Platform Architecture

AI-Native Investment Architecture

Continuos development of digital traders, quantitative models, and automated execution infrastructure powered by integrated LLM-based qualitative systems and ML forecasting engines. Working to provide customers with reliable and timely technology.

Data Ingestion
Market Data News Feeds Filings
β†’
Intelligence Layer
LLM Analysis ML Models Factor Extraction
β†’
Signal Generation
Ensemble Scoring Risk Assessment Portfolio Optimization
β†’
Execution
Digital Agents Towards Smart Routing Order Management
01

Digital Trading Agents

Work on Autonomous execution and portfolio management systems

Core Capabilities needed by investors

  • Execution automation engines with adaptive slicing algorithms
  • Market-regime detection and strategy adaptation mechanisms
  • Reinforcement learning-inspired execution policies
  • Risk-adjusted position sizing with dynamic volatility targeting
  • Multi-venue smart order routing with latency optimization
  • Real-time P&L tracking and attribution analysis

Agent Architecture Policy

  • Event-driven state machines for order lifecycle management
  • Modular strategy composition with hot-swappable components
  • Concurrent execution across multiple asset classes
  • Fail-safe mechanisms and circuit breakers for risk control
v1.0 in development

DY-Agent Alpha architecture

Type Multi-Asset Execution
Latency < 10m
Asset Classes Equities, FX, ETFs
Strategy Mode Adaptive VWAP/TWAP
Risk Framework Real-time VaR monitoring
02

Developing ML Forecasting Models

Quantitative prediction and risk estimation systems

Time-Series Models Active
Fundamental ML, adding LSTM, GRU, Transformer architectures
Factor Models Active
Researching : Fama-French, PCA, Dynamic factor loadings
Volatility Estimation Active
Future release: GARCH, EGARCH, Stochastic volatility
Regime Classification Active
Planning: HMM, Gaussian Mixture Models

Forecasting Framework Summary

  • Multi-horizon prediction spanning intraday to quarterly timeframes
  • Ensemble methods combining neural and statistical approaches
  • Feature engineering with technical, fundamental, and alternative data
  • Online learning with incremental model updates
  • Cross-validation with walk-forward optimization

Risk Modeling Path

  • Portfolio-level VaR and CVaR estimation
  • Tail risk assessment with extreme value theory
  • Correlation matrix estimation and regime-dependent covariance
  • Stress testing with historical and synthetic scenarios
03

Qualitative Intelligence Layer

LLM-powered analysis and signal extraction systems

NLP Pipeline

  • Real-time news ingestion from global financial sources
  • Sentiment analysis with context-aware embeddings
  • Entity extraction and relationship mapping
  • Event detection and impact classification
  • Multi-language support for European market coverage(future release)
  • Regulatory filing analysis and key disclosure extraction

Signal Processing

  • Earnings call transcript analysis and tone detection
  • Automated report summarization with key metrics extraction
  • Cross-document coherence checking and fact verification
  • Temporal signal decay modeling and recency weighting
v1.2

InsightLLM-QA

Function Qualitative-to-Quant Signal
Input News, Filings, Transcripts
Output Sentiment Scores [-1, 1]
Languages EN, DE, FR, ES, IT, NL
Latency < 10s per document
04

Hybrid Intelligence Layer

Integrated qualitative and quantitative signal fusion

Qualitative Signals

Sentiment Score Event Impact Tone Analysis News Flow
Signal Fusion
Weight Calibration
Cross-Validation
Ensemble Scoring
Confidence Bounds

Quantitative Signals

Price Momentum Volatility Regime Factor Exposures Technical Patterns

Fusion Architecture

  • Joint signal representation with shared latent space
  • Dynamic weight adjustment based on signal quality metrics
  • Cross-validated ensemble scoring with out-of-sample validation
  • Multi-horizon probability estimates with uncertainty quantification
  • Adaptive learning rates responsive to market regime changes
  • Correlation-aware signal combination preventing redundancy
05

Research Platform Policy

Continuous innovation and model development infrastructure

Model Lifecycle

  • Continuous retraining with expanding window methodology
  • Automated hyperparameter optimization via Bayesian methods
  • A/B testing framework for production deployment
  • Model versioning with Git-based lineage tracking

Data Quality

  • Automated anomaly detection in incoming data streams
  • Data quality scoring with completeness metrics
  • Missing value imputation with domain-aware techniques
  • Outlier detection and robust preprocessing pipelines

Governance

  • Model risk management with performance monitoring
  • Reproducible research with containerized environments
  • Audit trails for regulatory compliance
  • Documentation automation and knowledge graphs

Prototyping

  • Rapid experimentation environment
  • Distributed computing for large-scale model training
  • GPU acceleration for deep learning workloads
  • Feature store for consistent data access patterns
06

API & Integration Layer

Institutional-grade connectivity and data distribution, to be released in 2026

Interface Architecture

  • RESTful API with OpenAPI 3.0 specification
  • WebSocket connections for real-time streaming data
  • FIX protocol support for institutional order routing
  • GraphQL endpoints for flexible data queries
  • Rate limiting and quota management per client tier

Endpoint Categories

  • Signal distribution endpoints with confidence intervals
  • Execution services with smart order routing
  • Portfolio analytics and risk metrics APIs
  • Historical data access with flexible aggregation
  • Backtesting-as-a-Service for strategy validation

Security & Reliability

  • TLS 1.3 encryption for all connections
  • OAuth 2.0 and API key authentication
  • 99.9% uptime SLA with redundant infrastructure
  • DDoS protection and traffic anomaly detection
REST API Example
POST /v1/signals/generate
Authorization: Bearer <token>
Content-Type: application/json
 
{
  "symbols": ["AAPL", "GOOGL"],
  "horizon": "1d",
  "models": ["ensemble"]
}
WebSocket Stream
WSS wss://api.diligent-yield.com
 
{
  "subscribe": ["signals"],
  "symbols": ["SPY", "QQQ"],
  "frequency": "real-time"
}

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