1998issue C051-3
Return-path volatility and the recovery burden
Each completed trade rescales the capital available for the next trade, so the size and order of returns change ending wealth even when the list of percentage outcomes looks similar. This evaluation treats return volatility as the spread of those equity changes, then uses Monte Carlo simulation to compare swing size while a drawdown limit and volatility position sizing keep the path inside a recoverable bound.
- Sequential compounding means the size and order of returns change ending wealth even when the list of percentage outcomes looks similar.
- Restoring a 20 percent advance after a 20 percent decline requires a 50 percent gain on the remaining equity.
- When both paths reused the same random draws and only the lower-volatility path took half of each gain or loss, that path finished ahead in about 80 of 100 trials.
- Exposure can be judged before entry and during a position by whether the allowed return range keeps equity swings inside a bound the account can recover from.
Sequential compounding and the recovery burden
Each completed trade rescales the capital available for the next trade. Under sequential compounding, the ending equity of one trade becomes the starting capital of the next, so the size and order of returns change ending wealth even when the list of percentage outcomes looks similar.
Restoring a 20 percent advance after a 20 percent decline requires a 50 percent gain on the remaining equity. That extra lift is the recovery burden: the larger percentage gain required to restore a prior equity level after a decline.
Return volatility of the equity path
In this evaluation, return volatility is the period-to-period variability of those percentage equity changes, not the variability of an entry signal alone. It is the spread of period-to-period percentage changes in account equity produced by a trade sequence.
Monte Carlo campaigns that isolate swing size
Monte Carlo simulation here means repeated random or matched-seed return sequences used to compare how two volatility settings finish after many paths. Across 100 independent random campaigns started from the same equity, the half-range return path finished ahead in about 60 to 70 trials.
Holding the random seed fixed isolates swing size as the variable under test, so a better finish cannot be blamed on a luckier set of trades. A matched-seed comparison feeds two equity series the same random returns while shrinking the period gains and losses of one series.
When both paths reused the same random draws and only the lower-volatility path took half of each gain or loss, that path finished ahead in about 80 of 100 trials. A practical system check prefers smaller losses even when wins shrink with them, because less ground then has to be recovered after a decline.
Bounds that keep swings recoverable
A drawdown limit is a precommitted bound on loss or exposure, set from equity, volatility, and stop distance, so one sequence cannot force an outsized recovery. Volatility position sizing scales exposure so the allowed gain-and-loss range stays inside a chosen bound before entry and while the position is open.
Exposure can be judged before entry and during a position by whether the allowed return range keeps equity swings inside a bound the account can recover from.
All readings on this track · 26 readings
- 1989Known-bias price streams for system evaluation
- 1989Constructing holdout-checked synthetic prices from chaos maps
- 1991Closing the feedback loop in auction market simulations
- 1994Evaluating eight-year election regimes
- 1995Constructing equity versus leverage with resampled trades
- 1995Stock leverage from compounded returns and margin cost
- 1996Constructing and optimizing volatility position-sizing profiles
- 1996Constructing fade rules from an empirical deviation histogram
- 1998Return-path volatility and the recovery burden
- 1999Monte Carlo checks beyond a single historical path
- 2002Monte Carlo equity paths from win rate and profit factor
- 2003Checking a small system edge with Monte Carlo simulation
- 2003Evaluate a mechanical trading system with a stop-loss order and Monte Carlo simulation
- 2005Evaluating momentum rank rotation under portfolio constraints
- 2009Bounded risk and rank rotation in portfolio construction
- 2010A calendar for walk-forward robustness and Monte Carlo evaluation
- 2012Treat the optimize view as an evaluation protocol
- 2013Entry, exit and abstention as one testable procedure
- 2014Evaluating stop and target bounds before fading large moves
- 2014Evaluating system exits when markets get noisier
- 2015Memory-bearing synthetics for dominant-cycle spectra
- 2015Constructing next-session implied-volatility forecasts by reordering closes
- 2016Mechanical systems as a gated factory
- 2017Four-sleeve Monte Carlo annual-loss bounds
- 2018When to retire a mechanical system with Monte Carlo drawdown limits
- 2020Treat mechanical system design as a revalidation lifecycle