Suppose that for a given setup, price hits target before it reaches invalidation 70% of the time. Great, right? But getting filled is not guaranteed.
The more right you are about your trade setup, the less likely you are to get filled
By extension, getting filled actually lowers the Bayesian probability that you are right
Should I sacrifice some RRR to get filled more often?
The eternal dilemma of contrarian trading
Suppose that for a given setup, price hits target before it reaches invalidation 70% of the time. Great, right? But getting filled is not guaranteed.
Scenario 2: If only 40 out of 100 limits get filled, expected hit rate is 25%
Both scenarios have 30 losses in expectation, but the hit rate in the first is >2x better simply because more trades were taken
EV = hitrate * avg_win_R + (1-hitrate) * avg_loss_R
So we want argmax(EV), and we can compute this by seeing how hitrate and avg_win_R affect the EV of the setup.
Here's a real example where I completed this optimization.
- the average win R tends to increase ("W:PR")
- the number of losses ("L") stays constant at 31
- but the number of trades taken decreases ("#")
- so the hit rate decreases, from a max of 67.7% to a min of 11.4%.
- Understand the tradeoff between RRR and hit rate. I talked about limit orders in this thread but a similar relationship applies to market entries too
- There are no easy answers here. Only the prospect of hard work collecting good data and learning from it.
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@Captain_Kole1 @melodyofrhythm @ape_rture @realadamli @7ommyZero @voicelessFvoice
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