Dekalog Blog

Just a short update.

The results of the first set of tests of optimising an indicator via the framework of training a neural net are in, and this post is a presentation of these results and a reflection on this in more general terms.

Following on from my previous post, I have been experimenting with various tweaks to the basic set-up of expressing an indicator in the form of a neural net, shown again below to avoid the necessity of having readers flip between posts.

Recently I started investigating relative rotation graphs with a view to perhaps implementing a version of this for use on forex currency pairs.

At the end of my previous post I said that I would be looking into using tick volume to create a new indicator, and this post is about the work I have done on this idea.

Over the last few weeks/months I have found it necessary to revisit the basic infrastructure of my trading/computing set up due to increasing slowness of the various computing routines I have running.

Below is code for an Octave compiled C++ .oct function to smooth price data.

Longtime readers of this blog are almost certainly aware that over the last few years I have posted several times about Oanda's OrderBook and PositionBook data and what can be done with it.

As part of my investigations into forex news trading I have found it necessary to obtain forex tick level data for back testing purposes and below I provide code to achieve this using Dukascopy's Node library, being called from Octave and using some system

Since my last post about end of initial testing I have been working on Oanda API functions in Octave to programmatically place entry orders and associated take profit and stop orders for a future possible forex news trading system.

Following on from my previous post I have completed the same tests as outlined in that post on other currencies and the summary results are: USD - an average of 0.38% return per trade EUR - an average of 0.22% return per trade GBP - an average of 0.77% re…

My first test of trading forex news announcements is to test the efficacy of breakouts immediately following a news announcement related to the US dollar, specifically, only the high impact news as shown on the forexfactory calendar in red.

This post, as the title suggests, is about trading forex news releases and, incidently, is a small update to the appearances of my PositionBook chart and OrderLevels chart.

During the course of writing this blog I have visited the idea of using Kalman filters several times, most recently in this February 2023 post.

In my previous post I suggested three different approaches to using PositionBook data other than directly using this data to create new, unique indicators.

Since my last post I have been trying to create new indicators from PositionBook data but unfortunately I have had no luck in doing so.

In my previous post I said I was trying to develop new indicators from the results of my new PositionBook optimisation routine.

Just over a year ago I previewed a new chart type which I called a "PositionBook Chart" and gave examples in this post and this one.

Recently I responded to a Quantitative Finance forum question here, where I invited the questioner to peruse certain posts on this blog.

Managed to code it up and get it working, but at the end of the day I couldn't see any value added over just averaging the output of the indicators I was trying to fuse together via Kalman filtering.

In the Spring of 2012 and again in the Spring of 2019 I posted a series of posts about the Kalman Filter, which readers can access via the blog archive on the right.

As a quick follow up to my previous post I thought I'd show an example of how one could possibly use my new PositionBook chart as a trade set-up.

It has been almost 6 months since I last posted, due to working on a house renovation.

This post is about using OrderBook/PositionBook features as input to simple machine learning models after previous investigation into the relevance of such features.

In my previous post I talked about how I planned to use constrained optimization to create features from Oanda's OrderBook and PositionBook data, which can be downloaded via their API.