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

I haven't seen this tradeoff articulated on CT. Allow me to explore this, starting with an example of trading with limit orders:

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 1: If 100 out of 100 limits get filled, expected hit rate is 70%
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
Then we have RRR. Setting entries closer to invalidation increases RRR but decreases the probability of getting filled, since entries are further away from the current price. And as above, getting filled less often results in a lower hit rate. So RRR trades off against hit rate.
Where to place the limit orders though? The optimal place to enter maximizes the EV of the setup, where

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.
The optimum lies where the marginal +EV benefit from increased RRR is perfectly offset by the -EV cost of decreased hit rate. This is like optimizing output with MR=MC in economics - we want to find the best combination of two related variables. There may be multiple local optima
Of course, actually determining the optimal place to enter requires good data and quantitative analysis. It doesn't have to be in a spreadsheet; for instance it could be based on past charts logged in a journal.

Here's a real example where I completed this optimization.
This shows the tradeoff. As "Entry" gets closer to "Inval",
- 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%.
The highest EV in terms of R ("EVR") of 0.72R occurs not at the extremes where RRR or hitrate are maxed out but somewhere in the middle. It turns out that the extremes where RRR or hitrate were maxed out actually exhibited the lowest expected values.
In conclusion:
- 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.

💪
Tagging some people who I think would appreciate this:
@Captain_Kole1 @melodyofrhythm @ape_rture @realadamli @7ommyZero @voicelessFvoice

More from Trading

TradingView isn't just charts

It's much more powerful than you think

9 things TradingView can do, you'll wish you knew yesterday: 🧵

Collaborated with @niki_poojary

1/ Free Multi Timeframe Analysis

Step 1. Download Vivaldi Browser

Step 2. Login to trading view

Step 3. Open bank nifty chart in 4 separate windows

Step 4. Click on the first tab and shift + click by mouse on the last tab.

Step 5. Select "Tile all 4 tabs"


What happens is you get 4 charts joint on one screen.

Refer to the attached picture.

The best part about this is this is absolutely free to do.

Also, do note:

I do not have the paid version of trading view.


2/ Free Multiple Watchlists

Go through this informative thread where @sarosijghosh teaches you how to create multiple free watchlists in the free


3/ Free Segregation into different headers/sectors

You can create multiple sections sector-wise for free.

1. Long tap on any index/stock and click on "Add section above."
2. Secgregate the stocks/indices based on where they belong.

Kinda like how I did in the picture below.

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This is a pretty valiant attempt to defend the "Feminist Glaciology" article, which says conventional wisdom is wrong, and this is a solid piece of scholarship. I'll beg to differ, because I think Jeffery, here, is confusing scholarship with "saying things that seem right".


The article is, at heart, deeply weird, even essentialist. Here, for example, is the claim that proposing climate engineering is a "man" thing. Also a "man" thing: attempting to get distance from a topic, approaching it in a disinterested fashion.


Also a "man" thing—physical courage. (I guess, not quite: physical courage "co-constitutes" masculinist glaciology along with nationalism and colonialism.)


There's criticism of a New York Times article that talks about glaciology adventures, which makes a similar point.


At the heart of this chunk is the claim that glaciology excludes women because of a narrative of scientific objectivity and physical adventure. This is a strong claim! It's not enough to say, hey, sure, sounds good. Is it true?
Recently, the @CNIL issued a decision regarding the GDPR compliance of an unknown French adtech company named "Vectaury". It may seem like small fry, but the decision has potential wide-ranging impacts for Google, the IAB framework, and today's adtech. It's thread time! 👇

It's all in French, but if you're up for it you can read:
• Their blog post (lacks the most interesting details):
https://t.co/PHkDcOT1hy
• Their high-level legal decision: https://t.co/hwpiEvjodt
• The full notification: https://t.co/QQB7rfynha

I've read it so you needn't!

Vectaury was collecting geolocation data in order to create profiles (eg. people who often go to this or that type of shop) so as to power ad targeting. They operate through embedded SDKs and ad bidding, making them invisible to users.

The @CNIL notes that profiling based off of geolocation presents particular risks since it reveals people's movements and habits. As risky, the processing requires consent — this will be the heart of their assessment.

Interesting point: they justify the decision in part because of how many people COULD be targeted in this way (rather than how many have — though they note that too). Because it's on a phone, and many have phones, it is considered large-scale processing no matter what.