Automated%20Technical%20Stock-Trading%20Agent

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Automated Technical Stock-Trading Agent

• For the Penn-Lehman Automated Trading Project
• Michael Mallin

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The Penn-Lehman Automated Trading Project (PLAT)

• An investigation of designing and implementing
  software-based agents to trade in financial
  markets
• Joint effort between the University of
  Pennsylvania and the Equity Capital Management
  Group of Lehman Brothers, one of the worlds
  largest brokerage houses and investment banks
• Actualized through the Penn Exchange Simular, a
  program that can run various types of trading
  simulations

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Penn eXchange Server (PXS)

• PXS a trading simulation program that runs on a
  Linux machine and allows an unbounded number of
  agents to trade with each other, placing buy and
  sell orders on a single stock for part or all of
  a simulated single day of trading
• Each agent is a compiled C program that adheres
  to a specified API
• PXS can be executed in one of three modes ?

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PXS Execution Modes

• Real-time trading simulation engine receives
  buy and sell orders from agents, and also reads
  in orders in real-time from the Island Electronic
  Crossing Network (ECN)
• Historical trading simulation engine receives
  buy and sell orders from agents, and also reads
  in orders from an historical log of a real days
  trading
• Agent-only simulation engine receives buy and
  sell orders from agents only

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PLAT 2004/5

• This year, focus was to run in mode 3, to
  facilitate more controlled studies of agent
  performance
• So how is a market created?
• Strategy-Based Agents
• Agents designed to trade in an effort to maximize
  their profits (for an entire single day of
  trading)
• Background Agents
• Organizers of the project wrote an assortment of
  parameterized agents
• Technically equivalent to strategy-based agents,
  but serve very different purpose
• Designed to trade not in an effort to profit, but
  in an effort to mimic a certain macro market
  behavior

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PLAT Background Agents

• Vanilla Background Agent
• Reads in numbers from a normal distribution
  (normal distribution data file given as a
  parameter)
• Numbers represent deltas from top buy/sell order
  book prices
• Takes these parameters
• Volume number of shares for each order it places
• Distance additional delta to subtract/add to top
  buy/sell order book prices
• Frequency frequency with which agent places
  orders, in seconds
• Assymetric Background Agent
• Same as Vanilla Background Agent but takes two
  Distance params (buy and sell)

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What kind of stock-trading agent can you write
for PXS?

• Automated trading
• A PXS agent is a computer program that makes
  trading decisions based on an algorithmic
  approach to reading in specified types of inputs
  and using them to decide how best to maximize its
  profit.
• This input is the only thing at its disposal, so
  unlike a human trader, the agent cannot consult
  any additional sources of information, and it
  cannot act on soft judgement.
• Day trading
• A PXS simulation runs over the course of at most
  one day agents are expected to liquidate by the
  end of the simulation. Therefore no long-term
  profitability considerations are relevant.
• Parameterized strategies
• A large part of an agents logic can be provided
  at run-time in the form of arguments. The shell
  of the algorithm is hard-coded but many factors
  can and should be parameterized.

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What kind of stock-trading agent can you write
for PXS? (contd)

• Single-stock trading
• PXS is at least for the moment designed to
  simulate trading only a single stock.
• Technical trading
• This is a widely-used term on Wall Street. The
  meaning that I intend is that trading decisions
  are based purely on the numbers the inputs at
  the agents disposal consist essentially of
  prices and volumes of other agents (either
  programs or real traders, depending on the mode)
  current and historical orders, and the price
  history of the simulated stock for the fictional
  or real trading day.
• As such, the agent is precluded from using a vast
  number of contextual elements that many
  real-world trading strategies employ, such as
  fundamental analysis of the company whos stock
  is being traded, or news releases, or global
  economic information.
• It is important to note that simply being an
  automated agent does not rule out using such
  inputs (a computer agent could parse AP news
  releases, for example).

