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Machine Learning and artificial intelligence are everywhere. The [course_title] course focuses on the use of machine learning in the field of trading.

You will be introduced to the concepts of machine learning that you need to implement in trading. The course teaches you the algorithmic steps from information gathering to market orders. You will learn how to apply probabilistic machine learning approaches to trading decisions.

Apart from these, the course covers statistical approaches like linear regression, KNN and regression trees and show you how to apply them to actual stock trading situations.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

Edukite courses are free to study. To successfully complete a course you must submit all the assignment of the course as part of the assessment. Upon successful completion of a course, you can choose to make your achievement formal by obtaining your Certificate at a cost of £49.

Having an Official Edukite Certification is a great way to celebrate and share your success. You can:

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Course Credit : Udacity

Course Curriculum

Lesson 1: 00-00 Introduction
Introduction 00:02:00
Three parts to the course 00:01:00
Textbooks 00:01:00
Prerequisites 00:01:00
Lesson 2: 01-01 Reading and plotting stock data
Introduction 00:01:00
Data in CSV files 00:02:00
Quiz: Which fields should be in a CSV file? 00:01:00
Real stock data 00:05:00
Pandas dataframe 00:03:00
Example CSV file 00:01:00
Quiz: Read CSV 00:02:00
Select rows 00:01:00
Compute max closing price 00:02:00
Quiz: Compute mean volume 00:01:00
Plotting stock price data 00:02:00
Quiz: Plot High prices for IBM 00:01:00
Plot two columns 00:01:00
Lesson 3: 01-02 Working with multiple stocks
Working with multiple stocks 00:01:00
Pandas dataframe recap 00:01:00
Problems to solve 00:02:00
Quiz: NYSE trading days 00:01:00
Building a dataframe 00:02:00
“Joining” dataframes 00:02:00
Create an empty data frame 00:02:00
Join SPY data 00:04:00
Quiz: Types of “join” 00:01:00
Read in more stocks 00:02:00
Quiz: Utility functions for reading data 00:02:00
Obtaining a slice of data 00:03:00
More slicing 00:03:00
Problems with plotting 00:01:00
Quiz: How to plot on “equal footing”? 00:01:00
Plotting multiple stocks 00:02:00
Quiz: Slice and plot two stocks 00:01:00
Normalizing 00:02:00
Lesson 4: 01-03 The power of NumPy
What is NumPy? 00:01:00
Relationship to Pandas 00:02:00
Notes on Notation 00:02:00
Quiz: Replace a slice 00:01:00
Creating NumPy arrays 00:02:00
Arrays with initial values 00:02:00
Quiz: Specify the datatype 00:01:00
Generating random numbers 00:04:00
Array attributes 00:03:00
Operations on ndarrays 00:04:00
Quiz: Locate maximum value 00:01:00
Timing python operations 00:01:00
How fast is NumPy? 00:02:00
Accessing array elements 00:03:00
Modifying array elements 00:02:00
Indexing an array with another array 00:02:00
Boolean or “mask” index arrays 00:02:00
Arithmetic operations 00:03:00
Learning more NumPy 00:01:00
Lesson 5: 01-04 Statistical analysis of time series
Are you ready? 00:01:00
Global statistics 00:02:00
Compute global statistics 00:02:00
Rolling statistics 00:03:00
Quiz: Which statistic to use? 00:01:00
Bollinger Bands 00:03:00
Computing rolling statistics 00:02:00
Calculate Bollinger Bands 00:01:00
Daily returns 00:03:00
Quiz: Compute daily returns 00:01:00
Cumulative returns 00:03:00
Lesson 6: 01-05 Incomplete data
Introduction 00:01:00
Pristine data 00:02:00
Why data goes missing 00:05:00
Why this is bad – what can we do? 00:03:00
Quiz: Pandas fillna() 00:01:00
Using fillna() 00:02:00
Quiz: Fill missing values 00:01:00
Lesson 7: 01-06 Histograms and scatter plots
Histograms and scatterplots 00:01:00
A closer look at daily returns 00:02:00
Quiz: What would it look like? 00:01:00
Histogram of daily returns 00:03:00
How to plot a histogram 00:02:00
