Runtime Fund Launch: Our Core Investment Thesis
Dear Investors and Partners,
Before discussing markets, performance, or short-term outcomes, I want to begin with the foundation: why this strategy exists at all.
This first letter outlines our core investment thesis. It is intended to be durable — a reference point that future letters will build upon rather than restate.
At its core, our work is guided by a simple belief:
Long-term compounding is not a forecasting problem.
It is a portfolio engineering problem.
Modern compute finally allows us to approach it that way.
The Problem We Are Solving
Investing is the allocation of capital across uncertain outcomes, through time, under constraints.
Most portfolios are not engineered under this lens. They are assembled — often via static allocations, intuition-driven adjustments, or collections of rules that may work in isolation but interact poorly under stress.
Markets are adaptive systems. Two portfolios with identical long-term averages can produce radically different outcomes depending on:
- when you start
- how deep drawdowns go and how long they last
- whether the system can recover after adverse periods
Our objective is to engineer portfolios that compound across many possible paths — not just the most convenient one.
Simplicity and Lessons from Physics
In physics, the most powerful theories are not those with the most rules, but those with the fewest, deepest explanatory principles. Maxwell unified electricity, magnetism, and light with a small set of equations — not because nature is simple, but because those laws operate at the right level of abstraction.
The same idea applies to portfolio design.
Strategies built from many ad-hoc rules, thresholds, and exceptions may appear sophisticated, but they are often brittle. Each additional parameter introduces another way the system can fail when conditions change.
We deliberately seek explanatory depth over surface complexity: systems whose behavior emerges from foundational principles rather than accumulated patches.
Elegance is the signature of a system that generalizes.
Why Systematic Approaches Matter
Human judgment is essential in defining strategy intent — but it does not scale reliably when decisions must be made repeatedly, under uncertainty, and under stress.
Systematic approaches elevate judgment to the level of design, and then enforce that design consistently:
- decisions are rule-based, not reactive
- every rule can be tested historically
- behavior under stress is examined explicitly
- changes are deliberate, not emotional
As Charlie Munger observed, "The big money is not in the buying and selling, but in the waiting." (investopedia.com)
Waiting only works if the system is designed to survive the periods in between.
Risk as a Design Constraint
In our framework, risk is not an afterthought — it is a primary design input.
Rather than optimizing for returns first and managing risk later, we embed explicit guardrails directly into the optimization process:
- maximum drawdown and capital loss thresholds
- stress testing during historically adverse periods
- sensitivity analysis across start dates and holding periods
- consistency checks between in-sample and out-of-sample behavior
Strategies that only work when started at the "right time" are not acceptable.
Volatility will occur. Losing periods are unavoidable. What matters is whether drawdowns remain bounded, understood, and recoverable. We do not control when stress arrives — we control how the system engages with it.
Why Compute and AI Matter
Modern compute allows us to operationalize this philosophy.
We use AI and large-scale computation not to predict markets, but to systematically explore and validate portfolio behavior:
- evaluate large populations of strategy variations
- test across multi-decade historical data
- separate discovery from validation
- identify structural weaknesses before capital is deployed
AI here is not a black box. It is a search, testing, and discipline engine.
As Jim Simons advised, "Be guided by beauty... There is beauty in things that work really well." (MIT Sloan)
In this context, beauty means: few moving parts, clear constraints, repeatable behavior. When a system is simple at its core, it is easier to test, easier to stress, and harder to break.
Our Edge
Our edge does not come from a single signal or forecast.
It comes from:
- a purpose-built research and testing platform
- foundational rules designed for robustness
- explicit risk guardrails at the system level
- continuous iteration rather than static design
But most importantly, it comes from accumulated work that cannot be shortcut.
Our system has been refined through large-scale simulation — a scale that requires years of infrastructure and compute investment to replicate.
And unlike backtests alone, we have 5.5 years of realized, live performance — and counting. The system has operated through multiple market regimes, and that history informs every refinement.
Even if every detail were published tomorrow, a competitor would still face years of work to rebuild the infrastructure, re-run the experimentation, and accumulate the operational learning we already possess.
Looking Ahead
Future letters will discuss how the system behaves across different environments and what we are learning from live operation.
They will not attempt to predict markets or rationalize short-term noise.
The purpose of this communication is alignment — around discipline, rigor, and long-term thinking.
Thank you for your trust.
Sincerely,
Deniz
This communication is for informational purposes only and does not constitute an offer to sell or a solicitation of an offer to buy any security. Any offer or solicitation will be made only pursuant to definitive offering documents and in accordance with applicable law.
