- Performance Simulations
- Round Turn Trade Simulations
- Stylized facts & Round Turn tradeDef
- Empirical examples
- Future Work
- Conclusion
updated 28 May 2018
Pat Burns - "If we generate a random subset of the paths, then we can make statistical statements about the quality of the strategy."
Jaekle & Tomasini - "Changing the order of the performed trades gives you valuable estimations about expected maximum drawdowns."
Lopez de Prado et al - "…because the signal-to-noise ratio is so weak, often the result of such calibration is that parameters are chosen to profit from past noise rather than future signal."
Harvey et al - "We argue that most claimed research findings in financial economics are likely false."
What all these methods have in common, is an element of random sampling based on some constraint. What we propose in blotter:::txnsim() is the random sampling of round turn trades bound by the constraint of the stylized facts of the observed strategy.
chart.Posn("longtrend", Symbol="GSPC")
set.seed(333) lt.wr <- ex.txnsim('longtrend',n=1000,replacement=TRUE,chart=FALSE,tradeDef="flat.to.flat") plot(lt.wr)
## mean median stddev maxDD sharpe totalPL ## txnsim.wr.longtrend.664 1 96.0 681 28.0 4 1 ## txnsim.wr.longtrend.640 2 554.5 744 28.0 9 2 ## txnsim.wr.longtrend.955 3 23.0 120 198.0 44 3 ## txnsim.wr.longtrend.951 4 51.0 62 711.0 55 4 ## txnsim.wr.longtrend.870 5 554.5 770 10.5 10 5 ## txnsim.wr.longtrend.945 7 554.5 431 70.0 25 6 ## txnsim.wr.longtrend.503 8 554.5 886 38.0 5 7 ## txnsim.wr.longtrend.660 9 554.5 459 154.5 28 8 ## txnsim.wr.longtrend.935 10 554.5 559 66.0 21 9 ## longtrend 6 554.5 846 7.0 6 10
## mean median stddev maxDD sharpe totalPL ## 0.0060 0.5539 0.8452 0.0070 0.0060 0.0100
chart.Posn(Portfolio='bbands',Symbol="AAPL",TA="add_BBands(on=1,sd=SD,n=N)")
win_rep <- names(bb.wr$ranks[,6][which(bb.wr$ranks[,6]==1)]) chart.Posn(Portfolio=win_rep,Symbol="AAPL",TA="add_BBands(on=1,sd=SD,n=N)")
Round turn trade Monte Carlo simulates random traders who behave in a similar manner to an observed series of real or backtest transactions. We feel that round turn trade simulation offers insights significantly beyond what is currently available as open source and txnsim() in particular is well suited to evaluating the question of "skill versus luck or overfitting".
Burns, Patrick. 2006. "Random Portfolios for Evaluating Trading Strategies." http://www.burns-stat.com/pages/Working/evalstrat.pdf
Tomasini, Emilio & Jaekle, Urban. 2009. "Trading Systems: A New Approach to System Development and Portfolio Optimization"
Bailey, David H, Jonathan M Borwein, Marcos López de Prado, and Qiji Jim Zhu. 2014. “The Probability of Backtest Overfitting.” http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2326253.
Harvey, Campbell R., and Yan Liu. 2015. “Backtesting.” SSRN. http://ssrn.com/abstract=2345489.
Peterson, Brian G. 2017. "Developing & Backtesting Systematic Trading Strategies." http://goo.gl/na4u5d
Thank You for Your Attention
Thanks to Brian Peterson, Joshua Ulrich, all the contributors to quantstrat and blotter, to the R/Finance committee and sponsors, R community and last but certainly not least, UIC and Mary Deering.