spb/tools-spboucher-ai

1{"slug":"high-frequency-finance-research","title":"High-Frequency Finance Research","published":true,2 "description":"A working stack for intraday market-microstructure and asset-pricing research: where the minute bars and fundamentals come from, how to store tens of billions of rows on one machine, and which libraries carry the econometrics. Assembled from tools used in academic finance work rather than trading.",3 "items":[4  {"tool":"firstrate-data","blurb":"Bulk intraday bars and fundamentals for US equities, ETFs, futures and FX; the primary raw input."},5  {"tool":"databento","blurb":"Normalized tick and order-book data with point-in-time symbology for exchange-level research."},6  {"tool":"polygon-io","blurb":"REST and streaming market data with a developer-friendly API."},7  {"tool":"sec-edgar","blurb":"Filings, XBRL facts and full-text search from the SEC's own public APIs."},8  {"tool":"fred","blurb":"Macro series from the St. Louis Fed for event studies and controls."},9  {"tool":"apache-parquet","blurb":"The columnar on-disk format for a multi-terabyte bar archive."},10  {"tool":"duckdb","blurb":"Query a Parquet lake with SQL from a notebook without a database server."},11  {"tool":"polars","blurb":"Fast DataFrame transformations over Arrow memory for resampling and feature construction."},12  {"tool":"clickhouse","blurb":"When a single node is not enough: a columnar server for tick-level aggregations."},13  {"tool":"arch","blurb":"Volatility models and bootstrap methods for realized-variance work."},14  {"tool":"statsmodels","blurb":"Regression, time-series and robust inference in Python."},15  {"tool":"linearmodels","blurb":"Panel, IV and asset-pricing (Fama–MacBeth) estimators missing from statsmodels."},16  {"tool":"jupyter","blurb":"The notebook environment that ties the exploration together."}17 ]}18