Category: Deep learning Automated trading

Hello. In this course, we will take a closer look at machine learning and deep learning algorithm trading for quantitative trading. In particular, we will focus on the basic concepts of deep learning and forward propagation. The mechanism of algorithm trading can be a powerful tool for analyzing and predicting market data, and mastering it can upgrade your trading strategy to the next level.

1. What is Algorithm Trading?

Algorithm trading is a continuous electronic trading system that uses computer programs to automatically execute trades in the market. These systems are based on algorithms that analyze market data and generate buy and sell signals. By utilizing machine learning and deep learning techniques in this process, we can recognize patterns in data and establish more sophisticated trading strategies.

1.1 Advantages of Algorithm Trading

• Exclusion of Emotion: Computers do not feel emotions, allowing for patient trading.
• Fast Execution: Algorithms can respond quickly to market changes, gaining an edge.
• Data Analysis: Machine learning algorithms can analyze large amounts of data and make better judgments than humans.
• Backtesting: Algorithms can validate the effectiveness of strategies by simulating based on historical data.

2. Basic Concepts of Machine Learning

Machine learning is a field of computer science that involves learning from data and making predictions. It develops algorithms to identify patterns in data and predict future outcomes. In the financial market, machine learning can be used in various ways, including price prediction, risk management, and investment strategy development.

2.1 Supervised Learning vs Unsupervised Learning

Machine learning can be broadly divided into supervised and unsupervised learning.

• Supervised Learning: When input data and correct labels are provided, the model learns from them to make predictions on new data. For instance, in stock price prediction, a model is created by learning from historical stock price data and the corresponding accurate stock prices.
• Unsupervised Learning: This is a method used to find patterns or structures in data when there are no labels present. Techniques such as clustering or dimensionality reduction in the stock market fall under unsupervised learning.

3. Basic Concepts of Deep Learning

Deep learning is a subset of machine learning based on artificial neural networks. It uses multi-layer neural networks to automatically extract features from data and perform prediction or classification tasks. Deep learning shows strong performance, especially in solving complex problems, and has been successfully applied in various fields such as image recognition and natural language processing.

3.1 Structure of Neural Networks

A neural network consists of an input layer, hidden layers, and an output layer.

• Input Layer: This is the layer where the features of the data provided to the model are inputted. In trading, factors such as stock prices, trading volume, and news data can be inputted.
• Hidden Layer: This is the intermediate layer that processes the input data. Multiple hidden layers can be used, increasing the complexity of the model and enabling more sophisticated learning.
• Output Layer: This is the layer where the final results of the model are outputted. Stock prices and buy/sell decisions fall under this category.

4. Forward Propagation

Forward propagation refers to the process of processing input data in a neural network to produce output. In this process, the neurons in each layer multiply the inputs they receive from the previous layer by weights and add biases, then pass them through an activation function to generate the final output.

4.1 Steps of Forward Propagation

1. Preparing Input Values: Prepare the characteristic data to be input into the model.
2. Applying Weights and Biases: Each input value is multiplied by the corresponding weight and accumulated. Then, the bias is added.
3. Applying Activation Function: An activation function is applied to the sum of weights and biases to generate output values. Common activation functions include Sigmoid, ReLU, and Hyperbolic Tangent (tanh).
4. Generating Output Values: The final prediction results are generated in the last output layer.

4.2 Mathematical Representation

Let weights be W, biases be b, and input values be X, then the output Y of a neuron can be expressed as follows:

    Y = activation(W * X + b)
    

4.3 Importance of Activation Functions

Activation functions introduce non-linearity to the neural network, enabling it to learn complex patterns. For example, the ReLU function has the following formula:

    f(x) = max(0, x)
    

This function outputs 0 for negative inputs, maintaining non-linear characteristics and helping enhance the expressiveness of the neural network.

5. Training Neural Networks and Backpropagation

This method trains the model based on the error between the output values generated by forward propagation and the actual labels. Here, the backpropagation technique is introduced, distributing the error according to each weight and connection in the network to adjust the weights.

