Rosetta Analytics Launches RL One Strategy
Systematic investment manager 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. The exposure could range from 100% long to 100% short; these predictions are then implemented with unleveraged long or short positions in the S&P 500 Index™ E-mini futures.
Julia Bonafede, CFA, co-founder of Rosetta Analytics, said: “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.”
Rosetta’s existing deep-learning strategies – DL One and DL Two – were funded by a US institutional investor and have been live since September 1, 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% long or short S&P 500 E-Mini futures, and DL Two implements this signal as 100% long S&P 500 E-Mini futures or 100% 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.
Angelo Calvello, PhD, co-founder of Rosetta Analytics, said: “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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