import MetaTrader5 as mt5 import numpy as np from qiskit import QuantumCircuit, transpile, QuantumRegister, ClassicalRegister from qiskit_aer import AerSimulator from qiskit.visualization import plot_histogram from Crypto.Hash import SHA256 import pandas as pd from datetime import datetime, timedelta def initialize_mt5(): if not mt5.initialize(): print("MT5 initialization error") return False return True def get_price_data(symbol="EURUSD", timeframe=mt5.TIMEFRAME_D1, n_candles=256, offset=0): """Retrieves price data from MT5""" rates = mt5.copy_rates_from_pos(symbol, timeframe, offset, n_candles) if rates is None: return None return pd.DataFrame(rates) def prices_to_binary(df): """Converts price movements into a binary sequence""" binary_sequence = "" for i in range(1, len(df)): binary_sequence += "1" if df['close'][i] > df['close'][i-1] else "0" return binary_sequence.zfill(256) def calculate_future_horizon(current_data, future_data, horizon=10): """Calculates the binary horizon of future prices""" future_binary = "" current_price = current_data['close'].iloc[-1] for price in future_data['close']: future_binary += "1" if price > current_price else "0" current_price = price return future_binary.zfill(horizon) def calculate_trend_ratio(binary_sequence): """Calculates the ratio of 1/0 in a binary sequence""" ones = binary_sequence.count('1') zeros = binary_sequence.count('0') total = len(binary_sequence) return ones/total, zeros/total def predict_horizon(quantum_counts, horizon=10): """Predicts the binary horizon based on the top-10 most probable states""" total_counts = sum(quantum_counts.values()) # Get the top-10 states with the highest probability top_10_states = sorted(quantum_counts.items(), key=lambda x: x[1], reverse=True)[:10] predicted_binary = "" # For each position in the horizon for i in range(horizon): weighted_ones = 0 weighted_zeros = 0 # Calculate weighted probabilities for each state for state, count in top_10_states: if len(state) > i: weight = count / total_counts if state[i] == '1': weighted_ones += weight else: weighted_zeros += weight # Determine the bit based on weighted probabilities predicted_binary += "1" if weighted_ones > weighted_zeros else "0" return predicted_binary def predict_trend(binary_sequence): """Predicts the trend based on the 1/0 ratio""" ones_ratio, zeros_ratio = calculate_trend_ratio(binary_sequence) return "BULL" if ones_ratio > 0.5 else "BEAR" def verify_prediction(current_data, future_data): """Verifies the accuracy of the prediction""" actual_trend = "BULL" if future_data['close'].iloc[-1] > current_data['close'].iloc[-1] else "BEAR" return actual_trend def sha256_to_binary(input_data): hasher = SHA256.new() hasher.update(input_data) return bin(int(hasher.hexdigest(), 16))[2:].zfill(256) def qpe_dlog(a, N, num_qubits): qr = QuantumRegister(num_qubits + 1) cr = ClassicalRegister(num_qubits) qc = QuantumCircuit(qr, cr) for q in range(num_qubits): qc.h(q) qc.x(num_qubits) for q in range(num_qubits): qc.cp(2 * np.pi * (a**(2**q) % N) / N, q, num_qubits) qc.barrier() for i in range(num_qubits): qc.h(i) for j in range(i): qc.cp(-np.pi / float(2 ** (i - j)), j, i) for i in range(num_qubits // 2): qc.swap(i, num_qubits - 1 - i) qc.measure(range(num_qubits), range(num_qubits)) return qc def analyze_market_state(price_binary, num_qubits=22): a = 70000000 N = 17000000 qc = qpe_dlog(a, N, num_qubits) simulator = AerSimulator() compiled_circuit = transpile(qc, simulator) job = simulator.run(compiled_circuit, shots=3000) result = job.result() counts = result.get_counts() best_match = max(counts, key=counts.get) dlog_value = int(best_match, 2) return dlog_value, counts def compare_horizons(real_horizon, predicted_horizon): """ Compares the direction of the real and predicted horizons Calculates the predominant direction (more 1s or 0s) and compares them """ # Count the number of 1s in the real and predicted horizons real_ones = real_horizon.count('1') pred_ones = predicted_horizon.count('1') # Determine the predominant direction