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From market endpoints to research-ready data
Quantitative trading strategies depend on the historical records that feed them. Acquiring free market data through custom web scrapers lowers upfront capital costs and expands research flexibility, but it introduces operational risks like protocol shifts, rate limits, and missing fields. Building a transparent pipeline around raw WebSocket connections ensures data transformations remain visible and verifiable before strategy testing begins.
What’s inside:
Data trade-offs: Free feeds provide rapid experimentation and broad market access, but lack the service-level guarantees, corporate-action histories, and delisting data typical of institutional vendors.
Managing protocol fragility: Relying on unofficial endpoints exposes pipelines to breakage when vendors adjust message headers, change packet envelopes, or enforce stricter rate limits.
Maintaining a tight dependency envelope: Constructing sockets and HTTP requests using standard library modules keeps data transformations visible and prevents third-party wrappers from hiding parsing errors.
Handling raw WebSocket transport: Direct management of TCP connections, TLS handshakes, frame masking, and ping-pong liveness maintains link stability across large multi-symbol requests.
Parsing TradingView message envelopes: Reading length-prefixed packet markers prevents buffer corruption, while explicit session and adjustment flags document whether prices reflect raw or split-adjusted trades.
Enforcing structured schema validation: Formatting raw bars into typed NumPy arrays preserves missing volume as unallocated values rather than zero, protecting liquidity filters from distortion.
Harvesting and de-duplicating universes: Paginating scanner endpoints allows researchers to build multi-asset ticker lists, though static current-day universes carry survivorship bias that requires explicit research controls.
