from jesse.strategies import Strategy
import numpy as np
class SmaCrossover(Strategy):
"""
Simple Moving Average Crossover Strategy
Goes long when fast EMA crosses above slow EMA
Goes short when fast EMA crosses below slow EMA
"""
def __init__(self):
super().__init__()
self.ema_fast = 10
self.ema_slow = 30
self.position_size = 0.02 # 2% of portfolio
self.stop_loss_pct = 0.02 # 2% stop loss
self.take_profit_pct = 0.04 # 4% take profit
def hyperparameters(self):
return [
{"name": "ema_fast", "default": 10, "min": 5, "max": 20},
{"name": "ema_slow", "default": 30, "min": 20, "max": 50},
{"name": "position_size", "default": 0.02, "min": 0.01, "max": 0.1},
{"name": "stop_loss_pct", "default": 0.02, "min": 0.01, "max": 0.05},
{"name": "take_profit_pct", "default": 0.04, "min": 0.02, "max": 0.1},
]
def should_long(self) -> bool:
if len(self.candles) < self.ema_slow:
return False
fast_ema = self._calculate_ema(self.ema_fast)
slow_ema = self._calculate_ema(self.ema_slow)
# Buy signal when fast EMA crosses above slow EMA
return fast_ema[-1] > slow_ema[-1] and fast_ema[-2] <= slow_ema[-2]
def should_short(self) -> bool:
if len(self.candles) < self.ema_slow:
return False
fast_ema = self._calculate_ema(self.ema_fast)
slow_ema = self._calculate_ema(self.ema_slow)
# Sell signal when fast EMA crosses below slow EMA
return fast_ema[-1] < slow_ema[-1] and fast_ema[-2] >= slow_ema[-2]
def go_long(self):
qty = self.balance * self.position_size / self.close
self.buy = qty, self.price
self.stop_loss = qty, self.price * (1 - self.stop_loss_pct)
self.take_profit = qty, self.price * (1 + self.take_profit_pct)
def go_short(self):
qty = self.balance * self.position_size / self.close
self.sell = qty, self.price
self.stop_loss = qty, self.price * (1 + self.stop_loss_pct)
self.take_profit = qty, self.price * (1 - self.take_profit_pct)
def should_cancel_entry(self):
return True
def _calculate_ema(self, period: int) -> np.ndarray:
"""Calculate Exponential Moving Average"""
if len(self.candles) < period:
return np.full(len(self.candles), np.nan)
close_prices = self.candles[:, 2] # Close prices are at index 2
ema = np.zeros(len(close_prices))
ema[0] = close_prices[0]
multiplier = 2 / (period + 1)
for i in range(1, len(close_prices)):
if not np.isnan(close_prices[i]):
ema[i] = (close_prices[i] - ema[i - 1]) * multiplier + ema[i - 1]
else:
ema[i] = ema[i - 1]
return ema
def update_position(self):
# Close long position if short signal appears
if self.is_long and self.should_short():
self.liquidate()
# Close short position if long signal appears
elif self.is_short and self.should_long():
self.liquidate()
