Automated trading and portfolio optimization algorithm using deep learning and machine learning. Optimal portfolio composition targeting multiple cryptocurrency assets through deep learning.

In recent years, data analysis in the financial markets and the resulting automated trading systems have gained significant attention. In particular, the cryptocurrency market has become an interesting market for many investors due to its volatility. This course covers how to implement automated trading systems for cryptocurrencies and portfolio optimization algorithms using deep learning and machine learning techniques.

1. Concept of Automated Trading Systems

An automated trading system refers to a system that generates trading signals through computer programs and executes trades based on them. Such systems are appealing to many investors because they make trading decisions based on data-driven analysis rather than relying on human emotions or intuition.

2. Overview of Deep Learning and Machine Learning

Deep learning is a type of machine learning based on artificial neural networks, which automatically learns features from data to perform predictions or classifications. It shows outstanding performance, especially when combined with large amounts of data and high-performance computing power. Machine learning techniques can be used for price predictions of financial assets such as stocks, options, and futures.

2.1 Machine Learning Algorithms

There are various machine learning algorithms, among which several are introduced:

  • Linear Regression: Models the linear relationship between a dependent variable and one or more independent variables.
  • Decision Tree: A tree-structured model that creates decision rules to classify data.
  • Random Forest: Combines multiple decision trees to make more accurate predictions.
  • Support Vector Machine: A method that finds a hyperplane that maximally separates data distributions.
  • Artificial Neural Network: Mimics the human brain to learn complex patterns from data.

2.2 Deep Learning Algorithms

Deep learning uses algorithms such as:

  • Multi-layer Perceptron: A grid-structured model consisting of input, hidden, and output layers.
  • Convolutional Neural Network (CNN): A structure mainly used for image analysis, recognizing local patterns in hidden layers.
  • Recurrent Neural Network (RNN): A structure suitable for time series data that stores previous information in memory for future predictions.

3. Portfolio Optimization

Portfolio optimization is the process of determining the investment proportions across multiple assets to maximize returns and minimize risk. Deep learning and machine learning techniques greatly assist in solving these optimization problems.

3.1 Modern Portfolio Theory

Modern Portfolio Theory (MPT), developed by Harry Markowitz, aims to optimize asset allocation based on expected returns, volatility, and correlations. The goal of MPT is to construct a portfolio that provides the optimal return at a given level of risk.

3.2 Portfolio Optimization Using Deep Learning

The process of portfolio optimization using deep learning proceeds in the order of data collection, data preprocessing, model selection and training, and result evaluation.

4. Example Code

Below is a simple code example that performs portfolio optimization using Python. 


# Import necessary libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import yfinance as yf
from scipy.optimize import minimize

# List of cryptocurrency assets
cryptos = ['BTC-USD', 'ETH-USD', 'XRP-USD', 'LTC-USD', 'BCH-USD']

# Download data
data = yf.download(cryptos, start='2020-01-01', end='2023-01-01')['Adj Close']

# Calculate log returns
returns = np.log(data / data.shift(1))

# Define portfolio performance function
def portfolio_performance(weights):
    mean_return = np.sum(returns.mean() * weights) * 252
    portfolio_volatility = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights)))
    return portfolio_volatility, mean_return

# Define objective function
def min_fun(weights):
    return portfolio_performance(weights)[0]  # Minimize volatility

# Set constraints and boundaries
constraints = ({'type': 'eq', 'fun': lambda x: np.sum(x) - 1})
bounds = tuple((0, 1) for asset in range(len(cryptos)))

# Initial weights
initial_weights = [1. / len(cryptos)] * len(cryptos)

# Execute minimization
optimal_weights = minimize(min_fun, initial_weights, method='SLSQP', bounds=bounds, constraints=constraints)

# Output optimal weights
print("Optimal Weights: ", optimal_weights.x)
print("Maximum Expected Return: ", portfolio_performance(optimal_weights.x)[1])
print("Minimum Volatility: ", portfolio_performance(optimal_weights.x)[0])