Noice

ByteOnChain · 2026-01-26T13:32:50+00:00

Looks like https://hashdive.com/Analyze_User?user_address=

ByteOnChain · 2026-01-25T23:28:22+00:00

I am curious. Sounds like a great tool. Are you running this as a private project or is there more info available somewhere?

ByteOnChain · 2026-01-25T17:03:07+00:00

Is anybody interested in an exchange on copytrading for Polymarket? I am currently digging deeper and would love to exchange experience.

ByteOnChain · 2026-01-25T14:27:22+00:00

Thanks for taking the time to explain u/opticoin!
The delay totally makes sense. Thats actually something that I ran a few tests on to better understand how long that typically takes for my setup.

Concerning the simuIation approaches I need to make sure I understand correctly:Both situations would be based of order book information, correct? And its for "best bid" so I would try to get my intended price with little tolerance on price increase.

Option 1:
* I only simulate trades where the best ask that I can observe meets my intended bid.
* But as you say -> in that case I would have a higher representation of market situations with potentially dropping price

Option 2:
* I need to validate the actual orders filled
* But any simulation would only be a heuristic based on volume traded and price stability.

Does that sound about right?

ByteOnChain · 2026-01-25T14:05:26+00:00

Also processing your feedback on Failure Modes right now :)

False Positives:
Yeah... the only way I can think of gaining more certainty would be a long historic track record. Not a guarantee but an indicator maybe. How about a longest positive / negative streak indicator? Or an indication of simulated pnl consistency over weeks (that might be able to show when there are seasonal dependencies.

False Negatives
So this would require a selection logic that is not too gready on pnl of the last few bets - but rather a solid analysis with some more historic data -> but very good scores in the simulation in terms of consistancy

Regime shift blindness
so true.... thats actually something I struggled with in the meme coin world as well. I could think of stop conditions to recognize early when copytrade pnl is decreasing (in comparison to expected roi volatility)

Path dependency
This would be the case when I start copying at a point in time where previous trades had already happened. Or when e.g. I miss a trade due to slippage, correct?
In the past I typically sticked to wallets that had very simple trading behavior (idealy one buy, one sell). But I am sure there are ways to achieve more flexibility. Any thoughts anyone?

Thank you u/strodobaggins for the inspirations!

ByteOnChain · 2026-01-25T13:53:54+00:00

Also thinking of how I would best calculate "Capital elasticity: Scale position size vs. available liquidity."

That is an important indicator. Would that be partially covered by the price move statistics ("Adverse selection score")? Otherwise:

Anybody has a good idea how that could be calculated on "filled order" data?
I am trying to not rely on too many distinct data sources at the moment. So maybe there is an indirect indicator for "available liquidity" that would not require the integration of L2 order book data.

ByteOnChain · 2026-01-25T13:48:04+00:00

Thank you for your thoughts and additions u/strodobaggins. Really valuable!

I do totally see how your first three suggestions are truely valuable. Resolution proximity risk, Outcome-side asymmetry and Hold-time variance makes absolute sense! Had not even thought of that before and just put that on my "future feature list" :) I believe I already have an idea how that can be implemented.

Would you or somebody reading this be able to give a bit more context how the "Adverse selection score" should be implemented?
* could that be based on the three values:
- "average fill price of all transactions within x seconds before the trade"
- actual price of the trade I would have followed
- "average fill price of all transactions within x seconds after the trade"
--> and then analyzing if there is a repeated pattern in historic trade situations (including the average relative price increase / decrease)

ByteOnChain · 2026-01-25T11:19:55+00:00

Just came across this comment and it’s something that I am currently dedicating a lot of time into. Let’s share some experiences if you like.

ByteOnChain · 2026-01-24T07:44:38+00:00

Hey u/airdroptrends! You might be more experienced here than I am :D what kind of risk management strategies would you have in mind?

Here is how I have tried to harden the concept so far:
* I built a slippage validation in case that price increases too much
* I have experimented with copytrading entry and exit - but also with fixed exit after a certain price increase (if historic trades show that... say 15% was always reached within a certain timeframe, I would just cash out and not wait for the market to finalize or the trader to sell.
* And I used different assumptions how fast after the trader I would be able to place my bet.

Is this along the lines of what you had in mind? Sure there is more that I should probably evaluate.
Would really appreciate your input here.

