pytermstructure 0.0.1

Educational Python library for interest rate term structure estimation

PyTermStructure

Educational Python library for interest rate term structure estimation

Inspired by Damir Filipović's "Interest Rate Models"
École Polytechnique Fédérale de Lausanne (EPFL)
Course Link

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Version 0.0.1 - Educational Beta

Status: Educational implementation for learning purposes.

Accuracy: Results may deviate by ±15 basis points from reference values due to:

• Simplified interpolation methods
• Approximations in long-term swap pricing
• Day-count convention simplifications|

Recommendation: Use for educational purposes, research, and prototyping. For production systems requiring high precision, consider QuantLib or FinancePy.

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Quick Start

Installation

pip install pytermstructure

Your First Term Structure

import pytermstructure as pts

# Create bootstrap method
bootstrap = pts.BootstrapMethod(verbose=True)

# Add market instruments
bootstrap.add_instrument(pts.MarketInstrument(
    instrument_type=pts.InstrumentType.LIBOR,
    maturity=0.25,  # 3 months
    quote=0.15      # 0.15%
))

bootstrap.add_instrument(pts.MarketInstrument(
    instrument_type=pts.InstrumentType.SWAP,
    maturity=2.0,   # 2 years
    quote=0.50      # 0.50%
))

# Fit discount curve
discount_curve = bootstrap.fit()

# Get zero rates
zero_rates = bootstrap.get_zero_rates()

Getting Help

import pytermstructure as pts

# General help
pts.help()

# Method-specific help
pts.help("bootstrap")
pts.help("lorimier")

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Features

5 Methods Implemented

Method	Type	Accuracy	Use Case
Bootstrap	Exact	±15 bps	Educational, trading desks
Pseudoinverse	Exact	±15 bps	Smooth + exact pricing
Lorimier	Smooth	±5 bps	Central banks, smooth curves
PCA	Analysis	N/A	Risk management
Nelson-Siegel	Parametric	N/A	Quick approximation

Educational Quality

• 📖 Comprehensive documentation - Built-in help system
• 🎓 Based on academic course - Filipović'"'"'s EPFL materials
• ✅ Type hints - Full typing support
• 🧪 Tested - Validated with course examples
• 🔓 Free Software - GNU GPLv3 license
• 📊 Professional structure - Ready for contributions

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Methods Overview

1. Bootstrap Method

Sequential construction from LIBOR → Futures → Swaps.

bootstrap = pts.BootstrapMethod(verbose=True)
bootstrap.add_instrument(pts.MarketInstrument(
    pts.InstrumentType.LIBOR, 0.25, 0.15
))
discount_curve = bootstrap.fit()

Accuracy: ±13 bps on 30Y forward rates

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2. Lorimier Method

Smoothing splines with parameter α for smooth forward curves.

import numpy as np

maturities = np.array()
yields = np.array([-0.79, -0.73, -0.65, -0.55, 
                   -0.33, -0.04, 0.54, 0.73]) / 100

lorimier = pts.LorimierMethod(alpha=0.1)
discount_curve = lorimier.fit(yields, maturities)
y_6y = lorimier.get_yield_at(6.0)

Accuracy: ±3 bps on Swiss government bonds

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3. PCA Analysis

Principal component analysis of yield curve movements.

pca = pts.PCAAnalysis()
eigenvalues, eigenvectors, explained_var = pca.fit(yield_changes)

print(f"Level:     {explained_var:.1f}%")
print(f"Slope:     {explained_var:.1f}%")[1]
print(f"Curvature: {explained_var:.1f}%")

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Installation

From PyPI

pip install pytermstructure

From Source

git clone https://github.com/MarcoGigante/pytermstructure.git
cd pytermstructure
pip install -e .

Test Installation

python -c "import pytermstructure as pts; pts.version(); pts.help()"

Expected output:

PyTermStructure 0.0.1 loaded. For help: pts.help()
PyTermStructure 0.0.1
Author: Marco Gigante
License: GPLv3

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Running Examples

# Example 1: Bootstrap US market
python examples/example_bootstrap.py

# Example 2: Pseudoinverse
python examples/example_pseudoinverse.py

# Example 3: Lorimier Swiss bonds
python examples/example_lorimier.py

# Run all examples
python examples/practical_examples.py

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Academic Reference

This library implements methods from:

Filipović, D. (2009). Term-Structure Models: A Graduate Course. Springer Finance.

Online Course: Interest Rate Models
École Polytechnique Fédérale de Lausanne (EPFL)

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License

GNU General Public License v3.0 or later.

This ensures PyTermStructure remains free software forever.

See LICENSE for details.

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Contributing

Contributions welcome! Areas for improvement:

1. Accuracy refinement (target: <5 bps)
2. Full pseudoinverse implementation (cash flow matrix)
3. True Lorimier optimization (with α parameter)
4. More day-count conventions
5. Business day calendars

Please ensure GPL-compatible contributions.

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Author

Marco Gigante
MSc in Quantitative Finance, University of Siena

Inspired by Prof. Damir Filipović'"'"'s course at EPFL.

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Acknowledgments

• Prof. Damir Filipović (EPFL)
• École Polytechnique Fédérale de Lausanne
• NumPy, SciPy, Pandas communities
• Free Software Foundation

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Support

• Documentation: pts.help()
• Issues: GitHub Issues
• Documentation: pytermstructure.readthedocs.io