Data/Targets modification solutions

After some brainstorming I came up with a few possible solutions that might help in giving the input data more meaning and relation to the targets, and making the targets cleaner, more “interpret-able” and “guessable”.

I. Possible additions to the trading strategy

1. Large moving averages, such as SMA(100), SMA(200), SMA(300) to determine the overall trend, to help filter out whipsaws. Possible uses are also the slopes of those SMA’s and other characteristics that the NN can deduce on its own.

2. Stochastics, RSI, MACD, either left on their own or possibly combined into one indicator giving an output value of the [-1,+1] range. There can be several values of of each, not strictly limited to the defaults of RSI(14) and Stochastics (8,3,3) or (5,3,3), nor the MACD of (12,26,9). Of course I will need to create extra TA functions in MATLAB.

3. ADX (including ATR, both needing to be built), StdDev, both need to be created as well.

Most of those are trend definers such as ADX and the large value SMA’s, while others are confirmators and come second in order of importance, such as MACD, RSI, Stochastics. StdDev is just to measure how far away is a price straying, to help predict where the trend is going.

a uSDJPY H1 chart with SMA(100) in green, SMA(200) in yellow, with ADX, MACD, Stochastics and StdDev in widnows below.

A USDJPY H1 chart with SMA(100) in green, SMA(200) in yellow, with ADX, MACD, Stochastics and StdDev in widnows below.

II. Possible changes to the target data

1. Form target data into optimal TP/SL and not rely on an exit signal but on hitting either of them. Problems to solve would be to use multiple entries or not. Try to create a mechanism to use trailing stop as well.

2. Smooth TP data, making it more prediction friendly, instead of pure noise. That means a change to the output values in a way that will not distort their meaning but make them more uniform.

III. Changes into input methods

(By modified I mean that the data will have to pass through several filters and functions to create a certain meaning out that is not clearly deduced out of its raw value, before it’s fed to the network. By raw, I mean that the data will be presented as context-free numbers).

Method 1: Raw Inputs + Raw Targets

Method 2: Raw Input + Modified Targets

Method 3: Modified Inputs + Raw Targets

Method 4: Modified Inputs + Modified Targets

I’m more inclined towards Method 4, I’m also planning to use a NN to help attach a meaning to a combination of indicators.

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