Building an automated trading system is becoming increasingly important in the financial markets. Among them, machine learning and deep learning algorithms have established themselves as powerful tools for discovering patterns in data and supporting decision-making based on them. In this article, we will explain in detail how to implement algorithmic trading using PyTorch 1.4.
1. Understanding Machine Learning and Deep Learning
Machine learning is a technique that learns from data to perform prediction or classification tasks. Deep learning is a subset of machine learning that allows for complex modeling using artificial neural networks. The reason deep learning has received attention in financial markets in recent years is its effectiveness in processing large amounts of data and detecting non-linear relationships.
1.1 Basic Concepts of Machine Learning
Machine learning is a system that learns behavior through observed data. We model the relationship between given input (X) and output (Y) and generate predictions for new inputs. Machine learning models can be broadly classified into three categories:
- Supervised Learning: Learns using labeled data. For example, training a model based on historical data and trading signals for price prediction.
- Unsupervised Learning: Uses unlabeled data. Analyzes data and discovers patterns using techniques such as clustering.
- Reinforcement Learning: An agent learns optimal behavior by interacting with the environment. It can be used to determine buy and sell signals for stocks.
1.2 Development of Deep Learning
Deep learning models are composed of layers, each transforming input data to generate the final output. Deep Neural Networks (DNN) are based on these rules, and various architectures such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) exist.
2. Introduction to PyTorch 1.4
PyTorch is an open-source machine learning library developed by Facebook, designed specifically for optimizing and building deep learning models. Its intuitive syntax and dynamic computation graph make it popular among data scientists and researchers.
2.1 Installing PyTorch
To install PyTorch, you need Python and pip. You can install PyTorch using the following command:
pip install torch==1.4.0 torchvision==0.5.0You are now ready to start a machine learning project using PyTorch.
3. Data Collection and Preprocessing
The data needed for the automated trading system can be collected from various sources. Using APIs like Yahoo Finance API, Alpha Vantage, and Quandl allows you to easily obtain stock data.
3.1 Data Collection
For example, the method for collecting data on a specific stock using the yfinance library is as follows:
import yfinance as yf
stock_data = yf.download("AAPL", start="2020-01-01", end="2023-01-01")3.2 Data Preprocessing
Since the collected data often contains missing values, it needs to be handled. You can manipulate the DataFrame and handle missing values using the Pandas library.
import pandas as pd
stock_data = stock_data.fillna(method='ffill')4. Feature Selection and Model Building
Feature selection has a significant impact on the performance of machine learning models. We can use various financial indicators and technical indicators as features for stock price prediction.
4.1 Feature Generation
Generating technical indicators like Simple Moving Average (SMA) and Relative Strength Index (RSI) can provide useful information for the model to learn.
stock_data['SMA'] = stock_data['Close'].rolling(window=20).mean()
stock_data['RSI'] = compute_rsi(stock_data['Close'], window=14)4.2 Dataset Splitting
By splitting the data into training and testing sets, we can evaluate the generalization performance of the model. An 80-20 ratio is typically used.
from sklearn.model_selection import train_test_split
train_data, test_data = train_test_split(stock_data, test_size=0.2, random_state=42)5. Model Training
It is now time to build and train the model. We will explain how to build an artificial neural network using PyTorch.
5.1 Model Configuration
In PyTorch, neural network models can be defined by inheriting from nn.Module. Below is an example of a simple feedforward neural network:
import torch
import torch.nn as nn
class SimpleNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(SimpleNN, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, output_size)
self.relu = nn.ReLU()
def forward(self, x):
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
return x
model = SimpleNN(input_size=3, hidden_size=10, output_size=1)5.2 Model Training
To train the model, you need to define a loss function and an optimization algorithm. Below is an example using Mean Squared Error (MSE) as the loss function and the Adam optimization algorithm:
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
for epoch in range(100):
model.train()
inputs = torch.from_numpy(train_data[['SMA', 'RSI']].values).float()
labels = torch.from_numpy(train_data['Close'].values).float().view(-1, 1)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()6. Model Evaluation and Prediction
After the model is trained, you can evaluate its performance using the test dataset. RMSE (Root Mean Squared Error) can be used to check the accuracy of the stock price prediction model.
model.eval()
with torch.no_grad():
test_inputs = torch.from_numpy(test_data[['SMA', 'RSI']].values).float()
predictions = model(test_inputs).numpy()
rmse = np.sqrt(np.mean((predictions - test_data['Close'].values) ** 2))
print(f'RMSE: {rmse}') 7. Building a Real Trading System
The process of building a real trading system based on the model includes signal generation, order execution, and portfolio management.
7.1 Signal Generation
The method for generating trading signals using the trained model is as follows:
signal = np.where(predictions > test_data['Close'].values, 1, 0)7.2 Order Execution
To execute actual orders according to the signals, an API is needed. For example, the Alpaca Trading API can be used.
import alpaca_trade_api as tradeapi
api = tradeapi.REST('APCAAPI-KEY-ID', 'APCAAPI-SECRET-KEY', base_url='https://paper-api.alpaca.markets')
for i in range(len(signal)):
if signal[i] == 1:
api.submit_order(
symbol='AAPL',
qty=1,
side='buy',
type='market',
time_in_force='GTC'
)8. Conclusion
In this tutorial, we explored how to build an automated trading system based on machine learning and deep learning using PyTorch 1.4. We covered various stages from data collection to model training, predictions, as well as executing orders in practice and managing portfolios. Machine learning and deep learning technologies will continue to evolve and play an increasingly important role in the financial markets.
To build more advanced models in the future, it is recommended to experiment with various architectures and algorithms. AI-based investment strategies will gradually become more powerful over time, and you should continuously improve your automated trading strategies accordingly.
References
- “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” – Aurélien Géron
- PyTorch Documentation
- Financial Machine Learning by Marcos Lopez de Prado