Quant Trading using Machine Learning

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Course Summary :

Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. 

This course takes a completely practical approach to applying Machine Learning techniques to Quant Trading

Let’s parse that.

Completely Practical: This course has just enough theory to get you started with both Quant Trading and Machine Learning. The focus is on practically applying ML techniques to develop sophisticated Quant Trading models. From setting up your own historical price database in MySQL to writing hundreds of lines of Python code, the focus is on doing from the get go.

Machine Learning Techniques: We'll cover a variety of machine learning techniques, from K-Nearest Neighbors and Decision Trees to pretty advanced techniques like Random Forests and Gradient Boosted Classifiers. But, in practice Machine Learning is not just about the algorithms. Feature Engineering, Parameter Tuning, Avoiding overfitting; these are all a part and parcel of developing Machine Learning applications and we do it all in this course. 

Quant Trading: Quant Trading is a perfect example of an area where the use of Machine Learning leads to a step change in the quality of the models used. Traditional models often depend on Excel and building sophisticated models requires a huge amount of manual effort and domain knowledge. Machine Learning libraries available today allow you to build highly sophisticated models that give you much better performance with much less effort. 

What's Covered: 

Quant Trading : Financial Markets, Stocks, Indices, Futures, Return, Risk, Sharpe Ratio, Momentum Investing, Mean Reversion, Developing trading strategies using Excel, Backtesting

Machine Learning: Decision Trees, Ensemble Learning, Random Forests, Gradient Boosted Classifiers, Nearest Neighbors, Feature engineering, Overfitting, Parameter Tuning

MySQLSet up a historical price database in MySQL using Python. 

Python Libraries : Pandas, Scikit-Learn, XGBoost, Hyperopt

A Note on Python: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs.

Mail us about anything - anything! - and we will always reply :-)

What are the requirements?

  • Working knowledge of Python is necessary if you want to run the source code that is provided.

What am I going to get from this course?

  • Develop Quant Trading models using advanced Machine Learning techniques
  • Compare and evaluate strategies using Sharpe Ratios
  • Use techniques like Random Forests and K-Nearest Neighbors to develop Quant Trading models
  • Use Gradient Boosted trees and tune them for high performance
  • Use techniques like Feature engineering, parameter tuning and avoiding overfitting
  • Build an end-to-end application from data collection and preparation to model selection

Pre-Requisites :

Prerequisites: Working knowledge of Python is necessary if you want to run the source code that is provided. Basic knowledge of machine learning, especially ML classification techniques, would be helpful but it's not mandatory.

Target Audience :

  • Yep! Quant traders who have not used Machine learning techniques before to develop trading strategies
  • Yep! Analytics professionals, modelers, big data professionals who want to get hands-on experience with Machine Learning
  • Yep! Anyone who is interested in Machine Learning and wants to learn through a practical, project-based approach

Curriculum :

