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A Sneak Peek into Artificial Intelligence Based HFT Trading Strategies

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This presentation was delivered by QuantInsti director and co-founder Mr Sameer Kumar at webinar 'A Sneak Peek into Artificial Intelligence Based HFT Trading Strategies' organized by QuantInsti on 27th February 2015.

In this webinar, Mr Sameer Kumar has explained how machine learning techniques can help you in designing better trading strategies. He also explained about alpha in trading and how you can extract it by applying the knowledge of market structure and order flow. It will help you understand how to use machine learning for predicting asset paths. Webinar was attended by participants who has interest in understanding the high frequency trading and using Artificial Intelligence for trading from India, US, UK, Spain and Sweden.

Following are the topics that are covered in this presentation,

1) Economic Concepts
2) AI and Machine Learning
3) Support Vector Machines
4) Building sample model using machine learning
This presentation will give you a basic understanding of how artificial intelligence based HFT trading strategies work, it will explain you how AI based technologies are changing the way you do trading, and how you can increase your profits by making best out of it.

Published in: Economy & Finance
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A Sneak Peek into Artificial Intelligence Based HFT Trading Strategies

  1. 1. A Sneak Peek to AI to HFT based trading Strategies Friday, 27th February, 2015 Mr. Sameer Kumar Head, Technology at iRageCapital, Director and Faculty at QuantInsti WEBINAR
  2. 2. Agenda • Economic Concepts • AI and machine learning • Building sample model using machine learning • Introduction to QuantInsti
  3. 3. Economic Concepts • Stock Market • Stock price and volume • Stock Market data – Broadcast/TBT • Indicators - moving avg crossover, spread etc.
  4. 4. AI and Machine Learning  Artificial Intelligence is intelligence of machines, where intelligent agent (system) perceives its environment and takes action which maximizes its chances of success.  Machine Learning is a subset of AI dedicated to classification and finding patterns and extrapolate it to new data.  There are hedge funds purely based on AI. e.g.. rebellion research, KFL capital etc.
  5. 5. Machine Learning  Supervised and Unsupervised learning  Unsupervised learning is the ability to find patterns in a stream of data without labeling the data. e.g SOM  In Supervised learning, we specify the classes/labels of training data. e.g. SVM
  6. 6. Support Vector Machines  SVMs are supervised learning models that analyze data and recognize patterns.  SVMs were originally proposed by Boser, Guyon and Vapnik in 1992 and gained increasing popularity in late 1990s.  SVMs are currently among the best performers for a number of classification tasks ranging from text to genomic data.
  7. 7. Binary classification can be viewed as the task of separating classes in feature spaces: wTx + b = 0 wTx + b < 0 wTx + b > 0 f(x) = sign(wTx + b) Support Vector Machines
  8. 8. Classification Margin  Distance from example xi to the separator is  Examples closest to the hyperplane are support vectors.  Margin ρ of the separator is the distance between support vectors. r ρ Support Vector Machines
  9. 9. Common Kernels Polynomial: Gaussian radial basis function For the polynomial, we choose the degree, while for radial basis we choose gamma parameter. Cost parameter is used to control over fitting of the model. Parameters that the user must choose )(tanh),( ),( )1.(),( 22 2/||||       x.yyx yx yxyx yx kK eK K p Neural net:
  10. 10. SVM: Applying Class Labels  Class labels are nothing but a way to identify which class this data point belongs. e.g.. if tomorrow's close is greater than today's close price, we can label “+1” and if its lesser, we can label “-1”. so these two are class labels.  We have to manually assign these labels, so we probably need to use R/excel to assign these labels to a large dataset.
  11. 11. Nifty Training Data Nifty Data from Yahoo Add indicators ( cross over, sma, lma, lag 1..5 ) Add labels ( Up, Down, Stationary ) nifty<-read.csv( "http://ichart.finance.yahoo.com/table.csv?s=^NSEI&a=08&b=16&c=2006&d=02&e=27&f=2015&g=d&ig nore=.csv") nifty$Sma=filter(nifty$Close,rep(1/7,7),sides=1) nifty$Lma=filter(nifty$Close,rep(1/21,21),sides=1) b=c(diff(0.1*nifty$Close),0) nifty$direction = ifelse(b>c,1,0) nifty$direction = ifelse(b < -c,-1,nifty$direction)
  12. 12. Nifty Prediction ( SVM )  SVM Learning Applied svm(formula = dir ~ . - label - Date - Close, data = nifty_train) Parameters: SVM-Type: C-classification SVM-Kernel: radial cost: 1 gamma: 0.125 Number of Support Vectors: 1289 > nifty_pred = predict(nifty_svm,nifty_train) > table(nifty_pred,nifty_train$dir) nifty_pred -1 0 1 -1 25 6 6 0 294 1103 313 1 31 19 58 Accuracy for predicting “Stationary” is 97.7%
  13. 13. Nifty Prediction ( SVM )  SVM plot using Lag2 and Lma ( keeping others constant)
  14. 14. Nifty Prediction ( Neural Network ) nifty_nn = neuralnet(label~Sma+Lma+Lag1+Lag2+Lag3+Lag4+Lag5+cross,data=nifty_train, hidden=3)
  15. 15. Nifty Training Data ( Tick By Tick)  Nifty TBT data contains all the orders and trades happened over entire day. It has over couple of millions of orders in a single day.  Add indicators ( based on paper – Modeling HF Limit order book dynamics with SVM )
  16. 16. Further Learning Models : HMM, decision tree, random forest, KNN etc. Weka : UI tool to experiment with different classification algorithms  Machine learning courses ( popular one is taught by Andrew NG on coursera. ) Books :
  17. 17. About QI & EPAT Quantinsti Quantitative Pvt Ltd. Quantinsti developed the curriculum for the first dedicated educational program on Algorithmic and High-Frequency Trading globally (EPAT) in 2009. Launched with an aim to introduce its course participants to a world class exposure in the domain of Algorithmic trading, it provides participants with in- house proprietary tools and other globally renowned applications to rise steeply on the learning curve that they witness during the program. Executive Program in Algorithmic Trading (EPAT) • 6-months long comprehensive course in Algorithmic and Quantitative Trading. • Primary focus on financial technology trends and solutions. • It is an online live interactive course aimed at working professionals from diverse backgrounds such as trading-brokerage services, Analytics, Quantitative roles, and Programming & IT industry. • Get placement assistance and internship opportunities with leading global firms after the program
  18. 18. Thank you! To Learn Automated Trading Email: contact@quantinsti.com Connect With Us: SINGAPORE 11 Collyer Quay, #10-10, The Arcade, Singapore - 049317 Phone: +65-6221-3654 INDIA A-309, Boomerang, Chandivali Farm Road, Powai, Mumbai - 400 072 Phone: +91-022-61691400

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