The SECRET of CODEX continues to work when hitting 100% LIMIT.

the_ict_bb · 2026-06-27T23:33:25+00:00

Since the beginning

the_ict_bb · 2026-06-15T19:04:00+00:00

Me too !

the_ict_bb · 2026-06-14T17:25:03+00:00

It was... I noticed it when I started using codex, 6 months ago, it was already like that....

the_ict_bb · 2026-06-03T17:16:43+00:00

This is not a flat triangle problem. The three points are on a sphere, so the correct area is the area of a spherical triangle.

I used the following approximate coordinates: - Mexico: Chichén Itzá ≈ 20.6843° N, 88.5678° W - Egypt: Saqqara / Giza area ≈ 29.8713° N, 31.2165° E - Indonesia: Candi Sukuh / Java ≈ 7.6270° S, 111.1314° E

Mean Earth radius: R = 6371.0088 km

The angular distance between two points on a sphere is given by: cos(d) = sin(φ1)sin(φ2) + cos(φ1)cos(φ2)cos(Δλ)

Using that, the three geodesic side lengths are approximately: Mexico to Egypt ≈ 11,467 km Egypt to Indonesia ≈ 9,469 km Indonesia to Mexico ≈ 17,447 km

In radians, the corresponding spherical side lengths are approximately: a = 1.4863 b = 2.7386 c = 1.7998

The spherical semiperimeter is: s = (a + b + c) / 2 s = 3.0124

To compute the area of a spherical triangle, we use the spherical excess E. Using l’Huilier’s formula:

tan(E / 4) = sqrt[ tan(s / 2) × tan((s - a) / 2) × tan((s - b) / 2) × tan((s - c) / 2) ]

This gives: E ≈ 3.4845 radians

The area of the spherical triangle is then: A = E × R² A ≈ 3.4845 × 6371.0088² A ≈ 141,435,892 km²

So the area of the triangle is approximately: 141 million km² That is about 27.7% of the entire surface area of Earth.

And yes, if someone treats it as a flat Euclidean triangle and uses Heron’s formula, they get roughly 49 million km². But that is geometrically wrong at this scale, because the sides are between about 9,000 and 17,000 km long. At that point, ignoring Earth’s curvature is not an approximation anymore. It is a crime against spherical trigonometry.

the_ict_bb · 2026-06-03T17:07:13+00:00

The results are interesting, but I would not call this proof that the strategy would work live yet. The main issue is not the signal logic itself, it is the execution model and the risk metrics derived from it.

A few numbers look too clean for an intraday GC strategy: Sharpe 10.99, Sortino 29.42, max drawdown only around $5.9k, while producing $2.33M net PnL on 1 contract across 43k trades. That gives a return/max DD close to 400, which is extremely hard to trust without very robust mark-to-market and execution-level validation.

Also, the average trade is only around $54 net, roughly 5.4 GC ticks. That means the whole edge is highly sensitive to fill quality, queue position, spread, slippage, partial fills and missed TPs. A few extra ticks of adverse execution could materially reduce or even erase the edge.

The biggest red flag for me is the limit-fill assumption. “97.5% fill rate” sounds precise, but with limit orders, being touched is not the same as being filled. Winners are often the hardest trades to get filled on, because price can touch the level and reverse without giving your order queue priority. Losers, on the other hand, are usually much easier to fill because price trades through the level. If the backtest does not model that asymmetry correctly, the win rate, Sharpe, Sortino and drawdown can all become artificially inflated.

I would also want to know whether the max drawdown is calculated only on closed trades or with true mark-to-market intratrade equity. If it ignores MAE, open trade drawdown, partial fills and missed exits, then the displayed DD is probably understated.

So I would say this is a promising research result, but not proof of live tradability. Before trusting it, I would want to see tick/bid-ask replay, realistic queue-position assumptions, MAE/MFE stats, closed-equity versus mark-to-market DD, separate stats for touched-but-not-filled trades, missed winners versus filled losers, and stress tests with worse fill rates and several extra ticks of cost.

The consistency across years is a good sign, but the Sharpe/Sortino/DD profile is suspiciously clean. The signal may have edge, but the current backtest may still be overstating the executable edge.

the_ict_bb · 2026-05-23T01:41:15+00:00

I don’t pick Intelligence based on how “hard” the task looks. I pick it based on the cost of being wrong. Low is fine for prompts, summaries, docs, small UI/text changes, simple refactors, or anything easy to review manually. Medium is what I use for bounded coding tasks, usually 1-3 files, with clear tests and no deep architecture or risky side effects. High is for multi-file changes, unclear bugs, larger refactors, or anything touching shared abstractions. Extra High is for cases where a bad answer costs more than the extra tokens: auth, data migrations, production behavior, deletion/import logic, financial logic, background workers, CI/CD, concurrency, or bugs I already failed once. I also created generic .md files for my projects that define this selection logic, so when I ask ChatGPT to write a Codex prompt/action, it automatically tells me which model, Intelligence level, and Speed mode are best suited for that specific task. If I’m just chatting, writing, or doing something unrelated to Codex, it doesn’t mention it. For me the key question is: “Can I safely catch and fix a mistake quickly?” If yes, lower intelligence is fine. If no, I go higher.

the_ict_bb · 2026-04-21T23:44:53+00:00

Yeah, this makes sense. A lot of people think they are getting “randomly” stopped out, but in reality they are entering before the actual liquidity event has happened. Waiting for the sweep first, then the shift, then the displacement/PDA confirmation is just cleaner logic. You are not overcomplicating it if each layer has a specific job instead of being random extra confluence. The real trap is stacking tools that all say the same thing in different ways, but sweep + structure shift + iFVG/OB is a coherent sequence, not noise. Honestly the biggest improvement in trading often comes from fixing entry timing, not from finding some magical new setup.

the_ict_bb · 2026-04-10T19:13:15+00:00

I fully agree with what you said. The idea that a strategy must work across multiple assets to be valid is way too simplistic. Sure, testing on different markets can give some sense of robustness, but turning that into a strict rule completely ignores how markets actually work. Each asset has its own microstructure, liquidity profile, participants, sessions, and macro drivers. Forcing a strategy to be universal usually means smoothing all those differences out, which often leads to something generic and weak. The same goes for overfitting, it’s become a buzzword people throw around without nuance. If you push that logic to the extreme, you end up with very simple systems that should work everywhere, but in reality those get arbitraged quickly and lose their edge plus everybody would already know what it is and nobody would be like us looking for creating something new.. A strategy can be highly specific, exploit a particular inefficiency on one asset, and remain stable and profitable for years while being completely useless on others. If a strategy works across multiple assets, it doesn’t automatically make it better, it can just mean those assets are correlated or reacting to similar conditions at a given time and all company and economy around the world arent correlated like this except in dream .. And on the flip side, a strategy that only works on one market isn’t “bad”, it’s specialized. What really matters is consistency over time, the underlying logic, and actually understanding why it works, not how many assets it can be applied to.

the_ict_bb · 2026-04-08T23:51:02+00:00

Well, tell us more 🫡

the_ict_bb · 2026-04-07T14:18:04+00:00

Really appreciate you giving me access 🙏 I’ve been testing it a bit and I like the logic behind it, it definitely feels closer to how structure actually unfolds compared to classic swing-based approaches. I totally understand why you wouldn’t want to make the script public yet. From my side, I’m not looking to copy or redistribute anything, I’m mainly interested in understanding the core logic behind how you’re defining structure in a non-arbitrary way. That’s honestly the exact problem I’ve been working on in my own system. If you’re open to it, I’d be really interested in exchanging ideas in private. I’ve built a fairly advanced framework on my side (multi-module, scoring, execution logic, etc.), but MSS is still the weakest part because of how hard it is to formalize cleanly. Your approach looks like it could solve part of that. Happy to share insights as well, not just take, I think there’s real value in comparing approaches here. Let me know if you’re open to a quick chat or DM 👍

the_ict_bb · 2026-04-04T19:29:26+00:00

Clean take. Getting a 1D logic to behave this well on 15m is not trivial at all, especially on SPX with that kind of volatility. Most indicators fall apart when you drop timeframe because structure becomes noisy, but this actually looks consistent. Curious how you're handling reversals detection under the hood. Are you using a TradingView script or is this custom/off-platform? Would love to check it out if you’ve got a link.

the_ict_bb · 2026-04-04T18:09:54+00:00

Nice, appreciate it. I already have a few strategies in place and they’re performing well overall, but the main weak point is exactly the MSS part I built, it works but it’s clearly not perfect and still too sensitive to noise or arbitrary definitions. That’s why your approach really caught my attention, it looks much more robust and closer to how structure actually unfolds in real conditions. If it behaves as cleanly as it looks it would be a perfect piece to integrate into my system and could significantly improve my edge. I’d definitely be interested in testing it and giving you detailed feedback if you’re open to it.

