Financial data platform for analysts, quants and AI agents.
A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
Analysis on systematic trading strategies (e.g., trend-following, carry and mean-reversion). The result is regularly updated.
A fixed income library for pricing bonds and bond futures, and derivatives such as interest rate swaps (IRS), cross-currency swaps (XCS) and FX swaps. Contains tools for full curveset construction with market standard optimisers and automatic differentiation (AD) and risk sensitivity calculations including delta and cross-gamma.
Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics
A statistical arbitrage strategy on treasury futures using mean-reversion property and meanwhile insensitive to the yield change
Fixed income tools for R
Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics
Quantitative analysis of Fixed Income Securities, including bond pricing models, yield curve fitting, PCA analysis, bond returns predictability and fixed income derivatives.
DRIP Fixed Income is a collection of Java libraries for Instrument/Trading Conventions, Treasury Futures/Options, Funding/Forward/Overnight Curves, Multi-Curve Construction/Valuation, Collateral Valuation and XVA Metric Generation, Calibration and Hedge Attributions, Statistical Curve Construction, Bond RV Metrics, Stochastic Evolution and Optio…
US Treasuries Yield Curve Data
Python class and jupyter iPython notebook for pricing a fixed coupon bond
AAD enabled and scripting included derivatives modeling.
Standardised Bloomberg Fixed Income Processing
Basic package for fitting yield-curves and other things.
A (very) fast Rust library for quantitative finance.
Python methods to create a Ho-Lee binomial interest rate model for fixed income security pricing: caps, swaps, bonds, etc.
Contains Python code and files used to estimate shadow rate using Krippner's K-ANSM(2) with an estimated lower bound term structure model
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