19 research outputs found
Market Impact in Trader-Agents:Adding Multi-Level Order-Flow Imbalance-Sensitivity to Automated Trading Systems
Financial markets populated by human traders often exhibit "market impact",
where the traders' quote-prices move in the direction of anticipated change,
before any transaction has taken place, as an immediate reaction to the arrival
of a large (i.e., "block") buy or sell order in the market: e.g., traders in
the market know that a block buy order will push the price up, and so they
immediately adjust their quote-prices upwards. Most major financial markets now
involve many "robot traders", autonomous adaptive software agents, rather than
humans. This paper explores how to give such trader-agents a reliable
anticipatory sensitivity to block orders, such that markets populated entirely
by robot traders also show market-impact effects. In a 2019 publication Church
& Cliff presented initial results from a simple deterministic robot trader,
ISHV, which exhibits this market impact effect via monitoring a metric of
imbalance between supply and demand in the market. The novel contributions of
our paper are: (a) we critique the methods used by Church & Cliff, revealing
them to be weak, and argue that a more robust measure of imbalance is required;
(b) we argue for the use of multi-level order-flow imbalance (MLOFI: Xu et al.,
2019) as a better basis for imbalance-sensitive robot trader-agents; and (c) we
demonstrate the use of the more robust MLOFI measure in extending ISHV, and
also the well-known AA and ZIP trading-agent algorithms (which have both been
previously shown to consistently outperform human traders). We demonstrate that
the new imbalance-sensitive trader-agents introduced here do exhibit market
impact effects, and hence are better-suited to operating in markets where
impact is a factor of concern or interest, but do not suffer the weaknesses of
the methods used by Church & Cliff. The source-code for our work reported here
is freely available on GitHub.Comment: To be presented at the 13th International Conference on Agents and
Artificial Intelligence (ICAART2021), Vienna, 4th--6th February 2021. 15
pages; 9 figure
Exploring Narrative Economics:A Novel Agent-Based-Modeling Platform that Integrates Automated Traders with Opinion Dynamics
In seeking to explain aspects of real-world economies that defy easy
understanding when analysed via conventional means, Nobel Laureate Robert
Shiller has since 2017 introduced and developed the idea of Narrative
Economics, where observable economic factors such as the dynamics of prices in
asset markets are explained largely as a consequence of the narratives (i.e.,
the stories) heard, told, and believed by participants in those markets.
Shiller argues that otherwise irrational and difficult-to-explain behaviors,
such as investors participating in highly volatile cryptocurrency markets, are
best explained and understood in narrative terms: people invest because they
believe, because they have a heartfelt opinions, about the future prospects of
the asset, and they tell to themselves and others stories (narratives) about
those beliefs and opinions. In this paper we describe what is, to the best of
our knowledge, the first ever agent-based modelling platform that allows for
the study of issues in narrative economics. We have created this by integrating
and synthesizing research in two previously separate fields: opinion dynamics
(OD), and agent-based computational economics (ACE) in the form of
minimally-intelligent trader-agents operating in accurately modelled financial
markets. We show here for the first time how long-established models in OD and
in ACE can be brought together to enable the experimental study of issues in
narrative economics, and we present initial results from our system. The
program-code for our simulation platform has been released as freely-available
open-source software on GitHub, to enable other researchers to replicate and
extend our workComment: To be presented at the 13th International Conference on Agents and
Artificial Intelligence (ICAART2021), Vienna, 4th--6th February 2021. 18
pages; 14 figure
Phenotypic and functional analyses show stem cell-derived hepatocyte-like cells better mimic fetal rather than adult hepatocytes
Background & Aims: Hepatocyte-like cells (HLCs), differentiated from pluripotent stem cells by the use of soluble factors, can model human liver function and toxicity. However, at present HLC maturity and whether any deficit represents a true fetal state or aberrant differentiation is unclear and compounded by comparison to potentially deteriorated adult hepatocytes. Therefore, we generated HLCs from multiple lineages, using two different protocols,
for direct comparison with fresh fetal and adult hepatocytes.
Methods: Protocols were developed for robust differentiation. Multiple transcript, protein and functional analyses compared HLCs to fresh human fetal and adult hepatocytes.
