Algorithmic trading from A to Z using Python

Algorithmic trading from A to Z using Python. Algorithmic trading (also known as automated trading, automated stock trading, or computer-aided trading) is a form of investment management in which a system uses mathematical models to buy and sell securities. Algorithmic trading is often used in order to increase the speed, efficiency, and profitability of financial markets.

Algorithmic traders use computers to identify patterns in stock prices and trade based on those patterns. They can use a variety of algorithms to make trades, including technical analysis, trend following, and position sizing. Algorithmic traders also use bots to automate their trades. Bots are software programs that automatically execute trades on behalf of an individual or organization.

To be successful as an algorithmic trader, you need to have a good understanding of financial markets and the mathematical models that underlie them. You also need to be able to identify patterns in stock prices and trade accordingly.

Technical analysis is the practice of analyzing securities and other economic data to identify patterns and trends. This information can then help traders make informed investment decisions.

Backtest is the process of testing an algorithm by running simulated trades against historical data to see how it performs. This helps traders ensure that their algorithm is working as intended and that it will not encounter any unforeseen problems during live trading. MetaTrader 5 live trading course teaches students how to trade the market using the MetaTrader 5 platform.

Algorithmic trading from A to Z using Python

You will Learn

  • Create an algorithmic trading strategy from A to Z (data import to live trading)
  • Put any algorithm in live trading using MetaTrader 5 and Python
  • Data Cleaning using Pandas
  • Guided tour thought the main algorithmic trading strategy (Technical Analysis, Price action, Machine Learning)
  • Manage financial data using Numpy, Pandas and Matplotlib
  • Python programming for algorithmic trading
  • Create scaling, intraday and swing trading strategies
  • Import stock price from Yahoo Finance and from your broker

Do you want to create algorithmic trading strategies?

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

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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, price action, machine learning 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 techniques to reduce the drawdown and maximize your returns.

You will learn and understand  quantitative analysis used by portfolio managers and professional traders:

  • Modeling: Technical analysis (Moving average, RSI), price action (Support, resistance) and Machine Learning (Linear regression).
  • Backtesting: Do a backtest properly without error and minimize the computation time (Vectorized Backtesting).
  • Portfolio management: Combine strategies properly (Strategies portfolio).

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.

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