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Algorithmic Trading A-Z with Python, Machine Learning & AWS

Build your own truly Data-driven Day Trading Bot | Learn how to create, test, implement & automate unique Strategies.
Instructor:
Alexander Hagmann
6,776 students enrolled
English [Auto]
Build automated Trading Bots with Python and Amazon Web Services (AWS)
Create powerful and unique Trading Strategies based on Technical Indicators and Machine Learning / Deep Learning.
Rigorous Testing of Strategies: Backtesting, Forward Testing and live Testing with paper money.
Fully automate and schedule your Trades on a virtual Server in the AWS Cloud.
Truly Data-driven Trading and Investing.
Python Coding and Object Oriented Programming (OOP) in a way that everybody understands it.
Coding with Numpy, Pandas, Matplotlib, scikit-learn, Keras and Tensorflow.
Understand Day Trading A-Z: Spread, Pips, Margin, Leverage, Bid and Ask Price, Order Types, Charts & more.
Day Trading with Brokers OANDA & FXCM.
Stream high-frequency real-time Data.
Understand, analyze, control and limit Trading Costs.
Use powerful Broker APIs and connect with Python.

Welcome to the most comprehensive Algorithmic Trading Course. It´s the first 100% Data-driven Trading Course!

In this rigorous but yet practical Course, we will leave nothing to chance, hope, vagueness, or hocus-pocus!

Did you know that 75% of retail Traders lose money with Day Trading? (some sources say >95%)

For me as a Data Scientist and experienced Finance Professional this is not a surprise. Day Traders typically do not know/follow the five fundamental rules of (Day) Trading. This Course covers them all in detail!

1. Know and understand the Day Trading Business

Don´t start Trading if you are not familiar with terms like Bid-Ask Spread, Pips, Leverage, Margin Requirement, Half-Spread Costs, etc.

Part 1 of this course is all about Day Trading A-Z with the Brokers Oanda and FXCM. It deeply explains the mechanics, terms, and rules of Day Trading (covering Forex, Stocks, Indices, Commodities, Baskets, and more).

2. Use powerful and unique Trading Strategies

You need to have a Trading Strategy. Intuition or gut feeling is not a successful strategy in the long run (at least in 99.9% of all cases). Relying on simple Technical Rules doesn´t work either because everyone uses them.

You will learn how to develop more complex and unique Trading Strategies with Python. We will combine simple and also more complex Technical Indicators and we will also create Machine Learning- and Deep Learning- powered Strategies. The course covers all required coding skills (Python, Numpy, Pandas, Matplotlib, scikit-learn, Keras, Tensorflow) from scratch in a very practical manner.

3. Test your Strategies before you invest real money (Backtesting / Forward Testing)

Is your Trading Strategy profitable? You should rigorously test your strategy before ‘going live’.

This course is the most comprehensive and most rigorous Backtesting / Forward Testing course that you can find.

You will learn how to apply Vectorized Backtesting techniques, Iterative Backtesting techniques (event-driven), live Testing with play money, and more. And I will explain the difference between Backtesting and Forward Testing and show you what to use when. The backtesting techniques and frameworks covered in the course can be applied to long-term investment strategies as well!   

4. Take into account Trading Costs – it´s all about Trading Costs!

“Trading with zero commissions? Great!” … Well, there is still the Bid-Ask-Spread and even if 2 Pips seem to be very low, it isn´t!

The course demonstrates that finding profitable Trading Strategies before Trading Costs is simple. It´s way more challenging to find profitable Strategies after Trading Costs! Learn how to include Trading Costs into your Strategy and into Strategy Backtesting / Forward Testing. And most important: Learn how you can control and reduce Trading Costs.

 

5. Automate your Trades

Manual Trading is error-prone, time-consuming, and leaves room for emotional decision-making.

This course teaches how to implement and automate your Trading Strategies with Python, powerful Broker APIs and Amazon Web Services (AWS). Create your own Trading Bot and fully automate/schedule your trading sessions in the AWS Cloud!

Finally… this is more than just a course on automated Day Trading:

  • the techniques and frameworks covered can be applied to long-term investing as well.

  • it´s an in-depth Python Course that goes beyond what you can typically see in other courses. Create Software with Python and run it in real-time on a virtual Server (AWS)!

  • we will feed Machine Learning & Deep Learning Algorithms with real-time data and take ML/DL-based actions in real-time!

What are you waiting for? Join now. As always, there is no risk for you as I provide a 30-Days-Money-Back Guarantee!

Thanks and looking forward to seeing you in the Course!

+++ IMPORTANT NOTICE +++

In some countries (Japan, Russian Federation, South Korea, Turkey) CFD/FOREX Trading is not permitted and residents cannot create an account on OANDA or FXCM (Online Brokers). For the heart of this course (Coding, Creating Strategies, Backtesting & Forward Testing Strategies) you don´t need a Broker account. Therefore, this course is a great choice even without a Broker account. But please keep in mind that some parts (Trading and Implementation) won´t work for you! Thanks a lot for your understanding! 

