News Sentiment Trading Strategies
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- Live Trading
- Learning Track
- Prerequisites
- Syllabus
- About author
- Testimonials
- Faqs
Learn News Trading Strategies
- Understand different sources and types of news as well as their impact
- Retrieve news pertaining to a company
- Filter the news data using qualitative parameters
- Utilise the news to calculate the sentiment score of any stock using LLM models and VADER
- Set trading rules based on the sentiment scores.
- Examine a use case of how LLM can be used to calculate sentiment.
- Apply technical indicators to optimise the strategy
- List the challenges pertaining to news-based trading and learn how to counter them

News Based Trading Skills
Strategies
- Buy the Rumour Sell the Event
- VADER-based Sentiment Analysis
- Optimise Strategy Using Technical Indicators
Concepts & Trading
- Retrieving News Data
- Identifying Sentiment from News Headline
- Sentiment Score
- LLM Models
Python
- Pandas
- Numpy
- Matplotlib
- Talib

learning track 8
This course is a part of the Learning Track: Advanced Algorithmic Trading Strategies
Course Fees
Full Learning Track
These courses are specially curated to help you with end-to-end learning of the subject.
Course Features
- Community
Faculty Support on Community
- Interactive Coding Exercises
Interactive Coding Practice
- Capstone Project
Capstone Project Using Real Market Data
- Trade & Learn Together
Trade and Learn Together
- Get Certified
Get Certified
Prerequisites
To start with the course, you need to have a basic understanding of financial markets. A basic knowledge of Python, including pandas dataframe, matplotlib, and loops for strategy implementations.
News Trading Strategies Course
- IntroductionAn approach to trading that focuses on the retrieval of news data and analysing the sentiment to make data-driven decisions is news-based trading. This section serves as a preview of the course and introduces the course contents. The interactive methods used in this course will assist you in not only understanding the concepts but also answering all questions regarding sentiment analysis. This section explains the course structure as well as the various teaching tools used in the course, such as videos, quizzes, coding exercises, and the capstone project.
About News-Based Trading
In this section, you will understand why you should trade the news and how it can help you in improving your ability to make well-informed decisions and capitalise on market opportunities in a dynamic and ever-changing landscape. Having access to reliable and timely news sources is essential for traders to interpret and respond to market-moving events effectively. Therefore, in this section you will also learn about some of the sources of news.Why Trade the News?2mTrading the News5mBenefits of Trading the News5mTrading Volatility5mTrading the News and Technical Analysis5mPrice Movements5mNews-Based Trading and Volatility5mSources of News5m 08sCompany News5mOfficial Announcements5mExpert Opinions and Research Reports5mImportance of Research Reports5mUnstructured News5mGeneral Public Influence5mRole of Media Houses5mAdditional Reading on News Based Trading2mFAQs on News Based Trading2m- Types of News ReleasesNews comes in various forms so in this section we’ve broadly classified news into two categories - planned and unexpected news. After completing this section, you will learn how news can be categorised and get an idea of the impact that news can have on the market.Planned News3m 45sCategorising News Releases5mCorporate News Releases5mIdentifying Planned News5mFOMC News Release5mEconomic News Releases5mImpact of Planned News Releases5mUnexpected News3m 31sPlanned and Unexpected News5mIdentifying Unexpected News5mTrading the Unexpected News5mUnexpected Events5mNews Based Trading Practices5mAdditional Reading on Impact of News Based Trading2m
- Buy the RumourIn this section, you will examine the reason why traders buy a stock on a rumour which has not been officially confirmed.Buy the Rumour1m 08sRole of Information in Trading5mRumours on Product5mVerification of Rumour5mView Regarding Product Launch5m
