Automated Trading Software Development
Impala Intech created automated trading software for improved client satisfaction for a trading firm
Client’s Goal - Revolutionizing Trading With Automation
Our client for this project was an established proprietary trading firm in the EU region. The client wanted to
- Create a trading software that will leverage automation at every step
- Make the trading process more convenient for existing experienced clients
- Lower the bar for new traders to enter the market
The Challenge for Impala
Business Challenges
- Develop a custom web-based quantitative trading platform
- Creating a data-driven platform capable of executing client-specific trading strategies for the cryptocurrency market based on a vast amount of historical and current data
- Increasing trading and other operational efficiency by eliminating delays
Technical Challenges
- Developing a system that could incorporate various data sources, such as transaction volumes and alternative data requests
- Creating a trading system that offers real-time information with zero delays
How Impala Intech Made it Possible
ML Implementation & Testing
We deployed the developed main system on the main server, connecting server gateways to provide connectivity with cryptocurrency exchanges, data mapping, and training ML models to test the final implementation
Marketing Data Module
The trading system is designed as a geo-distributed architecture To accommodate exchanges located in different regions. The central system functions as the hub that gathers real-time data from multiple exchanges
Order Management System
This system handles the order book, helping the client to keep track of orders in real-time while handling multiple orders parallelly. The module includes order creation, sending, and execution status monitoring
Position Manager
Position manager allows traders with real-time visibility in all the current trends as well as offering balance control and an overview of all the remaining funds. It also allows traders to monitor all their existing portfolios and assess exposure to different assets
Risk Manager
The module ensures that all the purchases have been made with acceptable price ranges with the help of risk parameters
Our Tech Stack
AWS
SciPy
Scikit-Learn
C#
Python
The Success of Our Solution
The upgraded system now processes trading information 97% faster compared to before. While the response time was 2-3 seconds before, the update has reduced the overall time to 34 milliseconds
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