TSL Adds Strategy Automation to QuantHouse Backtesting Suite - The Industry Spread

Ricardo Esteves

Ricardo Esteves has seen business and economics through many lenses. He joined the Financial Services Industry in 2009, and has been a financial journalist since 2011. He holds a degree in Business Administration and has experience producing real-time news, from both buy-side and sell-side, as well as for retail traders, brokers and service providers. Esteves' work has appeared in a variety of online publications including FX Street and FinanceFeeds.


TSL Adds Strategy Automation to QuantHouse Backtesting Suite

July 1, 2020

Trading System Lab (TSL) has added its machine learning capabilities as part of QuantHouse’s cloud backtesting suite, QuantFactory. The service provides a fully configurable environment in which clients can develop, backtest, optimize, and implement quantitative trading strategies that can later be executed in a standalone, live-trading environment.

TSL’s machine learning capabilities automate the design and development of trading strategies. Design automation delivers thousands of strategies per second, reduces time to market, and is interoperable with all data, markets, frequency, and programming languages. The product increases the scope of trading strategies available, increases the number of markets an individual can monitor, and respond to and, incorporates a wider range of data sources.

Salloum Abousaleh, Managing Director – Americas, QuantHouse, said: “Machine learning increases the scope of trading strategies available and the number of markets and data sources that an individual can process and respond to. QuantFactory and TSL combined, drastically reduce the time to engineer and deploy algorithmic trading strategies and automatize what is often a tedious manual process. This collaboration is part of our ongoing commitment to simplify access to quantitative trading that enables our clients to reduce cost, improve quality, decrease time to market and expand their universe of novel strategies through Machine Learning.”

Mike Barna, CEO, Trading System Lab, commented: “We are delighted to deliver our machine learning capabilities to the global QuantHouse community. Our integration with QuantFactory allows QuantHouse clients to rapidly deploy new strategies without writing a single line of code while leveraging QuantHouse’s leading research and backtesting environment helps optimize and deploy the trading models generated by our platform.”

QuantHouse provides end-to-end systematic trading solutions including innovative market data services, algo trading platform and infrastructure products. The firm is a subsidiary of IRESS. In regard to automation, the firm has recently completed the first phase of its infrastructure process automation program using cloud-based robot agents to demonstrate their commitment to delivering superior performance and resilience for their ever-growing client base. QuantHouse will be able to rapidly add resources to a number of their in-house processes with minimal human intervention.

QuantHouse has recently opened its doors to real-time and historical data for cash cryptocurrency pairs through its single API via DVeX, an electronic digital asset exchange owned and operated by DV Chain, part of the DV Trading group of companies.

In late 2019, the firm launched Historical Data on-Demand designed to dramatically speed up the research, development, and backtesting phase of any trading strategy. While critical for successful trading, the research, development and backtesting phase has been a historically lengthy and onerous process as market participants have to identify data sources, align formats and code to those data sources, allocate storage capacity to download the necessary files and ultimately incorporate into their trading models to assess the viability of their trading strategy. QuantHouse’s offering includes up to 10 years of historical data on-demand for the US, European and Asian-Pacific markets, available via a web portal and without the need to integrate an API. Historical data can be replayed over prior time periods with the results then being refined and adjusted in order to optimize trading performance, according to the company’s announcement.

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