The Future of Quant Trading: Physics‑Powered AI
Project QAE applies concepts from robotics, quantum mechanics, and classical mechanics to model markets as articulated systems moving through high‑dimensional state spaces—revealing structure that traditional statistics cannot. Quaternion state, kinematic chains, and topological analysis underpin an institutional‑grade platform.
Revolutionary AI for Sophisticated Traders
Physics-Powered Market Analysis
28‑dimensional kinematic feature vectors reveal hidden motion patterns.
Seamless AI Integration
MCP and APIs integrate with Claude and custom agents effortlessly.
Intelligent Command Center
Real-time portfolios, kinematics, and alerts in a professional dashboard.
Institutional-Grade Security
Bank-level encryption, secure endpoints, and audit trails.
Flexible Deployment
Self-hosted Pro or managed Enterprise across infrastructures.
Beyond Traditional Quant
Momentum, acceleration, and directional shifts ahead of the crowd.
Innovations
Project Kinematic Quant – Quantum Alpha Engine (QAE) models markets as articulated robotic systems moving through high-dimensional state spaces. We unify quaternion state representation, kinematic chains, physics‑inspired dynamics, and topological analysis to reveal hidden market structure traditional methods miss.
| Feature | Traditional Quant | Kinematic Quant |
|---|---|---|
| State Representation | 2D Vectors | Quaternion 4D Rotations |
| Dynamics Modeling | Statistical | Lagrangian Mechanics |
| Regime Detection | Time-Series | Persistent Homology (GUDHI) |
Physics‑Inspired Dynamics
QAE applies Lagrangian mechanics where L = T − V to capture market motion. Quaternion states support robust rotational dynamics in 4D, while kinematic chains model portfolio relationships.
Example (Python)
pythondef system_lagrangian(state, params):
"""Compute L = T - V for quaternion-based market state."""
q = state["quaternion"] # [w, x, y, z]
omega = state["angular_velocity"] # shape: (3,)
I = params["inertia_tensor"] # shape: (3, 3)
# rotational kinetic energy T = 0.5 * omega^T I omega
T = 0.5 * float(omega.T @ (I @ omega))
# potential energy from market configuration
V = float(params["potential"](state))
return T - V
Trusted by Professional Teams
“Kinematic's motion‑based factors revealed structural flows we couldn't see before. It's now core to our research stack.”
“We saw measurable alpha improvements within weeks. The institutional experience is best‑in‑class.”
How It Works
Explore the full architecture with diagrams and physics code on the Architecture page.
Connect & Authenticate
Secure auth and instant integration via MCP-compatible APIs.
Analyze Market Motion
Real-time kinematics produce your unique 28‑dimensional feature space.
Build Intelligent Models
Use built-in AI chat or connect preferred agents to craft strategies.
Deploy & Monitor
Launch with confidence and monitor in a professional command center.
Technical Specifications
API Preview
curl -H "Authorization: Bearer $TOKEN" https://api.kinematic.quant/v1/kinematics/features?symbol=ES
API-First
MCP-compatible endpoints with secure auth and granular scopes.
Architecture
Modular pipeline: ingestion → kinematic features → model orchestration.
Performance
<50ms inference latency on optimized paths with vectorized ops.
Tooling
TypeScript SDK, Python notebooks, and example strategies.
Choose Your Deployment
Free Trial
- ✓Full Engine access
- ✓Basic dashboard
- ✓API docs & samples
- ✓Email support
Pro (Self-Hosted)
- ✓Complete platform
- ✓Unlimited API calls
- ✓Advanced dashboards
- ✓Priority support
- ✓Integration assistance
Enterprise (Managed)
- ✓Managed cloud
- ✓Dedicated infra
- ✓24/7 premium support
- ✓Custom features
- ✓White‑label
Why Kinematic Quant?
Traditional quant treats markets as static data points. We see dynamic motion systems. By applying kinematics to financial data, our capture momentum, acceleration, and directional change— insights correlation matrices simply cannot detect.
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