2,437 research outputs found
Fools Rush In: Competitive Effects of Reaction Time in Automated Trading
We explore the competitive effects of reaction time of automated trading
strategies in simulated financial markets containing a single exchange with
public limit order book and continuous double auction matching. A large body of
research conducted over several decades has been devoted to trading agent
design and simulation, but the majority of this work focuses on pricing
strategy and does not consider the time taken for these strategies to compute.
In real-world financial markets, speed is known to heavily influence the design
of automated trading algorithms, with the generally accepted wisdom that faster
is better. Here, we introduce increasingly realistic models of trading speed
and profile the computation times of a suite of eminent trading algorithms from
the literature. Results demonstrate that: (a) trading performance is impacted
by speed, but faster is not always better; (b) the Adaptive-Aggressive (AA)
algorithm, until recently considered the most dominant trading strategy in the
literature, is outperformed by the simplistic Shaver (SHVR) strategy - shave
one tick off the current best bid or ask - when relative computation times are
accurately simulated.Comment: 12 pages, 9 figures. Author's accepted manuscript. Published in
ICAART 2020: Proceedings of the 12th International Conference on Agents and
Artificial Intelligence, pages 82-93. Valletta, Malta, Feb. 2020. V2 edits:
source code links moved from reference list to footnote
Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market
We report successful results from using deep learning neural networks (DLNNs)
to learn, purely by observation, the behavior of profitable traders in an
electronic market closely modelled on the limit-order-book (LOB) market
mechanisms that are commonly found in the real-world global financial markets
for equities (stocks & shares), currencies, bonds, commodities, and
derivatives. Successful real human traders, and advanced automated algorithmic
trading systems, learn from experience and adapt over time as market conditions
change; our DLNN learns to copy this adaptive trading behavior. A novel aspect
of our work is that we do not involve the conventional approach of attempting
to predict time-series of prices of tradeable securities. Instead, we collect
large volumes of training data by observing only the quotes issued by a
successful sales-trader in the market, details of the orders that trader is
executing, and the data available on the LOB (as would usually be provided by a
centralized exchange) over the period that the trader is active. In this paper
we demonstrate that suitably configured DLNNs can learn to replicate the
trading behavior of a successful adaptive automated trader, an algorithmic
system previously demonstrated to outperform human traders. We also demonstrate
that DLNNs can learn to perform better (i.e., more profitably) than the trader
that provided the training data. We believe that this is the first ever
demonstration that DLNNs can successfully replicate a human-like, or
super-human, adaptive trader operating in a realistic emulation of a real-world
financial market. Our results can be considered as proof-of-concept that a DLNN
could, in principle, observe the actions of a human trader in a real financial
market and over time learn to trade equally as well as that human trader, and
possibly better.Comment: 8 pages, 4 figures. To be presented at IEEE Symposium on
Computational Intelligence in Financial Engineering (CIFEr), Bengaluru; Nov
18-21, 201
Time Matters: Exploring the Effects of Urgency and Reaction Speed in Automated Traders
We consider issues of time in automated trading strategies in simulated
financial markets containing a single exchange with public limit order book and
continuous double auction matching. In particular, we explore two effects: (i)
reaction speed - the time taken for trading strategies to calculate a response
to market events; and (ii) trading urgency - the sensitivity of trading
strategies to approaching deadlines. Much of the literature on trading agents
focuses on optimising pricing strategies only and ignores the effects of time,
while real-world markets continue to experience a race to zero latency, as
automated trading systems compete to quickly access information and act in the
market ahead of others. We demonstrate that modelling reaction speed can
significantly alter previously published results, with simple strategies such
as SHVR outperforming more complex adaptive algorithms such as AA. We also show
that adding a pace parameter to ZIP traders (ZIP-Pace, or ZIPP) can create a
sense of urgency that significantly improves profitability.Comment: 22 pages. To be published in A. P. Rocha et al. (Eds.), ICAART 2020,
LNAI 12613, 2021. arXiv admin note: substantial text overlap with
arXiv:1912.0277
Speed Traps: Algorithmic Trader Performance Under Alternative Market Structures
Using laboratory experiments, we illustrate that trading algorithms that prioritize low latency pose certain pitfalls in a variety of market structures and configurations. In hybrid double auctions markets with human traders and trading agents, we find superior performance of trading agents to human traders in balanced markets with the same number of human and Zero Intelligence Plus (ZIP) buyers and sellers only, thus providing a partial replication of Das et al. (2001). However, in unbalanced markets and extreme market structures, such as monopolies and duopolies, fast ZIP agents fall into a speed trap and both human participants and slow ZIP agents outperform fast ZIP agents. For human traders, faster reaction time significantly improves trading performance, while Theory of Mind can be detrimental for human buyers, but beneficial for human sellers
Critical Market Crashes
This review is a partial synthesis of the book ``Why stock market crash''
(Princeton University Press, January 2003), which presents a general theory of
financial crashes and of stock market instabilities that his co-workers and the
author have developed over the past seven years. The study of the frequency
distribution of drawdowns, or runs of successive losses shows that large
financial crashes are ``outliers'': they form a class of their own as can be
seen from their statistical signatures. If large financial crashes are
``outliers'', they are special and thus require a special explanation, a
specific model, a theory of their own. In addition, their special properties
may perhaps be used for their prediction. The main mechanisms leading to
positive feedbacks, i.e., self-reinforcement, such as imitative behavior and
herding between investors are reviewed with many references provided to the
relevant literature outside the confine of Physics. Positive feedbacks provide
the fuel for the development of speculative bubbles, preparing the instability
for a major crash. We demonstrate several detailed mathematical models of
speculative bubbles and crashes. The most important message is the discovery of
robust and universal signatures of the approach to crashes. These precursory
patterns have been documented for essentially all crashes on developed as well
as emergent stock markets, on currency markets, on company stocks, and so on.
The concept of an ``anti-bubble'' is also summarized, with two forward
predictions on the Japanese stock market starting in 1999 and on the USA stock
market still running. We conclude by presenting our view of the organization of
financial markets.Comment: Latex 89 pages and 38 figures, in press in Physics Report
Why hasn’t high-frequency trading swept the board? Shares, sovereign bonds and the politics of market structure
In today’s trading of liquid financial instruments, there are two main contending agencements (in Callon’s ‘actor-network’ sense of combinations of humans and nonhuman elements that manifest distributed agency): one agencement yokes together automated high-frequency trading (HFT) and open, anonymous electronic order books; the other is organized above all around the distinction between ‘dealers’ and ‘clients’. Drawing upon interviews with 321 market participants, we examine differences in the relative presence of the two agencements. We focus in this article on the processes that have given rise to especially sharp differences between the trading of shares and of sovereign bonds, and between the trading of the latter in the US and Europe. The article contributes to two literatures: the sociological literature on trading (especially on HFT), which we argue needs expanded to encompass what can be called ‘the politics of market structure’; and the nascent political-economy literature on the processes shaping how sovereign bonds are traded. In terms of underlying theory, we advocate far greater attention in actor-network economic sociology to the state and its agencies and a stronger focus in political economy on materiality.Introduction Cases and data sources An agencement triumphant: The transformation of US and European share trading A partial colonization: The trading of US Treasurys An agencement blocked: sovereign bonds in Europe Conclusion Reference
- …