- Sell at the EventIn this section, you will discuss a contrarian strategy where you have bought the stock on the basis of rumours but sell it on the day of the event irrespective of the event’s outcome.Sell at the Event2m 4sReasoning for Stock Buy5mReaction to News on Product Launch5mSelling Stock5mOutcome of Buy the Rumour Sell the Event5mObservation of Buy the Rumour and Sell the Event5mRisk of Buy the Rumour and Sell the Event5mConsistency of Buy the Rumour Sell the Event5m
Backtest the Buy Rumour and Sell Event Strategy
You have seen the logic of the trading strategy where you will buy the stock on the rumour and sell at the event. Now, it is time to backtest and analyse this strategyBuy the Rumour Sell the Event5mRead the Data5mFind the Date 20 Days Prior to Event5mList the Dates From Event and 20 Days Prior to Event Date5mGenerate Trading Signal for Buy Rumour Sell the Event5mCalculate Daily Returns5mCalculate Portfolio Strategy Daily Returns5mCalculate Cumulative Returns5mCalculation of CAGR5mCalculate the Sharpe Ratio5mCalculate Maximum Drawdown5mLimitations of Buy the Rumour Sell the Event
The buy the rumour and sell at the event strategy is not without its flaws. In this section, you will analyse the strategy and its limitations. And further, explain how it can be improved.Limitations of Buy the Rumour and Sell at the Event2mTrading Strategy Application Boundary5mLogic of Buy the Rumour Sell the Event5mInfluence of Other Factors on Strategy5mInference on Buy the Rumour Sell the Event Strategy5mFAQs2mRetrieving and Storing Textual Data
Step 1 of creating a news sentiment analysis strategy is to retrieve news-related data. In this section, you will retrieve news data and learn how to store it in a format which can be easily retrieved and analysed.Retrieving and Storing Textual Data3m 2sWeb Scraping5mWebsite Crash5mPrevent Server Crash5mAPIs5mLoad Balancing5mBenefit of APIs5mStore Sentiment Data5mTweepy API2mAlpaca API2mWorking With Pickle File5mFetching News Data5mQualitative Analysis
The quality of data that we use also determines the effectiveness of our strategy. Therefore, in this section, you will delve into various methods to enhance the data quality. We will focus on refining and filtering the data through Python to elevate its overall quality and reliability.Qualitative Analysis3m 59sGoal of Qualitative Analysis5mContentless Articles5mDetermining Relevance5mDefine Novelty5mImportance of Novelty5mDuplicate News Articles5mQualitative Analysis5mRemove HTML Tags5mDrop Duplicate Articles5mEnsuring Novelty5mSort the News Data5mTransform the Data5mSimilarity Score5mTest on Trading the News and News Data16mFAQs on Qualitative Analysis2m- Using News to Your AdvantageIn this section we will discuss how we can use the news data to our advantage.How to Use News to Your Advantage?2m 36sCalculating Sentiment Scores5mChallenge in ML Approach5mVADER5mLexical-Based Approach5mAdvantage of VADER5mPositive Sentiment Score5mLexical-Based Sentiment Analysis5mSentiment Dictionary5mLabelled Data5mFull Form of VADER5m
- Sentiment Score Using VADERIn this section, you will learn about how VADER calculates sentiment score for a text. In addition to this, this section also covers the concepts such as how VADER accounts for sentiment intensity and the limitations of VADER.How Vader Calculates the Sentiment Score6m 46sWhy is VADER preferred?5mPurpose of VADER's Compound Score5mVADER's Lexicon Dictionary5mCalculation of Compound Score5mThe Range of Compound Score5mRange of Sentiment Scores5mHow VADER Accounts for Sentiment Intensity1m 43sSentiment Intensity - Capitalisation5mVADER’s Rule for Sentiment Intensity5mImpact of Sentiment Intensity5mEffect of Contrastive Conjunctions5mEffect of Punctuation Rule of VADER5mAdditional Reading for Sentiment Score Using VADER2mFAQs on Sentiment Score Using VADER2mSection Recap2m
