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2017issue C0116-19

Four-sleeve Monte Carlo annual-loss bounds

A mixed-instrument book is framed as a search for the best available risk-reward tradeoff among random variables. Monte-carlo-resampling turns a blotter into thousands of synthetic years so four independent-sleeves can be judged against a drawdown-limit on the annual-loss tail.

  • A usable risk sample covers several market conditions and meets a trades-per-parameter floor so profit-factor and win-rate estimates are statistically meaningful.
  • Monte-carlo-resampling rebuilds an annual outcome distribution from daily profits because a lone equity path is not treated as a risk test.
  • Four independent-sleeves are presented as generally adequate to cut the chance of an annual loss; eight holdings meet diminishing returns and expected profit is not said to rise.
  • A drawdown-limit bounds time at risk, simultaneous exposure, and risk-of-ruin before the next order is placed.
Entries in this reading3 entries

A book of random variables

A mixed-instrument book is framed as seeking the best available risk-reward tradeoff among random variables, not as a requirement to pursue high returns.

A usable risk sample is described as a track record that covers several market conditions and contains enough trades for profit-factor and win-rate estimates to be statistically meaningful. Systems with several free parameters are assigned a trades-per-parameter floor of 30 historical trades per parameter, so a two-length moving-average crossover is given a minimum of 60 trades to limit optimization fit.

A single equity path is not a risk test

A lone equity path is rejected as a risk test because repeated tests of the same rules can still produce large equity-curve differences. Risk-of-ruin is described as higher before an account has built a cushion.

Monte-carlo-resampling of daily profits

Monte-carlo-resampling converts a trade history into daily profits, draws those daily profits with replacement 260 times to form one synthetic year, and repeats that year 5,000 or 10,000 times to build an outcome distribution.

That resampled annual distribution is treated as approximately normal, so expectation and the one-sigma and two-sigma points can be read from it as a sigma-band. In the fully invested single-name illustration, breakeven-or-better sits outside the lower one-sigma point while the chance of a yearly loss remains high.

Four independent-sleeves and the loss tail

Raising the number of independent simultaneous holdings by a factor of four is stated to halve the variance of the annual distribution, because doubling independent samples reduces variance by the square root of two. Independent-sleeves are parallel, unsynchronized holdings that each take their own signals so daily results are not locked to a single name.

Splitting the same capital across four unsynchronized, independently signaled holdings is presented as generally adequate to cut the chance of an annual loss in a well-specified system. Eight holdings would halve variance again but meet diminishing returns and capital limits, and expected profit is not said to rise.

Time at risk and the drawdown-limit

A cyclic-hold that averages about 10 days from trough to peak is contrasted with trend following as a way to keep average time at risk shorter. Trend following is characterized by tentative entries, quick exits on failed onsets, a higher profit-factor, and a lower win rate.

A drawdown-limit is a pre-trade and in-trade bound on time at risk, simultaneous exposure, and the chance of early ruin, set before the next order is placed.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
24 of 26 in the Monte Carlo simulation track
201836-39 pp.Next on Monte Carlo simulationWhen to retire a mechanical system with Monte Carlo drawdown limitsBuild a mechanical trading system by encoding an identified market behavior pattern as one procedure for entry, exit, and related decisions.
All readings on this track · 26 readings
  1. 1989Known-bias price streams for system evaluation
  2. 1989Constructing holdout-checked synthetic prices from chaos maps
  3. 1991Closing the feedback loop in auction market simulations
  4. 1994Evaluating eight-year election regimes
  5. 1995Constructing equity versus leverage with resampled trades
  6. 1995Stock leverage from compounded returns and margin cost
  7. 1996Constructing and optimizing volatility position-sizing profiles
  8. 1996Constructing fade rules from an empirical deviation histogram
  9. 1998Return-path volatility and the recovery burden
  10. 1999Monte Carlo checks beyond a single historical path
  11. 2002Monte Carlo equity paths from win rate and profit factor
  12. 2003Checking a small system edge with Monte Carlo simulation
  13. 2003Evaluate a mechanical trading system with a stop-loss order and Monte Carlo simulation
  14. 2005Evaluating momentum rank rotation under portfolio constraints
  15. 2009Bounded risk and rank rotation in portfolio construction
  16. 2010A calendar for walk-forward robustness and Monte Carlo evaluation
  17. 2012Treat the optimize view as an evaluation protocol
  18. 2013Entry, exit and abstention as one testable procedure
  19. 2014Evaluating stop and target bounds before fading large moves
  20. 2014Evaluating system exits when markets get noisier
  21. 2015Memory-bearing synthetics for dominant-cycle spectra
  22. 2015Constructing next-session implied-volatility forecasts by reordering closes
  23. 2016Mechanical systems as a gated factory
  24. 2017Four-sleeve Monte Carlo annual-loss bounds
  25. 2018When to retire a mechanical system with Monte Carlo drawdown limits
  26. 2020Treat mechanical system design as a revalidation lifecycle
All 51 readings tagged Monte Carlo simulation
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