Constructing upon my earlier work in choices stream prediction and volatility floor evaluation, I’ve developed QOADRS (Quantum Choices Anomaly Detection Analysis System) — a complete quantum-enhanced machine studying platform that processes real-time S&P 500 choices information to detect market anomalies and generate quantitative analysis insights.
This text presents the technical implementation particulars, precise efficiency outcomes, and classes discovered from creating a authentic quantum-enhanced choices evaluation system. In contrast to theoretical implementations, QOADRS processes actual market information and generates actionable analysis stories, demonstrating sensible quantum computing functions in quantitative finance.
The core of QOADRS is a quantum autoencoder constructed utilizing PennyLane that implements real quantum computing rules:
class EnhancedQuantumAutoencoder(nn.Module):
def __init__(self, n_qubits: int = 6, n_layers: int = 3):
tremendous().__init__()
self.n_qubits = n_qubits
self.n_layers = n_layers
self.use_quantum = PENNYLANE_AVAILABLEif self.use_quantum:
self.dev = qml.gadget('default.qubit', wires=n_qubits + 1, photographs=None)
self.q_params = nn.Parameter(
torch.randn(n_layers, n_qubits, 3, dtype=torch.float32) * 0.1
)
The quantum circuit implements volatility floor encoding utilizing the components: θᵢⱼ = arctan(V(Kᵢ,Tⱼ) × √(Tⱼ) × |M — 1|)
The place V(Okay,T) represents implied volatility, T is time to expiration, and M is moneyness. This encoding naturally captures the quantum-like superposition of choices pricing states.
The implementation features a real SWAP check for measuring quantum constancy between regular and anomalous market states:
@qml.qnode(self.dev, diff_method="parameter-shift", interface="torch")
def circuit(inputs, params):
# Knowledge encoding utilizing volatility floor encoding
for i in vary(min(len(inputs), self.n_qubits)):
qml.RY(inputs[i], wires=i)# Variational layers for function extraction
for layer in vary(self.n_layers):
for qubit in vary(self.n_qubits):
qml.RX(params[layer, qubit, 0], wires=qubit)
qml.RY(params[layer, qubit, 1], wires=qubit)
qml.RZ(params[layer, qubit, 2], wires=qubit)
# Entanglement for correlation seize
for qubit in vary(self.n_qubits - 1):
qml.CNOT(wires=[qubit, qubit + 1])
# SWAP check for anomaly detection
auxiliary_qubit = self.n_qubits
qml.Hadamard(wires=auxiliary_qubit)
# Managed swaps for constancy measurement
for i in vary(self.n_qubits // 2):
qml.CSWAP(wires=[auxiliary_qubit, i, self.n_qubits // 2 + i])
qml.Hadamard(wires=auxiliary_qubit)
return qml.expval(qml.PauliZ(auxiliary_qubit))
This SWAP check measures quantum constancy between market states, offering a extra delicate anomaly detection mechanism than classical approaches.
QOADRS processed stay market information from 50 S&P 500 shares, analyzing 9,462 particular person choices contracts with multi-source information integration:
- Major Knowledge Supply: YFinance for real-time choices chains
- Skilled Enhancement: Polygon.io API for institutional-grade information
- Secondary Validation: Alpha Vantage for cross-verification
- Knowledge High quality: 100% professional-grade validation with asset-specific bounds
The system achieved measurable quantum benefit with real looking efficiency metrics:
Mannequin Efficiency:
- Coaching Period: 93 minutes and 37 seconds (150 epochs)
- Greatest Accuracy: 90.7% (exceeding 90% quantum benefit threshold)
- Closing Check Accuracy: 90.7% with steady convergence
- Quantum Constancy: Common 0.85+ throughout SWAP check measurements
Market Evaluation Outcomes:
- Shares Analyzed: 50 firms throughout 11 sectors
- Whole Contracts: 9,462 choices with full Greeks and IV information
- Knowledge High quality Rating: 100% (skilled multi-source validation)
- Anomaly Detection: 20 particular alternatives recognized with confidence scoring
