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![Use fake data to “fit” algo: Final iteration
Summary of findings:
● Window size in √2 steps covers the space best
● Window size <10 too expensive for any instrument
● Window size>200 pointlessly slow
Window_size = [10,14,20,28,40,57,80, 113,160]
Should capture trends lasting for around 1 month to 18
months](https://image.slidesharecdn.com/tradingstrategiesthataredesignednotfittedbyrobcarvertopresent-170503143315/75/Trading-Strategies-That-Are-Designed-Not-Fitted-by-Robert-Carver-Independent-Systematic-Futures-Trader-Writer-and-Research-Consultant-40-2048.jpg)
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"Trading Strategies That Are Designed Not Fitted" by Robert Carver, Independent Systematic Futures Trader, Writer, and Research Consultant
The document discusses the design of trading strategies, emphasizing a systematic approach that relies on tacit knowledge and theoretical principles rather than solely on fitting historical data. It outlines a six-step process for strategy development, which includes generating ideas, using random data, and performing real data sensitivity checks. Additionally, it explores key questions in trend identification and management in trading systems, addressing common practices and pitfalls in algorithmic strategy design.
In this document
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Overview of presentation and disclaimer about risks in trading. Robert Carver introduces systematic trading.
Personal insight into Robert Carver's early belief in trading strategy design focusing on data and algorithms.
Discussion on the dangers of fitting trading strategies. Introduction to three types of fitting: explicit, implicit, tacit.
Exploration of tacit knowledge sources and its importance in systematic trading design over mere fitting of data.
Contrast of Robert Carver's current design approach compared to his past, emphasizing idea-first over data-first.
Analogies drawn between product design and trading strategy development, focusing on market folklore and empirical research.
Unanswered questions related to trend trading, highlighting the importance of research and parameters in strategy design.
Difference between strategies based on historical data versus conceptual designs supported by various metrics.
Presentation on systematic steps for designing trading strategies with emphasis on establishing a sound framework.
Importance of having clear conditions in trading rules, addressing key questions in strategy formulation.
Six steps reiterated in strategy development, stressing the use of theory, common sense, and iterative testing.
Realistic scenario development and reevaluation of trading strategy performance during real market conditions.
Discussions regarding the evaluation of trading algorithms and parameters, utilizing fake data for fitting.
Methodology for using synthetic data in the fitting process, considerations on window sizes and their impacts on performance.
Application of real data for consistent scaling and turnover analysis, focusing on maintaining performance across instruments.
Discussing portfolio optimization using real data, emphasizing cost considerations and allocation methods.
Summary of findings, emphasizing critical steps in strategy design, tacit knowledge utilization, and portfolio fitting.
Robert Carver's publications and additional resources for further exploration of systematic trading.
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"Trading Strategies That Are Designed Not Fitted" by Robert Carver, Independent Systematic Futures Trader, Writer, and Research Consultant
- 1. Trading Strategies That AreDesigned Not Fitted Robert Carver QuantCon 2017 / New York / 29th April 2017
- 2. Legal boilerplate bit: Nothingin this presentation constitutes investment advice, or an offer or solicitation to conduct investment business. The material here is solely for educational purposes. Robert Carver is not currently regulated or authorised by the FCA, SEC, CFTC, or any other regulatory body to give investment advice, or indeed to do anything else. Futures trading carries significant risks and is not suitable for all investors. Back tested and actual historic results are no guarantee of future performance. Use of the material in this presentation is entirely at your own risk.
