finasys 0.1.4

Financial data processing, feature engineering and AI agent toolkit for Python

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

Documentation: finasys Docs

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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. Learn more via our official documentation or start contributing via this GitHub repo.

Quick Start

import finasys as fs

# Load stock data (auto-cached with DuckDB)
df = fs.load("AAPL", start="2024-01-01")

# Add technical indicators + returns in one call
df = fs.features.add_all(df)

# Generate an LLM-ready summary
print(fs.agents.summarize(df))

Install

pip install finasys

Optional extras:

pip install finasys[langchain]   # LangChain tool integration
pip install finasys[pandas]      # Pandas interop
pip install finasys[all]         # Everything

Features

Data Sources (fs.load())

• Single fs.load() entry point for Yahoo Finance, CSV, and Parquet files
• Standardized OHLCV column names across all sources
• DuckDB-backed local caching (second call is instant)
• Multi-symbol fetching with automatic alignment

df = fs.load("AAPL", start="2024-01-01")
df = fs.load(["AAPL", "GOOGL", "MSFT"], start="2024-01-01")
df = fs.load("./data/prices.csv")

Feature Engineering (fs.features)

• 15+ technical indicators: RSI, MACD, Bollinger Bands, ATR, VWAP, OBV, Stochastic, ADX, CCI, Williams %R, MFI, ROC, Momentum
• Returns: simple, log, cumulative, drawdown
• Rolling statistics: mean, std, min, max, skew, z-score
• Lag features with built-in look-ahead bias protection
• Calendar features: day of week, month, quarter
• Cross-sectional: rank, percentile, z-score across symbols

All implemented in pure Polars expressions -- no ta-lib C dependency, 10-100x faster than pandas-ta.

df = fs.features.rsi(df, period=14)
df = fs.features.macd(df)
df = fs.features.returns(df, periods=[1, 5, 21])

Target / Label Engineering (fs.features)

• Forward returns for regression targets
• Ternary classification labels (up/flat/down) with configurable thresholds
• Triple-barrier labeling (Lopez de Prado method) -- the gold standard for financial ML
• Volatility-adjusted labels that adapt to the current regime

# Forward returns for regression
df = fs.features.forward_returns(df, periods=[1, 5])

# Classification labels
df = fs.features.classify_returns(df, period=5, thresholds=(-0.01, 0.01))

# Triple-barrier method
df = fs.features.triple_barrier_labels(df, profit_take=0.02, stop_loss=0.02, max_holding=10)

# Volatility-adjusted labels (adapts to regime)
df = fs.features.volatility_adjusted_labels(df, period=5, vol_multiplier=1.0)

Distribution Features (fs.features)

• Rolling kurtosis, skewness, tail ratio -- capture fat-tail dynamics
• Rolling Jarque-Bera normality test
• Z-score of returns vs rolling distribution

df = fs.features.rolling_kurtosis(df, window=30)
df = fs.features.rolling_skewness(df, window=30)
df = fs.features.tail_ratio(df, window=30)
df = fs.features.zscore_returns(df, window=30)

Market Regime Features (fs.features)

• Volatility regimes from fast/slow rolling volatility
• Trend strength via rolling Hurst-style approximation
• Combined market states such as trending/high-volatility or ranging/low-volatility
• Breakout flags and strength scores

df = fs.features.volatility_regime(df, fast_window=21, slow_window=63)
df = fs.features.trend_strength(df, window=63)
df = fs.features.market_state(df)
df = fs.features.breakout_detection(df, window=20)

Risk & Performance Metrics (fs.stats)

• Sharpe, Sortino, Calmar ratios
• Value at Risk (historical, parametric, Cornish-Fisher)
• Conditional VaR (Expected Shortfall)
• CAPM alpha/beta, information ratio
• Max drawdown duration tracking
• Dual mode: scalar for reporting, rolling columns for ML features

