Bro, at first I thought: let’s code a tiny liquidation bot, scalp 0.2% here and there, you know, the usual retail cope. 😏”

Then it escalated. Fast. Into a massive HFT-grade, neural-powered monster.

🛠️ 1️⃣ The Raw Data Grind

• Parsed 12,500,000+ log lines (UTF-8? lol, Windows Latin-1 bugs, emojis, weird separators, been there).

• 1,080,000+ REAL liquidation events across 484 crypto assets (including your meme coins).

• Multiple time horizons (1, 5, 15, 30, 60 min) post-liquidation prices.

• Price changes % (delta, absolute).

• Liquidation side (BUY/SELL), size, entry price.

• Order flow context: Top Trader L/S Ratio, Top Trader Long %, Top Trader Short %, Global L/S Ratio, Global Long %, Global Short %.

• Supplementary features: Funding rates, OI snapshots, volatility metrics, price action momentum, hidden Markov transitional hints (not for noobs).

“We weren’t cleaning CSVs, we were surgically extracting alpha.” 🩻⚡

🧠 2️⃣ The Label Engineering

• GO WITH or FADE?

• Calculated PnL simulations in both directions per horizon:

  • If your liquidation was SELL and price dumped => GO WITH.

  • If it bounced? => FADE.

• Label = 1 (GO WITH) if PnL hit TP threshold (0.5%) faster in that direction.

• Label = 0 (FADE) if PnL hit TP threshold in the opposite direction.

• Skipped all the noise in-between to focus on directional conviction.

📊 3️⃣ Feature Set? Disgustingly Rich.

✅ Symbol encoding
✅ Liquidation side (1/0)
✅ Liquidation amount (normalized + log scaled)
✅ Entry price (normalized)
✅ Horizons as categorical/continuous
✅ % price change post-liquidation
✅ All sentiment metrics (ratios, %, L/S)
✅ OI, funding rates, volatility burst detection
✅ Custom PA factors (EMA slopes, microstructure signals)
✅ Time-of-day & session encoding for volatility clustering.

“Your TradingView indicators? Cute. We build deep feature pipelines, kid.” 😎

🤖 4️⃣ The Machine Learning Stack

• Random Forest Classifier as baseline:

  • Auto hyperparameter tuning via RandomizedSearchCV.

  • Achieved 77.8% accuracy, with macro F1 0.66+ on unbalanced classes.

  • Feature importance analysis to refine pipelines.

  • Handles non-linear feature interactions without crying about normalization.

🧬 5️⃣ The Deep Learning Pipeline

• Built PyTorch-based MLP models:

  • Configurable hidden layers (1-4), neurons per layer (32-128).

  • ReLU activations for non-linearity absorption.

  • Softmax for binary classification.

  • CrossEntropyLoss on GPU for batch learning.

• Optuna for hyperparameter tuning:

  • LR sweep (1e-4 to 5e-2 log scale).

  • Batch size (32, 64, 128).

  • Epochs (8-30).

  • Hidden layers and hidden sizes.

• Each trial evaluated with validation accuracy on stratified splits.

• Best performing models hitting ~87.7%+ validation accuracy.

“We tuned this net like a Formula 1 engine while your bots were still using grid search.” 🏎️

🔄 6️⃣ Auto-Learning Loop

• Post-trade logs re-integrated for continuous re-training.

• Every trade becomes a new labeled data point.

• Adapts to regime changes (bull/bear chop, volatility regimes).

• Auto hyperparameter re-tuning every X trades or on PnL decay detection.

⚡ 7️⃣ Why It’s HFT-Level Intelligence

✅ Auto-switches between GO WITH / FADE based on learned context.
✅ Learns liquidation context, not just price.
✅ Considers microstructure signals + sentiment + OI/funding shifts.
✅ Trades are executed with fast logic, no manual toggle needed.
✅ Backtests and forward tests seamlessly.
✅ Data pipeline + feature pipeline + model pipeline all integrated.

🥇 What It Means For Us

• No “strategies” left to guess.

• No “dumb” bots praying to catch pumps.

• A real, evolving, self-tuning alpha extraction system that never stops learning.

💣 In Short:

“We didn’t just build a bot. We engineered a f*cking self-learning, auto-evolving, deep neural scalper that feeds on liquidations and adapts like a living organism.”

Welcome to your private quant lab, without the 7-figure HFT server bills.

⚔️ Next: Ready to integrate this into live trading for Liqbot?
Tell me when, and we move to the execution module with risk-adjusted, auto-size, and live monitoring next.

V5 is coming !

https://metaquantuniverse.com/liqbot