Getting Started

1
What is Algorithmic Trading / Course Overview
2
How to get the best out of this course
3
Did you know...? (what Data can tell us about Day Trading)
4
Student FAQ
5
*** LEGAL DISCLAIMER (MUST READ!) ***

+++ PART 1: Day Trading, Online Brokers and APIs +++

1
Our very first Trade
2
Long Term Investing vs. (Algorithmic) Day Trading
3
Overview & the Brokers OANDA and FXCM

Day Trading with OANDA A-Z: a Deep Dive

1
OANDA at a first glance
2
How to create an Account
3
FOREX / Currency Exchange Rates explained
4
Our second Trade - EUR/USD FOREX Trading
5
How to calculate Profit & Loss of a Trade
6
Trading Costs and Performance Attribution
7
Margin and Leverage
8
Margin Closeout and more
9
Introduction to Charting
10
Our third Trade A-Z - Going Short EUR/USD
11
Netting vs. Hedging
12
Market, Limit and Stop Orders
13
Take-Profit and Stop-Loss Orders
14
A more general Example
15
Trading Challenge

FOREX Day Trading with FXCM

1
FXCM at a first glance
2
How to create an Account
3
Example Trade: Buying EUR/USD
4
Trade Analysis
5
Charting
6
Closing Positions vs. Hedging Positions
7
Order Types at a glance
8
Trading Challenge

Installing Python and Jupyter Notebooks

1
Introduction
2
Download and Install Anaconda
3
How to open Jupyter Notebooks
4
How to work with Jupyter Notebooks
5
Tips for Python Beginners

Trading with Python and OANDA/FXCM - an Introduction

1
Overview
2
OANDA: Commands to install required packages
3
OANDA: How to install the OANDA API / Wrapper
4
OANDA: Getting the API Key & other Preparations
5
OANDA: Connecting to the API/Server
6
OANDA: How to load Historical Price Data (Part 1)
7
OANDA: How to load Historical Price Data (Part 2)
8
OANDA: Streaming high-frequency real-time Data
9
OANDA: How to place Orders and execute Trades
10
Trading Challenge
11
FXCM: Commands to install required packages
12
FXCM: How to install the FXCM API Wrapper
13
FXCM: Getting the Access Token & other Preparations
14
FXCM: Connecting to the API/Server
15
Troubleshooting: FXCM Server Connection Issues
16
FXCM: How to load Historical Price Data (Part 1)
17
FXCM: How to load Historical Price Data (Part 2)
18
FXCM: Streaming high-frequency real-time Data
19
FXCM: How to place Orders and execute Trades
20
Trading Challenge

Conclusion and Outlook

1
Conclusion and Outlook

+++ PART 2: Pandas for Financial Data Analysis and Introduction to OOP +++

1
Introduction and Downloads Part 2

Introduction to Time Series Data in Pandas

1
Importing Time Series Data from csv-files
2
Converting strings to datetime objects with pd.to_datetime()
3
Indexing and Slicing Time Series
4
Downsampling Time Series with resample()
5
Coding Exercise 1

Financial Data Analysis with Pandas - an Introduction

1
Getting Ready (Installing required library)
2
Importing Stock Price Data from Yahoo Finance
3
Initial Inspection and Visualization
4
Normalizing Time Series to a Base Value (100)
5
The shift() method
6
The methods diff() and pct_change()
7
Measuring Stock Performance with MEAN Returns and STD of Returns
8
Financial Time Series - Return and Risk
9
Financial Time Series - Covariance and Correlation
10
Coding Exercise 2
11
Simple Returns vs. Log Returns
12
Importing Financial Data from Excel
13
Simple Moving Averages (SMA) with rolling()
14
Momentum Trading Strategies with SMAs
15
Exponentially-weighted Moving Averages (EWMA)
16
Merging / Aligning Financial Time Series (hands-on)

Advanced Topics

1
Helpful DatetimeIndex Attributes and Methods
2
Filling NA Values with bfill, ffill and interpolation
3
Timezones and Converting (Part 1)
4
Timezones and Converting (Part 2)

Object Oriented Programming (OOP): Creating a Financial Analysis Class

1
Introduction to OOP and examples for Classes
2
The Financial Analysis Class live in action (Part 1)
3
The Financial Analysis Class live in action (Part 2)
4
The special method __init__()
5
The method get_data()
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34 hours on-demand video
33 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion
Algorithmic Trading A-Z with Python, Machine Learning & AWS
Category:
Finance & Accounting, Investing & Trading
Teacher
Daniel W
Price:
$218.98 $169
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