Technical Analysis with Python for Algorithmic Trading

Use Technical Analysis and Indicators for (Day) Trading. Create, backtest and optimize TA Trading Strategies with Python

This course clearly goes beyond rules, theories, vague forecasts, and nice-looking charts. (These are useful but traders need more than that.) This is the first 100% data-driven course on Technical Analysis. We´ll use rigorous Backtesting / Forward Testing to identify and optimize proper Trading Strategies that are based on Technical Analysis / Indicators.

What you’ll learn

  • Convert Technical Indictors into sound Trading Strategies with Python..
  • Make proper use of Technical Analysis and Technical Indicators..
  • Create and backtest combined Strategies with two or many Technical Indicators..
  • Visualize Technical Indicators and Trend/Support/Resistance Lines with Python and Plotly..
  • Exponential Moving Average (EMA) strategies.
  • Relative Strength Index (RSI) strategies.
  • Backtest and Forward Test Trading Strategies that are based on Technical Analysis/Indicators..
  • Create interactive Charts (Line, Volume, OHLC, etc.) with Python and Plotly..
  • Use Pandas, Numpy and Object Oriented Programming (OOP) for Technical Analysis and Trading..

Course Content

  • Guide to quantitative trading –> 5 lectures • 14min.
  • Successful Backtesting of Algorithmic Trading Strategies –> 5 lectures • 33min.
  • Machine Learning with Python –> 4 lectures • 16min.
  • Automating Stock Investing Technical Analysis With Python –> 8 lectures • 1hr.

Technical Analysis with Python for Algorithmic Trading

Requirements

  • An internet connection capable of streaming HD videos..
  • A desktop computer (Windows, Mac, or Linux) capable of storing and running Anaconda. The course will walk you through installing the necessary free software..
  • Basic Coding Skills in Pandas, Numpy and Matplotlib.

This course clearly goes beyond rules, theories, vague forecasts, and nice-looking charts. (These are useful but traders need more than that.) This is the first 100% data-driven course on Technical Analysis. We´ll use rigorous Backtesting / Forward Testing to identify and optimize proper Trading Strategies that are based on Technical Analysis / Indicators.

This course will allow you to test and challenge your trading ideas and hypothesis. It provides Python Coding Frameworks and Templates that will enable you to code and test thousands of trading strategies within minutes. Identify the profitable strategies and scrap the unprofitable ones!

 

The course covers the following Technical Analysis Tools and Indicators:

  • Interactive Line Charts and Candlestick Charts
  • Interactive Volume Charts
  • Trend, Support and Resistance Lines
  • Simple Moving Average (SMA)
  • Exponential Moving Average (EMA)
  • Moving Average Convergence Divergence (MACD)
  • Relative Strength Index (RSI)
  • Stochastic Oscillator
  • Bollinger Bands
  • Pivot Point (Price Action)
  • Fibonacci Retracement (Price Action)
  • combined/mixed Strategies and more.

 

This is not only a course on Technical Analysis and Trading. It´s an in-depth coding course on Python and its Data Science Libraries Numpy, Pandas, Matplotlib, Plotly, and more. You will learn how to use and master these Libraries for (Financial) Data Analysis, Technical Analysis, and Trading.

 

Quantitative trading summed up

  • Quantitative trading uses statistical models to identify opportunities
  • Quant traders usually have a mathematical background, combined with knowledge of computers and coding
  • There are four components in a quant system: strategy, backtesting, execution and risk management
  • Some common strategies include mean reversion, trend following, statistical arbitrage and algorithmic pattern recognition
  • While the majority of quants work for hedge funds and investment firms, there are many retail traders too

 

DIY quant trading

The majority of quant trading is carried out by hedge funds and investment firms. These will hire quant teams to analyse datasets, find new opportunities and then build strategies around them. However, a growing number of individual traders are getting involved too.

The required skills to start quant trading on your own are mostly the same as for a hedge fund. You’ll need exceptional mathematical knowledge, so you can test and build your statistical models. You’ll also need a lot of coding experience to create your system from scratch.

Many brokerages and trading providers now allow clients to trade via API as well as traditional platforms. This has enabled DIY quant traders to code their own systems that execute automatically.