Quantitative backtesting

The Historical Data Layer for Quantitative Research

Test trading and investment strategies on 30+ years of clean, adjusted price history — with fundamentals, technical indicators, earnings data, and alternative datasets to build and validate multi-factor models across the full equity universe.

30+ years, adjusted OHLCV

Price & Fundamental History

Adjusted end-of-day and intraday OHLCV data across 80,000+ global equities, alongside historical financial statements, ratios, and earnings surprises — the complete dataset for price-based and fundamental-driven strategy research.

SMA, RSI, MACD, COT, insider trades

Technical Indicators & Alternative Data

Pre-computed technical indicators (SMA, EMA, RSI, MACD, ADX), Commitment of Traders reports, and insider trading disclosures — additional signal inputs for multi-factor strategy development and alternative data research.

What you can build

Quantitative backtesting infrastructure powered by FMP.

Price-based strategy testing

Adjusted EOD and intraday OHLCV history across global equities, forex, commodities, and crypto for momentum, mean-reversion, and technical strategy research.

Fundamental factor research

Historical financial statements and ratios to build and backtest value, quality, and growth factors across the global equity universe.

Event-driven research

Earnings surprise history, dividend announcements, insider trading disclosures, and IPO data for event-study analysis and signal construction.

Alternative signal research

Commitment of Traders data, Senate and House trading disclosures, institutional ownership and ESG ratings as non-traditional signal inputs for multi-factor model development.

Data Built for Rigorous Research

Bulk historical data endpoints for efficient retrieval across large portions of the equity universe — reducing the time between hypothesis and test.

Bulk access

Corporate-action-adjusted price history and consistent fundamental data formatting to minimise look-ahead bias and data cleaning in backtesting pipelines.

Clean & adjusted

Quant researchers choose FMP for the historical breadth, data cleanliness, and programmatic accessibility that rigorous strategy backtesting demands.