Create a trading strategy from scratch, backtest it and optimize it
Basics in Python and Maths for algorithmic trading
Advanced algo trading concepts like Hurst exponent and how to adapt your strategy to your data
Understand how to create and use technical indicators with Python
Backtest your strategy without error using vectorized backtesting
Learn many financial metrics: Sortino ratio, alpha, beta,…
Learn how to analyze your drawdown
Add a stop loss on your strategies
Combine different technical indicators to double your earnings
MetaTrader 5 live trading using Python
Do you want to create algorithmic trading strategies?
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 to monetize and diversify your knowledge?
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 technical analysis to create robust strategies. You will perform quantitative analysis to find patterns in the data. Once you will have many profitable strategies, we will learn how to perform vectorized backtesting. Then you will apply portfolio and risk management techniques to reduce the drawdown and maximize your returns.
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You will learn and understand quantitative analysis used by portfolio managers and professional traders:
- Modeling: Technical analysis (Moving average, RSI) and condition combination.
- Backtesting: Do a backtest properly without error and minimize the computation time (Vectorized Backtesting).
- Risk management: Manage the drawdown(Stop loss), combine strategies properly (Strategies portoflio).
Why this course and not another?
- This is not a programming course nor a trading course or a machine learning course. It is a course in which statistics, programming and financial theory 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.
Introduction
READ ME
Install the environments
How to break into algorithmic trading field?
Introduction
Algorithmic trading vs quantitative trading
Create trading system
Contract for Difference (CFD)
Entry & Exit signals
Risk return couple
Prerequisites
Python basics
Introduction
Type of object: Number
Type of object: String
Type of object: Logical operations / 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
Python for data science
Introduction
Numpy: Array
Numpy: Random
Numpy: Indexing / Slicing / transformation
Pandas: Serie and DataFrame
Pandas: Cleaning and selection data
Pandas: Conditional selection
Matplotlib: Graph
Matplotlib: Toolbox
Matplotlib: Scatter
Basics statistics
Introduction
Population versus sample
Application: create google stock price sample
Central tendency measure: The mean
Application: Compute mean Google return + Annualization of returns
Central tendency measure: The median
Extreme value problem? Compute the median
Central tendency measure: The percentile
Application: Understand Google return distribution
Dispersion measure: The variance
Application: Compute variance returns + Variance annualization
Dispersion measure: The standard deviation
Application: Compute the volatility + Annualize the volatility
Relationship measure: Covariance / covariance matrix
Application: Assets covariance
Relationship measure: Correlation
Application: Assets correlation
DOWNLOAD summary sheet about descriptive statistics
Import and manage the data
Introduction
Import & manage data from Metatrader 5
Import & manage data from Yahoo finance
Your first algorithmic trading strategies
Introduction
Simple moving average
Strategy explanation
How to verify our trading position?
Compute the profit of a trading strategy
How to automate the strategy?
Most important video: Performance depending of the data!
Vectorized Backtesting
Introduction
Sortino ratio computation
Beta ratio computation (CAPM metric)
Alpha ratio computation (CAPM metric)
Drawdown function: creation
Drawdown function: application
Backtesting function (1)
Backtesting function (2)
Backtest our strategy
Intermediate trading strategies: Combine different signals
Introduction
Recap
Compute the RSI
Add multiple conditions to take a position
Verify if the positions are correctly implemented
Compute the profits
Apply a stop loss (SL) on your returns
Automate the strategy
Compare the same strategy using different data sources
Create a portfolio of trading strategies
Advanced concepts to optimize you strategies
Introduction
What is the Hurst exponent?
How to find if is this asset is Trending or not?
How to find if is this asset is Mean reverting or not?
How to find if is this asset follows a random walk or not?
Adapt your strategy to your data!
MetaTrader 5 live trading
Introduction
Install a library on Jupyter
Initialize the platform
Get data broker
Send orders on the market using python
Get current positions
Run structure creation
Close all positions
Live Trading application: random signal
Live Trading application: moving averages + rsi
How to go deeper into the algorithmic trading
Bonus lecture