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Real-Time Agent Inputs

• PXS provides rich API for accessing buy and sell
  limit order book information
• What is a limit order book?
• Limit Order trader specifies both volume and
  price of stock (e.g. 1000 shares of MSFT _at_ 24),
  and order gets queued in buy or sell book
• Order price will be below last price of stock
• Market Order trader need only specify volume
  (or also a price that is higher/lower than top
  order in sell/buy book)
• When market order arrives, gets executed with top
  order(s) in opposing book
• So a limit order is guaranteed price but not
  execution a market order is guaranteed execution
  but not price (because books could change before
  execution)

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Real-Time Agent Inputs (contd)

• What is a limit order book? (contd)
• Two order books buy and sell each consisting
  of list of all limit orders
• Buy book in descending order of price
• Sell book in ascending order of price
• (i.e. by attractiveness to opposing side)
• Relatively recent development on Wall Street is
  automation of order book maintenance and order
  execution by ECNs
• Even more recent is publication (by ECNs) of
  real-time order book data this is at the heart
  of PXS
• In agent-only mode, PXS order books consist only
  of agent orders

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Technical Implementation

• Agent implements its strategy in function called
  updateAgentOrder()
• Function called by engine at regular intervals of
  approximately three seconds for the entire course
  of the simulation, which runs (at most) from 930
  to 1600 i.e. for 23,400 seconds
• So updateAgentOrder() called 7,800 times
• Therefore agent does not implement any sort of
  looping mechanism each call to
  updateAgentOrder() is expected to make a local
  decision, the result of which is to place zero to
  n orders (potentially a mix of buy and sell) at
  that moment in time

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My Agent High-Level Description

• Hybrid of two distinct strategies that it
  switches between depending on what it considers
  to be the state of the market
• At a given time, agent operates in one distinct
  mode
• When it switches from one mode to the other,
  makes clean break from former mode before making
  switch
• Modes
• Market-making strategy aims to profit on
  consistent and directionally neutral oscillation
  of stocks price
• Trend-trading strategy aims to profit on
  consistent movement of the stocks price in one
  direction, followed by either a flattening of
  price behavior or a switch to other direction

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My Agent Design Motivation

• Simple market-making strategies did very well in
  the host of simulations that I ran with the
  technical agents supplied by PLAT as well as in
  prior PLAT competitions
• Simple market-making also made intuitive sense
  consistently high volatility of stock in single
  day
• But
• Compelling to try and further enhance
  profit-making potential by identifying and
  exploiting actual price trends if and when they
  occur
• Trend-trading didnt do that well in some cases
  (Sherstov and Stone) but seemed that tweaking
  parameters and controlling when it was run could
  enhance performance
• (And thinking about programming different
  variations on the market-making theme seemed not
  as interesting to code ?)

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My Agent updateAgentOrder()

• updateAgentOrder()
• begin
• if (at a designated trading time)
• if (in strategy-based trading time)
• tradeBasedOnStrategy()
• else
• withdrawOrders()
• doSingleLiquidation(remainingLiqui
  dationOpps)
• end

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My Agent updateAgentOrder()

• Explanation
• If function called at calculated point at which
  agent makes trades (based on interval param given
  to agent), and current time is still before point
  at which makes sense to close up its daily
  position (hard-coded at 1530 the day ends at
  1600), agent trades/does not trade based on its
  strategy
• Otherwise, withdraws any of its unmatched orders
  on the books, and performs single iteration of
  liquidation
• The concept of single iteration of liquidation
  based on liquidation strategy of spreading out
  liquidation over multiple trades, to avoid
  placing orders of very large volumes that can
  have negative effect on price and liquidity
• arg remainingLiquidationOpps tells
  doSingleLiquidation() how many additional times
  it expects to call it
• So doSingleLiquidation() makes single decision
  but from a more global perspective.

