FMEvalDatasets is a curated dataset of historical price data across equities, bonds, commodities, REITs, ETFs, cryptocurrencies, and FX, designed for evaluating foundation models on forecasting, regime detection, and time-series reasoning.
The dataset provides a structured, high-quality benchmark for assessing the performance of foundation models—particularly large language and time-series models—on tasks involving historical financial market data. It enables standardized evaluation across forecasting, anomaly detection, pattern recognition, and temporal reasoning tasks in finance.
| Financial Instrument | Count | Geographic Coverage / Remarks |
|---|---|---|
| FX | 8 | G7 currency pairs |
| EM FX | 14 | Emerging markets (Asia, Latin America, Middle East) |
| Equities | 6 | USA, Europe, Singapore, Kazakhstan |
| Crypto | 6 | Major cryptocurrencies (e.g., BTC, ETH, BNB) |
| ETF | 5 | Global ETFs excluding US, plus US REIT ETF |
| MSCI | 19 | Global indices covering all continents |
| Rates | 10 | Sovereign yields across US, Europe, Japan, Malaysia, Singapore |
| REIT | 5 | Real estate investment trusts (US and China) |
| Volatility | 22 | Volatility across the 22 FX pairs |
- ~10 years of historical coverage
- Includes bull, bear, and high-volatility regimes for robustness testing
- Cleaned, aligned, and normalized time series
- Organized into train/validation/test splits with optional rolling windows
- Optional derived features: returns, volatility, moving averages, technical indicators
- Price Direction Forecasting (classification)
- Return Prediction (regression)
- Volatility Estimation
- Market Regime Detection
- Event Impact Analysis (price behavior around known market events)
- Benchmarking foundation models for financial time-series tasks
- Evaluating generalization across asset classes and market conditions
- Fine-tuning or pretraining models on market structure and dynamics
- Financial ML researchers and quantitative modelers
- Developers of financial foundation models (LLMs + time-series models)
- Institutions focused on systematic trading, risk management, or macroeconomic modeling