Autonomous Learning Investment Strategies
Explore the future of discretionary investment management with SYGMA1’s autonomous learning strategies, powered by our neural network AI, guiding your portfolio with unparalleled precision and insight.
Autonomous Learning Investment Strategies
An Autonomous Learning Investment Strategy (“ALIS”) refers to an approach to investing in financial markets that relies on advanced machine learning and artificial intelligence (AI) technologies to make investment decisions. In this strategy, AI algorithms are employed to autonomously learn from historical market data, analyze current market conditions, and adapt to changing market dynamics without the need for human intervention.
The acronym was introduced to define the emerging ‘third wave’ of investment managers, representing a progression beyond the initial two waves associated with fundamental discretionary and quantitative investing.
The goal of an Autonomous Learning Investment Strategy is to improve investment performance, enhance risk management, and potentially identify investment opportunities that might be missed by traditional human-driven approaches.
SYGMA1: The Neual Network Ai
SYGMA1 is an acronym that stands for Systematic Global Macro which is the underlying type of strategy our system is programmed to execute and 1 represents the first edition we’ve created.
SYGMA1 consists of Artificial Neural Network Systems (”ANNs”) that drive our autonomous learning investment strategies. SYGMA1 processes a vast amount of data at any given second to detect price trends and patterns that result in placing optimized and unbiased trades. A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain.
SYGMA1 uses Deep Learning which consists of interconnected nodes or neurons in a layered structure that resembles the human brain. As in any Deep Learning AI system, SYGMA1 creates an adaptive system that uses it to learn from its mistakes or “past experience” to improve continuously.
Total Return: 413.26%
Annualized Return: 26.32 %
Standard Deviation: 10.30%
Sharpe Ratio: 2.37
SYGMA-ABR, is a Systematic Global Macro Strategy meticulously engineered for daily income generation. At its core, this strategy utilizes systematic, data-driven methodologies to automatically adapt to the complexities of global economic shifts and dynamic market conditions. A key distinguishing feature is its Absolute Return Profile, ensuring consistent profitability regardless of prevailing market directions. This resilience is complemented by SYGMA-ABR's capacity to generate daily returns, engaging in strategic trades across short-term and midterm timeframes. This dual focus on systematic global macro principles and an absolute return approach defines the essence of the SYGMA-ABR strategy.
Total Return: 1758.93%
Annualized Return: 51.82%
Standard Deviation: 16.55%
Sharpe Ratio: 3.01
SYGMA-PRO, a strategic counterpart to SYGMA-ABR, mirrors its foundational approach with a distinctive edge. The key differentiator lies in SYGMA-PRO's application of additional leverage on trades, coupled with more advanced risk management policies meticulously crafted to accommodate and mitigate the increased leverage. Tailored for seasoned professional investors with a comprehensive understanding of derivatives, SYGMA-PRO stands as a sophisticated choice, offering a nuanced investment experience that aligns with the discerning requirements of those well-versed in the intricacies of financial markets and derivatives trading.
Introducing SYGMA-E, a bespoke systematic global macro strategy meticulously designed for precision trading within the energy markets. Uniquely tailored for energy companies, SYGMA-E serves as a strategic hedge to energy production, ingeniously crafted to generate additional income. This specialized strategy epitomizes our commitment to providing tailor-made solutions, ensuring that energy companies navigate market dynamics with strategic finesse, ultimately enhancing their financial resilience and optimizing returns in the dynamic energy landscape.