Backtesting systems have one job that everything else depends on: the data the strategy sees in research has to be the data it will see in production. When those diverge, a backtest becomes a story about a market that never existed, and the live results quietly disappoint. Most of that divergence comes from sourcing research and production data differently.
The problem
Strategies that look profitable in backtest diverge live because research and production read different prices, with subtle differences in venue selection, aggregation, and timestamp semantics.
How SiftingIO handles it
Use historical OHLCV over REST for interval-based research and WebSocket updates for live consumers. Keep the strategy's sampling interval explicit, and check price basis, volume meaning, market sessions, and adjustment conventions before comparing historical and live results.
Compare historical bars with live observations
Historical bars provide open, high, low, close, volume, and a bucket timestamp. Live ticks provide individual price updates. They serve different stages of a strategy workflow and should not be assumed to reconstruct an identical series. Validate source conventions and your bar-building method on a representative window before moving from research to live use.
Aggregated prices that do not flicker
A backtest run against a single venue inherits that venue's outages and quirks, and the strategy quietly learns to trade noise that will not be there tomorrow. SiftingIO aggregates each market across multiple sources into one fair price, so when an individual source goes quiet the series stays stable. The signal you fit is the market, not one venue's gaps.
Attribution when a strategy misbehaves
When a live strategy drifts from its backtest, the first honest question is what data it actually saw. Audit logs record every read with a timestamp, so you can reconstruct the exact inputs a strategy consumed and attribute a divergence to the data or rule it out, instead of blaming the model by default.