quantlib

Minimal, self-contained CLI tools and library for quantitative finance.

Subcommands

• corr: Compute correlation matrices over time from returns.
• costs: Calculate Sharpe Ratio (SR) costs for instruments based on spread and fees.

Modules

• estimators: Volatility and Forecast Scalar estimators — contains robust_vol_calc and mixed_vol_calc for daily volatility estimation, and forecast_scalar for scaling forecasts.
• accounts: P&L calculation framework — contains account_forecast for generating P&L curves from forecasts and prices.

Install (editable - for developers)

From the repo root:

• cd quantlib
• python -m pip install -e .

This installs the quantlib command.

• uv sync --extra dev

Docker

Pull a published image from GitHub Container Registry:

• docker pull ghcr.io/rodionlim/quantlib-st:latest

Run a quick correlation query by piping a CSV into the container (one-liner):

• cat sample_data/returns_10x4.csv | docker run --rm -i ghcr.io/rodionlim/quantlib-st:latest corr --min-periods 3 --ew-lookback 10

When publishing the image the Makefile also tags and pushes :latest in addition to the versioned tag.

Package Sample Usage

Correlation

import pandas as pd
import numpy as np

from quantlib_st import correlation

# Sample data
data = pd.DataFrame(
    np.random.randn(100, 3),
    columns=['Asset_A', 'Asset_B', 'Asset_C'],
    index=pd.date_range(start='2020-01-01', periods=100, freq='D')  # daily dates
)
# Compute correlation matrix
corr_matrix = correlation.correlation_over_time(
    data,
    is_price_series=False,
    frequency='D', # resampling purpose
    interval_frequency='7D',
    date_method='expanding',
    ew_lookback=50,
    min_periods=10
)
print(corr_matrix.as_("jsonable"))
print(corr_matrix.as_("long"))