Using data mining technologies to find currency trading rules
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Using Data Mining Technologies to find Currency Trading Rules. A. G. Malliaris M. E. Malliaris Loyola University Chicago. Multinational Finance Society, Rome, Italy, June 26-29, 2011. MOTIVATION.

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Using data mining technologies to find currency trading rules
Using Data Mining Technologies to find Currency Trading Rules

A. G. Malliaris

M. E. Malliaris

Loyola University Chicago

Multinational Finance Society, Rome, Italy, June 26-29, 2011


  • If it is possible to isolate some assets that have consistent patterns of co-movement, then this knowledge can be used in two ways:

    to make money if one has moved and the other has not moved yet, or,

    to build a more diverse portfolio by including assets that move in opposite directions


  • Are there certain currency markets that move up or down together sufficiently often for us to form a conclusion about their inter-relationships?

  • Data from eight major currencies, over ten years, was studied to see if there are movement rules among these currencies that might be stable over time.

Data mining

  • Data Mining is the search for meaningful patterns in large data sets

  • Meaningful patterns are easily understood, valid on new or test data with some degree of certainty, and potentially useful

  • Models are produced using a set of values of indicators at a particular time

  • The goal is to produce a model that generalizes well on future observations

  • Association analysis

    • Association analysis is a popular data mining method that originated with the study of market baskets to see which items people purchased at the same time.

    • Association analysis generates a set of rules of the form IF A THEN B

    Association analysis1

    • The set of rules that is generated also have values of support and confidence

    • Support: percent of times that some combination of inputs (also called antecedents) occurs in the data set.

    • Confidence: when the antecedent combination does occur, reflects the percent of time that the output, or consequent, is also true.


    • Generalized Rule Induction is an association analysis methodology that was introduced in 1992 by Smyth and Goodman

    • GRI is an effective, parsimonious method for detecting relationships in a large set of variables

    Decision trees

    • Decision trees divide up a large collection of records into successively smaller sets of records by applying a sequence of simple decision rules.

    • A good decision tree model consists of a set of rules that results in homogeneous groups; that is, it separates records into groups where a single class predominates for each group

    • The final result of these splits is often represented graphically in a tree structure.


    All Data

    Growing a tree

    • All Decision Trees begin with a root node.

    • They employ a strategy that grows the tree by making a series of locally optimum decisions about which attribute to use for partitioning the data.

    • The goal of the algorithm is to partition the records into successively purer subsets based on the values of the target field.

    Classification regression trees c rt

    • Unlike many methods from statistics, C&RT did not exist before machine learning methods were available.

    • C&RT is a decision tree methodology that generates only binary splits at each stage

    • All decisions are based on the value of a single target variable


    • January 2000 through July 2009

    • Downloaded from Bloomberg.

    • These prices were split into two disjoint sets for training and validation.

    • Data from January 1 2000 to June 30 2008 was used as the training set (2215 rows)

    • July 1 2008 to July 21 2009, used as the validation set (276 rows).

    • To study the simultaneous market movements, all data was transformed into “Up” or “Down”

    The eight currencies

    Original data was daily cash closing prices for the price of 1 US Dollar in the foreign currency that day

    Australian Dollar, Japanese Yen

    British Pound, Euro, Swiss Franc

    Canadian Dollar, Mexican Peso, Brazilian Real

    Relative movement

    In order to view them all in a similar scale, the Mexican Peso has been multiplied by 10 and the Japanese Yen by 100 for the graph.

    Data mining tool

    • Methodologies were run using the SPSS product Clementine

    • There were two runs of each model for GRI

      • First, the Australian dollar and Japanese Yen were inputs, with the Euro, the Swiss Franc and the British Pound as possible outputs.

      • Second, the Australian dollar, Japanese Yen, the Euro, the Swiss Franc and the British Pound were inputs, with the Mexican Peso, the Brazilian Real and the Canadian dollar as outputs.

    • One C&RT tree was created for each of the six

      GRI output targets


    • Each model developed a large number of rules and paths.

    • Here, we display one Up rule and one Down rule for each of the target markets.

    • Rules selected were those that did well not only on the training set, but also on the validation set.


    • Today, the daily volume of currency transactions in currency futures, forwards, swaps and options dominates all other types of trading volumes.

    • Whether currencies move together or independently is a matter of importance for investors wishing to spread the impact of their portfolio decisions.

    • The results of this study suggest that there is reason to believe that co-movement among some specific markets exists over relatively long periods of time.