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Forex: Seasonal Trader
Trade when you spot a
pattern with high odds, stay
clear when the odds are low
This tool is designed to
exploit short-term trading patterns, and intended for traders, arbitrage plays over time
and to assist long-term investors in fine-tuning entry and exit points. It uses end-month
data to illustrate seasonal trading patterns. A rise represents an increase in value of
the currency against the Dollar or global benchmark. For currencies it means total return
(i.e. forex gains or losses plus interest on short-term deposits).
Each series shows the
cumulative gain or loss as the year progresses. Thus, starting from zero at the beginning
of the year, a 1% gain in January followed by a 2% gain in February show up as +3% for
February. A fall of 1% during the next month would show up as +2% for March.
In order to identify
possible changes in behaviour patterns over time, separate series are shown for short,
medium and long-term averages. The time-frames selected are 4, 12 and 24 years to reflect
respectively one, three and six electoral cycles, as discussed in the GuideBook.
The shorter the time-scale, the stronger the line, in recognition that more recent
experience has greater significance. Thus the white line is
the short-term series, the light grey line is the medium-term series and the dark grey line is the long-term series.
To indicate of the odds of
making profits or losses in any month of the year, white percentage figures are attached to the longest time-scale. These odds are calculated using
up to 24 years of data, where available. Thus, for example, 67% against a rising month
shows that the changes of a profit in that month are 2 in 3. Equally 67% against a fall
shows that the chances of a loss in that month are 2 in 3. Conversely 33% indicates that
there is only 1 in 3 chance of making profits in that month.
To achieve the most reliable
results take action where all series demonstrate a similar seasonal pattern - especially
where confirmed by the odds shown.
While the database is as
broad as possible, it does not cover all situations. In some cases the longer-term series
in individual charts may be missing and in other cases there may be no data at all. There
are several reasons for this. It
may not be possible to compile a suitable long run of data from available sources.
Exchange controls or hyperinflation in the past may distort historical data in some
countries. Euro conversion renders analysis of legacy currencies irrelevant
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