QuantOffice Energy
Advanced systematic trading solutions
for energy markets

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Market Connectivity

Connect your strategies and algorithms to tradable energy assets across the globe. Combine it with the extendable and ever-growing list of connectors for cross-asset trading, hedging, and risk management.

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Strategy Development

Use the QuantOffice Studio together with the data stored in TimeBase to develop and deploy advanced custom trading strategies, models, and algorithms, supporting .NET, and Python. Use MS Visual Studio Code or Visual Studio for effective coding and debugging. The execution server also provides an ultra-low latency API for building and running custom Java algos.

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Backtesting and Paper Trading

Backtest your models and algorithms using Deltix’s advanced proprietary simulators. Run them with playback or live data and simulated paper-trading accounts along with pre-trade risk limits enacted in the most realistic test harness available before going live.

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Live Trading

Deploy ready-to-go strategies and algos on Deltix's Trading Server to trade on single or multiple exchanges simultaneously. Trade fully automatically or semi-automatically in a high-performance, safe, and controlled environment. Monitor your trading ecosystem with a multitude of tools and applications addressing the needs of different business users.

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Risk Control and Monitoring

Set and monitor pre-trade risk limits on any combination of attributes such as global, trader, account, exchange, team, etc. Use standard risk measures, such as exposures, draw-down, frequency, size, volume of trades, etc. and easily develop your custom limits with the provided SDK. Monitor the risk state of the system via out-of-the-box applications or develop your own screens.

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Comprehensive Deployment Options

Deploy the entire system or parts of it on the cloud, in-house, collocated or with any combination of these for unmatched flexibility depending on your business and budgetary preferences.

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High Performance Application Servers

Gain an instant advantage over your competition by utilizing QuantOffice's high-performance ecosystem, designed to process millions of messages per second with low latencies using generic out-of-the-box hardware, tunable to microseconds where demanded.

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Efficiency
out-of-the-box

Reduce TTM and TCO by leveraging hundreds or man/years of our investment to deliver battle-tested, time-proven high-performance products, tools, libraries, and APIs to build the most demanding research and trading capabilities covering the full cycle of systematic trading on energy markets.

About QuantOffice Energy

QuantOffice Energy is an advanced systematic trading platform tailored specifically for energy markets. Our high-performance application ecosystem covers a whole set of well-integrated functionalities from data acquisition to model research and strategy development, to paper trading, to production trading with full stack of risk controls and sub-millisecond latencies.

With rich flexible multi-platform API, wide range of deployment routes, i.e. cloud, co-located, or on premises, QuantOffice Energy offers the best in class tools to implement energy trading systems with lowest TCO and highest ROI. Combined with EPAM engineering and implementation services our value proposition is the highest available in the industry to-date.    

QuantOffice Energy includes out-of-the-box market data connectors to data vendors and exchanges, supporting all major energy asset classes, along with other exchanges and FX LPs. Setting up the universe is often as simple as selecting your trading venues and the instruments of interest in QuantOffice Universe Configurator – everything else is pre-configured. Often, with little effort, the data is streamed and stored on the server in TimeBase - a proprietary, high-performance, enterprise-grade time-series database.

A wide range of data analysis tools and means are available for the researcher. These include the usage of streaming APIs (Java, Python, C#, C++), with a wide range of available commercial and open-source tools, like Jupyter, that are not integrated into the QuantOffice Energy suite. The SQL-dialect query engine is at the researcher's disposal as well, and representation of the data as Py pandas is supported too, etc. QuantOffice itself is a powerful and reliable tool to work with time series, especially high-frequency data, providing high-level effective, and convenient API with no-code/low-code means

QuantOffice Energy platform offers an unmatched variety of development capabilities. QuantOffice gives the user a choice to develop C# or Python models, strategies, and algorithms in a convenient, efficient all-inclusive package. A complete set of financial libraries and our non-invasive code-generator/co-pilot increase the productivity of the programmer by an order of magnitude. You can debug the strategy code either with historical data stored in TimeBase or with streamed real-time data, whichever suits you best.

The integration of your components or trading logic coded in different languages and environments, such as Java, C++, R, etc., is also supported via the rich and flexible API of QuantOffice, TimeBase, or Strategy Server.

QuantOffice Studio covers a comprehensive set of development and testing capabilities for the trading model lifecycle. Universal Strategy Runner is built in the QuantOffice Studio for a user to develop, run, debug, and refine the strategy code in a single integrated sandbox. Backtesting is a natural continuation of this process when a single run can be defined for tables of parameters, different calendars and custom sessions, different lists of instruments and use a variety of simulators, from coarse bar-based to substantially more precise L2 (MBP and MBO) simulators.

