Machine Learning and Deep Learning Algorithm Trading, Financial Statement Data

Methods using machine learning and deep learning techniques to automate investment decisions in the financial markets are increasingly being adopted. In particular, financial statement data plays a crucial role in assessing a company’s financial condition and evaluating the value of its stock. This course provides detailed explanations on how to build a trading system based on financial statement data using machine learning and deep learning algorithms.

1. Overview of Machine Learning and Deep Learning

Machine Learning and Deep Learning are subfields of artificial intelligence that analyze data and learn patterns to make predictions. The basic idea of machine learning is to train a model using data and to use this model to predict new data.

1.1 Machine Learning

Machine learning primarily uses algorithms to analyze data and recognize patterns. The main classification methods in machine learning are as follows:

  • Supervised Learning: The model learns to predict outcomes when input data and labels are provided.
  • Unsupervised Learning: Focuses on discovering patterns in data based on unlabeled data.
  • Reinforcement Learning: An agent learns optimal behaviors through a reward system.

1.2 Deep Learning

Deep learning is a subfield of machine learning based on artificial neural networks. It is particularly powerful in learning complex data patterns and is widely used in fields such as image and speech recognition, and natural language processing.

2. Importance of Financial Statement Data

Financial statements are essential information for understanding a company’s financial condition, playing a critical role for stock investors. The main types of financial statements include:

  • Income Statement: Represents a company’s profitability and costs.
  • Balance Sheet: Shows the assets, liabilities, and equity at a specific point in time.
  • Cash Flow Statement: Indicates the cash inflows and outflows of a company.

2.1 Financial Metrics

Financial metrics derived from financial statements provide tools for numerically analyzing a company’s performance. Key financial metrics include:

  • Earnings Per Share (EPS): The value obtained by dividing net income by the number of outstanding shares, used to evaluate the profitability of a stock.
  • Return on Equity (ROE): The value obtained by dividing net income by shareholders’ equity, used to assess a company’s financial performance.
  • Debt Ratio: The ratio obtained by dividing total liabilities by total assets, indicating a company’s financial health.

3. Machine Learning and Deep Learning Algorithm Trading

It is possible to develop trading strategies utilizing machine learning and deep learning models. In this process, we will explore how to effectively use financial statement data.

3.1 Data Collection

Data collection for financial statements can be done using various APIs or web scraping techniques. Stock data can be obtained through APIs such as Yahoo Finance and Alpha Vantage.


import pandas as pd
import requests

# Example: Fetching data through the Yahoo Finance API
def get_financial_data(ticker):
    url = f"https://query1.finance.yahoo.com/v10/finance/quoteSummary/{ticker}?modules=financialData"
    response = requests.get(url)
    return response.json()

data = get_financial_data("AAPL")
print(data)

3.2 Data Preprocessing

Data preprocessing is a crucial step in improving the performance of machine learning models. This includes handling missing values, data normalization, and feature selection.


# Example of data preprocessing
def preprocess_data(data):
    # Remove missing values
    data = data.dropna()
    
    # Encoding categorical variables
    data = pd.get_dummies(data)
    
    # Normalization
    from sklearn.preprocessing import MinMaxScaler
    scaler = MinMaxScaler()
    scaled_data = scaler.fit_transform(data)
    return scaled_data

processed_data = preprocess_data(data)

3.3 Model Selection

Choosing the right model is one of the important decisions when building a trading system. Essential machine learning models and techniques include:

  • Linear Regression
  • Decision Trees
  • Random Forest
  • Support Vector Machines
  • Neural Networks

3.4 Model Training and Evaluation

The trained model should be utilized with an evaluation system to assess its performance. It is crucial to prevent overfitting and enhance generalization performance during this process. Commonly used evaluation metrics include:

  • Accuracy
  • Precision
  • Recall
  • F1 Score

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Splitting the dataset
X_train, X_test, y_train, y_test = train_test_split(processed_data, target, test_size=0.2)

# Model training
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Model evaluation
predictions = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, predictions))

4. Trading System Using Deep Learning

Deep learning models are powerful in learning patterns from complex data. Libraries such as Keras and TensorFlow make it easy to build deep learning models.

4.1 Designing Deep Learning Architecture

When designing the architecture of a deep learning model, the following elements should be considered:

  • Input Layer
  • Hidden Layers
  • Output Layer
  • Activation Functions

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

# Building a deep learning model
model = Sequential()
model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# Compiling the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

4.2 Model Training and Evaluation

Train the model with the training data and evaluate its performance using evaluation metrics.


# Model training
model.fit(X_train, y_train, epochs=100, batch_size=10, verbose=0)

# Model evaluation
loss, accuracy = model.evaluate(X_test, y_test)
print("Accuracy:", accuracy)

5. Building an Actual Trading System

It is essential to have a system that makes actual trading decisions based on the predictions of the model. To achieve this, an automated trading system (Trading Bot) can be built.

5.1 Signal Generation Before Trading

Signal generation is the step where buy or sell decisions are made based on the predictions of the model.


def generate_signal(predictions):
    signals = []
    for prediction in predictions:
        if prediction >= 0.5:
            signals.append(1)  # Buy
        else:
            signals.append(0)  # Sell
    return signals

signals = generate_signal(predictions)

5.2 Executing Trades

To execute actual trades, a method to send orders through an API is used. For example, the Alpaca API can be utilized.


import alpaca_trade_api as tradeapi

# Alpaca API setup
api = tradeapi.REST('YOUR_API_KEY', 'YOUR_SECRET_KEY', base_url='https://paper-api.alpaca.markets')

# Executing orders
for signal in signals:
    if signal == 1:
        api.submit_order(
            symbol='AAPL',
            qty=1,
            side='buy',
            type='market',
            time_in_force='gtc'
        )
    else:
        api.submit_order(
            symbol='AAPL',
            qty=1,
            side='sell',
            type='market',
            time_in_force='gtc'
        )

6. Conclusion

Algorithmic trading using machine learning and deep learning helps make strong investment decisions through financial statement data. By implementing the methods described in this course, you can build and operate your own automated trading system. Continuously collecting data and updating models will enable you to maximize performance.

7. References