5.1 Loss Function

The loss function measures the difference between the model’s predicted values and the actual values. It generally has the following form:

    Loss(y_true, y_pred) = (y_true - y_pred)^2
    

5.2 Weight Updates

Weights are updated based on the gradients of the error obtained through backpropagation. The Gradient Descent algorithm is used to update each weight as follows:

    W_new = W_old - learning_rate * gradient
    

Here, learning_rate indicates the speed at which the weights are adjusted.

6. Practical Example: Stock Price Prediction

Now we will take a closer look at an example of stock price prediction using machine learning and deep learning. In this example, we will build a simple neural network model that learns from historical stock price data to predict future prices.

6.1 Data Collection

Stock price data can be collected through Yahoo Finance API or other financial data services. The collected data requires preprocessing, which includes the following steps:

• Selecting date, closing price, and trading volume to create a data frame.
• Handling missing values and performing normalization.
• Splitting into training data and testing data.

6.2 Model Design and Implementation

We’ll design a simple forward propagation-based deep learning model. The following model can be constructed using Python’s Keras and TensorFlow libraries:

    from keras.models import Sequential
    from keras.layers import Dense

    model = Sequential()
    model.add(Dense(units=64, activation='relu', input_dim=number_of_features))
    model.add(Dense(units=32, activation='relu'))
    model.add(Dense(units=1, activation='linear'))

    model.compile(optimizer='adam', loss='mean_squared_error')
    model.fit(X_train, y_train, epochs=100, batch_size=32)
    

6.3 Result Visualization

The model’s prediction results can be visualized to check the differences from the actual values. The following charts can be generated using the Matplotlib library:

    import matplotlib.pyplot as plt

    plt.plot(y_test, label='True Price')
    plt.plot(predictions, label='Predicted Price')
    plt.legend()
    plt.show()
    

7. Conclusion

In this course, we learned about algorithm trading using machine learning and deep learning, especially focusing on the concept of forward propagation and a practical example. These techniques will greatly assist in making data-driven decisions in the financial market. Over the long term, they can form the foundation for improving your trading strategy and investment performance. I hope you continue to learn and experiment to develop your own model.

8. References

• Ian Goodfellow, Yoshua Bengio, and Aaron Courville, “Deep Learning”, MIT Press.
• Alexander Elder, “Trading for a Living”.
• Andreas C. Müller and Sarah Guido, “Introduction to Machine Learning with Python”.

In recent years, the use of machine learning and deep learning in financial markets has surged. Effective implementation of algorithmic trading requires data collection, analysis, predictive modeling, and performance evaluation. This article will explore linear regression, one of the machine learning techniques, along with regularization methods to effectively regulate it, and explain how it can be applied to trading.

1. Basics of Algorithmic Trading

Algorithmic trading is a system that automatically executes buy and sell orders when specific conditions are met. This system has various strategies and can improve predictive accuracy through machine learning techniques. Key elements of algorithmic trading include:

• Data Collection: Collecting historical price data, trading volumes, technical indicators, etc.
• Modeling: Creating models based on the collected data.
• Testing: Validating the performance of the model.
• Execution: Automatically executing trades when optimal trading signals are generated.

2. Overview of Machine Learning and Deep Learning

Machine learning is an algorithm that learns and predicts from data. Deep learning, a subset of machine learning, uses artificial neural networks to learn more complex patterns. These two technologies can be powerful tools for extracting meaningful insights from financial data. Through machine learning and deep learning, we can learn from historical data to predict future price movements.

3. Linear Regression and Its Importance

Linear regression is one of the simplest and most widely used algorithms in machine learning. The basic concept is to model the linear relationship between input variables and output variables. It is important because it can be applied to various financial problems, such as stock price prediction and risk assessment.

3.1 Mathematical Foundation of Linear Regression

The basic formula for linear regression is as follows:

Y = β0 + β1X1 + β2X2 + ... + βnXn + ε

Here, Y is the variable to be predicted, X is the independent variable, β is the regression coefficient, and ε is the error term. The goal of linear regression is to estimate the β values based on the given data.

3.2 Limitations of Linear Regression

Basic linear regression can suffer from issues like overfitting, where the model is too closely fitted to the training data, resulting in poor generalization capabilities. Regularization methods are needed to resolve this.

4. Regularization of Linear Regression

Regularization is a technique to prevent the model from becoming too complex. It helps improve model performance, with two main methods: Lasso and Ridge regularization.

