Machine Learning and Deep Learning Algorithm Trading, Strategy Backtesting using Backtrader

Quant trading, also known as algorithmic trading, is a method of automatically executing trades in financial markets through data analysis and algorithms. This article will detail how to establish trading strategies using machine learning and deep learning, and how to backtest these strategies using the Backtrader library.

1. Basics of Machine Learning and Deep Learning

Machine learning and deep learning are technologies that analyze large amounts of data to find patterns and make predictions based on those patterns. These two concepts are deeply interconnected and play a very important role, especially in financial markets.

1.1 What is Machine Learning?

Machine learning is a subfield of artificial intelligence that involves creating models capable of learning and making predictions from data. It is generally classified into supervised learning, unsupervised learning, and reinforcement learning.

1.2 What is Deep Learning?

Deep learning is a specific methodology of machine learning that uses artificial neural networks to learn complex patterns. Deep learning requires large-scale data and powerful computing power and is applied in various fields such as image analysis, natural language processing, and speech recognition.

2. Overview of Algorithmic Trading

Algorithmic trading involves creating computer programs that automatically execute actual trades. In this process, a model that generates trading signals is necessary, and the following considerations must be made during the model building phase.

2.1 Data Collection

The most important first step in algorithmic trading is to collect reliable data. Various financial information such as stock price data and trading volume data must be gathered.

2.2 Feature Engineering

This is the process of defining features to learn based on the collected data. Rather than simply using price changes, various indicators like moving averages, volatility, and RSI (Relative Strength Index) can be added.

2.3 Model Selection

It is important to choose an appropriate model among machine learning and deep learning models. Each model has its strengths and weaknesses, so it is essential to choose a model that fits the characteristics of the data and the objectives.

3. Introduction to Backtrader

Backtrader is a powerful backtesting framework implemented in Python that helps evaluate the performance of algorithms using financial data and various strategies.

3.1 Installing Backtrader

pip install backtrader

3.2 Basic Features of Backtrader

  • Strategy Implementation: Custom strategies can be implemented.
  • Data Feeding: Data can be sourced from various providers for use.
  • Performance Analysis: The performance of trading strategies can be evaluated in various ways.

4. Strategy Development Using Machine Learning

Now, let’s move on to the stage of strategy development using machine learning models. For example, you can use SVM (Support Vector Machine) or LSTM (Long Short-Term Memory) models.

4.1 Data Preparation

First, import the necessary libraries, load stock data, and create features and labels.


import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import classification_report

4.2 Model Training

In this step, split the data into training and testing sets, then train the model.


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = SVC(kernel='linear')
model.fit(X_train, y_train)

4.3 Prediction and Evaluation

Evaluate and predict the model on the test data.


y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

5. Running the Backtest

Now we will use the model we created to execute a backtest in Backtrader.

5.1 Strategy Implementation


import backtrader as bt

class MyStrategy(bt.Strategy):
    def __init__(self):
        pass

    def next(self):
        # Implement model predictions and trading logic
        pass

5.2 Execution and Result Analysis


cerebro = bt.Cerebro()
cerebro.addstrategy(MyStrategy)
data = bt.feeds.YahooFinanceData(dataname='AAPL')
cerebro.adddata(data)
cerebro.run()
cerebro.plot()

6. Conclusion

In this tutorial, we covered the basics of algorithmic trading using machine learning and deep learning, as well as the procedures for backtesting strategies using Backtrader. Algorithmic trading can be improved through iterative learning and experimentation. In this process, understanding data and technical skills play crucial roles. I hope this article serves as a helpful first step into the world of quantitative trading.

7. References

If you wish to gain deeper knowledge about the content covered here, it is recommended to refer to the following materials.

  • Machine Learning with Python
  • Quantitative Trading: How to Build Your Own Algorithmic Trading Business
  • Backtrader Documentation – https://www.backtrader.com/docu/