- Limitations of VADERThis section covers the limitations of VADER for Sentiment Analysis of news headlines.Limitations of VADER for Sentiment Analysis1m 13sLimitations of VADER - I5mVADER to Analyse Sarcasm5mLimitations of VADER - II5mInterpretation of Financial Terms by VADER - I5mInterpretation of Financial Terms by VADER - II5mAdd New Words to VADER5m
Calculate Sentiment Score in Python
This section covers the implementation of VADER in Python for calculating the sentiment score of words and news headlines. This section also covers the VADER methods to access its lexicon and update it with financial words.VADER Score Calculations5mCreate The Analyzer Object5mGenerate Sentiment Scores of a News Headline5mCalculate Compound Score of News Headline5mSentiment Intensity of Headlines5mAdd New Words in VADER Dictionary5mAccess the Vader Lexicon5mPython Code to Update VADER Lexicon5mUpdate the VADER Lexicon5mSentiment Score of a Word5mCalculate Sentiment Scores of News Headlines5mSentiment Score of a News Headlines5mAdditional Reading for Calculate Sentiment Score In Python2mFAQs on Calculate Sentiment Score In Python2mSection Recap2mA Guide to Vader Library and Its Methods2mTest on Sentiment Analysis With VADER14m- Challenges in Calculating Sentiment of NewsCalculation of Sentiments in news articles is not as straight-forward as we thought. In this section, you will lean about the challenges in effectively calculating news sentiments and how to overcome them.Challenges in Calculating Sentiments of News2mLimitations of Using First or Last Few Sente2mLimitations of Selectively Checking Sentences2mCalculating Sentiment of News Articles10mMethod to Split Article into Sentences2mLast Five Sentences2mAppropriate Keyword2m
- Sentiment Analysis Using LLMsLarge Language Models (LLMs) harness the power of artificial intelligence to make our life easier. LLMs are inherently using sentiment analysis for this purpose. Thus, you will see how you can perform sentiment analysis using LLM.Sentiment Analysis Using LLMs2m 5sUsage of LLMs5mSteps in Sentiment Analysis5mLLM-Based Sentiment Analysis5mPassing the Data5mBenefits of LLMs5mLLMs and Its Use Cases2mLimitations of LLMs in Sentiment Analysis2mSentiment Analysis Using Gemini5m
- Calculation of Daily News Sentiment ScoreIn this section, you will calculate the news sentiment score for each day using VADER. This will help us in creating a strategy around sentiment analysisCalculate Daily News Sentiment5mExtract the Date From a Column5mCalculate Compound Sentiment Score for Headline5mFilter Out News with Non-Zero Sentiment Scores5mCalculate Normalised Sentiment Score for the Day5mSum the Normalised Sentiment Scores for Each Date5m
Buy the Rumour Sell the Event With Sentiment Analysis
The buy the rumour and sell the event strategy did not include any news-related analysis. In this section, you will seek to improve your trading strategy by using sentiment scores which were calculated in the previous sections.Buy the Rumour Sell the Event Using Sentiment Scores2mPurpose of Incorporation of Sentiment Scores5mDrawback of Not Considering News Events5mSignificance of Setting Sentiment Score Based Threshold5mRolling Sentiment Score5mCalculation of Average Sentiment Score5mDecision when Average Sentiment Score is Positive5mAction if Day is Prior to Event5mTime of Exit5mIncorporating Sentiment in Buy the Rumour Sell the Event2mIncorporating Sentiment in Buy the Rumour Sell the Event5mCalculate Rolling 20-Day Mean of Sentiment Scores5mJoin Sentiment Score and Price in One Dataframe5mGenerate Trading Signal for Sentiment Score and Event Day Condition5m- Analysis of Buy Rumour Sell Event Using Sentiment Scores StrategyYou have backtested the Buy the Rumour Sell at the Event Using Sentiment Scores Strategy in the previous section. Now, you will check if there is a way to improve the strategy performanceAnalysis of the Buy the Rumour Sell at the Event Using Sentiment Scores Strategy2mLimitation of Buy the Rumour Sell the Event Strategy5mEnhancement of Buy Rumour Sell Event With Sentiment Scores Strategy5mAction if Sentiment Score is Negative5mElimination of Condition in Enhanced Strategy5m