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My Agent tradeBasedOnStrategy()

• tradeBasedOnStrategy()
• begin
• read current price P
• add P to list of prices
• if (weve already built a full list of
  prices)
• calculate the price regression slope
  PS
• add PS to the list of price
  regression slopes
• if (PS is near zero)
• tradeBasedOnMarketMaking()
• else
• if (weve already built a full
  price regression slope list)
• calculate second regression
  PSS
• tradeBasedOnTrend()
• end

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My Agent tradeBasedOnStrategy()

• Explanation
• Maintains two lists
• Local price history over (parameterized) specific
  interval of time
• Local history of price regression slopes over
  (parameterized) specific interval of time
• Adds latest price and price regression slopes to
  two lists, shifting out oldest elements in each
• If price slope is near zero (the concept of
  near is quantified by an epsilon argument given
  to the agent), agent seeks to profit from
  market-neutral trading via market-making mode
• Otherwise, agent concludes there is some price
  trend, and seeks to profit on it via
  trend-trading mode

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My Agent tradeBasedOnMarketMaking()

• / N and mmVolume are user-supplied params /
• tradeBasedOnMarketMaking(N, mmVolume)
• begin
• place buy order just in front of Nth
  order in buy book
• for mmVolume shares
• place sell order just in front of Nth
  order in sell book
• for mmVolume shares
• end

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My Agent tradeBasedOnMarketMaking()

• Explanation
• Simple market-making strategy that always places
  pair of buy and sell orders
• Buy below last price
• Sell above the last price
• How far below and above is not computed directly
  from last price, but by stepping just in front of
  the Nth order in both books
• Agent steps exactly in middle of the Nth and the
  (N-1)th orders
• If N 1, steps in the middle of the 1st order
  and the last price
• (Any order in buy book is lt last price, and any
  order in sell book is gt last price either can
  be equal if only part of that order was just
  matched.)

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My Agent tradeBasedOnTrend()

• / pSlope slope of price regression line
• pSlopeSlope slope of price regression
  slopes line
• tfVolume is user-supplied param /
• tradeBasedOnTrend(pSlope, pSlopeSlope, tfVolume)
• begin
• if (pSlope gt 0 and pSlopeSlope gt 0)
  //price rising
• place buy order in front of 1st order
  in buy book
• for tfVolume shares
• else if (pSlope lt 0 and pSlopeSlope lt 0)
  //price falling
• place sell order in front of 1st
  order in sell book
• for tfVolume shares
• else //trend reversal
• withdrawOrders()
• doSingleLiquidation(1)
• end

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My Agent tradeBasedOnTrend()

• Explanation
• Buys if price on the rise and sells if on the
  decline, provided that price acceleration is
  monotonic in the relative direction
• If neither case holds, closes out position that
  it built up based on last trend, seeking to
  collect its profits
• Implicit Assumption (or bug ?) Agent never in
  risk of going immediately from trend trading mode
  to market making mode without first closing out
  its position this is because if the price slope
  gets to near zero, there must have first been a
  trend reversal

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My Agent doSingleLiquidation()

• / remainingLiquidationOpps remaining number of
• chances for liquidation /
• doSingleLiquidation(remainingLiquidationOpps)
• begin
• get current share holdings C
• if (C lt 0)
• get total number of order shares in
  sell queue SQ
• if (-C / remainingLiquidationOpps lt
  SQ)
• place buy market order for C /
  remainingLiquidationOpps
• shares
• else
• place buy market order for SQ
  shares
• else if (C gt 0)
• get total number of order shares in
  buy queue BQ
• if (C / remainingLiquidationOpps lt
  BQ)
• place sell market order for C /
  remainingLiquidationOpps
• shares
• else
• place sell market order for BQ
  shares

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My Agent doSingleLiquidation()

• Explanation
• Examines agents current share position
• If non-zero, seeks to (wholly or partially) zero
  it out by placing trades in the opposite
  direction
• Volume in which it places these trades determined
  by its total position vis-à-vis the current size
  of the opposing order book

