System Architecture

Overview: Three‑Body Inspired Tri‑Environment

The QAE mirrors the three‑body problem: Development (Physics Laboratory), Live Trading (Mission Control), and Client (Observatory). Data flows, control signals, and feedback loops create complex orbital interactions analogous to gravitational dynamics.

graph TD
  DEV["Physics Laboratory (Dev)"] -->|Data Pipelines| LIVE["Mission Control (Live Trading)"]
  LIVE -->|Signals / Telemetry| CLIENT["Observatory (Client)"]
  CLIENT -->|Feedback / Controls| LIVE
  DEV -->|Model Artifacts| LIVE

Data Flow

From ingestion to deployment, kinematic feature transforms, Lagrangian dynamics, and topological analysis (persistent homology) power strategy orchestration.

graph LR
    subgraph "🎯 Data Sources - Fluid Inlets"
        A1[Exchange APIs<br/>High-Pressure Feed]
        A2[Market Data Vendors<br/>Turbulent Flow]
        A3[Alternative Data<br/>Laminar Stream]
        A4[Proprietary Data<br/>Controlled Injection]
    end

    subgraph "🔄 Dagster Orchestration - Fluid Mixing Chamber"
        B1[Data Assets<br/>Pressure Vessels]
        B2[Resources<br/>Flow Regulators]
        B3[Schedules & Sensors<br/>Pumps & Valves]
        B4[Observability<br/>Flow Meters]
    end

    subgraph "⚗️ Kedro Transformation - Chemical Reactor"
        C1[Data Catalog<br/>Reaction Chamber]
        C2[Nodes<br/>Catalysts]
        C3[Pipelines<br/>Reaction Pathways]
    end

    subgraph "🚀 Kinematic Engine - Plasma Accelerator"
        D1[Quaternion Mapping<br/>Ionization Chamber]
        D2[Kinematic Chain<br/>Particle Accelerator]
        D3[Feature Generation<br/>Fusion Reactor]
    end

    subgraph "🎯 ML Pipeline - Energy Conversion"
        E1[Model Training<br/>Thermodynamic Process]
        E2[Feature Engineering<br/>Heat Exchanger]
        E3[Prediction Generation<br/>Energy Output]
    end

    A1 --> B1 --> C1 --> D1 --> E1
    A2 --> B2 --> C2 --> D2 --> E2
    A3 --> B3 --> C3 --> D3 --> E3
    A4 --> B4

Conservation Laws

Quantity	Analogy	Financial Interpretation
Energy	L = T − V	Risk budget vs. alpha potential (kinetic vs. potential)
Momentum	p = m·v	Trend persistence and capital mass effects
Angular Momentum	L = r × p	Rotational regimes in quaternion space

Physics‑Inspired Code

Lagrangian (Python)

def system_lagrangian(state, params):
    q = state["quaternion"]
    omega = state["angular_velocity"]
    I = params["inertia_tensor"]
    T = 0.5 * float(omega.T @ (I @ omega))
    V = float(params["potential"](state))
    return T - V

Financial Energy Conservation (Python)

# Financial Energy Conservation Law
class FinancialEnergyConservation:
    def __init__(self):
        self.total_system_energy = 0

    def calculate_portfolio_energy(self, positions, velocities, accelerations):
        """E = (1/2)mv² + kinetic_energy_from_acceleration"""
        kinetic_energy = 0.5 * sum(
            mass * velocity**2 for mass, velocity in zip(positions, velocities)
        )
        potential_energy = sum(
            0.5 * mass * acceleration**2 for mass, acceleration in zip(positions, accelerations)
        )
        return kinetic_energy + potential_energy

    def validate_energy_conservation(self, initial_energy, final_energy, tolerance=1e-6):
        """Check if energy is conserved within tolerance"""
        return abs(initial_energy - final_energy) < tolerance

Momentum Conservation (Python)

# Financial Momentum Conservation
class MomentumConservation:
    def calculate_portfolio_momentum(self, positions, velocities):
        """p = mv (position * velocity)"""
        return sum(pos * vel for pos, vel in zip(positions, velocities))

    def validate_trade_execution(self, pre_trade_momentum, post_trade_momentum):
        """Validate that momentum is conserved during trade execution"""
        return abs(pre_trade_momentum - post_trade_momentum) < 1e-10

Angular Momentum (Python)

# Market Rotation Angular Momentum
class AngularMomentum:
    def calculate_market_angular_momentum(self, quaternion_states):
        """L = Iω (Angular momentum from quaternion rotations)"""
        angular_momenta = []
        for q in quaternion_states:
            # Extract rotation axis and angle
            axis, angle = q.to_axis_angle()
            # Calculate angular momentum vector
            L = q.w * axis  # Simplified calculation
            angular_momenta.append(L)
        return sum(angular_momenta)

    def detect_regime_change(self, angular_momentum_history):
        """Detect market regime changes from angular momentum shifts"""
        threshold = 0.1
        current = angular_momentum_history[-1]
        previous = angular_momentum_history[-2]
        return abs(current - previous) > threshold