Options open interest, volume, gamma and other metrics can be helpful in foreshadowing large and small risk events in the underlying stock markets.
Our programs analyze options data along hundreds of parameters to identify risk patterns and directional trends.
Using the machine learning parameters, our models adjust the position size of a target ETF once per day to avoid tail risk. In the back-test, these models often show both increased investment return and decreased volatility.
The goal with this approach is stable growth over time. These are not "get rich quick" models. While many investment ideas appeal to fear or greed, our models are designed for peace of mind.