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Machine Learning for Trading

Programming

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Introduction

Three parts to the course

Textbooks

Prerequisites

Introduction

The Data

Our Stock Data

Pandas Dataframe

Actual CSV

Interesting Stuff

Compute max closing price

Plotting stock price data

Plot two columns

Working with multiple stocks

Pandas dataframe recap

Problems to Solve

How We re Going to do it

Joining SPY

Create an exmpty data frame

Join SPY data

Read in more stocks

Slicing

More slicing

Problems with Plotting

Plotting multiple stocks

Normalizing

What is NumPy

Relationship to Pandas

Notes on Notation

Creating NumPy arrays

Arrays with initial values

Generating random numbers

Array attributes

Operations on ndarrays

Timing python operations

How fast is NumPy

Accessing array elements

Modifying array elements

Indexing an array with another array

Boolean or mask index arrays

Arithmetic operations

Learning more 1

Are you ready

Global statistics

Compute global statistics

Rolling statistics

Bollinger Bands

Computing rolling statistics

Daily returns

Cumulative returns

Introduction

Pristine Data

Why Data Goes Missing

Why this is bad what can we do

Using fillna

Histograms and scatterplots

A closer look at daily returns

Histogram of daily returns

How to plot a histogram

Computing histogram statistics 1

Plot two histograms together

Scatterplots

Fitting a line to data points

Slope does not equal correlation

Scatterplots in python 1

Real world use of kurtosis

Overview

Daily portfolio values

Portfolio statistics

Sharpe ratio

Computing sharpe ratio

But wait there s more

Putting it all together 1

What is an optimizer

What is an optimizer Part 2

Minimizer in Python

Convex problems

Building a parameterized model

Minimizer finds coefficients

Minimizer finds coefficients in python

Minimizer finds coefficients in python

Wrapping up optimizers

What is portfolio optimization

The difference optimization can make

Framing the problem

Ranges and constraints

Overview

Types of funds

Liquidity and capitalization

Incentives for fund managers

Two and twenty

How funds attract investors

Hedge fund goals and metrics

The computing inside a hedgefund

Overview

What is in an order

The order book

How orders affect the order book

How orders get to the exchange

How hedge funds exploit market mechanics

Additional order types

Mechanics of short selling Entry

Mechanics of short selling Exit

What can go wrong

Overview

Why company value matters

The value of a future dollar

Intrinsic value

Book value

Market capitalization

Why information affects stock price

Summary

The Capital Asset Pricing Model

Definition of a portfolio

The market portfolio

The CAPM equation

CAPM vs active management

CAPM for portfolios

Implications of CAPM

Arbitrage Pricing Theory

Risks for hedgefunds

Two stock scenario

Two stock CAPM math

How does it work

CAPM for hedge funds summary

Technical versus fundamental analysis

Characteristics

When is technical analysis valuable

When is technical analysis valuable part 2

A few indicators Momentum

A few indicators Simple moving average

A few indicators Bollinger Bands

Normalization

TA wrap up

Lesson Overview

How data is aggregated

Stock splits

Dividends

Adjusting for dividends

Survior bias

Our hypothesis

EMH assumptions

Origin of information

3 forms of the EMH

Is the EMH correct

Overview

Grinold s fundamental law

The coin flipping casino

Coin flip casino risk 1

Coin flip casino risk 2

Coin flip casino lessons

Coin flipping lessons

Coin casino real world

11 IR IC and breadth

IR IC and breadth cont

The fundamental law

Overview

What is risk

Visualizing return vs risk

Can we do better

The importance of covariance

Mean variance optimization

The efficient frontier

Overview

The ML problem

Supervised regression learning

Supervised regression learning cont

How it works with stock data

Example at a fintech company

Price forecasting demo

Backtesting

ML tool in use

Problems with regression

Problem we will focus on

Introduction

Parametric regression

K nearest neighbor

Kernel regression

Training and testing

Learning APIs

Example for linear regression

Overview

A Closer Look at KNN Solutions

Metric 1 RMS error

In Sample vs Out Of Sample

Cross Validation

Roll Forward Cross Validation

Metric 2 Correlation

Overfitting

Overview

Ensemble learners

Bootstrap aggregating bagging

Bagging example

Boosting

Summary

Overview

The RL problem

Mapping trading to RL

Markov decision problems

Unknown transitions and rewards

What is optimal

RL summary

Overview

What is Q

Learning Procedure

Update Rule

05 Two Finer Points

The Trading Problem Actions

Creating the State

Discretizing

Q Learning Recap

Overview

Dyna Q Big Picture

Learning T

Learning R

Dyna Q Recap

Interview with Tammer Kamel Part 1

Interview with Tammer Kamel Part 2

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Machine learning empowers traders to accelerate and automate one of the most complex, time-consuming, and challenging aspects of algorithmic trading, providing a competitive advantage beyond rules-based trading.

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