1989issue C111-11
Constructing holdout-checked synthetic prices from chaos maps
An irregular series is treated as the output of a bounded feedback generator, then scaled into a synthetic price. No conforming procedure is written until a hold-out projection has been compared with unused observations.
- Treat an irregular series as deterministic chaos: the output of a nonlinear recurrence with feedback, not a path that lacks causal structure.
- A growth map keeps the seed and the rescaled growth factor inside the open unit interval; after a short transient its attractors do not depend on the seed.
- A synthetic price is the generated series after light smoothing, a chosen basis, accumulation of one-period changes, and scaling to the target market range.
- Holdout optimization shrinks the cumulative absolute gap on an earlier window, then judges a projection only against unused observations before any further forecast or conforming procedure is written.
A fixed construction order
In this construction an irregular series is treated as the output of a nonlinear recurrence with feedback, not as a path that lacks causal structure. That reading is deterministic chaos: apparent irregularity produced by a known nonlinear recurrence with feedback rather than by a process with no recoverable structure.
The work follows a fixed order. A bounded feedback generator is built first. That output is then scaled into a synthetic price. No conforming procedure is written until a hold-out projection has been compared with unused observations.
Start with a bounded growth map
A linear growth term combined with a quadratic feedback term is rewritten as a standardized one-step map. That growth map requires both the seed and the rescaled growth factor to stay inside the open unit interval.
After a short transient the standardized map settles on attractors that do not depend on the seed. One illustration homes in on 0.375 while seeds range from 0.01 to 0.60.
Identify the map by attractor count
Map classes are identified by attractor count, which rises as the growth factor rises. One pair of illustrations uses 0.76 for two attractors and 0.89 for eight. A chaotic regime is said to begin once the factor exceeds 0.8924.
When scheduled revisits disappear, a seed change of 1% or less can produce an uncorrelated path. The governing parameters cannot be recovered from a fragment of the series even though the unit bounds still hold.
Widen the irregular range
The one-dimensional map is judged too simple and too often mixed for price work. A modulated map drives the observed series from two separate feedback maps so that purely irregular output occupies a wider parameter range.
Scale the output into a synthetic price
A synthetic price is built by lightly smoothing the generated series, choosing a basis, treating the result as a one-period change, and accumulating those changes. A higher basis lowers the implied synthetic direction. The path is then scaled to the target market range.
Check the unused tail before any rule
Holdout optimization first minimizes the cumulative absolute gap between the real series and the normalized synthetic path. The fit is stopped before the data end. A projection is then compared with the unused tail before any further forecast or conforming procedure is written.
One construction example fitted the synthetic path over 120 days and then projected 75 trading days. Judgment was mainly trend and turning-point alignment. The horizon is limited to intermediate use unless the fit is very close.
Parameter search is advised to refine small growth-factor neighborhoods rather than scan the full range coarsely, because changes of less than 1% in growth, seed, or basis can rewrite the synthetic path.
A conforming procedure is a single mechanical rule set for entry, exit, and abstention. It is written only after the hold-out projection is accepted as a construction check.
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