Machine Learning and Deep Learning Algorithm Trading, Data Collection and Preparation

Using machine learning and deep learning in quantitative trading is a very useful approach to developing effective trading strategies. However, the most important first step is ‘data collection and preparation’. This course will explain in detail the importance of data, methods for collection, preprocessing, and how to train algorithms using the prepared data.

1. Importance of Data

Data forms the foundation of machine learning and deep learning. The performance of algorithms highly depends on the quality of the data used. Here are some reasons why data is important:

  • Reliability: High-quality data enables the model to make more accurate predictions.
  • Representativeness: The model should reflect various market conditions to make generalized predictions.
  • Volume: A large amount of data provides the necessary information for algorithms to learn patterns.

2. Methods for Data Collection

There are various ways to collect data, especially financial market data, which is mainly gathered through the following methods:

  • Utilizing APIs: [For example, real-time and historical data can be collected through APIs such as Alpha Vantage, Yahoo Finance, and Quandl.]
  • Web Crawling: Data can be extracted from websites using libraries like BeautifulSoup or Scrapy.
  • Data Providers: Data can be purchased from companies that specialize in providing specific data.

2.1 Example of Using APIs

For instance, the method to collect stock price data using the Alpha Vantage API is as follows:

import requests

api_key = 'YOUR_API_KEY'
symbol = 'AAPL'
url = f'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={symbol}&apikey={api_key}'

response = requests.get(url)
data = response.json()

The above code requests the daily stock price data for Apple Inc. (AAPL) and receives a response in JSON format.

2.2 Example of Web Crawling

Data collection through web crawling can be done as follows:

from bs4 import BeautifulSoup
import requests

url = 'https://finance.yahoo.com/quote/AAPL'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

price = soup.find('fin-streamer', {'data-field': 'regularMarketPrice'}).text

The above code is an example of crawling the current stock price of Apple from Yahoo Finance.

3. Data Preprocessing

Collected data must undergo a preprocessing phase before model training. Preprocessing enhances data quality, allowing algorithms to learn more effectively.

3.1 Handling Missing Values

Missing values indicate empty spots in the data analysis process, and there are several ways to handle them:

  • Delete the missing values.
  • Replace missing values with the mean or median.
  • Predict and replace based on other data.
import pandas as pd

data = pd.read_csv('data.csv')
data.fillna(data.mean(), inplace=True)

3.2 Data Normalization

Normalization is needed to unify the scale of data. This allows algorithms to converge more quickly.

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
data[['feature1', 'feature2']] = scaler.fit_transform(data[['feature1', 'feature2']])

3.3 Feature Engineering

Creating useful features for the model greatly impacts its performance. This can be done through methods such as:

  • Generating technical indicators based on historical price data
  • Analyzing the correlation between stock prices and other variables
data['SMA'] = data['close'].rolling(window=20).mean() # 20-day moving average

4. Preparing for Algorithm Training

Once data preprocessing is complete, the machine learning algorithm is ready for training. The training and test data should be separated for model evaluation.

from sklearn.model_selection import train_test_split

X = data[['feature1', 'feature2']] # Features
y = data['target'] # Target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

5. Conclusion

Data collection and preparation are the most crucial steps in machine learning and deep learning algorithm trading. By adopting the correct data collection methods and thorough data preprocessing, the performance of the model can be maximized. Afterward, the learned model can be used to develop actual trading strategies and validate the effectiveness of the strategies through backtesting.

This course has covered the entire process from data collection to preprocessing. The next step will discuss how to build algorithm trading models based on this data. Wishing you success in your quantitative trading!