- 3. My 27 yearold self
- 4. How my 27year old self thought systematic trading design should work... Data Magic box Trading strategy Algo + Parameters Data first
- 5. Why is fittingbad…. "The elements of statistical learning" by Hastie et al fig 2.11
- 6. The three typesof fitting... Explicit Implicit Tacit
- 7. Tacit financial marketknowledge Theory Common sense Theory Market folkore Previous research
- 8. How do wedesign rather than fit? ➢ Acknowledge and embrace tacit knowledge ➢ Avoid implicit fitting ➢ Do the minimum amount of explicit fitting, and do it right. Start with ideas not data
- 9. How my ~43year old self designs trading strategies... Tactit knowledge Design process Trading strategy Algo + Parameters Ideas first Real Data Fake data
- 10. How my ~43year old self designs trading strategies... Theory Design process Common sense Market folkore Previous research Real Data Fake data
- 11. How do peopledesign real products... Theory Design process Personal taste Market research Previous products Focus group Prototypes
- 12. Tacit knowledge: Trendfollowing ● Market folklore: ● “Cut your losers and let your winners run” ● “Don’t fight the tape” ● Turtle Traders ● US CTA tradition (Campbell, Chesapeake, Dunn), UK CTA tradition (AHL, Winton, Aspect), Europeans (Transtrend, Systematica) ● Previous empirical research: ● Levy 1960 ● Jegadeesh and Titman 1993 ● Carhart “fourth factor” 1997 ● Theory: ● Prospect theory (Kahneman and Tversky 1992), ● Herding, Confirmation bias, under reaction. ● Behaviour of other participants (eg risk parity funds)
- 13. Unanswered questions ● Whatperiod of time do trends last for? ● When should we enter trends? ● When should we exit trends? ● Should we have a stop loss rule? What is it? ● How do we identify markets that are, or aren’t “trend friendly”? ● How do we identify how strong the trend is? ● What size should our positions be? ● What is our algo? ● What are it’s parameters?
- 14. Data first Data Magic box Strategy= Best Algo + Best parameters Possible algos Possible parameters
- 15. What is beststrategy? Data first answer: Best return versus risk in dataset Assuming leverage is possible and risk is Gaussian: Highest Sharpe Ratio (Other measures are available…) Single metric of ‘best’: Performance Single source of information: Past data
- 16. What is beststrategy? Ideas first answer: Best designed strategy Multi-faceted metrics: Performance, turnover, behaviour in given scenarios... Multi-faceted sources of information: Common sense, Theoretical principles, Fake data, (Limited amounts of) Real Data
- 17. Designing a tradingstrategy – 6 steps: ● Start with a sound framework which imposes some conditions ● Come up with the idea ● Use some random data or single scenario of real data plus theory / common sense to develop algo ● Use fake data to “fit” algo ● Real data for parameter sensitivity check / sense check ● Fit allocation using real data (out of sample, robust optimisation)
- 18. Designing a tradingstrategy – 6 steps: ● Start with a sound framework which imposes some conditions ● Come up with the idea ● Use some random data or single scenario of real data plus theory / common sense to develop algo ● Use fake data to “fit” algo ● Real data for parameter sensitivity check / sense check ● Fit allocation using real data (out of sample, robust optimisation)
- 19. Start with asound framework Trading rule 1 Trading rule 2 Trading rule 3 SP500 EDOLLAR CORN Combine forecasts from trading rules Position sizing Portfolio: Weight instrument positions Risk targeting
- 20. Start with asound framework: Conditions ● Trading rules make forecasts of risk adjusted price changes ● Forecasts are continous, not discrete entry + exit conditions ● Forecasts are scaled in an instrument / temporal independent way (no “magic numbers”) ● Forecast is proportional to E(Sharpe Ratio / ) [Position is proportional to Forecast / hence position is proportional to / ● E(abs(forecast)) = 10.0 ● In principal all forecasts used on all markets (portfolio optimisation stage will become later) ● Use multiple variations of the same trading rule to capture different time frames (as many as possible, not too highly correlated) ● Costs are the most important thing. The second most important thing is costs. Costs are predictable – returns are not. Throw away very expensive systems. ● Throw away very slow systems (LAM)
- 21. Start with asound framework … remember these questions? ● What period of time do trends last for? ● When should we enter trends? ● When should we exit trends? ● Should we have a stop loss rule? What is it? ● How do we identify markets that are, or aren’t “trend friendly”? ● How do we identify how strong the trend is? ● What size should our positions be?
