Algorithmic-Trading-Code

Algorithmic Trading Platform

A Python-based platform for high-frequency trading analysis, designed to stream real-time stock data, analyze trends, and generate trading strategies for research purposes. Not intended for production trading or actual purchases—this is an experimental tool for exploring quantitative trading concepts.

Overview

This project simulates an algorithmic trading environment, integrating real-time data from Alpaca API, historical data via yfinance, and a time-series database (InfluxDB). It implements Momentum, Mean-Reversion, and Arbitrage strategies, visualizes signals with Dash, and backtests performance using Backtrader. The setup is containerized with Docker for portability and scalability.

Features

• Real-Time Data: Streams 1-minute stock bars (e.g., AAPL, SPY) using Alpaca API.
• Trend Analysis: Generates signals with three strategies:
  • Momentum: Based on price change thresholds.
  • Mean-Reversion: Using Simple Moving Averages (SMA).
  • Arbitrage: Analyzing price spreads between tickers.
• Visualization: Interactive Dash dashboard (http://localhost:8050) with price trends and signals.
• Backtesting: Simulates strategies on 5-day historical data, yielding $9109.53 from $10,000 (Momentum strategy).
• Data Storage: InfluxDB for efficient time-series management.
• Dockerized: Runs via Docker Compose with app and influxdb services.

Sample Backtest Plot

Caption: Backtest of Momentum strategy on AAPL (5 days, 1-min intervals), showing price trends and trades, ending with $9109.53 from $10,000.

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Technologies

• Python 3.12: Core language for scripting and logic.
• Alpaca-py (0.39.0): Real-time stock data streaming.
• InfluxDB (2.7): Time-series database with Flux queries.
• Backtrader (1.9.78.123): Backtesting framework.
• Dash (2.18.2) & Plotly (6.0.0): Interactive visualization.
• Pandas (2.2.3) & NumPy (2.2.3): Data analysis and computation.
• Yfinance (0.2.54): Historical data retrieval.
• Matplotlib (3.10.1): Static plot generation.
• Docker & Docker Compose: Containerization and orchestration.

Skills Demonstrated

• Python programming and asynchronous coding (asyncio).
• Algorithmic trading strategy design and backtesting.
• Quantitative analysis of financial data.
• Real-time data pipelines and time-series management.
• Data visualization (Dash, Matplotlib).
• Containerization and DevOps (Docker setup, networking).
• Troubleshooting (e.g., port conflicts, InfluxDB authentication).
• Linux command-line proficiency (Bash).

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Prerequisites

• Python 3.12: Installed locally for non-Docker runs.
• Docker: For containerized setup (Docker Desktop or CLI).
• InfluxDB Token: Generated from InfluxDB UI or pre-set.
• Alpaca API Keys: Free-tier keys from Alpaca.

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Setup Instructions

Local Run (Python)

1. Clone the repository:

   git clone https://github.com/yourusername/algorithmic-trading-code.git
   cd algorithmic-trading-code