4.1 Lasso Regularization

Lasso regularization is L1 regularization, which minimizes the sum of the absolute values of the regression coefficients. This method has the effect of setting some coefficients to zero, making it advantageous for feature selection. The objective function of Lasso is defined as follows:

J(β) = RSS + λΣ|βj|

Here, RSS is the Residual Sum of Squares, and λ is the regularization strength adjustment parameter.

4.2 Ridge Regularization

Ridge regularization is L2 regularization, which minimizes the sum of the squares of the regression coefficients. This method reduces all coefficients but does not set them to 0. The objective function for Ridge is as follows:

J(β) = RSS + λΣ(βj^2)

This method is effective in addressing multicollinearity issues.

5. Implementation of Regularization Using Shrinkage Methods

Shrinkage methods are performed as a combination of the above Lasso and Ridge algorithms. This approach is known as Elastic Net regularization, using both regularizations simultaneously to find the optimal model.

5.1 Key Characteristics of Elastic Net

Elastic Net balances L1 and L2 regularization to form a more robust predictive model. The objective function is as follows:

J(β) = RSS + λ1Σ|βj| + λ2Σ(βj^2)

This method is particularly useful when the number of variables is high, and the number of samples is low.

5.2 Implementation Using Python

The following is how to implement Elastic Net using Python’s sklearn library:

import numpy as np
import pandas as pd
from sklearn.linear_model import ElasticNet

# Load data
data = pd.read_csv('financial_data.csv')
X = data.drop('target', axis=1)
y = data['target']

# Create Elastic Net model
model = ElasticNet(alpha=1.0, l1_ratio=0.5)
model.fit(X, y)

# Predictions
predictions = model.predict(X)

The above code loads data from ‘financial_data.csv’, trains an Elastic Net model based on the target variable, and then makes predictions.

6. Performance Evaluation and Model Improvement

There are various metrics to evaluate model performance, including MSE (Mean Squared Error), RMSE (Root Mean Squared Error), and R² (Coefficient of Determination). These can be used to check the predictive accuracy of the model and to improve performance through appropriate adjustments in regularization strength.

6.1 Cross-Validation

Cross-Validation is a technique to evaluate the generalization ability of a model, using part of the data for training and the remainder for validation. This helps prevent overfitting and increases the reliability of the model.

6.2 Hyperparameter Tuning

Hyperparameter tuning can be performed to further enhance model performance. Methods such as Grid Search and Random Search can be used to find optimal regularization strength and ratios.

7. Conclusion

Algorithmic trading utilizing machine learning and deep learning enables data-driven investment decisions. By applying linear regression algorithms and shrinkage methods, we can create more robust and generalized models, providing sufficient advantages in real-world trading. It is anticipated that these techniques will continue to evolve and maximize trading efficiency.

8. References

• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer Science & Business Media.
• Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research.

From manual coding to learning filters for data

1. Introduction

Smart trading is already changing the paradigm of the financial markets. Automated trading using artificial intelligence is no longer a technology of the future but a technology of the present. This course systematically explains the basics to advanced concepts of trading using machine learning and deep learning. It covers the basics of manual coding and how to build machine learning models through various data filtering techniques.

2. Basic Concepts of Machine Learning

Machine learning is a branch of artificial intelligence that uses algorithms to allow computers to learn patterns from data and make predictions. Basically, algorithms learn correlations through large datasets, attempting to make predictions on new data as a result.

2.1 Types of Machine Learning

Machine learning can be broadly categorized into three types:

• Supervised Learning: Learning a predictive model using labeled data.
• Unsupervised Learning: Finding patterns or structures using unlabeled data.
• Reinforcement Learning: Learning to maximize rewards through interaction with the environment.

3. Advances in Deep Learning

Deep learning is a subfield of machine learning that uses artificial neural networks to learn more complex patterns. Recent advancements have led to groundbreaking achievements in fields such as image recognition and natural language processing, utilizing large amounts of data and high computing power.

3.1 Key Structures of Deep Learning

Deep learning consists of multiple layers of artificial neural networks. Each layer transforms the input data and passes it on to the next layer. As the number of layers increases, the ability to learn complex features improves.

