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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) |