Win 90% of Trades; Quantitative Investment Models in Excel
4.6 (8 ratings)
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Win 90% of Trades; Quantitative Investment Models in Excel

Use Excel to Model Investing Strategies; Back Test and Optimize Them so You Can Make More Informed Trading Decisions
4.6 (8 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
48 students enrolled
Created by Daniel Davis
Last updated 6/2017
English
Current price: $10 Original price: $200 Discount: 95% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 1.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • How to extract historic stock market data
  • How to calculate technical indicators in Excel
  • How to construct an investment model In Excel, built around technical indicators
  • At the end of the course, you will have built your own quantitative model
  • BONUS: You will Receive the Instructor's Version of the Excel Model
View Curriculum
Requirements
  • This Course is Designed for Beginners
  • Excel Skills will be Taught Along the Way
  • *You Do Not Need Extensive Math or Excel Skills to Succeed in this Course
Description

Have you ever heard of Quantitative Investing? Do you want to sharpen your skills within Microsoft Excel?  This course will take you through the process of building a quantitative investment model in Excel.  We will build a trading model based off of the technical indicators and model various buy and sell parameters to find optimal trades.   You can use the information you glean from this model to make informed trades with high probabilities of success.  

The model we will build in this course will simulate and back test a trading strategy, outputting your performance, probability of success and average profitability per trade. 

The model will be built around an analysis of technical indicators, namely the Relative Strength Index, which we will use to determine high probability trades for any stock you wish to analyze. 

Quantitative finance is all about making investing more of a science and less of an art.  If you are interesting in learning how to make trades with 80%-90% success rates, this course is for you.

Who is the target audience?
  • Anyone who Wants to Know how to Test Investment Strategies in Excel
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Curriculum For This Course
16 Lectures
01:19:57
+
Welcome to the Course
16 Lectures 01:19:57

Welcome to the course! Follow through this course to build your own quantitative investment model! Thank you for joining!

Preview 00:41

In this video we briefly discuss:

  • Quantitative finance 
  • Introduce the Inflection Point Model, the trading model we will build through this course.  You are welcome to keep and improve it.
  • Discuss what you will be able to do at the end of the course (aside from having an actionable trading model).
    • Be able to extract stock market data from the web
    • Be able to calculate Relative Strength values for any stock
    • Be able to build models around entry and exit points in a stock
    • Determine profitability/ define risks of a trading strategy
    • Use this information to make informed trades
Welcome to the Course!
03:44

Before we can build the Inflection Point Model, we must first get familiar with how it works and what information we will need in order to build it.  

Preview 07:56

See attached for Instructor's version of Inflection Point Model for your reference as you build your own version.  

We will be building a trading model called the Inflection Point Model.  This video introduces how the model looks in Excel, it's functionalities, and the optimization function. This is a peak into what your model will look like when it is complete. 

Preview 07:21

To build The Inflection Point Model we first need to extract historic stock data from the internet.  In this video, we will pull several years of GLD data and then perform some calculations on it in order to get the technical indicators needed to build the Inflection Point Model.  

Data Extraction and Calculating Relative Strength Index
07:31

We've just completed calculating the RSI. From here, we need to get Excel to take that data and clean it so that it can recognize when an entry or exit parameter is met. In this video, we will introduce how to do this. 

Coding Entry and Exit Triggers
05:08

We're about to write the heftiest code in the whole model.  Don't worry! It's not as hard as it looks.  In this video, we will introduce the profit and loss calculators and how they work on a conceptual level.  

Introducing Profit/Loss Calculators
04:02

Excel can calculate the profit and loss of any position that would be placed.  This video explains how to automatically determine the price changes from when you enter to when you exit a position.  

Coding Profit/Loss Calculators
05:15

Sometimes you will have to put multiple positions on at once (the RSI is below your entry trigger for multiple days).  In this video, we build the function that will identify when a second, third, fourth, etc. position must be placed.  Once Excel recognizes when a subsequent purchase must be placed, it will run a new set of profit/loss calculations. 

Coding Profit/Loss Calculators Part 2
04:45

Excel's formula bar only permits 64 levels of nesting.  In this video, we figure out a way to get around that limitation by adding a new column of profit/loss calculations that pick up where the previous column leaves off. 

Profit/Loss Calculators Part 3
04:54

This video shows the final steps of the calculations needed to determine the profit/ loss of the strategy you define.  Specifically, this video shows how to calculate the P/L for the subsequent purchases. 

Profit/Loss Calculators Part 4
05:11

Errant Trades are trades which are triggered by our entry parameter, however, the trade lasts long enough that Excel fails to recognize the exit parameter.  These are infrequent, however, to ensure the quality of our results, we have to build a function that will identify this situation if it happens.  This video reveals the code and steps necessary to build this function. 

Identifying Errant Trades/Quality Assurance
06:08

In this video we build out the calculations needed to build the summary data table (the table that shows how many trades we make, how many were successful, shows our total profit, errant trades and average profitability per trade). 

Compiling Performance Data
03:26

In this video we:

  • Calculate probability of success 
  • Find profitability of each trade, given the entry points we define
  • Determine average profitability per trade (Remember! That's profitability per single share traded)
Determining Probability of Success & Total Profit
05:44

Create a table that outputs your performance given every combination of entry and exit points.  From there, you can select the entry parameters that fit your personal risk tolerance.  On the second table, you will also see how you would have performed if you had stuck to your strategy for all of the years of GLD's data. 

Building Optimization Function
04:11

Some important qualities you need to remember when building your own models:

  • Ensure your formulas extend to cover all of your data. If you insert a new stock with more data than the model is currently designed to handle, you will need to pull the formulas down further. 
  • Stock picking: Ideal candidates for this strategy have volatile RSIs.  ETFs make excellent candidates for this strategy along with Blue Chips.  Not every stock will be a good candidate for this trading style. 
  • You will want to do this analysis on 15 different stocks and find the ones that are the most successful.  Add those to your watch list and monitor them until they pass through their buy triggers. 

Thank you!

Highlights of the Completed Model
04:00
About the Instructor
Daniel Davis
4.6 Average rating
7 Reviews
48 Students
1 Course
Professional Financial Analyst

Hello! I am a professional investment analyst who loves data analytics, financial modeling, and quantitative finance.  I have spent the last 10 years studying and investing in the stock market (and earning a B.S. in Finance along the way).  I have spent way too much time modeling investments in Microsoft Excel and use the knowledge that I glean from these models to make investment decisions.  I look forward to sharing what I have learned with you!