1999issue C081-2
Monte Carlo checks beyond a single historical path
A reader asked for resources on applying Monte Carlo simulation to the verification of trading systems, and also for chaos science applied to trading. An editorial reply said earlier articles had already borrowed from Monte Carlo simulation and listed that work in a group apart from chaos theory.
- Monte Carlo simulation is a statistical resampling method that generates many alternative evaluation paths from observed market sequences.
- System verification checks a specified trading rule with procedures that go beyond a single historical path.
- An editorial reply grouped earlier Monte Carlo topics, including risk of ruin and variable position size, separately from chaos theory.
- Editorial reading: place Monte Carlo simulation after the model so a stated forecast is evaluated against more than one historical path.
A request for verification resources
A reader asked for resources on applying Monte Carlo simulation to the verification of trading systems. The same request asked for coverage of Monte Carlo methods used to test trading systems and also for coverage of chaos science applied to trading.
Monte Carlo simulation is a statistical resampling method that generates many alternative evaluation paths from observed market sequences. System verification means checking a specified trading rule with procedures that go beyond a single historical run.
How earlier work was grouped
An editorial reply said earlier published articles had already borrowed from Monte Carlo simulation. That reply listed prior work in two separate groups: Monte Carlo simulation and chaos theory.
The Monte Carlo group named topics that included risk of ruin, statistical evaluation of trading systems, leverage and profitability, variable position size, and money management that used simulation together with chaos. Risk of ruin is the chance that a run of losses exhausts capital under a stated sizing rule. Variable position size is changing trade size across sequential signals instead of holding a constant quantity.
Research writing for system testing
A separate letter said research-focused articles were more useful than indicator how-to pieces for learning how to develop and test a personal trading system.
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