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https://github.com/statute8234/python-finance
Rating: (8/10) The code performs a linear regression analysis on stock data using the yfinance library, importing necessary libraries, selecting stock symbols, downloading data, fitting a model, making predictions, and visualizing results.
https://github.com/statute8234/python-finance
datetime matplotlib numpy pandas random sklearn yfinance
Last synced: about 2 months ago
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Rating: (8/10) The code performs a linear regression analysis on stock data using the yfinance library, importing necessary libraries, selecting stock symbols, downloading data, fitting a model, making predictions, and visualizing results.
- Host: GitHub
- URL: https://github.com/statute8234/python-finance
- Owner: Statute8234
- Created: 2024-01-03T19:50:48.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-17T01:39:14.000Z (10 months ago)
- Last Synced: 2024-03-17T06:21:16.483Z (10 months ago)
- Topics: datetime, matplotlib, numpy, pandas, random, sklearn, yfinance
- Language: Python
- Homepage:
- Size: 6.84 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Python-Finance
The code performs a linear regression analysis on stock data using the yfinance library, importing necessary libraries, selecting a stock symbol, downloading historical data, preparing data for analysis, splitting data into training and testing sets, fitting a linear regression model, making predictions, calculating metrics, and visualizing actual and predicted values using a scatter plot.[![Static Badge](https://img.shields.io/badge/matplotlib-blue)](https://pypi.org/project/matplotlib/)
[![Static Badge](https://img.shields.io/badge/numpy-purple)](https://pypi.org/project/numpy/)
[![Static Badge](https://img.shields.io/badge/datetime-yellow)](https://pypi.org/project/datetime/)
[![Static Badge](https://img.shields.io/badge/yfinance-red)](https://pypi.org/project/yfinance/)
[![Static Badge](https://img.shields.io/badge/pandas-yellow)](https://pypi.org/project/pandas/)
[![Static Badge](https://img.shields.io/badge/sklearn-red)](https://pypi.org/project/sklearn/)
[![Static Badge](https://img.shields.io/badge/random-orange)](https://pypi.org/project/random/)## Table of Contents
- [About](#about)
- [Features](#features)
- [Imports](#Imports)
- [Rating: 8/10](#Rating)# About
The code performs a linear regression analysis on stock data using the yfinance library, importing necessary libraries like matplotlib, numpy, datetime, yfinance, pandas, and modules from sklearn. It randomly selects a stock symbol, downloads historical stock data, prepares data for regression analysis, splits data into training and testing sets, fits a linear regression model to the training data, makes predictions on the testing data, calculates evaluation metrics like MSE, MAE, R-squared, and model coefficients, and visualizes the actual and predicted values using a scatter plot.
# Features
The code for linear regression analysis on stock data using the yfinance library includes library imports, stock symbol selection, historical data download, data preparation, data splitting, linear regression model fitting, prediction making, evaluation metrics calculation, and results visualization. It uses essential libraries like matplotlib, numpy, datetime, yfinance, pandas, and sklearn for data manipulation, analysis, and visualization. The code also randomly selects a stock symbol for analysis and downloads historical stock data using the yfinance library. The data is then processed, divided into training and testing sets, and the model is fitted to identify patterns. The model is then used to make predictions on the testing data. The code provides a comprehensive approach to analyzing stock data and predicting future trends based on historical patterns.
# Imports
matplotlib.pyplot, numpy, datetime, yfinance, pandas, sklearn.model_selection, sklearn.linear_model, sklearn.metrics, random
# Rating
The code is rated 8/10 for its functionality, readability, and use of libraries such as Matplotlib, NumPy, Pandas, and scikit-learn. It performs linear regression on stock data using Yahoo Finance, evaluates performance metrics like MSE, MAE, and R-squared, and visualizes predictions against actual values. The code is well-structured and easy to follow, separating tasks into logical sections for better understanding and maintenance.
The code randomly selects a symbol from a list of symbols, adding variability and making it suitable for testing with different stocks. However, there are some cons, such as misspelled variable names and a need for a truly random approach or additional randomness. The visualization is clear, but could be enhanced by adding more descriptive labels to the axes.
Error handling is lacking, and the use of magic numbers as start and end dates is not recommended. To improve, the variable names should be corrected, random selection should be considered, and the visualization should be enhanced by adding more descriptive labels to the axes. Error handling mechanisms should be implemented, and constants or variables should be defined for start and end dates instead of using magic numbers directly in the code.