The concepts covered in this course include the need for backtesting, working of technical indicators, and finally, the process of backtesting and the performance analysis of a strategy.
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Backtesting helps us evaluate our trading strategy objectively. In this section, you will understand the need as well as the process of backtesting.
In this section, you will gain an intuitive understanding of the technical indicators, parabolic SAR and the stochastic oscillator. You will also understand how to enter and exit a trade based on these technical indicators.
In this section, you will learn how to estimate your transaction costs and slippage so that your backtesting results are closer to live trading results.
In this section, you will create a strategy using the entry and exit rules based on the technical indicators. You would then backtest this trading strategy on historical data to see how it performs. The backtest is done using a python jupyter notebook.
This covers important performance metrics such as Sharpe Ratio, Compound Annual Growth Rate (CAGR), and Maximum Drawdown used to analyse the performance of the trading strategy. It also calculates and implements these metrics in Python using the Jupyter notebook.
This section will walk you through the steps involved in taking your trading strategy live. You will learn about the backtesting and paper trading platform, Blueshift. You will learn about code structure, various functions used to create a strategy and finally, paper trade on Blueshift.
Blueshift Paper Trading Template
Learn to install the Python environment in your local machine.
This section contains the summary as well as the downloadable zip file for all the Python codes and data files used in the course. You can tweak the strategies created in the course with your own data and ideas.