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Performance Optimization and Risk Management for Trading

Performance Optimization and Risk Management for Trading

Loại khoá học: Other Finance & Accounting

Generate Income and make a living with Day Trading / Algorithmic Trading. A quantitative & data-driven Python course.

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

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Mô tả

(Latest course update and full code review in May 2023!)


(How) Can I generate sustainable Income and make a living with Trading? - That is one of the most frequently asked questions in Day Trading / Algorithmic Trading.

This unique course provides the skills, knowledge, and techniques required to (realistically!) answer that question. The course uses rigorous quantitative methods and is 100% data-driven (Python coding required!). 


You will learn how to make use of the most powerful trading features and techniques:

  • Path-dependent Simulation techniques to find a sustainable level of Trading Income

  • Taking into account Taxation, Inflation and Shortfall Risk

  • Strategy Backtesting and Forward Testing

  • Strategy Optimization techniques (One/Many Parameter Optimization, Multi-Period Optimization, Smoothing, and more...)

  • Finding the optimal Degree of Leverage in Margin Trading (Kelly Criterion and more advanced techniques)

  • Improving Trading Performance and mange Risk with Stop Loss and Take Profits Orders

  • and more...


Important: these techniques and skills are highly relevant and must-knows for any Trader and any Trading activity:

  • for Assets like Forex (Currencies), Cryptocurrencies, Stocks, Indexes, Commodities, and more...

  • for Strategies based on Technical/Fundamental Analysis, Artificial Intelligence (Machine Learning & Deep Learning), Statistical Arbitrage, and more...

  • for Trading with Brokers like Interactive Brokers (IBKR), Binance, TD Ameritrade, Oanda, FXCM, and more...


Performance Optimization and Risk Management require... rigorous Performance and Risk Measurement. The course covers the following Metrics and Methods:

  • Mean-Variance Analysis

  • Risk-adjusted Return Metrics (e.g. Sharpe Ratio)

  • Downside Deviation and Sortino Ratio

  • Tail Risk Metrics

  • Maximum Drawdown, Maximum Drawdown Duration, and Calmar Ratio

  • Deep Analysis of Levered Trading and the Kelly Criterion

  • Compound Annual Growth Rate (CAGR)

  • Investment Multiple

  • and many more...


You´ll have the opportunity to practice what you have learned in various Coding Exercises/Challenges (real data and meaningful questions!).


This is not only a course on Performance and Risk Management for Trading. It´s an in-depth coding course on Python and its Data Science Libraries Numpy, Pandas, Matplotlib. You will learn how to use and master these Libraries for (Financial) Data Analysis, Optimization, and Trading. 

Please note: This is not a course for complete Python Beginners (check out my other courses!)


What are you waiting for? Join now and start improving your Trading Performance!

As always, there is no risk for you as I offer a 30-Days-Money-Back Guarantee!


Thanks and looking forward to seeing you in the Course!

Bạn sẽ học được gì

How to make a living with Trading (and what it requires)

How to optimize the Performance of Trading Strategies

How to manage & control the Risk of Trading Strategies

How to find the optimal degree of Leverage for Margin Trading

How to measure the Performance and Risk of Trading Strategies and Financial Instruments

How to make proper use of Stop Loss (SL) and Take Profit (TP) Orders

Advanced Python Coding (OOP, Pandas, Numpy, Scipy, Matplotlib, Seaborn)

How to optimize Trading Performance with single/multiple Parameter Optimization

How to optimize Trading Performance with Smoothing

How to calculate Risk, Return and the Sharpe Ratio (Mean-Variance Analysis)

How to calculate Downside Risk and the Sortino Ratio

How to calculate Maximum Drawdown, Maximum Drawdown Duration and the Calmar Ratio

How to calculate CAGR, Investment Multiple, compound Returns and more.

How to generate a sustainable income with Trading

Yêu cầu

  • 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.
  • Basic Python Coding Skills (Variables, Data Types, Lists, For Loops, Functions) -> This is not a Course for complete Python Beginners.
  • Basic Coding Skills in Pandas, Numpy and Matplotlib
  • Basic Knowledge or first practical experiences with Trading/Investing would be great (not mandatory but it helps)
  • Some high school level math & statistics skills would be great (not mandatory, but it helps)

Nội dung khoá học

22 sections

Getting Started

7 lectures
Welcome and Introduction
02:37
Did you know...?
02:37
Course Overview
04:33
Tips: How to get the most out of this course
05:27
Student FAQ
02:11
*** LEGAL DISCLAIMER (MUST READ!) ***
00:38
Course Materials / Download (Updated: May 2023)
01:57

An Introduction to Trading and Income Generation

5 lectures
Trading vs. Investing
14:39
[Article] Algorithmic Trading and how to start
02:27
How to generate sustainable Income with Trading
09:26
How to make a living with Trading
07:31
Example
04:37

