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from collections import deque
from decimal import Decimal
import pandas as pd
from shared.models import Candle, Signal, OrderSide
from strategies.base import BaseStrategy
class BollingerStrategy(BaseStrategy):
name: str = "bollinger"
def __init__(self) -> None:
super().__init__()
self._closes: deque[float] = deque(maxlen=500)
self._period: int = 20
self._num_std: float = 2.0
self._min_bandwidth: float = 0.02
self._quantity: Decimal = Decimal("0.01")
self._was_below_lower: bool = False
self._was_above_upper: bool = False
@property
def warmup_period(self) -> int:
return self._period
def configure(self, params: dict) -> None:
self._period = int(params.get("period", 20))
self._num_std = float(params.get("num_std", 2.0))
self._min_bandwidth = float(params.get("min_bandwidth", 0.02))
self._quantity = Decimal(str(params.get("quantity", "0.01")))
if self._period < 2:
raise ValueError(f"Bollinger period must be >= 2, got {self._period}")
if self._num_std <= 0:
raise ValueError(f"Bollinger num_std must be > 0, got {self._num_std}")
if self._quantity <= 0:
raise ValueError(f"Quantity must be positive, got {self._quantity}")
self._init_filters(
require_trend=False,
adx_threshold=float(params.get("adx_threshold", 25.0)),
min_volume_ratio=float(params.get("min_volume_ratio", 0.5)),
atr_stop_multiplier=float(params.get("atr_stop_multiplier", 2.0)),
atr_tp_multiplier=float(params.get("atr_tp_multiplier", 3.0)),
)
def reset(self) -> None:
self._closes.clear()
self._was_below_lower = False
self._was_above_upper = False
def _bollinger_conviction(self, price: float, band: float, sma: float) -> float:
"""Map distance from band to conviction (0.1-1.0).
Further from band (relative to band width) = stronger signal.
"""
if sma == 0:
return 0.5
distance = abs(price - band) / sma
# Scale: 0% distance -> 0.1, 2%+ distance -> ~1.0
return min(1.0, max(0.1, distance * 50))
def on_candle(self, candle: Candle) -> Signal | None:
self._update_filter_data(candle)
self._closes.append(float(candle.close))
if len(self._closes) < self._period:
return None
series = pd.Series(list(self._closes))
sma = series.rolling(window=self._period).mean().iloc[-1]
std = series.rolling(window=self._period).std().iloc[-1]
upper = sma + self._num_std * std
lower = sma - self._num_std * std
# Bandwidth filter: skip sideways markets
if sma != 0 and (upper - lower) / sma < self._min_bandwidth:
return None
price = float(candle.close)
# Track band penetration
if price < lower:
self._was_below_lower = True
if price > upper:
self._was_above_upper = True
# BUY: was below lower band and recovered back inside
if self._was_below_lower and price >= lower:
self._was_below_lower = False
conviction = self._bollinger_conviction(price, lower, sma)
signal = Signal(
strategy=self.name,
symbol=candle.symbol,
side=OrderSide.BUY,
price=candle.close,
quantity=self._quantity,
conviction=conviction,
reason=f"Price recovered above lower Bollinger Band ({lower:.2f})",
)
return self._apply_filters(signal)
# SELL: was above upper band and recovered back inside
if self._was_above_upper and price <= upper:
self._was_above_upper = False
conviction = self._bollinger_conviction(price, upper, sma)
signal = Signal(
strategy=self.name,
symbol=candle.symbol,
side=OrderSide.SELL,
price=candle.close,
quantity=self._quantity,
conviction=conviction,
reason=f"Price recovered below upper Bollinger Band ({upper:.2f})",
)
return self._apply_filters(signal)
return None
|
