"An investment in knowledge pays the best interest." — Benjamin Franklin
For traders worldwide with various strategies all agree on one thing, educating yourself and learning from reliable resources is a crucial step in the world of trading.
The following well-researched articles, crafted by experts, offer a concise overview of the crucial role sentiments play in trading and how to leverage them. We've compiled a selection of top articles that address various subjects, concepts, and approaches in sentiment trading.
Sentiment-based trading strategies operate based on market sentiment and the corresponding trends. These strategies typically hinge on the volatility of asset prices and their intrinsic value.
Here are the top blogs on Sentiment Trading that provided valuable insights to our readers in 2023.
As technology continues to advance, sentiment analysis techniques are expected to become more sophisticated, accurate, and integrated into trading platforms.
It gently introduces the intricate concepts of market sentiment and sentiment analysis, aiming to enhance decision-making skills and boost trading performance for investors and traders alike.
A complete guide for VADER Sentiment Analysis and how you can leverage it in Algorithmic Trading Models with the help of Python.
It empowers you to utilize the Simple Moving Average (SMA) as a core technical indicator through a practical demonstration. Additionally, you gain insights into how VADER assesses the Valence Score of textual inputs and sentences. Sentiments have become a driving force in financial markets in recent times.
Explore how this approach can be applied to potential outcomes in Algorithmic Trading.
Quants often rely on multiple data sources to enhance strategies that build the value of trading activities.
Explore valuable insights in this article on diverse data sources for crafting a robust trading strategy with reduced risks. Discover ways to leverage data from various sources for more effective trading without making any guarantees.
The article discusses:
- Fundamental Data
- Macroeconomic Data
- Earnings Calendar
- Financial News Data
- Twitter Data
- Sentiment Data
Ever wondered how the Bag of Words method creates vectorized representations for NLP tasks like Sentiment Analysis? Explore key terms, limitations, and the advantages of this approach. From Bag of Words vs Word2Vec to crafting an algorithmic trading pipeline, discover the simplicity and potential of this feature extraction method. Can Bag of Words predict price movements? Uncover the secrets in this captivating read!
The topics covered in this blog:
- Bag of Words Approach
- Limitations of Bag of Words
- Bag of Words vs Word2Vec
- Advantages of Bag of Words
Natural Language Processing, abbreviated as NLP, which finds extensive applications in the field of trading. It is primarily employed to assess market sentiment by analyzing data from sources such as Twitter feeds, newspaper articles, RSS feeds, and press releases. In this blog, we will delve into the foundational structure required to address NLP challenges from the viewpoint of traders.
With this blog uncover the steps that you need to follow for using NLP in trading!
In the quest to find the elusive alpha, data scientists and quant analysts have now shifted their focus on processing the tons of ‘big data’ churned out there by internet users. Using programs to understand and analyze human language is called natural language processing (NLP).
In this article, look at one of the popular libraries for natural language processing in Python- spaCy.
In this article, we will study some of the most widely used features of NLTK and use them in building a sentiment analysis model. Dealing with natural language data and processing it is the core of News based automated trading systems and thus an exciting skill from an algorithmic trader's perspective.
The topics covered are:
- How to install NLTK
- NLTK Corpus
- NLTK VADER Sentiment Analysis
- NLTK Tokenizers
- NLTK Stopwords
- NLTK Stemmers
- Train Sentiment Analysis model
Gain insights on how to build a machine learning model to predict asset values using historical data. It uses historical pricing data and Twitter sentiment indicators to build the model. See the potential for practical implementations and how you too can innovate your models.
This final project submitted by our EPATian Siddhant R Vaidya is a part of his coursework at QuantInsti. The scope of this project offers:
- The indicator can be further improvised and the thresholds can be optimized
- Employing machine learning for generating more effective sentiment scores
- For any other strategies, sentiment analysis can be used for risk management
- Using sentiment analysis for identifying black swan events
Market sentiments have a significant impact on investor behaviour within a particular asset or financial market. They represent the emotional and psychological bias reflected in various actions and pricing fluctuations.
One of the best ways to extract sentiment is from news articles. It is pivotal to understand different sources and types of news to understand their impact. Especially when you want to retrieve valuable information and filter news data with qualitative parameters. Learn how to automate the process of utilising news to identify the general sentiment using VADER and LLM models and trade accordingly to develop News Sentiment Trading Strategies.
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