IFVG Machine Learning Model

An invite-only, multi-layer Inversion Fair Value Gap framework. It detects hidden FVGs, confirms the inversion on the close, then filters each setup through liquidity sweeps, HTF bias, volume and a quality grade — with an adaptive Best Mode that ranks 11 internal filter combinations on virtual results, a chart-and-HTF IFVG ledger, automatic invalidation, a full Entry / SL / TP1-TP2-TP3 model and a premium checklist dashboard.

If you trade fair value gaps, you already know the real problem: there are too many of them. Every displacement leaves an imbalance, every imbalance looks tradable, and so you end up reacting to gaps that form in dead volume, against the higher-timeframe trend, with no liquidity taken — and you can't understand why the 'clean' setups keep failing. The issue was never the concept. It was that nothing was filtering the noise out before you acted.

IFVG Machine Learning Model PRO+ is built around one disciplined idea: don't chase the gap, wait for the inversion, then check the structure. It tracks hidden FVGs in memory, waits for price to invert and close through them, and only then runs the setup through six layers of context — a liquidity-sweep engine that marks where buy-side and sell-side liquidity was taken, an HTF market-structure bias engine (H1 + H4) that can require both timeframes to agree, relative-volume confirmation, and an adjustable quality filter measuring gap size, displacement body and range against ATR. What survives is drawn as a clean IFVG line with a full Entry / SL / TP1 (0.5R) / TP2 (1R) / TP3 (2R) model. What doesn't is filtered out before it ever clutters your chart.

Then there's the part that gives it its name. Behind the scenes the script runs 11 internal filter combinations as virtual trades and tracks how each one actually resolves. Its adaptive Best Mode promotes the combination that is currently ranking strongest on closed virtual results — so instead of you guessing whether to run 'Liq + Bias + Vol / Strict' or 'Bias Only / Loose', the engine surfaces the mode the data is favouring right now, and a checklist dashboard shows you exactly which layers are aligned before you commit. To be clear about what that means: this is internal, historical, on-chart adaptation — not an external AI, not future prediction, and not a promise that the chosen mode will keep performing. Markets change. It is a structured way to filter inversion setups on evidence, with every confirmation laid out in front of you — used as decision support alongside your own plan and risk, never as a signal to blindly execute.

From a blank chart to a read

1. Add IFVG Machine Learning Model PRO+ to any candlestick chart and start with the default settings.
2. Watch for a close-confirmed bullish or bearish IFVG line on your chart timeframe, with HTF-1 / HTF-2 context behind it.
3. Use the checklist dashboard to read which layers are aligned — HTF bias, liquidity taken, volume, quality and confirmation.
4. Leave Trade Mode on Best Mode to let the engine surface its strongest internal combination, or pick a fixed mode for full manual control.
5. Treat the projected Entry / SL / TP1-TP2-TP3 as a structure reference — it excludes spread, commission and slippage.
6. Combine the output with your own strategy and risk management, and test on replay or demo before any live use. It is decision support, not a trade command.