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)