Python Algorithmic Trading Dashboard with LSTM Predictions - Part 5

Step 1: Finalizing the Project

Here’s what your project should now include:
Core Components:

    Data Fetching: Pull historical and live data from Binance or Alpaca.
    LSTM Model: Predict price trends.
    Dashboard: Real-time visualizations with Dash.
    Live Trading: Execute buy/sell orders based on predictions.
    Authentication: Secure the dashboard.

Step 2: Secure Deployment

1. Prepare API Keys for Security

    Avoid hardcoding API keys in your scripts. Use environment variables:
        Install python-dotenv:

pip install python-dotenv

Create a .env file:

BINANCE_API_KEY=your_binance_api_key
BINANCE_SECRET_KEY=your_binance_secret_key
ALPACA_API_KEY=your_alpaca_api_key
ALPACA_SECRET_KEY=your_alpaca_secret_key

Load the keys in your script:

        from dotenv import load_dotenv
        import os

        load_dotenv()

        BINANCE_API_KEY = os.getenv('BINANCE_API_KEY')
        BINANCE_SECRET_KEY = os.getenv('BINANCE_SECRET_KEY')
        ALPACA_API_KEY = os.getenv('ALPACA_API_KEY')
        ALPACA_SECRET_KEY = os.getenv('ALPACA_SECRET_KEY')

 

2. Dockerize the Project

Containerize the application for easy deployment.

    Create a Dockerfile:

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt

COPY . .

CMD ["python", "app.py"]

Build and run the container:

    docker build -t trading-dashboard .
    docker run -p 5000:5000 trading-dashboard

Step 3: Deploy to the Cloud
 

Option 1: Deploy on AWS

    Set up an EC2 Instance:
        Launch an EC2 instance with a free-tier eligible AMI (e.g., Amazon Linux 2 or Ubuntu).
        Install Docker:

    sudo yum update -y
    sudo yum install docker
    sudo service docker start

Deploy the App:

    SSH into your EC2 instance and clone your project repository.
    Build and run the Docker container on EC2:

        docker build -t trading-dashboard .
        docker run -d -p 80:5000 trading-dashboard

    Access Your App:
        Open the EC2 instance's public IP in your browser.

Option 2: Deploy on Heroku

    Install the Heroku CLI:

brew tap heroku/brew && brew install heroku

Push Your Project:

    Create a Procfile:

web: python app.py

Deploy:

    git init
    heroku create
    git add .
    git commit -m "Initial commit"
    git push heroku main

Secure Environment Variables:

    Set environment variables on Heroku:

        heroku config:set BINANCE_API_KEY=your_binance_api_key
        heroku config:set BINANCE_SECRET_KEY=your_binance_secret_key

Step 4: Add Authentication

    Use Flask-Login for User Authentication:

pip install flask-login

Basic Setup:

from flask_login import LoginManager, UserMixin, login_user, login_required, logout_user

login_manager = LoginManager()
login_manager.init_app(app)

class User(UserMixin):
    pass

@login_manager.user_loader
def load_user(user_id):
    return User()

@app.route('/login', methods=['GET', 'POST'])
def login():
    # Add login form logic
    return "Login Page"

Secure routes like:

    @app.route('/secure-dashboard')
    @login_required
    def secure_dashboard():
        return "Secure Trading Dashboard"

Step 5: Next Features to Consider ?

    Push Notifications:
        Use Twilio or email notifications to alert on trades.
    Advanced Analytics:
        Integrate Plotly for richer visualizations.
    Risk Management:
        Add stop-loss and position-sizing algorithms.

 Hope this is helpful, and I apologize if there are any inaccuracies in the information provided.