Results: HLCs were comparable to those of other laboratories by multiple parameters. Transcriptional changes during differentiation mimicked human embryogenesis and showed more similarity to pericentral than periportal hepatocytes. Unbiased proteomics demonstrated greater proximity to liver than 30 other human organs or tissues. However, by comparison to fresh material,
HLC maturity was proven by transcript, protein and function to be fetal-like and short of the adult phenotype. The expression of 81% phase 1 enzymes in HLCs was significantly upregulated and half were statistically not different from fetal hepatocytes. HLCs secreted albumin and metabolized testosterone (CYP3A) and dextrorphan (CYP2D6) like fetal hepatocytes. In seven bespoke tests,
devised by principal components analysis to distinguish fetal from adult hepatocytes, HLCs from two different source laboratories consistently demonstrated fetal characteristics.
Conclusions: HLCs from different sources are broadly comparable with unbiased proteomic evidence for faithful differentiation down the liver lineage. This current phenotype mimics human fetal rather than adult hepatocytes
The SOLAS air-sea gas exchange experiment (SAGE) 2004
Author Posting. © The Author(s), 2010. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Deep Sea Research Part II: Topical Studies in Oceanography 58 (2011): 753-763, doi:10.1016/j.dsr2.2010.10.015.The SOLAS air-sea gas exchange experiment (SAGE) was a multiple-objective study investigating
gas-transfer processes and the influence of iron fertilisation on biologically driven gas exchange in
high-nitrate low-silicic acid low-chlorophyll (HNLSiLC) Sub-Antarctic waters characteristic of the
expansive Subpolar Zone of the southern oceans. This paper provides a general introduction and
summary of the main experimental findings. The release site was selected from a pre-voyage desktop
study of environmental parameters to be in the south-west Bounty Trough (46.5°S 172.5°E) to the
south-east of New Zealand and the experiment conducted between mid-March and mid-April 2004. In
common with other mesoscale iron addition experiments (FeAX’s), SAGE was designed as a
Lagrangian study quantifying key biological and physical drivers influencing the air-sea gas exchange
processes of CO2, DMS and other biogenic gases associated with an iron-induced phytoplankton
bloom. A dual tracer SF6/3He release enabled quantification of both the lateral evolution of a labelled
volume (patch) of ocean and the air-sea tracer exchange at the 10’s of km’s scale, in conjunction with
the iron fertilisation. Estimates from the dual-tracer experiment found a quadratic dependency of the
gas exchange coefficient on windspeed that is widely applicable and describes air-sea gas exchange in strong wind regimes. Within the patch, local and micrometeorological gas exchange process studies (100 m scale) and physical variables such as near-surface turbulence, temperature microstructure at the interface, wave properties, and wind speed were quantified to further assist the development of gas exchange models for high-wind environments.
There was a significant increase in the photosynthetic competence (Fv/Fm) of resident phytoplankton
within the first day following iron addition, but in contrast to other FeAX’s, rates of net primary
production and column-integrated chlorophyll a concentrations had only doubled relative to the
unfertilised surrounding waters by the end of the experiment. After 15 days and four iron additions
totalling 1.1 tonne Fe2+, this was a very modest response compared to the other mesoscale iron
enrichment experiments. An investigation of the factors limiting bloom development considered co-
limitation by light and other nutrients, the phytoplankton seed-stock and grazing regulation. Whilst
incident light levels and the initial Si:N ratio were the lowest recorded in all FeAX’s to date, there was
only a small seed-stock of diatoms (less than 1% of biomass) and the main response to iron addition
was by the picophytoplankton. A high rate of dilution of the fertilised patch relative to phytoplankton
growth rate, the greater than expected depth of the surface mixed layer and microzooplankton grazing
were all considered as factors that prevented significant biomass accumulation. In line with the limited
response, the enhanced biological draw-down of pCO2 was small and masked by a general increase in pCO2 due to mixing with higher pCO2 waters. The DMS precursor DMSP was kept in check through grazing activity and in contrast to most FeAX’s dissolved dimethylsulfide (DMS) concentration declined through the experiment. SAGE is an important low-end member in the range of responses to iron addition in FeAX’s. In the context of iron fertilisation as a geoengineering tool for atmospheric CO2 removal, SAGE has clearly demonstrated that a significant proportion of the low iron ocean may not produce a phytoplankton bloom in response to iron addition.SAGE was jointly funded through
the New Zealand Foundation for Research, Science and Technology (FRST) programs
