Maximizing Profit with an AI Bitcoin Trading Bot

Table of Contents:

1. Introduction to Bitcoin Trading Bot
2. Neural Networks for Bitcoin Value Prediction 2.1. Training with TensorFlow's Time Series Documentation 2.2. Historical Bitcoin Data from Binance API 2.3. Three Parts of the Project: Model Training, Prediction, and Execution of Paper Trades
3. Interface Setup and Usage of Binance Public API 3.1. Creating a Binance Account and API Key 3.2. Storing API Key Locally for Trading
4. Training Recurrent Neural Networks (RNN) with Time Windows 4.1. Input Sequence and Labeling Process 4.2. Loss Calculation and Performance Measurement 4.3. Preventing Overfitting with Validation Loss
5. Sample Window with Bitcoin Price and Predictions
6. Automation Script for Practical Application 6.1. Loading Trained Model and Querying Price Data 6.2. Trading Strategy: Threshold Approach 6.3. Executing Buy/Sell Orders Based on Statistical Significance
7. Trading Script Demonstration and Training Procedure
8. Customization and Optimization of Neural Network Models 8.1. Modified Window Generator Class for Rapid Prototyping 8.2. Implementing Different Models: Baseline, Linear Prediction, Dense Neural Network, and 1D Convolutional Network 8.3. Researching Alpha Calculation and Price Correlation
9. Using Alpha as Feedback for Model Training and Improvement
10. Generating Profit and Measuring Alpha
11. Comparing Performance of Different Models
12. Recognizing Relevancy and Responsiveness to Changing Market Conditions
13. Scalable Approach to Optimization 13.1. Creating and Evaluating Multiple Model Architectures 13.2. Integration with Trading Bot and Dynamic Model Switching
14. Future Opportunities for Improvement 14.1. Optimization Routine and Visualization with TensorBoard 14.2. Hosting Platform with User-Friendly Interface

Introduction to Bitcoin Trading Bot

Bitcoin trading has gained significant popularity in recent years, with many individuals and organizations looking for ways to automate their trading strategies. In this article, we will explore the development and functionality of a Bitcoin trading bot designed for the Capstone project of the Oregon State University Computer Science program. The trading bot utilizes neural networks to predict Bitcoin values and automate trades, offering potential benefits in terms of efficiency and generating profit. Let's dive into the details and understand the various components and techniques employed in this project.

Neural Networks for Bitcoin Value Prediction

2.1. Training with TensorFlow's Time Series Documentation

To train the neural network models used in this project, we leveraged TensorFlow's time series documentation. This valuable resource provided us with guidance on structuring and training the models using historical Bitcoin data. By following the recommended approach, we were able to implement neural networks capable of forecasting Bitcoin prices effectively.

2.2. Historical Bitcoin Data from Binance API

Our training dataset consisted of historical Bitcoin data obtained from the Binance API. This dataset was structured in a time series format, which matches the requirements for time series forecasting using neural networks. By accessing the Binance public API, we were able to retrieve the necessary data to train our models accurately.

2.3. Three Parts of the Project: Model Training, Prediction, and Execution of Paper Trades

The Bitcoin trading bot project can be divided into three distinct parts. The first part involves training the neural network models, where the model's architecture is optimized using various techniques. The Second part focuses on using the trained models to make predictions based on real-time Bitcoin price data. These predictions serve as the basis for decision-making in the trading process. The third part revolves around executing paper trades, where the bot simulates buying, selling, or holding Bitcoin based on the predicted prices. This part also includes visualizations of trade efficiency and generated Alpha, a measure of the bot's performance compared to a benchmark.

As each of our team members worked on different aspects of the project, we will now delve into the details of each component, highlighting the contributions made by each member.