QuantOffice Energy Quantitative Energy Trading Platform
QuantOffice Energy
Solution Overview
QuantOffice Energy is a fully customizable energy trading platform giving researchers and traders in the energy sector the choice of how the system interacts with the markets. The system is product-agnostic and can support all energy assets and instrument classes such as gas, power, oil, coal, carbon, spot, forwards, futures, and options. The solution can be fully customized to support users' requirements as these can vary greatly when trading different asset classes on the energy markets. The choice of data feeds and execution venues is completely open, as are the types of data supported; price (L1, L2, L3) news, weather, shipping, inventory, fundamental, etc.
The platform supports the creation of custom synthetic products which can be back-tested against multiple data sets; from historical market data to analytical data stored in our TimeBase database. The toolkit supports the fast development of custom algorithms that can be immediately accessed to ensure an energy provider’s book is fully balanced with no shortfalls. Integration with ETRM and CTRM systems for balance reconciliation is supported.
In addition, the platform comes with a custom UI designed for spot trading energy/power markets. The trader is able to manage hundreds of possible intervals in a given day for the intraday and auction sessions. Custom algos can also be designed to facilitate closing of positions to ensure no shortfalls occur.
Customer Problem
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Quants in the energy market are forced to build trading models across multiple platforms
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Standard UIs often don’t enable the trader to rapidly inspect their positions or fills across many different product intervals
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Lack of cross sector energy trading
EPAM Solution
QuantOffice Energy streamlines the process for building quantitative trading models by enabling traders and researchers to perform their analysis and initiate execution from simulation to production environments under one single eco-system. This, along with our custom-designed UIs for the energy sector, help ensure that traders can design their system to their liking regardless of the sector within energy.
The system supports and stores data from multiple sources; price (L1, L2, L3), weather, news, shipping, fundamental, political etc. This enables users to research, back test, optimize and trade their ideas. Execution is via multiple routes; exchanges, brokers and ECNs, and supports ultra-low latency trading.
Key Differentiators
Open and customizable framework
Rich, flexible, and powerful environment for creating and running custom trading strategies and bots
Benefits
Analyze differences in fundamental and market data
TimeBase allows backtesting of data of any format type
An end-to-end system
Perform all research and trading using the same code
Store any form of data
TimeBase supports the streaming and storage of any data type
Custom built UI for energy markets
Customizable UI adapting to spot and derivative energy markets
Features
- Custom-built UI: special UI that allows users to visualize all intraday trading intervals in one location
- Analysis & optimization: allows users to analyze the results of back-testing and optimize for improved results
- Simulation with live data: simulate with live data and analyze the effectiveness of the strategy
- Multiple choices for trading execution: we have over 100+ different connectors to choose from for market/analytical data and execution routing
- High performance applications: designed to process millions of messages per second with low latencies using generic out-of-the-box hardware, tunable to microseconds
Use Cases
Trading Research
Problem Statement
Many platforms tie e users to leveraging their proprietary data, offering little flexibility on the data types they can back test against
Solution Proposed
To create a database that allows the user to use any source or format of data
Achieved Results
Our TimeBase database allows the user to source their own market data, and allows them to use any data format against which to back test their models
Risk Management
Problem Statement
Many systems in the energy markets do not offer customizable risk rules, or otherwise limit the user to their internal risk rules which may not be up to par with a company’s risk parameters
Solution Proposed
Create a platform that allows users to build their own defined risk rules, while also offering them many prebuilt risk rules
Achieved Results
Risk Manager allows users to ensure their risk parameters meet their internal teams’ parameters set by the risk team
Additional Information
Questions & Answers
What types of data can be used for back testing?
How can your system be deployed?
Tech Requirements
Windows and Linux, on-prem, and AWS, Azure and GCP are all supported
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