This repository contains a collection of finance-related projects leveraging Python to perform data analysis, trading strategy development, and stock price prediction. These projects demonstrate the power and versatility of Python in the world of finance and aim to provide practical applications for anyone interested in expanding their knowledge of finance and data analysis.
- Exploratory Data Analysis of Cryptocurrency Historical Data
- Interactive Backtesting of Trading Strategies with IPyWidgets
- Moving Average Crossover Trading Strategy with Python
- Stock Closing Price Prediction using Decision Trees
- Technical Stock Analysis (RSI) with Python
- Predicting Stock Price with Machine Learning - Linear Regression
In this project, we perform an exploratory data analysis on historical data of various cryptocurrencies. The goal is to gain insights into the performance and trends of different cryptocurrencies, as well as to identify potential patterns or anomalies.
This project demonstrates the use of IPyWidgets to create an interactive backtesting environment for trading strategies. Users can input different parameters, such as the time period, to test and optimize their trading strategies.
This project focuses on implementing a moving average crossover trading strategy using Python. The moving average crossover strategy is a popular technical trading rule that generates buy and sell signals based on the crossing of two moving averages.
In this project, we use decision trees to predict the closing price of a stock based on historical data. Decision trees are a popular machine learning technique for making predictions and can provide an intuitive and easy-to-understand representation of the prediction process.
This project demonstrates the calculation and interpretation of the Relative Strength Index (RSI) using Python. The RSI is a popular technical indicator used to measure the strength or weakness of a stock's price movement over a given period.
In this project, we use linear regression, a basic machine learning algorithm, to predict stock prices based on historical data. The project demonstrates how to preprocess data, train a linear regression model, and evaluate its performance.