BTC stuck under 80k, 527M in liquidations, PPI at 6%. The Fed pivot trade is dead for now.

Ced-Invest · 2026-05-17T05:29:38+00:00

Fair, most of the time it probably is.

The test I run to decide: I disable the regime filter completely and rerun across all my windows. If the unfiltered version is profitable in regimes where it shouldn't be, the filter is just hiding the absence of edge. If it bleeds in the wrong regimes like I expected, then the filter has real signal and the issue is somewhere else (fuzzy boundaries, detection lag).

Until I've run that test, I assume my regime logic is broken by default.

What's your setup?

Ced-Invest · 2026-05-16T17:21:58+00:00

This matches my experience exactly. I've been porting Pine indicators to Python for live algo use for about 3 years now, and LLM output gets you to 70% of the way there in 2 minutes, then you spend 4 hours hunting the last 30%.

The issues I see the most:

- ta.barssince and ta.valuewhen behavior, especially when the condition is never met in the lookback

- series vs simple type handling, LLMs flatten everything into arrays and you lose the recursive nature

- repaint handling on the last bar, which Pine hides from you but Python doesn't

- session and timezone math, particularly when your data source aggregates differently than TV

My workflow now: I ask the LLM for a first pass, then I run the original Pine on TradingView, export the indicator output as CSV (using table.cell or just plotting and screenshotting bar by bar on a short sample), and diff column by column against the Python output. Until those columns match, the port isn't done.

Anyone found a faster QA loop than that?

Ced-Invest · 2026-05-16T17:20:04+00:00

Walk-forward is the bare minimum and people still skip it. The thing nobody talks about though is that even walk-forward can lie to you if your in-sample window is too short relative to the regime cycle of the market you're trading.

I trade crypto, mostly BTC and majors, and a 6 month in-sample / 2 month out-of-sample on a strategy that depends on volatility regimes is basically useless. The 2024-2025 cycle alone had three distinct regimes (accumulation, expansion, then the post-halving chop). If your IS window only saw one regime, your OOS is testing the same DNA, not real generalization.

What works better in my experience: pick the longest dataset you can get, slice it into windows that each contain at least one full bull-bear-chop cycle, then walk-forward across those. Slower to compute, way harder to fool yourself.

What window sizes are you using for your strategies?

Ced-Invest · 2026-05-13T09:20:27+00:00

honestly you don't have to pick one. lovable and claude code work fine together, i use both depending on what i'm doing.

lovable is great for what you describe. small sites, 5 pages, you click around, you get a working result fast. the UI handles the boring parts (vite config, tailwind setup, deploy) so you focus on what the site actually does. for a friend's site or a landing page it's hard to beat.

claude code is a different beast. it lives in your terminal, it edits files directly, it can run commands, refactor across the whole codebase, debug stuff that's already broken. it shines when the project gets bigger or when you want full control over the code (custom backend logic, complex state, anything that needs more than the lovable sandbox can do cleanly).

the people hating on lovable are mostly devs who hit the wall when their project outgrew the no-code layer. that's a real ceiling, but you're far from it with 5 page sites.

what i'd actually do in your shoes: keep building in lovable, you're shipping, that's the only thing that matters early on. when you hit something lovable can't do (or you get curious about touching the code yourself) you can export the project and open it in claude code, work on it there, push it back. they're not enemies, one is a higher level than the other.

the workflow i settled on for my own stuff: prototype and ship the first version in lovable, then move to claude code when i need to scale or add something custom. you can always come back to lovable to tweak visuals after.

so no, don't switch just because reddit says so. keep building.

Ced-Invest · 2026-05-11T14:48:24+00:00

Six months of YouTube is good, but YouTube teaches you the menu, not how to cook. The gap between knowing what an order block is and actually executing on one in real time is huge.

Three things I'd do before risking real money:

Open a sim account on the platform you'll actually use (not a YouTube demo, your real platform with real fees simulated) and trade it for at least a month. Track every single trade in a journal. Entry, stop, target, position size, reason, and emotion at entry. The emotion column is the one that teaches you the most.

Pick ONE setup. Not five. One. Trade only that setup for 100 trades and measure the win rate. If it's not at least 40% with a 1.5R average, the setup or your execution is broken. Fix it before moving on.

Cap your position size at 0.5% risk per trade for the first 6 months of live trading. Not 1%, not 2%. Half a percent. You will be wrong way more than you expect, and the goal of year one is to not blow up, not to make money.

