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Exploiting Bitcoin prices patterns with Deep Learning.
| Date | Stars |
|---|---|
| 2026-07-31 | 530 |
| 2026-08-06 | 530 |
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# When Bitcoin meets Artificial Intelligence
[]()
Exploiting Bitcoin prices patterns with Deep Learning. Like OpenAI, we train our models on raw pixel data. Exactly how an experienced human would see the curves and takes an action.
<p align="center">
<img src="https://bitcoin.org/img/icons/opengraph.png" width="100">
</p>
So far, we achieved:
- [x] Download Bitcoin tick data
- [x] Convert to 5-minute data
- [x] Convert to Open High Low Close representation
- [x] Train a simple AlexNet on 20,000 samples: accuracy is 70% for predicting if asset will go UP or DOWN. Training is done on [NVIDIA DIGITS](https://github.com/nvidia/digits) and with the Caffe framework.
- [x] Quantify how much the price will go UP or DOWN. Because the price can go UP by epsilon percent 99% of the time, and pulls back by 50%
- [ ] Train on **1,000,000+** samples (at least)
- [ ] Apply more complex Conv Nets (at least Google LeNet)
- [ ] Integrate bar volumes on the generated OHLC (Open, High, Low, Close) image
- [ ] Use CNN attention to know what's important for which image. Maybe only a fraction of the image matters for the prediction
## Results on 20,000 samples (small dataset)
<p align="center">
<img src="assets/1.png" width="500">
<br><i>Training on 5 minute price data (Coinbase USD)</i>
</p>
<hr/>
<p align="center">
<img src="assets/2.png" width="500">
<br><i>Some examples of the training set</i>
</p>
<hr/>
## Illustration of the dataset from CoinbaseUSD
```
price_open price_high price_low price_close volume close_price_returns close_price_returns_bins close_price_returns_labels
DateTime_UTC
2017-05-29 11:55:00 2158.86 2160.06 2155.78 2156.00 21.034283 0.000000 (-0.334, 0.015] 5
2017-05-29 12:00:00 2155.98 2170.88 2155.79 2158.53 47.772555 0.117347 (0.015, 0.364] 6
2017-05-29 12:05:00 2158.49 2158.79 2141.12 2141.92 122.332090 -0.769505 (-1.0322, -0.683] 3
2017-05-29 12:10:00 2141.87 2165.90 2141.86 2162.44 87.253402 0.958019 (0.713, 1.0623] 8
```
- Scroll right to see all the columns!
- Volumes are displayed in BTC.
- Returns are in percentage and are computed on the close prices.
## How to get started?
```
git clone https://github.com/philipperemy/deep-learning-bitcoin.git
cd deep-learning-bitcoin
./data_download.sh # will download it to /tmp/
python3 data_generator.py /tmp/btc-trading-patterns/ /tmp/coinbaseUSD.csv 1 # 1 means we want to use quantiles on returns. 0 would mean we are interested if the bitcoin goes UP or DOWN only.
```
If you are interested into building a huge dataset (coinbase.csv contains around 18M rows), it's preferrable to run the program in background mode:
```
nohup python3 -u data_generator.py /tmp/btc-trading-patterns/ /tmp/coinbaseUSD.csv 1 > /tmp/btc.out 2>&1 &
tail -f /tmp/btc.out
```
If you ever see this error:
```
_tkinter.TclError: no display name and no $DISPLAY environment variable
```
Please refer to this solution: https://stackoverflow.com/questions/37604289/tkinter-tclerror-no-display-name-and-no-display-environment-variable
## Run with Docker
To build the docker image just execute
```
docker build -t dlb .
```
from the repository folder and then run the container
```
docker run -it --name dlb -v $PWD:/app dlb /bin/bash
```
the current folder will be mounted into `/app`. To verify the correct mount
execute inside the container
```
root@c11ef702a6d6:/app# mount| grep app
/dev/sda2 on /app type ext4 (rw,relatime,errors=remount-ro,data=ordered)
```
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c6d5e0ee98f239e6, topic:deep-learning