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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
def _compute_rsi(series: pd.Series, period: int) -> float | None:
"""Compute RSI using Wilder's smoothing (EMA-based)."""
if len(series) < period + 1:
return None
delta = series.diff()
gain = delta.clip(lower=0)
loss = -delta.clip(upper=0)
avg_gain = gain.ewm(com=period - 1, min_periods=period).mean()
avg_loss = loss.ewm(com=period - 1, min_periods=period).mean()
rs = avg_gain / avg_loss.replace(0, float("nan"))
rsi = 100 - (100 / (1 + rs))
value = rsi.iloc[-1]
if pd.isna(value):
return None
return float(value)
class RsiStrategy(BaseStrategy):
name: str = "rsi"
def __init__(self) -> None:
self._closes: deque[float] = deque(maxlen=200)
self._period: int = 14
self._oversold: float = 30.0
self._overbought: float = 70.0
self._quantity: Decimal = Decimal("0.01")
def configure(self, params: dict) -> None:
self._period = int(params.get("period", 14))
self._oversold = float(params.get("oversold", 30))
self._overbought = float(params.get("overbought", 70))
self._quantity = Decimal(str(params.get("quantity", "0.01")))
def reset(self) -> None:
self._closes.clear()
def on_candle(self, candle: Candle) -> Signal | None:
self._closes.append(float(candle.close))
if len(self._closes) < self._period + 1:
return None
series = pd.Series(list(self._closes))
rsi_value = _compute_rsi(series, self._period)
if rsi_value is None:
return None
if rsi_value < self._oversold:
return Signal(
strategy=self.name,
symbol=candle.symbol,
side=OrderSide.BUY,
price=candle.close,
quantity=self._quantity,
reason=f"RSI {rsi_value:.2f} below oversold threshold {self._oversold}",
)
elif rsi_value > self._overbought:
return Signal(
strategy=self.name,
symbol=candle.symbol,
side=OrderSide.SELL,
price=candle.close,
quantity=self._quantity,
reason=f"RSI {rsi_value:.2f} above overbought threshold {self._overbought}",
)
return None
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