# Scalar metrics (whole-series)
sharpe = fs.stats.sharpe_ratio(df)                         # => 1.47
var = fs.stats.value_at_risk(df, confidence=0.95)           # => -0.0216
cvar = fs.stats.cvar(df, confidence=0.95)                   # => -0.0285

# Rolling metrics (ML features)
df = fs.stats.sharpe_ratio(df, window=63)                   # adds sharpe_63
df = fs.stats.value_at_risk(df, window=63)                  # adds var_63

Portfolio Analytics (fs.portfolio)

• Correlation and covariance matrices for multi-symbol DataFrames
• Pairwise rolling correlation
• Weighted and equal-weight portfolio returns
• Minimum-variance portfolio weights

df = fs.load(["AAPL", "GOOGL", "MSFT"], start="2024-01-01")
corr = fs.portfolio.correlation_matrix(df)
portfolio = fs.portfolio.equal_weight_returns(df)
weights = fs.portfolio.minimum_variance_weights(df)

Data Quality Checks (fs.quality)

• Missing business-day gaps
• Outlier flags
• Suspected split flags
• Completeness reports with nulls, duplicates, zero-volume days, gaps, and flags

gaps = fs.quality.detect_gaps(df)
df = fs.quality.flag_outliers(df)
df = fs.quality.detect_splits(df)
report = fs.quality.completeness_report(df)

Smart Profiler (fs.profiler)

• One-call data quality assessment for financial time series
• Detects: missing dates, price outliers, suspected stock splits, zero-volume days
• Distribution analysis: skewness, kurtosis, Jarque-Bera normality test, tail ratio
• LLM-ready text summaries and JSON-serializable structured reports

# Text summary (great for LLM system prompts)
print(fs.profiler.profile_summary(df))
# DATA PROFILE | 252 rows x 7 columns
# Quality issues: 9 missing dates; 11 price outliers
# Returns distribution: skew=0.501, kurtosis=3.647, non-normal (JB p=0.0000)

# Full structured report
report = fs.profiler.profile(df)
report.quality.missing_dates      # ['2024-01-15', '2024-02-19', ...]
report.distribution.is_normal     # False
report.to_dict()                  # JSON-serializable

AI Agent Tools (fs.agents)

• LLM-ready summaries of financial DataFrames
• Tool definitions in OpenAI function-calling format
• Extended tools for risk reports, portfolio analysis, stock screening, quality checks, and profile summaries
• Context extraction for RAG-style usage
• Schema descriptions for system prompts
• LangChain integration (optional)

summary = fs.agents.summarize(df)
tools = fs.agents.tools(symbols=["AAPL", "GOOGL"])

from finasys.agents.langchain import get_tools
lc_tools = get_tools(symbols=["AAPL"])

Composable Pipelines (fs.FeatureSet)

Serializable, reproducible feature pipelines with 21 built-in step classes.

pipeline = fs.FeatureSet([
    fs.features.RSI(period=14),
    fs.features.Returns(periods=[1, 5, 21]),
    fs.features.RollingStats(windows=[5, 21]),
    fs.features.RollingKurtosis(window=30),
    fs.features.VolatilityRegime(),
    fs.features.ForwardReturns(periods=[1, 5]),
    fs.features.TripleBarrier(profit_take=0.02, stop_loss=0.02),
])
df = pipeline.transform(df)
pipeline.save("pipeline.json")  # version control your feature engineering

Why finasys?

	finasys	pandas-ta	ta-lib
Engine	Polars (fast)	pandas (slow)	C library
Install	pip install finasys	pip install pandas-ta	Requires C build tools
ML Targets	Triple-barrier, vol-adjusted labels	None	None
Risk Metrics	Sharpe, VaR, CVaR, alpha/beta	None	None
Data Profiling	Financial-specific quality checks	None	None
AI Agent support	Built-in	None	None
Caching	DuckDB auto-cache	None	None
Look-ahead protection	Built-in	None	None

License

Apache-2.0