I like the simplicity of the directional futures trade. However, the large notional values can be a problem for smaller accounts.
Many system traders believe that it is easier to build consistently profitable swing trading systems over day trading systems. Yet, many futures accounts are not properly capitalized to swing trade futures.
While the CME is planning to introduce Micro futures in May, one of the possibilities today is to trade CME index or futures options as a proxy for the futures contract.
After talking to my options friend, he suggested that for directional trades that buying deep in the money calls will perform most similar due to the high delta. I do think near the money calls are very interesting too because of the higher vega(?).
However, I am most interested in vertical spreads, the credit spreads and the debit spreads. Because I like the risk limited nature of the spread. A bullish put credit spread, for example, involves buying a lower put strike and selling a higher put strike which creates a floor and ceiling. The highest payoff is achieved when both options expire worthless. A call debit spread should be priced equivalent and involves buying a lower call and selling an upper call.
As for how the returns of a futures system might change when selling credit spreads, I suspect that the volatility of the individual trade returns will be lower but that the overall return is likely less, as well. As for potential difficulties, trading automation would be more complicated. I can also see that it might be more difficult to extract maximal value at the trade exit signal, as well. It will be important to pick the expiry that offers maximum movement with good risk to reward ratio.
Have you attempted to trade any of your systematic futures strategies with options? Please let me know in the comments what worked and didn’t work.
Curtis is passionate about markets. He has developed top ranked futures strategies. His core focus is (1) applying machine learning and developing systematic strategies, and (2) solving the toughest problems of discretionary trading by applying quantitative tools, machine learning, and performance discipline.
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