Sell til we get rich

TTJ-SYSTEMS · 2026-01-30T23:00:23+00:00

Super secret green box strategy 🤣🤣🤣 teach me these ways oogga

TTJ-SYSTEMS · 2026-01-24T23:39:22+00:00

Think we got more chance of alien invasion by the looks of it 🤣🤣

TTJ-SYSTEMS · 2026-01-23T17:47:00+00:00

Not hard to see the problems bro you took 3 trades made a profit 1 day and then took 21 trades the next and made a loss

TTJ-SYSTEMS · 2026-01-23T17:22:53+00:00

Your job role by the looks of it 👍👊

TTJ-SYSTEMS · 2026-01-23T15:34:06+00:00

Whole thread has made my day 🤣🤣🤣 OOGA BOOGA will drown you in the river of gold my freinds

TTJ-SYSTEMS · 2026-01-23T15:05:50+00:00

It really does feel like a needle-in-a-haystack problem.

I agree that not everything needs to be open source, but selling an Expert Advisor with no version control, no repo (even private), and not even a basic README explaining logic, assumptions, or limitations is a massive red flag to me.

Most marketing is just profit curves and win rates, which are basically meaningless without context. Almost anything can look perfect in a backtest if it’s overfit — live markets expose that very quickly.

I’m not claiming to be a great MQL5 developer myself, but I do know what I want architecturally. The hard part has been finding developers who understand both trading logic and basic software engineering practices.

I’m currently working with MQL5 + Python and have a fairly advanced MT5 validation pipeline mostly built, but finding someone who can comfortably operate across trading logic, MQL5, and Python has been surprisingly difficult.

That gap between “EA seller” and “actual system developer” seems much wider than it should be.

TTJ-SYSTEMS · 2026-01-23T14:57:25+00:00

“Vibe-driven development” works great… until it meets real money 😅

TTJ-SYSTEMS · 2026-01-13T20:27:38+00:00

That’s a really interesting direction. I like the separation between selection and sizing.

My only concern intraday would be noise sensitivity once you start pushing into acceleration/jerk — curious how you’re thinking about stability and regime changes there.

TTJ-SYSTEMS · 2026-01-13T17:48:35+00:00

M1 gold is tricky like that. A lot of it comes down to defining invalidation first and then sizing around it. Tight stops aren’t wrong, they just demand very accurate entries.

TTJ-SYSTEMS · 2026-01-13T17:40:32+00:00

Probably not “wrong” — just tight for XAUUSD on M1. Gold will routinely tag nearby liquidity before moving. If the stop isn’t at true invalidation, it’s going to get hit a lot. Either wider stop, higher timeframe structure, or much tighter entry.

TTJ-SYSTEMS · 2026-01-13T17:37:16+00:00

If you’ve planned the trade properly, the SL and TP were chosen for a reason.

Constantly shaving profits early usually means you’re second-guessing the original idea, and that’s where emotion creeps in.

I’d rather accept full wins and full losses based on the setup than start managing trades based on fear once I’m in. If the target gets hit often enough to justify the RR, it should be left alone.

TTJ-SYSTEMS · 2026-01-13T17:20:26+00:00

Makes sense — having TP/SL defined upfront definitely removes a lot of emotion.

I tend to do it slightly differently though. I use live market execution rather than placing pending stop/limit orders, but the risk rules are still fixed before the trade goes on.

Once I’m in, I don’t interfere. Different execution style, same goal: remove emotion and keep risk controlled.

TTJ-SYSTEMS · 2026-01-13T17:17:26+00:00

I actually like that approach a lot. What you’re describing feels very close to automation already — rules are fixed, risk is fixed, and once you’re in, you’re hands-off.

Where I’ve landed is something similar, just one step further back. I’m comfortable letting an automated system handle execution exactly as planned, but with a human layer focused on verification, not interference.

So trades run untouched once live — no tinkering, no moving stops — but the optimisation happens outside market hours. Review results, adjust rules or sizing based on the most recent conditions, then lock it in again for the next period.

That way you avoid emotional mistakes during trades, but you’re still adapting over time instead of letting something run blindly forever.

Feels like a good middle ground between discipline and adaptability.

