Everything your fund knows, explained so you know it too, from your first prediction market to the guardrails around your money.
What a prediction market is, how prices become probabilities, what shares pay, and why the crowd is sometimes wrong, followed through one soccer match.
Opusfund vs a DIY AI trading agent: why the model is the easy 10%, custody, enforced guardrails, memory and daily accountability are the hard part you’d be rebuilding.
Opusfund vs sports betting apps: bookmaker margins vs market prices, betting against the house vs trading against the crowd, and impulse vs process.
Opusfund vs trading on Polymarket yourself: same markets, different jobs, coverage, discipline, one chat vs many tabs, and what the Chair keeps.
Opusfund vs Polymarket trading bots: speed vs understanding, scripts vs a mandate, silence vs written reasons, and why setup complexity is the hidden cost of bots.
Opusfund vs copy trading: mirrored strangers vs your own mandate, unknown reasons, mismatched sizing, misaligned incentives vs explained, governed trades.
Trading yourself vs chairing an AI fund: hours, coverage, discipline and the knowing-doing gap, what each path demands, and what the Chair keeps.
Why Opusfund trades are gasless: what gas is, who covers the network toll, which transfers still cost a fraction of a cent, and why it matters.
Opusfund vs trading bots: rigid rules vs reasoning, silence vs written explanations, parameters vs guardrails, and why novelty breaks one, not the other.
Funding an Opusfund with USDC on Polygon: what a stablecoin is, why the fund trades in dollars, how deposits and withdrawals work, and the safety rules.
Non-custodial wallets explained: what keys are, custodial vs non-custodial, how an AI trades from a wallet it cannot empty, and the backup rule that matters.
What Opusfund is: your own AI-run hedge fund on prediction markets, one AI CEO, plain-text rules, guardrails, a non-custodial wallet, daily memos.
Setting up an Opusfund step by step: set up your AI CEO, write the mandate, set caps and whitelisted addresses, fund with USDC, and read the first memo.
The conviction floor: why an AI fund needs a minimum-conviction gate, how you settle it with your CEO at launch, and how moving it changes the fund’s temperament.
The morning memo of an AI-run fund: positions opened and closed with reasons, the honest scorecard, the watchlist, and why written accountability matters.
Inside the AI CEO’s decision pipeline: scan markets, form probabilities, demand edge, clear your gates, size by conviction. Every reason written down.
The guardrails of an AI-run fund: caps, conviction floor, whitelisted addresses and sign-offs, plain-text rules, enforced in code on every bet.
A five-step pre-trade read for any prediction market: question wording, resolution rules, price history, liquidity, and time to resolution, with the checklist.
An AI hedge fund is a full operation run by one AI inside human-written rules: what your CEO does, what the Chair keeps, and how it differs from a bot.
Combos (parlays) bundle several legs into one bet. Every leg must hit. How the price works, why they add variance not EV, and when your CEO builds one.
Risk-to-reward ratios explained: how payoff shape works in prediction markets, why R/R alone misleads, and how probability completes the picture.
When to take profit in prediction markets: selling before resolution, the “would I buy it now?” test, exits written at entry, and why the memo shows every one.
Conviction is evidence-weighted confidence: how it’s graded, how it differs from certainty and enthusiasm, and how the conviction floor turns it into a filter.
Position sizing explained: why survival beats maximization, how edge and conviction set size, what caps protect, and why oversizing kills good strategies.
Edge is a nameable, sourced reason the market price is wrong. Where real edge comes from, how to test a claimed one, and why no edge means no bet.
Expected value explained with real numbers: how to compute a bet’s average worth, why positive EV loses sometimes, and why repetition turns it into a business.
Leverage explained: how a 2x position works, why losses multiply as fast as gains, what liquidation means, and how a disciplined fund caps it.
Funding rates on perpetual futures: why they exist, who pays whom, what positive vs negative funding signals, and how a fund reads and budgets them.
Liquidity decides your real trading cost: order books, spread, depth and slippage in prediction markets, and why thin markets punish size.
Maker vs taker fees explained: who provides liquidity, who consumes it, why patience is cheaper, and Opusfund’s exact rates, 0.5% maker, 1% taker.
Prediction markets vs perpetual futures: how each pays, how each loses, how risk differs, and why one fund trading both rooms beats specializing in either.
Why a prediction-market price is a probability: what 62¢ really says, what payouts follow, how prices move on news, and how mispricing becomes opportunity.
A prediction market lets people bet on real-world questions, and turns the betting into a live probability. How markets, prices, and shares work.
Perps (perpetual futures) let you bet on a price going up or down, long or short, with no expiry. How they work, what funding does, and where the risk lives.
Each one links to the guide or answer that explains it properly.