(C01X0204) "Drivers and Mitigation of Global Change" and (C01X0223) "Ocean
Ecosystems: Their Contribution to NZ Marine Productivity." Funding was also provided for
specific collaborations by the US National Science Foundation from grants OCE-0326814
(Ward), OCE-0327779 (Ho), and OCE 0327188 OCE-0326814 (Minnett) and the UK Natural
Environment Research Council NER/B/S/2003/00282 (Archer). The New Zealand
International Science and Technology (ISAT) linkages fund provided additional funding
(Archer and Ziolkowski), and the many collaborator institutions also provided valuable
support
Modelling human choices: MADeM and decision‑making
Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)
Methods Matter: A Trading Agent with No Intelligence Routinely Outperforms AI-Based Traders
There's a long tradition of research using computational intelligence
(methods from artificial intelligence (AI) and machine learning (ML)), to
automatically discover, implement, and fine-tune strategies for autonomous
adaptive automated trading in financial markets, with a sequence of research
papers on this topic published at AI conferences such as IJCAI and in journals
such as Artificial Intelligence: we show here that this strand of research has
taken a number of methodological mis-steps and that actually some of the
reportedly best-performing public-domain AI/ML trading strategies can routinely
be out-performed by extremely simple trading strategies that involve no AI or
ML at all. The results that we highlight here could easily have been revealed
at the time that the relevant key papers were published, more than a decade
ago, but the accepted methodology at the time of those publications involved a
somewhat minimal approach to experimental evaluation of trader-agents, making
claims on the basis of a few thousand test-sessions of the trader-agent in a
small number of market scenarios. In this paper we present results from
exhaustive testing over wide ranges of parameter values, using parallel
cloud-computing facilities, where we conduct millions of tests and thereby
create much richer data from which firmer conclusions can be drawn. We show
that the best public-domain AI/ML traders in the published literature can be
routinely outperformed by a "sub-zero-intelligence" trading strategy that at
face value appears to be so simple as to be financially ruinous, but which
interacts with the market in such a way that in practice it is more profitable
than the well-known AI/ML strategies from the research literature. That such a
simple strategy can outperform established AI/ML-based strategies is a sign
that perhaps the AI/ML trading strategies were good answers to the wrong
question.Comment: To be presented at the IEEE Symposium on Computational Intelligence
in Financial Engineering (CIFEr2020) in Canberra, Australia, 1-4 December
2020; 8 pages; 3 figures. arXiv admin note: text overlap with
arXiv:2009.0690
Automated Creation of a High-Performing Algorithmic Trader via Deep Learning on Level-2 Limit Order Book Data
We present results demonstrating that an appropriately configured deep
learning neural network (DLNN) can automatically learn to be a high-performing
algorithmic trading system, operating purely from training-data inputs
generated by passive observation of an existing successful trader T. That is,
we can point our black-box DLNN system at trader T and successfully have it
learn from T's trading activity, such that it trades at least as well as T. Our
system, called DeepTrader, takes inputs derived from Level-2 market data, i.e.
the market's Limit Order Book (LOB) or Ladder for a tradeable asset. Unusually,
DeepTrader makes no explicit prediction of future prices. Instead, we train it
purely on input-output pairs where in each pair the input is a snapshot S of
Level-2 LOB data taken at the time when T issued a quote Q (i.e. a bid or an
ask order) to the market; and DeepTrader's desired output is to produce Q when
it is shown S. That is, we train our DLNN by showing it the LOB data S that T
saw at the time when T issued quote Q, and in doing so our system comes to
behave like T, acting as an algorithmic trader issuing specific quotes in
response to specific LOB conditions. We train DeepTrader on large numbers of
these S/Q snapshot/quote pairs, and then test it in a variety of market
scenarios, evaluating it against other algorithmic trading systems in the
public-domain literature, including two that have repeatedly been shown to
outperform human traders. Our results demonstrate that DeepTrader learns to
match or outperform such existing algorithmic trading systems. We analyse the
successful DeepTrader network to identify what features it is relying on, and
which features can be ignored. We propose that our methods can in principle
create an explainable copy of an arbitrary trader T via "black-box" deep
learning methods.Comment: To be presented at the IEEE Symposium on Computational Intelligence
in Financial Engineering (CIFEr2020) in Canberra, Australia, 1-4 December
2020; 8 pages; 4 figure