The meeting provided an in-depth walkthrough of the Inverted Liquidity Model (ILM), a trading model designed to capitalize on market reversals by leveraging liquidity concepts and fair value gaps.
Key components covered included liquidity identification, daily trading routines, model entry/exit rules, the importance of data-driven journaling, and discretionary elements like EMA bias and risk management.
The session emphasized actionable steps, including trade journaling, weekly reviews, and top-down technical analysis for trade preparation, with practical examples and references to supplementary resources.
Action Items
Ongoing – All Attendees: Download the free ILM trading model PDF and the trade journal sheets from provided links.
Next trading session – All Attendees: Apply the ILM model in a demo environment before trading live; review results and adjust strategy as needed.
Sundays – Interested Attendees: Join the free weekly live outlook session for level marking and model review.
Ongoing – All Attendees: Record every trade using the provided Google Sheets journal for ongoing analysis and improvement.
Model Overview and Core Concepts
ILM (Inverted Liquidity Model) specializes in trading reversals by identifying where liquidity (market orders) is likely to be present and where fair value gaps are invalidated.
Key concepts include:
Liquidity: Areas where buy/sell orders are concentrated (highs/lows, trendlines, equal highs/lows).
Sell-side and buy-side liquidity: Locations of probable stop orders (short cover/buy back, long exit/sell back).
Fair Value Gaps (FVG): Gaps in price action representing leftover orders; their invalidation signals potential reversals.
EMA (15-minute) bias: Used to quickly determine trending versus ranging markets.
Daily Routines and Trade Preparation
Daily trading routine:
Review losses on weekends for improvement.
Pre-session: Identify draw and liquidity for high timeframes and active session.
During session: Follow a checklist—ensure it’s a major session (NY or London), conduct top-down/low-timeframe analysis, mark key levels (session highs/lows, clusters, trendlines).
Post-trade: Journal every trade with key metrics (free Google Sheets tool provided), focusing on data-driven refinement.
Avoid trading on weekends unless trading crypto; major sessions provide higher opportunity.
ILM Model Rules and Application
Two main model types:
Retest/Quick Entry (trending markets): Look for fair value gap inversion after a liquidity sweep, enter on retest, use EMA for directional bias.
Rangebound Conservative/Lick-to-Lick (ranging markets): Trade frequent but smaller moves within defined ranges, taking quick profits.
Entry: Wait for price to sweep liquidity, produce a fair value gap, then invert (close beyond gap). Enter on retest or immediately, stops below/above the level, and take profit at the next liquidity zone.
Trade management: Maximum two trades per day. Move stops to break even after hitting TP1 (1–2.5R), take 80% profit at TP1, remainder at TP2.
Emphasis on discretion when countertrading relative to EMA distance and observed liquidity clusters.
Data, Journaling, and Continuous Improvement
Rigorous journaling is required to maintain and sharpen an “edge” in trading.
Key trade data tracked includes long/short ratios, win rates per session, and performance by weekday.
The provided Google Sheets journal automates much of the tracking and is recommended over paid alternatives.
Community, Resources, and Support
All resources (full PDF guide, Google Sheets journal, EMA indicators) are provided for free via linked resources.
Weekly live trading analysis sessions are offered every Sunday; participants are encouraged to attend for actionable insights.
Feedback and questions are welcomed via comments or the Discord group.
Decisions
ILM model recommended as primary reversal strategy — based on proven user results, adaptability to all market conditions, and front-tested data supporting profitability.
Open Questions / Follow-Ups
Are there additional edge-case scenarios for ILM not addressed in the main guide?
Would attendees benefit from deeper dives into model variants or additional case studies? Please provide feedback or specific requests in the community channels.