Multi-Strategy Trading Utilizing Market Regimes
Transcript of Multi-Strategy Trading Utilizing Market Regimes
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Multi-Strategy Trading Utilizing Market Regimes
Hynek Mlnarık 1
Subramanian Ramamoorthy 2
Rahul Savani 1
1Warwick Institute for Financial ComputingDepartment of Computer Science
University of Warwick
2Institute of Perception, Action and BehaviourSchool of Informatics
University of Edinburgh
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The Portfolio Allocation Problem
Dynamically allocate working capital in a portfolio of instruments
– over time, as market conditions continually change.
Classic problem with established theory, e.g., mean-varianceoptimization and modern extensions.
These are “model-based” methods. So, one makes assumptions(e.g., known expected returns) that may turn out to betroublesome.
This issue spurred research into “model-free” approaches.
![Page 3: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/3.jpg)
The Portfolio Allocation Problem
Dynamically allocate working capital in a portfolio of instruments– over time, as market conditions continually change.
Classic problem with established theory, e.g., mean-varianceoptimization and modern extensions.
These are “model-based” methods. So, one makes assumptions(e.g., known expected returns) that may turn out to betroublesome.
This issue spurred research into “model-free” approaches.
![Page 4: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/4.jpg)
The Portfolio Allocation Problem
Dynamically allocate working capital in a portfolio of instruments– over time, as market conditions continually change.
Classic problem with established theory, e.g., mean-varianceoptimization and modern extensions.
These are “model-based” methods. So, one makes assumptions(e.g., known expected returns) that may turn out to betroublesome.
This issue spurred research into “model-free” approaches.
![Page 5: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/5.jpg)
The Portfolio Allocation Problem
Dynamically allocate working capital in a portfolio of instruments– over time, as market conditions continually change.
Classic problem with established theory, e.g., mean-varianceoptimization and modern extensions.
These are “model-based” methods. So, one makes assumptions(e.g., known expected returns) that may turn out to betroublesome.
This issue spurred research into “model-free” approaches.
![Page 6: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/6.jpg)
The Portfolio Allocation Problem
Dynamically allocate working capital in a portfolio of instruments– over time, as market conditions continually change.
Classic problem with established theory, e.g., mean-varianceoptimization and modern extensions.
These are “model-based” methods. So, one makes assumptions(e.g., known expected returns) that may turn out to betroublesome.
This issue spurred research into “model-free” approaches.
![Page 7: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/7.jpg)
“Model-free” Portfolio Allocation
Point of departure: Classic work on optimal bet sizing (Kelly1956, Breiman 1961) - how much to bet given odds?
Constantly rebalanced portfolios (Thorp 1971, Markovitz 1976,Bell+Cover 1988, Algoet+Cover 1988) - keep relative allocationof capital constant (still assuming known market returndistributions).
Universal portfolio (Cover 1991) - Sequential portfolio allocationto match the best constantly rebalanced portfolio in hindsight(for an arbitrary market process).
Many extensions and follow-on work: multiplicative updates(Helmbold et al. 1998), efficient online computation (Kalai et al.2002), Anticor (Borodin et al. 2004), kernel-weighted allocation(Gyorfi et al. 2006).
![Page 8: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/8.jpg)
“Model-free” Portfolio Allocation
Point of departure: Classic work on optimal bet sizing (Kelly1956, Breiman 1961) - how much to bet given odds?
Constantly rebalanced portfolios (Thorp 1971, Markovitz 1976,Bell+Cover 1988, Algoet+Cover 1988) - keep relative allocationof capital constant (still assuming known market returndistributions).
Universal portfolio (Cover 1991) - Sequential portfolio allocationto match the best constantly rebalanced portfolio in hindsight(for an arbitrary market process).
Many extensions and follow-on work: multiplicative updates(Helmbold et al. 1998), efficient online computation (Kalai et al.2002), Anticor (Borodin et al. 2004), kernel-weighted allocation(Gyorfi et al. 2006).
![Page 9: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/9.jpg)
“Model-free” Portfolio Allocation
Point of departure: Classic work on optimal bet sizing (Kelly1956, Breiman 1961) - how much to bet given odds?
Constantly rebalanced portfolios (Thorp 1971, Markovitz 1976,Bell+Cover 1988, Algoet+Cover 1988) - keep relative allocationof capital constant (still assuming known market returndistributions).
Universal portfolio (Cover 1991) - Sequential portfolio allocationto match the best constantly rebalanced portfolio in hindsight(for an arbitrary market process).
Many extensions and follow-on work: multiplicative updates(Helmbold et al. 1998), efficient online computation (Kalai et al.2002), Anticor (Borodin et al. 2004), kernel-weighted allocation(Gyorfi et al. 2006).
![Page 10: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/10.jpg)
“Model-free” Portfolio Allocation
Point of departure: Classic work on optimal bet sizing (Kelly1956, Breiman 1961) - how much to bet given odds?
