AI trading assistant
Project description
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
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
- pypi
pip3 install netrade
Usage
This step devided into 2 step
Training
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
Project details
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