polars-greeks 0.1.1

High-performance Black-Scholes options pricing and Greeks calculation plugin for Polars

polars-greeks

High-performance Black-Scholes options pricing and Greeks calculation plugin for Polars.

Built with Rust for maximum performance, providing both DataFrame batch processing and scalar calculation interfaces.

Features

• 🚀 High Performance: Rust implementation with zero-copy data processing
• 📊 Dual API: DataFrame batch processing + scalar direct calculation
• 🎯 Complete Greeks: Delta, Gamma, Theta, Vega, Rho, Vanna, Volga, Charm, Speed, Zomma
• 💾 Memory Efficient: Pre-allocated vectors, avoid unnecessary computations
• ✅ Well Tested: Comprehensive test suite with mathematical accuracy validation

Quick Start

Installation

pip install polars-greeks

DataFrame Batch Processing

import polars as pl
import polars_greeks as greeks

df = pl.DataFrame({
    "spot": [100.0, 105.0, 95.0],
    "strike": [100.0, 100.0, 100.0],
    "time_to_expiry": [0.25, 0.25, 0.25], 
    "volatility": [0.2, 0.2, 0.2],
    "is_call": [True, False, True]
})

result = df.select(
    greeks.df(
        pl.col("spot"),
        strike=pl.col("strike"),
        time_to_expiry=pl.col("time_to_expiry"),
        volatility=pl.col("volatility"),
        r=pl.lit(0.05),
        q=pl.lit(0.0),
        is_call=pl.col("is_call"),
        greeks=["delta", "vega", "gamma", "theta"]
    ).alias("greeks_result")
).select(
    pl.col("greeks_result").struct.field("delta").alias("delta"),
    pl.col("greeks_result").struct.field("vega").alias("vega"),
    pl.col("greeks_result").struct.field("gamma").alias("gamma"),
    pl.col("greeks_result").struct.field("theta").alias("theta")
)

Scalar Direct Calculation

import polars_greeks as greeks

result = greeks.scalar(
    spot=100.0,
    strike=100.0,
    time_to_expiry=0.25,
    volatility=0.2,
    r=0.05,
    q=0.0,
    is_call=True,
    greeks=["delta", "vega", "gamma"]
)

print(result)
# {'delta': 0.56, 'vega': 19.8, 'gamma': 0.019}

Supported Greeks

• delta: Price sensitivity to underlying asset price changes
• gamma: Delta sensitivity to underlying asset price changes
• theta: Price sensitivity to time decay
• vega: Price sensitivity to volatility changes
• rho: Price sensitivity to interest rate changes
• vanna: Delta sensitivity to volatility changes
• volga: Vega sensitivity to volatility changes
• charm: Delta sensitivity to time decay
• speed: Gamma sensitivity to underlying asset price changes
• zomma: Gamma sensitivity to volatility changes

Performance

• Zero-copy processing: Direct operations on Polars Series
• Batch optimization: Single pass calculation of multiple Greeks
• Selective computation: Only calculate requested Greeks
• Memory efficient: Pre-allocated vectors, no unnecessary allocations

Typical performance: 100K+ calculations per second for multiple Greeks.

Requirements

• Python ≥ 3.8
• Polars ≥ 0.20.0

License

MIT