Applied algorithms on 10-year trading dataof Google stock to design trading strategies and make predictions based on the model built.
Columbia FinTech Boot Camp Homework - Program that leverages Python and Pandas to analyze and compare historical performance of several investment portfolios.
Using the Sharpe ratio to analyze Facebook and Amazon stocks.
Design of a mathematic model for cross-border ETF arbitrage strategy
In this financial analysis we take a deep dive into how the adoption of cryptocurrencies is challenging the traditional financial infrastructure across international lines. Our main questions to answer are: What is the risk to reward ratio of investing in cryptocurrency? Are cryptocurrencies correlated to more standard stocks like the S&P 500? A…
Estadísticas básicas para analizar activos, portafolio de inversión o estrategías de trading algoritmico
Use pandas to calculate and compare profitability and risk of different investments using the Sharpe Ratio.
Analyzing portfolio optimizing strategies
Model to filter out specified number of bad stocks each month from our portfolio and add better performing stocks from a given wide range of stocks.
Python implementation of Hansen's Superior Predictive Ability (SPA) Test for evaluating multiple strategy performances, adjusting for correlation effects and multiple comparisons to identify statistically significant strategies. This version doesn't use bootstrapping and uses a loglog correction.
This repository is for me to practice with Python and Pandas.
Quantitative analysis techniques with Python and Pandas, for investors to determine which portfolio is performing the best across many areas: volatility, returns, risk, and Sharpe ratios.
Project to determine if ETFs v. Stocks support optimal portfolio optimization & diversification during market disruptions
evaluate the performance among various algorithmic, hedge, and mutual fund portfolios and compare them against the S&P 500 Index.
The course uses online data from yahoo finance and additional data for offline practice. Codes and dataset (URLs) for the online open source is shared in this repo.
Standard Deviations, Rolling Windows, Beta, Sharpe Ratios, Weighted Returns. Portfolio comparisons.
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