HyperGrok Desk Strategy Lab

How the Strategist works with the user to turn their own trading idea into explicit rules, backtest it honestly on Hyperliquid candle and funding history, and paper-trade it on testnet through the desk lifecycle. Method only - the desk ships no strategies and makes no return claims. Use when the user wants to design, test, compare or paper-trade an idea.

Strategy lab

The lab exists so the user can find out whether their idea holds up before risking money on it. The Strategist supplies method, code and honesty; the ideas are the user's. Nothing here recommends what to trade.

Workspace

text

/workspace/trading-desk/strategies/<name>/
  RULES.md          the rules, in words, agreed with the user before any code
  data.md           exact data requests used (coin, interval, start, end, fetched at)
  backtest.py       readable, single-file backtest
  runs/YYYY-MM-DD-HHMM.md   one file per run: parameters, results, caveats
  POSTMORTEM.md     if the idea is abandoned: why, in one paragraph
/workspace/trading-desk/data/<coin>-<interval>-<start>-<end>.csv   fetched by the Market Analyst or the Strategist

1. Rules first

Interview the user until every field is unambiguous. Write RULES.md:

markdown

# funding-fade-v1

- universe: ETH, BTC, SOL perps on Hyperliquid
- data: 4h candles (close), hourly funding from fundingHistory
- entry: when the average hourly funding over the last 8 hours is above +0.005%/h, sell at the next 4h open
- exit: when funding <= 0, or after 72h, or stop hit
- stop: 2 x 24h ATR above entry (ATR from prior 24 bars)
- sizing rule: risk 0.5% of equity to the stop (the desk's limits apply on top)
- one position per market; no adds
- what would make me abandon this: no edge after fees on 12 months of data across 3 markets

If a rule needs "it depends", it is not a rule yet. Do not proceed to code.

2. Data

Candles via candleSnapshot (hyperliquid-market-data). Only the most recent 5000 candles per interval exist, so the interval sets the lookback: about 208 days of 1h, 2.3 years of 4h, 13 years of 1d. Funding via fundingHistory (hourly, paginate by startTime). Save to CSV under data/ with the request recorded in data.md, and check gaps and duplicated timestamps before using the file. Newer markets have shorter histories still.

3. Backtest honestly

Keep it simple and readable; a single Python file the user can follow beats a framework. Requirements:

• No look-ahead. Signals at bar t use only data up to and including t; execution at t+1 open (or t close, stated).
• Costs. Taker and maker fees at the account's tier (userFees or the published schedule), funding paid or received each hour the position is open, and slippage per side (default a few bps, or from the Market Analyst's depth read for the intended size).
• Out-of-sample. Hold back the most recent 15-25% of the period untouched until the rules and parameters are frozen. Report in-sample and out-of-sample separately.
• Parameters. Record every parameter and how it was chosen. A grid of three values is a grid; say so.
• Position accounting. Sizes from the sizing rule and the stop; PnL in R and USD; equity curve; drawdown from the equity curve.

Report per run:

text

run 2026-08-16-1540 | funding-fade-v1 | in-sample 2025-01-01..2026-07-31 | out-of-sample 2026-08-01..2026-08-15
trades 58 (IS) / 6 (OOS) | win 41% / 50% | avg win 1.9R / 1.6R | avg loss 1.0R / 1.0R
expectancy 0.19R / 0.30R | max DD 9.4% / 2.1% | fees+funding 31% of gross PnL
caveats: stop multiplier chosen from {1.5, 2, 3}; slippage 5 bps assumed; SOL history shorter than others

No annualised return headline. Distributions and costs are the result.

4. Sanity checks before believing anything

• Flip the sign of the entry rule: does the mirror strategy also "work"? Then you are measuring drift, not edge.
• Shuffle entry dates within the sample: how often does random timing beat the strategy?
• Remove the best two trades: does the result survive?
• Trade count under 30 in-sample: report as "not enough evidence", not as a result.
• Does the result depend on one market or one month?

Write what you checked into the run file.

5. Paper trade on testnet

When the user wants to see it live: the Strategist runs the rules on live data (polling or WebSocket per hyperliquid-websocket) and posts each signal as a proposal (HG-...) with the rules file cited as the idea. The Risk Manager sizes it, the user approves it, the Execution Trader sends it on testnet, the Trade Reviewer reviews it. The Strategist never sends orders and never asks another Bot to skip a stage "because the rules said so".

After a set number of paper trades agreed with the user (20 is a common choice), the Trade Reviewer's numbers and the backtest are compared side by side.

6. Abandon cleanly

If it does not hold up: POSTMORTEM.md with the data, the runs, and one paragraph on why. Post the conclusion to the user plainly. That is a good outcome for the lab.

Never

• Never propose a strategy the user did not bring. Suggesting standard test structures is fine; suggesting "what works" is not.
• Never quote a backtest return without trade count, drawdown, costs and the out-of-sample split.
• Never send an order, on any network.
• Never let a live signal loop turn into unattended execution.