Moreover, if required, a user can develop a custom strategy runner using QuantOffice API. The assemblies and portfolios of strategy can also be backtested using the QuantOffice Studio extension called Multi-Strategy Runner. Another QuantOffice Studio extension is called Optimizer. It can be used to run Brute Force or Genetic Optimization processes directly with your strategies as-is. Alternatively, by utilizing QuantOffice API, a user can integrate a 3rd-party optimization framework of a choice to work with the strategies developed in QuantOffice. The results of backtesting can be stored in BacktestExporer for further refinement, fine-grain lifecycle management of the strategy, and rerun with current data and group access.

The strategy can be deployed at any time with live data supplied by out-of-the-box market data connectors offered by QuantOffice Energy to be test-run in an environment closely resembling live trading. Playback of historical data “as-live” is supported as well.

Paper trading, involving the Risk Manager for creating realistic scenarios of live strategies execution in combination with QuantOffice and Ember trading simulators, is the most critical test before finally running them in a live trading ecosystem.

Risk rules and limits are defined in Risk Manager application before switching the system to live mode. The application provides out-of-the-box risk rules together with SDK to define and manage custom risk rules. The user is in full control of the type of restraints the system must impose on the strategy that breaches the risk limits, ranging from rejecting the order and keeping going to stopping the strategy or “kill-switching” the entire system, depending on the severity of the breach.

When all aspects of the trading system are ready, tested, and proven to work as expected, the ready-to-go strategies are switched from Paper trading to Live trading using QuantOffice Energy trading connectors activated on Execution Server (Ember). QuantOffice Energy offers a variety of easily customizable Live monitoring applications: Trading Console, Strategy Server Monitor, Ember Monitor, and Risk Monitor. All the financial transactions are stored in the trading history warehouse. Out-of-the-box Integrations with widely popular IT tools such as Grafana, Graylog, Kafka, and more, a special FIX drop-copy are also provided as part of the ecosystem.

Case Studies

A large producer, trader, transporter and supplier of natural gas

Load large historic data sets (Level 2 price data, and other types of unstructured data) into TimeBase.

Create, research, backtest and optimize strategies QuantOffice development suite. In addition, create Python strategies.

Connect the real-time market data feeds to the Aggregator to facilitate the live streaming of data to the QuantOffice strategies. Deploy these strategies on the Execution Server for paper trading and ensure that the newly acquired data is included in both the real-time system and the historical dataset.

Use the strategies deployed on Execution Server for market making gas and other energy products directly on Trayport and other markets.

Use the strategies deployed on Execution Server for trading gas and other energy products directly on Trayport and other markets.

Hedge FX exposure of cross currency trades and positions.

Monitor and trade FX positions systematically.

A large electrical generator, who trades all types of power, oil and other energy products

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Employ the vast historical dataset present in TimeBase, which comprises level 2 price data, news, pipeline details, weather, macroeconomic calendars, and shipping information. This data originates from other databases within the organization.

Create, backtest and optimize strategies in QuantOffice strategy development suite.

Continually transmit real-time data feeds for all the necessary data types from the internal messaging bus to Aggregator. The Aggregator streams this live data to the QuantOffice strategies being utilized, which are then deployed on Execution Server for paper trading. The newly received data is also added to the pre-existing historical dataset.

Use the strategies deployed on Execution Server to send alerts (for multiple energy product types) for the trading desk. These are entered by the traders manually in the internal trading application.

A large electrical generator, who trades all types of power and oil products

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Load large historic data set (level 2 price data, news, pipeline information, weather, macro economic calendars, shipping information) from multiple sources into TimeBase.

Create, backtest and optimize strategies in QuantOffice strategy development suite.

Continually transmit real-time data feeds for all the necessary data types from the internal messaging bus to Aggregator. The Aggregator streams this live data to the QuantOffice strategies being utilized, which are then deployed on Execution Server for paper trading. The newly received data is also added to the pre-existing historical dataset.

Use the strategies deployed on Execution Server for sending alerts about medium and long term spread opportunities (for multiple energy product types) to the management team, along with relevant calculations and links to news articles. These trading options are discussed at the board level, after which considerable trading positions are initiated by the trading desk.

Use the strategies deployed on Execution Server for trading oil via CQG.

QuantOffice Energy Ecosystem

TimeBase Admin

Universe Configurator

Backtest Explorer

Back Testing Environment

Execution
Server Monitor

Risk Manager

TimeBase Admin

Universe Configurator

Strategy
Server Monitor

Trading Environment