual moving average parameter combinations.
- A heuristic score ranks parameter sets using win rate, profit factor, drawdown, and a volatility bonus.
- The highest-scoring parameters are selected per token, and top-ranked tokens enter a trading whitelist.
- Historical selection across both tokens and parameters risks compounded overfitting without out-of-sample validation.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.