# How AI Rankings Work ### Not All Winning Wallets Are Worth Copying This is the core problem PolyKopy solves. Most people look at one thing: **PnL** That is lazy analysis. A wallet can show profits for reasons that do not translate well to copy trading: * lucky streaks * one oversized winner * extreme risk-taking * impossible timing * thin liquidity * chaotic entries * short-term noise PolyKopy’s AI rankings are built to go deeper. We are not trying to find wallets that merely *won.* We are trying to find wallets that are actually **worth following.** *** ### What the Rankings Aim to Measure The AI ranking system is designed around real-world copyability. That includes signals like: #### Consistency Does this wallet show repeatable behavior over time? #### Risk Profile Are returns coming from sensible behavior or reckless swings? #### Copyability Would following this wallet realistically make sense for a user? #### Liquidity Conditions Does this wallet operate in ways that can be copied cleanly? #### Stability Over Time Does the wallet still look strong outside a short hot streak? #### Behavioral Quality Does the wallet trade with structure, or chaos? These factors matter far more than one flashy screenshot. *** ### Why Copyability Matters A wallet can be profitable and still terrible to copy. Examples: * enters too fast * exits too fast * trades thin markets * overuses oversized risk * relies on perfect timing * behaves inconsistently That is why PolyKopy focuses on **copyability**, not just profitability. Because your results depend on what can actually be followed in the real world. *** ### What AI Helps Do Better Manual wallet analysis is slow and messy. Humans tend to: * chase recent winners * overreact to big numbers * miss hidden risk * ignore poor consistency * get baited by short-term streaks AI helps process patterns at scale across large wallet sets faster and more objectively. That gives users a cleaner shortlist to evaluate. *** ### Rankings Are a Tool, Not Autopilot The rankings help surface stronger candidates faster. They are not meant to replace judgment. Best use case: * AI narrows the field * You choose what fits your bankroll * Your settings control execution * Performance tracking guides refinement That is the smart loop. *** ### Why Rankings Improve Over Time As markets evolve, strong ranking systems should evolve too. Wallet behavior changes.\ Market conditions change.\ What matters can shift. PolyKopy is built with the mindset that discovery should stay adaptive, not static. *** ### What Users Should Remember The #1 ranked wallet is not automatically the best wallet for *you*. Fit still matters: * risk tolerance * bankroll size * strategy style * diversification goals * comfort level Use rankings to find signal.\ Use judgment to apply it. *** ### The Bottom Line PolyKopy’s AI rankings are built to identify wallets that make sense to copy — not just wallets that got lucky. That is a massive difference. > **Anyone can rank winners. We rank wallets worth following.** --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://polykopy.gitbook.io/polykopy-docs/ai-wallet-discovery/how-ai-rankings-work.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.