Transcript Modelling the High-Frequency FX Market Trading Activity

Modelling the FX Market Traders’ Behaviour:
an Agent-Based Approach
M. Aloud, E. Tsang and R. Olsen
University of Essex
Oct 2012
The “Biology” of Markets
(Richard Olsen)
• How was biology studied?
Richard Olsen
Forex
Olsen Ltd / OANDA
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Observe
Copy
Measure
Generalize
…
Agent-based Markets Studies
Why?
2. interaction
Artificial
Market
3. Observe
Agent n
5. Modify
models
4. Compare and
contrast
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Prices, wealth, etc
Observed market data
(“stylized facts”)
All rights reserved, Edward Tsang
Card Payment
Agent 2
Experimenter
1. modelling
CHASM, an artificial
market platform
1. modelling
Agent 1
• E.g. supply prices
“All models are wrong, but some are useful” (Box 1987)
The core flow of the research.
Dataset
• A unique high-frequency dataset of OANDA’s individual traders’ historical
transactions at an account level made available on anonymous basis.
• The sampling period covers 2.25 years, from January, 1 2007 to March, 5
2009.
• The dataset includes about 147 million transactions carried out by 45,845
different accounts trading in 48 different currency pairs under the same
terms and conditions.
• Each transaction includes the subsequent details: the transaction type, the
transaction timestamp, the traded currency pair, the executional price, the
units and the amount traded.
Observed Stylized Facts
• The trading activity in the FX market exhibits intraday periodic patterns,
namely seasonality.
• The intraday seasonality exhibits a double U-shape or Camel-shape.
• The intraday patterns can be explained by considering the structure of the
worldwide main FX market centres business trading hours in a day.
Intraday seasonality of EUR/USD trade numbers.
An Agent-Based FX Market
• FX ABM simulates the intraday trading activity at the level of a FX
market-maker market.
• The market is populated by a number of agents who participate in the
market in terms of buying and selling currencies.
• At any time t in the market, an agent x is capable of holding:
– a risk free asset in the form of cash, and
– a risky asset in the form of a currency pair, or one of them.
• FX ABM has a number of distinguishing features:
– modelling of heterogeneity in different forms;
– modelling of a simple behavioural constraint trading strategy;
– and modelling of asynchronous nature of trading;
ZI-DCT0
• The agents are modelled as zero-intelligence directional-change event
trading (ZI-DCT0) agents.
• The central idea behind ZI-DCT0 is that an agent will look into the price
time series based on intrinsic time rather than physical time notation.
• Intrinsic time adopts an event based system in contrast to physical time
which adopts a point based system.
• The intrinsic time basic unit is an event.
• An event is defined as the total price movements exceeding a given
threshold.
• The threshold is defined by the trader.
• Two different groups of agents are identified based on ZI-DCT0: contrary
agents and trend-following agents.
4500.0
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Directional Changes Definition
Threshold (%)
Overshoots
5% Direction Changes in FTSE100, 2010
FTSE100
5900.0
5700.0
5500.0
5300.0
5100.0
4900.0
4700.0
Results
• FX ABM reproduces the stylized facts of the trading activity as exhibited in
the real high-frequency FX market to a satisfactory extent as demonstrated
by the statistical analysis.
Intraday seasonality of trade numbers from the FX ABM. The
results are averaged over 30 independent simulations, each
conducted over a one simulated month, and with different initial
seeds of the random number generators, and different time
horizons over the 2.25 years of data samples.
Summary of FX-ABN
The essential elements for modelling the trading activity in FX market are:
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The role of heterogeneity and simplicity.
Distribution of agents’ initial wealth and their profit objectives.
ZI-DCT0 agents.
Behavioural constraints.
Asynchronous trading time windows.
Initial activation conditions.
Characteristics of an order size.
The generation of limit orders.
The level of the agent’s risk appetites.
Number of agents in ABM.
Conclusions
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We want to study markets as a hard science
Agent-based Market a good approach
We observed >45,000 traders for 2.25 years
We established stylised facts about them
We have built a model FX-ABM
We have reproduced traders’ collective
behaviour in FX market
• More work is needed
Supplementary Documents
Artificial Finance Market Conclusions
• Platform developed
– It supports a wide range of experiments
• Conditions for stylized facts identified in
endogenous, realistic market
• Agents must be competent and realistic
– Some must observe fundamental values
• Learning agents (EDDIE-based):
– Statistical properties of returns and wealth distribution
changed
– No need for fundamental trader!
17 July 2015
All rights reserved, Edward Tsang & Serafin Martinez jaramillo
Artificial Payment Card Market:
A Multi-Agent Approach
Biliana Alexandrova-Kabadjova, CCFEA, Essex
Edward Tsang, CCFEA, Essex
Andreas Krause, Management School, Bath
Why Model-based Markets?
• Possible futures
– Real data only shows one history
– Simulation could reveal possible crises
– One could block the paths to crises
• Establish causal relations
– Change agent bahaviour or market conditions
– Observe differences
– Differences are due to controlled changes
17 July 2015