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
SiftingIO provides one canonical mid, bid, and ask across multiple venues with consistent timestamps. The same WebSocket stream powers research backtests, replayed from history, and live consumption, so the function under test is identical end to end.
The same series in research and production
Here the historical tick and OHLCV endpoints replay the exact canonical mid, bid, and ask that the live WebSocket publishes. The function under test reads an identical price series whether it runs over history or over the live stream, so a result in backtest carries a real expectation into production rather than an artifact of two feeds that almost agree.
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.