“Algos for Alpha”
Systematic Trading in Global Macro Markets
Quant Invest APAC Conference
Shanghai, December 8th 2011
Today’s Message
• Algorithms Give Consistency
• Consistency is an ‘Edge’
• Experience/Empirical Evidence
– Human Traders are bad at Consistency
– Human Traders can make money, but few are
consistent
• Consistent over Time
• Consistent with the Fund Goals
The ABC of Investing
• Alpha
– Non-Correlated Excess Returns
• Beta
– General “Class” Investments
• Profit from the movement of the asset class
• Carry
– Yield Gap
• Profit from holding asset, avoid losses by quick exit
Comparison
Investment
Type
Alpha Version
Beta Version
Carry Trade
Property
Renovate an old
house
Buy an apartment off
plan
Buy to Rent with 90%
Financing
Stocks
Visit a company and
get to know it , see
value where others
haven’t yet
Index Tracker or
Large Portfolio
Minimize Slippage
Buy High-Dividend
Stocks, financed at
low rate
Industry
Innovate a new
process
Participate, own a
process/factory
Finance and Build a
Utility
FX, Interest
Rates
Buy Lower
Sell Higher
(Index Product?)
Emerging Markets
AUDJPY
Alpha Investing
• ‘Alpha’ Returns (i.e. more than ‘Risk Free’)
come from
– Work, Effort, Application
– EDGE
– Cons: Being ‘Involved’ doesn’t generate returns
– Pros: The Return is Uncorrelated
• So what Leads us to use Algorithms?
• We need an EDGE.. Otherwise we shouldn’t Trade!
You want Alpha, What’s your Edge?
• Excess Uncorrelated Returns require an
advantage
– Information and Spread
– Client Flow gives the ‘Sell-Side’ both of these
– Arbitrage
– Tax, Regulatory, Capital Base, Access
– Analysis
– Inconsistent Excess returns are down to ‘Luck’ not Analysis
Systematic Trading
• The Analysis is important…… but
• The Edge of Systematic Trading is ‘CDF’
– Consistency
– Discipline
– Focus
• Searching for Alpha without CDF
– Retail FX: 80% of Leveraged Clients lose half of
their capital within 3 months (Source: Interviews with UK Brokerages)
Consistency
• Being Consistent when Trading
– Means matching the Fund’s long term goals when
choosing:
• Trade Entry Point, Trade Size, Trade Exit Point
– 2nd & 3rd above are often overlooked, but turn out to be more
important than the 1st
– Keeps you from ‘blowing up’
• You stay active longer, your analytical edge can show
Calibrate For Consistency
• What is your response to a 1% Profit or Loss?
– How about a 0.10%, 5%, 10%, 15% Profit or Loss?
•
•
•
•
-0.10% is of similar importance to +0.10%
-1% is more important that +1%
-5% is way more important than +5%
-10% means we are stopped out for the year,
+10% is a very good month.. (is it too good to be true?)
• -15% means we have broken our covenants…
+15% is our annual target
Picture of how we feel about P&L
Loss
Profit
‘Utility’
How do you feel about
the P&L?
There’s no Profit that can make up for a -5% Loss
Incentive is: Win Small and Often, and Never Lose Big
Utility for the ‘O.P.M.’ Trader
At this point lose your
Job, so you don’t care
about anything worse
Incentive is: Bet Big to
Win Big
For some traders, it may
even be better to lose
more beyond a certain
point….
“Other People’s Money” Traders’ Incentive is: Bet Big to Win Big
How do Algorithms Help?
• Base your Algo on your Utility Function
– Keeps the Trade/Risk Profile consistent with Goals
• The FM can Market the Fund to Investors who
want that particular ‘Utility’ Profile
• Can be Aggressive or Conservative
– but Trading will be CONSISTENT
• The FM will be in the game for the long
term
Trade Entry Analysis
• Purpose: Profitability over the Long Term
– So you have to be Trading over the Long Term
• Generate Trade Ideas
– Model of the Market
• “Model” is a very loose term
– Combine with your Trade Management
parameters
– Look for Patterns that work
• (take care when Testing!!)
