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. Through a user-friendly and intuitive interface market operators can 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. Other than backtesting, based on playback of recorded 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 fake markets, which continuously mirror the corresponding markets.

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 a 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, i.e. a distributed cache for low-latency market data and shared internal information produced by any 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 will trigger the execution of related actions.

algorithmicpath provides market operators with an interactive 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 complete with a toolkit of pre-defined open strategies, which end-users can modify and extend other than designing and implementing new strategies in critical areas such as:

  • pre/post trade activities for "execution policy" compliance
    • 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 backtesting or live market data feeds complemented by mirrored markets via exchangepath
  • Multiple strategy managers to automatically 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) in order to provide a unique, distributed, consistent layer to 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 analyze execution activities. This allows traders to write colocated cooperating distributed strategies, e.g. a strategy reading market-1 data events, extracting signals from the monitored market to be notified to remotely colocated strategies which will trade in 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 traders to:
    • create strategies that can cooperate among them either locally or over a network, while graphically deciding the testing and deploying-running locations (markets colocation)
    • compile strategies into machine language to minimize 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 behavior 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 end-users can focus on the business logic (described in terms of events and related actions) rather than bothering about complex programming features.