Algorithmic Trading with Python: Technical Analysis Strategy

Algorithmic Trading with Python: Technical Analysis Strategy

Algorithmic Trading with Python: Technical Analysis Strategy

Data Importation, Modeling, Algorithmic Trading, Portfolio management, Live Algo Trading using Python! Bot included.

Language: english

Note: 4.2/5 (207 notes) 38,692 students

Instructor(s): Lucas Inglese

Last update: 2021-10-19

What you’ll 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 programming
  • 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

 

Requirements

  • Some python knowledge are welcoming but not necessary

 

Description

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.

 

Who this course is for

  • Everyone who wants to learn MT5 live trading using python
  • Students in finance
  • Professional in finance
  • Professional in data science
  • Students in data science

 

Course content

  • Introduction
    • Read me
    • Install the environments
    • Join our community for free to improve your knowledge in algorithmic trading
  • Chapter 1: 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
  • Chapter 1 (following): 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
  • Chapter 2: Import the data
    • Install a library on Google Colaboratory
    • Use Yfinance Library
    • Others import ways
  • Chapter 3: 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
  • Chapter 4: Vectorized Backtesting
    • Introduction
    • 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
  • Chapter 5: Find best assets for the strategy
    • Introduction
    • What is the Hurst exponent?
    • Computations of metrics to create a dataset
    • Find best assets sector for our strategy
    • Find the best criterion to select the assets
  • Chapter 6: Find best parameters for our strategy
    • Introduction
    • HeatMap optimization parameters
    • Automate the process
    • Find best parameters: HeatMap method
    • Find best parameters: Statistical method
    • Backtest the strategy using the best parameters
    • Automate the process
  • Chapter 7: Portfolio optimization methods
    • Introduction
    • Find best hyper parameter for the assets
    • Create a portfolio of trading strategy
    • Sortino ratio criterion
    • Min variance criterion
    • Mean Variance Skewness Kurtosis criterion
  • Chapter 8: MetaTrader 5 Live Trading using Python
    • Introduction
    • 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
  • Chapter 9: VPS
    • Initialize the VPS
    • Install the softwares
    • Put your algorithm in live trading

 

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

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