lyle pakula, ae capital asx grains forum 2016€¦ · • algotrading / trade execution. o...

Not to be distributed without the express permission of AE Capital management. Private and Confidential.

Lyle Pakula, AE CapitalASX Grains Forum 2016

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Aussie Drivers

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≠high correlation doesn’t maketh the driver

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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

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Indicator Risk Model Trade+ =

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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