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Rethinking Technical Analysis

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This is a presentation by Kingsley Jones, CEO of Jevons Global Pty Ltd.
Kingsley has been involved in financial markets for close to twenty-five years. During that time, traditional quantitative analysis rose to prominence, as did the importance of technology in trading. In this talk, Kingsley will reprise some of the key ideas that have developed in trading: 1) the importance of order-book analysis; 2) the marriage of technical analysis and behavioural finance; and 3) the role of high performance computing in processing large data sets. We illustrate the confluence of all three on the speaker's own technical indicator: the cost-basis sentiment index. We show how it lies at the intersection of these three trends and show how to speed it up using some mathematical ideas from the theory of parallel computing.

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Rethinking Technical Analysis

  1. 1. RETHINKING TECHNICAL ANALYSIS Kingsley Jones
  2. 2. IN BRIEF... RETHINKING TECHNICAL ANALYSIS ... MY JOURNEY • Origins estimating the cost-basis of the market • Order books understanding how prices are made • Linkages tying current sentiment to market history • Computation pushing performance with parallelism Rethinking Technical Analysis: New Foundations and Faster Computation
  3. 3. COST BASIS THEORY (2002) SentimentValuation Vs. “Unrealised profits and losses of investors in stocks drive investor sentiment.”
  4. 4. COST BASIS FOUNDATIONS When new trading occurs the Average Cost Basis moves toward the new prices The Average Cost Basis is near the centre of the Volume at Price distribution New Average Cost BasisOld Average Cost Basis 11.20 8.90 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 8.00 10.00 12.00 14.00 16.00 18.00 Price %Volume 11.20 8.90 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 8.00 10.00 12.00 14.00 16.00 18.00 Price %Volume Volume Traded at Higher Price 𝑡 𝑡 𝑡 𝑡 𝑡−1
  5. 5. COST BASIS MOVING AVERAGE Estimated Cost Basis MTW US Equity - MANITOWOC COMPANY INC 0 10 20 30 40 50 60 28-Feb-01 28-Feb-03 28-Feb-05 28-Feb-07 28-Feb-09 28-Feb-11 Price -80% -60% -40% -20% 0% 20% 40% 60% 80% P&L VWAP CB (VWAP) P&L
  6. 6. SUPPORT & RESISTANCE Investors are far more likely to have their cost of entry level at prices where large volume traded in the recent past 8 10 12 14 16 18 28-Jan-10 28-Apr-10 28-Jul-10 28-Oct-10 28-Jan-11 Time Price 11.20 8.90 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 8.00 10.00 12.00 14.00 16.00 18.00 Price %VolumeKey price levels act as support when reached from above and resistance when met from below Volume-at-Price ChartTraditional Price Chart
  7. 7. ORDER-BOOK SLOPE (2002) Source: Jevons Global (2019) and Credit Suisse (2002). Order book dynamics as the link to technical patterns.
  8. 8. COST-BASIS DISTRIBUTION (2002) Source: Jevons Global (2019) and Credit Suisse (2002).
  9. 9. A MAD MAD NON-NORMAL WORLD Embrace non-parametric methods for probability forecasting. Source: Bloomberg Finance (2011).
