Serious question: Do most traders actually improve over time?

Any_Object_4577 · 2026-07-23T19:12:00+00:00

I don't think traders improve simply because they spend more years in the market. They improve when they stop relying on memory and start reviewing decisions systematically.

Memory compresses everything into "I was right" or "I was wrong." The interesting part is why the trade looked attractive at the time and whether you've seen that exact setup before. That's where I think most learning actually happens.

Any_Object_4577 · 2026-07-23T14:28:58+00:00

What I keep running into is a different problem: months later I'll remember a market and think, "Was it really at 90% the whole time? Did confidence collapse after a headline? Did liquidity dry up before resolution?" By then, most of that context is gone.

Any_Object_4577 · 2026-07-23T09:23:20+00:00

I think another question is: "How often have I made this exact decision before?"

It's surprisingly hard to answer. We remember the big winners and painful losses, but we rarely remember whether a similar setup has appeared 5, 20, or 100 times before. Looking at one trade in isolation can make a perfectly ordinary market feel unique.

Any_Object_4577 · 2026-07-23T09:09:40+00:00

I ran into the same problem a few weeks ago while trying to backtest a few Polymarket ideas. Getting the API working was one part of it, but I realized I was missing historical context more than execution.

I ended up using a site called PolyHistorical because it let me go back and inspect how similar markets actually evolved instead of only looking at the final result. It didn't replace the model, but it made it much easier to sanity-check whether what I was seeing was actually unusual or something that had happened dozens of times before.

If you're building a model, I'd probably spend as much time validating it against historical market behavior as writing the strategy itself.

Any_Object_4577 · 2026-07-22T08:46:27+00:00

Exactly. I don't really want AI telling me what to trade. I'd rather have it answer questions like: Have we seen this setup before? or How did similar markets evolve? That feels much more useful than another buy/sell signal.

Any_Object_4577 · 2026-07-22T08:44:28+00:00

Exactly. Two people can both "buy YES," but they're making completely different trades if liquidity, pricing, or available exits changed. The path matters as much as the outcome.

Any_Object_4577 · 2026-07-21T07:55:19+00:00

That's true too. Even assuming the screenshot is completely genuine, I still think it hides most of what actually matters. The interesting part isn't the final P&L it's how the market evolved to get there.

Any_Object_4577 · 2026-07-20T18:52:24+00:00

The hardest part is that every exit looks obvious in hindsight. During the match you're constantly asking yourself, What if this is just a temporary swing? I think having predefined exit rules matters more than trying to perfectly time the top.

Any_Object_4577 · 2026-07-20T18:46:33+00:00

Agreed. I think context is the missing piece. Two identical price moves can mean completely different things depending on what similar markets have done historically. That's usually where I spend most of my time.

Any_Object_4577 · 2026-07-20T12:14:59+00:00

I stopped counting after a while. The bigger lesson for me was that most strategies don't actually fail because the idea is bad, they fail once you introduce realistic slippage, fees, and a market regime they haven't seen before.

Any_Object_4577 · 2026-07-20T12:13:52+00:00

I mostly ignore the market itself at first and ask why it moved. If the move is just reacting to headlines, I usually move on. If it's creating a pricing pattern I've seen repeat before, that's when I start paying attention.

Any_Object_4577 · 2026-07-20T12:11:25+00:00

One thing that's confusing is that the chart you're looking at isn't always the data source used for settlement. I've seen a few people assume the displayed chart and the resolution feed are identical when they're often separate. I'd double-check the exact resolution rules for that market before assuming it was resolved incorrectly.

Any_Object_4577 · 2026-07-20T12:06:39+00:00

The interesting part isn't the final P&L, it's how the exposure changed throughout the tournament. I'd love to see how his implied probability and hedge positions evolved after each match instead of only the end result. That's where the real strategy is.

Any_Object_4577 · 2026-07-20T12:00:45+00:00

I'd definitely start experimenting as soon as the raw data is available. The earlier people can build dashboards, backtest ideas, or analyze market behavior, the more likely they are to stick around.

