Perpetual futures pricing for this coin: the funding rate traders pay to hold a position, total open interest, and how both moved over 24h. Funding is the tether between the perp and spot; positive means longs are paying shorts.

Funding is a small fee traders pay each hour to keep a bet open. When it is positive, the people betting the price will RISE are paying the people betting it will fall, which means the crowd is leaning bullish. Negative means the opposite. Open interest is how much money is riding on those bets in total: if it climbs while the price climbs, new money is arriving rather than old bets being closed.

The classic use is the cash-and-carry (basis) trade: hold the coin, short the perp, and collect funding while the two prices stay tied together — a market-neutral yield. Funding-rate arbitrage is the same idea run across venues. In the other direction, traders fade a crowded-long setup when funding is very positive but the price has stopped rising, and watch OI-price divergence: rising open interest on a falling price means shorts are piling in.

Design a new trading strategy for AAVE built on perp funding and open interest. Read the live signal from /api/v1/derivatives/summary?coin=AAVE. Invent the rules yourself — do not just reimplement carry — and write out entry, exit, position sizing and the single assumption the edge depends on. Then validate it on /api/v1/backtesting/funding?symbol=AAVE plus /api/v1/backtesting/klines, with taker fees charged both sides. If it does not beat buy-and-hold, say so and explain why.

Hourly funding and open-interest history comes from /backtesting/funding?symbol=X, going back to May 2023 for Hyperliquid perps. Pair it with /backtesting/klines for matching price bars so funding lines up with the candle it was charged on. Note that open interest is only populated for Hyperliquid — Binance rows carry null.

Using CryptoDataAPI, backtest a funding-carry trade on AAVE. Pull hourly funding from /api/v1/backtesting/funding?symbol=AAVE and matching price bars from /api/v1/backtesting/klines. Simulate holding spot and shorting the perp whenever annualised funding is above 10%, exiting when it drops below 0. Charge taker fees both sides. Report net return after fees and funding, max drawdown, and how much of the return came from funding versus price. Tell me if the edge disappears after costs.

The live Hyperliquid order book for this coin, aggregated into price bands: how much size rests on the bid versus the ask, and how far the book goes before it thins out.

This is the queue of orders waiting to be filled. Bids are people waiting to buy, asks are people waiting to sell. If one side is much bigger, there is more demand on that side. The part that matters is not the price at the very top of the list, but how much is stacked up a little further away, because a big order has to eat through it. Less stacked up means the price moves further on the same trade.

Execution strategies live here. VWAP and TWAP orders slice a large trade across time so it never eats more of the book than it has to, and iceberg orders hide the true size. Order-book imbalance (OBI) is a short-term signal built directly from the bid/ask sizes you see here. Depth is also how you tell a real wall from a spoof — a genuine one keeps absorbing trades instead of vanishing.

Design a new trading strategy for AAVE built on order-book depth. Read the live book from /api/v1/hyperliquid/l2-book?coin=AAVE. Use the shape of resting liquidity — not just the top of book — to define entries, exits and how you would size to avoid paying slippage. State the assumption it depends on, then validate on /api/v1/backtesting/snapshots?data_type=liquidity_depth. Report honestly if depth turns out not to predict anything.

Order-book history is snapshotted every 5 minutes: /backtesting/snapshots?data_type=liquidity_depth for the banded depth you see here, and data_type=hl_l2_books for the raw ladders. The daily bundle at /backtesting/daily-snapshots/{date} carries a liquidity_depth block too if you only need one reading per day.

Using CryptoDataAPI, test whether order-book imbalance predicts short-term returns on AAVE. Pull /api/v1/backtesting/snapshots?data_type=liquidity_depth, compute (bid_depth - ask_depth) / (bid_depth + ask_depth) per snapshot, and measure the forward 1h and 4h return in each imbalance decile. Report the spread between top and bottom decile and whether it survives a realistic spread cost.