BalanceEvolution 0.11

Monte Carlo simulations of the trading account evolution based on the winning ratio and risk-reward relation of a trading plan.

This package allows the user to estimate the evolution of a trading account after a prescribed number of trades using a Monte Carlo (MC) algorithm. The input of the algorithm is the winning ratio and the risk-reward relation associated with a trading plan, which supposedly has been extensively tested by the user.

The mc module implements a single class, Simulation, which should be instantiated by the user and which provides a number of methods and properties, such as the ones for calculating the average balance of the MC-generated trading paths, giving an estimate of the long run performance of a trading plan, and, perhaps more importantly, the probability of ruin, which is the probability of zeroing out a trading account when the trader strictly follows a trading plan.

Basic Usage Example

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# Estimate of the probability of ruin of a trading strategy

# Input variables
winratio=0.40     # Winning ratio of the strategy (must be between 0 and 1)
capital=1000.0    # Initial capital
profit=20.0       # Profit per winning trade (must be a positive number)
loss=-10.0        # Loss per losing trade (must be a negative number)
npaths=1000       # Number of independent trading paths
ntrades=1000      # Maximum number of trades per trading path

# Run a Monte Carlo simulation
from BalanceEvolution import mc

s=mc.Simulation()
s.setparams(winratio,capital,profit,loss,npaths,ntrades)
s.run()

# Plot trading paths
s.plottradingpaths()

# Plot histogram of final account balances
s.plothist()

# Average balance, standard devation, maximum and minimum
s.getavgbalance()

# Avgerage consecutive wins, stdandard deviation, maximum and minimum
s.getavgconsecutivewins()

# Avgerage consecutive losses, stdandard deviation, maximum and minimum
s.getavgconsecutivelosses()

# Probability of ruin
print(s.ruinprob)