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 BTC mid price ((best bid + best ask) / 2). The goal is to make the user money on this trade and the highest gains you can.

Trade constraints (fixed, do not change them)

• Instrument: BTC-USD perpetual.
• Direction: you must choose exactly one of "long" or "short".
• Leverage: you must choose an integer between 500 and 1000 inclusive.
• The position is closed at take-profit or by liquidation. Ideally the trade plays out within 30 minutes or less.
• There is no stop-loss other than liquidation. Treat liquidation as the stop.
• Take profit is expressed as the move in the BTC PRICE in your favor, in basis points (1 bp = 0.01%), not the return on margin.
• Don't just pick smallest leverage because it is safest. optimize for big gains as well.

PaperTrade execution mechanics (read carefully, they drive target sizing)

Entry and exit both fill at the Hyperliquid BBO mid. There is no spread, no slippage, no funding, and no fee on notional.

Liquidation ("bust"). The position is hard-liquidated when the mid crosses the bust price, and the full isolated margin is forfeited. The bust 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

   700x →  9.5 bp

   600x → 11.9 bp

   500x → 15.2 bp

Asymmetric impact haircut on wins. Losses are never charged anything extra. Winning closes go through three steps: (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 with BTC's current parameters:

  scale = 0.90 / (1 + 1/(move × 15000) + 100000/(1e6 × move × 814.598))

  move  = |exit − entry| / entry, after the 0.2 bp deadband

The haircut depends only on move size, not position size. Net share of the raw gain the trader keeps, and the resulting return on margin:

  move    keep   return on margin @500x   @1000x

   3 bp   49%        7%                   15%

   5 bp   61%       15%                   30%

   8 bp   69%       28%                   55%

  10 bp   72%       36%                   72%

  15 bp   77%       58%                  116%

  20 bp   80%       80%                  159%

  30 bp   82%      124%                  247%

  50 bp   85%      212%                  423%

Implication: very small take-profits are heavily taxed, so the target needs to be large enough to 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. Weigh these together.

How to decide

The decision is entirely yours. Pull the market data described below, and if you can look up anything else that helps (current news, scheduled macro events, liquidation heatmaps, other

exchanges' order flow, anything you consider useful), do so and factor it in. Be decisive: you must return a direction. Express doubt through the confidence field and through your

choice of 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. Use the current time for endTime and work backwards for

startTime.

1. Candles (fetch all three intervals): {"type":"candleSnapshot","req":{"coin":"BTC","interval":"1m","startTime":<now - 60 min>,"endTime":}}

   {"type":"candleSnapshot","req":{"coin":"BTC","interval":"5m","startTime":<now - 6 h>,"endTime":}}

   {"type":"candleSnapshot","req":{"coin":"BTC","interval":"15m","startTime":<now - 24 h>,"endTime":}}

   Response is an array of candle objects: t = open time ms, T = close time ms, o/h/l/c = open/high/low/close as strings, v = volume in BTC, n = number of trades. Oldest first.

2. Current market context: {"type":"metaAndAssetCtxs"}

   Response is [meta, ctxs]. Find the index of the entry with name "BTC" in meta.universe, then read ctxs[thatIndex]. Useful fields: markPx, oraclePx, premium, funding (hourly rate,

positive means longs pay shorts), openInterest (in BTC), dayNtlVlm (24h notional volume USD), prevDayPx.

3. Order book: {"type":"l2Book","coin":"BTC","nSigFigs":5}

   Response has levels[0] = bids and levels[1] = asks, each an array of {px, sz, n}, best price first. Best bid is levels[0][0].px, best ask is levels[1][0].px. mid = (best bid + best

ask) / 2. This mid is exactly the price PaperTrade will fill and liquidate at.

Other useful queries if you want them: {"type":"fundingHistory","coin":"BTC","startTime":} for recent funding, {"type":"recentTrades","coin":"BTC"} for the latest prints. Rate

limit is generous (1200 weight per minute); these calls are weight 2 to 20 each.

Metrics

Look up any additional data and calculate any metric that could help your decision. Work in basis points of price so results compare directly with the bust distance and

take-profit. Some that are 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 and whether it is expanding or contracting
• Distance from session VWAP and from 1m/5m EMAs
• Rate of change and EMA slope on 1m and 5m; agreement across 1m/5m/15m
• Cumulative volume delta and average trade size from recentTrades
• Book imbalance within 10 bp of mid; resting liquidity between entry and take-profit or bust
• markPx vs. oraclePx premium and current funding
• Open interest change vs. price change
• Position within the 24h range; distance to prior-day high/low/close and recent swing points
• Time of day (US open, funding settlement) and scheduled macro releases in the next hour

Output

Return ONLY a single JSON object, no markdown fences, no commentary before or after. Use exactly these keys and types:

{

"direction": "long" | "short",

"leverage": <integer 500-1000>,

"take_profit_bp": <number, favorable price move in basis points, e.g. 12 means 0.12%>,

"take_profit_price": <number, the BTC price at which to close, 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 you based the trade on>,

"data_time_utc": "<ISO 8601 timestamp of when you fetched the data>",

"reasoning": "<2-4 sentences, plain text, the main evidence for the direction and why this leverage and target fit current volatility, the bust distance, and the haircut>",

"risks": "<1-2 sentences, what would most likely cause liquidation>"

}

Numbers must be plain JSON numbers, not strings. take_profit_price must equal entry_mid × (1 + take_profit_bp/10000) for a long or entry_mid × (1 − take_profit_bp/10000) for a short,

where entry_mid = (best bid + best ask) / 2 from your live l2Book fetch.