run the .ipynbs to open the jupyter notebooks
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System Design & Architecture: To be led by Christopher, outlining the main components of the trading system and how they will interact.
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Algorithm Development: Chris and Russell will collaborate to design, test and optimize our trading algorithms using Backtrader.
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Interface Design: Victor will take the helm in designing a sleek, intuitive, and interactive interface using Plotty Express, ensuring the system is user-friendly and efficient.
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Data Management: Victor and Russell: data sourcing, formatting and data management.
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Integration & Testing: The team will collaborate in this final phase to integrate all system components, followed by rigorous testing to ensure optimal performance.
Our research questions are as follows:
- Does a neural network or a svm model perform better for stock market predictions?
- How can we fully automate this process, with very little user intervention?
- Can we gather sentiment data, live and hourly to make this work?
- Will PCA help NN's or SVM models perform better?
We will use the following datasets in our project:
- Augomento.AI dataset
- Kaggle/Alternative.me Fear and Greed dataset
- Data cleaning and formatting
- Algorithm development
- Visualization
- Integration
- Back testing
- Paper trading
Our project will be divided into the following tasks:
Data Cleaning and Formatting
We will use Pandas to clean and format the cryptocurrency dataset. This will involve removing any errors in the data, converting the data to the correct format and creating any new columns that are needed.
Algorithm Development
We will use a machine learning model to create our predictions and implement our algorithm using the predicted buy and sell signals. We hope with machine learning it can develop a trading strategy that can identify and exploit profitable trading opportunities in the cryptocurrency market.
Visualization
We will use Plotty Express and backtraderto visualize our data. These visualizations will help us to understand the data and to identify any potential trading opportunities.
Integration
We will use backtrader to integrate the algorithms and visualizations required.
Paper Trading
Once we are satisfied with the performance of our system in backtesting, we will deploy it to a paper trading account. This will allow us to test our system in real-world market conditions without risking any real money.
Optimization
We will monitor the performance of our trading system in paper trading and make adjustments to our algorithm as needed. Our goal is to optimize our system to generate the highest possible returns.
We believe that this project has the potential to develop a machine learning system that can create profitable algorithmic trading system for cryptocurrencies. We are committed to working hard to complete this project on time and within budget. We look forward to sharing our results with the class and the instructional team.