Section 1 - You, This Course and Us
      1 : You, This Course and Us02:00
    Section 2 - Developing Trading Strategies in Excel
        2 : Are markets efficient or inefficient?10:27
        3 : Momentum Investing11:31
        4 : Mean Reversion06:30
        5 : Evaluating Trading Strategies - Risk And Return00:00
        6 : Evaluating Trading Strategies - The Sharpe Ratio10:16
        7 : The 2 Step process - Modeling and Backtesting03:48
        8 : Developing a Trading Strategy in Excel11:42
        9 : Download for sec 2
      Section 3 - Setting up your Development Environment
          10 : Installing Anaconda for Python00:00
          11 : Installing Pycharm - a Python IDE03:55
          12 : MySQL Introduced and Installed (Mac OS X)07:03
          13 : MySQL Server Configuration and MySQL Workbench (Mac OS X)17:32
          14 : MySQL Installation (Windows)06:31
        Section 4 - Setting up a Price Database
            15 : Programmatically Downloading Historical Price Data06:23
            16 : CodeAlong - Dowloading Price data from Yahoo Finance14:39
            17 : CodeAlong - Downloading a URL in Python07:38
            18 : CodeAlong - Downloading Price data from the NSE13:55
            19 : CodeAlong - Unzip and process the downloaded files05:21
            20 : Manually Download data for 10 years
            21 : CodeAlong - Download Historical Data for 10 years06:26
            22 : Inserting the Downloaded files into a Database10:10
            23 : CodeAlong - Bulk loading downloaded files into MySQL tables15:12
            24 : Data Preparation04:16
            25 : CodeAlong - Data Preparation12:43
            26 : Adjusting for Corporate Actions08:41
            27 : CodeAlong - Adjusting for Corporate Actions 115:29
            28 : CodeAlong - Adjusting for Corporate Actions 2 08:47
            29 : CodeAlong - Inserting Index prices into MySQL00:00
            30 : CodeAlong - Constructing a Calendar Features table in MySQL06:53
            31 : Download for sec 4
          Section 5 - Decision Trees, Ensemble Learning and Random Forests
              32 : Planting the seed - What are Decision Trees?17:00
              33 : Growing the Tree - Decision Tree Learning18:03
              34 : Branching out - Information Gain18:51
              35 : Decision Tree Algorithms07:50
              36 : Overfitting - The Bane of Machine Learning 19:03
              37 : Overfitting Continued11:19
              38 : Cross Validation18:55
              39 : Regularization07:18
              40 : The Wisdom Of Crowds - Ensemble Learning16:39
              41 : Ensemble Learning continued - Bagging, Boosting and Stacking18:02
              42 : Random Forests - Much more than trees12:28
              43 : Download for sec 5
            Section 6 - A Trading Strategy as Machine Learning Classification
                44 : Defining the problem - Machine Learning Classification15:51
                45 : Download for sec 6
              Section 7 - Feature Engineering
                  46 : Know the basics - A Pandas tutorial11:41
                  47 : CodeAlong - Fetching Data from MySQL18:34
                  48 : CodeAlong - Constructing some simple features07:27
                  49 : CodeAlong - Constructing a Momentum Feature08:42
                  50 : CodeAlong - Constructing a Jump Features 05:52
                  51 : CodeAlong - Assigning Labels03:12
                  52 : CodeAlong - Putting it all together18:08
                  53 : CodeAlong - Include support features from other tickers06:34
                  54 : Download for sec 7
                Section 8 - Engineering a Complex Feature - A Categorical Variable with Past Trends
                    55 : Engineering a Categorical Variable00:00
                    56 : CodeAlong - Engineering a Categorical Variable 06:46
                  Section 9 - Building a Machine Learning Classifier in Python
                      57 : Introducing Scikit-Learn00:00
                      58 : Introducing RandomForestClassifier00:00
                      59 : Training and Testing a Machine Learning Classifier15:01
                      60 : Compare Results from different Strategies 05:44
                      61 : Using Class probabilities for predictions03:11
                    Section 10 - Nearest Neighbors Classifier
                        62 : A Nearest Neighbors Classifier06:49
                        63 : CodeAlong - A nearest neighbors Classifier04:16
                        64 : Download for sec 10
                      Section 11 - Gradient Boosted Trees
                          65 : What are Gradient Boosted Trees? 00:00
                          66 : Introducing XGBoost - A python library for GBT11:51
                          67 : CodeAlong - Parameter Tuning for Gradient Boosted Classifiers09:21
                        Section 12 - Introduction to Quant Trading
                            68 : Financial Markets - Who are the players?16:38
                            69 : What is a Stock Market Index?03:13
                            70 : The Mechanics of Trading - Long vs Short positions00:00
                            71 : Futures Contracts 00:00
                            72 : Download for sec 12


Instructor :

Loonycorn A 4-ppl team;ex-Google.


Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) working in tech, in the Bay Area, New York, Singapore and Bangalore. Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum Navdeep: longtime Flipkart employee too, and IIT Guwahati alum We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Unanth! We hope you will try our offerings, and think you'll like them :-)


Average Rating
 (1 Reviews)



posted 10 month before


Good course. Your teaching style and code examples are easy to understand the concept.