This project uses different machine learning models to try and create a trading algorithm that predicts early signals for entry / exit strategies. The basis of this algorithm uses Simple Moving Average (SMA).
The three different models created are described below:
-
Baseline Model with the following paremeters:
- 4-day fast SMA
- 100-day long SMA
- Accuracy -> 0.55
-
Tuned model (hyperparemter):
- 4-day fast SMA
- 140-day long SMA
- Accuracy -> 0.56
-
KNN (K Nearest Neighbors) model with baseline paremters:
- 4-day fast SMA
- 100-day long SMA
- Accuracy -> 0.52
We are able to see from the three strategies that the tuned hyperparemter model performs the best, the baseline 2nd best and KNN coming in last.
If you would like to clone the repository, type "git clone https://github.com/kheller18/machine-learning-trading-bot.git". In the terminal, with the conda dev environment activated, install the following packages and dependencies before running the crime analysis application. To understand how to install these, refer to the Usage
-
csv - Used to store data
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Jupyter Lab - version 3.4.4 - Used to create and share documents that contain live code, equations, visualizations and narrative text.
-
pandas - For data analysis.
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pathlib - version 1.0.1 - This was used to locate through the directory or file path.
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scikit-learn - version 1.2 - Tools for data analysis
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imbalanced-learn - version 0.10.1 - Tools for data analysis
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NumPy - version 1.24.0- Provides tools when dealing with classification with imbalanced classes
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TensorFlow -version 2.0.0- Deep neural network creation
-
Keras -version 2.9.0- Deep neural network creation
After cloning the repository locally, you'll need to have the packages listed in Installation installed on your machine. To do so, you'll need to activate your conda dev environment and running the following commands:
```
pip install pandas
pip install hvplot
pip install jupyterlab
pip install scikit-learn
pip install imbalanced-learn
pip install numpy
pip install tensorflow
pip install keras
```
After all of these are installed, please refer to the Deployment section for instructions on how to view or edit the notebook.
MIT License
Copyright (c) 2023 Keenan Heller
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- There are currently no tests associated with this project.
- There is currently no live deployment of this notebook on a common server, but the user has the ability to run this notebook locally on their machine via:
Jupyter Lab: Navigate to root of the directory and type "jupyter lab machine_learning_trading_bot.ipynb".