Danh mục
Development
Web Development Data Storage Git Automation Database Design Web Scraping
Business
Business Analysis Communication Project Management Sales Forecasting Business Strategy Operations Management Human Resources Oil and Gas Industry
Finance & Accounting
IT & Software
Office Productivity
SAP Word Excel PowerPoint Excel VBA SharePoint
Personal Development
Persuasion Notion Workspace Mind Mapping
Design
Design Sketching Typography Game Design Digital Art zBrush 3D Animation
Marketing
SEO Marketing Strategy YouTube Marketing Digital Marketing Facebook Marketing Growth Hacking Twitter Marketing Instagram Marketing
Lifestyle
Beer Brewing Beauty Home Repair Energy Healing Bartending Leather Crafting Makeup Artistry Hypnotherapy
Music
Piano Guitar Singing Music Production Acoustic Guitar Fingerstyle Guitar Music Theory Bass Guitar
Duocmmo Tedu Tin học văn phòng Khoá học miễn phí Tất cả khoá học
Get link khoá học
Trang chủ
Development
Programming Languages
Python
Algorithmic Trading A-Z with Python, Machine Learning & AWS

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

Loại khoá học: Programming Languages

Build your own truly Data-driven Day Trading Bot | Learn how to create, test, implement & automate unique Strategies.

image
50.000 VND
145 VND
Đầy Đủ Bài Giảng
Học Online Tiện Lợi
Kích Hoạt Nhanh 2-5 Phút
Thanh toán tự động
Được phép tải xuống

Tổng quan

Nội dung khoá học

Đánh giá

Mô tả

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

*** MARCH 2023:  Course fully updated and now with an additional Broker: Interactive Brokers (IBKR)***


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, Interactive Brokers, 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 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!

Bạn sẽ học được gì

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, Interactive Brokers (IBKR) and FXCM.

Stream high-frequency real-time Data.

Understand, analyze, control and limit Trading Costs.

Use powerful Broker APIs and connect with Python.

Yêu cầu

  • No prior Python knowledge required. This course provides a Python Crash Course.
  • No prior Finance/Trading knowledge required. This course explains the Basics.
  • A desktop computer (Windows, Mac, or Linux) capable of storing and running Anaconda. The course will walk you through installing the necessary free software.
  • An internet connection capable of streaming HD videos.
  • Some high school level math skills would be great (not mandatory, but it helps)

Nội dung khoá học

37 sections

Getting Started

6 lectures
What is Algorithmic Trading / Course Overview
04:54
How to get the best out of this course
05:27
Did you know...? (what Data can tell us about Day Trading)
04:41
Test your knowledge
1 question
Student FAQ
05:16
*** LEGAL DISCLAIMER (MUST READ!) ***
01:31

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

5 lectures
Our very first Trade
01:45
Long Term Investing vs. (Algorithmic) Day Trading
04:26
Spot Trading vs. Derivatives Trading (Part 1)
08:24
Spot Trading vs. Derivatives Trading (Part 2)
08:59
Overview & the Brokers OANDA, IBKR and FXCM
06:41

Day Trading with OANDA A-Z: a Deep Dive

16 lectures
OANDA at a first glance
09:27
Creating a fully functional Demo Account - in all Countries/Regions!
00:33
How to create an Account ***Update May 2023***
09:55
FOREX / Currency Exchange Rates explained
08:33
Our second Trade - EUR/USD FOREX Trading
04:24
How to calculate Profit & Loss of a Trade
06:45
Trading Costs and Performance Attribution
11:18
Margin and Leverage
08:04
Margin Closeout and more
07:20
Introduction to Charting
04:50
Our third Trade A-Z - Going Short EUR/USD
07:03
Netting vs. Hedging
07:26
Market, Limit and Stop Orders
05:42
Take-Profit and Stop-Loss Orders
03:28
A more general Example
04:25
Trading Challenge
00:21

Stocks and FOREX Trading with Interactive Brokers (IBKR)

12 lectures
IBKR at a first glance
03:52
How to create a (Paper Trading) Account
04:05
How to Install the IB Trader Workstation (TWS)
03:15
TWS - First Steps
03:47
The first Trade (buying Stocks)
05:50
Trading Hours
04:33
Cash Account vs. Margin Account
03:59
Trading Costs (Stocks) - Commissions
09:18
Trading Costs (Stocks) - other (hidden) Costs
06:33
FOREX Trading: Cash vs. CFD
08:33
A complete CFD FOREX Trade
05:14
CFD Trade Analysis
03:11

