Updated 24 Apr 2019
WFAToolbox is MATLAB App for Walk-Forward Analysis using easy-to-use graphical user interface (GUI) to create advanced algorithmic trading strategies with MATLAB Toolboxes and custom functions.
Using only backtesting (in-sample) and out-of-sample testing is not enough to develop robust algorithmic trading strategy. Only walk-forward testing allows you to get real-world solutions.
The user-friendly interface allows you to do all the steps of trading strategy testing at the click of a button. This makes it accessible even for those who are somewhat familiar with MATLAB.
Using MATLAB language and Toolboxes (Machine Learning, Econometrics, Neural Network etc.) gives you access to all of the sophisticated models you might need for developing an advanced strategy.
HISTORICAL DATA SOURCES
You can load intraday and daily historical data for almost every type of assets: stocks, futures, bonds, forex, crypto etc. Datafeeds available: Alpha Vantage, IQFeed, OANDA, MetaTrader 4, Interactive Brokers etc.
WFAToolbox Team (2019). Walk-Forward Analysis (Algorithmic Trading) Toolbox (https://www.mathworks.com/matlabcentral/fileexchange/68453-walk-forward-analysis-algorithmic-trading-toolbox), MATLAB Central File Exchange. Retrieved .
Fixed minor bugs.
Fixed several issues with errors when running strategies and execution module.
Fixed Support Vector Machine (SVM) sample strategy issue with two assets notification.
Fixed: issue with wfa2_ResizeFigure function in parallel mode
Fixed GUI issues with tabs and hierarchical tree.
Fixed: bug with "strat_path" message
Feature: an optimization of equities for multiple strategies.
Feature added: a sum of equities of multiple strategies.
Added a video demonstration of the new connection:
Added connection video demonstrations:
Fixed: lag on plot text objects
Fixed bug: "Reference to non-existent field 'source'."
Inspired by: Files from webinar Machine Learning for Algo Trading, Algorithmic Trading with MATLAB - 2009 update, Algorithmic Trading with MATLAB - 2010, Cointegration and Pairs Trading with Econometrics Toolbox, Automated Trading with MATLAB - 2012, Files for webinar titled "Classifying Trading Signals using Machine Learning and Deep Learning", JSONlab: a toolbox to encode/decode JSON files, urlread2, Basic Genetic Algorithm, Technical Indicators, Complex Moving Average Indicators, findjobj - find java handles of Matlab graphic objects
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