Expert4x Grid Trend Multiplier -

class GridTrendMultiplier: """ Expert4x Grid Trend Multiplier Strategy

for i in range(1000): price += np.random.randn() * 0.5 if i > 200 and i < 600: # Uptrend price += 0.1 elif i > 600: # Downtrend price -= 0.05 prices.append(max(price, 10)) df = pd.DataFrame({ 'high': [p * (1 + abs(np.random.randn() * 0.002)) for p in prices], 'low': [p * (1 - abs(np.random.randn() * 0.002)) for p in prices], 'close': prices }, index=dates) expert4x grid trend multiplier

def update_multiplier(self, trend_strength: float): """ Update position multiplier based on trend strength """ if trend_strength > 50: # Strong trend - increase multiplier self.total_multiplier = min( self.max_multiplier, self.total_multiplier * self.trend_multiplier ) elif trend_strength < 25: # Weak trend - decrease multiplier self.total_multiplier = max( 1.0, self.total_multiplier / self.trend_multiplier ) def check_grid_execution(self, current_price: float, grid_levels: List[float], atr: float) -> Optional[Dict]: """ Check if price hit a grid level and execute order Returns: Order details if executed, None otherwise """ for level in grid_levels: # Check if price crossed a grid level if abs(current_price - level) / level < 0.0001: # Within 0.01% # Determine direction based on trend if self.current_trend == "BULLISH": direction = "BUY" stop_loss = level * (1 - 0.02) # 2% stop loss take_profit = level * (1 + self.grid_distance_pct / 100) elif self.current_trend == "BEARISH": direction = "SELL" stop_loss = level * (1 + 0.02) take_profit = level * (1 - self.grid_distance_pct / 100) else: # Neutral - alternate direction = "BUY" if len(self.open_positions) % 2 == 0 else "SELL" stop_loss = level * (1 - 0.02) if direction == "BUY" else level * (1 + 0.02) take_profit = level * (1 + self.grid_distance_pct / 100) if direction == "BUY" else level * (1 - self.grid_distance_pct / 100) position_size = self.calculate_position_size(level) order = { 'type': direction, 'entry_price': level, 'position_size': position_size, 'stop_loss': stop_loss, 'take_profit': take_profit, 'timestamp': datetime.now(), 'grid_level': level, 'multiplier': self.total_multiplier } return order return None 200 and i &lt

def get_performance_metrics(self) -> Dict: """ Calculate strategy performance metrics """ win_rate = (self.winning_trades / self.total_trades * 100) if self.total_trades > 0 else 0 profit_factor = 0 # Calculate profit factor gross_profit = sum(t['profit'] for t in self.closed_trades if t.get('profit', 0) > 0) gross_loss = abs(sum(t['profit'] for t in self.closed_trades if t.get('profit', 0) < 0)) profit_factor = gross_profit / gross_loss if gross_loss > 0 else float('inf') total_return = ((self.balance - self.initial_balance) / self.initial_balance) * 100 metrics = { 'total_return_pct': total_return, 'final_balance': self.balance, 'total_trades': self.total_trades, 'winning_trades': self.winning_trades, 'losing_trades': self.losing_trades, 'win_rate_pct': win_rate, 'profit_factor': profit_factor, 'max_drawdown_pct': self.max_drawdown, 'current_trend': self.current_trend, 'trend_strength': self.trend_strength, 'final_multiplier': self.total_multiplier, 'open_positions': len(self.open_positions) } return metrics 'close': prices }