Introduction

Backtests of trading strategies require processing historical market data, computing features, simulating trades. Backtests can take hours on single machines. Distributed computing frameworks (Spark, Dask) parallelize backtests across clusters. Benchmarking Spark vs Dask reveals trade-offs and optimal choice for quant backtesting.

Spark for Backtesting

Spark excels at large-scale data processing. Distribute historical data across cluster, compute features in parallel, simulate trades in parallel. Mature, widely used in finance. Steep learning curve; overhead for small backtests. Optimized for batch processing, not real-time.

Dask for Backtesting

Dask provides Pandas-like API, enabling easy scaling of Python code. Smaller overhead; faster for medium-sized backtests. Less mature than Spark; smaller ecosystem. Intuitive for researchers transitioning from single-machine Pandas workflows.

Benchmark Results

For small backtests (< 10GB data): Dask faster due to lower overhead. For large backtests (> 100GB): Spark more scalable. Hybrid approach: use Dask for research, migrate to Spark for production backtests. Benchmark on your data and hardware to optimize choice.

Conclusion

Careful framework selection optimizes backtest performance and reduces model development iteration cycles.