Thread: Konsep Pencocokan Pola EA

Originally Posted by ;

Dan apa yang bisa kita simpulkan untuk trading kita? Misalnya, algo Anda menemukan bahwa setelah katalima candle R-O-N-A-L ada kemungkinan candle 'D' - bagaimana pengaruhnya terhadap keputusan trading Anda? Masalah lain mungkin Anda menggunakan alfabet yang sangat besar. Mengenai contoh saya di atas, ketika kita mengasumsikan semua lilin terdistribusi hampir sama rata - maka peluang lilin D mungkin sekitar 0,4%... karena kami menyatakan, bahwa algo kami membuktikan D lebih mungkin dalam hal itu peluangnya mungkin meningkat menjadi mungkin 1%... Namun demikian...

Menggunakan alfabet sebenarnya memungkinkan lebih banyak peluang perdagangan daripada hanya menggunakan angka lurus. Dengan alfabet, hanya ada 255 jenis candle, dan dengan asumsi dua pola candle, kemungkinan menemukan pola yang identik adalah 1/65025. Saat menggunakan angka lurus, secara teoritis ada variasi tak terbatas. Anda dapat mencoba membuat semacam margin of error, dan bekerja seperti itu. Setidaknya dengan alfabet, itu menjaga margin kesalahan bagi saya.

Originally Posted by ;

Saya masih tidak mengerti mengapa Anda peduli ...

Masalah utamanya adalah tidak ada sistem yang sepenuhnya memperhitungkan kesenjangan. Menggunakan metode relasional yang Anda sebutkan sebelumnya, celah sama dengan lilin besar. Dengan cara saya menyusun kode, saya harus dapat menambahkan lebih banyak dimensi/parameter ke tiga yang sudah saya miliki kecuali saya salah memahami sesuatu dalam kalkulus multi-variabel. Untuk saat ini, satu-satunya hubungan antara candle yang sangat saya khawatirkan adalah apakah candle 'R' muncul setelah candle 'P' lebih sering daripada candle 'Q'. Dalam perdagangan langsung, kita akan sangat jarang menemukan celahnya, jadi saya tidak repot.

Originally Posted by ;

[edit] You could change the distribution of the 255 'characteristic slots' so one feature is more weighted than another.

I was thinking about the same thing. You can count how many different values of High - Open, Close - Low and Close - Open occured since 2000. You will possibly get something like this: High - Open - 50 different values Close - Low - 50 different values Close - Open - 100 different values In this example you can assign ca. half of the 'characteristic slots' to Close - Open.

Will pay attention to this thread.

Thanks for April Offer You Cannot Refuse. I refuse (but it is really really hard)! Subscribe. RR

Ronald, Let me know if I'm off base... Patterns need to be generalizations, so that they could be skewed by a certain percentage but still fit in the pattern... so that if a bars' range was 10 pips or 100 pips, it could still fit within a pattern. Would it be any use to agorize a bar by its range? An example... a bar range is always 100%. So, the open would be at 50% of its range (center of the bar), and the close could be at 75% (3/4 of the bar) of its range. Then you could classify the low of the current bar to the low of the previous bar as lt;100%, low of previous bar within the range of the current bar, etc. I am unsure at this time how to handle if the previous bar is part of engulfing pattern... Just throwing out an idea... Carl

Originally Posted by ;

Ronald, Let me know if I'm off base... Patterns need to be generalizations, so that they could be skewed by a certain percentage but still fit in the pattern... so that if a bars' range was 10 pips or 100 pips, it could still fit within a pattern. Would it be any use to agorize a bar by its range? An example... a bar range is always 100%....

Hey Carl, Your method identifies bars based on their relative shape. I'm not sure if you intended for this circumstance, but let's assume we have two bars which are perfect scaled replicas of each other. When looking at a chart, would you visually classify them as the same bar? I'm classifying bars based on their absolute shape, it would be easier to differentiate between patterns where the price is ranging and patterns where the price is trending. Ideally, I'd rather just write a neural network which could take into account all of this data. 2 layer is enough to come up with every possible continuous mathematical function, but I suspect adding more layers and nodes will allow the NN to create a conditional step function. It's similar to our own trading thinking.. -If the market price is moving sideways use this mindset -If the market is moving up, use this mindset -If there is undefinable chaos, use a different mindset. That's the hope anyway

Originally Posted by ;

Ideally, I'd rather just write a neural network which could take into account all of this data. 2 layer is enough to come up with every possible continuous mathematical function, but I suspect adding more layers and nodes will allow the NN to create a conditional step function. It's similar to our own trading thinking..

Hi Ronald, Are you writing a neural network based on FANN? Will you share a NN? If NN is based on FANN I hope
http://articles.mql4.com/868 may be useful.

Originally Posted by ;

...This is a newer concept EA which is programmed to find its own candle patterns. The pattern finding algorithm is simple: it checks the previous x closed bars and generates a string something like LLDSSL that means the bars from right (most recent) to left were long, short, short, doji, long, long. The EA then scans the chart history looking for a pattern of bars with that exact same sequence. The EA checks x bars after the sequence to see if that bar was long or short. The results are then added to the statistics generated by the EA...

Hey there Ronald... This reminds me of something I did a number of years ago for equities. My set-up was essentially a generalization of the same idea you are working with. The way I generalized the approach was that rather than trying to egorize candles into traditional patterns (long, doji, short, etc.) and then match sequences, I just generated sequences of percentage-based differentials for each of OHLC on each bar, as compared to the previous bar. By expressing all of these values from bar to bar in terms of similarity to the previous bar and then setting a specific number of bars to examine, I ended-up with a single mathematically derived value that expressed the overall similarity of all of OHLC for a set number of bars to the current condition. Then I would check the subsequent bars from the close historical match to see if it was indiive of a good move and to get an immediate term signal to trade as per the previous matching pattern. Anyway, I just thought I would mention this as this kind of set-up it might be something to add to your pattern recognition to give a generalization effect. (To elaborate, my system did something like this, for the past n bars being examined: open_2% = (open_2-open_1)/open_1 open_3% = (open_3-open_2)/open_2 open_n% = (open_n-open_n-1)/open_n-1 This was done also for n bars for high, low, and close. The next step was to find and sum the differences between each bar position in the historical data to the same offset bar within the current bars (with the same bar to bar percentages computed for the current bars.) Finally, the sum of the sums of the differences for the past and current OHLC would be taken. Then it was a simple matter to evaluate that final result -- the closer the number was to zero, with each of OHLC results also being close to zero, indied similarity between the current bars and the historical bars. Once similarity was determined, large moves either up or down in the past data were of interest as the current conditions might be expected to behave in a similar manner.) Anyway, I thought I would mention that, as you might be able to make your pattern matching more abstract and less dependent on human-recognizable constructions using a set-up like that. Theo Buitendyk.

The, can you tell us the result of your implementation ?

I wonder, what was the result of the implementation of this method ? The idea sounds very interesting..