supervised learning strategy of market making
DESCRIPTION
Supervised Learning Strategy of Market Making. Presented by Ori Gil Supervisor : Gal Zahavi. Winter 2011. Project Overview. Introduce to basic concepts. Display the main models in the project: Roll model ( 1984 ). Glosten-Milgron model ( 1985 ). - PowerPoint PPT PresentationTRANSCRIPT
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Supervised Learning Strategy
of Market Making
Presented byOri Gil
Supervisor : Gal Zahavi
Control and Robotics Laboratory
Winter 2011
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Introduce to basic concepts.
Display the main models in the project:Roll model (1984).Glosten-Milgron model (1985).
Implement a profitable automated market maker
in TASE:Basic strategy – trading threshold.Supervised learning strategy – training set and test set.
Simulation on TASE data and conclusions.
Project Overview
Control and Robotics Laboratory 2
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Basic Concepts
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Market Liquidity
Market Making
Bid-Ask Spread
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Control and Robotics Laboratory
Price Process
0 1 2 3 4 5 6 71730
1735
1740
1745
1750
1755
1760
1765
t [hours]
Sha
re P
rice
[0.0
1 N
IS]
AskBidPrice
Leumi’s share (03/01/2010)
5
Price Process
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Control and Robotics Laboratory
Price Process
0 1 2 3 4 5 6 71730
1735
1740
1745
1750
1755
1760
1765
t [hours]
Sha
re P
rice
[0.0
1 N
IS]
AskBidPrice
Leumi’s share (03/01/2010)
5
2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5
1751.5
1752
1752.5
1753
1753.5
1754
1754.5
1755
t [hours]
Sha
re P
rice
[0.0
1 N
IS]
AskBidPrice
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The Models
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ExtendedGM Model
Roll Model (1984)
Glosten-Milgrom Model (1985)
Liquidty Population
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Roll Model (1984)
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Roll Model (1984)
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GM Model (1985)
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Market Makers (uninformed)Uninformed TradersInformed Traders
Market population
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GM Model (1985)
Control and Robotics Laboratory
The event tree of a trade:
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Analyzing GM Model: Finding The Bid-Ask spread
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Market Making Algorithm – Basic approach
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Estimating μ from Bid(t-1) and Ask(t-
1)[GM Model]
Submitting bid and ask
orders :Bid(t)=Bid(t-1)Ask(t)=Ask(t-1)
μ ≤ M
μ > M
Cancelling open orders and
holding trade work until new order arrives
Waiting for new order to arrive at the
market μ ? M
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Gathering training set
from TASE quotesD(t)={X(t),Y(t)}
Market Making Algorithm – Supervised learning approach
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Bid priceAsk price
Informed proportion µV probability δ
Gathering training set
from TASE quotesD(t)={X(t),Y(t)}
Gathering training set
from TASE quotesD(t)={X(t),Y(t)}
Market Making Algorithm – Supervised learning approach
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Gathering training set
D(t)={X(t),Y(t)}from TASE quotes
Gathering training set
from TASE quotesD(t)={X(t),Y(t)}
Running the learned function on the
training set (Multi-linear regression)
Market Making Algorithm – Supervised learning approach
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Control and Robotics Laboratory
Gathering training set
from TASE quotesD(t)={X(t),Y(t)}
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Running the learned function on the
training set (Multi-linear regression)
Market Making Algorithm – Supervised learning approach
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Market Making Algorithm – Supervised learning approach
Control and Robotics Laboratory
Running our mm strategy on test set and compare results against historical data
Gathering training set
from TASE quotesD(t)={X(t),Y(t)}
Next-step” forecast
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Running the learned function on the
training set (Multi-linear regression)
Producing optimal mm
strategy (OLS)
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Control and Robotics Laboratory
0 1 2 3 4 5 6 71730
1735
1740
1745
1750
1755
1760
1765
1770
t [hours]
Pric
e [0
.01
NIS
]
PriceVhigh
Vlow
0 1 2 3 4 5 6 70
20
40
60
80
t [hours]
[%
]
Proportion of informed traders
0 1 2 3 4 5 6 720
40
60
80
100
t [hours]
1-
[%]
probabilty of Vhigh
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Parameters Measure – GM+Roll
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Market Making - Basic Strategy
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“Next-step” Forecast
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Training
0 0.2 0.4 0.6 0.8 1 1.2 1.41732
1734
1736
1738
1740
1742
1744
1746
1748
1750Bid Regression
t [hours]
Pric
e [0
.01
NIS
]
Training outputTraining predicted
0 0.2 0.4 0.6 0.8 1 1.2 1.41735
1740
1745
1750Ask Regression
t [hours]
Pric
e [0
.01
NIS
]
Training outputTraining predicted
Test
1 2 3 4 5 6 71744
1746
1748
1750
1752
1754
1756
1758
1760
1762
1764Bid Regression
t [hours]
Pric
e [0
.01
NIS
]
Test outputTest predicted
1 2 3 4 5 6 71746
1748
1750
1752
1754
1756
1758
1760
1762
1764
1766Ask Regression
t [hours]
Pric
e [0
.01
NIS
]
Test outputTest predicted
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Supervised Learning Strategy
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“Next-step” Forecast
Supervised Learning Strategy
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ConclusionsKnowing informed traders population at the market
improves our market making performance.
Adding supervised learning solution to the model showed even better performance.
The project has shown success in bringing learning techniques to building market-making algorithms.
Future extensions of this study may include the refinement of the learning techniques.
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BIBLIOGRAPHY Cont R., Stoikov S. and Talreja R., 2010, "A Stochastic Model for Order Book Dynamics,
Operations Research, 58, pp. 549–563.
Das, S., 2005. "A Learning Market-Maker in the Glosten-Milgrom Model" Quantitative Finance, 5, 169-180.
Glosten L. R., and P. R. Milgrom, 1985, “Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders,” Journal of Financial Economics, 14, 71–100.
Huang, R.D. and H. R. Stoll, 1997, “The components of the bid-ask spread: A General approach”, Review of Financial Studies 10, 995-1034.
Roll, R., 1984, “A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market”, Journal of Finance, 39, 1127–1139.
TASE website, http://www.tase.co.il/TASEEng/Homepage.htm.
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Thank You!
Questions?
Control and Robotics Laboratory