Algorithmic Trading with Real Python Hands-On Examples

What you'll learn
Backtesting strategy with technical indicators
Parameter Optimization
Simulation Backtesting with stocks in NASDAQ100
Screening stock system with NASDAQ100, S&P500
Requirements
Basic python programming
Description
In this course you will learn to build your own backtesting system from scratch and illustrating with plotly library. We mainly use technical indicator for backtesting such asExponential Moving Average (EMA), Moving average convergence divergence (MACD), Bollinger Band, Bollinger Band + ADX and 3 EMA channels.
We mainly use functions relating to pandas and DataFrame being able to deal with large time-series data. For illustrate result, we use plotly library which is beautiful and easy to understand.
During learning backtesting, you will learn manyperformance measurement. For example, win rate, compound annual growth rate (CAGR) , expected returns and maximum drawdown.
Next, you will learn to doparameter optimization and compare many performance measurement in each parameter.
You will learn to simulate your strategies with stocks in NASDAQ100 ,also you can add anyfactors in yourtrading plan such as position sizing, ranking stocks, cutting loss, taking profit ,transaction cost and other conditions.
Lastly, you can makescreening system which you can find stocks having interesting buy signals everyday. In this course, I introduce you 2 screening system which are oversold stock and strong uptrend stock. However, you can adapt your trading idea to build your own screening system.
Learning python coding in this course is starting from scratch and you can have further developing by yourselves.
Who this course is for
Traders who interest in systematic investing, algorithmic trading or applying python programming skill to trading