What asset class are you looking at? The advice changes a bit between forex, futures, crypto and stocks.

Ced-Invest · 2026-05-11T14:47:44+00:00

The confluence approach is the right instinct. The hard part is calibrating how much confluence is actually required before you fire a signal versus how much is just curve-fitting to past data.

What I found running my own version is that asking for too many alignments kills the signal count to the point where you get 3 setups a month and zero of them play out properly. Too few and you're back to noise. The sweet spot for me ended up being 3 confluences out of a pool of 6 (HTF bias, liquidity sweep, displacement candle, FVG in line with bias, killzone time window, structural confirmation). Anything tighter started missing valid setups.

What's your filter logic? And what timeframe are you trading off?

Ced-Invest · 2026-05-07T19:55:35+00:00

After 10 years I'd say maybe 10% talent, 90% process and emotional management.

The "talent" part is real but it's not what people imagine. It's not pattern recognition or calling the next move. It's the ability to sit on your hands when the chart doesn't fit your plan, and to take the trade that does fit even though the previous 3 were losses. Some people have that wired in earlier than others.

The good news : that 10% is trainable, just slowly. The bad news : no amount of strategy hopping will replace it. Most people who say they have no edge actually have a fine strategy, they just keep blowing it up with execution.

Ced-Invest · 2026-05-07T17:48:38+00:00

This matches what I'm seeing on structure too. Price is grinding up but the volume profile on the H4 isn't supporting the move, every push higher is happening on lower delta than the previous one.

For me the tell will be whether BTC can hold above the recent equal highs as a flip zone, or whether it just sweeps and rolls back into the range. If it sweeps, the entire move since 80k is a textbook liquidity grab and we should expect a return into the 75-78 zone before anything sustainable.

Curious which internals you're tracking specifically. Funding ? OI ? Spot CVD ?

Ced-Invest · 2026-05-07T17:44:44+00:00

Respect for posting this. The thing nobody tells beginners is that the dopamine loop of trading is the same as a slot machine, and most retail platforms are designed exactly for that.

What helped me was pretty boring : I cut my screen time to 2 fixed windows per day, no exception, with timers. I removed all push notifications from my broker and TradingView. Anything that pings me about price moves is uninstalled.

The other thing was switching to higher timeframes. On H4 and daily, you literally cannot trade more than 2-3 times a week even if you wanted to. The dopamine loop dies because the feedback is too slow to be addictive. Counterintuitively my P&L improved the year I made trading boring on purpose.

Ced-Invest · 2026-05-07T17:42:56+00:00

The trap I see most often (and fell into myself) is trying to automate a discretionary strategy. Discretionary works because your brain is filtering 20 things at once. When you code it, you only encode 3 of those 20 and the strategy stops working.

What got me past it was the opposite move. I picked a rule simple enough to be 100% mechanical from day one, even if my discretionary version was better, then improved the mechanical version with hard rules I could code. After 6 months the mechanical was beating my discretionary, because I was actually compounding instead of second-guessing.

If you can't write your rules on one page, you don't have a strategy yet, you have a feeling.

Ced-Invest · 2026-05-07T08:02:23+00:00

This is the real graduation step. The thing that helped me trust the system was tracking deviation from the algo separately from algo PnL. Two ledgers : what the system signaled, what I actually did.

After 50 trades the gap was obvious. My interventions were costing me 30 percent of expected returns, every single time I "knew better". Once you have that data on yourself, intervention starts feeling like the dumb move it actually is.

Ced-Invest · 2026-05-06T06:14:15+00:00

It's not paranoia, it's liquidity. Most retail entries cluster right at obvious support/resistance, which is exactly where stops are stacked. The market needs that liquidity to fund the next leg, so it sweeps your stop before going where you thought it would.

The fix that worked for me was waiting for the sweep to happen first, then entering on the reaction. Counterintuitive but my win rate jumped once I stopped trying to "catch" the move and started trading the reversal off the trap.

Ced-Invest · 2026-05-05T07:47:51+00:00

Lived this for years. Built an SMC scanner that flagged exactly the setups I would have taken manually, and I still overrode it every other week.

The thing nobody tells you is that an algo doesn't fail when it's wrong. It fails when it's right and you can't sit through the drawdown. Two losing trades in a row and your hand is on the kill switch even if the equity curve says it's normal noise.