real_trend = "BULL" if real_ones > len(real_horizon)/2 else "BEAR" pred_trend = "BULL" if pred_ones > len(predicted_horizon)/2 else "BEAR" return "WIN" if real_trend == pred_trend else "LOSS" def analyze_from_point(timepoint_offset, n_candles=256, horizon_length=10): """ Analyzes the market from a given point in the past timepoint_offset - number of candles back from the current moment """ try: # Get historical data up to the event horizon point historical_data = get_price_data(n_candles=n_candles, offset=timepoint_offset + horizon_length) if historical_data is None: print("Failed to retrieve historical data") return # Get real data after the point (for prediction verification) future_data = get_price_data(n_candles=horizon_length, offset=timepoint_offset) if future_data is None: print("Failed to retrieve verification data") return # Determine the event horizon point horizon_point_price = historical_data['close'].iloc[-1] horizon_point_time = historical_data.index[-1] if isinstance(historical_data.index, pd.DatetimeIndex) else None print(f"\n=== ANALYSIS FROM EVENT HORIZON POINT ===") print(f"Price at event horizon point: {horizon_point_price:.5f}") if horizon_point_time: print(f"Time of event horizon point: {horizon_point_time}") # Convert historical prices into a binary sequence price_binary = prices_to_binary(historical_data) print("\nBinary price sequence before event horizon (last 64 bits):") print(price_binary[-64:]) # Analyze the market state market_state, quantum_counts = analyze_market_state(price_binary) # Display the matrix of probable projections print("\nMatrix of probable projections (top-20 states):") print("{:<22} {:<10} {:<10}".format("State", "Frequency", "Probability")) print("-" * 42) total_shots = sum(quantum_counts.values()) sorted_states = sorted(quantum_counts.items(), key=lambda x: x[1], reverse=True) for state, count in sorted_states[:20]: probability = count / total_shots * 100 print("{:<22} {:<10} {:.2f}%".format(state, count, probability)) # Get the real and predicted horizons real_horizon = calculate_future_horizon(historical_data, future_data, horizon_length) predicted_horizon = predict_horizon(quantum_counts, horizon_length) print("\n=== COMPARISON OF PREDICTION WITH REALITY ===") print("Real horizon after the point:") print(real_horizon) print("\nPredicted horizon of future prices:") print(predicted_horizon) # Compare the directions of the horizons result = compare_horizons(real_horizon, predicted_horizon) horizon_accuracy = sum(a == b for a, b in zip(real_horizon, predicted_horizon)) / horizon_length print(f"\nNumber of 1s in the real horizon: {real_horizon.count('1')}/{len(real_horizon)}") print(f"Number of 1s in the prediction: {predicted_horizon.count('1')}/{len(predicted_horizon)}") print(f"Bit match accuracy: {horizon_accuracy:.2%}") print(f"Direction comparison result: {result}") # Analyze the probability distribution print("\nAnalysis of probability distribution for event horizon bits:") print("{:<5} {:<10} {:<10} {:<10}".format("Bit", "Prob.1", "Prob.0", "Prediction")) print("-" * 35) for i in range(horizon_length): weighted_ones = 0 weighted_zeros = 0 # Calculate weighted probabilities for the top-10 states for state, count in sorted_states[:10]: if len(state) > i: weight = count / total_shots if state[i] == '1': weighted_ones += weight else: weighted_zeros += weight predicted_bit = "1" if weighted_ones > weighted_zeros else "0" print("{:<5} {:<10.2%} {:<10.2%} {:<10}".format( i+1, weighted_ones, weighted_zeros, predicted_bit )) except Exception as e: print(f"Error during analysis: {str(e)}") def main(): if not initialize_mt5(): return try: # Request the event horizon point offset offset = int(input("Enter the event horizon point offset (number of candles back from the current moment): ")) horizon_length = int(input("Enter the event horizon length (default is 10): ") or "10") # Perform analysis from the given point analyze_from_point(offset, horizon_length=horizon_length) finally: mt5.shutdown() if __name__ == "__main__": main()