https://github.com/simhayn/stock-analysis

Stock Market Analysis and Prediction. The main focused is on NVDA stock forecasting and interactive visualization.
https://github.com/simhayn/stock-analysis

Last synced: about 22 hours ago
JSON representation

Stock Market Analysis and Prediction. The main focused is on NVDA stock forecasting and interactive visualization.

• Host: GitHub
• URL: https://github.com/simhayn/stock-analysis
• Owner: simhayn
• Created: 2024-07-16T19:35:31.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-07-28T22:18:47.000Z (3 months ago)
• Last Synced: 2024-07-28T23:27:59.100Z (3 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 3.99 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Stock Market Analysis and Prediction✅📈

This project focuses on analyzing and predicting stock prices for NVDA, MARA, and CLSK. 

It's interactive graphs are available on kaggle notebook preview. You can enter the [link](https://www.kaggle.com/code/natalyyakobov/time-series-analysis-stocks?scriptVersionId=188679366)

to see them.

The analysis includes extracting data, performing exploratory data analysis (EDA), and forecasting stock prices using different models.

## Project Overview

- Data Extraction: Stock data for NVDA, MARA, and CLSK was extracted from stooq.

- EDA: Visualizations were created using cufflinks-plotly to explore stock returns, rolling averages, volatility, and Bollinger Bands.

- Prediction Models: The closing price of NVDA was predicted using: **SARIMAX** model with auto ARIMA and **LSTM** neural network.

## Steps

**Extracting Data**

- Set up the time range explicitly using dates.

- Pulled stock data from stooq.

**EDA**

- Visualized the data with cufflinks-plotly.

- Explored returns, rolling averages, volatility and Bollinger Bands.

**Forecasting**

- Used SARIMAX with auto ARIMA to predict NVDA's closing price.

- Compared the predictions with those from an LSTM neural network.

Hope you enjoy this project!