Algorithm Design And Evaluation Further Reveal Connection Between Investment And Trading Processes

1 / 25

# Algorithm Design And Evaluation Further Reveal Connection Between Investment And Trading Processes - PowerPoint PPT Presentation

Algorithm Design And Evaluation Further Reveal Connection Between Investment And Trading Processes. Introduction.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about 'Algorithm Design And Evaluation Further Reveal Connection Between Investment And Trading Processes' - anoush

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Algorithm Design And Evaluation Further Reveal Connection Between Investment And Trading Processes
Introduction

The design of effective algorithms for trading may sometimes seem an “art on its own”, full of rules of thumb and very disconnected from the investment process within which algos exist.

We show, via clear and practical examples, that such notion is misleading, in sometimes surprising ways. We then connect those examples with approaches that appropriately fix the algorithmic flaws presented. Those approaches fix those flaws mostly by keeping the initial investment decision in mind and knowing how to manage transaction costs.

A Novel Idea:A Tale Of Love And Hate (really novel!)
• Loved Algo FILLS MORE AT BETTER PRICES
• Loved has traded those 5 orders better than Hated… Right?
• Remark: 1-Day Stock Pr Chg = from order placement to 24h later.
Loved Algo Does Seem To Deserve Love
• “Flipping” Exercise: Buy at arrival plus slippage (that is, traded price), sell at 24h later price, keep net.
• From table, “Fill The Most At Best Prices” works:Lovedlets the fund pocket9.6 BP (\$98k) per order on average, whileHatedlets fund pocketonly 7.0 BP (\$72k) per order, on average.
• Loved Algo Is Better… RIGHT?

Loved fills more, at lower prices than Hated...

• Now fund seems to keep more money using Hated.
• Perhaps it is time to give more love to the Hated – and vice versa?

7.2 BP =

370k/ 500MM

7.7 BP =

390k/ 500MM

... But ??

* For simplicity – and without compromising results – transaction costs not shown in flipped sells (included only when fund really sells, not in potential profit exercise). If included when sale happens, transaction cost is appropriately taken into account, and no double count happens.

Confusion: Loved Algo Better Before, Worse Now  ?????
• Now, with Loved Algo, fund nets ONLY 7.2 BP per order.
• With Hated Algo, fund nets 7.7 BP per order.
• Which one is correct, once and for all?
• Loved algo jumps too hard at cheaper opportunities.
• Because initially lower prices tend to yield not as good medium term returns, the additional mkt impact from those “rushes to cheap” cancels the benefits from better prices. 
• Loved algo needs better TC management.
• Reinvest net x BP proceeds from each 24-hour investment horizon.
• 200 days/year.
• Compounding yields (1.000x)200-1 annual returns.
• Simple setup, but illustrates power of 1 BP saved.
The Conclusion From Love X Hate: Algorithms Depend Enormously On Appropriate TC Mgmt – Investment/Trading Together:The Trading X Alpha Orthogonality Principle
• Graphic Interpretation: 5-point slippage and alpha numbers can be represented as vectors.
• Correlations fill, net  angle
• Conclusions:
• Loved: traded more when alpha smaller  neg correl  should not jump so hard at better prices; be less afraid of unfavorable prices.
• Hated: traded more at good alphas pos correl  could trade some slippage for higher net even when forecasting well.

Fund’s alpha

Hated Slipp

Optimal Slipp

Optimal Net (orthogonal with fill)

Loved Slipp

• Vectors:slippage, alpha and fill rate’s numbers can be represented as R5 vectors. Correlations and angles between those vectors can be shown to be equivalent quantities.
• Since cannot see in R5, show above in plane.
Another Conclusion From Love X Hate: Opportunity Cost And Risk, Not Only Slippage, A Major Component In Evaluating Algorithmic Performance
• Tendency is to select a benchmark (say, arrival), and calculate average cost (slippage) with respect to it. Misleading, as gaming may make benchmark average look better for algos but yielding worse for fund’s returns.
• Different situations: appropriate transaction cost measurements should correct for different trading conditions (momentum, liquidity, etc).
Part II: Algorithms Cannot Avoid TC Management And Investment IdeaWhen Adapting To Market Movement, Keep Impact X Risk At Sight + Remember Initial Preferences
TC Management: Cannot Avoid The Trade-Off Between Risk (And Alpha) And Market Impact
• Strategy X can be represented by a percentage of volume rate (“POV”) or by a trade schedule.
• Adaptation: Should adapt the initial scheduled plan as trading evolves, without drifting away from initial cost/risk preferences.
Algorithm’s Adaptive Strategies: AIM – Has To Follow TC Management As Well
• Very Important:Reference price has to shift as achieved cost and prices change in order to reflect original preferences.
• Loved Algo’s flaw was that, even though it reacted to favorable prices, it lost sight of TC management and initial price X risk trade-off.
Investment & Trading Frontiers Connected: Losing Sight Of Alpha May Ruin Trade TC Mgmt
• Case in point: Loved Algo’s careless attack into cheaper prices.
• By seeing current price levels compared to arrival (or implementation) price, Loved Algo could have managed better its “greed”.
Ensuring Consistency between Investment & Trading Frontiers

HATED ALGO: not VWAP, since it could save slippage even at good alpha forecasts. But, like VWAP, could be more aggressive at cheaper prices (has some room for slippage).

LOVED ALGO: forgets planned alpha and values instant gratification without remembering its exact risk aversion...

Introductory Example:Who Performed Better?

Hated Algo gets the “curve ball” (9 COG 400k, 1 MSFT 10k)...

• Avg Cost:
• Hated Algo:-36BP
• Loved Algo:-11BP
• Conclusion:
• Loved Better Than Hated.
• Obvious, right?

Loved Algo gets the “beach breeze” (1 COG 400k, 9 MSFT 10k)...

Looking More Closely...
• Indeed, Loved’s Average of-11BP Better Than Hated’s -36BP.
• But, strangely...
• Hated “beats” Loved in direct “face-offs”:
• Hated trades MSFT better than Loved.
• Hated trades COG better than Loved.
• SO: IS Loved STILL BETTER THAN Hated? IS -11 BP ABOVE REALLY BETTER THAN -36 BP ABOVE? WHAT GIVES?...

Hated trades COG better than Loved

Hated trades MSFT better than Loved

In the table above, B’s average’s superiority comes into question.

• Avg Cost:
• Hated:-36BP
• Loved:-11BP
• But Hated better for each stock. How come?
• Hated beats Loved hand-to-hand and is thus better than Loved. Simple average is misleading due to uneven assignment of easy (MSFT) and hard (COG).

Hated trades COG better than Loved

Hated trades MSFT better than Loved

Should Be Able To Compare At Level Playing Fields
• Different algos, say from different brokers, may enjoy different perceptions in the buy-side firm which uses them.
• The firm may apply one algo for certain trade characteristics and other algo for other characteristics.
• For fair comparisons – and best use of algos – such possible differences should be taken into account.
Conclusions
• What looks good when seen in isolation may not be as good when seen as part of a process. Algos should be consistent with investment.
• TC management follows some basic ideas. Adapting the efficient frontier to adaptive trading (as in algos) goes beyond simple rules of thumb to include the investment plan.
• Comparing algos should take into account possible differences in their applications, even for similar type algos (like, two IS algos).