Netrade

Netrade is an AI trading assistant with human trader approach

How it works

This AI model will predict the price will go up or go down based on chart pattern and candlestick pattern

The data is available here

Chart Pattern

Chart pattern is enough for analyzing price will go up or go down as usualy traders do, here's some example:

Candle Stick

Bechause of chart pattern has a limitation we need candle stick pattern to decide when we should buy or sell, here is some example

first row is bearish candle stick pattern and the second row bulish candle stick

Performance

We've been tested this model in 1 week and here's the result:

model accuracy & loss

profit

Win & loss rate ( 61% win 39% loss )

Installation

github

git clone https://github.com/rizki4106/netrade.git

cd netrade && pip3 install torch torchmetrics scikit-image Pillow torchvision

pypi
```
pip3 install netrade
```

Usage

This step devided into 2 step

Training

download pre-trained model here https://github.com/rizki4106/netrade/releases/ but if you want to train with your own data, here's the step

Prepare The data
You should put your image in this pattern:

chart:
----up:
------image1.png
------image2.png
----down:
------image1.png
------image2.png
candle:
------image1.png
------image2.png
----down:
------image1.png
------image2.png

Make csv file that contain this field

chart_name	candle_name	path	label
filename.png	filename.png	down	0
filename.png	filename.png	up	1
filename.png	filename.png	down	0
filename.png	filename.png	down	0
filename.png	filename.png	up	1

Create image transformer

from torchvision import transforms

# this is for chart pattern
chart_transformer = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor()
])

# this is for candlestick pattern
candle_transformer = transforms.Compose([
    transforms.Resize((64, 64)),
    transforms.ToTensor()
])

Load your data with data loader

from netrade.data import NetradeDataLoader

# supposed you have created csv file like i mention above
frame = pd.read_csv("file_training.csv")

# load data and turn it into tensor
train_data = NetradeDataLoader(
    chart_dir="/path/to/root/chart-pattern/",
    candle_dir="/path/to/root/candle-stick/",
    frame=frame,
    chart_transform=chart_transformer,
    candle_transform=candle_transformer
) # this data loader will return [chart image, candle image and labels]

Create bathes

from torch.utils.data import DataLoader

dataloader = DataLoader(
    train_data,
    batch_size=16,
    shuffle=True
)

Run training loop

from netrade.core import Netrade
import torch

# initialize the model
netrade = Netrade()

# run training
model, history = netrade.train(X_train=dataloader, epochs=10)

# model is pure pytorch nn module that has been trained with your own data you can check it model.parameters()
# history is the result from training loop

print(history)

# save the model's state
torch.save(model.state_dict(), "name-it.pth")

Inference mode / Testing

If you want to run this model in production mode then here's the step

from netrade.core import Netrade
from torchvision import transforms
from PIL import Image

# initialize the model
netrade = Netrade(saved_state_path="path-to-saved-state.pth")

# create image transformer
# this is for chart pattern
chart_transformer = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor()
])

# this is for candlestick pattern
candle_transformer = transforms.Compose([
    transforms.Resize((64, 64)),
    transforms.ToTensor()
])

# read the chart pattern image and turn it into tensor
chart_image = chart_transformer(Image.open("chart.png")).unsqueeze(0)

# read candlestick image and turn it into tensor
candle_image = candle_transformer(Image.open("candle.png")).unsqueeze(0)

# run prediction
preds = netrade.predict(chart_image=chart_image, candle_image=candle_image)

# print the result
print(preds.argmax(1)) # 0 price will go down, 1 price will go up

License

worthmining/netrade

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