“The intelligent investor is a realist who sells to optimists and buys from pessimists.” - Benjamin Graham
Sentiment Trading strategies work on market sentiment and the trends around them. The strategies are often determined by the price and value of an asset that may fluctuate.
Market sentiments influence the behavior of investors for a specific asset or financial market. It is the emotional and psychological bias exhibited through certain activities and pricing dynamics.
These well-researched articles by experts give a brief overview of how sentiments play a pivotal role in trading. We have listed down various top articles that cover topics, concepts, approaches, etc. Here are the Top Blogs on Sentiment Trading that helped our readers in the year 2022.
This blog is a walkthrough guide for VADER Sentiment Analysis and how you can leverage it in Algorithmic Trading Models with the help of Python.
The blog enables you to use SMA as a primary technical indicator as a practical example. You also learn how VADER evaluates the Valence Score of an input text and sentence. Sentiments are driving financial markets in recent years. Learn how you too can use this approach to procure results through Algorithmic Trading.
This article is the first instalment that discusses the Sentiment Trading Strategy and Indicators. There are multiple indicators that can be used for market analysis like volatility, volume, fund flow, etc. Here you can read about the Put/Call Ratio and Arms Index or Short-term trading index (TRIN). It offers an overview of how two contrarian methods of investing can capitalize on general optimism or pessimism in the market.
Many Quants rely on multiple data sources to enhance strategies that build the value of trading activities. This article offers great pointers on various data sources that help develop a powerful trading strategy with minimum risks. You learn how you too can leverage the data through various sources and optimally use data for trading activities.
The article discusses:
- Fundamental Data
- Macroeconomic Data
- Earnings Calendar
- Financial News Data
- Twitter Data
- Sentiment Data
The article is a final project submitted by our EPATian Siddhant R Vaidya as 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
Social Media and Twitter help as alternative data sources to analyse market sentiments. The blog reviews the Tweepy library to procure real-time and historical data from Twitter. Read through the article to get insights on:
- Twitter and Sentiment Analysis
- The impact of social networks on market trends
- A Python Twitter API, Tweepy
- How to install and set up Tweepy?
- Authentication on Twitter API
- How to use Tweepy to get tweets
- Tweets pagination with cursors
- Building a naive sentiment indicator
The blog discusses in detail the Bag of Words approach that develops vectorized representations of text data. These representations enable the execution of NLP tasks like Sentiment Analysis. You too can learn relevant terms, limitations, and the critical advantages of the approach.
The article covers 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.
The blog provides an overview of sentiment analysis and showcases a basic model of sentiment analysis in R. Learn how you can develop a robust model using sentiment approaches.
Usually, sentiment analysis trading procures large sets of data and builds a sentiment score to offer signals during trading activities. This article shows how you can directly leverage a sentiment bar as an input in your strategy.
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