Computing histogram statistics 00:02:00
Quiz: Compare two histograms 00:01:00
Plot two histograms together 00:02:00
Scatterplots 00:02:00
Fitting a line to data points 00:02:00
Slope does not equal correlation 00:02:00
Quiz: Correlation vs slope 00:01:00
Scatterplots in python 00:05:00
Real world use of kurtosis 00:01:00
Lesson 8: 01-07 Sharpe ratio and other portfolio statistics
Overview 00:01:00
Daily portfolio values 00:05:00
Portfolio statistics 00:02:00
Quiz: Which portfolio is better? 00:02:00
Sharpe ratio 00:02:00
Quiz: Form of the Sharpe ratio 00:01:00
Computing Sharpe ratio 00:04:00
But wait, there’s more! 00:03:00
Quiz: What is the Sharpe ratio? 00:01:00
Putting it all together 00:01:00
Lesson 9: 01-08 Optimizers: Building a parameterized model
What is an optimizer? 00:03:00
Minimization example 00:02:00
Minimizer in Python 00:04:00
Quiz: How to defeat a minimizer 00:01:00
Convex problems 00:03:00
Building a parameterized model 00:04:00
Quiz: What is a good error metric? 00:01:00
Minimizer finds coefficients 00:01:00
Fit a line to given data points 00:06:00
And it works for polynomials too! 00:03:00
Wrapping up optimizers 00:01:00
Lesson 10: 01-09 Optimizers: How to optimize a portfolio
What is portfolio optimization? 00:01:00
The difference optimization can make 00:02:00
Quiz: Which criteria is easiest to solve for? L10 00:01:00
Framing the problem 00:02:00
Ranges and constraints 00:02:00
Lesson 11: 02-01 So you want to be a hedge fund manager?
Overview 00:01:00
Types of funds 00:03:00
Liquidity and capitalization 00:03:00
Quiz: What type of fund is it? 00:01:00
Incentives for fund managers 00:04:00
Two and twenty 00:02:00
Quiz: Incentives quiz 00:01:00
How funds attract investors 00:04:00
Hedge fund goals and metrics 00:06:00
The computing inside a hedge fund 00:06:00
Lesson 12: 02-02 Market Mechanics
Overview 00:01:00
What is in an order? 00:03:00
The order book 00:03:00
Quiz: Up or down 00:01:00
How orders affect the order book 00:04:00
How orders get to the exchange 00:04:00
How hedge funds exploit market mechanics 00:12:00
Additional order types 00:02:00
Mechanics of short selling: Entry 00:02:00
Quiz: Short selling 00:01:00
Mechanics of short selling: Exit 00:02:00
What can go wrong? 00:02:00
Lesson 13: 02-03 What is a company worth?
Overview 00:01:00
Quiz: What is a company worth? 00:01:00
Why company value matters 00:04:00
Quiz: The Balch Bond 00:02:00
The value of a future dollar 00:05:00
Intrinsic value 00:05:00
Quiz: Intrinsic value quiz 00:01:00
Book value 00:02:00
Market capitalization 00:01:00
Why information affects stock price 00:04:00
Quiz: Compute company value 00:01:00
Quiz: Would you buy this stock? 00:01:00
Summary 00:02:00
Lesson 14: 02-04 The Capital Assets Pricing Model (CAPM)
The Capital Asset Pricing Model 00:01:00
Definition of a portfolio 00:02:00
Quiz: Portfolio return 00:01:00
The market portfolio 00:04:00
The CAPM equation 00:04:00
Quiz: Compare alpha and beta 00:01:00
CAPM vs active management 00:03:00
CAPM for portfolios 00:02:00
Implications of CAPM quiz 00:01:00
Implications of CAPM 00:02:00
Arbitrage Pricing Theory 00:02:00
Lesson 15: 02-05 How hedge funds use the CAPM
Risks for hedge funds 00:01:00
Two stock scenario 00:03:00
Quiz: Two stock scenario quiz 00:01:00
Two stock CAPM math 00:03:00
Quiz: Allocations remove market risk 00:01:00
How does it work? 00:02:00
CAPM for hedge funds summary 00:01:00
Lesson 16: 02-06 Technical Analysis
Technical versus fundamental analysis 00:01:00
Characteristics 00:02:00
Quiz: Potential indicators 00:01:00
When is technical analysis valuable? 00:02:00
When is technical analysis valuable? (part 2) 00:04:00
A few indicators: Momentum 00:03:00
A few indicators: Simple moving average 00:04:00
A few indicators: Bollinger Bands 00:04:00
Quiz: Buy or sell? 00:01:00
Normalization 00:02:00
Wrap up 00:01:00
Lesson 17: 02-07 Dealing with Data
Lesson Overview 00:01:00
