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)
metrics = strategy.execute_strategy(df)
I'll help you create an feature. This is a trading strategy that combines grid trading with trend detection and position sizing multipliers. expert4x grid trend multiplier
print("\n" + "="*50) print("GRID TREND MULTIPLIER STRATEGY RESULTS") print("="*50) for key, value in metrics.items(): if isinstance(value, float): print(f"{key.replace('_', ' ').title()}: {value:.2f}") else: print(f"{key.replace('_', ' ').title()}: {value}") return strategy, metrics if == " main ": strategy, metrics = run_backtest()
def execute_strategy(self, price_data: pd.DataFrame, volume_data: Optional[pd.Series] = None) -> Dict: """ Main strategy execution loop Args: price_data: DataFrame with 'high', 'low', 'close' columns volume_data: Optional volume series Returns: Strategy performance metrics """ logger.info("Starting Grid Trend Multiplier Strategy") for i in range(len(price_data)): current_close = price_data['close'].iloc[i] current_high = price_data['high'].iloc[i] current_low = price_data['low'].iloc[i] # Use enough data for indicators if i < 50: continue # Get price series up to current point price_series = price_data['close'].iloc[:i+1] # Detect trend self.current_trend, self.trend_strength = self.detect_trend(price_series) # Update multiplier based on trend strength self.update_multiplier(self.trend_strength) # Calculate ATR atr_series = self.calculate_atr( price_data['high'].iloc[:i+1], price_data['low'].iloc[:i+1], price_data['close'].iloc[:i+1] ) current_atr = atr_series.iloc[-1] if not pd.isna(atr_series.iloc[-1]) else current_close * 0.01 # Calculate grid levels self.grid_levels = self.calculate_grid_levels(current_close, current_atr) # Check for grid execution order = self.check_grid_execution(current_close, self.grid_levels, current_atr) if order: self.open_positions.append(order) logger.info(f"Order executed: {order['type']} at {order['entry_price']:.4f} " f"with multiplier {order['multiplier']:.2f}") # Update existing positions closed_trades = self.update_positions(current_close) if closed_trades: for trade in closed_trades: logger.info(f"Trade closed: {trade['result']} with profit ${trade['profit']:.2f}") # Calculate final metrics metrics = self.get_performance_metrics() return metrics for i in range(1000): price += np
The strategy automatically adapts to market conditions, increasing exposure during strong trends while maintaining strict risk controls through position sizing and stop losses.
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) atr_value: float) ->
import pandas as pd import numpy as np from datetime import datetime from typing import Dict, List, Tuple, Optional import logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger()