from matplotlib import pyplot as plt
import numpy as np
import ipywidgets as widgets
from IPython.display import display

out_text = '最終金額 : {0:,} ,年間生活費 : {1:,} \n年間利息 : {2:,} , FIRE倍率 : {3}\n'

# FIREシミュレーター
def show_widgets():
    button = widgets.Button(description='シミュレーション')
    s0 = widgets.IntSlider(value=200000  , min=10000  , max=1000000, step=10000 , description='月の生活費:')
    s1 = widgets.IntSlider(value=2000000, min=0 , max=10000000, step=100000 , description='初期金額:')
    s2 = widgets.IntSlider(value=200000  , min=0 , max=1000000  , step=10000   , description='月の積立金:')
    s3 = widgets.FloatSlider(value=4.0, min=0.1, step=0.1, max=20.0, description='利率:')
    s4 = widgets.IntSlider(value=10  , min=1 , max=50  , step=1 , description='年数:')
    output0 = widgets.Output(layour={'border': '1px solid black'})
    output1 = widgets.Output()
    ax = plt.gca()
    
    def on_click(b: widgets.Button) -> None:
        ax.clear()
        cost = s0.value
        initial = s1.value
        reserve = s2.value
        rate = s3.value
        year = s4.value
        rand_x = []
        money = initial
        for i in range(1,year):
            money += reserve * 12
            money = int(money*(1+rate/100))
            rand_x.append(money)
        f_rate = money / (cost * 12)
        result = '{0:.4}倍'.format(f_rate)
        Interest = int(money*(rate/100))
        ax.plot(list(range(1,len(rand_x)+1)),rand_x)
        ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, loc: "{:,}".format(int(x))))
        with output0:
            output0.clear_output(wait=True)
            out = out_text.format(money, cost * 12 , Interest , result)
            if f_rate>25:
              out += 'FIRE達成しました!!'
            print(out)
        with output1:
            output1.clear_output(wait=True)
            display(ax.figure)
            
    button.on_click(on_click)
    box1 = widgets.Box([s0,s1])
    box2 = widgets.Box([s2,s3])
    box3 = widgets.Box([s4,button])
    display(box1 , box2 , box3 , output0,output1)
    plt.close()
    button.click()
    
show_widgets()

タグ ：

#プログラミング

#Python

#FIRE

#シミュレーター

#投資

#金融

import pandas as pd
import numpy as np
import warnings
warnings.simplefilter('ignore')
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

# 直接読み込む場合
df = pd.read_csv('https://atmtech.net/main/n225/Y2020-all.csv',header=None,names=['day','time','open','high','row','close'])

# 手元にダウンロードしてから行う場合
df = pd.read_csv('Y2020-all.csv',header=None,names=['day','time','open','high','row','close'])

# プレビュー
df.head()

df['time'] = df['time'].str.replace('0.395833333','9:30:00')

def time_sep(d):
    l = d['time'].split(':')
    return '{0:02}:{1:02}:{2:02}'.format(int(l[0]),int(l[1]),int(l[2]))

def day_sep(d):
    l = d['day'].split('/')
    return '{0}-{1:02}-{2:02}'.format(int(l[0]),int(l[1]),int(l[2]))

df['time'] = df.apply(time_sep,axis=1)
df['day'] = df.apply(day_sep,axis=1)
df.head()

df_9 = df[df['time']=='09:00:00'][['day','close']]
df_9 = df_9.reset_index(drop=True)
df_9.columns = ['day','stand']
df_9.head()

# 集計してみる
pd.pivot_table(merge_df,index='day', columns='time', values='rate')

# 箱髭図を作ってみる
fig = plt.figure(figsize=(16,10))
ax = fig.add_subplot(1, 1, 1)
sns.boxplot(x='time', y='rate', data=merge_df, ax=ax)
plt.xticks(rotation=90)
plt.show()

# 時間帯ごとに集計する
calc_df = merge_df[['time','rate']][merge_df['time']!='00:00:00'].groupby(['time']).agg([np.mean,np.median,np.std])
calc_df.columns = ['mean','median','std']
calc_df.head()

# 折れ線グラフにしてみる
plt.figure(figsize=(16,10))
plt.plot([1 for i in range(len(calc_df['mean']))])
plt.plot(calc_df['mean'],color='red',label="mean")
plt.plot(calc_df['median'],color='green',label="median")
plt.xticks(rotation=90)
plt.legend(fontsize=20)
plt.show()

# 価格差でフラグ値を付ける
merge_df['flg'] = merge_df['rate'] >=1

calc2_df = pd.crosstab(merge_df['time'], merge_df['flg'])
calc2_df.columns = ['down','up']
calc2_df = calc2_df.reset_index()
calc2_df = calc2_df[calc2_df['time']!='09:00:00']
calc2_df = calc2_df[calc2_df['time']!='00:00:00']

merge_df.head()

# 100%棒グラフを作る
plt.figure(figsize=(16,6))
plt.plot([121 for i in range(len(calc_df['mean']))])
height1 = np.array(calc2_df['down'])
height2 = np.array(calc2_df['up'])
p1 = plt.bar(calc2_df['time'], height1, color="red")
p2 = plt.bar(calc2_df['time'], height2, bottom=height1, color="green")
plt.xticks(rotation=90)
plt.legend((p2[0], p1[0]), ("UP","DOWN"))
plt.show()

money = 10000000
sel_close , buy_close = 0,0
sel_value , buy_value = 0,0
result , quantity = 0,0
result_df = pd.DataFrame()
for row in merge_df[(merge_df['time']=='09:00:00')|(merge_df['time']=='09:20:00')].iterrows():
    data = row[1]
    date = data[0]
    times = data[1]
    close = data[5]
    if times=='09:00:00':
        sel_close = close
        quantity = money//sel_close
        sel_value = quantity * sel_close
    elif times=='09:20:00':
        buy_close = close
        buy_value = quantity * buy_close
        result = (sel_value - buy_value)
        money = money + (sel_value - buy_value)
        tmp_df = pd.DataFrame([[date,sel_close,buy_close,sel_value,buy_value,result,money]],
                              columns=['date','sel_close','buy_close','sel_value','buy_value','result','money'])
        result_df = pd.concat([result_df,tmp_df])

result_df = result_df.reset_index(drop=True)
result_df.head()

plt.figure(figsize=(16,3))
plt.plot(result_df['money'],color='red')
plt.show()

plot_df = df[df['time']=='09:00:00']
plot_df = plot_df.reset_index(drop=True)
plt.figure(figsize=(16,3))
plt.plot(plot_df['close'],color='blue')
plt.show()

タグ ：

#Python

#プログラミング

#投資

#株

#売買シミュレーション