Funding Squeeze

from freqtrade.strategy import IStrategy
from datetime import datetime
import pandas as pd
import talib.abstract as ta


class FundingSqueezeStrategy(IStrategy):
    minimal_roi = {"0": 100.0}     # exits managed by the funding flip + time stop
    stoploss = -0.04
    trailing_stop = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True
    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:
        # Funding rate via the dedicated candle type. HL funds hourly.
        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)
            # Annualise 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

        # Recent up-move (squeeze fuel needs the move already started).
        dataframe["ret_24h"] = dataframe["close"].pct_change(24)
        dataframe["ret_4h"] = dataframe["close"].pct_change(4)
        dataframe["atr_24"] = ta.ATR(dataframe, timeperiod=24)
        dataframe["vol_avg20"] = dataframe["volume"].rolling(20).mean()
        return dataframe

    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        # Long when funding is deeply negative AND price is already rising AND
        # volume confirms (avoid dead-tape squeezes that can't propagate).
        dataframe.loc[
            (dataframe["funding_apr"] < -0.10)
            & (dataframe["ret_24h"] > 0.03)
            & (dataframe["ret_4h"] > 0.0)
            & (dataframe["volume"] > dataframe["vol_avg20"])
            & (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 — the squeeze has been
        # paid out and we're now competing with reset shorts and tired longs.
        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):
        # Squeezes resolve fast. If we haven't gotten paid in 24h the thesis is
        # invalidated; bail rather than wait for the funding flip.
        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": -0.04,
  "trailing_stop": true,
  "trailing_stop_positive": 0.02,
  "trailing_stop_positive_offset": 0.03,
  "trailing_only_offset_is_reached": true,
  "minimal_roi": { "0": 100.0 },
  "trading_mode": "futures",
  "margin_mode": "cross",
  "entry_pricing": { "price_side": "same" },
  "exit_pricing": { "price_side": "same" },
  "pairlists": [{ "method": "StaticPairList" }]
}