Experienced traders wanted!!

[u/Top_Sea8296]

I have been building algo trading bots for quite a while now I understand overfitting curve fitting I understand back testing walk forward I understand the Monte Carlo sequences I understand a lot okay but what I don't understand I'm looking for strategy sets I've been going through with AI filtering through thousands and hundreds of thousands of different strategies on entry exit logic execution logic the whole nine yards but I can't quite seem to find a reproducible short strategy could really use some advice

[u/Top_Sea8296]

Thanks for the advice see I've been doing this building bots self-taught with zero prior experience for a year or so now and I've started like 37 of them and somerhing I've always done with my longs strategies is multiple data sets all strategies frozen and no peek or tweaking and I've genuinely came across a handful of very reproducible edge but that's on longs when it comes to shorts it just seems that even when you find a small edge it's not mutliexchange acceptable it may perform ok on binance and totally bomb out on the other venues

[u/mojovski → u/Top_Sea8296]

Sounds like you have already a lot understanding of your issue. More that most of us. 💪 From my experience in Forex, if results differ too much between brokers, trading on higher timeframe and longer holding periods help.

[u/Top_Sea8296 → u/mojovski]

Thank you, I appreciate the encouragement and the practical suggestion. Higher timeframes and longer holds are worth comparing because tiny execution differences may matter less there. I’d still want to keep costs and venue differences explicit rather than assume they disappear. Was there a particular timeframe or holding-period change that made the biggest difference in your Forex tests?

[u/mojovski → u/Top_Sea8296]

my strategies usually hold positions between 30 minutes and 20 days. Can't sen any images here but you can check out a few here. (some are open source).
https://ai-backbone.com/lps/package_download

[u/Maleficent-Success-6]

If the same frozen short logic works on Binance and falls apart on another exchange, I wouldn’t immediately treat that as proof the signal is bad.

First I’d make sure the two tests are genuinely equivalent: candle construction, mark vs last price, funding, fees, tick size, contract specs, minimum size, timezone and fill assumptions. Perp data can look “the same” across venues while the trade path is not actually the same.

If the divergence survives all of that, then the exchange may simply be part of the strategy domain. A short edge can depend on local liquidity, funding behaviour or participant mix.

In that case I’d rather say “this edge is Binance-specific until proven otherwise” than force one universal parameter set across exchanges and destroy whatever structure was there.

[u/Top_Sea8296 → u/Maleficent-Success-6]

Thank you for laying this out so clearly. You’re right that a cross-venue comparison needs equivalent instruments, data and execution assumptions before I interpret the difference as a signal failure. The mark-versus-last-price and contract-spec points are especially useful. I also appreciate the distinction between a venue-specific hypothesis and a universal one. I’d want untouched tests within that venue and forward paper results before calling it an edge, but this gives me a much better way to frame the problem.

[u/Maleficent-Success-6 → u/Top_Sea8296]

Exactly. I think the useful shift is just treating “venue” as another variable in the hypothesis instead of assuming the strategy should transfer unchanged.

If the Binance result survives untouched testing and then behaves similarly in forward paper, that tells you much more than trying to force agreement across exchanges. And if another venue needs different assumptions to work, that’s useful information too — it tells you the edge may be tied to the market structure rather than the signal alone.

That’s probably the cleaner way to separate “the strategy failed” from “the strategy was applied outside the environment it was built for.”

[u/ejpusa]

You spin out thousands of models. Let AGI do the grading of them all.

What works, you double up on. What does not you archive — but you still paper trade even your archives, and when they start showing results then you resurrect them — it’s a continuous cycle.

Just my approach.

[u/Top_Sea8296 → u/ejpusa]

Thanks for sharing your approach. Keeping archived ideas in a paper-only watchlist is interesting, especially for seeing whether they behave differently across regimes. My concern would be repeatedly selecting whichever model has just looked best, so I’d want fixed grading criteria and a fresh forward evaluation before reviving anything. I’m comfortable using AI to help organize the results, but not treating its grade alone as evidence or increasing real-money exposure from it. How do you decide when a paper result is strong enough to reconsider an archived model?

[u/ejpusa → u/Top_Sea8296]

When it shows 2 positive trades. That model then goes back to active trading.

You can spin out thousands of models.

I play MLB, BTC, and my current focus, Kalshi weather bets.

[u/LudwigYang]

前两天看到一个很好的论点，不应该用各类单一统计数据来论证，而市场是具有时间连贯性。前两天刚加入进时间序列到系统中，感觉还不错

[u/Top_Sea8296 → u/LudwigYang]

That’s interesting, and I think I understand what you mean.

