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
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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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