https://github.com/uhstray-io/pystockbot
Platform & exchange agnostic Stock, Crypto, and Asset automated Machine Learning & AI Trading Bot
https://github.com/uhstray-io/pystockbot
automation docker machine-learning python scikit-learn statistical-analysis trading-algorithms
Last synced: 10 months ago
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Platform & exchange agnostic Stock, Crypto, and Asset automated Machine Learning & AI Trading Bot
- Host: GitHub
- URL: https://github.com/uhstray-io/pystockbot
- Owner: uhstray-io
- License: mit
- Created: 2024-07-18T01:26:13.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-06-13T20:13:02.000Z (about 1 year ago)
- Last Synced: 2025-06-13T21:24:22.469Z (about 1 year ago)
- Topics: automation, docker, machine-learning, python, scikit-learn, statistical-analysis, trading-algorithms
- Language: Jupyter Notebook
- Homepage: https://www.uhstray.io/
- Size: 31.4 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# PyStockBot
This is a project to create a stock bot that can predict the stock price of a company using machine learning.
**DISCLAIMER:** *In general, there is no preference given to any of these metrics, models, and resources, this is not a recommendation to use them. Besides implementing and categorizing them Uhstray.io and all contributors are not officially suggesting any opinion on any of these, Uhstray.io and any contributors are not financial advisors. Do your own due diligence and speak to a professional financial advisor before making any financial decisions.*
## Trading Design Architecture
## Contributing Guidelines
- [Review our Code of Conduct](https://www.uhstray.io/en/code-of-conduct)
- [Check our CONTRIBUTING.MD](./CONTRIBUTING.md)
## Installation
This project uses uv as a package manager. To install uv and the dependencies, run the following commands:
```bash
pip install -U uv
```
```bash
uv sync
```
## Running the code
Run the notebooks in the following order:
1. pull_data.ipynb
2. pull_events.ipynb
3. pull_dividends_splits.ipynb
4. prepare_dataset.ipynb
5. train_model.ipynb
6. analyze_model.ipynb
## Understanding the code
The code is divided into the following sections:
## Understanding the data
## Understanding the metrics
## Resources
### Data
### Articles
https://machinelearningmastery.com/xgboost-for-time-series-forecasting/
https://www.kaggle.com/code/faressayah/stock-market-analysis-prediction-using-lstm/notebook
### Videos
https://www.youtube.com/watch?v=vV12dGe_Fho
https://www.youtube.com/watch?v=z3ZnOW-S550
### Libraries
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html
https://pandas-datareader.readthedocs.io/en/latest/remote_data.html