CodeMot Model (Multi-Objective Trading, MOT) is an AI-driven quantitative trading engine designed around a single core question:
When should a trade be executed?
Instead of focusing on short-term price prediction, Mot Model prioritizes time-based decision making, combining multiple specialized AI models into a unified Time Decision Engine that outputs actionable trading states:
ENTER · HOLD · WAIT · EXIT
Traditional quantitative systems often fail not because the direction is wrong, but because the timing is wrong. Mot Model addresses this structural weakness by elevating trade timing to a first-class optimization objective.
Key advantages:
- Time-first decision logic (not price-first prediction)
- Multi-model collaboration with strict role separation
- Centralized Time Engine with final veto authority
- Explainable decision chain (no black-box trading)
- Adaptive behavior across market regimes
Data Layer
│ Market · News · Macro · Financials
▼
Feature Engineering
│ Time Slicing · Factor Construction · Noise Filtering
▼
Model Layer
│ LSTM | Transformer | XGBoost | CNN | Reinforcement Learning
▼
MOT Time Engine (Core)
│ Time Scoring · Multi-Objective Fusion · Risk Guard
▼
Execution Signals
ENTER · HOLD · WAIT · EXIT
All model outputs must pass through the MOT Time Engine. No individual model is allowed to place trades directly.
| Model | Primary Role |
|---|---|
| LSTM | Temporal trend detection & rhythm consistency |
| Transformer | Multi-factor attention & dominant signal identification |
| XGBoost | Statistical edge validation & risk confidence |
| CNN | Structural pattern recognition & false-break detection |
| Reinforcement Learning | Trade frequency, timing & execution path optimization |
Each model solves one clearly defined problem, reducing systemic instability.
- Trend consistency confirmed (LSTM)
- Dominant factors aligned (Transformer)
- Market structure validated (CNN)
- Statistical edge exceeds threshold (XGBoost)
- Trading rhythm within safe bounds (RL)
- Structural distortion or false breakout detected
- Excessive trading frequency or degraded rhythm
- Risk exposure exceeds system constraints
Any single veto forces the system to downgrade to WAIT or HOLD.
Mot Model is designed so that users do not need to understand the algorithms, but can clearly perceive whether trading is currently allowed.
Key UI signals:
- Signal Confidence – multi-model consensus strength
- Model Consensus Index – number and agreement of contributing models
- RL State – Enter / Hold / Reduce / Exit
- Risk Exposure Level – volatility and drawdown synthesis
Mot Model dynamically reallocates model weights based on market regime:
| Market Regime | System Response |
|---|---|
| Trending | Increase LSTM & Transformer influence |
| Range-bound | CNN + XGBoost dominate |
| High Volatility | RL tightens timing and exposure |
| Macro Events | Risk guard overrides signals |
This ensures the system reconstructs its decision logic in real time, rather than relying on fixed strategies.
AI should manage trading time, not guess prices.
Mot Model focuses AI capability on trade permission, timing, and rhythm control, delivering a trading engine that is:
- Executable
- Explainable
- Evolvable
This project is provided for research and system design purposes only. It does not constitute financial advice or investment recommendations. Trading involves substantial risk.
© Mot Model™ AI Quantitative Trading