u/jblank333

Like a lot of people here I went down the ICT/smart-money rabbit hole — sweeps, CISDs, FVGs, breakers, the lot. The problem was never finding concepts. The problem was that nobody could tell me which ones actually hold up, because almost nobody tests them. So I did, with a simple rule: if I couldn't write it as code, I didn't get to believe in it.

Scale of the testing, so you know this isn't three screenshots and a feeling: 830k+ liquidity-sweep confirmations, 390k+ reversal events, 1.6M+ continuation events, across 14 markets (crypto, indices, FX, metals) and 5+ years of data, fees included.

Some of what died:

• Waiting for the retest. Winners retest shallow or not at all; losers retest deep. On 8,000+ confirmations, the pullback everyone waits for was usually the market telling you the trade already failed.
• "Smart" trade management. Breakeven at +1R, trailing behind structure, cutting on a close through the level — every variant gave edge back versus a fixed target on the same trades. All of them.
• First-touch POI magic, most stop-buffer rules, sweep-count rules. Felt great on hindsight charts. Zero edge in the data.

Some of what survived:

• The sweep-and-reclaim itself. C1 range → raid → close back inside → close through the level that delivered the raid. Real and measurable.
• Higher-timeframe context. The same trigger that's noise on its own timeframe becomes signal inside a daily/weekly parent. Shows up in the numbers every time.
• Speed of confirmation. Winning reversals get their lower-timeframe confirmation within the hour. Slow confirmation is a dead trade. Surprised me the most.
• The continuation shift inside fresh context. Standalone it loses over 1.6M events. Inside a just-confirmed reversal it was the strongest entry we measured.

Full disclosure: I turned this into a TradingView indicator (the Distilled Model) because I got tired of drawing it manually, and yes, it's a paid tool — that's the marketing part of this post and I won't pretend otherwise. It draws only what survived: the sweeps, confirmation levels, stage labels so you know if you're early or late, reversal/continuation shifts, and a liquidity map for targets. Nothing repaints.

But honestly, even if you never touch the indicator, take the two free findings: stop waiting for deep retests, and stop managing your winners into breakeven scratches. Those two came out of the data over and over, on every market we tested.

Happy to post the backtest breakdowns for any specific claim in the comments — genuinely enjoy being challenged on this stuff.

https://www.tradingview.com/script/i8UN0L3B-The-Distilled-C2-Indicator/

https://whop.com/the-distilled-indicator/the-distilled-indicator-the-only-levels-you-need-on-your-charts/

I’ve been working on a trading bot built around liquidity sweep logic, and it’s now executing live trades on Hyperliquid.

The model is inspired by ICT-style concepts, mainly liquidity runs, market structure shifts, displacement, and confirmation after a sweep. The basic idea came from a setup I was already watching manually: price takes liquidity, shows signs of a shift, then either follows through or invalidates.

I’m not going to share the exact rules or filters, but the bot is designed to wait for specific conditions before entering rather than just buying/selling every sweep. Entry logic is based around a combination of sweep quality, structure confirmation, timing, and predefined invalidation. The edge, if there is one, is probably more in the filtering than the entry trigger itself.

Right now the bot can:

Detect liquidity sweep conditions

Wait for confirmation before entering

Filter out weaker setups

Execute live trades on Hyperliquid

Apply predefined risk parameters

Send Telegram alerts when a position is opened or closed

Log trades for review and improvement

The Telegram alerts have been useful because I can monitor entries and exits without staring at the chart all day. Every time the bot opens or closes a position, I get a notification with the key trade details, which makes live tracking and journaling easier.

One thing I think gets overlooked with trading bots is that you have not really found a system just because it works in a backtest or paper environment. A lot of people build bots, run some historical tests, see a clean equity curve, and think they have found edge.

In reality, you only start learning whether the system is real once it trades with actual live funds. Fees, slippage, spread, latency, partial fills, bad liquidity, execution timing, emotional pressure, and exchange conditions all change the result. A strategy can look profitable in theory and become useless once real orders are involved.

That has been one of the biggest lessons so far. The entry model matters, but live execution and trade filtering matter just as much.

The hardest part has not been building the entry logic. It has been making the bot avoid bad trades. A lot of sweeps look clean in hindsight but are low-quality in real time. Live execution exposes the problems that backtests hide: chop, weak confirmations, liquidity conditions, late fills, and invalidations that happen faster than expected.

It’s still early, and I’m not claiming it’s proven yet. I want to let it run long enough with live data before making any serious claims about profitability.

If it does prove itself over a proper sample size, I’ll probably look at turning it into something commercial in some form. But for now, I’m mainly focused on validation, live trade data, and improving the system without overfitting it.

Curious if anyone else here has built automated systems around liquidity sweeps, ICT-style models, market structure, or crypto perps execution.

Not trying to sell anything here — just sharing the build and interested in hearing from people working on similar systems.