Applied Sentiment Analysis - Trading & Forecasting

Name: Applied Sentiment Analysis - Trading & Forecasting
Rating: 4.3 (118 reviews)

Learn to develop and back-test sentiment and technical models to forecast the trend of assets with real applications.

Created bySofien KAABAR, CFA

Last updated 3/2022

English

What you'll learn

Understand the COT report and its strength in predicting the market's trend.
Understand basic concepts of Descriptive Statistics and their implications in trading.
Learn how to develop and use a COT model from scratch.
Learn how to analyze markets using Technical Analysis.
Learn how to combine both types of analyses to produce robust forecasts.
Back-test step-by-step the advanced strategies presented through Python.
Learn about other sentiment indicators (ISM PMI, VIX, Put/Call ratio, etc) and back-test other strategies.

Course content

4 sections • 59 lectures • 2h 49m total length

Getting Started3:05
Explore sentiment analysis with the Commitments of Traders report to inform trading across currencies, commodities, and equities, emphasizing risk management and smart money flow for weekly market timing.
The Different Types of Analyses3:56
Explore the four main analysis types—fundamental, technical, quantitative, and sentiment—and how they shape trading decisions, from intrinsic value and macroeconomic forecasting to text mining and sentiment signals.
Economic Intuition 1014:42
Conflicts & Model's Choice Dilemma1:46
Important: Read FIRST1:08
Intuition of Market Sentiment3:10
Explore market sentiment through a simple survey analogy, study the commitments of traders report, and link price moves with volume to spot uptrends and reversals on a weekly basis.
Derivatives4:06
Our Approach1:41
Downloading the COT Data Step-by-Step1:15
Handling the COT Charts6:18
Explore the commitment of traders charts by analyzing long and short hedgers and funds, compute net positions, and use correlation to spot extremes and reversals in asset prices.
Open Interest & Spreads1:06
Notes - Part 1 Recap3:26

Part 2: Quick Snapshot0:47
explore the basics of data analysis, learn about barriers and how to measure them, identify the three pillars of the framework, and preview behavioral finance impacts on trading.
Introduction to Descriptive Statistics3:29
EURUSD Statistical Analysis Template1:27
The 3 Golden Rules2:37
How to get the Commodities' COT values0:31
Application - Crude Oil4:02
Analyze crude oil prices against net funds and net hedgers using three golden rules, while noting look-ahead bias, fixed versus moving barriers, and divergence signals.
Introduction to Behavioral Finance1:24
Behavioral Finance Extra Notes3:32
Notes - Part 2 Recap2:45

Part 3: Quick Snapshot0:57
Decompose commercial participants to understand intentions, explore advanced strategies to boost signal frequency and performance, and back-test these ideas using four metrics and RSI-based technical analysis.
Correlation Analysis1:30
Commercial Consumers | Soybeans4:01
Explore advanced COT methods to analyze commercial consumer and producer hedgers in soybeans, assess long vs short positions, and apply Bollinger Bands based variable barrier analysis to time futures signals.
Commercial Producers | USDCAD3:53
Other COT Components2:02
Cross Asset COT Technique1:17
Downloading Anaconda0:42
Getting Familiar with SPYDER6:37
Numpy and Pandas basics1:58
Performance Evaluation3:43
Strategy 1: Fixed-Barrier COT Back-testing7:59
Apply fixed-barrier back-testing on audusd in spyder using numpy, pandas, and matplotlib. Hold for 10 periods, compute moving averages to set upper and lower barriers, and generate buy/sell signals.
Strategy 2: Variable-Barrier COT Back-testing4:43
Explore the variable barrier Bollinger bands back-testing for COT data, using a look-back to create a moving barrier, compare signals and equity to a fixed barrier, and tune parameters.
COT Delta Intuition0:47
A very simple yet extremely interesting transformation to the COT values that will show hidden signals.
Application - Gold4:13
Apply sentiment analysis to gold trading using COT delta rate of change, barrier and standard deviation methods, and net hedgers correlations to confirm contrarian signals.
Strategy 3: COT Delta Back-testing8:26
Explore back-testing the delta COT strategy on Kiwi dollar data using a rolling delta, barriers, and two-week holding periods, with Python, equity curves, and parameter optimization.
Normalization Intuition0:42
Strategy 4: Normalization Back-testing5:01
Equity Sentiment COT4:05
Using the COT on the stock market indices.
COT | RSI Intuition0:44
Using the relative strength index on the COT values in order to further optimize and smooth-out the predictions.
Strategy 5: COT | RSI Silver2:31
Beware of Technical Analysis Traps: Back-testing the RSI1:39
Combining Technical & Sentiment Analysis3:44
An example on how to combine Sentiment Analysis with Technical Analysis.
Notes - Part 3 Recap2:33