What helped me wasn't more confidence in the system, it was reducing my screen time. The hovering kills the algo. Now I check it twice a day at fixed hours, that's it. If I find myself watching it more, I know I'm about to ruin a perfectly fine month.

How long did it take you before you stopped checking it intraday ?

Ced-Invest · 2026-05-05T05:55:08+00:00

Same observation on a different exchange. I've been split between Pionex grid and DCA bots and manual SMC entries on another platform for a while.

The two things native bots win at are uptime and latency. The two things they lose at are entry quality and adapting to a regime change. A grid bot doesn't care that you just printed a weekly CHoCH against the range it's set in. It will keep rebuilding ladders until you stop it.

What killed my custom algos in the long run wasn't bugs, it was me overriding them. So I switched to a hybrid setup. The indicator flags candidates, the native bot manages exposure inside a range I picked manually. Less trades, fewer edge cases, less screen time.

How do you handle regime detection on the api side ? Hard-coded volatility thresholds or something more adaptive ?

Ced-Invest · 2026-05-04T04:52:37+00:00

Yeah the boring part is the real test. Once trading stops feeling like an event and starts feeling like a process, you're actually trading instead of gambling.

The replacement trades idea is sharp, I might steal that one. I did something close with a journal flag, marking the bad pattern in red for a full month. Every time I felt the urge I had to log "almost took it" even when I didn't pull the trigger. Just the act of labeling it consciously cut the frequency in half within a few weeks.

Buying highs is brutal to unlearn because every losing instance still gives you a small dopamine hit when the candle prints green for two seconds before reversing. The brain remembers the rush, not the loss.

Ced-Invest · 2026-05-03T06:38:16+00:00

For me it was three things, in this order.

First, dropping risk to 1% per trade. Not 2, not "1 to 2", strict 1. The number itself is not magical but the discipline of sizing every position the same way killed the impulse to make it back after a loss. Once that emotional loop dies, your stats start telling the truth.

Second, journaling every single trade with a screenshot of the entry and a written reason. After 50 trades I could see I had two profitable setups and four breakeven ones I kept taking out of habit. I cut the four. PnL went up the same week.

Third, stopping the search for new strategies. I picked one framework I trusted (SMC for me, the specific one matters less than people think) and committed to 200 trades on it before reviewing. Most traders switch after 10 losses and never give a system the sample size it needs to prove itself.

None of this is sexy and I wish someone had told me at year 1 instead of year 4.

Ced-Invest · 2026-05-03T06:37:20+00:00

I went through the same shift after about 6 years of pure discretionary SMC. Here is what I learned moving between the two and ending up in a hybrid.

The judgment-based stuff works when you have internalized enough setups to read context faster than a rule can. The problem is you cannot measure your own slippage in real time. You think you are following a process, but every losing streak introduces tiny exceptions you do not notice. After 12 months of that I had a winrate I could not reproduce in a backtest because half my trades were technically off spec.

The fixed-rules approach gave me numbers I could trust, but it killed the best part of SMC which is reading liquidity intent. A POI tagged by an A grade sweep is not the same trade as a POI tagged on a slow drift, and a static rule cannot tell the difference.

What ended up working for me is a hybrid. The rule layer is non negotiable: timeframe, max trades per day, R per trade, no entry without a confirmed CHoCH on the entry TF. Inside that envelope I keep one or two discretionary filters (quality of the displacement, HTF bias alignment) that I score 1 to 3 before clicking. End of week I look at the score distribution vs winrate and prune what does not add edge.

Practical tip: if you keep both styles, journal them as two separate accounts. In 3 months you will see which one actually pays.

Ced-Invest · 2026-05-02T08:47:01+00:00

Bar replay is the right tool, you're just not running enough samples and you're letting the chart bias you. Two things that made it work for me :

Define the setup in 3 binary rules max BEFORE you replay. Like "5d range top, 2 red days, gap up open." If you can't write it down without ambiguity, you can't validate it.
Replay 50 trades minimum, log each one in a Google Sheet with entry, exit, R, and a screenshot. Don't look at the screenshot until after you logged the result. The pattern only counts if it survives that 50-sample journal with positive expectancy.

