# -*- coding: utf-8 -*- """ProfitTarget simulation Automatically generated by Colab. Original file is located at https://colab.research.google.com/drive/1v8vVATArNHju8g3PnDMiAHJlLvNkv0Yk """ import numpy as np import matplotlib.pyplot as plt import seaborn as sns # Parameters initial_balance = 1000 target_balance = 200000 win_rate = 0.76 # 37% tgtBalance = f'${target_balance:,}' initialBal = f'${initial_balance:,}' # Time horizon years = 3 trades_per_year = 250 # roughly 1 trade per day total_trades = trades_per_year * years # Reward-Risk Ratio (to be varied in simulation) reward_risk_ratios = np.arange(0.5, 5.5, 0.5) # Risk per trade (to be varied in simulation) risk_percents = np.arange(0.5, 10.5, 0.5) / 100 # Monte Carlo simulation settings num_simulations = 500 np.random.seed(84) # for reproducibility # Function to simulate a single equity curve def simulate_equity_curve(win_rate, rr, risk_percent, initial_balance, total_trades): balance = initial_balance equity_curve = [balance] max_drawdown = 0 peak = balance current_drawdown = 0 consecutive_losses = 0 max_consecutive_losses = 0 for _ in range(total_trades): if np.random.rand() < win_rate: balance += balance * risk_percent * rr consecutive_losses = 0 else: balance -= balance * risk_percent consecutive_losses += 1 max_consecutive_losses = max(max_consecutive_losses, consecutive_losses) peak = max(peak, balance) current_drawdown = (peak - balance) / peak max_drawdown = max(max_drawdown, current_drawdown) equity_curve.append(balance) if balance <= 0: break # account blown return equity_curve, max_drawdown, max_consecutive_losses # Run Monte Carlo simulation across parameter grid results = [] for rr in reward_risk_ratios: for risk_percent in risk_percents: balances = [] drawdowns = [] consecutive_losses = [] for _ in range(num_simulations): eq_curve, max_dd, max_losses = simulate_equity_curve( win_rate, rr, risk_percent, initial_balance, total_trades ) balances.append(eq_curve[-1]) drawdowns.append(max_dd) consecutive_losses.append(max_losses) success_rate = np.mean(np.array(balances) >= target_balance) results.append({ 'RR': rr, 'Risk%': risk_percent * 100, 'Success Rate': success_rate, 'Median Final Balance': f'${np.median(balances):,.2f}', 'Median Max Drawdown%': f'{np.median(drawdowns)*100:.2f}%', 'Median Max Consecutive Losses': np.median(consecutive_losses) }) # Convert results to structured array for easier plotting import pandas as pd df_results = pd.DataFrame(results) # Plot: Heatmap of success rate vs RR and Risk% # Use keyword arguments for pivot pivot_success = df_results.pivot(index='Risk%', columns='RR', values='Success Rate') plt.figure(figsize=(10, 6)) sns.heatmap(pivot_success, annot=True, fmt=".2%", cmap='YlGnBu') plt.title(f'Success Rate to Reach {tgtBalance} in {total_trades} trades\n(Win Rate = {win_rate*100:.1f}%, Initial Balance = {initialBal})') plt.xlabel("Reward-Risk Ratio") plt.ylabel("Risk % per Trade") plt.tight_layout() plt.show() print("\n\n\n") # Find the "best" combination with at least 10% success rate optimal_combos = df_results[df_results['Success Rate'] >= 0.50].sort_values(by='Success Rate', ascending=True) # Show top 5 optimal combinations print(f"First 5 success rate>50% to Reach {tgtBalance} in {total_trades} trades:") display(optimal_combos.head().style.background_gradient(cmap='YlGnBu')) print("\n\n\n") #Part 2: Graphs of drawdowns, losing streaks and number of trades to achieve target # Results storage results = {} for rr_ratio in reward_risk_ratios: avg_max_drawdown = [] avg_losing_streak = [] median_trades_to_target = [] for risk_frac in risk_percents: max_drawdowns = [] max_losing_streaks = [] trades_to_target = [] for _ in range(num_simulations): equity = initial_balance peak_equity = initial_balance losing_streak = 0 max_losing_streak = 0 max_dd = 0 history = [] for t in range(total_trades): risk_amount = equity * risk_frac if np.random.rand() < win_rate: pnl = risk_amount * rr_ratio losing_streak = 0 else: pnl = -risk_amount losing_streak += 1 equity += pnl peak_equity = max(peak_equity, equity) drawdown = (peak_equity - equity) / peak_equity * 100 max_dd = max(max_dd, drawdown) max_losing_streak = max(max_losing_streak, losing_streak) history.append(equity) if equity >= target_balance: trades_to_target.append(t + 1) break else: trades_to_target.append(total_trades) max_drawdowns.append(max_dd) max_losing_streaks.append(max_losing_streak) # Store averages avg_max_drawdown.append(np.mean(max_drawdowns)) avg_losing_streak.append(np.mean(max_losing_streaks)) median_trades_to_target.append(np.median(trades_to_target)) # Save results for this R:R results[rr_ratio] = { "risk_percents": risk_percents * 100, "avg_drawdown": avg_max_drawdown, "avg_losing_streak": avg_losing_streak, "median_trades_to_target": median_trades_to_target } # Plot average drawdown plt.figure(figsize=(9, 5)) for rr_ratio in reward_risk_ratios: plt.plot(results[rr_ratio]["risk_percents"], results[rr_ratio]["avg_drawdown"], marker='o', label=f'R:R {rr_ratio}') plt.title(f'Target= {tgtBalance} in {total_trades} trades (Win Rate = {win_rate*100:.1f}%)\nAverage Max Drawdown vs % Risk per Trade') plt.xlabel('% Risk per Trade') plt.ylabel('Avg Max Drawdown (%)') plt.legend() plt.grid(True) plt.tight_layout() plt.show() print("\n\n\n") # Plot average losing streak plt.figure(figsize=(9, 5)) for rr_ratio in reward_risk_ratios: plt.plot(results[rr_ratio]["risk_percents"], results[rr_ratio]["avg_losing_streak"], marker='o', label=f'R:R {rr_ratio}') plt.title(f'Target= {tgtBalance} in {total_trades} trades (Win Rate = {win_rate*100:.1f}%)\nAverage Losing Streak vs % Risk per Trade') plt.xlabel('% Risk per Trade') plt.ylabel('Average Losing Streak (trades)') plt.legend() plt.grid(True) plt.tight_layout() plt.show() print("\n\n\n") # Plot trades to target plt.figure(figsize=(9, 5)) for rr_ratio in reward_risk_ratios: plt.plot(results[rr_ratio]["risk_percents"], results[rr_ratio]["median_trades_to_target"], marker='o', label=f'R:R {rr_ratio}') plt.title(f'Target= {tgtBalance} in {total_trades} trades (Win Rate = {win_rate*100:.1f}%)\nMedian Trades Needed to Reach Target vs % Risk per Trade') plt.xlabel('% Risk per Trade') plt.ylabel(f'Median Trades to Reach {tgtBalance}') plt.legend() plt.grid(True) plt.tight_layout() plt.show()