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Statistics:
Missing Link between Technical Analysis and
Algorithmic Trading
Manish Jalan
Managing Partner and Quantitative...
The statistical modeling
building blocks
Define End Goal Define Set of Rules
Collect
Data
Back-test Optimize Simulate
Conn...
Why Mathematics & Statistics?
Pure Technical Models
Moderate ROI when model is working
Large draw-downs when model stops
L...
The Mathematics
Data
Distributions
Time Series
Modeling
Market
Microstructure
4Statistics: Missing Link between Technical ...
The Volatility
5
2
1
1
( )
n
i
i
x
n
 

 
Volatility
Is deviation from mean
in daily, 5 min, 10 min etc.
5Statistics...
The normal distribution
Normal
Distribution
Most popular data distribution
Standard normal distribution curve
Source: Wiki...
Mean ix
n
 

Standard
deviation
2
1
1
( )
n
i
i
x
n
 

 
Variance
2 2
1
1
( )
n
i
i
x
n
 

 
Correlation
( ...
Normal vs. other distributions
CAUCHY
DISTRIBUTION
BETA
DISTRIBUTION
BINOMIAL
DISTRIBUTION
CHI-SQUARE
DISTRIBUTION
LAPLACE...
Behavior of the time-series of data
– Mean reverting, Trending or Random Walk
– 50-60% time series is random walk
– Focus ...
Mean and Variance
0
2
4
6
8
10
12
Constant Mean
0
2
4
6
8
10
Constant Variance
0
10
20
30
40
Increasing Mean
0
5
10
15
20
...
Mean reversion modeling
Co-integration: Stationary mean and variance
Time series is stationary when
– The mean is constant...
Variance Ratio Test: Test for variance alone
Useful when mean is varying w.r.t to the time
Ornstein-Uhlenbeck Process: Tes...
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
18.00%
20.00%
0 10 20 30 40 50
Growth
P/E Ratio
Cluster analysis...
Regression
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25
2
0.659
0.720
y x
R

...
1001.50 13
1001.00 19
1000.50 2
1000.00 17
999.50 9
999.00
10 998.50
4 998.00
16 998.00
7 998.00
Last
Traded
Price
Bid-
As...
Market microstructure
09:15 09:30 09:45 10:00 10:4510:3010:15
1005.00 1007.50 1004.50 1003.00 1008.00 1010.50 1009.50
5n ...
The spread
( )Spread Ticks BestAsk BestBid 
( )
( ) 10000
( )
2
BestAsk BestBid
Spread BP
BestAsk BestBid

 

Spread...
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
09:00 09:50 10:40 11:30 12:20 13:10 14:00 14:50
The market curve
Volume / ...
1055.00 2
1054.00 7
1053.00 15
1052.00 25
1051.00 31
1050.00
42 1049.00
20 1048.00
15 1047.00
11 1046.00
6 1045.00
1 2 1 3...
High frequency example
Short Term Upward
Momentum
10:00:00 10:00:30 10:01:00
Trades hitting the Bid
Trades lifted on the O...
21
Conclusion
Statistical modeling can help you reduce draw-downs in technical analysis
Statistics can help filter for hig...
22
Recommended referrals
Prop trading
• Statistical Arbitrage:
Algorithmic Trading
Insights and Techniques
by Andrew Pole
...
23
Manish Jalan
Managing Partner and Quantitative Research Head
SG Analytics, Pune/Mumbai, India
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Statistics - The Missing Link Between Technical Analysis and Algorithmic Trading by Manish Jalan at QuantCon 2016

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Trading leveraged derivatives using only technical analysis or speculative analysis can lead to windfall losses for even the most disciplined trader and investor. Statistics are often an ignored area of work when it comes to derivatives trading. Our talk shall focus upon how volatility can be used for dynamically adjusting the stop losses. It will talk about how correlation is an essential method to diversify the class of derivatives being traded or hedged. It will focus on co-integration as a key method to distinguish a mean reverting time series to a non-mean reverting time series. It will touch upon other essential time series econometrics like OU process, VRT as well as statistical tools like PCA, ARCH, GARCH etc. which are essential for derivatives pricing and forecasting the volatility.

Published in: Economy & Finance

Statistics - The Missing Link Between Technical Analysis and Algorithmic Trading by Manish Jalan at QuantCon 2016

