• Post category:StudyBullet-4
  • Reading time:11 mins read


Learning by doing! Apply statistics on Trading and quantitative finance. (Forex, crypto, stocks )

What you will learn

Find optimal stop loss & take profit using probability distribution

Understand Student test and apply it to portfolio management problem

Use probability distribution to compute the Value at Risk (VaR)

Compute correlation between assets properly

Understand the main financial statistics: mean, variance, standard-deviation, skewness, kurtosis, covariance, correlation, …

Compute conditional probability to create a strategy with 70% beneficial trades

Master combinatorial statistics

Learn the basis of probability: random variables, intersection, union, independency, conditional probability …

Learn bayes theorem

Learn the most used law of probability in finance: Bernoulli, Binomial, Poisson, Uniform, Exponential, Normal,…

Learn how statistical test works

Description

You already have knowledge in finance and you want to go deeper to monetize and diversify your knowledge?

You already have some trading knowledge and you want to learn about quantitative trading/finance?

You are simply a curious person who wants to get into this subject?

 

If you answer at least one of these questions, I welcome you to this course. All the applications of the course will be done using Python. However, for beginners in Python, don’t panic! There is a FREE python crash course included to master Python.

In this course, you will learn how to use statistics and probability to make your strategies stronger. You will learn the statistical methods used by the quantitative analyst to find the optimal stop loss and take profit and to perform a risk analysis (VaR). You will use the power of conditional probability to increase the beneficial trade to 70%.


Get Instant Notification of New Courses on our Telegram channel.


 

Through this example, you will learn and understand a lot of statistic and probability concepts used by portfolio managers and professional traders:

  • Descriptive statistics: mean, variance, standard deviation, covariance, correlation, skewness, kurtosis, …

  • Probability: random variable, union, intersection, independence, conditional probability, …

  • Hypothesis Test: understand the process, student test, ad-fuller test, …

Why this course and not another?

  • This is not a programming course nor a trading course or a statistic course. It is a course in which programming and statistic are used for trading.

  • This course is not created by a data scientist but by a degree in mathematics and economics specializing in mathematics applied to finance.

  • You can ask questions or read our quantitative finance articles simply by registering on our free Discord forum.

Without forgetting that the course is satisfied or refunded for 30 days. Don’t miss an opportunity to improve your knowledge of this fascinating subject.

English
language

Content

Introduction

READ ME

Descriptive statistics

Introduction
Population versus Sample
Application: create google stock prices sample
Central tendency measure: The mean
Application: compute mean Google return + Annualization of returns
Central tendency measure: The median
Application: Extreme value problem? Compute the median
Central tendency measure: The percentile
Application: Understand Google return distribution
QUIZ – central tendency measure
Dispersion measure: The variance
Application: Compute variance returns + Variance annualization
Dispersion measure: The standard deviation
Application: Compute the volatility + Annualize the volatility
QUIZ – dispersion measure
Relationship measure: Covariance / covariance matrix
Application: Assets covariance
Relationship measure: Correlation
Application: Assets correlation
QUIZ – Relationship measures

Exercise: Compute risk/return, correlation asset PROPERLY

Introduction
Correction exercise 1: Compute asset risk/return
Correction exercise 2: Compute portfolio correlation

Combinatorial statistics

Introduction
Arrangement: Permutation
Arrangement: Without repetition
Arrangement: With repetition
Combination: Without repetition
Combination: With repetition
QUIZZ arrangement / combination

Exercise: Find the number of combinations between forex pairs

Introduction
Correction exercise: Find the number of combinations between forex pairs(1)
Correction exercise: Find the number of combinations between forex pairs(2)

Probability

Introduction
What a random variable is?
Quiz – Random variable
Intersection
Union
Independent event
Complementary event
Quiz – Events
Conditional probability
Bayes theorem
Quiz – Conditional probability

Exercise: From 50% to 70% of good trade?

Introduction
Import some data
Compute basic probability
Create Bollinger bands
Computation for Bollinger bands strategy
Compute conditional probability of increase

Law of probability

Introduction
Law of probability
Bernoulli law
Binomial law
Poisson law
Continuous uniform law.
Exponential law
Normal law
QUIZ – Law of probability

Exercise: Find the best stop loss & take profit / Compute the VaR

Introduction
Compute a value at risk
Find the best stop loss & take profit

Hypothesis test

Introduction
Hypothesis test
Example + Error threshold
P value
Quiz – Hypothesis test

Exercise: You trading strategy really works or it’s randomness? / Bonus

Introduction
Student test: practice
AdFuller test: practice

How to go deeper in the algorithmic trading field?

Tip

BONUS 1: Python basics

Introduction
Type of object: Number
Type of object: String
Type of object: Logical Operation / Boolean
Type of object: Variable assignment
Type of object: Tuple and list
Type of object: Dictionary
Type of object: Set
Python structures: If / Elif / Else
Python structures: For
Python structures: While
Functions: Basics of function
Functions: Local variable
Functions: Global variable
Functions: Lambda function

BONUS 2: Python for data science

Introduction
Numpy: Array
Numpy: Random
Numpy: Indexing / slicing / transformation
Pandas: Serie and dataframe
Pandas: Cleaning and selection
Pandas: Conditional selection
Matplotlib: Graph
Matplotlib: Scatter
Matplotlib: Toolbox