Modeling Transaction Costs for Algorithmic Strategies
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Modeling Transaction Costs for Algorithmic Strategies Tomas Bok [email protected] Boston Algorithmic Trading Meetup April 24, 2013 © 2013 Tomas Bok Tuesday, May 28, 13
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Discussion of this presentation, and custom slippage model for you to test with, can be found at https://www.quantopian.com/posts/custom-slippage-modeling-transaction-costs-for-algorithmic-strategies If you're interested in learning more about modeling transaction costs, we've scheduled a webinar with Tom for June 26 at 2PM EDT. The webinar will be a Q&A based on this presentation. Bring your modeling questions to the webinar, and Tom will answer any questions you have. Please RSVP at https://attendee.gotowebinar.com/register/3673417022478449920 .
Transcript of Modeling Transaction Costs for Algorithmic Strategies
ModelingTransaction Costsfor Algorithmic Strategies
Tomas Bok [email protected]
Boston Algorithmic Trading MeetupApril 24, 2013
© 2013 Tomas Bok
Tuesday, May 28, 13
Taxonomy of T-CostsExplicit Costs
Broker CommissionsFees & TaxesTicket Charges
Benchmark Slippage
Opportunity Cost
} mostly independentof execution style
} highly dependenton execution style
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Algorithmic Trading Stack
Smart Order Router
Exchanges, ECNs, & Dark Pools
DirectOrders
Execution Algorithm
Investment StrategyParentOrders
ChildOrders
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Benchmark Slippage
timedecision
benchmark price}slippage
price
average realized price
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Factors that Drive Slippage
Small Orders: Large Orders:slippage is
primarily due tomarket impact
slippage isprimarily due to
spread
Price Actionaffects all orders
+luck𝛼 decay
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Forecasting Slippage
timedecision
benchmark price}slippage
price
average price
order size and sideexecution horizonstock-specific liquidity details(volume, spread, volatility, ...)price action (over trade horizon)
Typical Model Inputs
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Buy 200 DIS over 1m(1% participation)
Buy 2,000 DIS over 1m(10% participation)
Buy 20,000 DIS over 10m(10% participation)
Buy 120,000 DIS over 1h(10% participation)
0.9 bps
0.9 - 1.5 bps
2.9 bps
8.8 bps
Sample Slippage ForecastsBuy X shares of DIS at 10:00am
Source:ITG, Inc.
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Slippage Forecasting Methods
timedecision
benchmark price}slippage
price
average price
Generatepoint-estimateof slippage
Method A:Equation-Based
Generate bottom-up slippage estimate based on individually-simulated fills
Method B:Simulate Fills
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Slippage Forecasting Methods
Execution Algorithm
Investment StrategyParentOrders {Method A
Simulation Scope
ChildOrders
{Method BSimulation Scope
ParentOrders
Execution Algorithm
Investment Strategy
Generatepoint-estimateof slippage
Method A:Equation-Based
Generate bottom-up slippage estimate based on individually-simulated fills
Method B:Simulate Fills
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Method A: Equation-BasedAvg Price = Baseline Price +/- [ f(spread) + g(size,...) ]
Last PriceNext PriceBid-Ask Midpoint
BaselinePrice
✓4 bpsf(typical spread)f(starting spread)f(TWA spread)
SpreadCost
✓✓
0g(size, horizon, volume, volatility)
Impact
✓Horizon CloseHorizon VWAPHorizon TWA-Mid
✓✓✓
[basic f( ) = 0.5 x spread]
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Method B: Simulate Fills1. Generate stream of child orders: { time, size }
2. Generate stream of simulated fills: { size, price }
3. Avg Price = VWAP of fills
Ability to create child ordersTick dataLimit order modelMarket order modelImpact memory function
Requirementssizei x pricei
sizei
∑i
∑i
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Defining Strategy TimescaleStrategy Holding Period
milliseconds seconds minutes hours days weeks months
IntradayAlpha
HP = minutes to hoursExpected profit: ≤1 x spreadAlpha decay = fastTrading concern: ‘gas pedal’
High FrequencyTrading
HP = milliseconds to minutesExpected profit: .05 -.10 ¢Alpha decay = immediateTrading concern: latency
Low FrequencyQuant
HP = days to monthsExpected profit: ≥1%Alpha decay = slowTrading concern: liquidity
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Slippage Model SelectionLow Freq Method A (simulate parent order fills)
Use the data you have availableBe conservative
Intraday Method B (simulate child order fills)Bring execution algo into backtest...or break into 2 step process
HFT Method B+ (simulate direct order fills)Incorporate Level 2 dataIncumbents may find it easier to live-test
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Keeping It SimpleFocusing on ‘slippage-safe’ strategies
1. Avoid strategies that are overly cost-sensitive:intraday holding periodsexpected paper PNL ≤ 2 x spreadonly profitable with optimistic cost assumptionsrapid alpha decay
2. Stick to a liquid stock universe
3. Cap order sizes (≤ 25% 1-minute participation rate)
4. Assume at least a minute to execute orders
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Explicit Costs (US Equities)
Broker Commission
Taxes & Fees
Ticket Charges
TOTAL (1-way)
.05 - .20 cents+ net fees (.06¢) 0.5 - 1.0 cents
~.05 cents included
NA $1+ (or NA)
.15 - .30 cents 1 cent + tickets
Low FreqIntradayHFT
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Opportunity Cost
timedecision
price
price limit}+25 bps
Opportunity cost: effect of unexecuted shares on PNL
If you plan to trade with price limits or conditional execution strategies, backtest accordingly
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Golden Rules1. Think about transaction costs early and often
2. A simple cost framework is fine as long as you make conservative assumptions and “stay on the path”
3. To run more cost-sensitive strategies, be prepared to invest in a more sophisticated t-cost framework
4. Account for all three kinds of transaction costs
5. Backtest at full scale
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Image creditsSlides 3, 9: rack servers from dell.com; order tickets from www.silexx.comSlide 5: supermarket scale from www.racoindustries.com
© 2013 Tomas Bok
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