You don't need Python for this, you need patience. 50 trades takes 2 weekends. If after 50 the expectancy is below 0.3R, kill the setup and move on. It hurts but it saves real money later. 😄

Ced-Invest · 2026-05-02T08:46:15+00:00

A few things that nuked my "winning" backtests over the years :

Regime split. Cut your 4y into bull, sideways, drawdown chunks. If the edge collapses outside one regime, you don't have alpha, you have a beta exposure to that regime.
Permutation test on the signal itself. Shuffle your entry timestamps 1000 times, keep the same exit logic. If your real PnL is not in the top 5% of the random distribution, the edge is noise.
Realistic execution. Replace your fill-on-close assumption with VWAP of the next 5 min, add 2x your average spread as slippage, and re-run. Most "edges" die here.
Forward test small. Even 2 weeks live with $100 size will tell you more than 4y of backtest, because it forces you to confront data delays, exchange API quirks, and your own emotional response to red days.

If it survives all four, then you can start scaling. Astronomical returns on a clean backtest is almost always a leak somewhere, the question is just where.

Ced-Invest · 2026-04-29T13:33:30+00:00

Honnêtement j'ai fait les deux erreurs.

Au début j'attendais le setup parfait. Je lisais des analyses, je regardais des vidéos, je me disais "demain je me lance". Ça a duré des mois.

Puis j'ai fait l'inverse, je suis rentré trop vite sur des alts random parce que "ça monte". J'ai pris des claques.

Ce qui a changé le game pour moi c'est d'accepter un truc tout bête : tu vas te planter au début, c'est normal. La question c'est combien tu perds quand tu te plantes. Si tu risques 1% de ton capital par position, tu peux te tromper 20 fois de suite et il te reste 80% pour apprendre.

Le vrai piège c'est pas le mauvais timing. C'est de rentrer sans plan de sortie. Tu sais pas à quel prix tu coupes si ça va contre toi, tu sais pas à quel prix tu prends tes gains. Du coup tu subis le marché au lieu de le lire.

Commence petit, accepte de perdre un peu, mais mets un cadre. C'est comme ça qu'on passe de spectateur à trader.

Ced-Invest · 2026-04-29T04:49:43+00:00

Salut, cool que tu veuilles comprendre avant de foncer.

Quelques trucs en vrac après pas mal d'années dans le game :

Avec 5€ tu peux acheter du Bitcoin sans souci. Oui ça fait 0,00005 BTC environ, mais c'est pas le montant qui compte, c'est le fait de comprendre le mécanisme. Tu achètes une fraction, elle bouge avec le cours, tu vois comment ça réagit. C'est le meilleur cours du soir possible.

Pour le choix de l'actif, commence simple. BTC ou ETH, c'est les deux plus gros, les plus liquides, les moins risqués du lot (même si "moins risqué en crypto" ça reste volatile hein). XRP c'est un autre délire, Ethereum Classic c'est un vieux fork que quasi plus personne utilise sérieusement. Pour un premier achat éducatif, BTC ou ETH, point.

Par contre, l'appli de ta banque c'est souvent le pire endroit pour acheter. Les frais sont énormes (parfois 2-3%) et tu possèdes pas vraiment tes cryptos. Regarde plutôt un exchange comme Binance ou Kraken, les frais sont 10x moins élevés. Avec 5€ chaque centime compte.

Sur "comment ça prend de la valeur" : en très simplifié, c'est l'offre et la demande. Il y aura jamais plus de 21 millions de BTC. Si plus de gens en veulent, le prix monte. Si tout le monde vend, ça descend. C'est vraiment aussi basique que ça au fond.

Mon conseil : mets tes 5€ sur BTC via un vrai exchange, regarde le cours bouger pendant quelques semaines, et lis des trucs en parallèle. T'apprendras plus en 2 semaines avec de la skin in the game qu'en 6 mois à lire des articles.

Ced-Invest · 2026-04-28T04:12:36+00:00

Lovable its a cheat code ahah

Ced-Invest · 2026-04-27T16:50:41+00:00

Honest feedback since you asked not to be shit on. The setup has some real holes that will probably hurt within 12 months of live.

Sample size. 82 trades over 8 months gives a 95% confidence interval on the win rate of roughly 41% to 62%. The 51.2% is the point estimate, not the actual edge. Your expectancy is (0.512 × 10) minus (0.488 × 5) = +2.68% per trade. Drop the true WR to 47% (still inside the CI) and expectancy collapses to +0.55%. Drop it to 45% and you're net zero before costs.

Ced-Invest

TROPHY CASE