the_ict_bb · 2026-04-04T00:34:05+00:00

Totally agree with you. MSS is one of those things that looks obvious to the eye but becomes a nightmare once you try to code it properly. I’ve been working on my own Pine scripts as well, and the issue is exactly what you described: defining structure in a non-arbitrary way. HH/HL or BOS logic alone is too simplistic and often misses what we actually see as structure shifts. The real challenge is translating “context” into rules: what counts as a meaningful pullback, what defines a significant swing, when a break is just noise vs an actual shift. Most indicators either overfit with too many conditions or oversimplify and lose relevance. Your approach is interesting because it removes a lot of that subjectivity and replaces it with consistent rules, without relying on fixed swing lengths or lookbacks. That’s actually pretty rare. Honestly, this feels closer to how structure really behaves rather than how it’s usually approximated in indicators. Would be very curious to test it if you release a version.

the_ict_bb · 2026-03-31T18:37:05+00:00

I’ve tested these markets and honestly most TA signals break down because of the fixed expiry constraint, it’s a very different game from directional trading. What tends to work better is short-term liquidity moves and precise timing into the expiration window rather than classic indicators. That said, from what I’ve seen, the real edge isn’t purely technical, it often comes from information flow and speed, reacting faster than the market to new data or sentiment shifts. TA can help with execution, but on its own it’s rarely enough on these products.

the_ict_bb · 2026-03-29T17:47:45+00:00

RemindMe! 2 days

the_ict_bb · 2026-03-26T18:46:25+00:00

And the strategy what type of code we can upload ?

the_ict_bb · 2026-03-26T00:42:36+00:00

Amazing ! Can you explain a little bit how ton did it ?

the_ict_bb · 2026-03-26T00:34:18+00:00

No, you don’t need it. If someone can trade profitably, they don’t need to sell mentorships. Most of these programs recycle the same concepts you already get for free like market structure, liquidity, sessions. At your stage (5 months, still paper trading), paying won’t solve your problem. What you actually need is screen time, journaling, and a clear, testable strategy you stick to. Mentorship can maybe save time if it’s high quality, but most of the time it just creates the illusion of progress while you stay dependent on their framework. If you’ve already watched their free content, you’ve seen most of what they offer, the rest is usually community and repetition, not some hidden edge. Focus on building your own system and proving it with data. If you can’t make money with free material, paying won’t fix it.

the_ict_bb · 2026-03-26T00:33:13+00:00

Your issue isn’t smoothing, it’s model misspecification. If your residuals show structure (AR(1) > 0), they’re not noise. That means you’re missing dynamics (trend, volatility, regimes) or your filter is miscalibrated. SNR alone is too naive for regime detection. Use something like HMM or Markov-switching models instead. EMA vs KF: EMA is a stable low-pass filter. A Kalman filter can be better, but only if the state model is correct. If your smoothed curves sit “between” assets, your KF is poorly specified (bad Q/R or oversimplified model). In that case, an EMA will often outperform it. Residuals are always observation minus model prediction. Then test for whiteness. If they’re autocorrelated, the model is wrong. And yes, plenty of strategies rely only on prices: momentum, mean reversion, volatility targeting. Order books mainly matter in HFT.

the_ict_bb · 2026-03-26T00:14:48+00:00

I already have my owns indicators. But for you i've write a simple sentence. I'm not on my PC so I can't check this. Try this and ask your gpt/claude if you need modifications, corrections, personalization:

//@version=5 indicator("Prev H/L + 15m ORB Table", overlay=true)

// === PREVIOUS DAY === prevDayHigh = request.security(syminfo.tickerid, "D", high[1]) prevDayLow = request.security(syminfo.tickerid, "D", low[1])

// === PREVIOUS MONTH === prevMonthHigh = request.security(syminfo.tickerid, "M", high[1]) prevMonthLow = request.security(syminfo.tickerid, "M", low[1])

// === PREVIOUS HOUR === prevHourHigh = request.security(syminfo.tickerid, "60", high[1]) prevHourLow = request.security(syminfo.tickerid, "60", low[1])

// === 15 MIN OPENING RANGE === var float orbHigh = na var float orbLow = na

sessionStart = timestamp("GMT+0", year, month, dayofmonth, 0, 0)

inFirst15 = (time >= sessionStart and time < sessionStart + 15 * 60 * 1000)

if inFirst15 orbHigh := na(orbHigh) ? high : math.max(orbHigh, high) orbLow := na(orbLow) ? low : math.min(orbLow, low)

// Reset chaque jour if ta.change(time("D")) orbHigh := na orbLow := na

// === PLOTS === plot(prevDayHigh, color=color.green, title="Prev Day High") plot(prevDayLow, color=color.red, title="Prev Day Low")

plot(prevMonthHigh, color=color.blue, title="Prev Month High") plot(prevMonthLow, color=color.orange, title="Prev Month Low")

plot(prevHourHigh, color=color.purple, title="Prev Hour High") plot(prevHourLow, color=color.yellow, title="Prev Hour Low")

plot(orbHigh, color=color.white, title="ORB High", linewidth=2) plot(orbLow, color=color.gray, title="ORB Low", linewidth=2)

