Our Technology and Investment
RISE is at the epicenter of a major shift in the asset management industry towards fully AI-powered trading decision making. Human trading is quickly being superceded by high-speed, automated, data-driven algorithms. Within the next five years, the most successful asset managers will be pure technology companies. The only way to continue to generate profits from the financial markets will be by utilizing the latest advances in cognitive computing in order to make timely and accurate investment decisions in light of an explosion of data.
The company has applied to patent its process in the field of artificial intelligence and machine learning that enables the continuous identification and cultivation of market-beating investment opportunities.
AI is the Solution
Historically, decision-making in trading was based on a combination of human intuition and domain knowledge in finance and economics. With the progress of computing and information technologies, traders started formalizing and automating their trading ideas in the form of trading rules. The resulting rule-based systems were based on fundamental and market data analysis (such as price and volume information) as well as utilizing available public information about the traded assets (financial and macroeconomic reports, etc.)
The Rise approach—a powerful symbiosis of science, financial and behavioral theory—is to leverage AI and machine learning to constantly learn, find, produce, test and refine algorithms and investment strategies—thousands per month. The Rise engine is implemented by an interdisciplinary team of trading professionals, scientists and high- tech experts who constantly research and develop new ways of making Rise’s systems more competitive. Furthermore, the scaling limitations posed by human involvement in creating trading systems are being overcome by allowing the AI to play a larger role in the creative process.
A pure machine learning approach to automate the process of generating trading ideas allows a far greater scale of ideas to be generated and tested in a short amount of time, a vast departure from the unpredictable time spans and resource-intensive coding of other approaches.
The RISE Approach
Our patent-pending AI platform, enables investors to continuously discover, validate and implement new trading opportunities in both highly liquid and illiquid financial markets across the globe. The RISE technology-based financial solutions have outperformed competitive products since 2016.*
Human / machine hybrid trading strategies
We deal with sophisticated trading strategies that augment human domain expertise with machine learning techniques. Once these parameters are set, they are then optimized and validated by machine learning techniques which are applied to the human-created vectors to create a model, which is tested using trading algorithms.
Fully automated strategies
The pure AI approach means that the system receives raw data and identifies relations and patterns as well as trading methodologies to create profitable strategies. The result requires fewer costly analysts and traders and generates more diverse strategies that improve risk-return ratios along with reducing the dependency on unpredictable human resources.
Predictive and prescriptive models for AI in Trading
Rise is concerned with sophisticated trading strategies that augment human domain expertise with machine learning techniques. Once these parameters are set, they are then optimized and validated by machine learning techniques. Machine learning techniques are applied to the human-created vectors to create a model, which is tested using trading algorithms.
Implementing neural networks for trading
Independently of the choice between predictive and prescriptive models, the structure of the models must be properly chosen to achieve the desired general characteristics of the trading strategies. Within the autoregressive models, the immediate prediction or action – such as buy, sell, or do nothing – is modeled as a function of a fixed number of preceding observations.
Validating the Quality of Trading Systems
Trading systems must be cross-validated and checked for overfitting. If not done correctly, this is the spot where the most dangerous errors can be made. The core aspect of overfitting is when the model starts to "memorize" the data, instead of finding patterns. The solution is to bind the optimizable parameters of the strategy and thus reduce degrees of freedom.
Trading strategy development
All our generated trading strategies have a very low correlation with each other. Most of the new models start with a simple idea. The next step is to verify, whether a trading idea has potential and research it further. The process can take quite some time, as each trading idea differs in terms of where to look for deviations and anomalies in price or behavioral patterns. Our AI and machine learning systems speed up the process.
Performance and risk data for each of Rise’s strategies is the feed for our portfolio optimization. Additionally, all trading systems are developed to produce returns that do not correlate with one another. The ideal portfolio is selected depending on maximum Sharpe ratio or minimum volatility along the «efficient frontier» as shown in the image below.
The importance of risk management
In our view, effective risk management begins with the design of the trading strategy itself. Our more traditional methods for risk management are based on quantitative analysis of market data and behavioral finance. We have developed proprietary dashboards and analysis tools for clients so that they can review key risk and performance indicators in real-time whenever required.
*Cboe Eurekahedge Relative Volatility Index