DigiFi

digifi is a general purpose financial library and framework for financial modelling, portfolio optimization, and asset pricing. The purpose of this library is to provide basic functions and algorithms used in finance, but with enhanced memory safety, minimum number of dependencies and low computational requirements. digifi is composed of the following modules:

• utilities: Generic utility functions including time-value calculators, mathematical functions, loss functions and numerical solvers.
• statistics: Contains probability distributions (e.g., Normal, Binomial, Poisson) along with methods for computing covariance, skewness, kurtosis, and gamma and beta functions.
• random_generators: Algorithms for generating pseudo-random numbers from uniform and normal distributions, along with the algorithms that connect with probability distributions through a polymorphism to allow generation of pseudo-random numbers from any defined distribution.
• stochastic_processes: Stochastic processes for simulation of price action, and a builder of custom stochastic processes.
• financial_instruments: Pricing of different financial instruments (e.g., bonds, stocks, options) and their utility functions.
• portfolio_applications: Portfolio performance metrics, risk metrics, algorithm for optimizing portfolio of financial instruments for a given performance metric.
• lattice_models: Binomial and trinomial models for pricing options (European, American and Bermudan) with any payoff (e.g., Call, Straddle).
• corporate_finance: Functions for valuation of investment projects, and CAPM.
• technical_indicators: Trading indicators such as RSI, MACD, Bollinger Bands, etc.
• market_making: Automated Market Making (AMM) algorithm for simulation of transactions in liquidity pools.

Disclaimer

Note that the developers of this package do not accept any responsibility or liability for the accuracy or completeness of the code or the information provided.

Features

The following optional features are provided by digifi (Note: none of these features are the enabled by default):

• sample_data: Provides some sample data to test CAPM and portfolio optimization algorithms.
• plotly (Coming Soon): Plotting support.

Errors

In general, the functions and methods inside digifi propagate errors so that the users can decide on the error handling techniques applicable to rtheir use case. However, for some numerical solutions closures are used which can panic at runtime.

Citations

Some of the code and algorithms used are from external sources (aside from the dependencies of the library). Below is the list of the sources where the code extracts/algorithms were taken from, along with the purpose they serve in the library. We would like to express gratitude to the developers and academics who contributed to these projects.

1. Nelder-Mead Numerical Solver (https://github.com/to266/optimize): Only part of the optimize crate was used in the development of this library. Unfortunately, the crate itself is outdated, but it contains a really good implementation of the Nelder-Mead numerical solver. optimize is not a dependency to this library, but we have used an extract from its source code and slightly changed it to optimize it for our needs. Nonetheless, optimize is a great package and we hope there will be a renewed support for it.
2. Inverse CDF of Poisson Distribution (https://people.maths.ox.ac.uk/gilesm/codes/poissinv/paper.pdf): This paper covers the implementation of the method for approximating the inverse CDF of the Poisson distribution.

General information

If you would like to add a commit or an issue, please do so using the GitHub link to the project:

• https://github.com/Digital-Finance-DigiFi/digifi