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34
Reinforcement learning for optimized trade execution
 In ICML ’06: Proceedings of the 23rd international conference on Machine learning
, 2006
"... We present the first largescale empirical application of reinforcement learning to the important problem of optimized trade execution in modern financial markets. Our experiments are based on 1.5 years of millisecond timescale limit order data from NASDAQ, and demonstrate the promise of reinforcem ..."
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We present the first largescale empirical application of reinforcement learning to the important problem of optimized trade execution in modern financial markets. Our experiments are based on 1.5 years of millisecond timescale limit order data from NASDAQ, and demonstrate the promise of reinforcement learning methods to market microstructure problems. Our learning algorithm introduces and exploits a natural &quot;lowimpact &quot; factorization of the state space. 1.
Regret Minimization for Online Buffering Problems Using the Weighted Majority Algorithm ∗
"... Suppose a decision maker has to purchase a commodity over time with varying prices and demands. In particular, the price per unit might depend on the amount purchased and this price function might vary from step to step. The decision maker has a buffer of bounded size for storing units of the commod ..."
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Cited by 5 (0 self)
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Suppose a decision maker has to purchase a commodity over time with varying prices and demands. In particular, the price per unit might depend on the amount purchased and this price function might vary from step to step. The decision maker has a buffer of bounded size for storing units of the commodity that can be used to satisfy demands at later points in time. We seek for an algorithm deciding at which time to buy which amount of the commodity so as to minimize the cost. This kind of problem arises in many technological and economical settings like, e.g., battery management in hybrid cars and economical caching policies for mobile devices. A simplified but illustrative example is a frugal car driver thinking about at which occasion to buy which amount of gasoline. Within a regret analysis, we assume that the decision maker can observe the performance of a set of expert strategies over time and synthesizes the observed strategies into a new online algorithm. In particular, we investigate the external regret obtained by the wellknown Randomized Weighted Majority algorithm applied to our problem. We show that this algorithm does not achieve a reasonable regret bound if its random choices are independent
Reactive Multiword Synchronization for Multiprocessors
, 2004
"... Shared memory multiprocessor systems typically provide a set of hardware primitives in order to support synchronization. Generally, they provide singleword readmodifywrite hardware primitives such as compareandswap, loadlinked/storeconditional and fetchandop, from which the higherlevel sync ..."
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Cited by 5 (3 self)
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Shared memory multiprocessor systems typically provide a set of hardware primitives in order to support synchronization. Generally, they provide singleword readmodifywrite hardware primitives such as compareandswap, loadlinked/storeconditional and fetchandop, from which the higherlevel synchronization operations are then implemented in software. Although the singleword hardware primitives are conceptually powerful enough to support higherlevel synchronization, from the programmer's point of view they are not as useful as their generalizations to the multiword objects.
Optimal Security Liquidation Algorithms
, 2005
"... This paper develops trading strategies for liquidation of a financial security which maximize the expected return. The problem is formulated as a stochastic programming problem, which utilizes the scenario representation of possible returns. Two cases are considered, a case with no constraint on ris ..."
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Cited by 4 (2 self)
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This paper develops trading strategies for liquidation of a financial security which maximize the expected return. The problem is formulated as a stochastic programming problem, which utilizes the scenario representation of possible returns. Two cases are considered, a case with no constraint on risk and a case when the risk of losses associated with trading strategy is constrained by Conditional ValueatRisk (CVaR) measure. In the first case, two algorithms are proposed; one is based on linear programming techniques, and the other uses dynamic programming to solve the formulated stochastic program. The third proposed algorithm is obtained by adding the risk constraints to the linear program. The algorithms provide pathdependent strategies which sell some fractions of security depending upon price samplepath of security up to the current moment. The performance of the considered approaches is tested using a set of historical samplepaths of prices.
Optimal algorithms for ksearch with applications in option pricing
 In ESA 2007, Proceedings 15th Annual European Symposium
, 2007
"... Abstract. In the ksearch problem, a player is searching for the k highest (respectively, lowest) prices in a sequence, which is revealed to her sequentially. At each quotation, the player has to decide immediately whether to accept the price or not. Using the competitive ratio as a performance meas ..."
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Abstract. In the ksearch problem, a player is searching for the k highest (respectively, lowest) prices in a sequence, which is revealed to her sequentially. At each quotation, the player has to decide immediately whether to accept the price or not. Using the competitive ratio as a performance measure, we give optimal deterministic and randomized algorithms for both the maximization and minimization problems, and discover that the problems behave substantially different in the worstcase. As an application of our results, we use these algorithms to price “lookback options”, a particular class of financial derivatives. We derive bounds for the price of these securities under a noarbitrage assumption, and compare this to classical option pricing. 1
Delayed Information and Action in OnLine Algorithms
 39th IEEE symposium on Foundations of Computer Science
, 1998
"... Most online analysis assumes that, at each time step, all relevant information up to that time step is available and a decision has an immediate effect. In many online problems, however, the time relevant information is available and the time a decision has an effect may be decoupled. For example, ..."