4. Application of Machine Learning in Financial Markets

Machine learning and deep learning technologies are being utilized in various ways in the financial markets. Examples include stock price prediction, algorithmic trading, and risk management.

4.1 Stock Price Prediction

Machine learning models can analyze historical price data to predict future price fluctuations. This provides valuable information to investors and helps them make better decisions.

4.2 Algorithmic Trading

Algorithmic trading is a technique that uses computer programs to automatically execute trades in the market. It analyzes data in real-time to capture market opportunities and enables objective trading devoid of human emotions.

5. Basics of Manual Coding

Basic programming knowledge is required to build automated trading systems. Python is a widely used language for financial data analysis and machine learning.

5.1 Installing Python and Setting Up the Environment

Python is free to use and can be easily installed through distributions such as Anaconda. Install the necessary libraries (e.g., NumPy, pandas, scikit-learn, TensorFlow, Keras, etc.) to prepare the development environment.

6. Data Collection and Preprocessing

A reliable data collection is essential for model training. Data can be easily collected through APIs such as Yahoo Finance and Alpha Vantage.

6.1 Data Collection

For example, you can write code to fetch historical data for a specific stock using the Yahoo Finance API.

import pandas as pd
import yfinance as yf

data = yf.download('AAPL', start='2010-01-01', end='2023-01-01')
print(data.head())
        

6.2 Data Preprocessing

The collected data must undergo preprocessing steps such as handling missing values, normalization, and transformation. These processes can significantly affect the model’s performance.

# Handling missing values
data.fillna(method='ffill', inplace=True)

# Normalization
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
data[['Open', 'High', 'Low', 'Close']] = scaler.fit_transform(data[['Open', 'High', 'Low', 'Close']])
        

7. Model Training and Validation

Once the data is prepared, you can select and train a machine learning or deep learning model. Common models include linear regression, decision trees, random forests, and LSTM.

7.1 Model Selection

Here is an example of an LSTM model for stock price prediction. LSTM is a form of recurrent neural network that performs well with time series data.

from keras.models import Sequential
from keras.layers import LSTM, Dense

model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X_train, y_train, epochs=100, batch_size=32)
        

7.2 Model Validation

Model validation is the process of evaluating the model’s performance using test data. You can assess the model using evaluation metrics such as RMSE, MAE, and R².

8. Feature Selection Techniques

After training and validating the model, you can further enhance performance using feature selection techniques. Filtering techniques are performed through various statistical methods or machine learning approaches.

8.1 Statistical Methods

Significant features can be selected through statistical approaches such as correlation analysis and ANOVA.

8.2 Machine Learning Techniques

Feature importance analysis based on random forests can help identify influential features.

from sklearn.ensemble import RandomForestRegressor

model = RandomForestRegressor()
model.fit(X_train, y_train)
importance = model.feature_importances_
        

9. Analysis and Visualization of Results

You can analyze the predicted results of the model and visualize them to gain insights. Libraries like Matplotlib and Seaborn can be used to visually represent the outcomes.

import matplotlib.pyplot as plt

plt.plot(y_test, label='Actual Prices')
plt.plot(predicted_prices, label='Predicted Prices')
plt.legend()
plt.show()
        

10. Conclusion

This course has covered a wide range of topics, from the basics to applications of algorithmic trading through machine learning and deep learning. Machine learning technologies are playing an increasingly important role in the financial markets, and continuous learning and research are necessary. I hope you can implement better trading strategies through this course.

I wish you success in your study of new techniques and trends and hope you achieve successful results in the world of algorithmic trading.

The use of machine learning and deep learning technologies in quantitative trading has rapidly increased in recent years. In this article, we will explore the construction of trading systems utilizing machine learning and deep learning algorithms, as well as delve deeply into the generation of returns and benchmark inputs. This process helps redefine investors’ strategic approaches and pursue better decisions and profitability through automated systems.

1. Concepts of Machine Learning and Deep Learning

Machine learning refers to a set of algorithms that learn patterns from data to perform predictions. Deep learning, a subfield of this, uses artificial neural networks to understand and analyze complex data structures. Both technologies are capable of processing large amounts of data and automatically learning, leading to progressively improving performance.

