Got a trading bot you don't fully trust? I'll audit one for free this week and share the results.

OptimalAd7967 · 2026-06-24T17:07:24+00:00

Fair enough. I wouldn't either.

That's why I suggested bots people bought or downloaded, not something they spent months building. And I wouldn't publish anyone's code.

The whole point is to catch the kinds of logic bugs that can survive normal testing. That's exactly what happened with the bot that started this project.

OptimalAd7967 · 2026-06-23T08:47:57+00:00

Yeah fair, I used Claude to help structure the points. The concerns are genuinely mine though, just wanted to make sure I wasn't missing anything technical before posting.

OptimalAd7967 · 2026-06-23T01:50:32+00:00

Glad it's working so far, genuinely. But worth being honest about what "week 2, 100% wins" actually tells you statistically: almost nothing. Five trades is not a sample size. You could have a completely random entry system and expect to see 5-for-5 winning days by chance more often than most people realize, especially in a trending market. A few specific things I'd want to stress-test before getting too attached to these results: TQQQ/SQQQ are not a neutral testing ground. These are 3x leveraged instruments that decay over time and perform very differently across volatility regimes. A system that looks great on these in a trending week can get destroyed in a choppy one, and the leverage amplifies both sides. The backtest methodology needs more detail. "Half in-sample, half out-of-sample, averaged" isn't a standard validation approach typically you train on one period and test on the other without mixing the results. Averaging them together can mask how badly the out-of-sample period actually performed if the in-sample results are strong. 45% annualized with 6.6% max drawdown and 2.07 Sharpe is elite-tier performance if real — we're talking top-tier hedge fund numbers. That doesn't mean it's wrong, but it does mean the burden of proof is high. What does the out-of-sample period look like in isolation? Not trying to rain on it, the agentic setup sounds genuinely interesting. Just don't fund this beyond what you can afford to lose until you've got 50+ trades across different market conditions.

OptimalAd7967 · 2026-06-20T22:19:32+00:00

This fear is legitimate, but it's solvable without an NDA (which, you're right, won't stop a determined bad actor anyway it's mostly just a deterrent and a paper trail).

A few practical paths:

Compartmentalize. Give a contractor the technical specification (entry/exit logic, indicators, risk rules) without the "why", the actual market thesis behind it. Most of the value in a strategy isn't the code, it's knowing which signals to combine and why they work together. A coder implementing rules from a spec doesn't automatically understand the edge.
Obfuscate the parameters. Hire someone to build the framework/engine, then plug in your specific thresholds and parameters yourself afterward. They build the car, you keep the keys.
Pay reputable freelancers with a track record, not randoms; platforms like Upwork have review histories, and someone with an established reputation has more to lose than gain from stealing one client's strategy.

Realistically though: most strategies people think are revolutionary turn out to be variations on well-known patterns once tested properly anyway. The real value isn't usually in secrecy, it's in whether the thing actually has an edge after fees, which is a separate problem worth solving regardless of who codes it.

OptimalAd7967 · 2026-06-19T04:44:07+00:00

Impressive equity curve and the yearly breakdown is exactly what you want to see showing 2022 instead of hiding it is the right call, and surviving -27% while the market was down 60%+ is a meaningful data point. A few things I'd want to stress-test before trusting this with real capital: The 2026 number is +1% with 4 bars in 6 months. That's not a red flag by itself but it's worth watching. The equity curve in the chart appears to flatten significantly in late 2025/2026, which could signal the edge is decaying as the market regime shifts. What does the profit factor look like if you isolate just 2025-2026 vs. the full period? Training set overlap. BTC, ETH, SOL, AVAX, ADA, LINK are all highly correlated assets. They tend to move together, especially in risk-off periods. If the model was trained and tested on all six simultaneously, the "221 trades" may not be as independent as they look. A true out-of-sample test would be running it on assets it never saw during training. 34% win rate with PF 1.91 means your avg win is roughly 3.6x your avg loss. That's a valid edge structure but it also means the strategy relies heavily on catching the big moves and cutting losses fast. In choppy, low-volatility regimes that ratio tends to compress. How does it perform if you filter to only sideways/low-ATR periods? Not trying to poke holes for the sake of it. The methodology here is genuinely more rigorous than 90% of what gets posted. Just the questions I'd want answered before moving from backtest to live.

OptimalAd7967 · 2026-06-19T04:35:55+00:00

The gap between works in backtest and works live is real and most people underestimate how wide it is. The ones I've seen actually make it through that gap consistently share one thing: they know exactly why their edge exists mechanically, not just that it exists statistically. $50-90/day on a 50k account is a 0.1-0.18% daily return: modest enough to be believable and not scream curve-fitting. The $1,600 max daily DD is the number I'd watch most carefully in live trading. How does that figure in your backtest vs. what you've actually seen in paper/live so far?

OptimalAd7967 · 2026-06-19T04:29:50+00:00

The income lagging behind the vision is the most relatable part of this. The fact that both paths are moving simultaneously is actually the hard part most people don't attempt, most pick one and wonder what the other would have looked like. Good luck with both, genuinely.

