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"""Asian Session RSI Strategy — 한국시간 9:00~11:00 단타.
규칙:
- SOL/USDT 5분봉
- 매수: RSI(14) < 25 + 볼륨 > 평균
- 익절: +1.5%, 손절: -0.7%, 시간청산: 11:00 KST (02:00 UTC)
- 하루 최대 3회, 2연패 시 중단
"""
from collections import deque
from decimal import Decimal
from datetime import datetime
import pandas as pd
from shared.models import Candle, Signal, OrderSide
from strategies.base import BaseStrategy
class AsianSessionRsiStrategy(BaseStrategy):
name: str = "asian_session_rsi"
def __init__(self) -> None:
super().__init__()
self._rsi_period: int = 14
self._rsi_oversold: float = 25.0
self._rsi_overbought: float = 75.0
self._quantity: Decimal = Decimal("0.1")
self._take_profit_pct: float = 1.5
self._stop_loss_pct: float = 0.7
# Session: 00:00~02:00 UTC = 09:00~11:00 KST
self._session_start_utc: int = 0
self._session_end_utc: int = 2
self._max_trades_per_day: int = 3
self._max_consecutive_losses: int = 2
# State
self._closes: deque[float] = deque(maxlen=200)
self._volumes: deque[float] = deque(maxlen=50)
self._today: str | None = None
self._trades_today: int = 0
self._consecutive_losses: int = 0
self._in_position: bool = False
self._entry_price: float = 0.0
@property
def warmup_period(self) -> int:
return self._rsi_period + 1
def configure(self, params: dict) -> None:
self._rsi_period = int(params.get("rsi_period", 14))
self._rsi_oversold = float(params.get("rsi_oversold", 25.0))
self._rsi_overbought = float(params.get("rsi_overbought", 75.0))
self._quantity = Decimal(str(params.get("quantity", "0.1")))
self._take_profit_pct = float(params.get("take_profit_pct", 1.5))
self._stop_loss_pct = float(params.get("stop_loss_pct", 0.7))
self._session_start_utc = int(params.get("session_start_utc", 0))
self._session_end_utc = int(params.get("session_end_utc", 2))
self._max_trades_per_day = int(params.get("max_trades_per_day", 3))
self._max_consecutive_losses = int(params.get("max_consecutive_losses", 2))
if self._quantity <= 0:
raise ValueError(f"Quantity must be positive, got {self._quantity}")
if self._stop_loss_pct <= 0:
raise ValueError(f"Stop loss must be positive, got {self._stop_loss_pct}")
if self._take_profit_pct <= 0:
raise ValueError(f"Take profit must be positive, got {self._take_profit_pct}")
self._init_filters(
require_trend=False,
adx_threshold=25.0,
min_volume_ratio=0.5,
atr_stop_multiplier=1.5,
atr_tp_multiplier=2.0,
)
def reset(self) -> None:
super().reset()
self._closes.clear()
self._volumes.clear()
self._today = None
self._trades_today = 0
self._consecutive_losses = 0
self._in_position = False
self._entry_price = 0.0
def _is_session_active(self, dt: datetime) -> bool:
"""Check if current time is within trading session."""
hour = dt.hour
if self._session_start_utc <= self._session_end_utc:
return self._session_start_utc <= hour < self._session_end_utc
# Wrap around midnight
return hour >= self._session_start_utc or hour < self._session_end_utc
def _compute_rsi(self) -> float | None:
if len(self._closes) < self._rsi_period + 1:
return None
series = pd.Series(list(self._closes))
delta = series.diff()
gain = delta.clip(lower=0)
loss = -delta.clip(upper=0)
avg_gain = gain.ewm(com=self._rsi_period - 1, min_periods=self._rsi_period).mean()
avg_loss = loss.ewm(com=self._rsi_period - 1, min_periods=self._rsi_period).mean()
rs = avg_gain / avg_loss.replace(0, float("nan"))
rsi = 100 - (100 / (1 + rs))
val = rsi.iloc[-1]
if pd.isna(val):
return None
return float(val)
def _volume_above_average(self) -> bool:
if len(self._volumes) < 20:
return True # Not enough data, allow
avg = sum(self._volumes) / len(self._volumes)
return self._volumes[-1] >= avg
def on_candle(self, candle: Candle) -> Signal | None:
self._update_filter_data(candle)
close = float(candle.close)
self._closes.append(close)
self._volumes.append(float(candle.volume))
# Daily reset
day = candle.open_time.strftime("%Y-%m-%d")
if self._today != day:
self._today = day
self._trades_today = 0
# Don't reset consecutive_losses — carries across days
# Check exit conditions first (if in position)
if self._in_position:
pnl_pct = (close - self._entry_price) / self._entry_price * 100
# Take profit
if pnl_pct >= self._take_profit_pct:
self._in_position = False
self._consecutive_losses = 0
return self._apply_filters(Signal(
strategy=self.name,
symbol=candle.symbol,
side=OrderSide.SELL,
price=candle.close,
quantity=self._quantity,
conviction=0.9,
reason=f"Take profit {pnl_pct:.2f}% >= {self._take_profit_pct}%",
))
# Stop loss
if pnl_pct <= -self._stop_loss_pct:
self._in_position = False
self._consecutive_losses += 1
return self._apply_filters(Signal(
strategy=self.name,
symbol=candle.symbol,
side=OrderSide.SELL,
price=candle.close,
quantity=self._quantity,
conviction=1.0,
reason=f"Stop loss {pnl_pct:.2f}% <= -{self._stop_loss_pct}%",
))
# Time exit: session ended while in position
if not self._is_session_active(candle.open_time):
self._in_position = False
if pnl_pct < 0:
self._consecutive_losses += 1
else:
self._consecutive_losses = 0
return self._apply_filters(Signal(
strategy=self.name,
symbol=candle.symbol,
side=OrderSide.SELL,
price=candle.close,
quantity=self._quantity,
conviction=0.5,
reason=f"Time exit (session ended), PnL {pnl_pct:.2f}%",
))
return None # Still in position, no action
# Entry conditions
if not self._is_session_active(candle.open_time):
return None # Outside trading hours
if self._trades_today >= self._max_trades_per_day:
return None # Daily limit reached
if self._consecutive_losses >= self._max_consecutive_losses:
return None # Consecutive loss limit
rsi = self._compute_rsi()
if rsi is None:
return None
if rsi < self._rsi_oversold and self._volume_above_average():
self._in_position = True
self._entry_price = close
self._trades_today += 1
# Conviction: lower RSI = stronger signal
conv = min((self._rsi_oversold - rsi) / self._rsi_oversold, 1.0)
conv = max(conv, 0.3)
sl = candle.close * (1 - Decimal(str(self._stop_loss_pct / 100)))
tp = candle.close * (1 + Decimal(str(self._take_profit_pct / 100)))
return self._apply_filters(Signal(
strategy=self.name,
symbol=candle.symbol,
side=OrderSide.BUY,
price=candle.close,
quantity=self._quantity,
conviction=conv,
stop_loss=sl,
take_profit=tp,
reason=f"RSI {rsi:.1f} < {self._rsi_oversold} (session active, vol OK)",
))
return None
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