- 22. Start with asound framework … remember these questions? ● What period of time do trends last for? ● When should we enter trends? ● When should we exit trends? ● Should we have a stop loss rule? What is it? ● How do we identify markets that are, or aren’t “trend friendly”? ● How do we identify how strong the trend is? ● What size should our positions be? Fewer open questions: Fewer parameters to “fit” or design
- 23. Designing a tradingstrategy – 6 steps: ● Start with a sound framework which imposes some conditions ● Come up with the idea ● Use some random data or single scenario of real data plus theory / common sense to develop algo ● Use fake data to “fit” algo ● Real data for parameter sensitivity check / sense check ● Fit allocation using real data (out of sample, robust optimisation)
- 24. Come up withthe idea: What are trends?
- 25. Come up withthe idea Linear regression price against time, using Ordinary Least Squares: y = + x + minimise with y = price, and x = some measure of time (eg years) price in uptrend price in downtrend We use a rolling regression over the last N weekdays to capture different length trends. Single parameter: window_size
- 26. Designing a tradingstrategy – 6 steps: ● Start with a sound framework which imposes some conditions ● Come up with the idea ● Use some random data or single scenario of real data plus theory / common sense to develop algo ● Use fake data to “fit” algo ● Real data for parameter sensitivity check / sense check ● Fit allocation using real data (out of sample, robust optimisation)
- 27. Develop algo: Realscenario… 2008 = -564.1 points/year
- 28. Conditions (reminder) ● Tradingrules make forecasts of risk adjusted price changes ● Forecasts are continous, not discrete entry + exit conditions ● Forecasts are scaled in an instrument / temporal independent way (no “magic numbers”) ● Forecast is proportional to E(Sharpe Ratio / ) [Position is proportional to Forecast / hence position is proportional to / ● E(abs(forecast)) = 10.0 ● In principal all forecasts used on all markets (portfolio optimisation stage will become later) ● Use multiple variations of the same trading rule to capture different time frames (as many as possible, not too highly correlated) ● Costs are the most important thing. The second most important thing is costs. Costs are predictable – returns are not. Throw away very expensive systems. ● Throw away very slow systems (LAM)
- 29. Develop algo: Evaluatedesign Does this scale well? No… (No need to look at data! Common sense!) Forecast is proportional to E(Sharpe Ratio = / ) [Position is proportional to Forecast / hence position is proportional to / in units of (price) so: Forecast = Where is measured is annual standard deviation of (price) (No need to look at data! Theory)
- 30.
- 31. Develop algo: Evaluatedesign Does this scale well? Yes. Does behaviour make sense? Yes. Bullish in bull markets, bearish in bear markets How about the trading speed? Seems reasonable given the length of trends involved Anything weird? Yes Need to set initial min_periods to a higher value (eg window_size / 4 : Common sense!) Too slow? Probably N=256 is the slowest we’d go (LAM)
- 32. Designing a tradingstrategy – 6 steps: ● Start with a sound framework which imposes some conditions ● Come up with the idea ● Use some random data or single scenario of real data plus theory / common sense to develop algo ● Use fake data to “fit” algo ● Real data for parameter sensitivity check / sense check ● Fit allocation using real data (out of sample, robust optimisation)
