scholarly journals Càdlàg semimartingale strategies for optimal trade execution in stochastic order book models

2021 ◽  
Vol 25 (4) ◽  
pp. 757-810
Author(s):  
Julia Ackermann ◽  
Thomas Kruse ◽  
Mikhail Urusov
Keyword(s):  
Stochastic Order ◽  
Order Book ◽  
Optimal Trade Execution

scholarly journals Optimal Trade Execution in an Order Book Model with Stochastic Liquidity Parameters

2021 ◽  
Vol 12 (2) ◽  
pp. 788-822
Author(s):  
Julia Ackermann ◽  
Thomas Kruse ◽  
Mikhail Urusov
Keyword(s):  
Order Book ◽  
Optimal Trade Execution

scholarly journals Optimal Trade Execution and Absence of Price Manipulations in Limit Order Book Models

2010 ◽  
Vol 1 (1) ◽  
pp. 490-522 ◽  
Author(s):  
Aurélien Alfonsi ◽  
Alexander Schied
Keyword(s):  
Limit Order Book ◽  
Order Book ◽  
Limit Order ◽  
Optimal Trade Execution

scholarly journals An End-to-End Optimal Trade Execution Framework based on Proximal Policy Optimization

2020 ◽  
Author(s):  
Siyu Lin ◽  
Peter A. Beling
Keyword(s):  
Network Architecture ◽  
Short Term Memory ◽  
Order Book ◽  
Neural Network Architecture ◽  
Reward Function ◽  
Time Dependencies ◽  
Optimal Trade Execution ◽  
End To End ◽  
Policy Optimization ◽  
Fully Connected

In this article, we propose an end-to-end adaptive framework for optimal trade execution based on Proximal Policy Optimization (PPO). We use two methods to account for the time dependencies in the market data based on two different neural network architecture: 1) Long short-term memory (LSTM) networks, 2) Fully-connected networks (FCN) by stacking the most recent limit orderbook (LOB) information as model inputs. The proposed framework can make trade execution decisions based on level-2 limit order book (LOB) information such as bid/ask prices and volumes directly without manually designed attributes as in previous research. Furthermore, we use a sparse reward function, which gives the agent reward signals at the end of each episode as an indicator of its relative performances against the baseline model, rather than implementation shortfall (IS) or a shaped reward function. The experimental results have demonstrated advantages over IS and the shaped reward function in terms of performance and simplicity. The proposed framework has outperformed the industry commonly used baseline models such as TWAP, VWAP, and AC as well as several Deep Reinforcement Learning (DRL) models on most of the 14 US equities in our experiments.

A multilayer model of order book dynamics

2016 ◽  
Vol 2 (3) ◽  
pp. 37-52 ◽  
Author(s):  
Alessio Emanuele Biondo ◽  
Alessandro Pluchino ◽  
Andrea Rapisarda
Keyword(s):  
Order Book ◽  
Multilayer Model

Bid- and Ask-Side Liquidity in the NYSE Limit Order Book

2016 ◽  
Author(s):  
Tolga Cenesizoglu ◽  
Gunnar Grass
Keyword(s):  
Limit Order Book ◽  
Order Book ◽  
Limit Order
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