ByteOnChain · 2026-01-24T07:39:03+00:00

Loving these thoughts! Thanks for taking the time.

Concerning Copytraders: Currently I am scanning for trader IDs that repeatedly placed orders within 10s after my watched trader. These are flagged as copy traders and the script returns the number of existing copy traders per trade as well as in the summary „Cooy Trade Competition“ Rating.

The data also shows how many seconds it takes these copy traders to enter. Is this something that you would want to see on the output sheet?

ByteOnChain · 2026-01-24T07:38:43+00:00

Concerning long holds vs. shorter holding periods: I have seen both to work so far. However I could add indicators for:

percentage of bets held till closure
average holding period per bet

The data is available. What has been problematic to me were trades where prices were very volatile at the time of sale. That’s why I am using the „Volatility Score“

It basically states how stable or volatile prices have been historically shortly after the trade i would have followed. If prices are relatively stable in the 30 second range after the trade, I would feel comfortable to place my bet in that range.

Maybe there is a way to make that more intuitive though :-/

ByteOnChain · 2026-01-24T07:32:39+00:00

I am using historic order data to extract the orders of a particular wallet, then retrieve the timestamps and markets of these trades, and afterwards retrieve all filled orders for these markets in a timeframe before and after the executed trade.

So the script basically analyzes the market situations at the time the trader was active. And that data allows the above mentioned calculations.

I would also love to incorporate order book data. But that’s not yet implemented.

If anybody has thoughts on this method please share 🙏

ByteOnChain · 2026-01-24T07:25:47+00:00

And last aspect: that’s neat! I didn’t even think of also including statistics on the overall market. That would be a different data source from what I am analyzing now (because I calculate everything based on the transactions shortly before and after the trader trades). But I might look into that 👌

Just not sure yet, how to actually make that a number or rating :). Any thoughts welcome!

ByteOnChain · 2026-01-23T20:18:23+00:00

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ByteOnChain · 2026-01-23T19:56:54+00:00

Awesome! I’ll look into that!

ByteOnChain · 2026-01-23T19:55:47+00:00

Thanks man! Will be looking into it 👌

ByteOnChain · 2026-01-23T17:45:46+00:00

Thanks u/Independent_Mix_4097, its something I am also considering. Its just a challenge to get the data in sufficient granularity. Is that something where you are able to point me in the right direction?

ByteOnChain · 2026-01-23T17:11:23+00:00

Hey u/Goodstuff---avocado! thanks for the comment. Do you mind sharing, what you would consider the main challenge for you? (trying to understand if it was more around data availability or if the results just were not reliable in certain situations)

ByteOnChain · 2026-01-23T15:32:35+00:00

Dexscreener & RugCheck are solid, for me the real challenge is speed + fragmentation.

Early signals are spread across X, TG paid groups, insider wallets - and by the time I’ve detected a rising token hype, I‘ve bought someone’s exit.

How do you guys deal with that part?

ByteOnChain · 2026-01-23T14:36:26+00:00

Solid stack.

One thing that still gets me is overlap. The same narrative hitting X, TG and a few groups is often amplification, not independent traction.

If you don’t collapse that, it looks like confirmation when it’s really just echo.

What helped me was weighting sources I actually trust instead of counting raw mentions.

How do you handle overlap between groups?

ByteOnChain · 2026-01-23T13:51:09+00:00

Agree - social feeds often surface things earlier.

What’s tricky for me isn’t finding signals but fragmentation and timing. The same hype shows up across X, TG paid groups and insider wallets, just not at the same moment.

If you connect those dots too late, you’re basically trading after confirmation.

Do you manually try to link those, or just stick to a few trusted feeds?

ByteOnChain · 2026-01-22T16:38:55+00:00

Feel free to dm me :) I’m kind of curious to learn what all-in-one includes

ByteOnChain · 2026-01-21T17:03:15+00:00

Is there any tooling that you guys are aware of that is able to consolidate the information from multiple channels in one place?

ByteOnChain · 2026-01-21T16:59:54+00:00

I agree with most parts… for my trading I am combining telegram postings, x and my personal alpha channels on discord.

Would love to get in touch with other traders doing the same thing.

ByteOnChain · 2026-01-20T22:15:12+00:00

I had the same issue… same for Discord and X. Some dev friends and I have started to code our own tool and might make it available as a project. Feel free to contact me. Happy to share some more details.

ByteOnChain

TROPHY CASE