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It gives us a final pip count for wins and losses It tells us how big the average winner is, the average loser It tells us our win percentage which is how often we can expect to have a winning trade, based on the information we ve already provided ME: And you do this for all of your testing RODDY: Don t leave home without it! We never test without it We spreadsheet everything We have spreadsheets on top of bloody spreadsheets I can hardly keep track of all of em Banes, did you say that you ve only tested twenty trades so far ME: Yes! Twenty per currency pair So I actually finished testing one hundred, in total RODDY: One hundred s not enough, boy! You gotta do better than that! ME: How much better WINNIPEG GEORGE: Well, we spent the entire weekend testing.

other landmarks and constructs a vector [(x1 , y1 , h1 ), . . . , (xn , yn , hn )], where xj and yj are the coordinates of landmark j and hj is the distance in hops from vi to vj (j = 1, . . . , n). Landmark vi calculates a value, called the correction, by cvi =

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NL research systems like SNePS [236], AGFL [237], and XTAG [238] work with a morphological analyzer like PC-KIMMO [239] to analyze phrases to characterize the roles of words in those phrases, and thus to infer limited semantics through mutual constraint satisfaction. Such research systems are foundational to current standards such as the W3C standards for speech recognition [240] and synthesis [241]. One readily accessible and fairly comprehensive NL parser application relevant to AACR is the TRAINS system that demonstrates speech recognition and synthesis for a notional railroad train reservation system [41, 242]. Since grammars can be used for the analysis of speech and text and for the generation of spoken language, their features and limitations motivate both audio perception and speech synthesis. Grammars require lexical categories and associated rules. Since nearly every word has multiple lexical categories, parsers generate multiple hypotheses, typically in the form of a stack or hypothesis tree, eliminating or rank ordering hypotheses according to the rules of grammar and the methods of parsing the language. Although elusive for decades, RetrievalWare s ThingFinder exempli es commercial parsers capable of reliably nding named entities (people, organizations, places, etc.) embedded in general text in English and other languages [243]. WordNet lexical categories are the CRA <Self/> <Roles/> for <Words/> in the <User/> domain: Noun, Verb, Adjective, Article, Preposition, Auxiliary verb, Pronoun, Question determiner, Words that function like prepositional phrases, Proper names, and the word to . Words are characterized by their description in a lexicon that a parser uses to determine the potential lexical categories of the word, along with properties such as agreement, root, subcategories, and verb form: illustrative lexicon entries Word dog saw Category Noun Verb Agreement (agr 3s) (agr a1) Properties (root dog) (vform past) (subcat_np) (root see)

(xj xi )2 + (yj yi )2

(agr = agreement; 3s = third person singular; a1 = tense variable) The CRA <Self/> does not specify lexical entries, deferring that to the NL subsystem.

whose numerator is the sum of Euclidian distances from vi to every other landmark and the denominator is the sum of the number of hops from vi to every other landmark. The ratio represents the average distance of a hop between landmark vi and any other landmark. In a second round of advertisements and controlled network ooding, each landmark vi communicates its correction value cvi to all other nodes. Corrections are collected by each nonlandmark node. For example, the correction for landmark v1 is cv1 = (6 2)2 + (2 5)2 + (5 2)2 + (4 5)2 = 1.04 4+3

We got through about a thousand trades ME: [silence] RODDY: Yeah, Harry Did you do a thousand trades I think not! ME: How did you get that many done WINNIPEG GEORGE: We sat on the couch with our laptops one of us with charts and the other one with spreadsheets We had food brought in, and we watched basketball, baseball, and cartoons, and only took breaks to go to the bathroom We didn t shower, answer the phone, or interact with humankind in any way It was complete focus ME: And you tested the same system, over and over again WINNIPEG GEORGE: We actually did five hundred trades each on two variations of the same system The main system we tested was a combination of the CCI [Commodity Channel Index] oscillator with long-term support and resistance The second variation, that we tested simultane-.

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