From Possibility to
Probability

Our framework combines patterns, trial and error, and machine learning to identify repeatable market tendencies. We start with what markets actually did across thousands of observations.

Explore Our Approach Meet The Team

85,6%

Probabilidad de retorno positivo en una ventana de 12 meses (vs 76,6% del S&P)

82,3%

Probability of beating S&P 500 on a 12M window

3M+

Models rejected for every model we approve

Based on historical backtesting and probabilistic simulations. Results reflect model behavior under specific assumptions and do not represent guarantees of future performance.

When Stories Pretend To Be Signals

Most commentary about investing is narrative scaffolding - projecting paths that sound persuasive precisely because probability is absent. We invert this by starting with the data first.

Narrative Fallacy

We study millions of market moments looking for tendencies that reliably repeat. For every model we develop and keep, millions are rejected through our Darwinian iteration process.

Career Risk Bias

Our machine learning surfaces variables with predictive weight - like a brutally honest metal detector. The real craft lies in how we combine them through methodical experimentation.

Lingering Positions

Every position has an explicit time horizon and defined return target. Either one arrives and we exit, or the clock runs out and we exit. No lingering, no bargaining.

Lack of Auditability

We don't promise certainty - we offer a method where every decision must earn its place with numbers. Our process is "move incrementally and measure everything."

Our Probability-Driven Process

We study what markets have actually done across millions of moments, identify tendencies that survive testing, and translate them into rules you can underwrite.

1

Pattern Discovery

For each pattern we ask: How often does it work? When it works, how good? When it doesn't, how bad? How long to know which we have? These give us expected payoff and time window.

2

Rule Creation

We convert tendencies into clear rules like a recipe. Machine learning finds ingredients, our research crafts the combination. For every model we promote, ~3M are discarded.

3

Disciplined Execution

We spread capital across many signals - more ways to be right. Rules are employees, not kids. Any that stop earning keep get replaced. No egos, no favorites, just accountability.

People Who Answer To The Process

Our edge isn't a single model but how we pair machine learning with trial and error. Hans sees distributions and error bars; Felipe sets the search space from market experience.

Felipe Galleguillos

Luis Felipe Galleguillos

Chief Executive Officer

"Brings market experience to suggest which variables deserve testing. Sets search space using decades of seeing what behaviors travel together and where false positives hide."

Hans

Hans Lembach

Chief Data Scientist

"Mathematician who sees only distributions and error bars. No attachment to tickers or headlines. Runs experiments testing how variables interact across regimes."

Nuestros Fondos

Estrategias sistemáticas basadas en datos, probabilidad y disciplina.

Quantum Wave I

Fondo de Inversión Privado que invierte 100% en Renta Variable de EE.UU. con metodologías cuantitativas

Características:

  • • Fondo denominado en USD
  • • Rescates trimestrales a partir de 2027
  • • Fondo solo toma posiciones largas en acciones, no toma derivados ni invierte con endeudamiento

Documentos:

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Choosing A Wiser Way Forward

We target the short-to-medium horizon where price and flow dynamics dominate. Our rules-based engine combines evidence, iteration at scale, and accountable implementation.

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