6 personas
General-purpose starting points for broad crypto exposure, defensive posture, meme momentum, and DeFi yield.
Public persona catalog
Start from a concrete frame, not a blank agent.
Explore a library of starter trading personas built around real operating styles, risk preferences, market focus, and decision habits. Choose a frame that matches how you think, then bring it into paper mode and refine it with review data.
52 starter personas
4 practical tiers
Built for paper-mode reps
Instead of starting with an empty agent, browse proven starter frames by trading style, market focus, and narrative preference. The goal is not to copy blindly. It is to start from a clear hypothesis and improve it through paper-trading review.
52
Total personas
4
Tier groups
21
Largest tier
2026
Catalog year
Tier map
Four practical ways into the library.
The public catalog groups the personas by the question they answer first: broad default exposure, playbook construction, ecosystem loyalty, or narrative and macro framing.
21 personas
Playbook-focused personas for trend following, breakouts, mean reversion, range trading, event reactions, and liquidity behavior.
13 personas
Ecoystem-focused personas for Solana, Ethereum, Base, Jupiter, and other crypto communities with clear watchlists.
12 personas
Narrative and macro personas for AI, RWAs, infrastructure, geopolitical risk, and broader market themes.
Catalog
Browse the public starter library.
Click into any persona to inspect the public playbook, watchlist, operating brief, and starting constraints. Then use the product to run the workflow, review decisions, and tune the behavior over time.
Persona SEO summary
The Trading Boy persona library is a public catalog of paper-trading starting frames. Each persona describes a trading style, watchlist, risk posture, time horizon, setup preference, and decision habit so the agent starts from an explicit thesis instead of a blank prompt.
How to use a starter persona
- Pick one frame that matches the market behavior you want to practice.
- Run it in paper mode before trusting its rules or changing size assumptions.
- Review skipped trades, entries, exits, risk behavior, and journal notes.
- Turn repeated mistakes into a rule change, not a vague prompt edit.
Featured crawlable starter personas
These examples are rendered in raw HTML so search engines can understand the public catalog before the interactive filters load.
8 featured
Basic
Diamond Hands
Patient BTC accumulator that buys dips across verified BTC and BTC-adjacent assets.
Basic
Steady Eddie
Balanced portfolio manager that spreads risk across blue-chip crypto with disciplined sizing.
Strategy
Trend Rider
Trend-following trader that rides liquid market leaders until momentum breaks.
Strategy
Breakout Scout
Breakout trader that targets high-beta assets as they clear major resistance.
Strategy
Rubber Band
Mean-reversion trader that fades emotional extremes in high-volume meme assets.
Community
SOL Soldier
Solana ecosystem maximalist that trades SOL and top verified DeFi, infrastructure, and meme plays.
Community
ETH Purist
Ethereum ecosystem loyalist trading ETH, L2s, DeFi, ENS, restaking, and liquid staking.
Thematic
Neural Net
AI narrative trader that bets on the convergence of crypto and artificial intelligence.
No matching personas.
Try a different search or clear the tier filter.
Use it well
Turn a starter persona into your own trading process.
Pick one starting frame
Use the library to narrow the field quickly. Choose the tier that matches how you actually think about the market, then pick one persona instead of bouncing between twenty names.
Move it into paper mode
The real work starts after selection. Run the persona through the paper-trading workflow, review what it catches, and tighten the behavior around actual decision history.
Measure before you trust it
A persona is a starting thesis, not a finished system. Keep it in paper mode, study the decisions it makes, and only tighten rules after you have enough review data to see what is actually working.
Review path
Choose the persona by the question you are testing.
A persona should make a paper-trading hypothesis easier to inspect. Before running a sample, write down what the persona is supposed to prove, what market behavior would invalidate it, and which review page will judge the result.
Strategy question
Use trend, breakout, mean-reversion, or range personas when you want to compare a specific rule. Pair the sample with backtesting vs forward testing so historical evidence and fresh paper evidence stay separate.
Agent behavior question
Use AI-agent personas when you want to inspect prompts, output fields, risk behavior, and skipped trades. Score the result with the AI paper trading agent evaluation checklist.
Safety question
Use defensive or research-heavy personas when the main issue is scope control. Confirm the AI trading agent permission boundaries before adding data sources, alerts, or review automations.
Start from one persona. Refine it with real review data.
Public pages are for orientation. The product is where the agent gets shaped, reviewed, and corrected.