This repository contains my market prediction code. The goal is to share and evaluate various forecasting strategies using Machine Learning and AI. Email me at saurabh.nagda.mba24@oxford.said.edu
This project utilizes historical stock data to predict future stock prices using machine learning algorithms. The repository includes data preprocessing, feature engineering, model training, and evaluation scripts.
- Implementation of various machine learning algorithms
- Evaluation of different regression models for stock price prediction
- Data fetching from Yahoo Finance
- Data preprocessing and feature engineering
- Evaluation metrics to assess model performance
- Visualization of predicted vs. actual stock prices
- Added 2024 Sharpe Ratio to Previous Code to List 500 S&P 500 Stocks and their 2024 Returns and Standard Deviation
- Added 2024 Standard Deviation to Previous Code to List 500 S&P 500 Stocks and their 2024 Returns
- New year 2025 code to fetch 2024 returns of 500 S&P 500 companies
- Simple code to import daily trading volume for MSFT, GOOG, QQQ and SPY using yFinance
- Code to add Moving Average Convergence Divergence (MACD) to identify trend changes
- Added plots for 20-day moving averages for MSFT, GOOG, QQQ, and SPY
- Added Correlation Matrix for MSFT, GOOG, SPY, and QQQ
- Optimize Portfolio VaR by Adjusting Composition of MSFT and GOOG in the portfolio
- Calculate the Value at Risk (VaR) for a 50:50 portfolio Microsoft (MSFT) and Google (GOOG).
- Calculate the Sharpe Ratio for MSFT and GO using SPY as the proxy for market returns and standard deviation
- Calculate expected returns for MSFT and GOOG using CAPM
- Calculate VaR (Value at Risk) for GOOG and MSFT with 95% confidence intervals
- Calculate the market risk premium for MSFT and GOOG, using SPY as proxy for the Market
- Code to add Beta values of GOOG, MSFT
- Code to analyze EPS and P/E ratios for GOOG, MSFT, QQQ and SPY
- Starting code to analyze and plot growth and correlation between GOOG, MSFT, QQQ, and SPY
- Updated MSFT price prediction code to use the RandomForest machine learning prediction model
- Updated Polynomial Regression code to split the data into training and test data
- Program to run polynomial regression from Scikit-learn to determine the price of diamonds using length, width, and depth
- A simple code to invoke OpenAI LangChain and use it to tell a short story
- Code to read an SQL database stored locally and run some SQL queries to update the contents in the database
- A simple program to transfer Excel data to a local SQL database
- Code to update the price of NVDA from YFinance to chart Protective Put and Covered Call strategy after earnings
- Before NVDA earnings call today, 11/20/24, code used to chart Covered Call and Protective Put trading strategy
- Code to perform sentiment analysis for content related to the US from FT.com
- A new code added to generate a word cloud of the summary generated from FT.com
- Simple code to retrieve and summarize a webpage (not an LLM-based code)
- Excel data clean-up and summarization using Pivot Table in the code
- Python program to take an Excel file for product sales, clean it up for blanks, NaNs, and non-logical values, and write the file with sales and average price summaries
- Updated clustering into three categories: 'Low', 'Medium', and 'High' along with summary statistics of these categories
- Clustering of historical inflation data using K-Means from Scikit-learn
- Updated the forecasting model to use the RandomForest algorithm
- A program to find the lag between advertising spend and realized sales. The lag is determined by maximizing R-squared in simple OLS/linear regression
- Simple program to forecast MSFT stock price using a linear regression machine learning model