FOREX Day Trading with FXCM

9 lectures
***Important Info April 2023***
00:18
FXCM at a first glance
06:55
How to create an Account
06:35
Example Trade: Buying EUR/USD
03:07
Trade Analysis
03:44
Charting
01:16
Closing Positions vs. Hedging Positions
02:16
Order Types at a glance
04:01
Trading Challenge
00:19

Installing Python and Jupyter Notebooks

5 lectures
Introduction
01:32
Download and Install Anaconda
06:15
How to open Jupyter Notebooks
12:24
How to work with Jupyter Notebooks
17:25
Tips for Python Beginners
01:11

Excursus: How to avoid and debug Coding Errors (don´t skip!)

15 lectures
Introduction
02:59
Test your debugging skills!
10:40
Major reasons for Coding Errors
01:12
The most commonly made Errors at a glance
05:37
Omitting cells, changing the sequence and more
06:58
IndexErrors
04:50
Indentation Errors
03:18
Misuse of function names and keywords
02:32
TypeErrors and ValueErrors
03:41
Getting help on StackOverflow.com
06:23
How to traceback more complex Errors
10:22
Problems with the Python Installation
06:15
External Factors and Issues
04:13
Errors related to the course content (Transcription Errors)
04:11
Summary and Debugging Flow-Chart
07:15

API Trading with Python and Online Brokers- an Introduction

35 lectures
How to maximize your learning experience
1 question
Overview
01:07
OANDA: Commands to install required packages ***UPD August 23***
00:28
OANDA: Getting the API Key & other Preparations
05:02
OANDA: Connecting to the API/Server
07:34
***Important Notice Update August 2023***
00:07
OANDA: How to load Historical Price Data (Part 1)
07:55
OANDA: How to load Historical Price Data (Part 2)
04:11
OANDA: Streaming high-frequency real-time Data
03:46
OANDA: How to place Orders and execute Trades
10:18
Trading Challenge
00:17
IBKR API: Downloads and required Commands to install the Wrapper
00:08
IBKR: How to download and install the API Wrapper & other Preparations
02:42
IBKR: Connecting to the API
03:17
IBKR: Contracts
05:11
IBKR: How to get Market Data
06:51
IBKR: Data Streaming for Multiple Tickers
02:19
IBKR: Contracts (advanced)
05:38
IBKR: FOREX and CFD Contracts
02:46
IBKR: Creating Orders (Stock Trading)
08:40
IBKR: Creating Orders (CFD Trading)
01:23
IBKR: CFD Trade Information
05:20
IBKR: Positions and Account Values
03:49
IBKR: Historical Bars
06:26
***FXCM Important Info April 2023***
00:18
FXCM: Commands to install required packages
00:13
FXCM: How to install the FXCM API Wrapper
03:24
FXCM: Getting the Access Token & other Preparations
03:05
FXCM: Connecting to the API/Server
07:45
Troubleshooting: FXCM Server Connection Issues
01:52
FXCM: How to load Historical Price Data (Part 1)
06:30
FXCM: How to load Historical Price Data (Part 2)
05:24
FXCM: Streaming high-frequency real-time Data
06:38
FXCM: How to place Orders and execute Trades
07:26
Trading Challenge
00:17

Conclusion and Outlook

1 lectures
Conclusion and Outlook
00:44

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

1 lectures
Introduction and Downloads Part 2 ***Updated March 2023***
02:56

Introduction to Time Series Data in Pandas

5 lectures
Importing Time Series Data from csv-files
08:16
Converting strings to datetime objects with pd.to_datetime()
08:53
Indexing and Slicing Time Series
07:25
Downsampling Time Series with resample()
14:20
Coding Exercise 1
05:10

