Forex Automaton

During the past 10 years, liquidity in the FX markets is known to have been growing. One might expect that various non-commercial participants, including those with capabilities to research, fund and execute systematic and algorithmic trading strategies, exhaust the alpha-generating potential of this market and drive it towards efficiency. Do spot foreign exchange markets really evolve towards efficiency? I use the ForexAutomaton CERPI 1H.1M inefficiency index (hourly scale, monthly data accumulation period) to look at the trend of the past 10 years (2003-2012), and compare the picture with that of the evolution in the instantaneous ("non-predictive") correlation strength (CERCSI, pronounced "Sir-see") and volatility.

  Currency Exchange Rate Correlation Strength Index (CERCSI) is a measure of zero-lag correlation strength with a specified timeframe (time scale) and measurement interval. For quantities we report regularly, measurement interval is the same as period of measurement. The quantities reported in the Forex Correlation Analysis Reports 1H.1M section of the site are measured monthly at the end of a month and include the whole month of data. This article defines the way CERCSI is constructed.

Currency Exchange Rate Predictability Index (CERPI) is a measure of market inefficiency with a specified time frame (time scale) and measurement interval. For quantities we report regularly, measurement interval is the same as period of measurement. The quantities reported in the Forex Correlation Analysis Reports 1H.1M section of the site are measured monthly at the end of a month and include the whole month of data. This article defines the way CERPI is constructed.

Forex Automaton is launching a new type of information product: a monthly correlation analysis report. The reports are computer-generated and follow a pre-defined format. This document explains the purpose and structure of the reports and is intended to serve as a brief user manual to the new information product. It also collects in one place any other information necessary to understand the reports, which otherwise would have to be repeated in every report. The document explains the quantities shown in the plots and relationships between the plots.

Data accumulation for the present incarnation of Heidi began on December 1, 2010 and the first predictions (in back-testing mode) were generated for March 16, 2011. The present system was announced and began live operation on May 23. Now, eight months later, I take a look at the first statistically significant performance figures of merit and make an adjustment to one of the system parameters.

This post begins monthly peformance reviews for the Demi line of EUR/USD daily time frame trading systems. There are four systems in total, differing only according to the choice of the closing hour of the "day": 3, 5, 9 and 11 am Eastern time. The systems went live on August 25, 2010, after extensive back-testing. Since then, the parameters found in the course of the back-testing have been frozen. Even though the system output (see Demi section of the site) is available only to the registered members, the performance reviews will be available publicly.

The third year of ForexAutomaton's life has come to a closure. As usual, presented here is the summary of main problems that occupied me during the year, and the main achievements. For the first time, the annual report includes reviews of the actual trading and forecasting systems which have been running live on this web site since the beginning of 2010, free and accessible to the public. The report is structured around system performance and development while observational studies of market inefficiency take a back seat. The prediction engine remains a Black Box to the reader.

• Statistical predictability of the direction in which each of the extremes of the price-chart bar evolves is proved to be insufficient to  claim market inefficiency, and is understood to have mundane roots in the diffusion of price under conditions of limited price continuity,  as is borne out by the random walk model.

• Differences between hypothetically efficient and real FX markets manifest themselves in higher order correlations, namely fourth order cumulants among certain real data and their proprietary forecasts.

• A strategy attempting to take advantage of these properties of the real FX markets, Demi, has been designed and launched. Results of the first seven months of its live paper trading are reviewed.

• A system making predictions on the hourly time frame, named Heidi, has been launched.

• Following a detailed analysis of the intra-day seasonality patterns such as the alternation of trend following and mean-reversion, Heidi has been improved for at least some of the intra-day time periods by making it seasonality-cognizant.

• The oldest system, Danica, generating predictions on the day scale, has so far been unable to provide systematic positive correlations between its forecasts for the direction of daily close and the actual direction in live operation, but the cause seems to go beyond the choice of the adjustable parameter.

For clues as to what effects to expect in forex rates from the consequences of the March 11th earthquake in Japan, I look at historical data from 1995, the year of the Great Hanshin earthquake. Great Hanshin earthquake occurred on Tuesday, January 17, 1995, at 05:46 JST (16 January at 20:46 UTC). There are at least two topics to explore. We look for quantitative effects of repatriation of funds into Japan to fund the recovery efforts on the exchange rate of the Japanese Yen to the US Dollar. Another possible concern is the carry trade implications.

Hourly time scale in forex provides roughly 24 times more statistics, than the daily scale. Therefore, if a single adjustable parameter for all currency pairs is justifiable in the daily system optimization (Danica), 24 parameters can be adjusted for Heidi without jeopardizing significance of the results. The natural way of increasing the number of parameters is to split the 24-hour day into time window "seasons" and treat the seasons as independent pattern recognition problems in the code and as independent optimization problems on the stage of optimization.

This post is the first attempt to summarize intra-day seasonality findings from the six major exchange rates involving USD, focused on the hour-scale correlation structures. Taking advantage of the "non-trivial" (non-zero time lag) correlations in forex is complicated since their structure changes during the day, and a residual structure that survives the multi-day averaging is for this reason weaker than what may exist in a stable way at a certain time period during the day. Averaging is necessary to accumulate statistics and let the signals dominate the noise. But in doing that, I contain averaging within temporal classes or "bins", combining bars of the data recorded at the same or close times during different days, months and years of observation. When cyclicity of time is thus taken advantage of, a weak but significant and stable pattern of intra-day alternation between trend-like and mean-reversion behavior emerges.