Algo Applied to EURJPY
Potential Trades
EURJPY Entry Points
Potential Trade Entry Points Identified by Trend Reversal Algo
Overall P&L of these trades = close to zero, before costs/fees
Algorithm Enters & Manages 24 Trades
Trades Entered and Risk Managed by Algorithm
Short EURJPY (Algo is a ‘Carry-Buster’)
With S/L at 65bps on Size: 50% of AUM
Overall P&L of these trades = 6.66%, max loss = -0.35%
Impact of Trade Management
• Trade Management means
– Not Necessary for model to predict market
Direction
– Not necessary to be right >50% of the time
– Is Necessary to avoid choppy markets (without options)
• Trade Management is very Important
– Its pro-active (looking for Exits, Controlling Size)
– Not just Risk Measurement/Management
Simple Example
The Same TradeSignal can lead to very different outcomes…
Trade Management
• Trade Management is part of the Algorithm
• You are looking for Profitable Price Movement
after Trade Entry
• P&L = Sum of [Trade Size x (EntryPoint-ExitPoint)/ExitPoint]
• Trade Size, Entry Point & Exit Point are
Equally Important
• Double Trade Size and you lose all your Edge!
Real Example
Trade Management 2
• Trade Management gives Consistency
• Are your Returns the right “Shape”?
• Goal: Long Term Success
– Need Time to show this
– Avoid Large Drawdowns that can close the
business
Check Consistency: Larger Sample
3500 Trades, 19 FX Assets, over 7yrs, Trades vs Sample Market Returns
Algo has more
small losses…
Market has many more
around zero (= 1800)
Market Return Average = 0.03%
Algorithm Return Average = 0.25%
Trade Average life: 2.4 days
(Results Scaled to AUM)
Market has some big –ve
moves, Algorithm never
has loss more than -35bps
…& more
small gains
Algo matches/slightly
exceeds market on big
positive returns
Utility Function
Roughly equal small losses and gains
No Large losses
The Algo generates Alpha (Excess returns) by ‘catching’ the
market’s large gains, while avoiding the large losses
What is the Algorithm Doing?
• Generates Alpha
– Identify and Manage Trades
• Generate Profit from recognising Patterns
• Match Utility for consistent risk/reward profile
• Trades Consistently
• The Algorithm doesn’t have Off-Days or Days-Off!
• Creates Time for Performance to show
• Insulates you against large negative shocks
Does the Algorithm Change?
• The Market Dynamic can change, how should
an Algorithm?
– 2 Causes of Market-Dynamic Change:
• Change in Market Condition
– Parameterise the Condition
– Algorithm should be able to cope
• Change in Participants/Products
– Structured Products can dominate less liquid markets
– Tough to trade using an Algorithm in these markets
Example: Spot FX & ‘Carry’
USDJPY Carry: Currently close to zero, was 5%
(p.a.) in 2006-7. The Spot Market trades
differently in these periods, Pattern Recognition
must take this into account.
Carry is a Parameter for the Intraday Spot-Trading
Algorithm, even if its not a factor in P&L
Impact of Structured Products
Structures
unwound
in ‘09
Normal Market Patterns in areas
with no Strike Concentration
Vol collapses as spreads near zero,
Sell side is very long Gamma.
Vol then Spikes as Spread enters
negative Gamma area
Structures in the
market give Sell-side a
very skewed vol
profile. This changes
the market Dynamic
Difficult to Trade with an Algorithm
Market size too small w.r.t. Structures
Discipline & Focus
• Implementation
• Concentrate on building your Process
– Engineering rather than Trading
• Technology is Important
– Process and WorkFlow Technology (not HFT)
• Less risk of being Leap-Frogged
– Growth not limited by Hiring
• Need to maintain Liquidity relative to Markets
Examples and Metrics
4,000,000
Sherpa Funds, Short Time-Frame Rule Pair, Back-tested P&L
Jan 2005-Nov 2011, 5k Risk/Trade, 4400 trades total, 24hrs/day
1800
3,500,000
Max Monthly Drawdown < 3% of 5yr P&L
1600
3,000,000
2,500,000
1400
2,000,000
P&L, Reversal Rule
1,500,000
1,000,000
500,000
1200
P&L, Extension Rule
Short Time-Frame Portfolio P&L
1000
S&P Index
800
0
14-Dec-05
(500,000)
28-Apr-07
9-Sep-08
22-Jan-10
6-Jun-11
600
Algorithm Advantages for Alpha
• Algorithms are Consistent, Disciplined and Focused
– These are ‘Edge’ in the Search for Alpha: This generates Alpha
– They allow time for Analysis to work out: They make money over time
– They match Risk Appetite across Investors/‘Traders’/Business
• Algorithms avoid common Human Characteristics
– Trade Size, Changing Risk Appetite, Endowment Effect
• ‘Great Traders’ can do all this!!!
• Hard to find, assess or retain ‘Great Traders’
Is your Alpha Portfolio run by a ‘Great Trader’?
Systematic Trading in Global Macro Markets
Target +15% p.a. with Monthly Liquidity
Stop-Loss at -10%
email: Investors@TheSherpaFunds.com