lyle pakula, ae capital asx grains forum 2016€¦ · • algotrading / trade execution. o...
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Not to be distributed without the express permission of AE Capital management. Private and Confidential.
Lyle Pakula, AE CapitalASX Grains Forum 2016
Private and Confidential
Predictawhat?• “Predictability” is a user beware concept
o Plot “line ups”, Correlations, grange causality, etco What’s useful depends on your style and time scale of trading
• At AE, fundamental analysis must lead technical analysis
• For the technical analysis component, we generate a predictability measure customised to our style of tradingo Analyse price action and liquidity across multiple time scales and time horizons, kind of like an equalizer o We then look for predictability at time scales and time horizons consistent with our fundamental analysis
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Example of “AE Style” Driving
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• One market consistently predicting the direction of anothero Note how the blue market shifts before the red market
• Possible information flow but really, data analysis can never truly say anything about “information”
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• Iron Ore driving AUDUSD?
• YTD Percentage return o Iron Ore ~ +40%o AUDUSD ~ +5%
• Correlation ~ 12% insampleo Rolling 30 day [10% - 40%]
• AE Predictability - HIGH
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Systematic Trading• Systematic trading - any trading that follows a system, manual or
automatedo Here, we’ll do a quick overview of automated trading as I understand it
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Nomenclature
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• Systematic Trading• Algorithmic Trading• Automated Trading• Ultra High Frequency• High Frequency• CTA• Statistical Arbitrage • Index Arbitrage• Arbitrage• …
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Nomenclature
• Basically, given finance and economics is not a science, most definitions are pretty loose
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• Systematic Trading• Algorithmic Trading• Automated Trading• Ultra High Frequency• High Frequency• CTA• Statistical Arbitrage • Index Arbitrage• Arbitrage• …
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Basics
Therefore, tend to observe increasedo Correlationso Trading volumeso Positive and negative feedbacks
between the “indicator” and traded market.
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• Indicator could beo another market, combination of markets and/or
the same marketo Twitter, CPI, GDP, weather, etco Becomes very hard to reverse engineer the
indicator from the trading
• Trading generally occurs when the “indicator” changes valueo most signals are momentum or reversion
Indicator Risk Model Trade+ =
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Feedback Loops• Negative Feedback Loops
• mean-reversion, profit-take• post NFP oscillations
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• Positive Feedback Loops
• momentum systems, stop-loss • fat finger flash crash
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Ultra High Frequency (ms)• Market Making
o Domain of the brokers and prop firmso Definitely good and bad (predator) firms
• Whose filtering out your bad firms?
o Largely an arms race as well as a client list race in OTC markets
o Algo’s tend to reprice based on• your order (BAD! a-la Flash Boys)• firms internal book• high-frequency correlations • momentum, reversion, order book signals• human input (eg NFP)
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• Algo Trading / Trade Executiono Ubiquitous in the industryo Most algorithms “iceberg” and tend to be a
balance of “participation” versus “crossing”o Little predictive power in them but effective at
getting VWAP for you
• Arbitrageo Consequence of any segmented marketo Been around forever (Rothschild's anyone?)o Just looks more complicated today
eg index arbitrage, FX triangulation o Total arms race
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Hours to Months• CTA ~ 10% of all HF FUM
* BarclarHedge Database
o Essentially all trend following (large correlation among managers)
o Various time scales from intraday-monthlyo Treat markets as a time series,
ie no fundamental insightso Will trade any market with enough volume and a
good backtesto Probably makes markets more volatile
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• Statistical Arbitrage o Finance nomenclature for “pairs trading”o Generally mean-reversion style strategies with
time frames from hours to dayso Will “latch” onto highly correlated markets and
probably help those markets remain highly correlated
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Other Strategies
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• Similar at the core to everything else (ie momentum, mean reversion) but can be much more complicatedo eg short vol spike in options, n-asset cointegration,
hybrid market making, esoteric market pairs o Dynamic adaptation (ie machine learning)o Tend to be shorter term, minutes to days
• Generally better Sharpes with lower scalability
• Harder to explain and therefore harder to market, hence less well known and usually within prop firms
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Summary• Systematic Trading Affects on Markets?
o Broad given the broad nature of these systems
o Obviously increased volumes
o I suspect increased and more persistent cross-asset correlations
o I suspect increased volatility across various time scales• more momentum, break-outs, fair value deviations• more/faster reversion• and all probably not inline with fundamentals given
fundamentals are not at the core of most automated trading models
o But .. is automated trading inherently different/better/worse to other forms of speculative trading in general and of the past?
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• AUDUSD currently “driven” by o Aussie, Eurodollar rates (Carry & Fed Vol)o Iron Ore (China)o Short Sterling (Brexit Risk)o So far as the fundamental world is seen by AE