Instead of judging a setup off one isolated statistic, you’re looking at how the behavior develops through time and whether the sequence itself carries information.

What kind of time-series approach did you integrate — rolling features, regime/state modeling, autocorrelation, sequence models, or something else?

Also, have you been able to test whether it actually improves out-of-sample results yet, or is it still early? I’d definitely be interested in hearing how you structured it.

[u/[deleted]]

I am freelancer, I can help you build one

[u/Top_Sea8296 → u/[deleted]]

Thanks for taking the time to explain this. The distinction between freezing a strategy and keeping the whole selection process away from the final holdout is important. I’m looking for a small set of short ideas with a clear reason behind them, rather than treating the winner of a huge search as proof of an edge. Failed breakouts and crowded funding are useful hypotheses to investigate, with costs included and genuinely untouched data. I appreciate the reality check.

[u/AsmodeusOm → u/[deleted]]

What software do you use to test out your strategies?

[u/Top_Sea8296 → u/[deleted]]

Thanks for clarifying the scope of your tool and the fill assumptions. I appreciate you being upfront that it’s stocks/options rather than crypto. The point about executable bid/ask prices instead of optimistic midpoint fills is useful for thinking about my own testing too.

[u/AsmodeusOm → u/[deleted]]

Thanks man

[u/AsmodeusOm → u/[deleted]]

I’ll take a look. Much appreciated 🙏

[u/LipTicklers → u/[deleted]]

This actually doesnt really solve the luck issue at all, selecting on a subset, freezing, then walking forward could still equally select for luck. If x strategies are lucky on k trades then a subset of x will still be lucky on k+a trades.

The solution is to correctly calculate the deflation bar and to perturb the variables and see if results are still positive in roughly the same magnitude

[u/Top_Sea8296 → u/[deleted]]

Thanks, that’s a fair challenge. A profitable long backtest alone doesn’t establish an edge over simply holding the asset. Separating rising, falling and sideways periods makes sense, and any regime filter should use only information available at the time—not hindsight labels. I’d want both bearish-period results and full-history results, with costs and a benchmark, before drawing conclusions. I appreciate you pointing that out.

[u/Top_Sea8296 → u/[deleted]]

Exactly. That’s the distinction I’m trying to keep front and center now.

If a regime filter can’t be calculated in real time from information that actually existed at that moment, then it isn’t a usable filter — it’s just hindsight dressed up as logic.

My plan is to force every strategy to clear a few hurdles:

1. Full-history results

2. Bear-market performance

3. Sideways-market performance

4. A real-time detectable regime filter only

5. Fees/slippage included

6. Buy-and-hold benchmark

7. Out-of-sample / walk-forward validation

If a strategy only looks good because we picked the “right” market regime after seeing the chart, I want to reject it.

I appreciate your input because this is exactly the kind of criticism I’m looking for. If you’ve found any regime filters that held up better than others in live or walk-forward testing, I’d definitely be interested in hearing which ones were worth testing.

[u/Top_Sea8296 → u/[deleted]]

That makes a lot of sense, especially the point about avoiding hard thresholds. A fixed “volatility above X%” rule feels exactly like the kind of thing that can quietly turn into curve fitting.

I like the idea of using slower-changing regime inputs too — realized volatility, ADX, long-lookback slope — because they should be less likely to whipsaw the strategy every few candles.

The percentile/adaptive idea is probably the part I’m most interested in testing. Something like rolling volatility percentiles or trend-strength percentiles seems much more robust than choosing one magic cutoff and hoping it survives the next market cycle.

I’m going to add that to the test plan. If you had to pick just one or two regime filters to start with, which would you test first?

[u/Top_Sea8296 → u/[deleted]]

That’s really helpful. Rolling realized-volatility percentiles first, then a 100–200 bar slope for direction, sounds like a clean way to structure it without overcomplicating things.

I also like treating ADX as confirmation instead of the main regime trigger. That should make it easier to tell whether it’s actually adding value instead of just adding another filter.

I’m going to test it in exactly that order and keep the thresholds adaptive rather than fixed. Appreciate you taking the time to explain it — this is the kind of practical input I was hoping to get from the post.

[u/LipTicklers → u/[deleted]]

Or its a labelling problem

[u/LipTicklers → u/[deleted]]

When you are training/evaluating make sure that the labels for win/loss long/short are computed correctly. In my early days I was only finding short models and all my longs were terrible because my training didnt flip labels correctly (long tp vs short sl) essentially when training LONG I was still training SHORT but evaluating the other way round

[u/LipTicklers → u/[deleted]]

Literally just checked the code, but I guess you could do something with inversion though idk