  10. 10. VECTORIZATION OF EWMA Start with normal iterative single-step rule. Source: Jevons Global (2019). 𝑡 𝑡 𝑡 𝑡 𝑡−1 1 1 1 1 0 2 2 2 2 1 3 3 3 3 2
  11. 11. IDEA OF THE ALGORITHM... Unfold the iteration with a re-scaled price vector. Source: Jevons Global (2019). 1 1 1 1 1 0 2 2 2 2 2 1 3 3 3 3 3 2
  12. 12. UNFOLD AND THEN VECTORIZE... Express the target CB vector using vector operations. Source: Jevons Global (2019). 𝐶𝐵1 (1 − 𝛼1 ) = 𝛼1 (1 − 𝛼1 ) 𝑃1 + 𝐶𝐵0 𝐶𝐵2 (1 − 𝛼2 )(1 − 𝛼1 ) = 𝛼2 (1 − 𝛼2 )(1 − 𝛼1 ) 𝑃2 + 𝐶𝐵1 (1 − 𝛼1 ) 𝐶𝐵3 (1 − 𝛼3 )(1 − 𝛼2 )(1 − 𝛼1 ) = 𝛼3 (1 − 𝛼3 )(1 − 𝛼2 )(1 − 𝛼1 ) 𝑃3 + 𝐶𝐵2 (1 − 𝛼2 )(1 − 𝛼1 ) CUMBETA = (1 - α). CUMPROD CB = ((α * PRICE/CUMBETA ).CUMSUM + CB0) *CUMBETA
  13. 13. VECTORIZED-EWMA TEST MAGIC: the two columns CB and CB* are the same! Source: Jevons Global (2019). Date DSCD PI RI MV MVFF P UP NOSH VO TO CB YB alpha 1 - alpha cumprod alpha*P/cumprod cumsum CB* 24/10/1986 900998 1123.6 3804.85 9921.42 347.25 898 1104836 345.32 1.005589 1 345.32 345.32 345.32 27/10/1986 900998 1117.4 3784.49 9866.18 345.32 893 1104836 4288.6 0.003882 345.32 1 0.003882 0.996118 0.996118 1.345638863 346.6656389 345.32 28/10/1986 900998 1094.9 3709.03 9667.31 338.36 875 1104836 6323.6 0.005724 345.2802 0.979958 0.005724 0.994276 0.990417 1.955363518 348.6210024 345.2802 29/10/1986 900998 1094.9 3709.86 9667.31 338.36 875 1104836 3031.2 0.002744 345.2612 0.980012 0.002744 0.997256 0.9877 0.939876664 349.560879 345.2612 30/10/1986 900998 1134.9 3846.36 10020.86 350.73 907 1104836 14313 0.012955 345.332 1.015631 0.012955 0.987045 0.974904 4.660621472 354.2215005 345.332 31/10/1986 900998 1163.7 3944.73 10274.97 359.63 930 1104836 11115.1 0.01006 345.4759 1.04097 0.01006 0.98994 0.965096 3.74887395 357.9703745 345.4759 3/11/1986 900998 1163.7 3945.55 10274.97 359.63 930 1104836 4760.9 0.004309 345.5369 1.040786 0.004309 0.995691 0.960938 1.612694132 359.5830686 345.5369 4/11/1986 900998 1154.9 3916.68 10197.63 356.92 923 1104836 1381.7 0.001251 345.5511 1.032901 0.001251 0.998749 0.959736 0.465088007 360.0481566 345.5511 5/11/1986 900998 1192.5 4044.81 10529.08 368.52 953 1104836 14672 0.01328 345.8561 1.06553 0.01328 0.98672 0.946991 5.16781383 365.2159704 345.8561 6/11/1986 900998 1180 4003.2 10418.6 364.66 943 1104836 16042 0.01452 346.1291 1.053537 0.01452 0.98548 0.933241 5.673554312 370.8895247 346.1291 7/11/1986 900998 1178.7 3999.78 10407.55 364.27 942 1104836 5279.5 0.004779 346.2158 1.052147 0.004779 0.995221 0.928781 1.874153005 372.7636778 346.2158 10/11/1986 900998 1176.2 3992.12 10385.45 363.5 940 1104836 3261.6 0.002952 346.2669 1.049768 0.002952 0.997048 0.926039 1.15879854 373.9224763 346.2669 11/11/1986 900998 1177.5 3997.19 10396.5 363.88 941 1104836 2892.8 0.002618 346.313 1.050726 0.002618 0.997382 0.923615 1.031544644 374.9540209 346.313 12/11/1986 900998 1197.5 4065.98 10573.28 370.07 957 1104836 5708.5 0.005167 346.4357 1.068221 0.005167 0.994833 0.918842 2.080975954 377.0349969 346.4357 13/11/1986 900998 1195 4058.32 10551.18 369.3 955 1104836 2983.2 0.0027 346.4975 1.065809 0.0027 0.9973 0.916361 1.08817086 378.1231678 346.4975 14/11/1986 900998 1197.5 4067.64 10573.28 370.07 957 1104836 2175.3 0.001969 346.5439 