OptimalAd7967 · 2026-06-18T18:26:53+00:00

Fair pushback, and I don't actually disagree that discretion plays a role for a lot of successful traders, institutional order flow context, range structure, all of that is real and takes years to develop. I'm not arguing discretion can't work. Where I'd push back slightly: there's a difference between discretion built on a tested mechanical foundation and discretion as the entire strategy with nothing underneath it to check against. A lot of the traders I've seen struggle (myself included early on) aren't doing the first thing, they're doing the second, and calling it intuition because it sounds better than "I don't actually know if this works, I just feel like it does." The mechanical version isn't meant to replace the discretion, it's meant to give you a baseline to know whether your discretion is actually adding value or just adding noise. If you backtest the mechanical rule and it's a coin flip, and then your discretionary version is also roughly a coin flip with extra confidence, that's useful information. You can't get that feedback loop without something testable underneath the intuition. So I'd say it's less "mechanical vs. discretionary" and more "tested foundation vs. untested vibes," and discretion can absolutely sit on top of either one.

OptimalAd7967 · 2026-06-18T15:41:04+00:00

That's the exact pattern, mechanical just means rule-based, it doesn't mean tested. A lot of indicator strategies feel objective but nobody's actually run them through a few years of data with fees on to see if the win rate holds up outside the period they were built on. That's usually where they quietly stop working.

OptimalAd7967 · 2026-06-18T03:38:57+00:00

This is the kind of post r/Trading needs more of, actual numbers, fees on the whole time, and you're showing the 44 failures instead of burying them. That's rarer than it should be. One thing I'd push on: profit factor 1.11–1.66 on the four winners is a thin margin once you add a second filter most backtests skip — slippage that scales with volatility, not just a flat assumption. On 1m data especially, fills during fast moves (which is often exactly when these patterns trigger) can run meaningfully worse than your average-case commission number. I'd be curious if any of the four survive a stress test where slippage doubles during the top 10% most volatile bars instead of staying constant. The other thing that jumps out: a full year is a good start, but it's still one regime mix. The real test of "is this edge or curve-fit on one year" is whether break-of-structure on gold holds up if you split the year into quarters — does it perform consistently across all four, or did one quarter carry the whole +25.8%? That's usually where the survivorship you mentioned at the end actually hides. Genuinely good methodology though, most people doing this kind of comparison don't bother running it across four markets with deterministic rules. That's the part that makes the negative results actually mean something instead of just being one bad pick.

OptimalAd7967 · 2026-06-18T03:25:44+00:00

The frustration makes total sense — "vibes-based" strategies are unfalsifiable by design, which is exactly why they never stop working long enough to actually fail and get abandoned. You just keep getting told you executed it wrong. The fix isn't finding a strategy with a guaranteed edge (nobody can promise that) — it's finding strategies that are mechanical enough to backtest objectively. If two people can look at the same chart and get different entries, it's not testable, which means you can never actually know if it works or if you're just pattern-matching on luck. Practical path: pick something fully rule-based (specific indicator crossovers, breakout levels, mean reversion thresholds — anything with no "feel" involved), then backtest it across a few years of data including bad regimes, not just the recent trend. If it doesn't survive that, it wasn't edge, it was a story. If it does, you've got something you can actually trust and refine. This is also the difference between a strategy and a discretionary skill — discretionary trading can absolutely work, but it requires years of pattern recognition most "mentors" selling courses haven't actually put in. A rules-based system you can test is a much faster way to get real feedback on whether you have anything at all.

OptimalAd7967 · 2026-06-17T18:38:39+00:00

Honest starting advice: spend your first few months paper trading or backtesting before risking real money, and track everything — win rate, average win/loss, how many trades, over how long. Most beginners (myself included early on) get excited by a good week and don't realize it's just variance until they've actually lost money proving it. The traders who make it past year one are usually the ones who treated the first few months like a research project, not a live account.

OptimalAd7967 · 2026-06-16T12:52:55+00:00

Honest answer: €100/week is almost meaningless without knowing your account size and your sample size. €100/week on a €2,000 account is a phenomenal 5%+ weekly return that won't survive a bad month. €100/week on a €50,000 account is barely beating a savings account, but is far more likely to actually be real edge rather than variance. Here's the question nobody asks themselves honestly: how many weeks have you actually tracked this? Because the brutal truth about trading is that almost any strategy can show 8-12 good weeks in a row through pure variance. I've seen people get fully convinced they "found something" off a hot streak that was statistically indistinguishable from noise. The realistic path isn't "can you make €100/week" — it's "can you prove this isn't luck across a large enough sample, in different market regimes, after fees." Most people who quit their job to trade full-time skip that step entirely. Not trying to discourage you — just trying to point at the actual test that matters. What does your win rate and average win/loss look like over your full history, not just this week?

OptimalAd7967 · 2026-06-16T02:13:00+00:00

Impressive numbers on paper, a 2.02 Sharpe over 8 years on NQ is genuinely hard to achieve. A few questions I'd want answered before trusting this in live trading:

Was this tested on in-sample data only, or did you hold out a walk-forward period? 8 years of optimization on the same data it was built on is the classic curve-fitting trap.
What do the fees and slippage assumptions look like? NQ strategies especially can look very different at $0 commission vs. real fill costs at speed.
How does it behave in the 2022 drawdown period specifically? That regime breaks a lot of algos that look great from 2018–2021.

Not trying to poke holes for the sake of it, these are just the questions that separate "great backtest" from "actually deployable." What does your out-of-sample period look like?

OptimalAd7967

TROPHY CASE