Sentiment Analysis Strategy
The sentiment analysis strategy seeks to use the average sentiment score to generate trading signals. You will implement this strategy and analyse the strategy performance.Flow Diagram for Sentiment Analysis Strategy2mSentiment Analysis Strategy5mEntry Rule for Sentiment Analysis Strategy5mThreshold of Sentiment Score to Generate Trading Signals5mTime Frame of Average Sentiment Score5mGenerate Sentiment Based Trading Signal5m- Improving the Sentiment Analysis StrategyThe sentiment analysis based strategy was backtested and analysed. But now, you will see how you can improve the strategy further by using technical indicators.Analysing Sentiment Analysis Strategy Performance2mPerformance of Sentiment Analysis Strategy5mImprovement in Quality of Trading Signal5mCombined Strategy Improvement5mFlow Diagram for Improving Sentiment Analysis Strategy2mImproving Sentiment Analysis Strategy Using Technical Indicators5mCalculate the 14-Day Period RSI Indicator5mGenerate the Entry Signal for RSI Indicator5mGenerate Exit Signal for RSI Indicator5mEnhancing Sentiment Analysis Strategies With Technical Indicators5mExpanding Information Scope in Trading With Technical Indicators5mTest on Sentiment Analysis Based Strategies10mFAQs2mPart Summary2m
- Pitfalls of Trading the NewsWhile news-based trading presents valuable prospects for leveraging information to our advantage, it is not free of challenges. In this section, you will explore the key challenges associated with news-based trading and you will also learn some ways to counter these challenges.Common Pitfalls of News Based Trading4m 19sImportance of Credibility5mCredible Sources5mSentiment Forecasts5mLatency in Getting Data5mConfirming Signals5m
Capstone Project
In this section, you will use the learnings from the course to use VADER to design a trading strategy based on daily sentiment score of a stock. You will also check the performance of this trading strategy.- Live Trading on IBridgePyIn this section, you would go through the different processes and API methods to build your own trading strategy for the live markets, and take it live as well.Uninterrupted Learning Journey with Quantra2mSection Overview2m 2sLive Trading Overview2m 41sVectorised vs Event Driven2mProcess in Live Trading2mReal-Time Data Source2mCode Structure2m 15sAPI Methods10mSchedule Strategy Logic2mFetch Historical Data2mPlace Orders2mIBridgePy Course Link10mAdditional Reading10mFrequently Asked Questions10m
- Paper and Live TradingTo make sure that you can use your learning from the course in the live markets, a live trading template has been created which can be used to paper trade and analyse its performance. This template can be used as a starting point to create your very own unique trading strategy.Template Documentation10mTemplate Code File2m
- SummaryIn this section, we will summarise all the learnings of this course.Course Summary2mCourse Summary and Next Steps2mPython Codes and Data2m
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Faqs
- When will I have access to the course content, including videos and strategies?
You will gain access to the entire course content including videos and strategies, as soon as you complete the payment and successfully enroll in the course.
- Will I get a certificate at the completion of the course?
Yes, you will be awarded with a certification from QuantInsti after successfully completing the online learning units.
- Are there any webinars, live or classroom sessions available in the course?
No, there are no live or classroom sessions in the course. You can ask your queries on community and get responses from fellow learners and faculty members.
- Is there any support available after I purchase the course?
Yes, you can ask your queries related to the course on the community: https://quantra.quantinsti.com/community
- What are the system requirements to do this course?
Fast-speed internet connection and a browser application are required for this course. For best experience, use Chrome.
- What is the admission criteria?
