A program for financial portfolio management, analysis and optimisation.
-
Updated
Nov 4, 2023 - Python
A program for financial portfolio management, analysis and optimisation.
I perform time series analysis of data from scratch. I also implement The Autoregressive (AR) Model, The Moving Average (MA) Model, The Autoregressive Moving Average (ARMA) Model, The Autoregressive Integrated Moving Average (ARIMA) Model, The ARCH Model, The GARCH model, Auto ARIMA, forecasting and exploring a business case.
Implement a momentum trading strategy in Python and test to see if it has the potential to be profitable
Stock Portfolio Analysis using Python/Pandas
Simple Real Estate Return Analysis Open-source Web Application. Easy to use, developed as a side project to quickly analyze and compare Residential Properties.
A framework for detecting misreported returns in hedge funds.
This function optimizes portfolio weights based on a user-specified weighted linear combination of the Sortino ratio, Sharpe ratio, average total return, average downside risk, average standard deviation of returns, and max drawdown.
Randomly partitions time series segments into train, development, and test sets; Trains multiple models optimizing parameters for development set, final cross-validation in test set; Calculates model’s annualized return, improvement from buy/hold, percent profitable trades, profit factor, max drawdown
ML Coursework focused on solving Computational Finance and Risk Assessment models
DHL Online Retoure module for Magento
Simple predictive modelling task using time series data.
This is the repository with codes from the Coursera course by EDHEC business School about "Construction and Analyzing Portfolio using Python Pandas"
Performed base file system operations from the Linux Command Line Enviroment when implementing programs. Derived and employed basic algoritms from given problems Demonstrated basic programming concepts and constructs such as for/while/do-while loops, primative data types i.e. long, short, int, double, strings, char, arrays, pointers, variables, …
Magento - ReBOUND Connector
Generation and analysis of artificial and real portfolio returns
Add a description, image, and links to the returns topic page so that developers can more easily learn about it.
To associate your repository with the returns topic, visit your repo's landing page and select "manage topics."