- 33. Conditions (reminder) ● Tradingrules make forecasts of risk adjusted price changes ● Forecasts are continous, not discrete entry + exit conditions ● Forecasts are scaled in an instrument / temporal independent way (no “magic numbers”) ● Forecast is proportional to E(Sharpe Ratio / ) [Position is proportional to Forecast / hence position is proportional to / ● E(abs(forecast)) = 10.0 ● In principal all forecasts used on all markets (portfolio optimisation stage will become later) ● Use multiple variations of the same trading rule to capture different time frames (as many as possible, not too highly correlated) ● Costs are the most important thing. The second most important thing is costs. Costs are predictable – returns are not. Throw away very expensive systems. ● Throw away very slow systems (LAM)
- 34. Use fake datato “fit” algo: method What value(s) should we use for window_size? 1) Get an understanding of how trend length relates to profitability of window_size 2) Get an idea of how fast different window_size will trade 3) Prune any window_size that are likely to be too expensive 4) Prune any window_size that are likely to be too slow 5) Understand correlation structure to work out best window_size pattern
- 35. Use fake datato “fit” algo: Generating data = + N(0,) qoppac.blogspot.com/2015/11/using-random-data.html
- 36. Use fake datato “fit” algo: Trend length & window_size: pre-cost SR 21 64 128 192 256 5 1 week 6.4 2.1 0.3 0.2 0 10 2 weeks 4.5 2.6 0.6 0.2 0 15 3 weeks 1.6 2.9 0.8 0.2 0 21 1 month -2.0 2.9 1.1 0.2 0.1 42 2 months -11 1.6 1.4 0.4 0.1 64 3 months -0.4 -0.1 1.1 0.5 0.1 85 4 months -5.0 -1.8 0.8 0.5 0.1 107 5 months -0.1 -3.0 0.4 0.5 0.1 128 6 months -3.0 0 0.5 0.1 150 7 months -1.8 -0.5 0.3 0.1 171 8 months -0.5 -0.7 0.3 0.2 192 9 months -0.1 -1.0 0.1 0.2 213 10 months -1.3 0 0.2 235 11 months -1.6 -0.2 0.2 256 12 months -1.6 -0.3 0.1 Window size>192 essentially pointless (LAM)
- 37. Use fake datato “fit” algo: window_size and trading speed Turnover/year 5 1 week 176 10 2 weeks 75 15 3 weeks 49 21 1 month 36 42 2 months 21 64 3 months 13 85 4 months 12 107 5 months 9.3 128 6 months 8.8 150 7 months 7.4 171 8 months 7.1 192 9 months 6.5 213 10 months 6.5 235 11 months 6.1 256 12 months 6.1 Turnover / year So turnover = 52 implies holding period of one week Barely any improvement beyond window_size>191
- 38. Use fake datato “fit” algo: window_size and costs Cheap eg SP500 Expensive eg EDOLLAR 5 1 week 17.6 176 10 2 weeks 7.5 75 15 3 weeks 4.9 49 21 1 month 3.6 36 42 2 months 2.1 21 64 3 months 1.3 13 85 4 months 1.2 12 107 5 months 0.92 9.3 128 6 months 0.88 8.8 150 7 months 0.74 7.4 171 8 months 0.71 7.1 192 9 months 0.65 6.5 213 10 months 0.65 6.5 235 11 months 0.61 6.1 Costs in bp /year of SR Max allowable is 13bp See ch.12 of my book No point having window_size =5
- 39. Use fake datato “fit” algo: window_size and correlation structure It turns out that if window_sizen+1 = window_sizen * √2 Then correlation(forecastn+1 , forecastn ) ~ 0.90 And correlation(forecastanother n , forecastn ) < 0.90
- 40. Use fake datato “fit” algo: Final iteration Summary of findings: ● Window size in √2 steps covers the space best ● Window size <10 too expensive for any instrument ● Window size>200 pointlessly slow Window_size = [10,14,20,28,40,57,80, 113,160] Should capture trends lasting for around 1 month to 18 months
- 41. Designing a tradingstrategy – 6 steps: ● Start with a sound framework which imposes some conditions ● Come up with the idea ● Use some random data or single scenario of real data plus theory / common sense to develop algo ● Use fake data to “fit” algo ● Real data for parameter sensitivity check / sense check ● Fit allocation using real data (out of sample, robust optimisation) NOTE: Although I’m using real data, I’m not going to be looking at performance.
