My First Systematic Strategy

Part 1 - Before the First Line of Code

This series will be my transparent journey through that entire process. I will be building a probabilistic arbitrage bot for Polymarket's 15-minute Bitcoin binary options and I'll also be documenting every phase, from the initial idea validation to the final live-trading system. This is Phase 0: due diligence. Before writing a single line I'll ask three fundamental questions: Where are we hunting? What does theory tell us? And who are we hunting against?

The Hunting Ground: Why Polymarket?

The first step in any strategy is to define the universe or the specific market and product I intend to trade. My target is Polymarket's 15-minute "Up or Down" market for Bitcoin. This choice is deliberate for several reasons I will discuss throughout the upcoming articles that make it an attractive laboratory for a first systematic strategy.

Key Characteristics of the Market:

• Binary & Bounded: The outcome is a simple "yes" or "no," and the contract price is confined between $0.00 and $1.00. This simplifies the modeling problem given that I'm predicting a probability, not a price magnitude.
• High Frequency: With four contracts per hour and 24 hours a day this market generates a vast number of trading opportunities. I believe that this is crucial for a statistical strategy, which relies on the lLLN for its edge to materialize.
• Directly Observable Probability: The price of a contract on a prediction market is a direct, real-time reflection of the crowd's perceived probability of an event. If a contract for "BTC Up" is trading at $0.65 then the market is pricing in a 65% chance of that outcome. This gives us a clear benchmark to beat. My alpha is simply the difference between my model's hypothetically more accurate probability and the market's less accurate one: Edge = |P_model - P_market|.
• Potentially Inefficient: While major financial markets are hyper-competitive I believe that prediction markets often have a higher concentration of retail participants. This can lead to pricing that is more influenced by behavioral biases than by pure statistical reality whoch creates the exact inefficiencies I aim to exploit.

Sizing Up the Competition: The Competitive Landscape

I am definitely are not trading in a vacuum. The profitability of any strategy is determined not just by its logic, but by the environment it operates in. We must be keenly aware of the other players in the game.

Who else is in this market?

• Retail Participants
• Automated Market Makers
• Other Quant/Arbitrage Bots

This competitive analysis tells us two things: First, an edge might exist because the market is not dominated by alpha-seeking funds. Second, that edge will be constrained by the costs imposed by market makers thus the spread and the speed of other arbitrage bots. My due diligence must include a thorough analysis of these costs to ensure my model's predictions are strong enough to overcome them.

With this initial groundwork complete, we have a clear thesis. We've identified a promising market, acknowledged the theoretical hurdles, and assessed the competitive environment. The next crucial step is to move from theory to practice. In Part 2, we will get our hands on a sample of real data and perform a "napkin sketch" analysis to see if we can find the first hints of a profitable edge.