Financial Data Analysis with Python and Pandas - a (deep) Introduction

36 lectures
Introduction and Overview
04:02
Installing and importing required Libraries/Packages
01:59
Loading Financial Data from the Web
10:54
Initial Inspection and Visualization
12:22
[Article] Loading Data into Pandas - advanced topics
00:04
Normalizing Time Series to a Base Value (100)
06:35
Coding Challenge #1
05:20
Price changes and Financial Returns
09:03
Reward and Risk of Financial Instruments
05:57
Coding Challenge #2
00:14
Investment Multiple and CAGR
06:49
Compound Returns & Geometric Mean Return
04:17
Coding Challenge #3
00:09
Discrete Compounding
07:59
Continuous Compounding
05:53
Log Returns
02:21
Simple Returns vs Log Returns ( Part 1)
06:04
Simple Returns vs Log Returns ( Part 2)
05:42
Coding Challenge #4
00:10
Mid-Section Test
15 questions
Comparing the Performance of Financial Instruments
09:37
(Non-) Normality of Financial Returns
12:35
Annualizing Return and Risk
04:51
Resampling / Smoothing of Financial Data
07:44
Rolling Statistics
09:05
Coding Challenge #5
00:19
Short Selling and Short Position Returns (Part 1)
03:16
Short Selling and Short Position Returns (Part 2)
04:43
Short Selling and Short Position Returns (Part 3)
04:07
Coding Challenge #6
00:12
Covariance and Correlation
07:09
Portfolios and Portfolio Returns
03:57
Margin Trading and Levered Returns (Part 1)
04:56
Margin Trading and Levered Returns (Part 2)
08:52
Coding Challenge #7
00:14
Final Test
15 questions

Advanced Topics

6 lectures
Importing Financial Data from Excel
11:25
Merging / Aligning Financial Time Series (hands-on)
05:02
Helpful DatetimeIndex Attributes and Methods
06:24
Filling NA Values with bfill, ffill and interpolation
10:07
Timezones and Converting (Part 1)
04:36
Timezones and Converting (Part 2)
04:48

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

16 lectures
Introduction to OOP and examples for Classes
10:58
The Financial Analysis Class live in action (Part 1)
04:58
The Financial Analysis Class live in action (Part 2)
03:42
The special method __init__()
08:28
The method get_data()
06:49
The method log_returns()
03:21
String representation and the special method __repr__()
03:41
The methods plot_prices() and plot_returns()
05:21
Encapsulation and protected Attributes
04:02
The method set_ticker()
03:18
Adding more methods and performance metrics
05:51
Inheritance
09:01
Inheritance and the super() Function
06:47
Adding meaningful Docstrings
06:24
Creating and Importing Python Modules (.py)
04:19
Coding Exercise 3: Create your own Class
07:13

+++ PART 3: Defining and Testing Trading Strategies +++

6 lectures
Introduction to Part 3
06:13
Trading Strategies - an Overview
06:43
Downloads for Part 3 ***Updated May 2023***
00:04
Getting the Data
03:56
A simple Buy and Hold "Strategy"
05:20
Performance Metrics
06:33

Defining and Backtesting SMA Strategies

13 lectures
SMA Crossover Strategies - Overview
05:04
Defining an SMA Crossover Strategy
07:03
Vectorized Strategy Backtesting
08:21
Finding the optimal SMA Strategy
11:52
Generalization with OOP: An SMA Backtesting Class in action
10:24
Creating the Class (Part 1)
04:02
Creating the Class (Part 2)
09:06
Creating the Class (Part 3)
06:20
Creating the Class (Part 4)
04:58
Creating the Class (Part 5)
02:37
Creating the Class (Part 6)
04:54
Creating the Class (Part 7)
02:46
Creating the Class (Part 8)
04:19

Defining and Backtesting simple Momentum/Contrarian Strategies

10 lectures
Simple Contrarian/Momentum Strategies - Overview
03:45
Getting the Data
02:53
Excursus: Your FAQs answered
05:03
Defining a simple Contrarian Strategy
03:27
Vectorized Strategy Backtesting
04:31
Changing the Window Parameter
05:35
Trades and Trading Costs (Part 1)
08:51
Trades and Trading Costs (Part 2)
03:01
Generalization with OOP: A Contrarian Backtesting Class in action
08:07
OOP Challenge: Create the Contrarian Backtesting Class (incl. Solution)
05:04

Defining and Backtesting Mean-Reversion Strategies (Bollinger)

