I evaluated the strategy of stock system trading with Python.

#Introducing the library
import pandas as pd
import matplotlib.pyplot as plt
import random

#Function for evaluation
#asset initial capital
#What percentage of bet capital to invest
#probability Win rate
#win average profit margin
#loss average loss rate
#transaction number of transactions
def sim(asset, bet, probability, win, loss, transaction):
  result = []
  for i in range(transaction):

#Win if the random number is less than the winning percentage
    if random.random() < probability:

#Assets increase by the average profit margin multiplied by the invested assets.
#This time it is compound interest, but if you change this area, you can make it simple interest.
      asset = asset + (asset * bet * win)

#If you lose, the average loss rate multiplied by the invested assets will decrease.
    else:
      asset = asset - (asset * bet * loss)

#Store transaction results in a list.
    result.append(asset)    

  return result

#Function for evaluation 2
def mon(asset, bet, probability, win, loss, transaction=100, test=1):
  df = pd.DataFrame()

#Call the evaluation function for the number of trials and store it in the data frame.
  for i in range(test):
    df[i] = sim(asset, bet, probability, win, loss, transaction)

#graph display
#Settings for auxiliary lines
  xmin = -3
  xmax = transaction

#Bluff size setting
  plt.figure(figsize=(25, 15), dpi=50)
  plt.plot(df)

#Auxiliary line setting
  plt.hlines([asset], xmin, xmax, "blue")
  plt.show()

#Display of statistical elements
  print(df.iloc[transaction - 1].describe())

#Amount of funds 300, risk 20%, Win rate 61.54% average profit margin 5.83% average loss rate 4.63% Let's verify with 250 trades and 300 trials.
mon(300, 0.2, 0.6154, 0.0583, 0.0463, 250, 300)

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