Your only job is to produce one trade decision for a user on PaperTrade, a perp exchange on HyperEVM that executes and liquidates at the Hyperliquid mid price ((best bid + best ask) / 2) of the market you pick. The goal is to make the user money on this trade and the highest gains you can.
Trade constraints (fixed, do not change them)
- Market: exactly one of "BTC" (BTC-USD perpetual) or "ETH" (ETH-USD perpetual). Pick whichever has the better trade setup right now, not a default.
- Direction: exactly one of "long" or "short".
- Leverage: an integer between 750 and 1000 inclusive.
- The position closes at take-profit or by liquidation, ideally within 30 minutes. There is no other stop-loss: treat liquidation as the stop.
- Take profit is expressed as the move in the chosen market's PRICE in your favor, in basis points (1 bp = 0.01%), not the return on margin.
- Don't just pick the smallest leverage or a short take-profit because it is safest; optimize for big gains too.
PaperTrade execution mechanics (read carefully, they drive target sizing)
Entry and exit both fill at the Hyperliquid BBO mid. No spread, slippage, funding, or fee on notional. Same mechanics for BTC and ETH.
Liquidation ("bust"). The position is hard-liquidated when the mid crosses the bust price and the full isolated margin is forfeited. The trigger is about 4.8 bp CLOSER to entry than
the zero-equity distance (10000 / leverage bp), so:
bust distance (bp) ≈ 10000 / leverage − 4.8
1000x → 5.2 bp
900x → 6.3 bp
800x → 7.7 bp
750x → 8.5 bp
Asymmetric impact haircut on wins. Losses are never charged anything extra. Winning closes: (1) a 0.2 bp deadband is subtracted from the move, (2) the raw gain is multiplied by a
scale factor that is small for tiny moves and approaches 0.90 for big moves, (3) a 2% win fee is taken. Formula (positionMultiplier = 814.598 for BTC, 483.979 for ETH):
scale = 0.90 / (1 + 1/(move × 15000) + 100000/(1e6 × move × positionMultiplier))
move = |exit − entry| / entry, after the 0.2 bp deadband
The haircut depends only on move size, not position size. ETH's smaller multiplier taxes small moves harder. Net share of the raw gain kept, and return on margin:
move BTC keep @750x @1000x ETH keep @750x @1000x
3 bp 49% 11% 15% 42% 9% 12%
5 bp 61% 23% 30% 54% 20% 27%
8 bp 69% 42% 55% 64% 38% 51%
10 bp 72% 54% 72% 68% 51% 68%
15 bp 77% 87% 116% 73% 83% 110%
20 bp 80% 120% 159% 77% 115% 153%
30 bp 82% 185% 247% 80% 181% 241%
Implication: tiny take-profits are heavily taxed, so the target must clear the haircut while still being reachable before an adverse move of the bust distance becomes likely. Higher
leverage multiplies gains but shrinks the survivable adverse move. ETH usually moves more per minute than BTC: the same leverage busts sooner there but reaches a target sooner too.
How to decide
The decision is yours. Pull the market data below for BOTH markets, compare the setups, and pick the market with the clearer edge for the next 30 minutes (trend agreement, volatility
that fits the bust distance, levels nearby, order flow). Look up anything else that helps (news, macro events, liquidation heatmaps). Be decisive: return one market and one
direction, and express doubt through confidence, leverage and target, not by refusing.
Fetching the market data
Hyperliquid has a free public API with no authentication. Every request is an HTTP POST to:
https://api.hyperliquid.xyz/info
with header Content-Type: application/json and a JSON body. Timestamps are Unix milliseconds. Fetch at least the following for each coin ( = "BTC" and "ETH"), with the
current time as endTime.
-
Candles (fetch all three intervals):
{"type":"candleSnapshot","req":{"coin":"","interval":"1m","startTime":<now - 60 min>,"endTime":}}
{"type":"candleSnapshot","req":{"coin":"","interval":"5m","startTime":<now - 6 h>,"endTime":}}
{"type":"candleSnapshot","req":{"coin":"","interval":"15m","startTime":<now - 24 h>,"endTime":}}
Response is an array of candles: t = open time ms, T = close time ms, o/h/l/c = open/high/low/close as strings, v = volume in coin, n = number of trades. Oldest first.
-
Current market context (one call covers both coins):
{"type":"metaAndAssetCtxs"}
Response is [meta, ctxs]. Find the index of "BTC" (and "ETH") in meta.universe, then read ctxs[thatIndex]: markPx, oraclePx, premium, funding (hourly, positive = longs pay
shorts), openInterest (in coin), dayNtlVlm (24h notional USD), prevDayPx.
-
Order book:
{"type":"l2Book","coin":"","nSigFigs":5}
levels[0] = bids, levels[1] = asks, each an array of {px, sz, n}, best first. mid = (levels[0][0].px + levels[1][0].px) / 2: exactly the price PaperTrade fills and liquidates at.
Also useful: {"type":"fundingHistory","coin":"","startTime":} and {"type":"recentTrades","coin":""}. Rate limit is generous (1200 weight/min; calls weigh 2 to 20).
Metrics
Compute any metric that helps, for each market, in basis points of price so results compare with the bust distance and take-profit, and across BTC and ETH. Likely useful:
- How often recent 1m windows moved against a position by more than the bust distance before moving the take-profit distance in its favor
- Per-candle adverse excursion (high − open, open − low) on 1m and 5m vs. the bust distance
- Realized volatility per minute over the last 15/30/60 minutes, expanding or contracting
- Distance from VWAP and 1m/5m EMAs; EMA slope; agreement across 1m/5m/15m
- Cumulative volume delta from recentTrades; book imbalance within 10 bp of mid
- markPx vs. oraclePx, funding, open interest change vs. price change
- Position in the 24h range; distance to prior-day high/low/close and recent swings
- BTC/ETH relative strength: which leads, and whether ETH confirms or diverges from BTC
- Time of day (US open, funding settlement) and macro releases in the next hour
Output
Return ONLY a single JSON object, no markdown fences, no commentary. Use exactly these keys and types.
For IMD file-output jobs, write this JSON to artifacts/report.md (output "report"), not just the final chat reply.
{
"market": "BTC" | "ETH",
"direction": "long" | "short",
"leverage": <integer 750-1000>,
"take_profit_bp": <number, favorable price move in basis points, e.g. 14 means 0.14%>,
"take_profit_price": <number, the close price, consistent with direction and take_profit_bp from entry_mid>,
"bust_distance_bp": <number, 10000/leverage - 4.8>,
"bust_price_estimate": <number, entry_mid moved bust_distance_bp against the position>,
"expected_net_return_on_margin_pct": <number, after haircut and win fee, if take profit hits>,
"confidence": <number 0.0-1.0>,
"regime": "trending_up" | "trending_down" | "ranging" | "volatile_choppy",
"key_levels": { "support": <number>, "resistance": <number> },
"entry_mid": <number, the live mid price of the chosen market you based the trade on>,
"data_time_utc": "<ISO 8601 timestamp of when you fetched the data>",
"reasoning": "<2-4 sentences, plain text: why this market over the other, the main evidence for the direction, and why this leverage and target fit the volatility, bust distance and haircut>",
"risks": "<1-2 sentences, what would most likely cause liquidation>"
}
Use JSON numbers. All prices are in the chosen market. entry_mid = (bid+ask)/2 from its live l2Book; take_profit_price = entry_mid × (1 ± take_profit_bp/10000), + for long and − for short.