1.1 Basic Concepts of Machine Learning

There are typically three main types of machine learning:

• Supervised Learning: A learning method where the correct answers are provided along with input data, used in classification and regression problems.
• Unsupervised Learning: A learning method that discovers patterns in data without provided answers, used in clustering and dimensionality reduction.
• Reinforcement Learning: A method where an agent learns optimal actions through interaction with the environment. It is primarily used in games or complex decision-making problems.

1.2 Understanding Deep Learning

Deep learning utilizes multilayer artificial neural networks to extract and learn features from high-dimensional data. This approach is applied in various fields like image recognition and natural language processing, and it is increasingly gaining attention in the financial markets as well.

2. Basics of Algorithmic Trading

Algorithmic trading is a trading system that automatically executes trades based on predefined rules. It is utilized in various financial markets such as stocks, bonds, and foreign exchange, enhancing the consistency and speed of trading. The performance of algorithms largely depends on the quality of data and the design of the algorithm.

2.1 Advantages of Algorithmic Trading

• Speed: Can execute trades thousands of times faster than humans.
• Accuracy: Performs consistent rule-based trading without emotional decisions.
• Strategy Testing: Provides the ability to test various strategies based on historical data.

3. Generation of Returns and Benchmark Inputs

To evaluate the performance of trading algorithms, the first requirement is accurate calculation of returns and a benchmark for comparison. Returns are fundamentally calculated based on the change in value of investment assets over a specific period.

3.1 Calculating Returns

Returns can be calculated using the following formula:

    Return (R) = (Final Value - Initial Value) / Initial Value
    

In actual trading, factors such as transaction fees and slippage must be considered, as these elements can significantly impact returns. Therefore, returns for each trade must be calculated based on trading data and accumulated to derive the overall return.

3.2 Importance of Benchmarks

To evaluate the performance of trading strategies, it is necessary to establish an appropriate benchmark. A benchmark generally represents the average market performance of the same asset class, such as setting the S&P 500 index as a benchmark. This allows for assessing the relative performance of the strategy. For example, it is possible to generate benchmark returns as follows:

    Benchmark Return (BR) = (Benchmark Final Value - Benchmark Initial Value) / Benchmark Initial Value
    

4. Designing and Building Machine Learning Models

When designing a machine learning model, it is essential to first prepare an appropriate dataset and select features and models. These processes have a direct impact on the performance of algorithmic trading.

4.1 Data Collection

To establish a trading strategy, financial data must be collected. This data includes stock prices, trading volumes, financial indicators, news data, and more. This data can be collected via APIs or through financial data providers.

4.2 Feature Engineering

Feature engineering is a crucial process for enhancing the performance of machine learning models. It generates critical information to be input into the model. For example, technical indicators (e.g., moving averages, RSI) can be calculated from historical price data to be used as features.

4.3 Model Selection

Model selection is extremely important in machine learning. The fundamental models that can be used are as follows:

• Linear Regression: Simple and interpretable but does not well explain non-linear relationships.
• Decision Trees: Can effectively learn non-linear patterns.
• Random Forests: Improves performance based on multiple decision trees.
• Neural Networks: Learns complex patterns and performs strongly across various data types.

5. Building an Automated Trading System

After building and training the model, it needs to be transitioned into an automated trading system. At this stage, a method for generating trading signals and placing actual orders based on these signals is required.

5.1 Generating Trading Signals

Trading signals are generated based on the predictions of the machine learning model. For instance, if it is predicted that a specific stock has a 70% probability of rising, it can be set as a buy signal for that stock. Signals are categorized as buy, sell, or hold.

5.2 Executing Orders

Once a signal is generated, actual orders must be executed. This can be done by connecting to a trading platform via APIs. Trading can be conducted using the APIs of various exchanges, and details such as order type (market, limit, etc.) must be configured during this process.

6. Performance Evaluation and Hyperparameter Tuning

The model’s performance should be regularly evaluated, and hyperparameter tuning should be conducted to improve performance. This includes retraining the model with new data and analyzing various performance metrics.

