On the Development of Systematic Crypto Trading Strategies: Lessons from Bayesian Optimization and Game Theory

The above graph illustrates the liquidity premium in Bitcoin during a portion of November 2012 when it was significantly more illiquid than the present-day. After WordPress announced its acceptance of BTC, the illiquidity discount decreased as there was an additional widely used outlet to spend Bitcoin.
The above graph illustrates the return of strategies over a period of time. As the strategy is deployed for longer, it becomes less effective due to changing market conditions and the interactions of other firms in the space. For this reason, improvements or new strategies need to be deployed when return hits an inflection point in order to maintain consistent returns over a period of time. The labels given to the three S-curves only represent one of many possible scenarios in regard to our strategy development.
This image is a sample plot of our portfolio allocation system which dynamically allocates AUM to various strategies during changing market conditions. Short strategies will perform best during downturns while volatility trading can squeeze out gains from resistance levels. During an upswing, long-focused strategies will generate the most return.

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