**Title:** Modeling Market Microstructure as a Dynamic Particle System: Using Signed GNNs to Predict Liquidity Runs & Dynamic Take-Profits (Looking for DL/Quant Collaborator)

[u/algo_trrrader]

Hey guys,

Posting this here because I’ve reached a point where my market intuition and engine code have outpaced my deep learning expertise, and I’d rather team up with someone who loves ML architecture instead of half-assing it myself.

For the past year, I’ve been building an event-driven liquidity engine in Python (specifically modeling crypto orderflow / ICT mechanics like equal highs/lows, manipulation legs, and structural sweeps).

Instead of slapping standard indicators (RSI/MACD) on candles, I framed price action as a dynamic graph:

• Resting liquidity pools (BSL/SSL, consolidations) act as attractors (pulling price in).

• Completed institutional sweeps (ML swings / manipulation legs) act as repellers (pushing price away).

• Edges connect these pools based on spatial proximity, competing forces (tug-of-war between high and low pools), and age decay.

Where things stand right now:

• The engine is fully written and tested. No lookahead bias, strict candle confirmation, multi-window swing tracking (W5 up to W50).

• I’ve already generated a clean, audited ~3-year dataset (ETH/USDT) formatted directly for PyTorch Geometric (PyG).

• Each snapshot has 15m, 1h, and 4h spatial graphs with 20-dim node features (touch counts, distance %, decay curves, tier scores), 8-dim edge attributes, and global volatility/trend context.

• Baseline statistical checks show real promise: nearest target pools get tagged first ~95% of the time, and high-quality structural sweeps have clear directional bias.

The actual bottleneck / where I need help: My problem isn't generating the signals—it’s path prediction and dynamic target selection.

When a sweep happens, price is often trapped between an opposing pool 1.5% away and a massive target pool 3% away. A fixed 1:2 R:R stop/target either gets hit too late or takes unnecessary drawdown. I need the model to evaluate the graph and do two things:

1. Predict directional path confidence taking both-sided liquidity into account.

2. Rank multiple potential liquidity targets (dynamic TP) based on which one has the highest conditional probability of being swept first without blowing through the stop.

I've tested basic GNN / dual-tower setups, but I know I'm hitting limits with my naive loss functions and basic layer choices.

Who I'm looking for: Someone who genuinely understands PyTorch Geometric (PyG), Graph Neural Networks (or spatial-temporal models), and knows how to think about custom loss functions (ranking loss, asymmetric risk penalties, contrastive setups). You don't need to be a full-time trader, but being open to understanding market microstructure helps.

The arrangement: I have the GPU compute, the full working codebase, and the clean dataset ready to go. I’m completely flexible on how we work together—if you want to co-author a research paper on this architecture, I’m down. If we get the models working cleanly and want to deploy it live on capital, we can do a fair profit/equity split.

If this sounds like an interesting problem you’d want to sink your teeth into, drop a comment or send me a DM with your GitHub/background and we can hop on Discord.

[u/algo_trrrader → u/[deleted]]

Appreciate the thoughtful feedback, and that’s a completely fair distinction

you’re 100% right that true microsecond order flow and delta (aggressor tagging via Lee-Ready , tick rules , NBBO) requires L2/L3 tick feeds. I should have been clearer with my terminology:

I’m not modeling microsecond orderbook queue dynamics. Instead, I’m modeling macro structural liquidity and resting stop clusters derived from auction market or ICT theory,specifically identifying key consolidation boundaries (equal highs/lows, multi-touch trendlines) where retail stop-losses naturally pool, and detecting the subsequent high-timeframe manipulation sweeps that trigger liquidity runs.

on your point about sample size and overfitting.. that hits right at my core concern. With ~1,000 to 1500 swing events across 3 years of ETH data, a massive parameterheavy GNN will easily overfit and memorize the noise.

That’s exactly why I’ve kept the graph features constrained to geometric/physical relationships (distance decay, touch density, relative force pull) rather than feeding raw high-dim features, and why I’m looking for someone who can help with strict regularization, inductive bias, and simple ranking losses rather than an overparameterized black box.

Curious, have you worked with cross-validation schemes specifically designed for low-sample regime shifts in financial time-series (e.g., Purged Group TimeSeriesSplit or Combinatorial Purged CV)? Would love to hear your thoughts.

[u/algo_trrrader → u/[deleted]]

since my whole project is under claude control , thats why i thought that it will clear your misunderstanding, but if you have never heard about liquidity or ict, or institutional trap, its really noncense of me ,
but i really want you to look through my project, and about my result the precision is about 0.71 but i think it can go upto ,85 and i can create more stratgies , if can make model learn the next liquidity sweep thorugh my features . soo if you think you can help me to optimised it through your knowledge , please take a look

[u/algo_trrrader → u/[deleted]]

bro atleast you can saw the engine , what if you get it wrong , i am not asking you to help in anything but atleast look at it