Recently, I shared a trade idea regarding UBER call options. I am not a professional, do not provide financial advice, never share my personal performance nor make any performance claims whatsoever, no ideas should be construed as buy/sell recommendations.
However, for education, if I had of traded UBER and keep in mind this could be a paper trading account then it may have looked something like this:
This shows I bought 3 SEP 13 calls for 65 cents with strike price 33 and 4 calls with strike 33.5 for 55 cents. Multiplying .65*100*3+.55*100*4= $415. My maximum risk on the trade was $415, no matter what happened. My max reward was unlimited while approximate two standard deviation move might yield a return of $1,500-$2,000.
Important to note, I only would receive the max reward if I hold into expiration. At expiration, if the market made a fast move below my call strikes then they would expire worthless. As such, holding into expiration is extremely risky. It makes sense to me to take half off for a risk free trade, at minimum, but with all the uncertainty, I decided not to hold until expiration– though still maintain a long term position.
Now, let us review the exits. On the exit, I received $2.1*100*3 credit totals to $630, and a $1.10*100*4 credit, which totaled $440 for a total of $1070. My credit minus my original debit yields $655.
My R reward/risk on this trade was thus 655/415= 1.57.
I was not as precise with the entry as I could have been. With shorter holding periods, getting a good price entry is more important and/or position going immediately in favor.
The trade was highly discretionary. In future, I think developing a system to identify the highest probability setups would be favorable that I could then scan with my own discretion, yielding a multiple factor edge.
The author 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. You can contact the author at firstname.lastname@example.org.
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