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My Forex experience and some Forex related information that might be useful to other traders
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Statistical Forex System — Gathered Information
Earlier I’ve described the statistical Forex systems and introduced the problem of choosing the right timeframe to gather the statistics for such systems. Today I will try to describe the problem of choosing the right information that is collected for the statistical trading system.

Gathering the statistics over a chosen period of time for the given market instrument is the next step to create a successful statistical Forex strategy. But what data should be used for this statistics? Is it a good idea to record bare chart data? Should you gather any additional information? Here is my view on all possible statistics type that can be used in the process:

Pure market quotes. This includes high, low, close and open rates for bars and bid or ask rates for ticks (if you think that tick-based statistics is a good idea). This method of statistics gathering is the most obvious. You gather the market quotes then compare them with the current situation and decide whether to buy, sell or hold. But there is a problem with the changing of quotes range. For instance, 1 year ago EUR/USD was in 1.4000-1.5500 range, a month ago it was far above 1.5500 level, so the data gathered in another price range would be completely useless. Alternatively a normalization of some sort can be used to store such statistics — e.g. store not a quote like 1.5404 but its relation to the next bar’s open price — 1.5404/1.5423 = 0.998768073. This way you’ll have data that is informative in any price range, but still uses no indicators or other complex calculations.

Indicators. These are probably the best data to be recorded as the statistics. Even standard MetaTrader indicators allow recording a lot of information and then using it to compare with real-time current market situation. With a large part of the indicators the normalization similar to the one used with the raw market data will be necessary. It’s probably a good idea to use indicators that change in the certain range — like RSI, DeMarker, Stochastics, Larry William’s Percentage Range, Money Flow Index, etc. The length of the arrays of the indicator values recorded for each tick or bar is also an important parameter of the statistics gathering. Remember that the longer this length is the more uninformative this statistics becomes. Ideally, it’s better to use single value of each indicator that is unique for the current bar or tick.

Additional information. It can include the time of the day to capture the trends and patterns that are specific for some trading sessions only. Another parameter that fall into this category is the day of the week — trading usually differs depending on the busyness of the day (often with less price action on Fridays). The statistics can also note if the day is some major holiday, current daylight saving time mode for the major countries and the volumes of the trades (although in Forex they are not very informative).

Complex calculations. This can include not only calculations based on the market data and indicators, but incorporate the additional information such as time and the day of the week into the calculations. In this case the produced number-formatted statistics would be easy to compare to the real market data. Considering the current development level of the PC industry it wouldn’t be a hard task to incorporate even the most complex calculations in the MetaTrader expert advisor that utilizes a statistical Forex strategy. Additionally these calculations can be accompanied by the various pivot points and resistance/support levels to help with choosing the position’s parameters.
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Statistical Forex System — Choosing Statistics Timeframe
In one of the previous posts I’ve introduced the statistical Forex system definition and marked up the important problems that should be solved in the process of its creation. Here I will try to explain more about the problem of the statistics gathering.

Choosing the timeframe for the statistics that will be gathered for your system consists of two parts — choosing the chart timeframe discreteness and choosing the length of the period over which the statistics will be gathered.

Choosing the right chart timeframe is a matter of balance between the uninformative short-term data with many samples and the small quantity of the more specific information. If you choose a tick or 1-minute timeframe discreteness you’ll have to face a large amount of data and a lot of CPU power used up on gathering, calculating and finding this data; finding some patterns in these vast arrays of information wouldn’t be an easy task and the further strategy building will very complicated in this case. On the other hand, using daily or weekly periods will give you too little information. For example, one year of the market analysis of D1 charts will give you just a little more than 200 data samples. In my opinion, considering the 24 hours a day and 5 days a week nature of the Forex market, the best choices here are M30, H1 or H4 timeframes as they give you a fair amount of samples with a decent informativity, because such samples will have a greater variation. Alternatively you can use multi-timeframe statistics, but that would lead to a really complex system, which, of course, will have a better potential.

Sampling period’s length is an important parameter of the statistics gathering. Using a small period will allow you to recognize the most up-to-date rate patterns and your strategy will probably benefit from them in the short to medium term. Unfortunately, short sampling period can contain too little of these patterns and if the market changes they will probably fail to help with the recognition of the changed price dependencies. Long sampling period will give a very wide array of patterns which can be used in comparing. But the difference between the market today and the market several years ago can bee too large, so those patterns can lead your system to a high inaccuracy ratio. Getting statistics over the past 2-3 years is a balanced decision here. You catch more than one long-term trend and you get a lot of the medium- and short-term trends caught into your statistics with such period, while really outdated data isn’t spoiling your statistics.

Of course, these decisions should also depend on your system, the nature of the data you will be collecting and the timeframe that it will use in the actual trading. But don’t forget the negative and positive sides of the different data timeframe and the sampling periods — try to avoid the extreme values that could possibly ruin your strategy.
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Forex Technical Analysis for 08/11—08/15 Week

EUR/USD trend: sell.
GBP/USD trend: sell.
USD/JPY trend: buy.
EUR/JPY trend: sell.

Floor Pivot Points
Pair	3rd Sup	2nd Sup	1st Sup	Pivot	1st Res	2nd Res	3rd Res
EUR/USD	1.4157	1.4577	1.4791	1.5211	1.5425	1.5845	1.6059
GBP/USD	1.8372	1.8759	1.8984	1.9371	1.9596	1.9983	2.0208
USD/JPY	105.41	106.43	108.31	109.33	111.21	112.23	114.11
EUR/JPY	159.79	162.55	163.95	166.71	168.11	170.87	172.27

Woodie’s Pivot Points
Pair	2nd Sup	1st Sup	Pivot	1st Res	2nd Res
EUR/USD	1.4526	1.4688	1.5160	1.5322	1.5794
GBP/USD	1.8759	1.8984	1.9371	1.9596	1.9983
USD/JPY	106.43	108.31	109.33	111.21	112.23
EUR/JPY	162.55	163.95	166.71	168.11	170.87

Camarilla Pivot Points
Pair	4th Sup	3rd Sup	2nd Sup	1st Sup	1st Res	2nd Res	3rd Res	4th Res
EUR/USD	1.4656	1.4831	1.4889	1.4947	1.5063	1.5121	1.5179	1.5354
GBP/USD	1.8872	1.9041	1.9097	1.9153	1.9265	1.9321	1.9377	1.9546
USD/JPY	108.59	109.38	109.65	109.91	110.45	110.71	110.98	111.78
EUR/JPY	163.06	164.21	164.59	164.97	165.73	166.11	166.49	167.64

Tom DeMark’s Pivot Points
Pair	EUR/USD	GBP/USD	USD/JPY	EUR/JPY
Resistance	1.5635	1.9790	110.27	169.49
Support	1.5001	1.9178	107.37	165.33

Fibonacci Retracement Levels
Pairs	EUR/USD	GBP/USD	USD/JPY	EUR/JPY
100.0%	1.5631	1.9758	110.36	169.47
61.8%	1.5389	1.9524	109.25	167.88
50.0%	1.5314	1.9452	108.91	167.39
38.2%	1.5239	1.9380	108.57	166.90
23.6%	1.5147	1.9290	108.14	166.29
0.0%	1.4997	1.9146	107.46	165.31