Technology
Strike Stock AI Technology
Spectral-Temporal Fusion Transformers (STFT)
Strike Stock AI is pioneering Spectral-Temporal Fusion Transformers—a revolutionary neural architecture that combines frequency-domain signal processing with state-of-the-art deep learning to predict market movements before they happen.
Traditional AI trading models learn from price and volume alone. We feed our transformers spectral intelligence—phase coherence, harmonic convergence, cross-asset resonance patterns—features invisible to conventional models. The result: prediction accuracy that compounds with every coherence event.
This is the next generation of AI-powered market intelligence.
How It Works
The Architecture
A three-layer architecture engineered for precision: spectral engines reveal the market’s hidden structure, transformers convert those signals into high-accuracy forecasts, and a real-time production pipeline delivers sub-second execution across live markets.
Before any neural network sees the data, our proprietary spectral engines extract the market's hidden structure:
Quantum Resonance Detector (QRD)
Single-instrument frequency-domain analysis. QRD applies sliding-window FFT across multiple proprietary time scales, computing real-time phase coherence between price, volume, and volatility signals.
Key Capabilities:
- Multi-scale FFT decomposition with proprietary window selection
- Real-time phase coherence scoring (0-1 confidence metric)
- Multi-frequency harmonic convergence detection
- 5-millisecond analysis latency per instrument (data-feed limited to 60-second intervals)
Quantum Matrix Engine (QME)
Multi-instrument spectral correlation. QME treats the entire market as one unified, high-dimensional system—detecting when AAPL, MSFT, NVGA lock in phase, when sectors rotate, when risk-on/risk-off dynamics shift across the entire spectrum.
Key Capabilities:
- Cross-asset phase coherence analysis at scale
- Sector-wide resonance pattern detection
- Market regime shift identification via spectral signatures
- Inter-market correlation (equities, indices, volatility products)
Think of it this way: QRD reads individual heartbeats. QME sees the entire organism breathing. Together, they generate the features that feed our transformers.
Our STFT architecture is built on Temporal Fusion Transformers (TFT)—a proven neural architecture specifically designed for time-series forecasting with interpretable attention mechanisms.
But we've supercharged it. While standard TFTs train on OHLCV data, our models consume 100+ spectral features per timestep:
- Phase coherence scores across multiple frequency pairs
- Harmonic resonance metrics
- Cross-signal phase stability
- Regime shift energy indicators
- Multi-asset phase-lock structures
Every minute, our transformers ingest these spectral signatures and predict forward price movements with quantified uncertainty bounds.
Training Infrastructure:
- PyTorch Lightning for distributed training
- Multi-horizon prediction (5, 10, 20 bars ahead)
- Quantile loss for probabilistic forecasting
- Attention visualization for interpretability
- Production checkpointing and validation
The result: Models that don't just see price patterns—they see the spectral mechanics driving those patterns.
Our spectral engines and transformers run on battle-tested, institutional-grade infrastructure:
Data Layer:
- InfluxDB: Time-series database optimized for microsecond-resolution spectral features
- Kafka: Real-time streaming pipeline processing market feeds at scale
- PostgreSQL: Relational storage for model predictions, scanner alerts, and historical patterns
Compute Layer:
- Python: Core application logic, scientific computing (NumPy, SciPy, PyTorch), API services
- C/C++/C#: Performance-critical FFT kernels and low-latency signal processing
- CUDA: GPU-accelerated transformer inference for real-time predictions
- Polygon.io: Institutional-grade market data feeds (1-minute OHLCV aggregates)
Architecture:
- Real-time WebSocket data ingestion
- Event-driven processing pipeline with <10ms latency
- Distributed model serving for sub-second inference
- Horizontal scaling: hundreds of tickers today, thousands tomorrow
This isn't research code. This is production AI infrastructure running live markets every trading day.
Research
From Research to Products
Our STFT technology powers real-world trading products:
Spectral Scanner
Real-time market-wide monitoring. When QRD/QME detect phase coherence events, our transformers instantly evaluate the signal strength and predict the magnitude of the impending move. Live alerts before price reacts.
Spectral Trading Studio
Full-featured dashboard with interactive spectral analytics, transformer-powered predictions, attention heatmaps showing what features drove each forecast, and daily AI-generated market intelligence reports.
Automated Execution
Automated Execution: STFT predictions feed directly into our execution layer—from signal detection to position sizing to order routing—all driven by spectral intelligence and neural network confidence scores.
Why This Matters
Traditional Indicators: Lagging, backward-looking, everyone sees them
Black-Box AI: Opaque, unexplainable, no physics foundation
Strike Stock STFT: Physics-based features + explainable AI + real-time execution
✓ Spectral intelligence from aerospace and biomedical signal processing
✓ Transformer architecture proven in language and time-series forecasting
✓ Production-grade infrastructure built for institutional scale
✓ Interpretable predictions with attention mechanisms showing feature importance
We’re not predicting the market with tea leaves or retrofitted NLP models. We’re teaching neural networks to read the market’s spectral signature in real time.
