The investment objective of Lynx Constellation is to apply machine learning techniques to forecast and trade global markets in fixed income, equity, foreign exchange and commodities, attempting to maximize risk-adjusted returns while controlling for correlation to traditional asset classes (such as equities and bonds) and trend-following.
Lynx Constellation trades a diversified portfolio of approximately 90 exchange-traded futures and over-the-counter (“OTC”) foreign exchange markets globally. The program employs a multi-model approach applying a range of machine learning techniques to identify and capture market phenomena over varying time frames. The models are designed to detect multivariate, linear and non-linear relationships in markets by learning from features. Some of these relationships are based on investor tendencies and behavioral biases, while others are based on complex factors such as seasonality. The models are designed to adapt as the market environment changes, automatically learning from new data rather than following static, explicit rules.
Signals are generated by analyzing large data sets from multiple sources. Noise in the data is reduced by employing filtering and dimensionality reduction techniques. Lynx Constellation focuses primarily on price information that can be observed independently or obtained from multiple providers, as data quality is an imperative.
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