Algorithmic

GATElab's algorithmicpath is a scalable, high-performance and low-latency Open Algorithmic Environment (OAE). It provides access to built-in industry standard algorithmic strategies as well as an intuitive user interface to build and test proprietary algorithmic models, quickly and easily.

algorithmicpath can be seamlessly integrated in the traderpath platform or any third party platform. Through a user friendly and intuitive interface, market operators can design, test, validate and maintain their own models for trading, quoting, pricing and hedging via a standard language before releasing them into the production environment.

algorithmicpath can also process high volumes of fast-moving market data from multiple sources and take action in the markets to decide, monitor and analyze your firm’s MiFID best execution compliance policy, whilst meeting all reporting, data and trade history requirements.

At the core of the algorithmicpath architecture is a high performance blackboard used as a central repository for low-latency market data and shared internal information produced by each strategy. Once new or updated data has been written onto the blackboard, events will be fired, and thus trigger the execution of corresponding actions.

algorithmicpath provides market operators with a tool to create/modify strategies, monitor their execution and tune parameters quickly when market conditions change, giving utmost flexibility and control to end-users.

algorithmicpath comes with a set of pre-defined open strategies written by GATElab, but end-users can modify and extend existing strategies or writes new strategies in critical areas such as:

  • pre/post trading activities for "execution policy" compliance
    • discovering liquidity and defining trading venues
    • executing and controlling execution results
    • evaluating transaction costs
  • arbitrage, spread trading, pricing and hedging
  • market making across different markets

ALGORITHMICPATH FEATURES

  • Develop unique automated trading strategies on a trader's desktop, with enhanced Python language support for fast and easy building of complex strategies
  • Multi-asset class, cross market strategy toolkit of pre-packaged open-sourced algorithms
  • Reuse existing strategies in a new environment and build an algo library
  • Evaluate, test and then refine strategies via backtesting
  • Multiple strategy managers can automatically share strategy events across the same network through a blackboard
  • Utilize GATElab's ultra-low latency trading gateways or existing networks
  • Algo monitoring and performance reporting
  • Seamless integration with existing systems