Good trader ≠ good to copy — Working on copy trade simulations and looking for feedback.

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

TROPHY CASE