6.1 Performance Evaluation Metrics

Several metrics can be used to evaluate performance:

• Sharpe Ratio: Indicates the return relative to risk; a higher value indicates better investment efficiency.
• Maximum Drawdown: Represents the maximum loss of an investment portfolio. Reducing this metric is important.
• Average Return: Indicates the average return over a specified period.

6.2 Hyperparameter Tuning

Hyperparameter adjustments are necessary to maximize the model’s performance. This process can be conducted via grid search or random search, testing various hyperparameter settings to find the optimal combination.

7. Conclusion

The world of algorithmic trading utilizing machine learning and deep learning is vast and captivating. With these technologies, investors can make more effective quantitative decisions. I hope this course has enhanced your understanding of returns and benchmark input generation, and helped you build a practically applicable trading system.

7.1 Additional Resources and References

Further information can be found in the following resources:

• QuantInsti: Algorithmic Trading
• Towards Data Science: Machine Learning for Trading
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

In the modern financial market, investors are utilizing various technical methods and tools to successfully realize profits. In particular, machine learning and deep learning technologies are receiving increasing attention in the field of algorithmic trading, playing a crucial role in maximizing the efficiency of data analysis and prediction. This course will cover the basics to advanced concepts of algorithmic trading using machine learning and deep learning, and provide an in-depth explanation of alpha factor engineering for predicting returns.

1. Basic Concepts of Machine Learning and Deep Learning

Machine learning is a technology that learns patterns from data to make predictions or decisions. Deep learning is a subset of machine learning that learns more complex data representations based on artificial neural networks.

1.1 Types of Machine Learning

• Supervised Learning: Learns based on a dataset with labels.
• Unsupervised Learning: Finds patterns in data without labels.
• Reinforcement Learning: Learns to maximize rewards by interacting with an environment.

1.2 Fundamentals of Deep Learning

Deep learning typically performs tasks using multi-layer artificial neural networks (ANN). Each layer receives inputs, multiplies them by weights, and produces outputs through an activation function.

2. Basics and Strategies of Trading

2.1 Understanding Algorithmic Trading

Algorithmic trading is a method of automatically executing trading strategies using computer algorithms. This allows for trades to be executed when specific conditions are met, eliminating emotional elements.

2.2 Traditional Trading Strategies

• Trend Following Strategy: A strategy that follows the price trends of the market.
• Market Neutral Strategy: Seeks profit regardless of market direction.

3. Alpha Factor Engineering

3.1 Concepts of Alpha and Beta

Alpha represents the excess return of an investment’s performance, while beta indicates the relationship with market volatility. It is important for investors to design strategies that increase alpha.

3.2 Definition and Development of Alpha Factors

Alpha factors are indicators for predicting returns of specific strategies. They are used to predict stock returns. Developing alpha factors requires various data analysis techniques.

4. Generating Alpha Factors through Machine Learning

4.1 Data Preparation and Preprocessing

To generate alpha factors, it is necessary to first collect appropriate data and preprocess it. This includes handling missing values, feature scaling, and normalization.

4.2 Model Selection and Training

There are several types of machine learning models, and it is essential to choose the appropriate model considering the characteristics of each model and the data. Options include regression analysis, decision trees, random forests, and neural networks.

5. Algorithmic Trading Using Deep Learning

5.1 Neural Network-Based Models

The artificial neural networks used in deep learning exhibit excellent performance in learning complex patterns in data. For example, Long Short-Term Memory (LSTM) networks are effective for processing time series data.

5.2 Hyperparameter Tuning

To maximize model performance, it is essential to appropriately adjust hyperparameters. This is crucial for creating a model optimized for the given dataset.

6. Performance Evaluation and Risk Management

6.1 Performance Evaluation Metrics

To evaluate a model’s performance, various metrics can be used. For example, the Sharpe ratio, alpha, beta, and maximum drawdown are among the multiple criteria available.

6.2 Risk Management Strategies

Constructing and managing an investment portfolio considering the risk-free rate of return is necessary to reduce investor losses. Various risk management techniques should be utilized to ensure the stability of trading strategies.

Conclusion

Algorithmic trading utilizing machine learning and deep learning will play an essential role in future investment strategies. It is important to build strategies that enhance profitability and minimize risk through effective alpha factor engineering. I hope this course helps you understand the basic concepts and acquire the knowledge and skills needed for practical application.