Cloud or local compute for backtesting

Disciplined backtest evaluation, overfitting risk, and the need to account for multiple testing are important considerations.

Backtesting infrastructure choices are usually framed as a simple scale decision indeed. In practice, the decision is also about where data accumulates, how costs behave as research volume changes, how easy it is to recreate a result later, and which dependencies become hard to unwind.

Data gravity is the tendency for applications and workflows to move toward where large datasets already live. In backtesting, this matters because historical market data, derived features, and intermediate research outputs can become large and operationally expensive to move. When a workflow is built around remote services, the surrounding tools often follow the data. That can make cloud compute convenient for shared access and elastic execution, but it can also make the research stack more dependent on the storage, orchestration, and service boundaries of that environment.

Cost shape is one of the clearest tradeoffs. Cloud spending is usually operational expenditure tied to usage. The mechanism is straightforward: more runs, larger instances, longer execution times, and more attached services increase the bill. Local compute is usually capital expenditure paid up front, then amortized over the useful life of the hardware. That shifts the economics from variable usage pricing to fixed capacity with ongoing maintenance and power costs.

Reproducibility is about obtaining the same analytical result when the same code, parameters, and data are used again. In systematic research, reproducibility is not a cosmetic property. It is a control against accidental changes in data handling, selection, and evaluation. The Sonar material emphasizes statistical controls around backtest evaluation, especially around multiple testing and selection bias. The backtest overfitting audit describes the problem of repeatedly testing variants until one appears strong by chance, and the deflated Sharpe ratio glossary explains why observed performance statistics can be overstated when many trials are considered. Reproducibility depends on controlling the research process and recording the number and nature of trials.

The practical mechanism that links infrastructure to reproducibility is environment control. Any setup that allows unnoticed changes in data snapshots, dependency versions, execution order, or hidden preprocessing steps can make later reruns diverge from earlier ones. Any setup that tightly versions data, code, configuration, and evaluation protocol can improve reproducibility. It is the process implication that follows from Sonar’s emphasis on disciplined evaluation and bias control.

Lock in usually happens quietly. It emerges when a team builds its research flow around specific storage formats, job schedulers, managed data services, or proprietary metrics pipelines. Over time, those choices shape how data is accessed, how experiments are launched, and how results are reviewed. The same pattern can also occur locally through bespoke internal tooling and machine specific dependencies.

Backtest quality depends on disciplined experimental design, not just raw compute access. Infrastructure matters because it shapes that process.

Treat compute choice as part of research design. Ask how the environment will preserve data lineage, trial counts, parameter history, and evaluation discipline.

Drafted with AI assistance from cited sources. Reviewed and approved by Sonar Sciences Quant & Research Team.