7 lectures
Mean-Reversion Strategies - Overview
05:41
Getting the Data
02:00
Defining a Bollinger Bands Mean-Reversion Strategy (Part 1)
04:29
Defining a Bollinger Bands Mean-Reversion Strategy (Part 2)
09:22
Vectorized Strategy Backtesting
05:48
Generalization with OOP: A Bollinger Bands Backtesting Class in action
07:20
OOP Challenge: Create the Bollinger Bands Backtesting Class (incl. Solution)
04:12

Trading Strategies powered by Machine Learning - Regression

11 lectures
Machine Learning - an Overview
06:40
Linear Regression with scikit-learn - a simple Introduction
08:17
Making Predictions with Linear Regression
03:11
Overfitting
06:23
Underfitting
04:05
Getting the Data
01:39
A simple Linear Model to predict Financial Returns (Part 1)
03:08
A simple Linear Model to predict Financial Returns (Part 2)
06:40
A Multiple Regression Model to predict Financial Returns
05:36
In-Sample Backtesting and the Look-ahead-bias
03:48
Out-Sample Forward Testing
04:30

Trading Strategies powered by Machine Learning - Classification

9 lectures
Logistic Regression with scikit-learn - a simple Introduction (Part 1)
05:22
Logistic Regression with scikit-learn - a simple Introduction (Part 2)
06:11
Getting and Preparing the Data
02:51
Predicting Market Direction with Logistic Regression
03:38
In-Sample Backtesting and the Look-ahead-bias
02:20
Out-Sample Forward Testing
03:25
Generalization with OOP: A Classification Backtesting Class in action
10:59
The Classification Backtesting Class explained (Part 1)
07:00
The Classification Backtesting Class explained (Part 2)
04:24

Advanced Backtesting Techniques

15 lectures
Introduction to Iterative Backtesting ("event-driven")
04:18
A first Intuition on Iterative Backtesting (Part 1)
06:07
A first Intuition on Iterative Backtesting (Part 2)
05:06
Creating an Iterative Base Class (Part 1)
04:27
Creating an Iterative Base Class (Part 2)
02:35
Creating an Iterative Base Class (Part 3)
02:14
Creating an Iterative Base Class (Part 4)
07:30
Creating an Iterative Base Class (Part 5)
05:42
Creating an Iterative Base Class (Part 6)
04:15
Creating an Iterative Base Class (Part 7)
06:36
Creating an Iterative Base Class (Part 8)
06:49
Adding the Iterative Backtest Child Class for SMA (Part 1)
05:52
Adding the Iterative Backtest Child Class for SMA (Part 2)
08:56
Using Modules and adding Docstrings
05:05
OOP Challenge: Add Contrarian and Bollinger Strategies
07:25

+++ PART 4: Real-time Implementation and Automation of Strategies +++

2 lectures
Introduction and Overview
01:38
Downloads for Part 4 *** Updated May 2023 ***
00:04

Implementation and Automation with OANDA (UPDATED!)

26 lectures
Updating the Wrapper Package (Part 1)
00:06
Updating the Wrapper Package (Part 2)
03:13
**Weekend and Bank Holiday Alert**
00:11
Historical Data, real-time Data and Orders (Recap)
09:19
Preview: A Trader Class live in action
05:05
How to collect and store real-time tick data
05:35
Storing and resampling real-time tick data (Part 1)
08:23
Storing and resampling real-time tick data (Part 2)
05:48
Storing and resampling real-time tick data (Part 3)
04:39
Storing and resampling real-time tick data (Part 4)
07:49
Storing and resampling real-time tick data (Part 5)
04:08
Working with historical data and real-time tick data (Part 1)
08:08
Working with historical data and real-time tick data (Part 2)
06:27
Working with historical data and real-time tick data (Part 3)
03:44
Defining a simple Contrarian Strategy
06:01
Placing Orders and Executing Trades
06:30
Trade Monitoring and Reporting
09:09
Trading other Strategies - Coding Challenge
01:48
Implementing an SMA Crossover Strategy (Solution)
04:18
Implementing a Bollinger Bands Strategy (Solution)
03:15
Machine Learning Strategies (1) - Model Fitting
05:26
Machine Learning Strategies (2) - Implementation
06:47
Importing a Trader Module / Class
02:56
Excursus: Printing all ticks in a Command Prompt/Terminal
00:15
Running a Python Trader Script
07:02
Outlook: What is (still) missing?
00:18

