Finding Your Edge: A Framework for Designing Winnable Games

This article draws from Kris Longmore's framework on market edges and applies the "Working Backwards" methodology I learned during my time at Amazon. Written for smaller firms trying to compete with larger, more established players.

Key ideas in brief

Four types of edges exist: Arbitrage, Information Asymmetry, Risk Premia, and Flow
They differ in size, skill requirements, and accessibility
Working backwards from your desired end state helps design a winnable game

Part 1: The Four Types of Edge

Before you can find your edge, you need to understand what edges actually exist in financial markets. Kris Longmore at Robot Wealth has a useful framework that categorizes market edges into four types. My take: they're in descending order of both potential size and the skill/resources required to capture them.

1. Arbitrage

What it is: Simultaneously buying and selling the same (or equivalent) asset in different markets to profit from price differences. Pure arb = risk-free profit.

Examples:

Statistical arbitrage between correlated securities
Index arbitrage (ETF vs. underlying basket)
Cross-exchange crypto arbitrage
Options put-call parity violations

Reality check: True arbitrage is nearly extinct for anyone without serious infrastructure. The HFT giants have this locked down. We're talking:

Sub-millisecond execution
Co-located servers at exchanges
Billions in infrastructure investment
PhD quant teams optimizing every microsecond

For most firms, arb isn't a viable game. The infrastructure cost alone prices out 99.9% of participants. When arb opportunities do appear, they last microseconds before being closed by firms spending $100M+ annually just to maintain their speed advantage.

Edge size: Massive when it exists, but requires massive capital and infrastructure to access.

Accessibility: Essentially zero for smaller firms.

2. Information Asymmetry

What it is: Knowing something material before others do, or understanding publicly available information better than the market.

Two flavors:

A) True information edge (legally grey to illegal)

Insider knowledge about company actions
Early access to economic data
Being "higher up in the food chain" on deal flow

B) Superior analysis edge (legal but hard)

Deep industry expertise that helps interpret public information
Proprietary data sources (satellite imagery, credit card data, web scraping)
Relationships with management teams, suppliers, customers

Reality check: The legal version of info asymmetry often comes from deep industry experience. A 20-year pharma executive might genuinely understand FDA approval dynamics better than the market. A former energy trader might read oil inventory reports with more nuance.

But here's the problem: most of us don't have that. And if we do have domain expertise, translating it into tradeable signals is its own skill.

The illegal version (actual insider trading) obviously isn't a sustainable strategy. And the grey areas (expert networks, channel checks) are increasingly scrutinized by regulators.

Edge size: Can be substantial, but requires either career-level expertise or legal risk.

Accessibility: Hard for smaller firms unless there's genuine domain expertise from prior careers.

3. Risk Premia

What it is: Getting paid to hold risks that others don't want to hold. You're essentially providing insurance to the market.

Examples:

Selling options (collecting premium for taking on tail risk)
Carry trades (borrowing low-yield currency to buy high-yield)
Credit spreads (compensation for default risk)
Volatility risk premium (IV systematically exceeds RV)

The pitch sounds great: "The market overpays for protection, so you can collect premium by being the seller."

Reality check: This works... until it doesn't. Risk premia strategies share a common profile:

Many small wins
Occasional catastrophic losses
Negative skew (small profits, fat tail losses)

The volatility risk premium is real—IV does exceed RV on average. But the premium exists because of the left tail events. When those tails hit, they hit hard. Think:

2008 financial crisis wiping out short vol funds
COVID crash in March 2020
XIV blowing up in Volmageddon

Consider how PFOF (Payment for Order Flow) affects retail traders as a cautionary example.

When retail traders sell options through most brokers, orders get routed to market makers who pay for the privilege of filling them. Why would they pay? Because they're confident they're getting the better end of the trade. The bid-ask spread shown isn't the "real" spread—it's the spread after the MM has already extracted value.

This means even if the risk premium theoretically exists, a meaningful chunk is being skimmed before the position is even entered. Retail option sellers often have a negative edge despite the premium appearing positive on paper. This is a structural disadvantage that smaller professional firms can avoid through proper execution infrastructure.

Carry trades are similar. The forward points that make carry attractive exist because the market is pricing in the currency risk. When those risks materialize (think Yen carry unwind in August 2024), leveraged carry positions get destroyed.

Bottomline on risk premia: Works if there's a good model to understand why the market is overpricing a particular risk. Without that model, the strategy amounts to collecting pennies in front of steamrollers while MMs and execution costs take their cut.

Edge size: Moderate, but eroded by execution costs and blowup risk.

Accessibility: Technically accessible to everyone, but most lose money due to fees, PFOF, and tail events.

4. Flow

What it is: Trading based on the flow of money through markets—trends, momentum, support/resistance, positioning.

