finmath 0.1.16

A financial math library

finmath

finmath is a high-performance financial mathematics library written in C++ with Python bindings using pybind11. The library includes various functions for calculating compound interest, option pricing (including Black-Scholes and Binomial Tree models), and time series analysis. The goal is to provide a fast and reliable tool for financial calculations that can be used in Python.

Installation

Prerequisites

• C++17 or later
• Python 3.6 or later
• pybind11 (for Python bindings)
• CMake 3.5 or later (for building the library)
• ninja For optimizing cmake build

Build Instructions

1. Clone the repository:

   git clone https://github.com/prajwalshah19/finmath.git
   cd finmath
   

2. Create a build directory:

   mkdir build
   cd build
   

3. Run CMake:

   cmake ..
   

4. Build the library:

   make
   

5. Install the Python bindings (optional):

   pip install .
   

This will build the finmath library and create the Python bindings so that you can use finmath directly in Python.

Usage

Python

After installing the finmath library, you can import and use it in Python:

import finmath

# Example: Calculate compound interest
principal = 1000
rate = 5
time = 10
frequency = 4
result = finmath.compound_interest(principal, rate, time, frequency)
print(f"Compound Interest: {result}")

# Example: Calculate option price using Black-Scholes model
option_price = finmath.black_scholes(finmath.OptionType.CALL, 95, 100, 1, 0.05, 0.2)
print(f"Black-Scholes Option Price: {option_price}")

# Example: Calculate rolling volatility over a 22-day window
prices = [100, 101, 102, 100, 99, 98, 100, 102, 103, 104, 105]  # Example price series
vol = finmath.rolling_volatility(prices, 22)
print(f"Rolling Volatility: {vol}")

C++

If you want to use the library directly in C++:

#include "finmath/InterestAndAnnuities/compound_interest.h"
#include "finmath/OptionPricing/black_scholes.h"
#include "finmath/TimeSeries/rolling_volatility.h"
#include <iostream>
#include <vector>

int main() {
    double principal = 1000;
    double rate = 5;
    int time = 10;
    int frequency = 4;

    double compound_result = compound_interest(principal, rate, time, frequency);
    std::cout << "Compound Interest: " << compound_result << std::endl;

    std::vector<double> prices = {100, 101, 102, 100, 99, 98, 100, 102, 103, 104, 105};
    auto vol = rolling_volatility(prices, 22);
    std::cout << "Rolling Volatility: " << vol.back() << std::endl;

    return 0;
}

Benchmarking

You can compare the performance of finmath functions with other implementations (e.g., gs_quant) to see the speedup provided by the C++ implementations:

import timeit
import gs_quant.timeseries as ts
import functions
import finmath

# Example data for 1000 days
prices = ts.generate_series(1000)

# Timing the finmath C++ implementation of rolling volatility
def test_cpp_implementation():
    return finmath.rolling_volatility(prices.tolist(), 22)

cpp_time = timeit.timeit(test_cpp_implementation, number=100)
print(f"C++ implementation time: {cpp_time:.6f} seconds")

Contributing

We welcome contributions to finmath! To contribute:

1. Fork the repository.

2. Create a new branch for your feature or bug fix:

   git checkout -b feature/my-new-feature
   

3. Commit your changes:

   git commit -m "Add some feature"
   

4. Push the branch to your fork:

   git push origin feature/my-new-feature
   

5. Open a Pull Request on the original repository.

License

This project is licensed under the AGPL-3.0 License - see the LICENSE file for details.

Acknowledgments

• Inspired by the gs_quant library for financial time series analysis.
• Thanks to pybind11 for making it easy to integrate C++ and Python.