superior-trade/superior-skills/grid-trading

from freqtrade.strategy import IStrategy
from freqtrade.persistence import Trade
from datetime import datetime
import pandas as pd
class EthGridStrategy(IStrategy):
    minimal_roi = {"0": 100.0}   # never auto-close on ROI; partials handled in adjust_trade_position
    stoploss = -0.30             # safety net, deeper than the deepest ladder rung
    trailing_stop = False
    timeframe = "15m"
    process_only_new_candles = True
    startup_candle_count = 200
    can_short = False
    position_adjustment_enable = True
    max_entry_position_adjustment = 5   # 5 ladder rungs below entry
    max_dca_multiplier = 6.0            # 1 + 5 adds
    def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        # 24h VWAP on 15m bars (96 bars).
        tp = (dataframe["high"] + dataframe["low"] + dataframe["close"]) / 3.0
        pv = tp * dataframe["volume"]
        dataframe["vwap_24h"] = (
            pv.rolling(96).sum() / dataframe["volume"].rolling(96).sum()
        )
        return dataframe
    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        # First grid rung: 1% below 24h VWAP.
        dataframe.loc[
            (dataframe["close"] <= dataframe["vwap_24h"] * 0.99)
            & (dataframe["volume"] > 0),
            "enter_long",
        ] = 1
        return dataframe
    def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        # Hard close on band breakout up.
        dataframe.loc[
            dataframe["close"] >= dataframe["vwap_24h"] * 1.06,
            "exit_long",
        ] = 1
        return dataframe
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake, max_stake: float,
                            leverage: float, entry_tag, side: str, **kwargs) -> float:
        return proposed_stake / self.max_dca_multiplier
    def adjust_trade_position(self, trade: Trade, current_time: datetime,
                              current_rate: float, current_profit: float,
                              min_stake, max_stake: float,
                              current_entry_rate: float, current_exit_rate: float,
                              current_entry_profit: float, current_exit_profit: float,
                              **kwargs):
        if trade.has_open_orders:
            return None
        n_entries = trade.nr_of_successful_entries
        n_exits = trade.nr_of_successful_exits
        # Ladder buys: every -1% from average entry, up to 5 adds.
        if n_entries <= 5 and current_profit <= -0.01 * n_entries:
            filled = trade.select_filled_orders(trade.entry_side)
            first_stake = filled[0].stake_amount_filled if filled else (min_stake or 10)
            return (first_stake, f"grid_buy_{n_entries}")
        # Partial profit-take: every +1.5% above avg entry, up to 3 ladders.
        if n_exits < 3 and current_profit >= 0.015 * (n_exits + 1):
            return (-(trade.stake_amount / 4.0), f"grid_tp_{n_exits}")
        return None

{
  "exchange": { "name": "hyperliquid", "pair_whitelist": ["ETH/USDC"] },
  "stake_currency": "USDC",
  "stake_amount": 1000,
  "dry_run_wallet": {"USDC": 10000},
  "timeframe": "15m",
  "max_open_trades": 1,
  "stoploss": -0.30,
  "minimal_roi": { "0": 100.0 },
  "entry_pricing": { "price_side": "same" },
  "exit_pricing": { "price_side": "same" },
  "pairlists": [{ "method": "StaticPairList" }]
}