Tagged “market-data”
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What a Reddit Sentiment Feature Actually Measures
Scraping a subreddit is easy; the feature you build from it is a model you never validated, sampled from a population that keeps changing.
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Backtesting on a News Archive You Queried Today
A news archive answers the question you ask now, not the one your strategy asked then. The timestamp, survivorship, and duplication traps.
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The Python Tooling Stack for Crypto Quant Research
The Python libraries quant researchers use for crypto: data access, pandas wrangling, backtesting engines, and a reproducible research workflow.
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The Exchange Abstraction Layer, and What It Hides
A unified exchange client normalizes symbols, candles, and errors across venues. What that abstraction genuinely gives you, and what it cannot paper over.
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Where Historical Crypto Data Comes From
The four categories of crypto price history, what each is good for, and the pagination, rate-limit, and coverage problems you inherit from each one.
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Data Quality Checks for Crypto Price History
A validation suite for OHLCV history: index integrity, invariant violations, gaps, duplicates, stale bars, and outliers you should not delete blindly.
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Resampling OHLCV Candles Without Corrupting Them
Aggregating candles to a longer interval has exact rules per column, and label conventions that silently shift your data a whole bar into the future.
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Survivorship Bias in Crypto Datasets
Crypto datasets quietly delete their failures. Why a point-in-time universe matters more here than in equities, and how to build one from what you have.
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Auditing Your Code for Lookahead Bias
Lookahead bias is a code bug, not a concept. A practical audit for the places future information leaks into a backtest, and how to catch each one.