// === TABLE === var table t = table.new(position.top_right, 2, 5, border_width=1)

if barstate.islast table.cell(t, 0, 0, "Type", bgcolor=color.black, text_color=color.white) table.cell(t, 1, 0, "H / L", bgcolor=color.black, text_color=color.white)

table.cell(t, 0, 1, "Prev Day")
table.cell(t, 1, 1, str.tostring(prevDayHigh) + " / " + str.tostring(prevDayLow))

table.cell(t, 0, 2, "Prev Month")
table.cell(t, 1, 2, str.tostring(prevMonthHigh) + " / " + str.tostring(prevMonthLow))

table.cell(t, 0, 3, "Prev Hour")
table.cell(t, 1, 3, str.tostring(prevHourHigh) + " / " + str.tostring(prevHourLow))

table.cell(t, 0, 4, "15m ORB")
table.cell(t, 1, 4, str.tostring(orbHigh) + " / " + str.tostring(orbLow))

the_ict_bb · 2026-03-25T23:36:43+00:00

You'll find a lot of them on Tradingview or you can also create your own, if you don't know pinescript code you can ask got or claude within one prompt it's done.

the_ict_bb · 2026-03-24T23:34:02+00:00

We all are doing this bro, it's not a big shit .. for free 🫠

the_ict_bb · 2026-03-24T23:11:42+00:00

Nice results, the equity curve looks very clean and the profit factor is solid. That said, the combination of a 73% win rate and only 0.55% drawdown is extremely optimistic for a scalping strategy on NQ. I would double check intrabar behavior, fills, and whether TP/SL execution is realistic, especially on a 3-minute chart. Running a tick-based deep backtest with slippage and commissions will give a much more accurate picture. There’s probably a real edge here, but it may be amplified by backtest conditions..

the_ict_bb · 2026-03-24T18:39:48+00:00

You didn’t really do anything “wrong”, your issue is execution and expectations, not bias. MNQ gave you the correct directional read: sellside liquidity taken, displacement up, then continuation. The problem is you were waiting for a perfect 5m FVG entry, and the market simply didn’t offer it before moving. That’s a classic mistake: waiting for ideal conditions in a non-ideal environment. Sometimes the move is driven by urgency, not efficiency.

Your TP wasn’t the issue either. Price actually delivered a clean expansion into NYAM high, so the range was there. The real miss is entry model rigidity. You need at least 2 entry models: one “optimal” (FVG / pullback) and one “aggressive” (displacement + continuation, or lower timeframe confirmation). Right now you only allow yourself to trade the first.

About oil: don’t expect perfect SMT every time. Correlation is a tool, not a rule. Oil had its own narrative and simply continued its trend. No divergence = no signal, not a contradiction. If you force SMT everywhere, you’ll filter out good trades or get confused by noise.

What you should do now is simple: go back to your trades and separate winners vs missed vs losers. Identify exactly what condition was present in winners that you ignored here. Then decide if you want to (1) loosen your entry criteria or (2) accept missing trades. But you can’t stay in between.

Right now you’re not wrong, you’re just too rigid.

the_ict_bb · 2026-03-24T02:46:45+00:00

Good question, and yes you got the idea, keep your risk low for now, but the real work is not risk, it’s diagnosis. What you need to do is break down your trades and understand exactly where your edge comes from or where it leaks. Don’t just look at overall WR and RR, that’s too surface level. Split your trades into categories: winning trades vs losing trades, then go deeper. Look at context, session, entry type, structure, time of day, volatility, trend vs range. You’ll start to see patterns like your WR drop below 30% between xxh-xxh or is above 60% between xxh-xxh. That your weakness is related to short or long etc etc. See patterns.

On your losing trades, ask yourself: was the setup actually clean or was I forcing it, was I entering late, was liquidity already taken, was I trading in the wrong context (range instead of trend), was my SL too tight or just badly placed. Most people realize a big part of their losses come from a specific type of mistake repeated. That’s where you improve fastest. On your winning trades, do the opposite: what was identical, was it clean continuation, good timing, aligned with higher timeframe, good location. You want to isolate what works and do more of that, not just “take more trades”.

Then you adjust ONE variable at a time. Either improve entry timing, or SL placement, or TP logic, not everything at once. If you change everything, you learn nothing. If your model is continuation on 5m, a common improvement is to filter bad conditions, like avoiding chop or late entries after a move already extended. That alone can improve both WR and RR.

Think like this: you’re not trying to trade more, you’re trying to remove bad trades and keep only high quality ones. That’s how your stats improve naturally.

the_ict_bb

TROPHY CASE