A Simple Breakout Trading Strategy in Python. Coding and Back-testing an Objective Systematic Breakout Strategyatic Breakout Strategy.
Note from Towards Data Science’s editors:_ While we allow independent authors to publish articles in accordance with our [rules and guidelines_](https://towardsdatascience.com/questions-96667b06af5), we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our [Reader Terms_](https://towardsdatascience.com/readers-terms-b5d780a700a4) for details._
Trading is divided into many different strategies that rely on trend-following, mean reversion, volatility, or other factors. Successful strategies rely on the current market state, for example, when markets are strongly trending, mean reversion strategies tend to fail and therefore we always have to adapt our market approach accordingly. Below is a breakout strategy that uses an indicator called the Donchian Channel. The basic idea is to make ranges as objective as we can (i.e. measurable) and then trade on the breakout (i.e. the start of a trend). The goal of the article is therefore to see whether this indicator can add value into our overall trading system or not. Does it provide good signals? Are the triggers to be taken seriously?
Created by Richard Donchian, this simple and great indicator is used to identify breakouts and reversals. Just like the Bollinger bands, it is used in an almost similar fashion. Our goal is to determine objectively a range exit by the surpass or break of any of the barriers. The way it is formed is by first calculating the maximum of the last n-period highs and the minimum of the last n-period lows, then calculating the average of them both. This gives us three measures: The Donchian upper band, the lower band, and the middle band. Here’s the mathematical formula followed later by the Python code used on an OHLC data structure.
Master Applied Data Science with Python and get noticed by the top Hiring Companies with IgmGuru's Data Science with Python Certification Program. Enroll Now
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
In this blog, we'll discuss the new applications of the data science in finance sector and how the developments in it revolutionize finance.
This Data Science with Python Tutorial will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python.