Rosetta Analytics launches RL One Strategy

Rosetta Analytics has launched RL One, a new reinforcement learning-driven investment strategy that aims to produce positive returns in any market environment. The strategy has been funded by a US institutional investor.

RL One is a long/short strategy that generates returns through deep reinforcement learning, a category of machine learning that reacts and learns from its environment by determining which decision will result in the highest risk/reward trade-off. The reinforcement learning model predicts optimal long or short exposure to the S&P 500 Index on a market-close to market-close basis. This exposure could range from 100 per cent long to 100 per cent short. These predictions are then implemented with unleveraged long or short positions in the S&P 500 Index E-mini futures.

As a next-generation quantitative investment manager, Rosetta Analytics uses proprietary advanced artificial intelligence models, such as deep learning and deep reinforcement learning, to create robust and scalable active investment strategies.
Rosetta’s existing deep-learning strategies – DL One and DL Two – were funded by a US institutional investor and have been live since 1 September, 2017. The deep-learning model driving DL One and DL Two generates a signal that offers a binary trading decision. DL One implements this signal as either 100 per cent long or short S&P 500 E-Mini futures, and DL Two implements this signal as 100 per cent long S&P 500 E-Mini futures or 100 per cent cash.
RL One takes Rosetta’s predictive capabilities to the next level by determining the optimal allocation of its trading signals, including the size of the trade and the extent to which it should be long or short across multiple asset classes. Rosetta has also successfully tested other multi-asset strategies, including a 22-stock long-only strategy and a US large cap-equities and US bonds long/short strategy.
During the day-to-day management of RL One, representations of S&P 500 Index stock-level returns and financial and macro-economic data – such as interest rates and spreads, commodity prices and currency pairs – act as inputs into the strategy’s reinforcement learning model. The result is a daily optimal allocation of capital between the S&P 500 and cash.
Leading the Rosetta Analytics investment team are co-founders Julia Bonafede, CFA, and Angelo Calvello, PhD. Bonafede is the former president of Wilshire Consulting who, at the time, managed an institutional consulting and OCIO firm with more than US$1 trillion in assets under advisement. Angelo has a proven track record, having co-founded Blue Diamond Asset Management AG and Impact Investment Partners AG. Earlier in his career, Angelo also held senior roles at Man Group and State Street Global Advisors.
Julia Bonafede, CFA, co-founder of Rosetta Analytics, says: “We believe investors shouldn’t compromise on earning consistent net-of-fee returns when actively allocating to risky assets. For too long, traditional active managers have consistently failed to provide promised returns to investors. Traditional quantitative models have been using the same quantitative methods to make investment decisions based on academic frameworks developed 50 years ago. It’s time for innovation and disruption. Traditional quantitative methods continue to produce homogeneous and suboptimal performance, whereas our next generation quantitative methods use powerful self-learning computational algorithms that can identify actionable insights in traditional and nontraditional data that are hidden from conventional investment processes. These insights provide a new and sustainable edge in investment decision-making.”
Angelo Calvello, PhD, co-founder of Rosetta Analytics, says: “We are excited to launch our RL One Strategy with its transformational and market-disrupting reinforcement learning model that reacts and learns from the environment to generate returns. Our approach has no preset notions and is continuously learning and adapting to market conditions. The successful live performance of our deep-learning strategies and the strength of the hypothetical performance of our reinforcement-learning prototype strategies demonstrates that deep learning and reinforcement learning can be used to find new commercially valuable insights undiscoverable by traditional quantitative methods.”

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