Algorithmic Trading with Real Python Hands-On Examples
Learn to build pactical backtesting system from scratch, optimization, quantitative trading simulation, screening system
Ratings 4.16 / 5.00
What You Will Learn!
- Backtesting strategy with technical indicators
- Parameter Optimization
- Simulation Backtesting with stocks in NASDAQ100
- Screening stock system with NASDAQ100, S&P500
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 as Exponential 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 many performance measurement. For example, win rate, compound annual growth rate (CAGR) , expected returns and maximum drawdown.
Next, you will learn to do parameter optimization and compare many performance measurement in each parameter.
You will learn to simulate your strategies with stocks in NASDAQ100 ,also you can add any factors in your trading plan such as position sizing, ranking stocks, cutting loss, taking profit ,transaction cost and other conditions.
Lastly, you can make screening 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 Should Attend!
- Traders who interest in systematic investing, algorithmic trading or applying python programming skill to trading