Implementation and Automation with IBKR

14 lectures
IBKR API - Recap
03:00
Streaming Tick Data
03:33
Streaming Tick Data for multiple Symbols
03:22
Streaming Bar Data
05:54
How to create a live Candle Stick Chart
01:48
Preparing the Data for Day Trading
05:04
Improving Code Efficiency
03:27
Define an SMA Day Trading Strategy
04:17
Creating Orders and Executing Trades
07:02
Trade Monitoring and Reporting
09:17
How to Stop a Trading Session
05:11
Trading other Strategies - Coding Challenge
02:48
Running a Python Trader Script
06:06
Outlook: What is (still) missing?
00:18

Implementation and Automation with FXCM

21 lectures
**Weekend and Bank Holiday Alert**
00:11
Historical Data, real-time Data and Orders (Recap)
09:31
Troubleshooting: FXCM Server Connection Issues
01:52
Preview: A Trader Class live in action
06:14
Collecting and storing real-time tick data
06:50
Storing and resampling real-time tick data (Part 1)
08:42
A Trader Class
05:41
Storing and resampling real-time tick data (Part 2)
08:18
Storing and resampling real-time tick data (Part 3)
03:16
Working with historical data and real-time tick data (Part 1)
06:07
Working with historical data and real-time tick data (Part 2)
05:55
Working with historical data and real-time tick data (Part 3)
03:46
Defining a Simple Contrarian Trading Strategy
04:47
Placing Orders and Executing Trades
07:33
Trade Monitoring and Reporting
06:31
Trading other Strategies - Coding Challenge
01:57
SMA Crossover and Bollinger Bands (Solution)
04:20
Machine Learning Strategies (1) - Model Fitting
05:26
Machine Learning Strategies (2) - Implementation
05:54
Excursus: Printing all ticks in a Command Prompt/Terminal
00:15
Running a Python Script
06:20

Cloud Deployment (AWS) | Scheduling Trading Sessions | Full Automation

12 lectures
Introduction and Motivation
02:49
Demonstration: AWS EC2 for Algorithmic Trading live in action
07:26
Amazon Web Services (AWS) - Overview and how to create a Free Trial Account
02:34
How to create an EC2 Instance
07:58
How to connect to your EC2 Instance
04:20
Getting the Instance Ready for Algorithmic Trading
07:00
**Weekend and Bank Holiday Alert**
00:11
How to run Python Scripts in a Windows Command Prompt
04:05
How to start Trading sessions with Batch (.bat) Files
04:02
How to schedule Trading sessions with the Task Scheduler
04:58
How to stop Trading Sessions (OANDA)
06:36
How to stop Trading Sessions (FXCM)
05:26

+++ PART 5: Expert Tips & Tricks, Case Studies and more +++

2 lectures
Overview
01:46
Downloads for PART 5 ***Updated June 2023***
00:04

Trading Hours, Spreads and Granularity - control and limit Trading Costs!

6 lectures
Introduction and Preparing the Data
04:57
The best time to trade (Part 1)
03:25
The best time to trade (Part 2)
03:20
Spreads during the busy hours
02:01
The Impact of Granularity
04:15
Conclusions
02:09

Working with two or many Strategies (Combination)

8 lectures
Introduction
02:06
Strategy 1: SMA
02:18
Strategy 2: Mean Reversion
02:26
Combining both Strategies - Alternative 1
05:21
Taking into account busy Trading Hours
02:32
Strategy Backtesting
01:41
Combining both Strategies - Alternative 2
02:48
Strategy Optimization
08:53

A Machine Learning-powered Strategy A-Z (DNN)

13 lectures
Project Overview
05:31
Installation of Tensorflow & Keras (Part 1)
00:20
Installation of Tensorflow & Keras (Part 2)
07:52
Getting and Preparing the Data
01:13
Adding Labels/Features
05:36
Adding lags
02:25
Splitting into Train and Test Set
02:00
Feature Scaling/Engineering
03:17
Creating and Fitting the DNN Model
08:00
Prediction & Out-Sample Forward Testing
07:02
Saving Model and Parameters
02:52
**Important Notices**
00:22
Implementation (Oanda & FXCM)
12:25

