import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.options.display.float_format = '{:.2f}'.format

# 250回のコイントスの賭けシミュレーター
def coin_250(default_assets=1000000,times=250,win_rate=0.55):
  kelly_criterion_percentage = (win_rate - (1 - win_rate))
  data = []
  assets = default_assets
  for i in range(times):
    stakes = int(assets * kelly_criterion_percentage)
    assets -= stakes
    result = np.random.choice([1,0],p=[0.55,0.45])
    if result == 1:
      assets += stakes * 2
    tmp =[assets,stakes, int(result)]
    data.append(tmp)
  
  # 250回の結果をデータフレームで集計
  df = pd.DataFrame(data,columns=["assets","stakes","result"])
  max_assets = df["assets"].max()
  min_assets = df["assets"].min()
  max_stakes = df["stakes"].max()
  min_stakes = df["stakes"].min()
  final_ps = int(df["assets"].tail(1).values[0])
  profit = final_ps - default_assets
  win_times = int(df["result"].sum())
  lose_times = len(df) - win_times

  group_id = (df["result"] != df["result"].shift()).cumsum()
  run_lengths = df.groupby(group_id)["result"].sum()
  run_lengths_0 = df.groupby(group_id).apply(lambda x: (len(x) - x["result"].sum()))
  # 最大連敗回数
  max_lose = run_lengths_0.max()
  # 最大連勝回数
  max_win = run_lengths.max()

  result_250 = [max_assets,min_assets,max_stakes,min_stakes,final_ps,profit,win_times,lose_times,max_win,max_lose]
  return result_250

times = 1000
results = []
for i in range(times):
  data = coin_250()
  results.append(data)

columns = ["最大資産","最小資産","最大掛金","最小掛金","最終資産","最終利益","勝利回数","敗北回数","最大連勝","最大連敗"]
df = pd.DataFrame(results,columns=columns)
df.describe()