Introduction to FX Data Mining

Andrew Kreimer

Algonell – Scientific FX Trading

FX, FOREX or Foreign Exchange Market

• The biggest market in the world

• ~5 ∗ 1012 (~5 trillion) daily trading volume (2015)

• 5 days a week, 24 hours a day, Monday – Friday

• Players: three main levels

• Banks, Investment Companies

• Brokers

• Traders

• Speculative nature – most of the trading is just for the difference

• The simple goal - gain difference in pips (1/10000 change of price)

• Buy low, sell high (Long)

• Buy EURUSD @ 1.01000 and then Sell @ 1.02000 +100pips gain

• Sell high, buy low (Short)

• Sell EURUSD @ 1.01000 and then Buy @ 1.00000 +100pips gain

Data Mining and Machine Learning• Data Mining

• Massive amounts of data (We have it in FX)

• Non trivial extraction of knowledge from data (We need it in FX)

• Data Mining methods

• Classification – spam & fraud detection

• Association – YouTube & Amazon

• Clustering – unstructured data

• Process Mining – log mining

• Text Mining – news mining

• Machine Learning

• Algorithms for knowledge discovery

• Neural Networks (Widely used in FX)

• Random Forest (Not appreciated as should be)

• Linear Regression (Too simple, but well known)

Algorithmic Trading or Quantitative Investment

• Trading algorithm

• Mathematics: Fibonacci, Chebysheb, Markov and etc.

• Data Mining

• Trading automatically

• Programming language: MQL, Java, C#, LUA, C++ and etc.

• No psychology

• Speed and robustness

• Deterministic

• Note: in this case it’s long term rather than HFT

Algorithmic FX Trading and Data Mining

• Historical Data

• GIGO – Garbage In Garbage Out

• Broker or Yahoo Finance?

• Programming skills and continuous debugging

• Creating ,implementing and testing (Currently done manually).

• Trial and Error – Explore!

WEKA – Waikato Environment for Knowledge Analysis

• Open Source Data Mining framework

• University of Waikato, Hamilton, New Zealand

• Provides

• Implementation of Machine Learning algorithms

• Data preprocessing filters

• Data visualization

• Software

• http://www.cs.waikato.ac.nz/ml/weka/

• Book

• Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) by Ian H. Witten et al.

• http://amzn.com/0123748569

Loading numerical data to WEKA

Numerical visualization in WEKA

Predicting Close Price with Random Forest

Loading nominal data to WEKA

Nominal visualization in WEKA

Clustering entry points with Simple K Means

Summary

• FX trading has infinite number of trading systems

• Data Mining can help us in creating unique trading models

• Tools and data are available

• Good luck with the mining!

Profit

FX

Data Mining

Historical Data

Algorithmic Trading

That’s all folks!Algonell – Scientific FX Trading

www.algonell.com