Data determines what an algorithmic trading strategy can see. Price history, corporate actions, fundamentals, order books, news, and alternative datasets each serve different strategy types and introduce different risks.
Historical price data is the baseline
Most strategies begin with bars, trades, quotes, or adjusted price histories. The data must include accurate timestamps, session calendars, corporate actions, and enough history to test different market regimes.
Live data must match production needs
A strategy that trades intraday needs timely data with predictable latency and error handling. A daily rebalancing model may not need tick-level feeds. Data quality should be matched to the decision frequency and execution risk.
Alternative data requires stronger validation
News, sentiment, web traffic, satellite, and transaction datasets can add information, but they also create alignment and survivorship problems. Traders should prove that the data would have been available when the strategy needed it.
Strategic takeaway
The best algorithmic trading data is clean, timely, relevant, and auditable. Strategy design should begin with what data can be trusted.
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