Pinned Repositories
advancing-into-analytics-book
Resources for _Advancing into Analytics: From Excel to R and Python_ by George Mount (O'Reilly Media, 2021)
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.
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
Financials
Financial statements analysis with data sourced from finance.yahoo.com. Needs Premium access to finance.yahoo.com.
Fixed-Income---Interest-Rate-Model-Option-Embeded-Bond-Pricing
ericlegoaec's Repositories
ericlegoaec/advancing-into-analytics-book
Resources for _Advancing into Analytics: From Excel to R and Python_ by George Mount (O'Reilly Media, 2021)
ericlegoaec/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.
ericlegoaec/BQL-api-for-Python
Contains the BQL function callable directly from Python. It needs the Excel add-in for Bloomberg
ericlegoaec/corp-bond-strategy
ericlegoaec/Data-Visualization
Data Visualization with Python
ericlegoaec/Financials
Financial statements analysis with data sourced from finance.yahoo.com. Needs Premium access to finance.yahoo.com.
ericlegoaec/FRED-data-scraper
Python project to scrape data from https://fred.stlouisfed.org/ economic data repository
ericlegoaec/Fundamental_analysis
ericlegoaec/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.
ericlegoaec/LiborMarketModel
Credit Value Adjustment (CVA) calculation for interest rate swaps using a risk neutral Libor Market Model (LMM) calibrated to european swaption implied volatilities.
ericlegoaec/multi-curve-affine-interest-rate-modelling
Simulation of short rate of an affine multi-curve example with discontinuites
ericlegoaec/NS-Forcasting
ericlegoaec/pyfolio
Portfolio and risk analytics in Python
ericlegoaec/python-for-excel
This is the companion repo of the O'Reilly book "Python for Excel".
ericlegoaec/SABRCalibration
A basic calibration of the SABR model
ericlegoaec/yahoo-finance
Python module to get stock data from Yahoo! Finance
ericlegoaec/yahoofinancials
A powerful financial data module used for pulling data from Yahoo Finance. This module can pull fundamental and technical data for stocks, indexes, currencies, cryptos, ETFs, Mutual Funds, U.S. Treasuries, and commodity futures.
ericlegoaec/DSLAB_Python
Data Science Labs in Python
ericlegoaec/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.
ericlegoaec/ExcelPySim
Excel+Python for reservoir simulation
ericlegoaec/ExcelWriteReader
Interop only interacts with open excel workbooks. Closed XML only reads from closed excel workbooks. This combines the two.
ericlegoaec/fastai
The fastai deep learning library, plus lessons and and tutorials
ericlegoaec/Financial-Analysis
A collection of financial analysis and simulations through statistical methods.
ericlegoaec/financial-risk-manager
ericlegoaec/Helloworld
ericlegoaec/M2MAA-SimulEtCopules
ericlegoaec/Publications
ericlegoaec/pyrb
Constrained and Unconstrained Risk budgeting / risk parity allocation in Python
ericlegoaec/scrape-reuters
Scrapes reuters for financial ratios
ericlegoaec/Stock-Strength
I dabble with web scraping and financial APIs to obtain financial data of publicly traded companies (mainly Apple to begin with) and use metrics such as the relative strength index and comparisons of market value and book value of stocks to determine whether investing in the company stock at the moment is worth it