Constantly rebalanced portfolios (Thorp 1971, Markovitz 1976,Bell+Cover 1988, Algoet+Cover 1988) - keep relative allocationof capital constant (still assuming known market returndistributions).
Universal portfolio (Cover 1991) - Sequential portfolio allocationto match the best constantly rebalanced portfolio in hindsight(for an arbitrary market process).
Many extensions and follow-on work: multiplicative updates(Helmbold et al. 1998), efficient online computation (Kalai et al.2002), Anticor (Borodin et al. 2004), kernel-weighted allocation(Gyorfi et al. 2006).
![Page 11: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/11.jpg)
Utilizing Market Context
Market processes are not entirely arbitrary
– how to utilize oddswithout overly restrictive assumptions?
Statistical view of Universal Portfolios (Belentepe 2005):Weights (constrained to a partition of unity) are conditionalexpectation of a multivariate normal distribution,w ∼ N
(Σ−1
t rt,1tΣt
).
Unconstrained version is the standard log-optimal investment.
Major contribution of universal algorithms is an online procedureto solve this problem, within a target portfolio class.
We seek online procedures that also allow us to utilize context inthe spirit of (non-parametric) statistics.
![Page 12: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/12.jpg)
Utilizing Market Context
Market processes are not entirely arbitrary – how to utilize oddswithout overly restrictive assumptions?
Statistical view of Universal Portfolios (Belentepe 2005):Weights (constrained to a partition of unity) are conditionalexpectation of a multivariate normal distribution,w ∼ N
(Σ−1
t rt,1tΣt
).
Unconstrained version is the standard log-optimal investment.
Major contribution of universal algorithms is an online procedureto solve this problem, within a target portfolio class.
We seek online procedures that also allow us to utilize context inthe spirit of (non-parametric) statistics.
![Page 13: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/13.jpg)
Utilizing Market Context
Market processes are not entirely arbitrary – how to utilize oddswithout overly restrictive assumptions?
Statistical view of Universal Portfolios (Belentepe 2005):Weights (constrained to a partition of unity) are conditionalexpectation of a multivariate normal distribution,w ∼ N
(Σ−1
t rt,1tΣt
).
Unconstrained version is the standard log-optimal investment.
Major contribution of universal algorithms is an online procedureto solve this problem, within a target portfolio class.
We seek online procedures that also allow us to utilize context inthe spirit of (non-parametric) statistics.
![Page 14: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/14.jpg)
Utilizing Market Context
Market processes are not entirely arbitrary – how to utilize oddswithout overly restrictive assumptions?
Statistical view of Universal Portfolios (Belentepe 2005):Weights (constrained to a partition of unity) are conditionalexpectation of a multivariate normal distribution,w ∼ N
(Σ−1
t rt,1tΣt
).
Unconstrained version is the standard log-optimal investment.
Major contribution of universal algorithms is an online procedureto solve this problem, within a target portfolio class.
We seek online procedures that also allow us to utilize context inthe spirit of (non-parametric) statistics.
![Page 15: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/15.jpg)
Utilizing Market Context
Market processes are not entirely arbitrary – how to utilize oddswithout overly restrictive assumptions?
Statistical view of Universal Portfolios (Belentepe 2005):Weights (constrained to a partition of unity) are conditionalexpectation of a multivariate normal distribution,w ∼ N
(Σ−1
t rt,1tΣt
).
Unconstrained version is the standard log-optimal investment.
Major contribution of universal algorithms is an online procedureto solve this problem, within a target portfolio class.
We seek online procedures that also allow us to utilize context inthe spirit of (non-parametric) statistics.
![Page 16: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/16.jpg)
Utilizing Market Context
Market processes are not entirely arbitrary – how to utilize oddswithout overly restrictive assumptions?
Statistical view of Universal Portfolios (Belentepe 2005):Weights (constrained to a partition of unity) are conditionalexpectation of a multivariate normal distribution,w ∼ N
(Σ−1
t rt,1tΣt
).
Unconstrained version is the standard log-optimal investment.
Major contribution of universal algorithms is an online procedureto solve this problem, within a target portfolio class.
We seek online procedures that also allow us to utilize context inthe spirit of (non-parametric) statistics.
![Page 17: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/17.jpg)
Portfolio Allocation – Our Approach
Dynamically allocate capital in a portfolio of trading strategies.
Use a set of primitives, i.e., simple strategies (e.g., rules used bytraders).
Individually, no primitive strategy is well suited (i.e., reliablyprofitable) under changing market contexts.
Represent changing market context by regimes - loosely, subsetsof strategies that are successful under this context.
Make use of historical data to non-parametrically modelregimes.
Devise online algorithm for dynamically rebalancing portfolio,shaped by contextual information.
![Page 18: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/18.jpg)
Portfolio Allocation – Our Approach
Dynamically allocate capital in a portfolio of trading strategies.
Use a set of primitives, i.e., simple strategies (e.g., rules used bytraders).
