Python Monte Carlo Trade Simulator

PythonTradeSim

copy/paste into Ipython Notebook

!pip install sympy

import numpy as np
import matplotlib.pyplot as plt
from sympy import symbols, solve

# Define the symbols
P_W = symbols('P_W')

# Parameters
edge = .05
risk_reward = 2

# Solve the equation without the edge, accounting for risk_reward
zero_sum_win_ratio = solve(P_W * risk_reward - (1 - P_W), P_W)[0]

# Add the edge
win_ratio = zero_sum_win_ratio + edge

initial_equity = 2400  # initial account equity
risk_per_trade_percent = 0.05  # risk per trade as a percentage of the account equity
num_trades = 6 * 20 * 1 
#num_trades = 100
trailing_dd = 2000
target = 3000
num_sims = 100
min_risk = 50  # minimum risk amount in dollars

# Initialize stats
max_drawdowns = []
average_drawdowns = []
average_returns = []
average_total_returns_no_dd = []
total_equity = 0
target_hit_counts = 0
trailing_dd_counts = 0

# Initialize a list to store all equity curves
all_equity_curves = []

# Monte Carlo simulation
for i in range(num_sims):
    equity = initial_equity
    high_equity = equity  # Initialize high equity with initial equity
    drawdown = 0
    max_dd = 0
    total_dd = 0
    equity_curve = []
    hit_trailing_dd = False  # flag to track if trailing drawdown was hit in this simulation

    for j in range(num_trades):
        risk_per_trade = max(min_risk, equity * risk_per_trade_percent)  # calculate risk per trade based on current equity

        if np.random.rand() < win_ratio:
            # Win trade
            equity += risk_reward * risk_per_trade
        else:
            # Lose trade
            equity -= risk_per_trade

        high_equity = max(high_equity, equity)  # Update high equity
        drawdown = max(0, high_equity - equity)  # Update drawdown
        max_dd = max(max_dd, drawdown)  # Update max drawdown
        total_dd += drawdown

        # Stop trading if trailing drawdown is hit
        if drawdown >= trailing_dd:
            hit_trailing_dd = True  # mark that trailing drawdown was hit in this simulation
            trailing_dd_counts += 1
            break  # stop trading for this simulation if trailing drawdown is hit

        # Stop trading if target is hit
        if (equity - initial_equity) >= target:
            target_hit_counts += 1
            break  # stop trading for this simulation if target is hit

        equity_curve.append(equity)

    max_drawdowns.append(max_dd)
    average_drawdowns.append(total_dd / num_trades)
    average_returns.append((equity - initial_equity) / num_trades)  # Corrected average return calculation

    total_equity += equity

 # Store the equity curve for this simulation along with its color
    if hit_trailing_dd:
        color = 'red'
    elif (equity - initial_equity) >= target:
        color = 'green'
    else:
        color = 'yellow'
    all_equity_curves.append((equity_curve, color))

    # Compute y-axis limits for the plots
    min_equity = min(min(curve) for curve, color in all_equity_curves if curve)
    max_equity = max(max(curve) for curve, color in all_equity_curves if curve)

    # Plot each equity curve with the computed y-axis limits
for equity_curve, color in all_equity_curves:
    if equity_curve:  # check if equity_curve is not empty
        plt.plot(equity_curve, color=color)

plt.ylim(min_equity, max_equity)
plt.show()  # display the plot once after all curves have been plotted



# Compute stats
max_dd_avg = np.mean(max_drawdowns)
avg_dd_avg = np.mean(average_drawdowns)
avg_return_avg = np.mean(average_returns)
avg_total_return_no_dd = np.mean(average_total_returns_no_dd) if average_total_returns_no_dd else 0
average_overall_equity = total_equity / num_sims
target_hit_ratio = target_hit_counts / num_sims
trailing_dd_ratio = trailing_dd_counts / num_sims  # updated to count only simulations that hit trailing drawdown

print(f'Zero Sum Win Ratio: {zero_sum_win_ratio}')
print(f'Edge: {edge}')
print(f'Win Ratio Total: {win_ratio}')
print(f'R: {risk_reward}')
print(f'% Risk: {risk_per_trade_percent}')
print(f'Target: {target}')
print(f'Trail DD: {trailing_dd}')
print(f'Min $ Risk: {min_risk}')
print(f'Number Trades: {num_trades}')
print(f'Number Sims: {num_sims}')
print(f'Average Maximum Drawdown: {max_dd_avg}')
print(f'Average Drawdown: {avg_dd_avg}')
print(f'Average Return per Trade: {avg_return_avg}')
print(f'Average Total Return (No DD Hit): {avg_total_return_no_dd}')
print(f'Average Overall Equity: {average_overall_equity}')
print(f'Target Hit Ratio: {target_hit_ratio}')
print(f'Trailing Drawdown Hit Ratio: {trailing_dd_ratio}')