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advancing-into-analytics-book

Resources for _Advancing into Analytics: From Excel to R and Python_ by George Mount (O'Reilly Media, 2021)

arbit-codes

Location for codes without a home

bcalc

Simple bond calculaor tools. calculates Price, duration and DV01, bootstraps yield/zero and forward curves.

black-litterman

Black-Litterman model is an asset allocation model that was first developed in 1990 at Goldman Sachs by Fischer Black and Robert Litterman after whom it was named. It was an attempt to modify the existing framework for asset allocation that was established by Harry Markowitz, known as the Mean-Variance Analysis or Modern portfolio theory. The key improvement that Black-Litterman model provides is that it addresses the views of the portfolio manager about the portfolio providing an additional qualitative input that adjusts the expected returns. The contribution to expected return of each of the portfolio asset about which a view is expressed is balanced against its contribution to overall portfolio risk.

blpapi-python

Bloomberg Python API

bond-pricing

Models for Fixed Income instruments pricing

bql-api-for-python

Contains the BQL function callable directly from Python. It needs the Excel add-in for Bloomberg

capm-credit

Mean-variance optimization on a bond portfolio

corp-bond-strategy

credit-default-swaps

A series of applications for pricing CDSs

data-visualization

Data Visualization with Python

deep_rough_calibration

C Bayer, B Stemper (2018). Deep calibration of rough stochastic volatility models.

derivative-analytics-with-python-yves-hilpisch

Codes given in "Derivative Analytics with Python - Yves Hilpisch" Book

dslab_python

Data Science Labs in Python

evolutionary-trading-strategies

This code illustrates the use of genetic programming to evolve financial trading strategies for a single equity stock. Individuals (strategies) are considered as functions of historical price data, outputting a position allocation. Strategy fitness evaluation is computed by simulating the strategy over historical financial data. Because financial investment requires a fundamental tradeoff between risk and return, strategies are evaluated on multi-objective fitness functions depending on profit and maximum drawdown of the strategy and ranging from very risk-prone to very risk-averse. The population of individual strategies is evolved using tournament selection, single-point crossover, and random mutation as evolutionary operators. Strategies with the best fitness at any stage in the evolutionary process are recorded in a ‘hall-of-fame’. At the end of the evolutionary process, strategies in the ‘hall-of-fame’ are evaluated over a set of test data and selected based on a train-test criterion which penalizes strategies that do not generalize well.

excelpysim

Excel+Python for reservoir simulation

excelwritereader

Interop only interacts with open excel workbooks. Closed XML only reads from closed excel workbooks. This combines the two.

fastai

The fastai deep learning library, plus lessons and and tutorials

finance

financial-analysis

A collection of financial analysis and simulations through statistical methods.

financial-risk-management

Python class calculating VaR and Monte Carlo Simulation

financial-risk-manager

financials

Financial statements analysis with data sourced from finance.yahoo.com. Needs Premium access to finance.yahoo.com.

finmarketpy

Python library for backtesting trading strategies and analyzing financial markets

fixed-income---interest-rate-model-option-embeded-bond-pricing

fred-data-scraper

Python project to scrape data from https://fred.stlouisfed.org/ economic data repository

fundamental_analysis

fundamentalanalysis

This package can scrape financial data from Yahoo Finance for multiple companies at once. This includes the ratios, balance sheets, income statements, cashflows and stock data.

gaussian-copula-model

Simulation stochastique simple de variables dépendantes avec copule gaussienne

helloworld