Algorithmicpath For Trading

algorithmicpath is a high-performance, low-latency and scalable 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 in a quick and easy manner.

Algorithmicpath can be seamlessly integrated with traderpath or any third-party trading platform. The intuitive interface enables users to seamlessly design, test, validate and maintain their own models for trading, pricing, quoting and hedging via a standard language and release them into the production environment. Besides back-testing, based on playing back canned market data, algorithmicpath leverages the exchangepath matching engine so that users can test algorithms with live data feeds from real markets and execute trading operations in simulated markets, which continuously emulate the corresponding actual markets.

Algorithmicpath can also process high volumes of fast-moving market data from multiple sources and take action across different markets to adhere to the firm’s MiFID best execution compliance policy, whilst meeting all reporting, data and trade history obligations.

The core of the algorithmicpath architecture is a high-performance blackboard, namely a distributed cache for low-latency market data and shared internal information produced by any given strategy. Once a new or updated data item has been written onto the local blackboard and propagated to remote nodes, events will be fired which trigger the execution of related actions.

Algorithmicpath provides users with an interactive tool to create/modify strategies, monitor their execution and fine tune parameters quickly when market conditions change, giving the utmost of both flexibility and control.

Algorithmicpath comes complete with a toolkit of pre-defined, open strategies. Users can modify and extend these to avoid designing and implementing new strategies from the ground up in critical areas such as:

  • pre/post trade activities to comply with the firm’s "execution policy"
  • discovering liquidity and routing orders to trading venues
  • executing and validating execution results
  • evaluating transaction costs
  • arbitrage, spread trading, pricing and hedging
  • market making across different markets

 

Algorithmicpath features

Enhanced Python support to develop fully automated trading strategies on any trader's desk

Multi-asset, multi-market strategy toolkit of packaged open-source algorithms

Reuse of existing strategies in a new environment and assembly of an algo library

Evaluation, testing and fine tuning of strategies via back-testing and live market data fed into emulated markets via exchangepath

Multiple automated strategy managers to dynamically share strategy-generated events across a LAN or WAN via a low-latency distributed blackboard

Direct connectivity to traderpath ultra-low latency trading gateways or third-party gateways

Strategy monitoring and performance reporting

Seamless integration with external services or data repositories

Algorithmicpath addresses two main concepts related to high performance and low latency:

Complex Event Processing (CEP) environment, which is the core of the architecture, focusing also on its distribution and cooperation over a network (LAN, WAN) to provide a unique, distributed and consistent layer for running strategies. In this scenario the CEP architecture is based on a high-performance distributed blackboard (i.e. a cache spread across several servers) used not only as a distributed repository for low-latency market data and historical data, but also to share internal data produced by each strategy implementing local or remote cooperation among them

The CEP engine can process high volumes of fast-moving market data (notified through the traderpath DMA platform) from several concurrent sources and perform actions in the market in tens of microseconds to decide, monitor and analyse execution activities. This allows traders to write co-located cooperating distributed strategies, e.g. a strategy reading one market’s data events, extracting signals from the monitored market to be notified to remotely co-located strategies which will trade in other markets under their supervision and vice versa

Graphical Integrated Development Environment (IDE) to further enhance the process of creating, testing and deploying strategies. This allows users to:

  • create strategies which cooperate among themselves either locally or over a network, while graphically implementing the test and deployment locations (markets colocation)
  • compile strategies into machine language to minimise execution latency to a 10-µs order of magnitude

The IDE is made up by three modules: Point & Click Event Editor, Guided Action Editor and Run-Time Dashboard.

The Point & Click Event Editor easily allows to graphically depict AND-ed or OR-ed Events-Actions behaviour of the strategy with input, state and relation parameters. The Guided Action Editor allows writing actions in a language-sensitive environment using enhanced Python. Via this IDE users can focus on the business logic (described in terms of events and related actions) rather than bothering about complex programming features.

Contacts

Luke Kolter - Gatelab

Luke Kolter

Sales Executive

+44 7980 775304

sales@gatelab.com

Antonella Perna - Gatelab

Antonella Perna

Sales Executive

+39 349 236 6309

sales@gatelab.com

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