Create, backtest and optimize TA Trading Strats with Python

Convert Technical Indictors into sound Trading Strats with Python.

Make proper use of Technical Analysis and Technical Indicators.

Create and backtest combined Strats with two or many Technical Indicators.

Visualize Technical Indicators and Trend/Support/Resistance Lines with Python and Plotly.

Exponential Moving Average (EMA) strats

Relative Strength Index (RSI) strats

Backtest and Forward Test Trading Strats 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.

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 Strats 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 strats within minutes. Identify the profitable strats 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 Strats 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 strats 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 strats 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.

(Day) Traders and Investors who want to professionalize their Business.

(Day) Traders and Investors who want to make proper use of Technical Analysis.

Technical Analyst and Chartist who want to improve their work/analysis with powerful Python Coding






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