TA Quant AI
TA Quant AI - Multi-Agent
Systematic Trading Intelligence
Five specialized agent classes. Three published research papers. 91 active strategies across Trend Following, Mean Reversion, Volatility Expansion, Cross-Exchange Arbitrage, and ML-Enhanced Prediction. Behavioral state inference from trade execution data, not price prediction.
Proven Alpha
Built for Professional Traders
91 active AI-driven strategies executed on real infrastructure with institutional-grade smart order routing, deterministic risk controls, and sub-10ms internal latency.
Five Agent Classes
Each class operates independently with its own objectives. Capital is allocated dynamically across agent recommendations through a multi-agent tournament - not single-strategy dependence. Intelligence, Execution, and Control layers operate in isolation, enabling independent auditing and deterministic risk enforcement.
Intelligence Layer
- Market Regime AgentsBull, bear, turbulent, low-vol classification
- Alpha Research AgentsCross-strategy signal synthesis
- Trend FollowingMomentum detection across timeframes
- Mean ReversionStatistical arbitrage signal layer
- Volatility ExpansionBreakout and range-compression detection
- Cross-Exchange ArbitrageSpread capture across 50+ venues
- ML-Enhanced PredictionTAQ foundation model signal layer
- Regime Detection78% accuracy, 5 market condition types
Execution Layer
- Execution Intelligence AgentsSmart order routing optimization
- Orderbook Depth AnalysisReal-time liquidity assessment
- Fee and Rebate OptimizationMaker/taker economics per venue
- Fill Probability ScoringHistorical execution quality weighting
- Latency Stability RoutingSub-10ms internal routing
Control Layer
- Risk and Exposure AgentsPosition and drawdown monitoring
- Portfolio OrchestrationMulti-agent tournament allocation
- Offline Policy TrainingNo live learning on production capital
- Expert Validation GatesHuman oversight before deployment
Execution and Risk Management
Deterministic, not probabilistic
Execution Intelligence
Smart order routing across 50+ exchanges optimized across four dimensions: orderbook depth and spread, fee economics and rebates, historical fill probability, and latency stability.
Risk and Exposure Agents
Real-time position monitoring with drawdown floors, exposure limits, and circuit breakers enforced independently of strategy logic. Capital protection is deterministic, not probabilistic.
Portfolio Orchestration
Multi-agent tournament synthesizes competing policy variants into final capital allocation. No single-strategy dependence. Dynamic weighting across all agent recommendations.
AISA Architecture
Multi-Agent Architecture
Strategy logic, execution, and risk management operate in distinct layers to prevent unauthorized trading and enable independent auditing. All strategy policy updates occur offline using logged market data. No live learning on production capital. Event-sourced pipeline logs all market data and decisions immutably, with expert validation gates before any strategy reaches production.
Proprietary Core
Behavioral State Inference Engine
TAQ formalizes trader behavior as a latent variable within a partially observable dynamical system. Self-supervised learning on 503,000 time series across crypto and traditional markets. No price prediction required - behavioral embeddings map directly to deterministic exchange controls: fees, throttling, and incentive mechanisms.
Latent Behavioral States
Infers low-dimensional behavioral representations capturing execution style, aggressiveness, stability, and regime sensitivity from trade execution sequences.
Adaptive Market Mechanisms
Behavioral embeddings enable deterministic exchange controls, fee schedules, and incentive mechanisms without relying on price prediction.
Regime-Aware Trading
Captures both short-term tactical behavior and long-term strategic identity, enabling adaptive strategies across market conditions.
Regulatory Alignment
Interpretability, auditable behaviors signals that support regulatory requirements while enabling adaptive yet deterministic market design.
Systematic Advantage
- ✓
Rule-based decisions eliminate emotional trading bias
- ✓
Consistent execution across all market regimes
- ✓
Deterministic risk controls bound every probabilistic strategy
- ✓
Offline policy training protects live capital from untested models
Discretionary Limitations
- ×
Emotional bias creates inconsistent decision-making
- ×
Performance varies with trader psychology and fatigue
- ×
No structural protection from behavioral drift
- ×
Limited scalability across regimes and instruments
Key Features
TAQ Foundation Model
Self-supervised behavioral state inference trained on 503,000 time series from Binance, Hyperliquid, dYdX, Coinbase, NASDAQ TAQ, and CME Futures. TCN backbone with 4 transformer layers. Median runtime 1.8 seconds.
Proof-of-Behavior Consensus
PoB consensus layer integrating behavioral embeddings with blockchain infrastructure. Greater than 90% fraud reduction versus Proof-of-Stake baseline. EVM and ZK-rollup deployment pathways.
Regime-Aware Strategy Selection
Market condition classification across bull, bear, turbulent, low-volatility, and ranging regimes. Strategy families are activated and deactivated based on current regime state. 78% regime detection accuracy.
Offline Policy Training
All strategy policy updates occur offline using logged market data. No live learning on production capital. Multi-agent tournaments compare competing variants before any strategy reaches deployment.
Rust-Native Backtesting
Strategies validated on real execution infrastructure, not synthetic data. Fee-adjusted return evaluation with exchange economic awareness. Signal generation cycle under 1 second.
Behavioral State Inference
Four interpretable market participant types with silhouette score 0.62. Infers execution style, aggressiveness, stability, and regime sensitivity from trade sequences without relying on price prediction.
Published Research
Three peer-level research papers underpinning the TAQ foundation model, Proof-of-Behavior consensus layer, and AISA agent system. Open to read.
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