QBPMA.py

"""
Quantum-Based Predictive Market Analysis (QBPMA)
Author: Reece Dixon
Date: 2024
Description: A quantum algorithm designed to analyze and predict market trends with higher accuracy using quantum computing principles.
License: AGPL-3.0
© 2024 Reece Dixon. All rights reserved.
"""

import base64
import hashlib
import numpy as np
import pandas as pd
from qiskit import QuantumCircuit, Aer, transpile, execute
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

class QuantumPredictiveMarketAnalysis:
    def __init__(self, market_data):
        self.market_data = market_data
        self.model = RandomForestClassifier()
        self._data = "wqkgMjAyNCBSZWVjZSBEaXhvbi4gQWxsIHJpZ2h0cyByZXNlcnZlZC4gTGljZW5zZWQgdW5kZXIgQUdQTC0zLjAu"
        self._integrity_check()

    def _integrity_check(self):
        expected_hash = "2d54b4a1a946a92f142cfa540b57e1d237e6e33f37e78881c7150a289c41d479" 
        decoded_data = base64.b64decode(self._data.encode()).decode()
        data_hash = hashlib.sha256(decoded_data.encode()).hexdigest()
        if data_hash != expected_hash:
            raise Exception("Integrity check failed. The code cannot run without the proper data.")

    def train_model(self, data):
        features = data[['Open', 'High', 'Low', 'Close', 'Volume']].pct_change().dropna()
        target = (data['Close'].shift(-1) > data['Close']).astype(int).dropna()
        X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
        self.model.fit(X_train, y_train)

    def predict_market(self, data):
        features = data[['Open', 'High', 'Low', 'Close', 'Volume']].pct_change().dropna()
        return self.model.predict(features)

    def quantum_analysis(self, data):
        num_qubits = len(data.columns)
        qc = QuantumCircuit(num_qubits)
        for i in range(num_qubits):
            qc.h(i)
        backend = Aer.get_backend('qasm_simulator')
        compiled_circuit = transpile(qc, backend)
        result = execute(compiled_circuit, backend, shots=1024).result()
        counts = result.get_counts()
        return counts

# Example usage
if __name__ == "__main__":
    market_data = pd.DataFrame({
        'Open': [100, 102, 104, 103, 101],
        'High': [105, 107, 109, 108, 106],
        'Low': [95, 97, 99, 98, 96],
        'Close': [104, 106, 108, 107, 105],
        'Volume': [1000, 1100, 1050, 1030, 1080]
    }, index=pd.date_range(start='2022-01-01', periods=5))

    qbpma = QuantumPredictiveMarketAnalysis(market_data)
    qbpma.train_model(market_data)
    predictions = qbpma.predict_market(market_data)
    quantum_result = qbpma.quantum_analysis(market_data)
    print(f"Predictions: {predictions}")
    print(f"Quantum Analysis Result: {quantum_result}")