Error Handling: How to make your Trading Bot more stable and reliable

19 lectures
Introduction
05:06
Python Errors (Exceptions)
01:40
try and except
02:39
Catching specific Errors
01:30
The Exception class
01:04
try, except, else
03:18
finally
03:28
Try again (...until it works)
04:34
How to limit the number of retries
02:39
Waiting periods between re-tries
04:01
Implementation with Oanda: V20 Connection Issues
03:54
Oanda Error Handling (Part 1)
05:53
Oanda Error Handling (Part 2)
08:03
Oanda Error Handling (Part 3)
03:19
Implementation with IBKR: Errors and Connectivity Issues
06:25
IBKR Error Handling
05:23
Implementation with FXCM: API/Server Issues
02:42
FXCM Error Handling (Part 1)
04:48
FXCM Error Handling (Part 2)
04:56

Adding Stop Loss and Take Profit to the Trading Bot

17 lectures
Introduction
02:48
Stop Loss Orders - Theory
07:46
Trailing Stop Loss Orders - Theory
00:52
Take Profit Orders - Theory
05:24
SL & TP - Use Cases and Conclusion
11:22
Oanda: Contrader Class without Take Profit & Stop Loss (Recap)
01:18
Oanda: How to create Stop Loss and Take Profit Orders
08:36
Oanda: Stop Loss and Take Profit Orders - Pitfalls
05:24
Oanda: Setting SL Distances and TP Prices in real-time
05:31
Oanda: Check for SL/TP Events
04:18
Oanda: Adding Stop Loss & Take Profit
08:15
Oanda Trading Bot: Final Python Script
00:06
IBKR: Contrader Class without Take Profit & Stop Loss (Recap)
01:54
IBKR: How to create Stop Loss and Take Profit Orders
11:29
IBKR: Stop Loss and Take Profit Orders - Pitfalls
05:08
IBKR: Adding Stop Loss & Take Profit
08:52
IBKR Trading Bot: Final Python Script
00:06

+++ APPENDIX: Python Crash Course +++

1 lectures
Overview
01:30

Appendix 1: Python (& Finance) Basics

80 lectures
Section Downloads ***Reviewed May 2023***
00:04
Intro to the Time Value of Money (TVM) Concept (Theory)
06:01
Calculate Future Values (FV) with Python / Compounding
03:29
***NEW*** Udemy Online Coding Exercises - Intro
04:28
Future Value
1 question
Calculate Present Values (PV) with Python / Discounting
02:38
Present Value
1 question
Interest Rates and Returns (Theory)
04:26
Calculate Interest Rates and Returns with Python
03:47
Interest Rates
1 question
Introduction to Variables
05:04
Variables
1 question
Excursus: How to add inline comments
02:50
Variables and Memory (Theory)
01:57
More on Variables and Memory
06:33
Addition Assignment
1 question
Variables - Dos, Don´ts and Conventions
03:49
The print() Function
04:09
print()
1 question
Coding Exercise 1
09:00
TVM Problems with many Cashflows
03:21
Intro to Python Lists
02:22
Creating Lists
1 question
Zero-based Indexing and negative Indexing in Python (Theory)
02:47
Indexing Lists
03:10
Indexing Lists
1 question
For Loops - Iterating over Lists
07:48
List Iteration
1 question
The range Object - another Iterable
04:56
Iterating over range objects
1 question
Calculate FV and PV for many Cashflows
07:35
The Net Present Value - NPV (Theory)
07:47
Calculate an Investment Project´s NPV
03:02
Calculating NPV
1 question
Coding Exercise 2
08:41
Data Types in Action
06:07
Strings
1 question
The Data Type Hierarchy (Theory)
03:30
Excursus: Dynamic Typing in Python
01:38
Build-in Functions
05:52
Functions
1 question
Integers
03:18
Floats
05:58
How to round Floats (and Integers) with round()
05:10
Rounding
1 question
More on Lists
05:15
Lists and Element-wise Operations
04:19
Element-wise Operations
1 question
Slicing Lists
04:33
Slicing Cheat Sheet
00:03
Slicing Lists
1 question
Changing Elements in Lists
02:44
Changing Lists
1 question
Sorting and Reversing Lists
03:48
Sorting Lists
1 question
Adding and removing Elements from/to Lists
09:33
Adding and Removing Elements
1 question
Mutable vs. immutable Objects (Part 1)
09:04
Mutable vs. immutable Objects (Part 2)
05:12
Coding Exercise 3
11:32
Tuples
06:50
Dictionaries
06:22
Dictionary
1 question
Intro to Strings
08:47
Capitalize Strings
1 question
String Replacement
04:10
String Replacement
1 question
Booleans
02:23
Operators (Theory)
04:37
Comparison, Logical and Membership Operators in Action
08:21
Booleans and Operators
1 question
Coding Exercise 4
08:56
Conditional Statements
09:04
Conditionals
1 question
Keywords pass, continue and break
09:37
Keywords
1 question
Calculate a Project´s Payback Period
04:35
Introduction to while loops
07:58
While Loop
1 question
Coding Exercise 5
00:04