Individually, no primitive strategy is well suited (i.e., reliablyprofitable) under changing market contexts.
Represent changing market context by regimes - loosely, subsetsof strategies that are successful under this context.
Make use of historical data to non-parametrically modelregimes.
Devise online algorithm for dynamically rebalancing portfolio,shaped by contextual information.
![Page 19: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/19.jpg)
Portfolio Allocation – Our Approach
Dynamically allocate capital in a portfolio of trading strategies.
Use a set of primitives, i.e., simple strategies (e.g., rules used bytraders).
Individually, no primitive strategy is well suited (i.e., reliablyprofitable) under changing market contexts.
Represent changing market context by regimes - loosely, subsetsof strategies that are successful under this context.
Make use of historical data to non-parametrically modelregimes.
Devise online algorithm for dynamically rebalancing portfolio,shaped by contextual information.
![Page 20: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/20.jpg)
Portfolio Allocation – Our Approach
Dynamically allocate capital in a portfolio of trading strategies.
Use a set of primitives, i.e., simple strategies (e.g., rules used bytraders).
Individually, no primitive strategy is well suited (i.e., reliablyprofitable) under changing market contexts.
Represent changing market context by regimes - loosely, subsetsof strategies that are successful under this context.
Make use of historical data to non-parametrically modelregimes.
Devise online algorithm for dynamically rebalancing portfolio,shaped by contextual information.
![Page 21: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/21.jpg)
Portfolio Allocation – Our Approach
Dynamically allocate capital in a portfolio of trading strategies.
Use a set of primitives, i.e., simple strategies (e.g., rules used bytraders).
Individually, no primitive strategy is well suited (i.e., reliablyprofitable) under changing market contexts.
Represent changing market context by regimes - loosely, subsetsof strategies that are successful under this context.
Make use of historical data to non-parametrically modelregimes.
Devise online algorithm for dynamically rebalancing portfolio,shaped by contextual information.
![Page 22: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/22.jpg)
Portfolio Allocation – Our Approach
Dynamically allocate capital in a portfolio of trading strategies.
Use a set of primitives, i.e., simple strategies (e.g., rules used bytraders).
Individually, no primitive strategy is well suited (i.e., reliablyprofitable) under changing market contexts.
Represent changing market context by regimes - loosely, subsetsof strategies that are successful under this context.
Make use of historical data to non-parametrically modelregimes.
Devise online algorithm for dynamically rebalancing portfolio,shaped by contextual information.
![Page 23: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/23.jpg)
Our Approach – Two Major Concepts
Weighted Strategies: Robust portfolios may be constructed byweighting the working capital allocated to primitive strategies.
A small change, but as we will argue, a very useful one.
Regimes: Characterize context by relative profitability ofprimitive strategies.
Good trading strategies exploit recurring market dynamics thatcan be more prevalent in some time periods than in others.Trends depend on hard to model latent variables - we seekalternate state description in an action-oriented representation.
![Page 24: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/24.jpg)
Our Approach – Two Major Concepts
Weighted Strategies: Robust portfolios may be constructed byweighting the working capital allocated to primitive strategies.
A small change, but as we will argue, a very useful one.
Regimes: Characterize context by relative profitability ofprimitive strategies.
Good trading strategies exploit recurring market dynamics thatcan be more prevalent in some time periods than in others.Trends depend on hard to model latent variables - we seekalternate state description in an action-oriented representation.
![Page 25: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/25.jpg)
Our Approach – Two Major Concepts
Weighted Strategies: Robust portfolios may be constructed byweighting the working capital allocated to primitive strategies.
A small change, but as we will argue, a very useful one.
Regimes: Characterize context by relative profitability ofprimitive strategies.
Good trading strategies exploit recurring market dynamics thatcan be more prevalent in some time periods than in others.Trends depend on hard to model latent variables - we seekalternate state description in an action-oriented representation.
![Page 26: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/26.jpg)
Our Approach – Two Major Concepts
Weighted Strategies: Robust portfolios may be constructed byweighting the working capital allocated to primitive strategies.
A small change, but as we will argue, a very useful one.
Regimes: Characterize context by relative profitability ofprimitive strategies.
Good trading strategies exploit recurring market dynamics thatcan be more prevalent in some time periods than in others.
Trends depend on hard to model latent variables - we seekalternate state description in an action-oriented representation.
![Page 27: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/27.jpg)
Our Approach – Two Major Concepts
Weighted Strategies: Robust portfolios may be constructed byweighting the working capital allocated to primitive strategies.
A small change, but as we will argue, a very useful one.
Regimes: Characterize context by relative profitability ofprimitive strategies.
Good trading strategies exploit recurring market dynamics thatcan be more prevalent in some time periods than in others.Trends depend on hard to model latent variables - we seekalternate state description in an action-oriented representation.
![Page 28: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/28.jpg)
On Trading Strategies
Distinguish generic behaviour of a trading rule (e.g., contrarianinvestment) from specific instantiations.