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My Agent User-Defined Parameters
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My Agent User-Defined Parameters (contd)
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Open Issues / Further Work

• (besides writing an entirely new agent)
• Partial Liquidation
• As explained above, when agent is in trend
  trading mode and perceives trend reversal, it
  liquidates, seeking to actualize its profit
• Current implementation attempts to do this in
  only one order (which is why doSingleLiquidation()
  called with value of 1 for remainingLiquidationOp
  ps arg)
• This simple solution could be dangerous if
  current position is very long or short

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Open Issues / Further Work (contd)

• Partial Liquidation (contd)
• Another implementation would be to spread
  liquidation over some interval, similar to way in
  which end-of-trading-day liquidation is done
• Design question then becomes one of determing the
  length of interval options include ?

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Open Issues / Further Work (contd)

• Use average length of price trend, as this is
  expected amount of time until next trend reversal
• Hard to estimate, but more importantly
• Inherently flawed, because if your moving from
  trend A to trend B, and A and B are the same
  duration, you will (in theory) break even! In
  fact, using an expected value means that you
  implicitly expect that this will happen!
• Compute reasonable length of time, based on
  level of current position and on some expectation
  of length of trend youre entering
• Could look at levels of regression slopes, or use
  order-book metric like VWAP differentials

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Open Issues / Further Work (contd)

• Residual Effect of Switching Strategy Modes
• Two possible mode transitions that can occur
  unbounded times during trading day (1) from
  market making to trend trading, or (2) from trend
  trading to market making
• Assumption (2) is not a problem, because price
  slope wont go to zero w/out first showing trend
  reversal (so agent does local liquidation)
• But (1) can be dirty because no guarantee that
  all orders placed were matched (which is one of
  the aims of market-making)
• So if agent does nothing, it can carry non-zero
  position into next mode

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Open Issues / Further Work (contd)

• Residual Effect of Switching Strategy Modes
  (contd)
• My agent currently allows this i.e. it does
  nothing to prevent it
• Claim This is sound
• Proof (sort of)
• There are two possible cases
• Agent is long/short when the switch is about to
  be made, and the trend is now up/down. Lets take
  the long/up case. So the price is on the rise,
  and the agent must have unmatched sell orders.
  But these orders should now be matched, as the
  price is rising! So this seems an impossible
  situation.
• Agent is long/short when the switch is about to
  be made, and trend is now down/up. In either of
  these two logically equivalent cases, the agents
  unmatched sell/buy orders will remain unmatched.
  But what is our alternative to leaving them on
  the books? We could withdraw them, but that is
  akin to accepting defeat. It is better to leave
  them, hoping for a future trend reversal that
  gives us at least a chance of matching the orders.

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Open Issues / Further Work (contd)

• Strength/Weakness of Predictive Model
• Agents understanding of the market based
  entirely on computing linear regressions of
  prices and price slopes
• Simply an implementation of agent described in
  Feng, Yu and Stone
• They explain in their article that this model
  produced premature trend reversal signals,
  leading to premature unwinding
• Idea for expanding it
• Incorporate order book-based metric as well, such
  as order book volume differences between buy and
  sell order books (see Subramanian) or VWAP
• Given agents design, this could be purely
  additive i.e. the strategies remain the same,
  but the logic of determing which one to use and
  when becomes more elaborate

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Why you should consider writing something for PLAT

• The PXS API is very clear and putting new ideas
  to work is relatively easy
• PLAT will continue to have competitions (as soon
  as their server is replaced)
• Algorithmic trading is hot in financial software
  (especially related to hedge funds) and salaries
  are high
• Algorithmic Trading Services will even consider
  sharing all or some of the downside risk for
  academics / quants who want to become involved
  with developing extremely high level, complex
  systems for these markets, and profit from the
  trading of these systems.
• Lots of AI techniques (e.g. machine learning) are
  employed
• Ive done a lot of the annoying work ?