Classifica(on- based Market Predic(on using Deep Neural Networks. Ma;hew Dixon, Ph.D., FRM Quiota LLC Qwafafew, Chicago
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1 Classifica(on- based Market Predic(on using Deep Neural Networks Ma;hew Dixon, Ph.D., FRM Quiota LLC Qwafafew, Chicago
2 Speaker Profile CEO and Founder of Quiota LLC, a trading technology and consul(ng firm for trading firms and fintech companies. Tenure- track Assistant Professor of Finance in the Stuart School of Business at the Illinois Ins(tute of Technology, Chicago. Held academic posi(ons at Stanford, UC Davis and the University of San Francisco. Ph.D. in Applied Math from Imperial College and M.Sc. in Parallel and Scien(fic Computa(on from Reading University, UK.
3 Is Deep Learning a Disrup(ve Technology for Algo- Trading? ü Capable of discovering complex hidden pa;erns in financial data ü Enable dynamical selec(on of the most important input signals ü Less prone to over- fisng ü Broad eco- system of open- source tools compa(ble with accelerator platorms x Less interpretable than classical econometrics models x Very compute intensive x Applica(on to (me series is a rela(vely new applica(on area for DNNs, e.g. LTSMs and RNNs
4 Learning Points 1 Take a systems view of algorithmic trading 2 Engineer automated trading decision frameworks using deep neural networks 3 Assess the performance of trading strategies 4 Use market micro- structure to improve forecas(ng accuracy
5 What is Algorithmic Trading?
6 Algo Trading Strategies Market data Rules Trading decision Mathema(cal opera(ons Input Output Configura(ons
7 Example: MAC Strategy fast moving average slow moving average Buy signal Sell signal
8 Strategy Universe Strategy configura(ons Configura(on c Trading decision s(c) U(lity U(s(c)) 1 buy 0 hold - 1 sell P&L / drawdown
9 How can we engineer a strategy producing buy / sell decisions with DNNs?
10 Classifiers Strategy configura(on c Features Classifier E.g. - Historic price returns at various lags - Moving averages - Oscillators Trading decision s(c) 1 buy 0 hold - 1 sell
11 Feature Engineering Raw features 5 minute mid- prices for 45 CME listed commodity and FX futures over the last 15 years Label - Lagged price differences from 1 to moving price averages with window size from 5 to pair- wise correla(on of returns Labels from posi(ve, neutral or nega(ve market returns Engineered features Normalized features Labeled feature set Final feature set consists of 9895 features and 50,000 consecu(ve observa(ons
12 Deep Neural Networks Literature Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classifica(on with deep convolu(onal neural networks. In Advances in neural informa(on processing systems, pages , R. Rojas. Neural Networks: A Systema(c Introduc(on. Springer- Verlag New York, Inc., New York, NY, USA, 1996.
13 Deep Learning Algorithm* Mini- batching in the stochas(c gradient method enables be;er u(liza(on of many- core accelerator platorm Sweep over the learning rate parameter space * M. Dixon, D. Klabjan and J. H. Bang, Classifica(on- based Market Predic(on using Deep Neural Networks, Forthcoming in Journal of Algorithmic Finance: h;p://papers.ssrn.com/sol3/papers.cfm?abstract_id= Prevent stagna(on of convergence by halving the learning rate
14 How can we meaningfully evaluate the performance of classification based strategies?
15 Walk Forward Op(miza(on
16 White noise Classifier Performance
17 Cumula&ve Unrealized P&L for a simple strategy Simple strategy: - Buy if signal is 1, - Hold at 0 and - Sell and take a short posi(on if signal is - 1 Ini(al capital is $100k
18 PL: Pla(num NQ: E- mini NASDAQ 100 Futures Quotes AD: Australian Dollar BP: Bri(sh Pound ES: E- mini S&P 500 Futures Quotes Sharpe Ra(os
19 Maximum Drawdown
20 Average Daily Returns
21 How to improve predic(ve accuracy? Predic(on at 5 minute intervals is very difficult Incorporate market microstructure into the features The labeling approach can lead to sequences of off- sesng posi(ons, unnecessarily racking up transac(on costs
22 How do we incorporate knowledge of market micro-structure?
23 Raw CME MDP3 Feed :::no_md_entries - num_groups: 1 ::::md_entry_px: md_entry_size: None ( ) security_id: [ZQM8] rpt_seq: 27 trading_reference_date: se;l_price_type: Rounded (4) md_update_ac(on: New (0) md_entry_type: Se;lement Price (6) :packet - (mestamp: :15: sequence_number: sending_(me: ::MDIncrementalRefreshDailySta(s(cs - transact_(me: match_event_indicator: EndOfEvent (128)
24 Process Limit Order Book Updates xxx (764660) SSN: ISN: Sent: Received: (72) ( 62) (55 ) ( 89) (81 ) xxx (764660) SSN: ISN: Sent: Received: Trade (Buy) xxx (764660) SSN: ISN: Sent: Received: (69) ( 62) (53 ) ( 89) (81 )
25 Data Issues Duplicate (me- stamps: xxx (72174) SSN: ISN: Sent: Received: (75) ( 69) (11 ) ( 37) (5 ) Xxx (72174) SSN: ISN: Sent: Received: (75) ( 69) (11 ) ( 36) (5 ) Iceberg orders: a large single order that has been divided into smaller lots for the purpose of hiding the volume Packet dropping Latency between the matching engine and market data processor can vary significantly
26 Labeling Approach t i p t i d u t i+1 d p t i+1 u p t i+1 d u d u t i+2 dd p t i+2 ud p t i+2 uu p t i
27 Performance Results at 0 ms Lead(me Actual\Predict % 9.8% 0.0% 0 7.8% 86.0% 6.3% 1 0.0% 14.0% 86.0%
28 Performance Results at 1ms Lead(me Actual\Predict % 7.6% 11.6% % 75.5% 12.8% % 9.5% 73.1%
29 Performance Results at 2ms Lead(me Actual\Predict % 16.1% 16.2% % 69.4% 15.4% % 12.1% 67.9%
30 Classifier Performance
31 Training Horizon
32 Bias Variance Trade- Off
33 Learning Points 1 Taken a systems view of algorithmic trading 2 Engineered automated trading decision frameworks using deep neural networks 3 Assessed the performance of trading strategies 4 Used market micro- structure to improve forecas(ng accuracy
34 Take Aways Restric(ng analysis to price history alone leads to unreliable predic(ve performance Unless an appropriate labeling approach is deployed, a good predic(on does not translate into a useful trade decision tool Deep Neural networks are best suited to mul(- instrument forecas(ng models
35 Appendix
36 Implementa(on Approach In designing an algorithm for parallel efficiency on a shared memory architecture, three design goals have been implemented: 1. The algorithm has to be designed with good data locality proper(es. 2. The dimension of the matrix or for loop being parallelized is at least equal to the number of threads. 3. BLAS rou(nes from the MKL should be used in preference to openmp parallel for loop primi(ves.
37 System Configura(on
38 Performance on the Intel Xeon Phi
39 Performance on the Intel Xeon Phi Speedup of the batched back- propaga(on algorithm on the Intel Xeon Phi rela(ve to the baseline for various batch sizes.
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