Strategies.
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Strategies Overview
DISLOCATION
Inception: 2007
Type: Machine Learning Stat-Arb
The Leibniz DISLOCATION program is a set of machine learning and AI-based equity statistical arbitrage strategies that aim to profit from "dislocation", or disagreement among market participants about security values. It generates consistent, low-volatility returns that are not correlated to the broader market. The program uses diversified, liquid S&P1500 names, with an average of 1000 live positions and up to 2000 orders per day. The portfolio is constructed using a combination of optimization techniques, including closed-form, stochastically robust, and meta-heuristic methods.
EMOTION
Inception: 2011
Type: Multi-Strategy Stat-Arb
The Leibniz EMOTION program is a highly systematic investment strategy with a proven track record dating back to 2011 at firms such as Soros, ZBI/Ziff Brothers Investments, Hermitage, and Schonfeld. The program trades globally diversified cash equities and ETFs, focusing on medium-term investor underreaction and short-term investor overreaction with low frequency and capacity constraints. EMOTION has demonstrated resilience against drawdowns and the ability to predict earnings surprises and identify ETF pairs with structural reasons for price divergence.
SI
Inception: 2011
Type: Multi Strategy CTA
The Systematic Intelligence Program (SI) is a fully systematic and globally diversified investment strategy, deploying assets in 80 different markets with highly liquid instruments to maximize diversification and limit volatility. Holding periods range from 3-40 days per sub-strategy. Risk exposures are adjusted daily and reduced within 1-2 days in trend reversals. SI consists of 3 sub-strategies which are dynamically weighted, avoiding overfitting and pre-established bias.
SWARM
Inception: 2015
Type: Machine Learning FX
The trading strategy uses a combination of neural networks and advanced statistical methods to trade a diverse range of financial products, primarily spot foreign exchange but also using futures market data. The sub-strategies are chosen using a Monte Carlo simulation and Markov Chain Monte Carlo process, and are combined into a single network with varying weightings. The strategy holds positions for varying lengths of time, typically 2 days, and has a high turnover. Since its inception in 2015, the strategy has evolved to become a system of non-optimized sub-strategies with unequal weightings, allowing for a more flexible and dynamic approach to the market.
REGIMENT
Inception: 2015
Type: Mid-Frequency FX
The REGIMENT investment program is a fully automated strategy that seeks to profit from volatility in the foreign exchange market using both directional and mean-reverting approaches. The program employs quantitative analysis, order routing algorithms, low latency technology and liquidity management to identify and exploit market inefficiencies. It is designed to adapt to different market conditions and risk regimes, using a diverse range of trading and hedging strategies. The program also includes a master risk layer that runs thousands of concurrent computations per second to manage risk.
GRAB+
Inception: 2016
Type: Pattern Recognition CTA
The GRAB strategy is a sophisticated approach to swing and trend-focused trading, utilizing elliptical cycles to detect tradeable patterns across 30 markets, executing trades through the most suitable sub-strategy based on daily observations. It incorporates advanced algorithms for entry and exit detection and active drawdown limiting for comprehensive risk management. Developed in 2013 and continuously improved through ongoing research, the latest version, GRAB+ is available from September 2019.
P-REACTION
Inception: 2016
Type: NLP Stat-Arb
Leibniz P-REACTION rigorously applies a data-driven framework using machine learning methods and natural language processing to predict how investor behavior impacts asset prices and leads to short-term mispricing surrounding earnings related news and events in a medium-frequency approach. P-REACTION digests and pre-acts as as well as reacts to many thousand events via its news-driven strategies in a fully systematic manner. Many years of development work and attention to detail now allow the strategy a reliable approach and fast reaction time to market moving news by exploiting behavioral biases and market inefficiencies.
SENTINEL
Inception: 2017
Type: AI/ML Multi-Strategy
SENTINEL is a deep learning-based, systematic trading program that utilizes daily sentiment data to predict price trends in a variety of global futures markets. The first sub- strategies went live in 2017. SENTINEL analyzes unstructured public data content from thousands of sources related to the economy and politics and uses proprietary tools to filter and map the data into price trend predictors. By doing this SENTINEL offers a unique perspective on potential market movements. SENTINEL uses supervised learning of classifiers across a broad spectrum of machine learning methodologies to improve prediction quality and to automatically create a diverse set of trading models and combines them into a multi-model, multi-scale strategy for high returns, maximized alpha/Sharpe and minimized drawdowns.
REDSTAR
Inception: 2018
Type: Commodity CTA
The REDSTAR investment program is a set of low-frequency, multi-model, multi-indicator and multi-timescale sub-strategies which originated at the largest natural gas producer. It exploits market effects in Futures markets by using technical approaches, systematically exploiting multiple time horizon effects by interpreting and positioning around commodities markets via momentum, mean reversion and term structure arbitrage approaches. The strategy targets market inefficiencies in German power, Emissions, Brent, Gas and Gasoil markets that still exist due to market deregulation, fragmentation and dependence on production and transportation infrastructure.
ROCKET
Inception: 2019
Type: Mid-Frequency CTA
The ROCKET program is a systematic, high-to-medium-frequency algorithmic strategy that trades US futures on the main market indices. It uses advanced mathematical algorithms to track the instant changes of available liquidity and react to them by taking advantage of either excess of liquidity supplied by buyers or sellers. It focuses on market microstructure and liquidity changes caused by recurring or unexpected events, and utilizes holding periods ranging from 1 second to tens of minutes. The program's mathematical foundation includes specially developed data transformation algorithms, taking into account the non-ergodic character of the traded instrument dynamics, commonly referred to as "memory".
