Algorithmic trading has been widely adopted by both the sell and buy-side market participants in the cash equity markets, but is now increasingly spreading to other liquid asset classes such as foreign exchange, commodities and energy markets. Reuters NewsScope Sentiment Engine (RNSE) enables clients to leverage a unique set of news sentiment, relevance, and novelty indicators for algorithmic trading systems as well as risk management and human decision support processes. The service utilizes a new linguistic model which has been developed specifically for this market and scores sentiment in milliseconds for news on 40 commodity and energy assets in addition to over 10,000 companies supported in the current offering.
The scores produced can be used by trading desks and quantitative research analysts to better model the movement of asset prices. Clients have access to historical data dating back to 2003, which allows them to back-test the systemâs applicability for their trading and investment strategies.
Mike Powell, Global Head of Enterprise Information, Thomson Reuters, commented:
âCommodity markets offer significant opportunities for institutional investors and proprietary traders to grow and diversify their investment strategies. Given the growth of the global commodities and energy markets, price volatility and increased adoption of this asset class into active trading strategies we are seeing customer demand for relevant quantitative solutions. The launch of this new module in RNSE draws on Thomson Reuters expertise in the commodity and energy markets as well as our commitment to deliver innovative solutions to our clients.â
RNSE is a key element within the Thomson Reuters Quantitative and Event Driven Trading product suite, which offers the financial community comprehensive real-time trading and back-testing investment tools.