GATElab algorithmicpath is a scalable high-performance and low-latency Open Algorithmic Environment (OAE) providing easy access to built-in industry standard algorithmic strategies for price-taking and market-making as well as an intuitive user interface to build proprietary models without bothering to tackle the complexity of algorithmic solutions.

Seamlessly integrated in the traderpath platform or any third party platform, algorithmicpath allows traders to 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.

Today GATElab announces that its OAE platform is ready to be delivered as a key component of any MiFID (Markets in Financial Instruments Directive) and MAD (Market Abuse Detection) compliance suite.

algorithmicpath can process high volumes of fast-moving market data from several sources and take action in the markets just within very few milliseconds to decide, monitor and analyze the firm MiFID best execution compliance, as well as to meet the reporting, reference data and trade history requirements.

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/updated data have been written onto the blackboard, events (simple, ORed, ANDed) will be
fired, thus triggering the execution of corresponding actions.

Designed as a fully-resilient scalable service, algorithmicpath provides a high-level development tool to create/modify strategies, monitor their execution and tune parameters within minutes when market conditions change, thus giving utmost flexibility and control to end users.

algorithmicpath, comes with a set of pre-defined open strategies written by GATElab, but it is strongly recommended that end-user feels free to modify, extend the existing ones or writes new ones in critical areas such as:

• pre/post trading activities for "execution policy" compliances

  • discovering liquidity and defining trading venues

  • executing and controlling execution results

  • evaluating transaction costs
    others

• arbitrage, spread trading, pricing and hedging

• market-making across different markets

• other strategies (where the limit is the fantasy

ALGORITHMICPATH FEATURES

• usage of Python as host language for writing quoting and trading algorithms

• definition of basic and complex parameters (structured market data)

• definition of rules based on AND-ed or OR-ed events and related actions to be executed

• market data change events, time events, data change events global to strategies

• definition of actions as Python functions that in turn can call:

  • global and simplified market access functions

  • pricing and risk functions

  • technical analysis functions

• multi-platform client-server architecture

• parallel execution of a large number of independent or cooperating strategies