Examples:

Trend following (price above moving average → long)
Support and resistance levels
Breakout strategies
Mean reversion
Sentiment and positioning extremes

Why it works: Markets aren't perfectly efficient. Information diffuses gradually. Economic regimes persist. Human behavior creates herding. These factors create trends that can be systematically captured.

The deeper source: Large flows are often the result of price insensitive buyers—participants who must transact regardless of price. Providing liquidity to price insensitive buyers is a great way to earn sustained alpha.

Who are price insensitive buyers? Those with a mandate:

Temporal mandates — monthly flows, tax loss harvesting, forced liquidations in margin calls
Allocation mandates — rebalancing at fixed frequencies, bond buying by domestic pension funds
Vol mandates — 10d RV breaching thresholds forcing vol targeting funds to act, RV spikes leading to systematic deleveraging

These flows are predictable in direction (if not always timing) and create persistent trends that can be captured by those positioned correctly.

The beautiful irony: Flow-based edges become more robust because everyone uses them.

Think about it. If millions of traders are watching the 50-day moving average, what happens when price crosses it?

Trend followers buy
Algorithms trigger entries
Stop losses get hit
More buying pressure
Price continues in same direction

The signals become reflexive. Support and resistance "work" partly because everyone believes in them. The 200-day SMA matters because the entire market treats it as meaningful. It's a self-fulfilling prophecy baked into market structure.

This is very different from the other edges. Arbitrage disappears when more people exploit it. Info asymmetry degrades when shared. Risk premia get competed away. But flow edges can actually strengthen with participation because the crowd behavior reinforces the signal.

The catch: Flow edges are:

Lower magnitude (trending markets don't give you 100x, they give you 10-20%)
Higher frequency (need more trades to compound the edge)
Subject to regime changes (trend following suffers in choppy, range-bound markets)
Requires discipline (many small losses before big wins)

Edge size: Smaller per-trade, but more consistent and accessible.

Accessibility: Available to everyone.

Part 2: Finding Your Edge (Going Upstream)

Given the landscape above, how do you actually find your edge?

The basic idea: move into areas where the lowest amounts of effort provide the largest quantum of returns.

Here's the uncomfortable truth. We're up against:

Smarter adversaries (more experienced)
Better funded adversaries (more resilient)
Better connected adversaries (better positioned)

We should assume they already know how we'll react by the time news reaches us.

So how do you find advantage against such opponents?

Two questions worth sitting with:

What are some things you have that they don't?
What can you do that they can't?

The Working Backwards Framework

During my time at Amazon, I learned a methodology called "Working Backwards." Instead of starting with your current capabilities and asking "what can I build?", you start with the desired end state and work backwards to figure out what's required to get there.

It's deductive logic applied to strategy design. And it's surprisingly useful for finding your trading edge.

The four questions:

Question 1: What is your end state?

Visualize it as clearly as possible. Not "I want to make money trading"—that's too vague. Get specific.

Sample answers:

Personal prop firm to deploy capital globally, legal structure to operate in the world
- Asset class independent
- Freedom to trade anywhere
- Capital leverage via firm structure
Small team with mostly automated workstreams
- Trading decisions systematized
- Risk management automated
- Anonymous, nimble, lean
Blockchain/Digital/AI native operating firm
- Tech leverage built into operations
- Not fighting the tools but using them

Why this matters: Your end state determines what edges are even relevant. If you want to run a systematic trend-following operation, you don't need info asymmetry. If you want to be a discretionary macro trader, you need a different skill stack.

Question 2: What will be your edge?

Given your end state, what specifically creates P&L? Not philosophy—mechanism.

Sample answers:

Finding asymmetric trades (limited downside, unlimited upside)
Being fluid between macro, market structure, momentum, and event driven
Geometric means and convex portfolio structures (see previous article on ergodicity)

The key: Be honest about where alpha actually comes from. Most traders can't articulate this clearly. "I'm good at reading charts" isn't an edge statement. "I systematically buy 30-day breakouts when IV is below 20th percentile" is closer.

Question 3: What games do you like playing?

This is underrated. Sustainability requires enjoying the process, not just the outcome.

Sample answers:

Reading about geopolitics and applying human behavior patterns as predictors
- History doesn't repeat, but human behavior does
Using options to increase returns and volatility
- Derivatives offer more levers than simple equity positions
Trying to figure out market structure and using positional trading
- Where are the flows? Who's positioned how? What happens when that unwinds?

Why this matters: If you hate what you're doing, you'll abandon it during drawdowns. The best strategy you can't stick with is worse than a mediocre strategy you execute consistently.

Question 4: What problem are you solving that not many others are?

This is where differentiation lives.

Sample answers:

Getting wealthy via trading → Common (everyone wants this)
Trading with knowledge about options → Getting more common
Constructing convex portfolios to chase asymmetric returns as a professional prop trader → Uncommon

The insight: The more crowded the problem space, the more competed away the returns. Finding an angle that's genuinely different—even if smaller in potential—often beats fighting in the crowded middle.