Installing Python and Jupyter Notebooks

4 lectures
Overview
01:41
Download and Install Anaconda
06:15
How to open Jupyter Notebooks
12:24
How to work with Jupyter Notebooks
17:25

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

37 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
06:50
Compound Returns & Geometric Mean Return
04:17
Coding Challenge #3
00:09
Discrete Compounding
08:00
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
13:02
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
Introduction to Currencies (Forex) and Trading
07:26
Short Selling and Short Position Returns (Part 2)
04:44
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

Performance and Risk Measurement - Overview

2 lectures
Introduction
01:47
Performance and Risk Metrics at a glance
03:34

Mean-Variance Analysis and the Sharpe Ratio

7 lectures
Introduction
07:11
Getting started
07:31
Return Metrics
02:35
Risk Metrics (Standard Deviation)
02:35
Risk-adjusted Return and the Sharpe Ratio
05:41
Putting everything together and Conclusion
06:25
Coding Challenge
00:17

Downside Risk and Sortino Ratio

6 lectures
Introduction
06:14
Getting started
01:45
Downside Deviation (Semi-Deviation)
03:55
Sortino Ratio
01:44
Putting everything toghether
05:10
Coding Challenge
00:19

Maximum Drawdown and Calmar Ratio

7 lectures
Introduction
08:02
Getting started
01:38
Maximum Drawdown
09:46
Calmar Ratio
01:30
Max Drawdown Duration
07:10
Putting everything together
05:30
Coding Challenge
00:19

Trading with Leverage and the Kelly Criterion

8 lectures
Introduction
05:21
Getting started
01:01
Recap: Leverage and Margin Trading
02:47
Finding the optimal degree of Leverage
03:49
The Kelly Criterion
02:34
The impact of Leverage on Reward & Risk
06:12
Putting everything together
04:33
Coding Challenge
00:15

Case Study A-Z: Performance Optimization and Risk Management for Trading

4 lectures
Introduction and Overview
02:36
[Article] Trading Strategies - Overview
02:11
[Article] The lifecycle of a Trading Strategy
03:33
A simple active Trading Strategy at a glance
07:17

Backtesting an active Trading Strategy - Introduction

9 lectures
Getting started
02:53
Intro to Backtesting: a Buy-and-Hold "Strategy"
02:54
Defining the Strategy
03:27
Vectorized Strategy Backtesting
08:42
Changing Strategy Parameters
05:35
Trades and Trading Costs (Part 1)
07:11
Trades and Trading Costs (Part 2)
08:17
Using a Backtester Class (v 1.0)
09:06
(optional) How to create the Class (OOP)
07:23

Strategy Optimization and advanced Performance Measurement

8 lectures
Introduction
00:14
Getting started
02:30
Finding the optimal value for the "window" Parameter
06:13
Relationship between the "window" Parameter and Performance
03:20
Using an updated Backtester Class (v 2.0)
05:31
Advanced Performance Reporting
05:51
Adding Performance Metrics to the Class (v 2.1)
06:53
Putting everything together: Advanced Performance Optimization (v 3.0)
06:30

Powerful: Strategy Optimization with Smoothing (the optimal data frequency)

9 lectures
Introduction
00:11
Getting Started
06:39
How strategies and impacted by random noise (Part 1)
05:45
How strategies and impacted by random noise (Part 2)
05:06
Data Resampling/Smoothing and Backtesting (Part 1)
06:58
Data Resampling/Smoothing and Backtesting (Part 2)
04:39
Using an updated Backtester Class (v 4.0)
02:36
Two Parameter Optimization: Frequency and Window
12:38
Using an updated Backtester Class (v 4.1)
08:59

Stop Loss and Take Profit for Trading Strategies

12 lectures
Introduction
00:17
Stop Loss Orders (Theory)
07:47
Getting started
01:43
Identify and label Trading Sessions
05:32
Cumulative/Compound Returns in a Trading Session
08:13
Adding Stop Loss (SL)
06:47
How Stop Loss impacts a Trading Strategy
05:32
Using an updated Backtester Class (v 5.0)
06:17
Take Profit Orders (Theory)
05:31
Adding Take Profit (TP)
05:04
Stop Loss & Take Profit - an deeper analysis
08:02
Final Conclusion
06:31

Adding Leverage to Trading Strategies: Margin Trading

7 lectures
Introduction
00:12
Getting started
01:43
The optimal degree of leverage (simplified)
04:14
A more realistic Approach (non-constant Leverage) (Part 1)
10:34
A more realistic Approach (non-constant Leverage) (Part 2)
05:27
Using an updated Backtester Class (v 6.0)
05:03
Revised: The optimal degree of leverage and the Kelly Criterion
06:21

Forward Testing (vs. Backtesting)