1.067888 0.001969 0.998031 0.914557 0.796699188 378.9198669 346.5439 17/11/1986 900998 1195 4059.97 10551.18 369.3 955 1104836 1414 0.00128 346.573 1.065576 0.00128 0.99872 0.913387 0.517459339 379.4373263 346.573 18/11/1986 900998 1197.5 4069.3 10573.28 370.07 957 1104836 1752.3 0.001586 346.6103 1.067683 0.001586 0.998414 0.911938 0.643619514 380.0809458 346.6103 19/11/1986 900998 1180 4010.6 10418.6 364.66 943 1104836 4877.8 0.004415 346.69 1.051833 0.004415 0.995585 0.907912 1.773252637 381.8541984 346.69 20/11/1986 900998 1188.7 4041.2 10495.94 367.36 950 1104836 2734 0.002475 346.7411 1.059465 0.002475 0.997525 0.905665 1.00374848 382.8579469 346.7411 21/11/1986 900998 1198.7 4076.06 10584.32 370.46 958 1104836 2018 0.001827 346.7844 1.068272 0.001827 0.998173 0.904011 0.748498577 383.6064455 346.7844 24/11/1986 900998 1210 4115.18 10683.76 373.94 967 1104836 2902.6 0.002627 346.8558 1.078085 0.002627 0.997373 0.901636 1.0895824 384.6960279 346.8558 25/11/1986 900998 1191.2 4052.18 10518.04 368.14 952 1104836 3941.8 0.003568 346.9317 1.061131 0.003568 0.996432 0.898419 1.461944181 386.1579721 346.9317 26/11/1986 900998 1188.7 4044.5 10495.94 367.36 950 1104836 4539 0.004108 347.0156 1.058627 0.004108 0.995892 0.894728 1.686798224 387.8447703 347.0156 27/11/1986 900998 1173.7 3994.24 10363.36 362.72 938 1104836 6888.4 0.006235 347.1135 1.044961 0.006235 0.993765 0.88915 2.543414541 390.3881848 347.1135 28/11/1986 900998 1176.2 4003.59 10385.45 363.5 940 1104836 2707.4 0.00245 347.1537 1.047087 0.00245 0.99755 0.886971 1.004268113 391.3924529 347.1537
  14. 14. LOOP BASED METHOD (384MS) Source: Jevons Global (2019).
  15. 15. VECTORIZED METHOD (2.8MS) Source: Jevons Global (2019).
  16. 16. MOORE’S LAW AND POWER WALL CPU clock speed has now plateaued! Gordon Moore Source: Intel (2011).
  17. 17. AMDAHL’S LAW AND SERIAL WORK We are now in the Age of Massive Parallelism! Source: Wikipedia (2015).
  18. 18. PARALLEL PREFIX SUM (SCAN) Source: “Parallel Prefix Sum (Scan) with CUDA” Chapter 39. GPU Gems NVIDIA (2019). There are work-efficient parallel cumulative sum algorithms.
  19. 19. SUPERCOMPUTER++ Source: Jevons Global (2019). NVDIA DGX-2 Deep-Learning Node
  20. 20. PARALLEL APIS ARE NOW CORE MPICH CUDA OpenCLOpenMP Source: Cray, Ohio State University, Argonne National Laboratory, Nvidia, OpenMP and OpenCL (2015).
  21. 21. ORIGINS…THE BEOWULF IN SCIENCE Source: Jevons Global (2019). ASCII-RED Pentium Cluster 1990s Beowulf Cluster Simulation Applications
  22. 22. THE NEOWULF AS CLOUD-NATIVE CODE Source: Jevons Global (2019).
  23. 23. THE NEOWULF AS MAKER PROJECT Source: Jevons Global (2019).
  24. 24. WHY INFINIBAND? Source: Jevons Global (2019).
  25. 25. NEOWULF TUNING Source: Jevons Global (2019).
  26. 26. AUTOMATION IN TRADING Source: Jevons Global (2019).
  27. 27. SUMMARY... KEY TAKEAWAYS • Physics is driving the move to parallelism • Conventional signal processing is largely serial • Simple parallelism is possible via compute farms • How far can we go in parallel back testing? Technical Analysis can be thought of as nonlinear signal processing – which we aim to parallelize.

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