Part 4: Quick Snapshot0:59
Present equity sentiment indicators and back-test them, review measures like University of Michigan consumer sentiment index and ISM index, plus put call ratio, VIX insights, and risk management.
Downloading the GEX/DPI Data Step-By-Step0:40
Back-testing the GEX/DPI15:09
Back-test the GEX and DPI to gauge their predictive power on the S&P 500, individually and in combination. Normalize, generate signals, and assess hit ratio, gross return, and equity curves.
UMCSI - Intuition0:49
The University of Michigan's Consumer Sentiment Index is one of the powerful leading economic indicators of the US economy.
Application - Dollar Index2:35
ISM - Purchasing Manager's Index1:34
Downloading the Put-Call ratio Data Step-By-Step0:13
Back-testing the Put-Call Ratio7:04
The VIX from another perspective1:53
A Discussion on Discretionary Trading8:26
Discussing the Trading framework from a discretionary point of view.
Part 1 Documents0:08
In this lecture, you will be able to download all of the documents we have used in Part 1.
Part 2 Documents0:01
In this lecture, you will be able to download all of the documents we have used in Part 2.
Part 3 Documents0:01
In this lecture, you will be able to download all of the documents we have used in Part 3.
Part 4 Documents0:01
In this lecture, you will be able to download all of the documents we have used in Part 4.
UPDATE: New Put-Call Ratio Data0:14

Requirements

Very basic knowledge of the financial markets (we will do a recap to refresh it, just in case).
Some basic concepts of Technical Analysis (It’s covered in case you’re just getting started).
Motivation and Willingness to experiment with data analysis in excel.
Some knowledge in Python is desired but not crucial.

Description

It’s time Market Sentiment analysis got a proper step-by-step guide. Why stick only with Technical and Fundamental Analysis when you are trading the markets? Adding this powerful tool to your arsenal will greatly enhance your skills and undoubtedly improve your risk-adjusted returns.

Whether you are just beginning or you’re already an experimented user of market sentiment analysis wanting to discover new techniques in this exciting field, then search no more! this course is for you.

It is specifically designed to guide you from A-Z with a focus on intermediate/advanced concepts that you will find very interesting through different applications and most importantly stepping-stones upon which you can build your super model and combine it with other promising types of analyses!

The CFTC publishes a goldmine every week, which is known as the Commitments of Traders Report – COT - that gauges the pulse and the feeling of the big market participants (i.e. the market movers).

Properly understanding and analyzing this report will allow the user to forecast the trend of the chosen asset (FX, equities, commodities) for the following days, weeks, and even months.

This course is split into 4 main parts:

1. Introduction to Market Sentiment.

2. Framework and COT Data Analysis.

3. Advanced Concepts & Back-testing through Python.

4. Other Sentiment Strategies (Put-call ratio, Dark pool index, Gamma exposure index, etc).

This is an APPLIED course, meaning we will not spend too much time with the boring theoretical albeit necessary stuff. There will be occasional notes for every major section to help you refresh your knowledge as you progress in the course. I will constantly be updating the sections with new sentiment and technical methods / strategies. All of the documents and code are downloadable.

Disclaimer: We acknowledge that trading comes with risks, and therefore, we have to stress that success in this field comes with proper risk management. No model is perfect and the views expressed in this course are only based on market sentiment analysis and are for educational purposes only. Always do your homework and let no one affect your judgement unless objective data is presented.

If you have a question/complaint (or even two), you can contact me through the Q&A section.

Who this course is for:

Anyone wanting to discover how to analyze FX, equity, and commodities markets from a sentiment point of view.
Professional and retail traders wanting to enlarge their toolkit.
Long-term investors wanting to gauge the trends and the the pre-crisis periods.
Researchers wanting to dive in deeper to develop more sophisticated sentiment models.
Anyone curious about a lesser-known trading technique.

Applied Sentiment Analysis - Trading & Forecasting

What you'll learn

Explore related topics

Course content

INTRODUCTION TO MARKET SENTIMENT12 lectures • 36min

FRAMEWORK AND DATA ANALYSIS9 lectures • 21min

ADVANCED CONCEPTS & BACK-TESTING THROUGH PYTHON23 lectures • 1hr 14min

OTHER SENTIMENT STRATEGIES15 lectures • 40min

Requirements

Description

Who this course is for: