SYNTHETIC - SPY

A SYNTHETIC ETF

True to its name, this strategy creates a synthetic ETF that trades the SPDR® S&P 500® ETF.

CAPITALIZING ON PRICE PREDICTIONS: The Synthetic-SPY trades by capitalizing on predicted price directions SPY ETFs. This is done through machine learning (ML) techniques which analyzes and signals entry and exit points based on historical price movements in combination machine learning.

• COMBINATION OF MULTIPLE MOVING AVERAGES: This is one of the primary measures that we assess as our ML engine works to produce changing historical moving averages that are dynamic to each ETF. In other words, these durations change based on the ETF among other market conditions.
• RETURN OVER MAXIMUM DRAWDOWN: This measures the differences between the asset’s point of maximum return and any subsequent dips. In other words, it’s the return on the largest difference between a price’s high point and subsequent low point. This is used in our ML engines to signal for exit points to avoid major drawdowns (losses) and entry points thereafter to maximize subsequent returns.

As a subset of artificial intelligence, machine learning focuses on technologies to allow computers to learn from complex datasets which is then used to make predictions and decisions.

ML-BASED TRADING: In contrast to traditional algorithmic trading, machine learning can spot patterns in enormous volumes of data. Machine learning can find relationships within historical data that can then be applied to trading strategies.

Our ML engines help us accelerate and automate some of the most complex and time-consuming aspects of algorithmic trading while providing us with advantages beyond traditional rule-based trading.