Appendix 2: User-defined Functions (required for OOP)

10 lectures
Section Downloads ***reviewed May 2023***
00:04
Defining your first user-defined Function
06:07
What´s the difference between Positional Arguments vs. Keyword Arguments?
05:35
How to work with Default Arguments
05:27
The Default Argument None
06:17
How to unpack Iterables
04:40
Sequences as arguments and *args
05:05
How to return many results
02:42
Scope - easily explained
08:16
Coding Exercise 6
00:04

Appendix 3: Numpy, Pandas, Matplotlib and Seaborn Crash Course

84 lectures
Downloads for this Section ***Updated May 2023***
00:04
Modules, Packages and Libraries - No need to reinvent the Wheel
07:52
Numpy Arrays
08:23
Numpy Arrays
1 question
Indexing and Slicing Numpy Arrays
03:13
Indexing and Slicing
1 question
Vectorized Operations with Numpy Arrays
03:56
PV with vectorized Numpy Code
1 question
Changing Elements in Numpy Arrays & Mutability
05:50
View vs. copy - potential Pitfalls when slicing Numpy Arrays
04:45
Changing elements in Arrays (and Copies)
1 question
Numpy Array Methods and Attributes
05:13
Methods
1 question
Numpy Universal Functions
03:59
Universal Functions
1 question
Boolean Arrays and Conditional Filtering
04:39
Conditional Filtering
1 question
Advanced Filtering & Bitwise Operators
06:11
Advanced Filtering
1 question
Determining a Project´s Payback Period with np.where()
04:50
Creating Numpy Arrays from Scratch
05:56
Numpy Arrays from Scratch
1 question
Coding Exercise 7
00:04
How to work with nested Lists
04:21
2-dimensional Numpy Arrays
03:51
How to slice 2-dim Numpy Arrays (Part 1)
05:36
How to slice 2-dim Numpy Arrays (Part 2)
02:03
Recap: Changing Elements in a Numpy Array / slice
03:39
How to perform row-wise and column-wise Operations
04:33
Coding Exercise 8
00:04
Intro to Tabular Data / Pandas
04:19
Create your very first Pandas DataFrame (from csv)
09:09
Loading a CSV-file into Pandas
1 question
Pandas Display Options and the methods head() & tail()
06:41
First Data Inspection
11:25
Summary Statistics
1 question
Coding Exercise 9
00:04
Selecting Columns
06:05
Selecting one Column with the "dot notation"
02:16
Selecting Columns
1 question
Zero-based Indexing and Negative Indexing
03:04
Selecting Rows with iloc (position-based indexing)
10:07
Slicing Rows and Columns with iloc (position-based indexing)
04:39
Position-based Indexing Cheat Sheets
00:02
Position-based Indexing 1
1 question
Position-based Indexing 2
1 question
Selecting Rows with loc (label-based indexing)
03:14
Slicing Rows and Columns with loc (label-based indexing)
10:21
Label-based Indexing Cheat Sheets
00:02
Label-based Indexing 1
1 question
Label-based Indexing 2
1 question
Summary, Best Practices and Outlook
06:30
Coding Exercise 10
00:04
First Steps with Pandas Series
03:53
Analyzing Numerical Series with unique(), nunique() and value_counts()
13:50
Analyzing non-numerical Series with unique(), nunique(), value_counts()
07:17
The copy() method
03:57
Sorting of Series and Introduction to the inplace - parameter
08:59
First Steps with Pandas Index Objects
05:57
Changing Row Index with set_index() and reset_index()
10:07
Changing Column Labels
03:20
Renaming Index & Column Labels with rename()
03:51
Filtering DataFrames (one Condition)
10:20
Filtering DataFrames by many Conditions (AND)
04:45
Filtering DataFrames by many Conditions (OR)
05:04
Advanced Filtering with between(), isin() and ~
08:35
Intro to NA Values / missing Values
08:52
Handling NA Values / missing Values
10:51
Exporting DataFrames to csv
02:14
Summary Statistics and Accumulations
10:26
Visualization with Matplotlib (Intro)
08:48
Customization of Plots
12:56
Histogramms (Part 1)
04:34
Histogramms (Part 2)
06:28
Scatterplots
07:18
First Steps with Seaborn
05:24
Categorical Seaborn Plots
13:33
Seaborn Regression Plots
12:21
Seaborn Heatmaps
08:17
Removing Columns
05:18
Introduction to GroupBy Operations
02:02
Understanding the GroupBy Object
08:05
Splitting with many Keys
06:49
split-apply-combine
09:36