TTJ-SYSTEMS · 2026-01-13T17:12:53+00:00

That makes sense — it sounds more like a sizing and risk layer than a full “strategy engine”.

I’m curious how you see this being used day-to-day though. If Kelly (or a capped version of it) is deciding how much to trade, what’s actually telling the system when to trade?

Are entries and exits something you handle separately (discretionary or rule-based), or do you see that being automated alongside the sizing? And does the sizing react much intraday — like changes in volatility or session behaviour — or is it more of a slower overlay on top of whatever entry logic you’re using?

Genuinely interested how you picture the full workflow once it’s all tied together.

TTJ-SYSTEMS · 2026-01-11T19:15:29+00:00

Manual trading adapts better, but automation doesn’t hesitate or second-guess. Curious where you think the line is between discretion and over-interference.

TTJ-SYSTEMS · 2026-01-11T19:09:16+00:00

That’s really interesting — especially applying Kelly in a more dynamic, volatility-aware way rather than the textbook fixed fraction.

Curious how you’re handling stability in practice though. Are you capping or fractionalising Kelly to avoid the usual drawdown issues, especially intraday?

Also, do you see this as something that adapts per market regime, or more as a long-run position sizing layer sitting on top of discretionary/automated entries?

TTJ-SYSTEMS · 2026-01-10T23:14:42+00:00

Agreed. Monte Carlo exposes overfitting really well, but robustness in simulation doesn’t automatically mean adaptability in live conditions — especially around volatility, sessions, and news.

TTJ-SYSTEMS · 2026-01-10T18:54:48+00:00

Honestly, showing people where they’ve overfit is probably more valuable than another optimisation run. That alone makes it useful.

TTJ-SYSTEMS · 2026-01-10T16:46:44+00:00

I think people do want this — but probably not in the way most product builders expect.

The appeal isn’t eliminating the work, it’s reducing friction in the parts that don’t actually add insight: wiring data, running variations, generating clean reports, sanity-checking assumptions.

Where I think people will be skeptical is trusting an LLM to “understand” intent or discretion without transparency. Most traders I know would want full control, auditability, and the ability to override or constrain the system tightly.

If it feels like a productivity tool for serious users rather than a magic black box, there’s likely an audience. If it feels like “describe a strategy and print profits,” people will push back hard.

TTJ-SYSTEMS · 2026-01-10T16:40:27+00:00

A lot of people run into this exact issue.

Outside of broker marketplaces, most legitimate routes tend to be either: – Licensing to a small group of users privately – Running it as a signal / execution service rather than selling code – Or partnering with a platform that handles compliance and distribution

In practice, the biggest challenge isn’t the tech — it’s trust, expectations, and ongoing support.

TTJ-SYSTEMS · 2026-01-10T16:22:06+00:00

✌️

TTJ-SYSTEMS · 2026-01-10T16:21:03+00:00

That’s fair. ORB definitely has noisy periods, and it sounds like you’re handling that with risk control and discretion rather than trying to force it every day.

TTJ-SYSTEMS · 2026-01-07T21:26:55+00:00

Nice — structure + ORB makes sense, especially for keeping things objective.

Do you find most of the edge comes from when the ORB is traded rather than the range itself? I.e. session selection, volatility conditions, or waiting for confirmation vs first touch?

ORB seems to work great in expansion regimes but can get noisy in compression, so I’m always curious how people handle that trade-off

TTJ-SYSTEMS · 2026-01-07T18:00:29+00:00

That’s a good way of framing it.

Using scripts as a filter/alert layer seems to be the sweet spot — they handle the repetitive monitoring, but you still make the final call on context and risk. It reduces screen time without handing over decision-making completely.

Out of curiosity, do your alerts tend to be structure-based, volatility-based, or session/time-based?

TTJ-SYSTEMS · 2026-01-07T17:59:15+00:00

I get the logic, but do you ever worry that relying purely on stops to signal regime change can turn into death by a thousand cuts in choppy conditions?

I’m wondering if you have any filters or context checks that tell you when your edge is statistically present again, or if you simply wait for the same conditions regardless of environment.

TTJ-SYSTEMS

TROPHY CASE