Trading Through the Lens of Perception Spheres

From Insider Reality to Algorithmic Abstraction

We often look at financial markets through the narrow prism of economics, risk management, and psychology. But what if different trading styles are actually manifestations of entirely different cognitive structures—or what we might call perception spheres?

As we move up the ladder of abstraction, our relationship with the market changes fundamentally. This evolution isn’t just about how we trade; it is also about where we play, how capital is deployed, how we experience time, and what we actually want to own. The playground expands, capital velocity shifts, time compresses, and the desire for physical ownership dissolves as perception reaches higher levels of abstraction.

If we examine the historical performance of the greatest practitioners across these tiers alongside their chosen arenas, a provocative pattern emerges: The higher the perception sphere, the higher the raw percentage return.

1. Concrete sphere – relational (insider) trading

At the base of the perceptual pyramid lies the most concrete, localized approach: direct, physical inside information. In theory, knowing exactly what a company is doing before anyone else gives you absolute control within a single, highly restricted micro-environment.

Yet, this sphere is fundamentally flawed. Not only is it legally restricted, but it represents a closed loop with zero systemic scalability. It is an evolutionary dead end—a localized distortion that inevitably collapses under the weight of regulation and trust.

2. General sphere – value investing (Warren Buffett)

Stepping up into legitimate, structured perception, we find Value Investing. Here, the playground is the individual corporate entity and the stock market, and the core motivation is true ownership.

Practitioners like Warren Buffett view the market through the lens of enterprise fundamentals. They analyze balance sheets, management quality, competitive moats, and the physical economic output of companies. Buffett’s legendary compound annual growth rate (CAGR) hovers around 19% to 22% over decades.

• The Will to Own & Time: Buffett actually wants to own the business. He pays the full price for the asset, and short-term price fluctuations matter little because he secures long-term value and dividends. He relies on permanent capital and linear, expansive time, anchoring his resources into physical economic structures that compound over decades.

• The Trade-Off: The trade-off is lower percentage returns compared to pure abstraction tiers; however, this is balanced by massive capacity, allowing Buffett to deploy hundreds of billions without breaking the model, simply because the capital rests quietly inside tangible assets.

3. Autonomous sphere – trend following (Ed Seykota)

Moving higher into abstraction, the trend follower abandons corporate balance sheets entirely. Consequently, the playground shifts from individual companies to commodities and futures markets (cotton, oil, metals, currencies).

Here, the trader’s motivation changes entirely: Seykota has zero desire to own the underlying physical asset. The only thing he wants to “own” is the correct estimate of the price movement.

• Margin & The Hedging Origin: Unlike Buffett, who pays the full price, Seykota trades on margin. He controls massive notional value with just a fraction of the capital (putting down a small margin deposit to secure exposure, rather than buying physical gold bars). Commodity markets were originally engineered for industrial producers and consumers to hedge future physical deliveries; speculators merely ride the waves of these commitments. Because the real-world volume of physical hedging and available liquidity is finite, trend-following strategies face a natural capacity ceiling.

• Time: Seykota surfs on cyclical time, detaching emotionally from the asset to treat time as a dynamic parameter of process and rhythm.

4. Essential sphere – Algorithmic & Statistical Arbitrage (Jim Simons)

At the pinnacle of modern trading sits the realm of pure quantification. Jim Simons and his Renaissance Technologies Medallion Fund achieved an estimated 66% gross annual return (translating to roughly 39% net to investors) over thirty years—a feat unmatched in financial history.

Simons and his team abandoned fundamental analysis, news, and human intuition altogether. Crucially, their playground became “everything and nothing” (Global Multi-Asset), and their motivation to own anything underlying is virtually nonexistent.

• Atemporal Presence & The Turnover Limit: Mathematics and algorithms are homeless—it is entirely irrelevant to a quant model whether it is processing Tesla stock or crude oil. Time is compressed into an atemporal presence as models hunt for statistical anomalies in the absolute “here and now.” However, because a quant fund must constantly churn capital through high-velocity data streams, it hits an absolute liquidity ceiling: the moment the fund gets too large, its own volume destroys the statistical edge, forcing Medallion to lock out external money.

The Paradox of Altitude, Capital, Time, and Ownership

Looking at these tiers reveals a profound structural truth: As trading styles ascend through higher perception spheres, shedding physical details and moving from true ownership to pure mathematical extraction, potential percentage returns increase.

• Value Investing demands full ownership and linear time, sacrificing peak percentage returns for massive, friction-free capital capacity in physical assets.

• Trend Following replaces physical ownership with margin leverage and cyclical time, riding the natural hedging waves of global commerce within physical market limits.

• Algorithmic Trading abolishes ownership entirely, operating in atemporal presence within a universal data playground, achieving astronomical percentage returns constrained only by high-velocity turnover limits.

Ultimately, how we trade is how we perceive reality, time, and property. The evolution of trading styles is a journey of human consciousness learning to navigate layers of complexity—proving that higher altitude commands a broader view, but demands a profound trade-off between the velocity of abstraction, the permanence of time, the will to own, and the mass of capital.

Note:
This blog post is the follow-up of the chapter 8.4 “Analogy with the way of trading”