Machine Learning and Deep Learning Algorithm Trading, How to Build Neural Networks from Scratch Using Python

In recent years, algorithmic trading using machine learning (ML) and deep learning (DL) technologies has gained attention in the financial markets. This article explains how to build a trading algorithm based on machine learning and deep learning using Python from start to finish. The topic is divided into several sections, each covering the fundamental concepts of algorithmic trading, data collection, preprocessing, model building, training, evaluation, and applying it to a real trading system.

1. Concept of Algorithmic Trading

Algorithmic trading refers to the process of automatically trading stocks, forex, futures, and other financial assets based on certain rules. Such algorithms can utilize machine learning and deep learning techniques to analyze market data and make trading decisions. This allows for the exclusion of human subjective judgments and the implementation of more systematic and consistent trading strategies.

1.1. Difference Between Machine Learning and Deep Learning

Machine learning refers to algorithms that learn patterns and make predictions using data. On the other hand, deep learning is a subset of machine learning that utilizes complex artificial neural networks to model nonlinear relationships. In complex data such as stock markets, deep learning can be more effective.

2. Data Collection

To build a trading algorithm, reliable data is needed first. Stock data can be collected from services such as Yahoo Finance, Alpha Vantage, and Quandl. In Python, data can be easily downloaded using libraries like ‘pandas_datareader’.

2.1. Collecting Data from Yahoo Finance

Here is a code example for collecting data from Yahoo Finance.

import pandas as pd
from pandas_datareader import data as pdr

# Collect data for a specific stock. For example, AAPL (Apple Inc.)
start = '2010-01-01'
end = '2022-01-01'
df = pdr.get_data_yahoo('AAPL', start=start, end=end)
print(df.head())

3. Data Preprocessing

After collecting data, it needs to be preprocessed into a format suitable for machine learning models. The preprocessing involves handling missing values, normalization, and label encoding. In particular, for stock data, the date can be set as the index, and price data volatility can be added for chart analysis.

3.1. Handling Missing Values

Missing values can decrease the performance of machine learning models, so they must be handled carefully. The most common method is to replace missing values with the mean or median.

df.fillna(method='ffill', inplace=True)  # Replace missing values with the previous value

3.2. Data Normalization

Adjusting the range of data to increase the convergence speed of the model is also important. Data normalization is typically performed using Min-Max scaling or Z-score normalization.

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(df[['Close']])  # Normalize close price data

4. Building the Neural Network Model

Now, based on the preprocessed data, we build a neural network model. Here, we will create a simple multi-layer perceptron (MLP) model using Keras and TensorFlow libraries.

4.1. Model Design

The model consists of multiple layers, each adding nonlinearity using an activation function. Below is an example of the design for a basic model.

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

model = Sequential()
model.add(Dense(64, activation='relu', input_dim=X_train.shape[1]))  # First hidden layer
model.add(Dense(32, activation='relu'))  # Second hidden layer
model.add(Dense(1, activation='linear'))  # Output layer (linear activation function is used for regression)

4.2. Model Compilation

When compiling the model, you need to specify the loss function and optimization algorithm. Typically, mean squared error (MSE) is used for regression problems.

model.compile(loss='mean_squared_error', optimizer='adam')

5. Model Training

To train the model, we need to learn from the training data and evaluate performance on the validation data. Generally, training and validation data are used in an 80:20 ratio.

history = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2)

6. Model Evaluation

After training is complete, we evaluate the model’s performance using test data. Metrics like RMSE (root mean square error) can be used to judge the model’s predictive capability.

from sklearn.metrics import mean_squared_error
import numpy as np

y_pred = model.predict(X_test)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f'RMSE: {rmse}')  # Print RMSE

7. Applying to Real Trading Systems

If the model’s performance is sufficiently good, it can be applied to actual trading. However, building a real trading system comes with significant difficulties, requiring consideration of various factors such as risk management and slippage.

7.1. Risk Management

Risk management is one of the most important elements of algorithmic trading. Typically, risk is managed by setting position sizes, stop-loss, and profit-taking levels.

7.2. Order Execution

In the order execution process, actual market orders are sent using a Broker API. By using libraries like ‘ccxt’, you can easily connect to the APIs of various exchanges.

import ccxt

exchange = ccxt.binance()  # Set up the exchange
order = exchange.create_market_order('BTC/USDT', 'buy', amount)  # Execute market order

Conclusion

In this article, we learned how to build a neural network model from scratch using Python for machine learning and deep learning algorithmic trading. We covered all processes from data collection, preprocessing, model building and training, evaluation, to real trading. Through this process, I hope to provide an opportunity to lay the foundations of algorithmic trading and further evolve your own trading strategies.

References