Search across markets

Rank many perps against each other and hold the best long and the worst short, then search the scores and settings with a genetic search that never sees the holdout.

The strategies elsewhere in these docs trade the market they read. A cross-sectional strategy ranks many markets against each other: every few days it takes the most traded perps on Hyperliquid or Lighter, ranks them by a score, holds the top share long and the bottom share short, and pays fees and each market's funding.

The score#

A score is a line of Flowscript that publishes metric("score", …), read on each market's daily bars. Momentum, skipping the last few days:

const close = bars.close(market, interval);
metric("score", close[skip] / close[lookback + skip] - 1);

Test one book#

backtest_rotation runs one book over the platform's full perp history (every perp on each venue, delisted ones too, with daily candles and funding), and reports:

• its return on the training span and on a holdout it never saw;
• 20 random books and stale versions of it, as baselines;
• its deflated Sharpe ratio, marked down for every setting you say you've tried.

Search#

start_strategy_search runs a genetic search over scores, their arguments and the book (how many markets, how often to rebalance, how much long and short). It never sees the holdout.

Read the best result's verdict first: promising only when the holdout, the baselines and the deflated Sharpe ratio all agree, otherwise not proven or no edge, with reasons.