6 lectures
Introduction
00:01
Getting started
01:10
Backtesting / Forward Testing - a simple (too simple?) approach
06:57
The Performance of Strategies over multiple Time Periods
08:50
Backtesting / Forward Testing a stable Strategy
03:46
More on selecting the right degree of Leverage
00:50

Sustainable Income Planning and the Impact of Taxes, Inflation and Risk

8 lectures
Introduction
02:57
Recap: A (too) simple Income Calculation
03:20
Introduction to Simulations (Part 1)
07:04
Introduction to Simulations (Part 2)
06:42
A path-dependent Simulation with Taxes and Income - Introduction
12:18
A path-dependent Simulation with Taxes and Income - many Simulations
03:24
Shortfall Risk and a Sustainable Income Level
04:16
Final Remarks
05:56

How to adjust the Framework to other Trading Strategies

9 lectures
Introduction
03:03
A Mean-Reversion Strategy - Overview
05:41
Getting started
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
Adjusting the framework and creating a Backtester Class (Part 1)
04:55
Adjusting the framework and creating a Backtester Class (Part 2)
04:37
The Backtester Class live in action
09:43

Appendix 1: Introduction to Statistics

30 lectures
Introduction
00:10
Statistics - Overview, Terms and Vocabulary
11:48
Population vs. Sample
06:52
Visualizing Frequency Distributions with plt.hist()
03:51
Relative and Cumulative Frequencies with plt.hist()
05:17
Measures of Central Tendency (Theory)
04:41
Coding Measures of Central Tendency - Mean and Median
03:42
Coding Measures of Central Tendency - Geometric Mean
03:50
Excursus: Why Log Returns are useful
02:35
Variability around the Central Tendency / Dispersion (Theory)
06:05
Minimum, Maximum and Range with Python/Numpy
02:05
Variance and Standard Deviation with Python/Numpy
03:14
Percentiles with Python/Numpy
03:35
Skew and Kurtosis (Theory)
03:48
How to calculate Skew and Kurtosis with scipy.stats
05:34
Coding Exercise 1
00:05
How to generate Random Numbers with Numpy
04:33
Reproducibility with np.random.seed()
03:38
Probability Distributions - Overview
06:33
Discrete Uniform Distributions
05:57
Continuous Uniform Distributions
04:11
The Normal Distribution (Theory)
05:21
Creating a normally distributed Random Variable
05:22
Normal Distribution - Probability Density Function (pdf) with scipy.stats
03:54
Normal Distribution - Cumulative Distribution Function (cdf) with scipy.stats
02:53
The Standard Normal Distribution and Z-Values
06:40
Properties of the Standard Normal Distribution (Theory)
02:52
Probabilities and Z-Values with scipy.stats
11:02
Confidence Intervals with scipy.stats
07:16
Coding Exercise 2
00:05

Appendix 2: Sampling, Estimation and Hypothesis Testing

29 lectures
Sample Statistic, Sampling Error and Sampling Distribution (Theory)
05:10
Sampling with np.random.choice()
04:00
Sampling Distribution
04:00
Standard Error
02:23
Central Limit Theorem (Coding Part 1)
04:15
Central Limit Theorem (Coding Part 2)
05:31
Central Limit Theorem (Theory)
03:38
Point Estimates vs. Confidence Interval Estimates (known Population Variance)
04:38
The Student´s t-distribution: What is it and why/when do we use it?
04:20
Unknown Population Variance - the Standard Case (Example 1)
04:33
Unknown Population Variance - the Standard Case (Example 2)
02:56
Student´s t-Distribution vs. Normal Distribution with scipy.stats
05:16
Bootstrapping with Python: an alternative method without Statistics
05:20
Coding Exercise 3
00:05
Hypothesis Testing (Theory)
10:34
Two-tailed Z-Test with known Population Variance
09:02
What is the p-value? (Theory)
03:19
Calculating and interpreting z-statistic and p-value with scipy.stats
03:53
One-tailed Z-Test with known Population Variance
05:54
Two-tailed t-Test (unknown Population Variance)
07:07
One-tailed t-Test (unknown Population Variance)
03:00
Hypothesis Testing with Bootstrapping
05:49
Testing for Normality of Financial Returns with scipy.stats
09:54
Coding Exercise 4
00:05
Covariance and Correlation - the Dataset (Part 1)
06:42
Covariance and Correlation - the data (Part 2)
05:42
Covariance and Correlation Coefficient (Theory)
06:50
How to calculate Covariance and Correlation in Python
05:02
Correlation and Scatterplots – visual Interpretation
04:59

Appendix 3: Introduction to Object Oriented Programming (OOP)

17 lectures
Introduction to OOP and examples for Classes
10:58
Required Packages
00:06
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: Create your own Class
07:13

What´s next? (outlook and additional resources)

1 lectures
Bonus Lecture
05:39

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