strategy-lab/to_explore/pyquantnews/107_RiskOfRuin.ipynb

This code simulates a trading strategy to assess the risk of ruin. It defines functions to simulate individual trades, an entire trading strategy, and calculate the risk of ruin over multiple simulations. The parameters include initial capital, win probability, average win/loss amounts, and the number of trades. The output is a plot showing how the probability of a winning trade affects the risk of ruin. This can help traders understand the robustness of their trading strategy.

In [ ]:

import numpy as np
import matplotlib.pyplot as plt

Simulate a single trade with given win probability and average win/loss amounts

In [ ]:

def simulate_trade(win_prob, avg_win, avg_loss):
    """
    Simulate a single trade with given win probability and average win/loss amounts.
    
    Parameters
    ----------
    win_prob : float
        Probability of a winning trade
    avg_win : float
        Average amount won per winning trade
    avg_loss : float
        Average amount lost per losing trade
    
    Returns
    -------
    float
        The result of the trade, positive for win and negative for loss
    """

    # Determine the trade outcome based on win probability and return the result
    if np.random.rand() < win_prob:
        return avg_win
    else:
        return -avg_loss

Simulate the entire trading strategy over a given number of trades

In [ ]:

def simulate_trading_strategy(initial_capital, trades, win_prob, avg_win, avg_loss):
    """
    Simulate the entire trading strategy over a given number of trades.
    
    Parameters
    ----------
    initial_capital : float
        Starting capital for the trading strategy
    trades : int
        Number of trades to simulate
    win_prob : float
        Probability of a winning trade
    avg_win : float
        Average amount won per winning trade
    avg_loss : float
        Average amount lost per losing trade
    
    Returns
    -------
    list
        Capital history as a list of capital values after each trade
    """

    # Initialize capital and history list
    capital = initial_capital
    capital_history = [capital]

    # Simulate each trade and update capital
    for _ in range(trades):
        capital += simulate_trade(win_prob, avg_win, avg_loss)
        capital_history.append(capital)

    return capital_history

Calculate the risk of ruin over a number of trading simulations

In [ ]:

def calculate_risk_of_ruin(initial_capital, trades, win_prob, avg_win, avg_loss, simulations=100):
    """
    Calculate the risk of ruin over a number of trading simulations.
    
    Parameters
    ----------
    initial_capital : float
        Starting capital for the trading strategy
    trades : int
        Number of trades to simulate
    win_prob : float
        Probability of a winning trade
    avg_win : float
        Average amount won per winning trade
    avg_loss : float
        Average amount lost per losing trade
    simulations : int, optional
        Number of simulations to run (default is 100)
    
    Returns
    -------
    float
        Proportion of simulations where the capital went to zero or below
    """

    # Initialize ruin count
    ruin_count = 0

    # Run the specified number of simulations
    for _ in range(simulations):
        capital_history = simulate_trading_strategy(initial_capital, trades, win_prob, avg_win, avg_loss)
        if min(capital_history) <= 0:
            ruin_count += 1

    return ruin_count / simulations

Define initial parameters for the trading simulation

In [ ]:

initial_capital = 10000
average_win = 110
average_loss = 100
trades = 1000

Calculate risk of ruin for varying win probabilities

In [ ]:

risk_of_ruins = []
steps = range(30, 60)
for step in steps:
    win_probability = step / 100
    risk_of_ruin = calculate_risk_of_ruin(initial_capital, trades, win_probability, average_win, average_loss)
    risk_of_ruins.append(risk_of_ruin)

Plot the risk of ruin versus probability of a winning trade

In [ ]:

plt.figure(figsize=(10, 6))
plt.plot(steps, risk_of_ruins, label='Risk of ruin')
plt.xlabel('Probability of a winning trade')
plt.ylabel('Risk of ruin')
plt.title("Probability of ruin versus probability of a winning trade")
plt.grid(True)
plt.show()

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