Qualitative Strategies: Set of strategy functions whosealgorithmic description is identical up to a fixed set ofparameters.
Allows us to target behaviours somewhat independently ofunknown parameters.
Quantitative Strategies: Specific subset of strategy functionsobtained by assigning parameter values.
Implementation requires fully specified quantitative strategies.
But, it is helpful to differentiate the static structure implied by thequalitative strategy from the dynamic evolution of regimes.
![Page 29: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/29.jpg)
On Trading Strategies
Distinguish generic behaviour of a trading rule (e.g., contrarianinvestment) from specific instantiations.
Qualitative Strategies: Set of strategy functions whosealgorithmic description is identical up to a fixed set ofparameters.
Allows us to target behaviours somewhat independently ofunknown parameters.
Quantitative Strategies: Specific subset of strategy functionsobtained by assigning parameter values.
Implementation requires fully specified quantitative strategies.
But, it is helpful to differentiate the static structure implied by thequalitative strategy from the dynamic evolution of regimes.
![Page 30: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/30.jpg)
On Trading Strategies
Distinguish generic behaviour of a trading rule (e.g., contrarianinvestment) from specific instantiations.
Qualitative Strategies: Set of strategy functions whosealgorithmic description is identical up to a fixed set ofparameters.
Allows us to target behaviours somewhat independently ofunknown parameters.
Quantitative Strategies: Specific subset of strategy functionsobtained by assigning parameter values.
Implementation requires fully specified quantitative strategies.
But, it is helpful to differentiate the static structure implied by thequalitative strategy from the dynamic evolution of regimes.
![Page 31: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/31.jpg)
On Trading Strategies
Distinguish generic behaviour of a trading rule (e.g., contrarianinvestment) from specific instantiations.
Qualitative Strategies: Set of strategy functions whosealgorithmic description is identical up to a fixed set ofparameters.
Allows us to target behaviours somewhat independently ofunknown parameters.
Quantitative Strategies: Specific subset of strategy functionsobtained by assigning parameter values.
Implementation requires fully specified quantitative strategies.
But, it is helpful to differentiate the static structure implied by thequalitative strategy from the dynamic evolution of regimes.
![Page 32: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/32.jpg)
On Trading Strategies
Distinguish generic behaviour of a trading rule (e.g., contrarianinvestment) from specific instantiations.
Qualitative Strategies: Set of strategy functions whosealgorithmic description is identical up to a fixed set ofparameters.
Allows us to target behaviours somewhat independently ofunknown parameters.
Quantitative Strategies: Specific subset of strategy functionsobtained by assigning parameter values.
Implementation requires fully specified quantitative strategies.
But, it is helpful to differentiate the static structure implied by thequalitative strategy from the dynamic evolution of regimes.
![Page 33: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/33.jpg)
On Trading Strategies
Distinguish generic behaviour of a trading rule (e.g., contrarianinvestment) from specific instantiations.
Qualitative Strategies: Set of strategy functions whosealgorithmic description is identical up to a fixed set ofparameters.
Allows us to target behaviours somewhat independently ofunknown parameters.
Quantitative Strategies: Specific subset of strategy functionsobtained by assigning parameter values.
Implementation requires fully specified quantitative strategies.
But, it is helpful to differentiate the static structure implied by thequalitative strategy from the dynamic evolution of regimes.
![Page 34: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/34.jpg)
On Trading Strategies
Distinguish generic behaviour of a trading rule (e.g., contrarianinvestment) from specific instantiations.
Qualitative Strategies: Set of strategy functions whosealgorithmic description is identical up to a fixed set ofparameters.
Allows us to target behaviours somewhat independently ofunknown parameters.
Quantitative Strategies: Specific subset of strategy functionsobtained by assigning parameter values.
Implementation requires fully specified quantitative strategies.
But, it is helpful to differentiate the static structure implied by thequalitative strategy from the dynamic evolution of regimes.
![Page 35: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/35.jpg)
Portfolio Allocation Algorithm: A Template
Three major steps:
(Optional) Use historical data to infer set of regimes, i.e.,relative order between strategies.
In-Sample: Identify the order (by performance) over regimeswithin a moving window, i.e., estimate current context.
Out-Sample: Allocate working capital assuming persistence ofthe identified in-sample context.
![Page 36: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/36.jpg)
Portfolio Allocation Algorithm: A Template
Three major steps:
(Optional) Use historical data to infer set of regimes, i.e.,relative order between strategies.
In-Sample: Identify the order (by performance) over regimeswithin a moving window, i.e., estimate current context.
Out-Sample: Allocate working capital assuming persistence ofthe identified in-sample context.
![Page 37: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/37.jpg)
Portfolio Allocation Algorithm: A Template
Three major steps:
(Optional) Use historical data to infer set of regimes, i.e.,relative order between strategies.
In-Sample: Identify the order (by performance) over regimeswithin a moving window, i.e., estimate current context.
Out-Sample: Allocate working capital assuming persistence ofthe identified in-sample context.
![Page 38: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/38.jpg)
Simplest Instantiation:
Trade with Best In-Sample Strategy
Profit
Time
Out−of−sampleIn−sample
In: Identify best quantitative strategy
Out: Allocate capital to the best in-sample strategy
![Page 39: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/39.jpg)
Simplest Instantiation:
Trade with Best In-Sample Strategy
Profit
Time
Out−of−sampleIn−sample
In: Identify best quantitative strategy
Out: Allocate capital to the best in-sample strategy
![Page 40: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/40.jpg)
Simplest Instantiation:
Trade with Best In-Sample Strategy
Profit
Time
Out−of−sampleIn−sample
In: Identify best quantitative strategy
Out: Allocate capital to the best in-sample strategy
![Page 41: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/41.jpg)
Performance: Trading with Best In-Sample
Strategy
-25000
-20000
-15000
-10000
-5000
0
5000
10000
15000
20000
Jan 06 Jul 06 Jan 07 Jul 07 Jan 08 Jul 08 Jan 09
in: 20, out: 5in: 20, out: 20in: 20, out: 45
800
1200
1600
2000
2400
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Observations: Trading with Best In-Sample
Strategy
Can be profitable. However,
Sensitivity to parameter choice, e.g., window size.
Need fine-grained trading to follow changing trends.
Wasteful chatter between different strategies.
Diversification could help solve some of these problems.
![Page 43: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/43.jpg)
Another Simple Instantiation: k–NN
Execute the weighted average action derived from k-nearest marketstates in a historical database.
This is more diversified, but tends not to suffice.– we will see a few empirical results later in this presentation.
![Page 44: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/44.jpg)
Another Simple Instantiation: k–NN
Execute the weighted average action derived from k-nearest marketstates in a historical database.
This is more diversified, but tends not to suffice.– we will see a few empirical results later in this presentation.
![Page 45: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/45.jpg)
Regimes - Layered Graph of Strategies
Regimes: weighted groups of strategies exhibiting correlatedbehaviour according to a fitness function
During training, weights are adjusted between strategiesaccording to fitness
Fitness of a regime is defined as a weighted sum of its strategyfitnesses, similar to a mixture model
The effective number of regimes may be significantly smaller than thenumber of underlying strategies.
Dimensionality reduction would aid the state identification step
Previous instantiations may be considered special cases
Possible to build predictive models in a space that is differentfrom standard latent variable time series models.
value
![Page 46: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/46.jpg)
Regimes - Layered Graph of Strategies
Regimes: weighted groups of strategies exhibiting correlatedbehaviour according to a fitness function
During training, weights are adjusted between strategiesaccording to fitness
Fitness of a regime is defined as a weighted sum of its strategyfitnesses, similar to a mixture model
The effective number of regimes may be significantly smaller than thenumber of underlying strategies.
Dimensionality reduction would aid the state identification step
Previous instantiations may be considered special cases
Possible to build predictive models in a space that is differentfrom standard latent variable time series models.
value
![Page 47: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/47.jpg)
Regimes - Layered Graph of Strategies
Regimes: weighted groups of strategies exhibiting correlatedbehaviour according to a fitness function
During training, weights are adjusted between strategiesaccording to fitness
Fitness of a regime is defined as a weighted sum of its strategyfitnesses, similar to a mixture model
The effective number of regimes may be significantly smaller than thenumber of underlying strategies.
Dimensionality reduction would aid the state identification step
Previous instantiations may be considered special cases
Possible to build predictive models in a space that is differentfrom standard latent variable time series models.
value
![Page 48: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/48.jpg)
Regimes - Layered Graph of Strategies
Regimes: weighted groups of strategies exhibiting correlatedbehaviour according to a fitness function
During training, weights are adjusted between strategiesaccording to fitness
Fitness of a regime is defined as a weighted sum of its strategyfitnesses, similar to a mixture model
The effective number of regimes may be significantly smaller than thenumber of underlying strategies.
Dimensionality reduction would aid the state identification step
Previous instantiations may be considered special cases
Possible to build predictive models in a space that is differentfrom standard latent variable time series models.
value
![Page 49: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/49.jpg)
Regimes - Layered Graph of Strategies
Regimes: weighted groups of strategies exhibiting correlatedbehaviour according to a fitness function
During training, weights are adjusted between strategiesaccording to fitness
Fitness of a regime is defined as a weighted sum of its strategyfitnesses, similar to a mixture model
The effective number of regimes may be significantly smaller than thenumber of underlying strategies.
Dimensionality reduction would aid the state identification step
Previous instantiations may be considered special cases
Possible to build predictive models in a space that is differentfrom standard latent variable time series models.
value
![Page 50: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/50.jpg)
Regimes - Layered Graph of Strategies
Regimes: weighted groups of strategies exhibiting correlatedbehaviour according to a fitness function
During training, weights are adjusted between strategiesaccording to fitness
Fitness of a regime is defined as a weighted sum of its strategyfitnesses, similar to a mixture model
The effective number of regimes may be significantly smaller than thenumber of underlying strategies.
Dimensionality reduction would aid the state identification step
Previous instantiations may be considered special cases
Possible to build predictive models in a space that is differentfrom standard latent variable time series models.
value
![Page 51: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/51.jpg)
Regimes - Layered Graph of Strategies
Regimes: weighted groups of strategies exhibiting correlatedbehaviour according to a fitness function
During training, weights are adjusted between strategiesaccording to fitness
Fitness of a regime is defined as a weighted sum of its strategyfitnesses, similar to a mixture model
The effective number of regimes may be significantly smaller than thenumber of underlying strategies.
Dimensionality reduction would aid the state identification step
Previous instantiations may be considered special cases
Possible to build predictive models in a space that is differentfrom standard latent variable time series models.
value
![Page 52: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/52.jpg)
Regimes - Layered Graph of Strategies
Regimes: weighted groups of strategies exhibiting correlatedbehaviour according to a fitness function
During training, weights are adjusted between strategiesaccording to fitness
Fitness of a regime is defined as a weighted sum of its strategyfitnesses, similar to a mixture model
The effective number of regimes may be significantly smaller than thenumber of underlying strategies.
Dimensionality reduction would aid the state identification step
Previous instantiations may be considered special cases
Possible to build predictive models in a space that is differentfrom standard latent variable time series models.
value
![Page 53: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/53.jpg)
Regimes - Layered Graph of Strategies
Regimes: weighted groups of strategies exhibiting correlatedbehaviour according to a fitness function
During training, weights are adjusted between strategiesaccording to fitness
Fitness of a regime is defined as a weighted sum of its strategyfitnesses, similar to a mixture model
The effective number of regimes may be significantly smaller than thenumber of underlying strategies.
Dimensionality reduction would aid the state identification step
Previous instantiations may be considered special cases
Possible to build predictive models in a space that is differentfrom standard latent variable time series models.
value
![Page 54: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/54.jpg)
Algorithm: REgime Detection and STrategy OPtimization
Training - Acquire regimes from data
Explicitly specified by expert
e.g., all possible combinations of strategies
Unsupervised learning
Find significantly correlated events among possiblecombinations of events e.g. using permutation tests
![Page 55: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/55.jpg)
Algorithm: REgime Detection and STrategy OPtimization
Training - Acquire regimes from data
Explicitly specified by expert
e.g., all possible combinations of strategies
Unsupervised learning
Find significantly correlated events among possiblecombinations of events e.g. using permutation tests
![Page 56: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/56.jpg)
Algorithm: REgime Detection and STrategy OPtimization
Training - Acquire regimes from data
Explicitly specified by expert
e.g., all possible combinations of strategies
Unsupervised learning
Find significantly correlated events among possiblecombinations of events e.g. using permutation tests
![Page 57: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/57.jpg)
Algorithm: REgime Detection and STrategy OPtimization
Training - Acquire regimes from data
Explicitly specified by expert
e.g., all possible combinations of strategies
Unsupervised learning
Find significantly correlated events among possiblecombinations of events e.g. using permutation tests
![Page 58: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/58.jpg)
Algorithm: REgime Detection and STrategy OPtimization
Trading - Allocate capital based on regime-level performance
In-sample period (Estimation of current regime):1 Compute values of all regimes by taking a weighted sum of the
strategy fitnesses in the in-sample period
2 Recalculate the weights of regimes proportionally to theseweights
Out-of-sample period (Trading): Multiplicative weight updatefor allocation between regimes
1
0
0.5
![Page 59: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/59.jpg)
Algorithm: REgime Detection and STrategy OPtimization
Trading - Allocate capital based on regime-level performance
In-sample period (Estimation of current regime):1 Compute values of all regimes by taking a weighted sum of the
strategy fitnesses in the in-sample period
2 Recalculate the weights of regimes proportionally to theseweights
Out-of-sample period (Trading): Multiplicative weight updatefor allocation between regimes
1
0
0.5
100
-100
0
![Page 60: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/60.jpg)
Algorithm: REgime Detection and STrategy OPtimization
Trading - Allocate capital based on regime-level performance
In-sample period (Estimation of current regime):1 Compute values of all regimes by taking a weighted sum of the
strategy fitnesses in the in-sample period
2 Recalculate the weights of regimes proportionally to theseweights
Out-of-sample period (Trading): Multiplicative weight updatefor allocation between regimes
1
0
0.5
100
-100
0
![Page 61: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/61.jpg)
Algorithm: REgime Detection and STrategy OPtimization
Trading - Allocate capital based on regime-level performance
In-sample period (Estimation of current regime):1 Compute values of all regimes by taking a weighted sum of the
strategy fitnesses in the in-sample period2 Recalculate the weights of regimes proportionally to these
weights
Out-of-sample period (Trading): Multiplicative weight updatefor allocation between regimes
1
0
0.5
100
-100
0
![Page 62: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/62.jpg)
Algorithm: REgime Detection and STrategy OPtimization
Trading - Allocate capital based on regime-level performance
In-sample period (Estimation of current regime):1 Compute values of all regimes by taking a weighted sum of the
strategy fitnesses in the in-sample period2 Recalculate the weights of regimes proportionally to these
weights
Out-of-sample period (Trading): Multiplicative weight updatefor allocation between regimes
1
0
0.5
100
-100
0
![Page 63: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/63.jpg)
Performance of RED-STOP Algorithm
-20000
-15000
-10000
-5000
0
5000
10000
15000
20000
Jan 06 Jul 06 Jan 07 Jul 07 Jan 08 Jul 08 Jan 09
maxknnRSRS - above 0RS - max
800
1200
1600
2000
2400
![Page 64: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/64.jpg)
Performance of RED-STOP Algorithm
-50000
-40000
-30000
-20000
-10000
0
10000
20000
Jan 06 Jul 06 Jan 07 Jul 07 Jan 08 Jul 08 Jan 09
maxknnRSRS - above 0RS - max
800
1200
1600
2000
2400
![Page 65: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/65.jpg)
Performance of RED-STOP Algorithm
-50000
-40000
-30000
-20000
-10000
0
10000
20000
Jan 06 Jul 06 Jan 07 Jul 07 Jan 08 Jul 08 Jan 09
maxknnRSRS - above 0RS - max
800
1200
1600
2000
2400
![Page 66: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/66.jpg)
Discussion
Relationship to alternate regime-switching models:
Possible to devise sophisticated time-series models based on EMand MCMC techniques but they can be fragile in on-line‘covariance-shifted’ scenarios.
We claim, and demonstrate, the benefit of a more direct‘action-oriented’ representation.
What is the role of historical data? Or, what happens in novelscenarios?
Data allows us to identify possible correlation patterns withinstrategy space– structure induced by latent market dynamics– no parametric assumptions regarding the latent dynamics
Structure in this space (e.g., low-dimensional regime subspaces)can be exploited to devise more efficient strategies.
![Page 67: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/67.jpg)
Discussion
Relationship to alternate regime-switching models:
Possible to devise sophisticated time-series models based on EMand MCMC techniques but they can be fragile in on-line‘covariance-shifted’ scenarios.
We claim, and demonstrate, the benefit of a more direct‘action-oriented’ representation.
What is the role of historical data? Or, what happens in novelscenarios?
Data allows us to identify possible correlation patterns withinstrategy space– structure induced by latent market dynamics– no parametric assumptions regarding the latent dynamics
Structure in this space (e.g., low-dimensional regime subspaces)can be exploited to devise more efficient strategies.
![Page 68: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/68.jpg)
Discussion
Relationship to alternate regime-switching models:
Possible to devise sophisticated time-series models based on EMand MCMC techniques but they can be fragile in on-line‘covariance-shifted’ scenarios.
We claim, and demonstrate, the benefit of a more direct‘action-oriented’ representation.
What is the role of historical data? Or, what happens in novelscenarios?
Data allows us to identify possible correlation patterns withinstrategy space– structure induced by latent market dynamics– no parametric assumptions regarding the latent dynamics
Structure in this space (e.g., low-dimensional regime subspaces)can be exploited to devise more efficient strategies.
![Page 69: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/69.jpg)
Discussion
Relationship to alternate regime-switching models:
Possible to devise sophisticated time-series models based on EMand MCMC techniques but they can be fragile in on-line‘covariance-shifted’ scenarios.
We claim, and demonstrate, the benefit of a more direct‘action-oriented’ representation.
What is the role of historical data? Or, what happens in novelscenarios?
Data allows us to identify possible correlation patterns withinstrategy space– structure induced by latent market dynamics– no parametric assumptions regarding the latent dynamics
Structure in this space (e.g., low-dimensional regime subspaces)can be exploited to devise more efficient strategies.
![Page 70: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/70.jpg)
Discussion
Relationship to alternate regime-switching models:
Possible to devise sophisticated time-series models based on EMand MCMC techniques but they can be fragile in on-line‘covariance-shifted’ scenarios.
We claim, and demonstrate, the benefit of a more direct‘action-oriented’ representation.
What is the role of historical data? Or, what happens in novelscenarios?
Data allows us to identify possible correlation patterns withinstrategy space– structure induced by latent market dynamics– no parametric assumptions regarding the latent dynamics
Structure in this space (e.g., low-dimensional regime subspaces)can be exploited to devise more efficient strategies.
![Page 71: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/71.jpg)
Discussion
Relationship to alternate regime-switching models:
Possible to devise sophisticated time-series models based on EMand MCMC techniques but they can be fragile in on-line‘covariance-shifted’ scenarios.
We claim, and demonstrate, the benefit of a more direct‘action-oriented’ representation.
What is the role of historical data? Or, what happens in novelscenarios?
Data allows us to identify possible correlation patterns withinstrategy space– structure induced by latent market dynamics– no parametric assumptions regarding the latent dynamics
Structure in this space (e.g., low-dimensional regime subspaces)can be exploited to devise more efficient strategies.
![Page 72: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/72.jpg)
Discussion
Relationship to alternate regime-switching models:
Possible to devise sophisticated time-series models based on EMand MCMC techniques but they can be fragile in on-line‘covariance-shifted’ scenarios.
We claim, and demonstrate, the benefit of a more direct‘action-oriented’ representation.
What is the role of historical data? Or, what happens in novelscenarios?
Data allows us to identify possible correlation patterns withinstrategy space– structure induced by latent market dynamics– no parametric assumptions regarding the latent dynamics
Structure in this space (e.g., low-dimensional regime subspaces)can be exploited to devise more efficient strategies.
![Page 73: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/73.jpg)
Conclusions
Framework for on-line multi-strategy trading.
Utilization of market context:
Learned from data.
Represented in terms of directly measurable quantities.
Many variations on a basic template - this is ongoing work.
Future Work:
Systematic empirical evaluation (across multiple markets)
Explore use of low-dimensional structure in regime space.
Risk-sensitive optimization and predictive-modelling.
![Page 74: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/74.jpg)
Conclusions
Framework for on-line multi-strategy trading.
Utilization of market context:
Learned from data.
Represented in terms of directly measurable quantities.
Many variations on a basic template - this is ongoing work.
Future Work:
Systematic empirical evaluation (across multiple markets)
Explore use of low-dimensional structure in regime space.
Risk-sensitive optimization and predictive-modelling.
![Page 75: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/75.jpg)
Conclusions
Framework for on-line multi-strategy trading.
Utilization of market context:
Learned from data.
Represented in terms of directly measurable quantities.
Many variations on a basic template - this is ongoing work.
Future Work:
Systematic empirical evaluation (across multiple markets)
Explore use of low-dimensional structure in regime space.
Risk-sensitive optimization and predictive-modelling.
![Page 76: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/76.jpg)
Conclusions
Framework for on-line multi-strategy trading.
Utilization of market context:
Learned from data.
Represented in terms of directly measurable quantities.
Many variations on a basic template - this is ongoing work.
Future Work:
Systematic empirical evaluation (across multiple markets)
Explore use of low-dimensional structure in regime space.
Risk-sensitive optimization and predictive-modelling.
![Page 77: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/77.jpg)
Conclusions
Framework for on-line multi-strategy trading.
Utilization of market context:
Learned from data.
Represented in terms of directly measurable quantities.
Many variations on a basic template - this is ongoing work.
Future Work:
Systematic empirical evaluation (across multiple markets)
Explore use of low-dimensional structure in regime space.
Risk-sensitive optimization and predictive-modelling.
![Page 78: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/78.jpg)
Conclusions
Framework for on-line multi-strategy trading.
Utilization of market context:
Learned from data.
Represented in terms of directly measurable quantities.
Many variations on a basic template - this is ongoing work.
Future Work:
Systematic empirical evaluation (across multiple markets)
Explore use of low-dimensional structure in regime space.
Risk-sensitive optimization and predictive-modelling.
![Page 79: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/79.jpg)
Conclusions
Framework for on-line multi-strategy trading.
Utilization of market context:
Learned from data.
Represented in terms of directly measurable quantities.
Many variations on a basic template - this is ongoing work.
Future Work:
Systematic empirical evaluation (across multiple markets)
Explore use of low-dimensional structure in regime space.
Risk-sensitive optimization and predictive-modelling.
![Page 80: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/80.jpg)
Conclusions
Framework for on-line multi-strategy trading.
Utilization of market context:
Learned from data.
Represented in terms of directly measurable quantities.
Many variations on a basic template - this is ongoing work.
Future Work:
Systematic empirical evaluation (across multiple markets)
Explore use of low-dimensional structure in regime space.
Risk-sensitive optimization and predictive-modelling.
![Page 81: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/81.jpg)
Conclusions
Framework for on-line multi-strategy trading.
Utilization of market context:
Learned from data.
Represented in terms of directly measurable quantities.
Many variations on a basic template - this is ongoing work.
Future Work:
Systematic empirical evaluation (across multiple markets)
Explore use of low-dimensional structure in regime space.
Risk-sensitive optimization and predictive-modelling.
![Page 82: Multi-Strategy Trading Utilizing Market Regimes](https://reader037.fdocuments.in/reader037/viewer/2022103010/584d0a3a1a28ab85738ffe29/html5/thumbnails/82.jpg)
Conclusions
Framework for on-line multi-strategy trading.
Utilization of market context:
Learned from data.
Represented in terms of directly measurable quantities.
Many variations on a basic template - this is ongoing work.
Future Work:
Systematic empirical evaluation (across multiple markets)
Explore use of low-dimensional structure in regime space.
Risk-sensitive optimization and predictive-modelling.