Introduction to FX Data Mining
This document introduces the topic of FX data mining. It discusses that the foreign exchange market has trillions in daily trading volume and operates globally. Data mining methods like classification, association, and clustering can be applied to the massive FX data to extract valuable trading insights. Machine learning algorithms like neural networks and random forests are widely used for knowledge discovery in FX. Algorithmic trading uses mathematics, data mining, programming, and machine learning to trade automatically without human psychology. Tools like WEKA provide implementations of algorithms and capabilities for data preprocessing, visualization, and model building that can help create unique FX trading systems by mining historical data.
Introduction to FX Data Mining
- 1. Introduction to FX Data Mining Andrew Kreimer Algonell – Scientific FX Trading
- 2. 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
- 3. 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)
- 4. 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
- 5. 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!
- 6. 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
- 7. Loading numerical data to WEKA
- 8. Numerical visualization in WEKA
- 9. Predicting Close Price with Random Forest
- 10. Loading nominal data to WEKA
- 11. Nominal visualization in WEKA
- 12. Clustering entry points with Simple K Means
- 13. 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
- 14. That’s all folks! Algonell – Scientific FX Trading www.algonell.com