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Most online analysis assumes that, at each time step, all relevant information up to that time step is available and a decision has an immediate effect. In many online problems, however, the time relevant information is available and the time a decision has an effect may be decoupled. For example, when making an investment, one might not have completely uptodate information on market prices. Similarly, a buy or sell order might only be executed some time later in the future. We introduce and explore natural delayed models for several wellknown online problems. Our analyses demonstrate the importance of considering timeliness in determining the competitive ratio of an online algorithm. For many problems, we demonstrate that there exist algorithms with small competitive ratios even when large delays affect the timeliness of information and the effect of decisions.
Selftuning reactive distributed trees for counting and balancing
"... Abstract. The main contribution of this paper is that it shows that it is possible to have reactive distributed trees for counting and balancing with no need for the user to fix manually any parameters. We present a data structure that in an online manner balances the tradeoff between the tree tra ..."
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Abstract. The main contribution of this paper is that it shows that it is possible to have reactive distributed trees for counting and balancing with no need for the user to fix manually any parameters. We present a data structure that in an online manner balances the tradeoff between the tree traversal latency and the latency due to contention at the tree nodes. Moreover, the fact that our method can expand or shrink a subtree several levels in any adjustment step, has a positive effect in the efficiency: this feature helps the selftuning reactive tree minimize the adjustment time, which affects not only the execution time of the process adjusting the size of the tree but also the latency of all other processes traversing the tree at the same time with no extra memory requirements. Our experimental study compared the new trees with the reactive diffracting ones on the SGI Origin2000, a wellknown commercial ccNUMA multiprocessor. This study showed that the selftuning reactive trees i) select the same tree depth as the reactive diffracting trees do; ii) perform better and iii) react faster. 1
SelfAdjusting Trees
, 2003
"... The reactive diffracting trees are known efficient distributed data structures for supporting synchronization. They not only distribute a set of processes to smaller groups accessing different parts of the memory in a global coordinated manner, but also adjust their size in order to attain efficient ..."
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Cited by 3 (1 self)
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The reactive diffracting trees are known efficient distributed data structures for supporting synchronization. They not only distribute a set of processes to smaller groups accessing different parts of the memory in a global coordinated manner, but also adjust their size in order to attain efficient performance across different levels of contention. However, the existing reactive adjustment policy of these trees is sensitive to parameters that have to be manually set in an optimal way and be determined after experimentation. Because these parameters depend on the application as well as on the system configuration, determining their optimal values is hard in practice. Moreover, because the reactive diffracting trees expand or shrink one level at a time, the cost of a multiadjustment phase on a reactive tree can become high. We argue that these two problems are not fundamental, and that it is possible to construct reactive trees that: (i) are selfadjustable with no need of fixing manually any parameter, and (ii) have the ability to expand or shrink many levels at one time. In this paper...
Reactive Spinlocks: A Selftuning Approach
"... Reactive spinlock algorithms that can automatically adapt to contention variation on the lock have received great attention in the field of multiprocessor synchronization, since they can help applications achieve good performance in all possible contention conditions. However, in existing reactive ..."
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Reactive spinlock algorithms that can automatically adapt to contention variation on the lock have received great attention in the field of multiprocessor synchronization, since they can help applications achieve good performance in all possible contention conditions. However, in existing reactive spinlocks the reaction relies on (i) some fixed experimentally tuned thresholds, which may get frequently inappropriate in dynamic environments like multiprogramming/multiprocessor systems, or (ii) known probability distributions of inputs. This paper presents a new reactive spinlock algorithm that is completely selftuning, which means no experimentally tuned parameter nor probability distribution of inputs are needed. The new spinlock is built on a competitive online algorithm. Our experiments, which use the Spark98 kernels and the SPLASH2 applications as application benchmarks, on a multiprocessor machine SGI Origin2000 and on an Intel Xeon workstation show that the new selftuning spinlock helps applications with different characteristics achieve good performance in a wide range of contention levels. 1.
Regret minimization algorithms for pricing lookback options
 In ALT
, 2011
"... Abstract. In this work, we extend the applicability of regret minimization to pricing financial instruments, following the work of [10]. More specifically, we consider pricing a type of exotic option called a fixedstrike lookback call option. A fixedstrike lookback call option has a known expirati ..."
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Abstract. In this work, we extend the applicability of regret minimization to pricing financial instruments, following the work of [10]. More specifically, we consider pricing a type of exotic option called a fixedstrike lookback call option. A fixedstrike lookback call option has a known expiration time, at which the option holder has the right to receive the difference between the maximal price of a stock and some preagreed price. We derive upper bounds on the price of these options, assuming an arbitragefree market, by developing twoway trading algorithms. We construct our trading algorithms by combining regret minimization algorithms and oneway trading algorithms. Our model assumes upper bounds on the absolute daily returns, overall quadratic variation, and stock price, otherwise allowing for fully adversarial market behavior. 1