Event driven trading new studies on innovative way. of trading in Forex market. Michał Osmoła INIME live 23 February 2016

Transcription

1 Event driven trading new studies on innovative way of trading in Forex market Michał Osmoła INIME live 23 February 2016

2 Forex market From Wikipedia: The foreign exchange market (Forex, FX, or currency market) is a global decentralized market for the trading of currencies. This includes all aspects of buying, selling and exchanging currencies at current or determined prices. Important Forex market features: Giant liquidity. Posibility of trading 24 hours a day, from 22:00 (UTC) on Sunday untill 22:00 (UTC) on Friday.

3 Macroeconomic announcements Macroeconomic indicators are information about the state and efficiency of a national economy. Features of macroeconomic announcements: Exact time of release is public information. Numeric format. Big news agency (like Bloomberg or Reuters) publish forecasts of the most meaningful indicators a few days before their release.

4 Event-driven trading

5 What cause such a reaction? Analysis confirm that the main factor which determine the direction of exchange rate movement is difference between actual and forecasted value of macroeconomic indicator. Behaviour of exchange rates right after macroeconomic indicator release is highly predictable for main macroeconomic news.

6 What is necessary to use this phenomenon in trading? Computer system able to get information about value of indicator immediately after announcement. Fast algorithm responsible for making a decision about taking a long/short position. Software which allow to place order instantly afrer release of indicator. Mechanism which monitor behaviour of exchange rate and decide about position close.

7 Modeling of EURUSD exchange rate First part of the research performed by INIME foundation checked the possibility of using ARIMA-GARCH and VEC models for make a short-term forecasts and apply them in closing-position system.

8 Unfortunately, performed analysis show that considered econometric models are too simple to explain complex nature of exchange rates. Forecasts obtain from ARIMA-GARCH and VEC models were useless in the context of optimal position closing. However, one interesting property of USDEUR, EURGBP and USDGBP exchange rates was discovered durning research. It was Granger casuality between given exchange rates after release US Nonfarm Payrolls indicator.

9 Granger Casuality test We say that a variable X that evolves over time Grangercauses another evolving variable Y if predictions of the value of Y based on its own past values and on the past values of X are better than predictions of Y based only on its own past values.

10 Application machine learning methods to Forex data Due to the fact that standard econometric methods are not useful in making short-term predictions, INIME foundation decided to try more sophisticated methods. We focus on two artificial intelligence concepts: Artifical Neural Networks Random Forests

11 Regression analysis Y dependent variable (real). X=(X1,...,Xn) independent variables (real). F(,β) some function from space X to space Y (model). β unknown parameters vector. O sample, i.e. set of pairs of observed values (x,y). E() - error function, defined on the sample O, describing the quality of assumptive model. Goal: Finding parameters vector β which minimize error function on sample O.

12 Artificial Neural Networks (ANN)

13 Neuron structure Source: Most common activation functions: Threshold function: Logistic function: Linear function: Hyperbolic tangent:

14 Scheme of feedforward artificial neural network

15 Data scaling ANN output often could be only the value from interval (0;1) or (-1,1) so we need to scale our data to proper format. It is a good manner to scale not only dependent variable but independent variables too. It often increase the speed of learning process and performance of ANN.

16 Backpropagation algorithm

17 Validation If we want to have reliable information about quality of obtained neural network, we need to perform some model test. Methods of validation: Simple dividing into learning set and test set (70/30). Dividing into learning set-validation set-test set (60/20/20). K-fold cross-validation.

18 Artificial Neural Networks advantages No need to know exact form of model. Could capture even very complicated structures of dependency between response and independent variables. Deal with noised data.

19 Random Forests Leo Breiman

20 Classification and regression tree (CART)

21 How to create CART? X set of independent variables in model Start at root node. Choose independent variable and split which minimize the sum of squared prediction errors over sample. Split sample O into two new subsets (new nodes). Check if stop rule is satisfied. If not search for split (across all leaf nodes which could be split) such that it maximize decrease of sum of squared errors over sample. The mean value of dependent variable over observation in choosen leaf node is prediction created by model for all cases which will get into that leaf.

22 How to build random forest? Set number of decision trees in forest. For each tree choose bootstrap sample from original sample O. Start create CART basing on given bootstrap sample. On each nodes split select at random k<<n independent variables which can be used to split operation. Create tree until maximum possible number of leaves will be reached (each observation will be in other leaf). Calculate mean of squared tree errors basing on observations which are not in bootstrap sample (OOB error). Repeat operation untill number of trees will be reached. The arithmetic mean of trees responses weighted by OOB error is prediction created by random forest for given observation.

23 Random forests advantages Do not overfit. One of the most effective machine learning techniques to deal with high-noise data. No need to know exact form of model function. Fast in calculation. Handles thousands of input variables without variable deletion. Gives estimates of what variables are important in the classification. OOB mean error is unbiased estimator of model error, no need to split data on test and learning set.

24 Using ANN and Random Forests in event-driven trading Goal: Predict maximum/minimum exchange rate value after release of macroeconomic indicator (on given time period).

25 Linear model

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27 Neural Network Act.f=logistic

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29 Random Forest Var.n=7

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31 Summary Mean absolute error: Linear model: Neural network: Random Forest: Mean squared error: Linear model: Neural network: Random Forest:

32 Further studies Influence of volatility and spread on exchange rate dynamic. The end of trend detection. Finding patterns before and after macroeconomic releases. Durability of macroeconomic indicator influence. Creating of more sophisticated opening algorithms. Developing existed strategies and creating new ones.

33 Thank you for your attention Michał Osmoła T: E: Adress: 13A/1 Cystersów St., Cracow NIP: REGON: KRS: