Algorithmic Trading with Python: Technical Analysis Strategy

Data Importation, Modelisation, Algorithmic Trading, Portfolio management, MT5 Live Trading using Python, Bot included.

4.15 (13 reviews)
Investing & Trading
6 hours
Oct 2021
last update
regular price

What you will learn

MT5 Live Trading using Python

Improve your Python skills

Create Algorithmic Trading strategies

Plot financial data

Vectorized Backtesting

Statistics like Sharpe ratio, Sortino ratio, beta

Combine Trading strategies using Portfolio Management Technic

Manage data using Pandas

Data Cleaning using pandas

Python programmation

Compare / Choose trading strategies

Quantify the risk of a strategy

Sortino portfolio Optimization

Minimum Variance Optimization

Mean Variance Skewness Kurtosis Optimization (not famous but one of the most used)

Import finance data from the broker

Import financial data from Yahoo Finance

Put your strategy on a VPS


You already have knowledge in python and you want to monetize and diversify your knowledge?

You already have some trading knowledge and you want to learn about algorithmic trading?

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. For beginners in python, don't panic there is a python course (small but condensed) to master this python knowledge.

In this course, you will learn how to program strategies from scratch. Indeed, after a crash course in Python, you will learn how to implement a strategy based on one of the most used technical indicators: the RSI. You will also learn how to combine strategies to optimize your risk/return using the portfolio techniques like Sortino portfolio optimization, min variance optimization, and Mean-Variance skewness kurtosis Optimization.

Once the strategies are created, we will backtest them using python. So that we know better this strategy using statistics like Sortino ratio, drawdown the beta... Then we will put our best algorithm in live trading.

You will learn about tools used by both portfolio managers and professional traders:

  • Live trading implementation

  • Import the data

  • Some reference algorithms

  • How to do a backtest

  • The risk of a stock

  • Python

  • What is a long and short position

  • Numpy

  • Pandas

  • Matplotlib

  • Why do you must diversify your investments

  • Sharpe ratio

  • Sortino ratio

  • Alpha coefficient

  • Beta coefficient

  • Sortino Portfolio Optimization

  • Min variance Optimization

  • Mean-Variance skewness kurtosis Optimization

Why this course and not another?

  • This is not a programming course nor a trading course. It is a course in which programming is used for trading.

  • This course is not created by a data scientist but by a degree in mathematics and economics specialized in Machine learning for 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.


Algorithmic Trading with Python: Technical Analysis StrategyAlgorithmic Trading with Python: Technical Analysis StrategyAlgorithmic Trading with Python: Technical Analysis StrategyAlgorithmic Trading with Python: Technical Analysis Strategy


Read me
Install the environments
Basics of Python
Type of object: Number
Type of object: String
Type of object: Logical operations and 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
Basics of Python for Data Science
Numpy: Array
Numpy: Random
Numpy: Indexing / Slicing / Transformation
Pandas: Serie and DataFrame
Pandas: Cleaning and selection data
Pandas: Conditional selection
Matplotlib: Graph
Matplotlib: Scatter
Matplotlib: Tools
Import the data
Install a library on Google Colaboratory
Use Finance Library
Others import ways
Algorithmic trading strategy
RSI Strategy Introduction
Create a strategy with the RSI - Explanation and computation of the RSI
Create a strategy with the RSI - Zone of Action
Create a strategy with the RSI - Buying signals
Create a strategy with the RSI - Selling signals
Create a strategy with the RSI - Strategy Example
Create a strategy with the RSI - Returns of the Strategy
RSI Function - Long Signals
RSI Function - Short Signals
RSI Function - Returns computation
Vectorized Backtesting
Sortino ratio computation
Beta ratio computation (CPAM metric)
Alpha ratio computation (CPAM metric)
Drawdown function creation
Drawdown function application
BackTesting Function (1)
BackTesting Function (2)
BackTesting RSI strategy
MetaTrader 5 Live Trading using Python
Install a library on Jupyter Notebook
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 signals
Live Trading application: RSI strategy


13 October 2021
if teach with example for Metatrader4 platform, it will more complete course. thanks you for awesome course


10/13/2021100% OFF
10/19/2021100% OFF
udemy ID
course created date
course indexed date
course submited by