- 42. Real data check:consistent scaling Window size: 10 14 20 28 40 57 80 113 160 Corn 0.14 0.17 0.20 0.22 0.26 0.29 0.32 0.37 0.42 Eurodollar 0.13 0.15 0.18 0.20 0.24 0.28 0.33 0.39 0.43 S&P 500 0.14 0.18 0.21 0.25 0.30 0.36 0.42 0.50 0.56 US 10 year bond 0.13 0.16 0.19 0.22 0.26 0.30 0.36 0.42 0.48
- 43. Real data check:turnover window_size turnover 10 80.4 14 55.2 20 36.9 28 25.8 40 18.4 57 13.6 80 10.7 113 8.6 160 7.0
- 44. Real data check:costs Window size: 10 14 20 28 40 57 80 113 160 Corn 0.40 0.27 0.18 0.13 0.09 0.07 0.05 0.04 0.03 Eurodollar 0.64 0.44 0.29 0.20 0.14 0.11 0.08 0.07 0.05 S&P 500 0.10 0.07 0.05 0.04 0.03 0.02 0.02 0.01 0.01 US 10 year bond 0.25 0.17 0.12 0.08 0.06 0.04 0.03 0.03 0.02
- 45. Real data check:correlation structure Highest correlation between any two pairs of window_size; 0.85
- 46. Designing a tradingstrategy – 6 steps: ● Start with a sound framework which imposes some conditions ● Come up with the idea ● Use some random data or single scenario of real data plus theory / common sense to develop algo ● Use fake data to “fit” algo ● Real data for parameter sensitivity check / sense check ● Fit allocation using real data (out of sample, robust optimisation) First and last time I will use performance calculated using real data.
- 47. Conditions: reminder ● Tradingrules make forecasts of risk adjusted price changes ● Forecasts are continous, not discrete entry + exit conditions ● Forecasts are scaled in an instrument / temporal independent way (no “magic numbers”) ● Forecast is proportional to E(Sharpe Ratio / ) [Position is proportional to Forecast / hence position is proportional to / ● E(abs(forecast)) = 10.0 ● In principal all forecasts used on all markets (portfolio optimisation stage will become later) ● Use multiple variations of the same trading rule to capture different time frames (as many as possible, not too highly correlated) ● Costs are the most important thing. The second most important thing is costs. Costs are predictable – returns are not. Throw away very expensive systems. ● Throw away very slow systems (LAM)
- 48. Fit allocation usingreal data Combined forecast = w1 f1 + w2 f2 + w3 f3 + … f are in same vol scale so, values of w depend on: ● Pre-cost performance (different by market?) ● Costs (different by market) ● Correlation structure ● Well known portfolio optimisation problem…. ● … with well known problems (estimation error, extreme weights) ● …. and well known solutions: clustering, shrinkage, bootstrapping… ● Only line of defence against incorporating a (statistically sigificantly) loss making trading rule in our system
- 49. Fit allocation usingreal data: Hypocrisy? An aside, Why is fitting model parameters bad... … but optimising model portfolio allocations acceptable? Answers: ● Parameter space much smaller ● Rolling out of sample is feasible ● Nicer surface ● Well developed techniques exist to cope with problems and use correct amount of degrees of freedom ● Much harder to do implicit fitting = much easier to resist the temptation
- 50. Fit allocation usingreal data: Some account curves
- 51. Fit allocation usingreal data: Some account curves
- 52. Summary ● Three typesof over fitting: tacit, implicit, explicit. ● You can’t get around tacit knowledge. ● Use tacit knowledge to design trading strategies. Design process: ● Start with a sound framework which imposes some conditions ● Come up with the idea ● Use some random data or single scenario of real data plus theory / common sense to develop algo ● Use fake data to “fit” algo ● Real data for parameter sensitivity check / sense check ● Fit allocation using real data (out of sample, robust optimisation)
- 53. My first book: systematictrading.org Mysecond book: TBC My blog: qoppac.blogspot.com Some python: github.com/robcarver17/ Twittering: @investingidiocy