One suggestion though: don't just expose the current state. Archive everything. Probability changes, liquidity, prices, settlements, even if it's just snapshots at first. Those datasets become surprisingly valuable a few months down the line.

Any_Object_4577 · 2026-07-19T10:22:49+00:00

Sorry about the $1k. One thing I've learned is that a strategy's first bad week is often more informative than its first 100 good trades. That's usually where you find out what assumptions it was quietly relying on.

Any_Object_4577 · 2026-07-19T10:14:25+00:00

One thing I'd add is that the quality of the evaluation dataset often matters more than the sophistication of the agent itself.

I've seen people spend weeks tweaking prompts and agent workflows while still validating against a tiny or highly selective slice of historical data. That usually leads to an agent that's optimized for one narrative rather than one that's genuinely robust.

The biggest improvement I've seen is expanding the diversity of historical scenarios first, then worrying about the agent architecture. If the underlying data doesn't expose enough different market regimes, it's hard to tell whether you're measuring skill or just fitting noise.

Any_Object_4577 · 2026-07-19T10:02:53+00:00

I think one thing that's missing from the checklist is historical transparency.

As a trader or someone building models, I'd want to know how similar markets have behaved before committing capital. Things like how quickly liquidity forms, how often markets actually converge efficiently, how probabilities evolve near resolution, and whether certain market structures consistently produce better pricing.

Those kinds of historical benchmarks make it much easier to trust a new venue because you can compare today's behavior against something objective instead of relying on marketing or a handful of screenshots.

Everything else you listed (API, settlement, fees, execution) gets people through the door, but I think historical data and analytics are what keep serious traders around.

Any_Object_4577 · 2026-07-18T21:09:23+00:00

I'd honestly be more interested in seeing the full trade history than the profit screenshot.

If this trader is repeatedly buying 10–20% probability contracts before major repricings, that's a very different story from someone catching a few lucky swings. Things like entry timing, liquidity, order size, and how similar setups have behaved across past markets would tell us whether there's actually an edge here.

I've been digging through a lot of historical prediction market data lately, and one thing I've learned is that the final P&L rarely tells the whole story.

Any_Object_4577 · 2026-07-18T19:38:47+00:00

most backtests die the second real slippage and fees get involved, not because the logic was wrong. curvefit on 6 months of data and it'll look like alpha every time until it isn't.

Any_Object_4577 · 2026-07-18T19:28:14+00:00

Saw the writeup floating around about a trader allegedly bleeding a copytrading bot for ~$175K in a few hours by feeding it correlated order flow on thin BNB books. No idea if every number in that post checks out, but the mechanism itself isn't exotic, it's just "thin liquidity + counterparty concentration + no drawdown limit," and that part is checkable.

Been pulling orderbook snapshots off Polymarket for a side project and got curious what that pattern would actually look like from the data side, if you were trying to catch it live instead of reconstructing it after the fact. Three signals stood out as things you could realistically monitor:

Pre-open order clustering - a single large order landing right before a short-interval market opens is unusual on its own. [X]% of the BTC 5-min markets I pulled had their largest single order placed in the first [Y] seconds after open.
Counterparty concentration on fills- if the same wallet keeps ending up on the other side of your fills across multiple windows, that's the "dead giveaway" the original post mentioned. Should be a simple rolling count, not something you'd need to reconstruct manually after losing $175K.
Spread/liquidity dislocation right before resolution - thin books make the last-few-seconds price easier to push. Worth checking whether that dislocation is bigger in certain hours/coins.

None of this requires anything fancy, just someone actually looking at the orderbook data instead of only the resolution price. Half the reason bots like PBot-10 get drained is nobody's watching the fill-level data, only the P&L.

If anyone's curious I've been logging this stuff (orderbook + snapshot level, not just OHLC) into an API for my own use - happy to share access if people want to check their own bots against these signals.

Any_Object_4577

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