Structural Advantages for Smaller Firms

Here's where it gets interesting. Smaller firms have disadvantages (less capital, less access, less infrastructure), but they also have advantages that larger institutions can't replicate.

Advantage 1: Long-term planning vs. monthly returns pressure

"All the money is made in the tails" — David Dredge

Large funds managing outside capital face quarterly redemptions. Pension funds face monthly reporting. Bank prop desks face daily P&L scrutiny.

Smaller firms with principal capital don't.

They can hold positions through drawdowns that would get an institutional PM fired. They can position for tail events that might take years to materialize. They can optimize for geometric mean growth while larger firms optimize for Sharpe ratios and benchmark tracking.

Requirement: Patience and proper portfolio construction to survive while waiting.

Advantage 2: Going into more difficult landscapes

Derivatives are harder than equities. Options have more moving parts—delta, gamma, vega, theta, skew. Most participants avoid them due to complexity.

That complexity is your moat.

By building fluency in derivatives, you skip over the long-only equity funds crowding the simple trades while keeping exposure to sector returns through index options.

Question to explore: How good are options at predicting price ranges of equities?

Not always, but often enough given they represent the crowdsourced wisdom from all market participants
Gamma levels, put/call skew, IV/RV relationships—all offer signals

Advantage 3: Early to new asset classes

Being early to blockchain and getting familiar with those tools = edge that erodes as the space matures.

Things to study:

On-chain analytics platforms
Global liquidity correlations with BTC price
Leverage dynamics on derivatives exchanges

The institutions are coming, but they're slow. Regulatory overhang, committee approvals, compliance sign-offs. By the time they're fully in, early participants have already captured the learning curve premium.

Advantage 4: Lesser nominal win requirements

"Take profits too soon" — The Zurich Axioms

Smaller firms trading principal capital don't need to beat a benchmark. They don't need to justify returns to external investors quarterly. The goal is compounding capital.

This enables:

Taking profits at smaller % gains
Exiting positions when risk/reward deteriorates (even if "more upside" exists)
Prioritizing capital preservation over return maximization

A 15% CAGR with low drawdowns compounds better than a 25% CAGR with 40% drawdowns over longer time horizons.

Advantage 5: Using options data to understand market maker positioning

MMs are usually option sellers. They hedge dynamically. This creates predictable flow patterns:

Near gamma levels, price action accelerates
Dealer hedging amplifies moves
Put/call skew reveals positioning

Understanding these dynamics offers insight into short-term price behavior that pure fundamental analysis misses.

Advantage 6: COT positioning and sentiment extremes

Commitment of Traders data shows how different market participants are positioned. When positioning gets very crowded, reversals become more likely.

This is pure flow analysis. No information edge required—just watching where the herd is positioned.

Putting It Together: The Edge Statement

After working through this framework, articulating your edge in one sentence becomes possible. Not philosophy—mechanism.

Sample answer:

"A small macro-based prop firm that chases asymmetric trades across asset classes using convex strategies and strict risk management. Small team with most operations automated. Goal: compound capital and beat S&P index returns annually."

This statement includes:

What: Macro-based, asymmetric trades, convex strategies
How: Strict risk management, automated operations
Benchmark: Beat S&P index annually (measurable)

The point is specificity. Vague statements ("we trade stocks") lead to vague execution. Specific statements create accountability and focus.

The Questions That Matter

To summarize, here are the questions worth answering before you start looking for your edge:

End State:

What does success look like in 5-10 years? Be specific.
What structure supports that? (Solo, team, fund, prop?)
What lifestyle does it enable?

Edge Definition:

What mechanism creates P&L?
Is it flow-based, info-based, or risk-premia-based?
Can you articulate it in one sentence?

Sustainability:

What games do you enjoy playing?
Can you execute this for years, including through drawdowns?
What would make you quit?

Differentiation:

What problem are you solving that's less crowded?
What structural advantages do you have?
What can you do that institutions can't?

Bottomline

Edge in financial markets isn't mysterious. It comes in four flavors:

Arbitrage — requires HFT infrastructure
Information asymmetry — requires domain expertise or legal risk
Risk premia — eroded by PFOF, fees, and tail risk
Flow — accessible to everyone, robust due to reflexivity

Finding your edge requires working backwards:

Define your end state clearly
Identify the mechanism that creates P&L
Pick games you enjoy playing
Solve problems that are less crowded

Structural advantages available to smaller firms:

Long-term horizon (no quarterly redemptions)
Derivatives fluency (complexity as moat)
New asset classes (learning curve premium)
Smaller nominal targets
MM positioning awareness
Sentiment/positioning extremes

The goal isn't to compete with Renaissance or Citadel.

The goal is to design a different game entirely—one with rules that favor your specific advantages. Work backwards from what winning looks like, then engineer the path to get there.