  1. 1. Statistics: Missing Link between Technical Analysis and Algorithmic Trading Manish Jalan Managing Partner and Quantitative Research Head SG Analytics, Pune/Mumbai, India APRIL, 2016
  2. 2. The statistical modeling building blocks Define End Goal Define Set of Rules Collect Data Back-test Optimize Simulate Connect to OMS Connect to Exchange Manage Risk Improve and Maintain Modeling Building 2Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  3. 3. Why Mathematics & Statistics? Pure Technical Models Moderate ROI when model is working Large draw-downs when model stops Long stretch of continuous bleeding in returns User might lose confidence Technical & Statistical Models Superior ROI when model is working Flattish ROI when model stops Shorter stretch of continuous flattish period User can diversify and make multi-models 3Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  4. 4. The Mathematics Data Distributions Time Series Modeling Market Microstructure 4Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  5. 5. The Volatility 5 2 1 1 ( ) n i i x n      Volatility Is deviation from mean in daily, 5 min, 10 min etc. 5Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  6. 6. The normal distribution Normal Distribution Most popular data distribution Standard normal distribution curve Source: Wikipedia 6Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  7. 7. Mean ix n    Standard deviation 2 1 1 ( ) n i i x n      Variance 2 2 1 1 ( ) n i i x n      Correlation ( , ) x y Cov x y r    Beta ( , ) ( ) s p s p Cov r r Var r   The normal distribution 7Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  8. 8. Normal vs. other distributions CAUCHY DISTRIBUTION BETA DISTRIBUTION BINOMIAL DISTRIBUTION CHI-SQUARE DISTRIBUTION LAPLACE DISTRIBUTION POISSON DISTRIBUTION EXPONENTIAL DISTRIBUTION 8Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  9. 9. Behavior of the time-series of data – Mean reverting, Trending or Random Walk – 50-60% time series is random walk – Focus should be on the other 40% Key elements: Mean and Variance Different behaviors – Mean reverting (E.g.: Pairs Trading) – Non-mean reverting (E.g.: Trend) – Constant variance (E.g.: Pairs Trading) – Increasing variance (E.g.: Trend) Time series modeling 9Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  10. 10. Mean and Variance 0 2 4 6 8 10 12 Constant Mean 0 2 4 6 8 10 Constant Variance 0 10 20 30 40 Increasing Mean 0 5 10 15 20 25 30 Increasing Variance 10Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  11. 11. Mean reversion modeling Co-integration: Stationary mean and variance Time series is stationary when – The mean is constant – The variance is constant Test for co-integration – If |r| < 1, the series is stationary – If |r| = 1, it is non-stationary (Random walk) Most popular test: ADF (Augmented Dickey Fuller) If ADF < -3.2 (95% probability of co-integrated series) 1t t ty ry e  11Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  12. 12. Variance Ratio Test: Test for variance alone Useful when mean is varying w.r.t to the time Ornstein-Uhlenbeck Process: Test for mean reversion alone Useful when only mean reversion rate matters Generic time series modeling ( ) ( ) ( ) k t t Variance r VR k k Variance r     ( )t t tdx x dt dW     12Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  13. 13. 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00% 18.00% 20.00% 0 10 20 30 40 50 Growth P/E Ratio Cluster analysis and PCA Grouping of similar data and pattern Useful in factor modeling PCA: To identify principal component 13Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  14. 14. Regression -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 2 0.659 0.720 y x R   Useful in identifying alpha-generating factors y mx c  14Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  15. 15. 1001.50 13 1001.00 19 1000.50 2 1000.00 17 999.50 9 999.00 10 998.50 4 998.00 16 998.00 7 998.00 Last Traded Price Bid- Ask Spread Price Ask QtyBid Qty Used in UHFT, HFT, Agency Trading Market microstructure 15Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  16. 16. Market microstructure 09:15 09:30 09:45 10:00 10:4510:3010:15 1005.00 1007.50 1004.50 1003.00 1008.00 1010.50 1009.50 5n  1006.70  2 1 ( ) 35.3 n i x     2.657  Market Price of Reliance in 5 min buckets 16Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  17. 17. The spread ( )Spread Ticks BestAsk BestBid  ( ) ( ) 10000 ( ) 2 BestAsk BestBid Spread BP BestAsk BestBid     Spread in BP Spread in Ticks 17Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  18. 18. 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% 09:00 09:50 10:40 11:30 12:20 13:10 14:00 14:50 The market curve Volume / Market curve BucketVolume VolumeRatio DaysTotalVolume  18Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  19. 19. 1055.00 2 1054.00 7 1053.00 15 1052.00 25 1051.00 31 1050.00 42 1049.00 20 1048.00 15 1047.00 11 1046.00 6 1045.00 1 2 1 3 1 4 1 5 0 1 2 3 5( ) ( ) ( ) ( )eqVB B B B B B     1 2 1 3 1 4 1 5 0 1 2 3 5( ) ( ) ( ) ( )eqVA A A A A A     ( , ) eq eq VA f Bid Ask VB  High frequency example – for execution Bid-Ask Density function using equivalent volumes 19Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  20. 20. High frequency example Short Term Upward Momentum 10:00:00 10:00:30 10:01:00 Trades hitting the Bid Trades lifted on the Offer 10:01:30 20Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  21. 21. 21 Conclusion Statistical modeling can help you reduce draw-downs in technical analysis Statistics can help filter for high probability trades Statistics can enhance the returns on capital deployed Technical analysis can be used for entry / exits and statistics can be used for filtering those entries and exits Statistics can help you re-fine your stop losses and portfolio optimization Statistics can help in making trade execution better and reduce slippages per trade Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  22. 22. 22 Recommended referrals Prop trading • Statistical Arbitrage: Algorithmic Trading Insights and Techniques by Andrew Pole • High-Frequency Trading: A Guide to Algorithmic Strategies and Trading Systems by Irene Aldridge • The Encyclopedia of Trading Strategies by Jeffrey Owen and Donna McCormick Agency trading • Algorithmic Trading and DMA: An introduction to direct access trading strategies by Barry Johnson • Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernset P. Chan Web forums Wilmott forum: www.wilmott.com Nuclear Phynance: www.nuclearphynance.com Statistics: Missing Link between Technical Analysis and Algorithmic Trading
  23. 23. 23 Manish Jalan Managing Partner and Quantitative Research Head SG Analytics, Pune/Mumbai, India

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