netrade 1.2.3

AI trading assistant

Netrade

Netrade is an AI trading assistant with human trader approach

Table of contents

1. How it works
2. Performance Result
3. Installation
4. Usage
   1. Training
5. Inference Mode or Testing
   1. Prepare Real-time Data
   2. Run in real world and real-time data

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

  you can do it by using data preprocessing helper easly

  from netrade.data import DataPreprocessing
  
  # initialize class
  chart = "/path/to/somwhere/chart/"
  candle = "/path/to/somewhere/candle"
  
  prep = DataPreprocessing(chart_path=chart, candle_path=candle)
  
  # create dataframe
  frame = prep.create_frame()
  frame.head()
  

• 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

Real-time Data

If you want to use this model in real time data, you should prepare the comodity price history i.e bitcoin or tesla stock price. in this example I'll be using yfinance to grab the historical data

from netrade.data import data_creation
import yfinance as yf
import matplotlib.pyplot as plt

#
ticker = yf.Ticker('BTC-USD')
data = ticker.history(period="7d", interval="15m")

# create chart image from realtime history data
# take the last 50 candle stick bar
chart_image = data_creation.create_image(data=data[-50:, :])

# take the last 3 candle stick bar
candle_image = data_creation.create_image(data=data[-3:, :])

# create_image returns PIL image class that you can use directly with pytorch

# you can show it by the way
plt.imshow(chart_image)
plt.imshow(candle_image)

Final Preparation

It's time to predict real - time price, let's put everything together

from netrade.data import data_creation
from netrade.core import Netrade
from torchvision import transforms
from PIL import Image
import yfinance as yf

# 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()
])

# load realtime data
ticker = yf.Ticker('BTC-USD')
data = ticker.history(period="7d", interval="15m")

# create chart image from real-time data
chart_image = data_creation.create_image(data=data[-50:, :])
candle_image = data_creation.create_image(data=data[-3:, :])

# turn image into tensor
chart_image = chart_transformer(chart_image)
candle_image = candle_transformer(candle_image)

# 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