Data & Tools of the Trade
Market data, vendors, APIs, and the open-source quant stack.
Every backtest, valuation, and execution algorithm stands on two things: data that means what you think it means, and tools that turn it into decisions. This track covers the anatomy of market data, the vendor and free-API landscape, and the open-source stack of QuantLib, Python backtesters, Rust libraries, and time-series databases, plus this site's own ranked directory of open-source tools.
Market Data
Market Data Types
Price history is only trustworthy once you understand trades, quotes, adjustment, and the biases that silently corrupt it.
The Data Vendor Landscape
Choosing a data source is a trade-off among cost, coverage, and licensing, not a search for the one best vendor.
Open & Free Data APIs
Free APIs are good enough for learning and research but carry fragility that keeps them out of production pipelines.
The Stack
QuantLib
QuantLib gives you production-grade pricing machinery for free, once you accept its learning curve.
Python Backtesting
Engine choice matters less than avoiding the look-ahead bias, overfitting, and cost-blindness that ruin most backtests.
The Rust Quant Stack
Rust earns its place in the hot loops of ingestion, feature computation, and simulation, rather than in exploratory research.
Time-Series Databases
Tick data demands columnar storage, and the modern choices trade query latency against retention cost and portability.