Unveiling Financial Markets (Forex/Stock) with News Sentiment Analysis Using Python.
Hello Guys,
It's me again... :)
In this article, I want to share my experience from my personal project. Although this project is still in the development stage, perhaps some of you would like to participate more seriously in its development so that it can benefit many people.
As we have all felt and experienced, in this digital information era, data is at the core of investment decisions, be it in the stock market or forex. Analyzing market sentiment is a very useful way to understand the movement of stock or currency prices. In this project, we will explore the steps to perform News Sentiment Analysis on financial market data using Python.
What is "News Sentiment Analysis"?
News Sentiment Analysis is a natural language processing technique used to extract and evaluate sentiments or opinions from news or text. In the context of the financial market, this means analyzing opinions, news, or tweets to understand the emotions or sentiments of investors towards an asset.
What are the main steps in this Project?
Certainly, several steps or stages are required to work on this project. Here is a general overview of the stages of this project:
- Data Collection: Gathering data from sources such as Twitter, financial news sites, or specialized platforms that provide sentiment data related to the market. But in this project, we use data collection techniques using 'web scraping'. The targeted website is https://www.forexfactory.com/calendar.
- Data Preprocessing: Cleaning and preparing data for analysis, including steps such as removing unused columns, eliminating characters (stop words, symbols), converting data, and text normalization.
- Sentiment Analysis: Using machine learning algorithms or models to evaluate news sentiment, such as Logistic Regression, Random Forest, or pipeline techniques.
- Visualization of Results: Displaying analysis results in the form of graphs or visualizations that can be understood to gain deeper insights.
- Building Machine Learning: Manually predicting news sentiment can be time-consuming and susceptible to subjective bias. With Machine Learning, we can utilize algorithms to automatically analyze news text, extract patterns, and predict sentiments with a fairly high level of accuracy.
What are the tools and programming languages used?
In this project, we will use Python as the main programming language, and several libraries such as:
- NLTK (Natural Language Toolkit), for text preprocessing.
- Scikit-learn, for building Sentiment Analysis models.
- Pandas and Matplotlib/Seaborn, for data manipulation and visualization.
I will explain step-by-step using Python for this project.
Okay, curious about this project?
What's the Idea Behind This Project?
Actually, the idea emerged when I was developing a Machine Learning project that could predict stock/forex prices. The idea came about because I saw immense potential for anyone interested in the financial market, especially investors or traders, to gain significant profits by utilizing this machine learning, coupled with 'News Sentiment'. The relationship between news and stock or forex movements is very close. They have a very high correlation. Economic, political, financial news, or other factors affecting a country's macroeconomic conditions or industry can have a significant impact on stock prices or currency exchange rates in the forex market. In addition, the challenge in News Trading is high volatility, meaning prices can move quickly and unexpectedly after news releases, increasing trading risks. There's also 'Slippage,' difficulty in getting the desired price due to rapid price changes after news releases. Furthermore, market reactions (sentiments) can sometimes be counteractive. Sometimes the market reacts in a contrary manner to the expected expectations from the news, making trading difficult to predict. Therefore, I came up with the idea of creating a Machine Learning model to predict news sentiment towards market prices.
In this section, I'll explain with the help of the Python programming language (Google Colab) to make it easier to understand.
Google Colab : https://colab.research.google.com/drive/13MdNp1gxW4Z5mCbScIgCnb4KcsYa7nL8?usp=sharing
Voila...
I haven't yet provided a description or explanation for each Python script. If there's anything to ask, please leave your comments in the section below.
Colmar, 19th December 2023, Winter.