def calculate_position_size(self, price: float, stop_loss_pct: float = 0.02) -> float: """ Calculate position size based on trend multiplier and risk management Args: price: Entry price stop_loss_pct: Stop loss percentage Returns: Position size in units """ # Base risk amount risk_amount = self.balance * self.risk_per_trade # Apply trend multiplier if self.current_trend == "BULLISH": position_multiplier = self.total_multiplier elif self.current_trend == "BEARISH": position_multiplier = self.total_multiplier else: position_multiplier = 1.0 # Calculate position size stop_loss_distance = price * stop_loss_pct position_size = (risk_amount * position_multiplier) / stop_loss_distance # Cap position size based on available balance max_position = self.balance * 0.1 / price # Max 10% of balance per trade position_size = min(position_size, max_position) return position_size
class GridTrendMultiplier: """ Expert4x Grid Trend Multiplier Strategy expert4x grid trend multiplier
def calculate_grid_levels(self, current_price: float, atr_value: float) -> List[float]: """ Calculate dynamic grid levels based on ATR Args: current_price: Current market price atr_value: Current ATR value Returns: List of grid price levels """ grid_spacing = max( current_price * (self.grid_distance_pct / 100), atr_value * 0.5 # Minimum half of ATR ) levels = [] for i in range(1, self.max_grid_levels + 1): # Calculate multiplier with trend bias multiplier = self.total_multiplier if self.current_trend == "BULLISH": up_level = current_price + (grid_spacing * i * multiplier) down_level = current_price - (grid_spacing * i * (1 / multiplier)) elif self.current_trend == "BEARISH": up_level = current_price + (grid_spacing * i * (1 / multiplier)) down_level = current_price - (grid_spacing * i * multiplier) else: up_level = current_price + (grid_spacing * i) down_level = current_price - (grid_spacing * i) levels.extend([up_level, down_level]) return sorted(levels) stop_loss_pct: float = 0.02) ->
The strategy automatically adapts to market conditions, increasing exposure during strong trends while maintaining strict risk controls through position sizing and stop losses. atr_value: float) ->