Description
Technical Analysis with Python for Algorithmic Trading is a training course on technical analysis and development of trading algorithms with the Python programming language, published by Udemy Academy. This training course focuses on short-term and daily trading and will introduce you to different strategies and different styles of financial market analysis. At the end of this course you will be able to turn your indicators and analytical style into comprehensible code for the computer and experience algorithmic trading up close. Backtesting and trading plan optimization is another important topic of this training course that the teacher has emphasized a lot. This course is completely data-driven, and numerous back tests and forward tests confirm this claim.
What you will learn in Technical Analysis with Python for Algorithmic Trading
- Interact with a variety of charts and graphs such as line charts and candlestick charts
- Familiarity with volume-based charts
- Familiarity with trend lines, resistance and support and ways to identify them
- Working with Simple Moving Average (SMA)
- Working with Moving Average (EMA)
- Analysis of different charts with Makdi indicator
- Familiarity with the RSI indicator and the development of trading strategies based on it
- Working with Stochastic Oscillator
- Analysis of different charts with Bollinger Bands
- Familiarity with Price Action analytical style and Pivot Point topic
- The Impact of Fibonacci Numbers on Forecasting the Future of Financial Markets
- Methods of combining different styles and indicators and achieving a single strategy
- Transform trading strategies into automated trading robots with Python programming language
- Familiarity with short-term deals and scalp
- Work with Pandas and Numpy libraries to analyze price and volume data of financial markets
- Introduction to Object Oriented Programming in Python
Course specifications
Publisher: Udemy
Instructors: Alexander Hagmann
Language: English
Level: Intermediate
Number of Lessons: 166
Duration: 13 hours and 32 minutes