Preprocessing: Sentence Recognition and N-grams
The development of algorithmic trading using machine learning and deep learning provides insights into the stock market, foreign exchange market, and cryptocurrency. This advancement heavily relies on the progress of data processing and preprocessing technologies. In this course, we will take an in-depth look at the preprocessing processes using sentence recognition and n-grams.
1. Basic Concepts of Machine Learning and Deep Learning
Machine learning is an algorithm that learns from data to make predictions. Deep learning is a subset of machine learning, based on artificial neural networks, which learns complex data structures. Both technologies are used in financial data analysis.
2. Importance of Data Preprocessing
Data preprocessing is an essential step to maximize the performance of machine learning models. Especially in fields like Natural Language Processing (NLP), the impact of data preprocessing on model performance is significant. Stock market data is often provided in text format, which necessitates an understanding of text preprocessing.
3. Sentence Recognition
Sentence recognition is one of the key processes in natural language processing, involving the collection of text data and converting that data into a meaningful form. The main steps of the sentence recognition process are as follows.
- Data Collection: You can utilize methods such as web scraping and API data collection.
- Text Cleaning: Clean the text by removing special characters and unnecessary spaces.
- Tokenization: Split sentences into words or sentence units.
- Part-of-Speech Tagging: Tag each word with its part of speech to understand the context.
4. N-gram Model
An n-gram refers to a sequence of ‘n’ consecutive words or characters. It is utilized in various NLP tasks such as language modeling, text classification, and sentiment analysis. The characteristics of n-gram models are as follows.
- N-word N-grams: Generate combinations consisting of ‘n’ words. For example, the 2-gram of “I go to school” is [“I”, “go”, “to”, “school”].
- Context Understanding: N-gram models allow a deeper understanding of the meaning of sentences.
- Frequency Analysis: By analyzing frequencies, you can identify frequently occurring n-grams and find specific patterns.
5. N-grams and Algorithmic Trading
Using n-gram models in trading can generate trading signals by analyzing personal sentiments from stock market news or social media. For example, if there are many positive mentions of a specific stock, strategies like considering buying can be employed.
6. Preprocessing Example
6.1 Sentence Recognition using Python
import pandas as pd
import re
from nltk.tokenize import word_tokenize, sent_tokenize
data = "I will win in the stock market today. The stock market is unpredictable."
# Sentence recognition
sentences = sent_tokenize(data)
print(sentences)
# Tokenization
tokens = [word_tokenize(sentence) for sentence in sentences]
print(tokens)
6.2 N-gram Generation
from nltk.util import ngrams
n = 2 # 2-gram
bigrams = list(ngrams(tokens[0], n))
print(bigrams)
7. Conclusion
Sentence recognition and n-gram models play a vital role in machine learning and deep learning-based algorithmic trading. Through these processes, we can effectively analyze text data and derive meaningful insights for investment decisions. In future lectures, we will specifically explore actual investment strategies utilizing these techniques.