Imagine you’re a skilled chef, keen to perfect a unique recipe. You meticulously gather ingredients, study their behaviors, and mix them in specific ways, hoping to create something delightful. Now picture your task intensified by the whims of changing seasons and unexpected guests—this is akin to what happens in the world of Bitcoin trading when you combine it with Artificial Intelligence (AI) and deep learning!
Getting Started
In this blog, we’ll explore how to develop a savvy AI model that predicts Bitcoin price patterns using deep learning techniques. Here’s how you can embark on this thrilling data-driven journey.
Preliminary Steps
First off, we will need to set up the environment and gather the necessary data. Here’s a simple roadmap:
- Clone the repository:
git clone https://github.com/philipperemy/deep-learning-bitcoin.git - Change directories:
cd deep-learning-bitcoin - Download data:
./data_download.sh - Run the data generator with quantile options:
python3 data_generator.py tmp btc-trading-patterns tmp coinbaseUSD.csv 1
Understanding the Model Training
To illustrate how our AI model learns from the data, envision a baker learning how to knead dough. Initially, the baker uses trial and error, refining their technique based on the texture and elasticity of the dough. Similarly, our AI model trains on historical Bitcoin data, adjusting its internal parameters every time it sees a new batch of price information.
We achieve this using a simple AlexNet model, achieving a 70% accuracy while predicting if the asset will go UP or DOWN based on Open High Low Close (OHLC) representation of price data. However, just like our baker who strives for perfection, we have goals for improvement:
- Train on **1,000,000+** samples
- Apply more complex Convolutional Networks (at least Google LeNet)
- Integrate volume data into our OHLC images
- Utilize CNN attention mechanisms for focused predictions
Results Analysis
Through training on a robust dataset, we can observe pronounced examples showcasing how returns fluctuate. Here’s a glimpse of how our dataset is organized:
price_open price_high price_low price_close volume close_price_returns
2017-05-29 11:55:00 2158.86 2160.06 2155.78 2156.00 21.034283 0.000000
Troubleshooting
As with any journey, we might face a hurdle or two! If you encounter errors such as:
- _tkinter.TclError: no display name and no $DISPLAY environment variable
Check out this solution for help.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Running with Docker
To make your life easier in managing dependencies and environment setups, running your project in Docker is a great choice. Here’s a quick guide:
- Build the Docker image:
docker build -t dlb . - Run the container with mounted volumes:
docker run -it --name dlb -v $PWD:app dlb /bin/bash
Verify that the current folder is correctly mounted within your container to ensure smooth operations.
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
Now, as you venture into the crossroad of Bitcoin and AI, may your predictions be sharper than ever!
