Try a run streamlit app with quant silver trading
- Bryan Downing
- 1 day ago
- 3 min read
Download the source for the following Python script and see the complete detailed strategy here:
Try to execute this but see below on how to run streamlit app
Coding breakdown
Coding Breakdown and Deployment Guide
Below is a detailed breakdown of the Python script and how to deploy it using Streamlit.
Coding Breakdown
1. Purpose and Features
The script is a Streamlit application for financial analysis and backtesting. It includes:
Data upload: Accepts user-uploaded CSV files for Silver and Copper price data.
Data visualization: Plots trends, OHLC (Open-High-Low-Close) charts, and statistical summaries.
Granger Causality Test: Evaluates causality between Copper and Silver prices.
Backtesting: Implements a simple trading strategy and computes performance metrics.
Downloadable Results: Allows users to download backtesting results as a CSV file.
2. Script Sections
Imports
Key libraries used:
Streamlit: For building the web app.
Pandas: For data manipulation.
NumPy: For numerical computations.
Matplotlib: For data visualization.
Statsmodels: For Granger causality tests.
Functions
load_data_from_csv(uploaded_files, asset_type="silver")
Purpose: Reads uploaded CSV files, validates column structure, and combines them into a single dataframe.
Key Features:
Ensures required columns (Date, Open, High, Low, Close) exist.
Converts Date to a datetime index.
Renames columns to distinguish between asset types (e.g., Silver Open).
generate_sample_data()
Purpose: Generates synthetic OHLC data for Silver and Copper when no files are uploaded.
Key Features: Simulates random price movements with realistic variations.
backtest_strategy(data, initial_capital)
Purpose: Simulates a trading strategy where Silver is bought/sold based on Copper price changes.
Key Features:
Tracks portfolio value, cash, and holdings over time.
Implements simple buy/sell signals based on Copper price trends.
calculate_metrics(portfolio_value, initial_capital, data)
Purpose: Computes performance metrics for the backtesting strategy.
Metrics:
Total return, annual return, volatility, Sharpe ratio, max drawdown, etc.
Streamlit Application
Sidebar Controls:
Allows users to upload CSV files for Silver and Copper data.
Toggles for enabling/disabling trends, summaries, and OHLC charts.
Inputs for backtesting parameters (e.g., initial capital).
Main Features:
Plots price trends and OHLC data.
Displays summary statistics and metrics (e.g., mean, volatility, correlation).
Conducts a Granger causality test between Copper and Silver prices (user-adjustable lags).
Performs backtesting and visualizes portfolio performance.
Download Option:
Allows users to download backtesting results as a CSV.
Error Handling
Ensures uploaded files have the correct format.
Fallback to synthetic data if no valid data is provided.
Deployment Guide
1. Set Up the Environment
Prerequisites
Ensure Python is installed on your system (preferably version 3.8 or later).
Install Required Libraries
Run the following command to install dependencies:
bash
pip install streamlit pandas numpy matplotlib statsmodels
2. Save the Script
Save the provided script as silver_market_analysis.py.
3. Run the Streamlit Application
Start the application by running:
bash
streamlit run silver_market_analysis.py
This command will:
Launch a local development server.
Open the app in your default web browser (usually at http://localhost:8501).
4. Deployment Options
Option 1: Deploy Locally
Keep the app running on your local machine by ensuring the streamlit process is active.
Option 2: Deploy on a Cloud Platform
You can deploy this Streamlit app to the cloud for public or private access. Popular options include:
Streamlit Community Cloud (Free):
Create a free account at Streamlit Community Cloud.
Push your script to a GitHub repository.
Deploy your app directly by linking your repository.
Heroku:
Create a requirements.txt file:
plaintext
streamlit
pandas
numpy
matplotlib
statsmodels
Create a Procfile:
plaintext
web: streamlit run silver_market_analysis.py
Deploy using Heroku CLI:
bash
heroku create
git push heroku main
AWS, GCP, or Azure:
Use a virtual machine or container (e.g., Docker) to host your app.
Example Dockerfile:
dockerfile
FROM python:3.9
WORKDIR /app
COPY . /app
RUN pip install -r requirements.txt
CMD ["streamlit", "run", "silver_market_analysis.py"]
Other Platforms:
Platforms like PythonAnywhere or DigitalOcean can also host your app.
5. Customizing and Scaling
For large-scale usage, consider:
Adding authentication (e.g., OAuth).
Using a database to store user-uploaded files.
Deploying with horizontal scaling (e.g., Kubernetes).
Conclusion
This guide enables you to run and deploy the Streamlit app for financial analysis. By following these steps, you can explore, visualize, and backtest financial data using a user-friendly web interface.
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