Quantum Market Price Distribution Visualization

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import numpy as np import matplotlib.pyplot as plt

# Defining the Quantum Market Equilibrium class class QuantumMarketEquilibrium: def init(self, base_price, volatility, quantum_uncertainty=0.1): """ Initialize the Quantum Market Equilibrium model.

Parameters: - base_price: The base price level - volatility: Market volatility - quantum_uncertainty: Quantum uncertainty factor """ self.base_price = base_price self.volatility = volatility self.quantum_uncertainty = quantum_uncertainty

def calculate_quantum_state(self, market_factors): """ Calculate the quantum state of the market.

Parameters: - market_factors: A dictionary containing market influences - monetary_policy: Impact of monetary policy - market_sentiment: Market sentiment effect - global_linkage: Global market linkage """ # Heisenberg uncertainty applied to price fluctuation uncertainty_factor = np.random.normal(0, self.quantum_uncertainty)

# Quantum superposition effect superposition_effect = ( market_factors['monetary_policy'] * np.sin(self.base_price) + market_factors['market_sentiment'] * np.cos(self.base_price) )

# Global market entanglement effect entanglement_effect = market_factors['global_linkage'] * np.exp(-self.volatility)

# Final equilibrium price calculation equilibrium_price = ( self.base_price * (1 + uncertainty_factor) + superposition_effect + entanglement_effect )

return equilibrium_price

def simulate_price_distribution(self, n_simulations=1000): """ Simulate the price distribution.

Parameters: - n_simulations: Number of simulations to run """ market_factors = { 'monetary_policy': np.random.uniform(-0.1, 0.1), 'market_sentiment': np.random.uniform(-0.2, 0.2), 'global_linkage': np.random.uniform(0, 0.3) }

prices = [self.calculate_quantum_state(marketfactors) for in range(n_simulations)] return np.array(prices)

def calculate_risk_metrics(self, prices): """ Calculate risk metrics for the simulated prices.

Parameters: - prices: Simulated price array """ var_95 = np.percentile(prices, 5) expected_shortfall = np.mean(prices[prices < var_95])

return { 'mean': np.mean(prices), 'std': np.std(prices), 'var_95': var_95, 'expected_shortfall': expected_shortfall }

def analyze_market_equilibrium(base_price=100, volatility=0.2): """ Analyze the market equilibrium using the Quantum Market Equilibrium model.

Parameters: - base_price: The initial price level - volatility: Market volatility """ model = QuantumMarketEquilibrium(base_price, volatility) prices = model.simulate_price_distribution() metrics = model.calculate_risk_metrics(prices)

# Print risk metrics print("Market Risk Metrics:") for key, value in metrics.items(): print(f"{key}: {value:.2f}")

# Plotting the price distribution histogram plt.figure(figsize=(10, 6)) plt.hist(prices, bins=50, alpha=0.7, color='blue', edgecolor='black') plt.axvline(metrics['mean'], color='red', linestyle='dashed', linewidth=2, label=f"Mean: {metrics['mean']:.2f}") plt.axvline(metrics['var_95'], color='orange', linestyle='dashed', linewidth=2, label=f"VaR(95%): {metrics['var_95']:.2f}") plt.title('Simulated Price Distribution Histogram') plt.xlabel('Price') plt.ylabel('Frequency') plt.legend() plt.show()

# Run the market equilibrium analysis analyze_market_equilibrium() 畫出直方圖