Real-Time Algorithmic Trading Engine
Budget: $250 – $750 USD
Description:
This project focuses on building a fully automated trading engine that can analyze multiple market instruments simultaneously and execute trades based on predefined trend signals. The system is designed in Python and connects to a mainstream broker API for real-time market data and order execution.
The trading logic runs independently for each instrument, allowing different timeframes, parameters, and risk settings to be applied concurrently. The engine also includes several configurable market filters such as volatility checks, gap detection, and earnings-date restrictions to avoid risky trades.
A simple yet functional dashboard will display active positions, trade direction, unrealized profit/loss, and recent trade activity. It will also provide manual override features — such as pausing trades, closing open positions, or editing watchlists during live sessions.
All trades, signals, and errors are logged for transparency, while the system maintains connection stability and reconnection handling to ensure uninterrupted operation during trading hours.
The project will be structured as a standalone Python application that can run locally or on a private server, with clear documentation provided for setup and operation. The initial version focuses on reliability, clean execution, and real-time monitoring, forming a solid base for future strategy or broker expansions.
This project focuses on building a fully automated trading engine that can analyze multiple market instruments simultaneously and execute trades based on predefined trend signals. The system is designed in Python and connects to a mainstream broker API for real-time market data and order execution.
The trading logic runs independently for each instrument, allowing different timeframes, parameters, and risk settings to be applied concurrently. The engine also includes several configurable market filters such as volatility checks, gap detection, and earnings-date restrictions to avoid risky trades.
A simple yet functional dashboard will display active positions, trade direction, unrealized profit/loss, and recent trade activity. It will also provide manual override features — such as pausing trades, closing open positions, or editing watchlists during live sessions.
All trades, signals, and errors are logged for transparency, while the system maintains connection stability and reconnection handling to ensure uninterrupted operation during trading hours.
The project will be structured as a standalone Python application that can run locally or on a private server, with clear documentation provided for setup and operation. The initial version focuses on reliability, clean execution, and real-time monitoring, forming a solid base for future strategy or broker expansions.