| The Algorithm | |
| import pandas as pd | |
| import numpy as np | |
| # Function to predict the future price | |
| def predict_price(data, c, n): | |
| # Extract relevant features from the data | |
| features = extract_features(data) | |
| # Apply the modified wave function to the features | |
| modified_features = apply_modified_wave_function(features, c, n) | |
| # Apply the refined modifications to the modified wave function | |
| refined_features = refine_modified_wave_function(modified_features) | |
| # Use a machine learning model to predict the future price | |
| predicted_price = model.predict(refined_features) | |
| return predicted_price | |
| # Function to place trades based on predicted prices | |
| def trade(data, c, n): | |
| # Predict the future price | |
| predicted_price = predict_price(data, c, n) | |
| # Buy or sell based on the predicted price | |
| if predicted_price > current_price: | |
| action = "buy" | |
| else: | |
| action = "sell" | |
| # Place the trade | |
| place_trade(action, quantity) | |
| # Function to extract relevant features from the data | |
| def extract_features(data): | |
| features = [] | |
| # Add relevant features to the list | |
| features.append(data["price"]) | |
| features.append(data["volume"]) | |
| features.append(data["moving_average"]) | |
| return features | |
| # Function to apply the modified wave function to the features | |
| def apply_modified_wave_function(features, c, n): | |
| modified_features = [] | |
| # Apply the modified wave function to each feature | |
| for feature in features: | |
| modified_features.append(c * np.abs(feature + 1j * feature) ** n) | |
| return modified_features | |
| # Function to refine the modified wave function | |
| def refine_modified_wave_function(modified_features): | |
| # Apply a normalization factor to the modified features | |
| normalized_features = normalize_features(modified_features) | |
| # Apply a filtering technique to the normalized features | |
| filtered_features = filter_features(normalized_features) | |
| return filtered_features | |
| # Function to normalize the features | |
| def normalize_features(features): | |
| # Apply a normalization function to each feature | |
| for i in range(len(features)): | |
| features[i] = (features[i] - min(features)) / (max(features) - min(features)) | |
| return features | |
| # Function to filter the features | |
| def filter_features(features): | |
| # Apply a filtering technique to select the most relevant features | |
| filtered_features = [] | |
| # Select features based on their correlation with the target variable (price) | |
| for feature in features: | |
| correlation = np.corrcoef(feature, data["price"])[0, 1] | |
| if abs(correlation) > 0.5: | |
| filtered_features.append(feature) | |
| return filtered_features | |
| # Function to place trades | |
| def place_trade(action, quantity): | |
| # Place the buy or sell order | |
| if action == "buy": | |
| order = Order(type="buy", quantity=quantity) | |
| execute_order(order) | |
| else: | |
| order = Order(type="sell", quantity=quantity) | |
| execute_order(order) | |
| # Function to execute trades based on predictions | |
| def execute_trades(data, c, n): | |
| for i in range(len(data)): | |
| trade(data[i:], c, n) | |
| # Load the data | |
| data = load_data() | |
| # Execute trades using the refined algorithm | |
| execute_trades(data, 0.5, 2) |