What´s next? (outlook and additional resources)

1 lectures
Bonus Lecture
05:40

Đánh giá của học viên

Chưa có đánh giá
Course Rating
5
0%
4
0%
3
0%
2
0%
1
0%

Bình luận khách hàng

Viết Bình Luận

Bạn đánh giá khoá học này thế nào?

Khoá học liên quan

Automated Trading Bot (Python hands-on bootcamp)

Automated Trading Bot (Python hands-on bootcamp)

25 VND
50.000 VND
100 Days of Code: The Complete Python Pro Bootcamp

100 Days of Code: The Complete Python Pro Bootcamp

140 VND
50.000 VND
MQL5 ADVANCED: Multi-Strategy & Multi-Symbol Expert advisors

MQL5 ADVANCED: Multi-Strategy & Multi-Symbol Expert advisors

90 VND
50.000 VND
MQL5 PROJECTS: Code the Black Swan Trading Strategy in MQL5

MQL5 PROJECTS: Code the Black Swan Trading Strategy in MQL5

55 VND
50.000 VND
MQL5 ADVANCED:Code Machine learning EAs with Neural Networks

MQL5 ADVANCED:Code Machine learning EAs with Neural Networks

90 VND
50.000 VND
MQL5 PROJECTS: Code the White Fairy Pyramiding strategy

MQL5 PROJECTS: Code the White Fairy Pyramiding strategy

60 VND
50.000 VND
Investing In Stocks The Complete Course! (17+ Hours)

Investing In Stocks The Complete Course! (17+ Hours)

145 VND
50.000 VND
MQL5 PROJECTS: Code the Sure Fire Hedging strategy with MQL5

MQL5 PROJECTS: Code the Sure Fire Hedging strategy with MQL5

60 VND
50.000 VND
CFA Level 1 Certification - Complete Exam Prep Course Bundle

CFA Level 1 Certification - Complete Exam Prep Course Bundle

55 VND
50.000 VND
Khóa Học Đầu Tư Chứng Khoán Phái Sinh (Phần 2)

Khóa Học Đầu Tư Chứng Khoán Phái Sinh (Phần 2)

160 VND
50.000 VND
Lập trình Python từ cơ bản đến nâng cao thông qua các dự án

Lập trình Python từ cơ bản đến nâng cao thông qua các dự án

25 VND
50.000 VND
Complete algorithmic forex trading and back testing system

Complete algorithmic forex trading and back testing system

120 VND
50.000 VND
Performance Optimization and Risk Management for Trading

Performance Optimization and Risk Management for Trading

110 VND
50.000 VND
Forex Algorithmic Trading with Python : Build a Grid Bot

Forex Algorithmic Trading with Python : Build a Grid Bot

85 VND
50.000 VND
Algorithmic Trading & Quantitative Analysis Using Python

Algorithmic Trading & Quantitative Analysis Using Python

100 VND
50.000 VND
The complete trading course Key Level and Market structure

The complete trading course Key Level and Market structure

100 VND
50.000 VND
image

Đăng ký get khoá học Udemy - Unica - Gitiho giá chỉ 50k!

Get khoá học giá rẻ ngay trước khi bị fix.

Get khoá học cho tôi