How data is aggregated 00:04:00
Quiz: Price anomaly 00:01:00
Stock splits 00:05:00
Quiz: Split adjustment 00:01:00
Dividends 00:02:00
Quiz: Dividends Quiz 00:01:00
Adjusting for dividends 00:04:00
Survivor bias 00:03:00
Lesson 18: 02-08 Efficient Markets Hypothesis
Our hypothesis 00:01:00
EMH assumptions 00:02:00
Origin of information 00:03:00
3 forms of the EMH 00:03:00
Quiz: The EMH prohibits 00:01:00
Is the EMH correct? 00:04:00
Lesson 19: 02-09 The Fundamental Law of active portfolio management
Overview 00:01:00
Grinold’s Fundamental Law 00:02:00
The Coin Flipping Casino 00:03:00
Quiz: Which bet is better? 00:01:00
Quiz: Coin-Flip Casino: Reward 00:02:00
Coin-Flip Casino: Risk 1 00:02:00
Coin-Flip Casino: Risk 2 00:02:00
Quiz: Coin-Flip Casino: Reward/Risk 00:01:00
Coin-Flip Casino: Observations 00:03:00
Coin-Flip Casino: Lessons 00:01:00
Back to the real world 00:01:00
IR, IC and breadth 00:02:00
IR, IC and breadth (cont.) 00:01:00
The Fundamental Law 00:02:00
Quiz: Simons vs. Buffet 00:01:00
Lesson 20: 02-10 Portfolio optimization and the efficient frontier
Overview 00:01:00
What is risk? 00:01:00
Visualizing return vs risk 00:01:00
Quiz: Building a portfolio 00:01:00
Can we do better? 00:02:00
Why covariance matters 00:04:00
Mean Variance Optimization 00:04:00
The efficient frontier 00:03:00
Lesson 21: 03-01 How Machine Learning is used at a hedge fund
Overview 00:01:00
The ML problem 00:02:00
Quiz: What’s X and Y? 00:01:00
Supervised regression learning 00:03:00
Robot navigation example 00:03:00
How it works with stock data 00:03:00
Example at a fintech company 00:02:00
Price forecasting demo 00:04:00
Backtesting 00:02:00
ML tool in use 00:02:00
Problems with regression 00:02:00
Problem we will focus on 00:02:00
Lesson 22: 03-02 Regression
Introduction 00:01:00
Parametric regression 00:04:00
K nearest neighbor 00:07:00
Quiz: How to predict 00:01:00
Kernel regression 00:02:00
Quiz: Parametric vs non parametric 00:02:00
Training and testing 00:03:00
Learning APIs 00:02:00
Example for linear regression 00:02:00
Lesson 23: 03-03 Assessing a learning algorithm
Overview 00:01:00
A closer look at KNN solutions 00:02:00
Quiz: What happens as K varies 00:02:00
Quiz: What happens as D varies 00:01:00
Metric 1 RMS Error 00:02:00
In Sample vs out of sample 00:01:00
Quiz: Which is worse? 00:01:00
Cross validation 00:01:00
Roll forward cross validation 00:01:00
Metric 2: correlation 00:01:00
Quiz: Correlation and RMS error 00:01:00
Overfitting 00:02:00
Quiz: Overfitting Quiz 00:01:00
Quiz: A Few other considerations 00:01:00
Lesson 24: 03-04 Ensemble learners, bagging and boosting
Overview 00:01:00
Ensemble learners 00:03:00
Quiz: How to build an ensemble 00:01:00
Bootstrap aggregating bagging 00:03:00
Quiz: Overfitting 00:01:00
Bagging example 00:02:00
Boosting 00:03:00
Quiz: Overfitation 00:01:00
Summary 00:01:00
Lesson 25: 03-05 Reinforcement learning
Overview 00:01:00
The RL problem 00:04:00
Quiz: Trading as an RL problem 00:01:00
Mapping trading to RL 00:02:00
Markov decision problems 00:03:00
Unknown transitions and rewards 00:03:00
What to optimize? 00:07:00
Quiz: Which approach gets $1M? 00:01:00
Summary 00:02:00
Lesson 26: 03-06 Q-Learning
Overview 00:01:00
What is Q? 00:03:00
Learning Procedure 00:04:00
Update Rule 00:05:00
Two Finer Points 00:02:00
The Trading Problem: Actions 00:03:00
Quiz: The Trading Problem: Rewards 00:01:00
Quiz: The Trading Problem: State 00:01:00
Creating the State 00:02:00
Discretizing 00:02:00
Q-Learning Recap 00:01:00
Lesson 27: 03-07 Dyna
Overview 00:01:00
Dyna-Q Big Picture 00:04:00
Learning T 00:02:00
Quiz: How to Evaluate T? 00:01:00
Learning R 00:02:00
Dyna Q Recap 00:01:00
Lesson 28: Interview with Tammer Kamel
Interview with Tammer Kamel (Part 1) 00:09:00
Interview with Tammer Kamel (Part 2) 00:11:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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