superior-trade/funding-rate-arbitrage
Use when writing a funding-rate-driven perp strategy on Superior Trade — anything described as funding harvest, funding arbitrage, funding rate carry, negative funding, paid to long, paid to short, basis trade. The strategy reads Hyperliquid hourly funding via `dp.get_pair_dataframe(candle_type="funding_rate")`, which is automatically downloaded for backtests.
npx skills add https://github.com/Superior-Trade/superior-skills --skill funding-rate-arbitrageA user wants to capture funding payments by being on the side that gets paid:
This is the most profitable of the six standard templates in our audit and the engine supports it natively. Promote this template when a user asks "what's a strategy that actually works?".
| Window | BTC/USDC:USDC 1h, 2026-01-01 → 2026-05-01 (BTC −13% over the window) |
|---|---|
| Trades | 55 |
| Win rate | 58.2% |
| Wallet PnL | +1.38% / +$13.76 |
| Profit factor | 1.57 |
| Sharpe | 1.52 |
| Max drawdown | 0.58% |
| Avg holding | 9h 40m |
| Backtest ID | 01kqyz3ejgy5b7tdemhb6gj9nf |
~+4% APR on a single pair through a market that fell 13%. A multi-pair scan (e.g. top 20 perps) compounds this.
The DataProvider exposes funding-rate candles directly. No Hyperliquid REST call from inside the strategy is needed for backtest — Freqtrade auto-downloads funding history when it sees a candle_type="funding_rate" request:
funding = self.dp.get_pair_dataframe(
pair=metadata["pair"],
timeframe="1h", # Hyperliquid funds hourly
candle_type="funding_rate",
)
The returned dataframe has the same shape as OHLCV — date, open, high, low, close, volume — but open is the funding rate at the start of that hour, expressed as a fraction (-0.0000135 = -0.0014% per hour). Annualize as funding_rate * 24 * 365.
The naive v1 (placeholder column filled with 0.0) produced 0 trades. v2 with dp.get_pair_dataframe(...) produced 55 trades and Sharpe 1.52.
from freqtrade.strategy import IStrategy
from datetime import datetime
import pandas as pd
import talib.abstract as ta
class FundingHarvestStrategy(IStrategy):
minimal_roi = {"0": 100.0} # let funding work; no profit-target exit
stoploss = -0.05
trailing_stop = False
timeframe = "1h"
process_only_new_candles = True
startup_candle_count = 30
can_short = False
def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
# Hyperliquid funds hourly — request 1h funding-rate candles.
try:
funding = self.dp.get_pair_dataframe(
pair=metadata["pair"],
timeframe="1h",
candle_type="funding_rate",
)
except Exception:
funding = pd.DataFrame()
if not funding.empty and "open" in funding.columns:
f = funding[["date", "open"]].rename(columns={"open": "funding_rate"}).copy()
dataframe = dataframe.merge(f, on="date", how="left")
dataframe["funding_rate"] = dataframe["funding_rate"].ffill().fillna(0.0)
# Annualize hourly funding: APR = rate * 24 * 365.
dataframe["funding_apr"] = dataframe["funding_rate"] * 24 * 365
else:
dataframe["funding_rate"] = 0.0
dataframe["funding_apr"] = 0.0
dataframe["atr_24"] = ta.ATR(dataframe, timeperiod=24)
return dataframe
def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
# Long when funding APR is deeply negative (shorts paying longs).
dataframe.loc[
(dataframe["funding_apr"] < -0.10) & (dataframe["volume"] > 0),
"enter_long",
] = 1
return dataframe
def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
# Exit when funding flips back to non-negative (no more carry).
dataframe.loc[(dataframe["funding_apr"] >= 0.0), "exit_long"] = 1
return dataframe
def custom_exit(self, pair: str, trade, current_time: datetime,
current_rate: float, current_profit: float, **kwargs):
# Hard timeout — the entry condition was wrong if we're still in
# after 24h without an exit signal.
elapsed_h = (current_time - trade.open_date_utc).total_seconds() / 3600.0
if elapsed_h >= 24:
return "timeout_24h"
return None
{
"exchange": { "name": "hyperliquid", "pair_whitelist": ["BTC/USDC:USDC"] },
"stake_currency": "USDC",
"stake_amount": 100,
"timeframe": "1h",
"max_open_trades": 1,
"stoploss": -0.05,
"minimal_roi": { "0": 100.0 },
"trading_mode": "futures",
"margin_mode": "cross",
"entry_pricing": { "price_side": "same" },
"exit_pricing": { "price_side": "same" },
"pairlists": [{ "method": "StaticPairList" }]
}
Pair format must be <COIN>/USDC:USDC (futures). BTC/USDC (spot) won't have funding rate data.
| Knob | Effect |
|---|---|
| -0.10 (entry threshold APR) | Stricter (-0.20) → fewer trades, only the deepest negative funding episodes. Looser (-0.05) → more trades, lower edge per trade. |
| >= 0.0 (exit threshold) | Stricter (>= -0.05) → exit before funding fully normalizes, lock more carry. |
| stoploss | Funding pays slowly. A tight stop (-0.02) gets shaken out by routine volatility. -0.05 is the sweet spot from the audit. |
| timeout_24h | Max holding. Funding episodes typically last 4–12h on majors; 24h is a safety net. |
can_short = True, enter_short when funding_apr > 0.30, exit_short when funding_apr <= 0.0. Profitable when alts are paying high positive funding (squeezes).StaticPairList with VolumePairList filtered to top 20 perps. Loop the same logic per pair. PnL compounds.BTC/USDC returns no funding rate — the column will be all zeros and zero trades fire. Always use BTC/USDC:USDC.dp.get_pair_dataframe(candle_type="funding_rate") is wired up for HL. Other exchanges may return empty.try/except plus the dataframe.empty check matters — if funding history isn't downloaded yet, the strategy must not crash. The reference above handles both.funding_rate is per-hour (HL funds hourly). Annualizing as * 365 instead of * 24 * 365 is off by 24×.docs/standard-strategies-audit.md, backtest 01kqyz3ejgy5b7tdemhb6gj9nfTake superior-trade/funding-rate-arbitrage from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.