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From raw market data to ML-ready features in five lines of code.

finasys is a toolkit for financial data processing — not manual wrangling — for ML pipelines and AI agents. It lets you go from raw market data to production-ready features in a few lines of code, whether you're building trading models, running portfolio analysis, or powering financial AI agents.

finasys is Polars-first — every indicator and feature runs as a native Polars expression, making it 10-100x faster than pandas-based alternatives with zero C dependencies (no ta-lib build headaches). It supports 37+ international markets, crypto, forex, commodities, and macro indicators out of the box.

Quick Start

import finasys as fs

df = fs.load("AAPL", start="2024-01-01")
df = fs.features.add_all(df)
print(fs.agents.summarize(df))

Install

pip install finasys

Key Modules

Module What it does
fs.load() Load from Yahoo Finance, CSV, Parquet with auto-caching
fs.features 15+ indicators, returns, rolling stats, lags, calendar, cross-sectional
fs.features (targets) Forward returns, classification labels, triple-barrier labeling
fs.features (distributions) Rolling skewness, kurtosis, tail ratio, Jarque-Bera
fs.stats Sharpe, Sortino, Calmar, VaR, CVaR, alpha/beta, drawdown duration
fs.profiler One-call data profiling: quality checks, distribution analysis
fs.agents LLM summaries, OpenAI tools, RAG context, LangChain integration

Why finasys?

  • Polars-first -- 10-100x faster than pandas-ta, zero C dependencies
  • Financial-native -- every function understands OHLCV, ticks, fundamentals
  • ML-ready -- target engineering, risk metrics, and data profiling built in
  • Agent-ready -- structured outputs designed for LLM consumption
  • Symbol-aware -- multi-symbol DataFrames work correctly out of the box