Hi All, I am presenting simple boiler point code that can quickly be applied to test technical indicator strategies using Python. The code: 1. downloads daily stock data from google, 2. calculates the short and long moving averages 3. generates the trading signals 4. calculates the daily returns 5. runs the moving average strategy and calculates the cumulative return 6. plots cumulative return of our simple strategy Here is the code ... enjoy trying it out and extend it as required: import numpy as np import pandas_datareader as datar import datetime import matplotlib.pyplot as plt date_start = datetime.datetime( 2017 , 1 , 1 ) date_end = datetime.datetime( 2017 , 6 , 30 ) data = datar.get_data_google( 'AAPL' , date_start , date_end) short_ma = 5 long_ma = 20 data[ 'short_ma' ] = data[ 'Close' ].rolling(short_ma).mean() data[ 'long_ma' ] = data[ 'Close' ].rolling(long_ma).mean() data[ 'masig' ] = data[ 'short_ma'
In a recent journal publication we investigate the viability of Type-2 fuzzy systems in high frequency trading. We propose Type-2 models based on a generalisation of the popular ANFIS model (ANFIS/T2). Type-2 models score significant risk adjusted performance improvements over Type-1. Benefits of Type-2 models increase with higher trading frequencies. Paper available at: http://www.sciencedirect.com/science/article/pii/S0957417416300203