BTC2 🥈

Algorithm Brief

BTC2_ALGORITHM
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Plain-English strategy summary
- Market: BTCUSD (paper)
- Style: aggressive pullback variant of BTC1.
- Bigger order size with lot-level net-after-fees profit ratio exits.

Key thresholds
- buy_pullback_ratio: 1.0%
- buy_notional: $350
- sell_notional: up to $700
- min_lot_profit_ratio: 0.5% net-after-fees on current min open lot

Flat re-entry extension
- If flat and price <= last_sell_price * (1 - buy_pullback_ratio), buy early.
- If still flat after reentry_wait_cycles, force one re-entry buy.
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from dataclasses import dataclass
from typing import Dict, Any

@dataclass
class Params:
    buy_pullback_ratio: float = 0.01
    buy_notional: float = 350.0
    sell_notional: float = 700.0
    min_lot_profit_ratio: float = 0.005
    reentry_wait_cycles: int = 2

def decide(snapshot: Dict[str, Any], p: Params = Params()) -> Dict[str, Any]:
    price = float(snapshot.get('price', 0.0))
    avg = float(snapshot.get('avg_price', price) or price)
    min_open_lot_price = float(snapshot.get('min_open_lot_price', avg) or avg)
    cash = float(snapshot.get('cash', 0.0))
    qty = float(snapshot.get('holding_qty', 0.0))
    notional = qty * price

    flat_cycles = int(snapshot.get('flat_cycles', 0) or 0)
    last_sell_price = float(snapshot.get('last_sell_price', 0.0) or 0.0)
    if qty <= 0 and cash >= p.buy_notional and price > 0:
        if last_sell_price > 0 and price <= (last_sell_price * (1.0 - p.buy_pullback_ratio)):
            return {'action': 'buy', 'reason': 'flat_reentry_drop_trigger', 'notional': p.buy_notional}
        if flat_cycles >= p.reentry_wait_cycles:
            return {'action': 'buy', 'reason': 'flat_reentry_forced_after_2_cycles', 'notional': p.buy_notional}

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