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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 — Complexity of a System
The final part of my introduction into statistical Forex system development is dedicated to the complexity of the resulting expert advisor (or automated program if you want to create such strategy for anything else than MetaTrader platform).

When traders think about Forex system, they come to a conclusion that a simple trading system can’t be profitable, because it doesn’t capture all the market parameters that influence the dynamics of the currency pairs. Partially I agree with this point of view but, in my opinion, the complexity of a Forex system should be limited. With the statistical Forex systems the complexity of different parts may vary.
1. The amount of different information and the number of data types that is gathered for the statistical system are the important parameters, which if increased produce a more complex system. One may decide to gather not a single timeframe information, but rather statistics from several timeframes and record not only the price quotes (or OHLC data for bars/candles) but also many indicators, calculations and other parameters. This will lead to rather large database of the statistics that would be hard to interpret in a right way, but if interpreted correctly it will surely yield better results than a more simple strategy.
2. Gathering statistics before the actual strategy running is extremely important and is a necessary step, in my opinion. But making a system that can continue gathering statistics when it runs in a real-time is also important. It shouldn’t add much complexity to your expert advisor, but it will help to react faster on the market changes. Of course, such on-the-fly data gathering can’t substitute the pre-running gathering.
3. Changing the way of the interpretation and statistics comparing is a really advanced method to add the complexity to your statistical system. Having several functions to compare the past and the current data can be helpful if you have some method to choose from these functions. Personally I couldn’t develop a system that would use such an interesting functionality.
4. The complexity of the actual order carrying and position handling is a rather obvious field for the system improvement and upgrading, but it hardly can be connected to the statistics that can be gathered. The only way that would work, in my opinion, is if your system uses real chart-to-chart comparison - that’s a really difficult method, but it opens a whole new set of opportunities for position and order adjustment. In other cases, simple buy/sell/hold decisions are the best market actions available for the statistical Forex systems.
Those are the most obvious ways to make your trading system more complex. Some minor changes can also improve it to make it react more flexibly on the market volatility and evolution. If you have experience with really complex Forex systems that are based on the trading statistics, feel free to comment on this post.
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Statistical Forex System — Decision Making
After statistical Forex systems were introduced in my blog, I’ve described the problems of timeframe selection and statistics gathering. Now it’s time to discuss the decision making problem of such trading systems.

When a completed strategy has enough statistical information and a sample from the current market situation it should have some methods of comparing the statistical information with the sample and make the decision regarding its further actions on the market. For the majority of the systems these decisions would be limited only to buy, sell, hold and close previous position actions, while more advanced systems may include position adjustment actions into their arsenal.

The most obvious way to make the decision for the statistical Forex system is to calculate the differences between the sample data and the data stored in the statistics and the lowest difference will point out the most probable recorded outcome. For example, if you recorded RSI indicator values and the current RSI reading is 75.2, while the lowest difference from your statistics is 0.1 and it suggests that the price goes down near that RSI level, then your system should probably generate a sell signal. This method looks simple, but it’s also flawed as the comparing multiple parameters of the two samples is impossible.

In general, quotes-derived parameters should be compared with some method similar to Euclidean distance (best distance, average distance, etc.) with possible weighing of the different parameters according to their importance. Meanwhile, the comparison of the time- and fact-based parameters should be rather strict — e.g. if you recorded some information specific for Fridays and it’s Monday today, then you should disregard this information.

Another noteworthy idea regarding decision making would also require a special statistics gathering method used in the system. Using self-organizing maps (or Kohonen maps) is a popular decision making method that is widely used in finance. Unfortunately, my own tests of the self-organizing maps within the statistical Forex systems (in a form of MetaTrader expert advisor) didn’t bring any interesting results. There are many other ways of utilizing the self-organizing structures to store and compare the quote-derived statistical information, but their complexity doesn’t look to be necessary in such systems.

Chart-to-chart comparison can be used if the statistics stored is a raw or normalized market data, which brings a lot of opportunities based on the graphical chart analysis and the difference calculations. It’s also necessary to note that such comparison would require a lot more CPU power and time to complete. It would also produce a more long-term aimed result than the immediate decision that would be true for the next bar or candle.

In my opinion, it’s optimal strategy to store the statistics in three separate «containers», where statistics in the first container would correspond to the buy action, in the second — to the sell action and third — hold action. Finding the best Euclid distance for the current market sample among all three «containers» gives you a hint for your next action. In this case, it’s more important to collect the right data and to format it in a right way for further comparison.
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Forex Technical Analysis for 08/19—08/23 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	1.4115	1.4388	1.4538	1.4811	1.4961	1.5234	1.5384
GBP/USD	1.7619	1.8066	1.8363	1.8810	1.9107	1.9554	1.9851
USD/JPY	106.64	107.48	108.99	109.83	111.34	112.18	113.69
EUR/JPY	156.39	158.89	160.59	163.09	164.79	167.29	168.99

Woodie’s Pivot Points
Pair	2nd Sup	1st Sup	Pivot	1st Res	2nd Res
EUR/USD	1.4357	1.4478	1.4780	1.4901	1.5203
GBP/USD	1.8066	1.8363	1.8810	1.9107	1.9554
USD/JPY	107.48	108.99	109.83	111.34	112.18
EUR/JPY	158.89	160.59	163.09	164.79	167.29

Camarilla Pivot Points
Pair	4th Sup	3rd Sup	2nd Sup	1st Sup	1st Res	2nd Res	3rd Res	4th Res
EUR/USD	1.4456	1.4573	1.4611	1.4650	1.4728	1.4767	1.4805	1.4922
GBP/USD	1.8252	1.8456	1.8525	1.8593	1.8729	1.8797	1.8866	1.9070
USD/JPY	109.22	109.86	110.08	110.29	110.73	110.94	111.16	111.80
EUR/JPY	159.99	161.15	161.53	161.92	162.69	163.07	163.46	164.61

Tom DeMark’s Pivot Points
Pair	EUR/USD	GBP/USD	USD/JPY	EUR/JPY
Resistance	1.5098	1.9331	111.76	166.04
Support	1.4675	1.8587	109.41	161.84

Fibonacci Retracement Levels
Pairs	EUR/USD	GBP/USD	USD/JPY	EUR/JPY
100.0%	1.5083	1.9256	110.66	165.58
61.8%	1.4921	1.8972	109.76	163.98
50.0%	1.4872	1.8884	109.49	163.48
38.2%	1.4822	1.8796	109.21	162.98
23.6%	1.4760	1.8688	108.86	162.37
0.0%	1.4660	1.8512	108.31	161.38