Forex Automaton

Our primary goal is to create a public information service providing financial markets forecasts, based on our proprietary forecasting tools: an automated trading system -- a Forex Automaton™. Our secondary goal is to quantify and monitor the very existence of sustainable opportunities for arbitrage profit-making. Or simply put, to monitor the degree to which these markets are more predictable than a "fair game" -- to a trader without access to insider information.

Forex Automaton was launched in April 2008 with the ambitious mission of leveraging the specific algorithmic know-how to create a trading signal service geared toward retail forex traders. From the very beginning a two-prong strategy was adopted: first, development of the trading system product whose usefulness relies on secrecy of the relevant know-how. Second, white-paper research focusing on statistical properties of the market time series, especially those aspects which are potentially interesting from the point of view of algorithmic trading, however counter-intuitive, technical and remote from the mainstream picture of forex trading they may be. As of now, it is mostly the second prong that's visible to the website visitor. This document summarizes the main findings to emerge so far from a year of studies, including some glimpses into the progress made on the black-box algorithmic trading system front.

This is the second post in a series dedicated to analyzing the timing of daily high and low for the popular foreign exchanges rates. Some of the motivations for such type of analysis have been outlined in the first post on the topic.

During the month of February, the second month of real-time documented performance, the system kept running on complete autopilot, with no code upgrades or parameter changes. This document consists of a summary section followed by 14 subsections, dedicated to the individual exchange rates tracked by the system. Those contain color-coded charts of the performance and details pertinent to the specific currency pairs. Usage strategies and effects of various approaches to selecting the forecasts to trade are discussed. For comparison with the previous month, you may want to take a look at January review.

The "Kelly Criterion" in quant folklore is based on the exposition and development of Kelly's work done by Edward Thorp. Acknowledging Thorp's contribution, I find the original article by Kelly conceptually more relevant to the realities of algorithmic trading as developed by ForexAutomaton. Our forecasting algorithm, as any forecasting tool, can be very naturally considered a case of the hypothetic communication channel discussed by Kelly, and the related mathematical objects, such as joint and conditional probability density distributions for the communicated "symbols" (forecasts and real quotes), are being already monitored here, despite the fact that the Kelly Criterion for capital allocation to trades remains to be coded.

I am revisiting the issue of the intermarket analysis on the day scale. The conclusion from the previous report on the subject was that, the rest of the algorithm being the same, intermarket analysis gives no advantage on this time scale and simpler analysis of the isolated markets should be preferred. In this report, data on the predictability of high and low are added and a bug related to the estimation of statistical precision of the data is fixed. Nevertheless, the conclusions remain the same.

I continue developing ways of using Danica's output for profitable trading. The strategy of combining protective stop with a profit target, setting these at the extremes of the previous trading day and trading in the direction of the forecast (referred to as the Day Range strategy and reported in the previous post is modified to remove the profit target. Much better results are obtained.

In the January performance review for the Danica trading system, an idea of a day-range trading strategy capitalizing on the high quality forecasts for the direction of daily high and low was expressed and a set of what-if charts for the forex pairs tracked was provided and discussed. This post is a deeper and more quantitative discussion of the strategy. Which forecasts should be acted upon? What is the expected profit per trade? How are profits/losses distributed statistically? -- these are the questions addressed.

In the course of the trading system optimization, best returns have been seen to be obtained by ignoring forecast moves  below a certain threshold, and acting on those above that threshold (that threshold was also called entry parameter). A small fraction of large returns has been seen to account for much of the positiveness of the Pearson correlation coefficient between actual logarithmic returns and their forecasts. Each Danica output contains forecasts for 14 forex rates, and a natural question is: how do I pick the ones to place trades? One might expect that the odds of success can be improved by selecting those markets where the next move is forecast to be large. I take version 0.5 of Danica forex system and study dependence of the correlation between the forecast and real logarithmic returns in day close on the relative strength of the forecast move.

Some opportunities for analysis are offered by the fact that the forex trading system such as Danica gives not just a forecast for the next close, but a combination of next high, low and close. This report is the first attempt to develop a selective approach to the forecasts, a discrimination algorithm of sorts, such that a decision to give the forecast further consideration or ignore it would be based on the information contained in the forecast itself.

This report gives a more detailed discussion of the optimization trade-offs made for Danica, as compared to the initial announcement. At the same time, it establishes a reference point for future studies of past performance.

It's now official: from now on, a forex forecast of low, high and close for the next 24-hour period will be posted on this site daily at 9am Eastern time. The system is named Danica following the naming convention where first names starting with D are assigned to systems with daily decision-making scale.

Just like logarithmic returns can be defined and analyzed for daily close, they can be defined for daily high and low. Japanese candlestick charting techniques, believed to have predictive power, study patterns formed by open, low, high and close as the time series progresses. In this report I extend application of my newly developed forecasting figure of merit, Pearson correlation coefficient between the real and predicted logarithmic returns, to the daily high and low, taking another look at the dependence of the prediction quality on the magnitude of a forecasting parameter nicknamed Fred. As the prediction quality is seen to be much better for the next high and low than it is for next close, I am contemplating ways of improving quality for close.

The intention of this post is to tie together several topics which appeared on my radar screen in the course of the trading system optimization. First, it has been understandably hard to fully rid oneself of  vestiges of the mainstream financial theory based on the postulate of market efficiency, while building a wealth-generating tool relying explicitly on demonstrable market inefficiencies. The realization that Sharpe ratio does not let one make an objective choice of a portfolio was  there from the beginning, and I recall perceiving this fact as a "necessary evil". Then came the understanding of the fact that an  artithmetic average of returns gives one a biased picture of long-term return, and consequently, Sharpe ratio is built around biased quantities.

 I approach pairs trading with the correlations tool-box and basic algebra. Let's consider two time series, a(t) and b(t). It will be understood that these are  taken on a fixed time scale (second, minute, hour, and the like). Most explanations of pair trading fail to communicate the importance of non-zero correlations at non-zero time lags --  let alone the importance of their constructive interference (to be explained). Meanwhile, it is these subtleties that make a difference between just another roulette-like source of random outcomes and a reliable, little-risk source of arbitrage profits. 

In the recent forex/CFD data, USD/CAD is negatively correlated with light oil (WTI) CFD. This is the same as saying that CAD, one of the commodity currencies, is positively correlated with oil. This is old news. In this article I take a deeper look at the issue and analyze the shape of the correlation peak. Analyzed on the hour time scale, the correlation peak is broad and somewhat asymmetric, indicating that it is much safer to rely on the guidance of USD/CAD in predicting the oil price, rather than other way round. The necessary caveat is that this is a time-integrated picture, covering a period from late August 2008.