There is no admission criterion. You are recommended to go through the prerequisites section and be aware of skill sets gained and required to learn most from the course.
- Is there a refund available?
We respect your time, and hence, we offer concise but effective short-term courses created under professional guidance. We try to offer the most value within the shortest time. There are a few courses on Quantra which are free of cost. Please check the price of the course before enrolling in it. Once a purchase is made, we offer complete course content. For paid courses, we follow a 'no refund' policy.
- Is the course downloadable?
Some of the course material is downloadable such as Python notebooks with strategy codes. We also guide you how to use these codes on your own system to practice further.
- Can the python strategies provided in the course be immediately used for trading?
We focus on teaching these quantitative and machine learning techniques and how learners can use them for developing their own strategies. You may or may not be able to directly use them in your own system. Please do note that we are not advising or offering any trading/investment services. The strategies are used for learning & understanding purposes and we don't take any responsibility for the performance or any profit or losses that using these techniques results in.
- I want to develop my own algorithmic trading strategy. Can I use a Quantra course notebook for the same?
Quantra environment is a zero-installation solution to get beginners to start off with coding in Python. While learning you won't have to download or install anything! However, if you wish to later implement the learning on your system, you can definitely do that. All the notebooks in the Quantra portal are available for download at the end of each course and they can be run in the local system just the same as they run in the portal. The user can modify/tweak/rework all such code files as per his need. We encourage you to implement different concepts learnt from different learning tracks into your trading strategy to make it more suited to the real-world scenario.
- If I plug in the Quantra code to my trading system, am I sure to make money?
No. We provide you guidance on how to create strategy using different techniques and indicators, but no strategy is plug and play. A lot of effort is required to backtest any strategy, after which we fine-tune the strategy parameters and see the performance on paper trading before we finally implement the live execution of trades.
- What does "lifetime access" mean?
Lifetime access means that once you enroll in the course, you will have unlimited access to all course materials, including videos, resources, readings, and other learning materials for as long as the course remains available online. There are no time limits or expiration dates on your access, allowing you to learn at your own pace and revisit the content whenever you need it, even after you've completed the course. It's important to note that "lifetime" refers to the lifetime of the course itself—if the platform or course is discontinued for any reason, we will inform you in advance. This will allow you enough time to download or access any course materials you need for future use.
- What is news sentiment trading, and how does it differ from traditional trading strategies?
News sentiment trading is a strategy that involves analysing and leveraging news sentiment data to make trading decisions. It differs from traditional trading strategies in that it places a significant emphasis on the analysis of news articles, social media, and other sources to gauge market sentiment. Traditional trading strategies often rely on technical analysis, fundamental analysis, or historical price patterns, whereas news sentiment trading focuses on real-time sentiment data to predict market movements.
- How is news sentiment measured or quantified in the context of trading?
Sentiment analysis algorithms assign a numerical value to news articles, social media posts, or other text data to gauge whether the sentiment is positive, negative, or neutral. These numerical values are then used to create sentiment indicators that traders can use for decision-making. Popular methods include lexicon-based analysis and machine learning models trained to recognize sentiment in text.
- Can you provide examples of successful news sentiment trading strategies or case studies?
Successful news sentiment trading strategies often involve identifying events or news releases that have a significant impact on specific assets or markets. For instance, trading algorithms may be designed to react to earnings reports, geopolitical events, or central bank announcements. A well-known case is trading around earnings season where sentiment analysis of corporate earnings reports can be used to predict stock price movements.
- What are the common challenges or limitations associated with news sentiment trading strategies?
Challenges and limitations of news sentiment trading strategies include:
- Data accuracy and noise in sentiment analysis.
- Over-reliance on sentiment data, which may not always capture market nuances.
- Rapid news cycles can lead to delayed reactions.
- Regulatory and ethical concerns, such as potential market manipulation.
- The need for robust risk management due to the inherent volatility of news-driven markets.