【Python Quantification】Bollinger Band Strategy with High Win Rate. Simulation Programming Implementation of Effects in Virtual Currency Market

Bollinger Bands are one of the commonly used indicators in financial technology analysis and proposed by John Bollinger in the 1980s. Bollinger bands reflect the "overbought" or "oversold" state of the market by counting the price fluctuation range, and are often used to assist in judging the timing of buying and selling. This video introduces the basic principles of Bollinger bands, and presents the implementation process of its strategy with Python code. It uses okb historical data as simulation data to help viewers apply Bollinger band strategies in quantitative transactions. 2. Introduction to the principle of Bollinger Band Bollinger Band is composed of three lines: Middle Band: Generally, it is the N-day simple moving average (SMA), such as the 20-day SMA; Upper Band: the standard deviation of the middle rail + K times (usually K = 2); Lower Band: the standard deviation of the middle rail - K times. The formula is as follows: Middle rail = SMA(N) Upper rail = Middle rail + K × Standard deviation (N) Lower rail = Middle rail - K × Standard deviation (N) Buying and selling signals Examples: When the price falls down the lower rail, it is considered that the market is oversold, and consider buying; when the price falls down the upper rail, it is considered that the market is oversold, and consider selling. The Bollinger Band strategy is a simple and practical quantitative technical analysis method. Through Python and related financial data packets, the development, testing and visualization of this strategy can be quickly realized. In actual investment, it is recommended to combine the Bollinger bands with other indicators or fundamentals, make comprehensive judgments, and control risks. #Python Quantitative#Quantitative Trading#Quantitative Trading Strategy#Bolling Band#Correspondence Circle Investment