2022年は金と銀のどっちに投資すべき？Pythonで分析した話

#モジュールをインポート
from pandas_datareader import data
import pandas as pd
from pylab import mpl, plt
import numpy as np
import datetime
plt.style.use('seaborn')
mpl.rcParams['font.family'] = 'serif'
%matplotlib inline

#データの期間
start = '2000-01-03'
end = datetime.date.today()

#金のデータ
gold_df = data.DataReader('GC=F', 'yahoo', start, end) 
gold_open = gold_df['Open']
gold_close = gold_df['Close']
gold_high = gold_df['High']
gold_low = gold_df['Low']

#銀のデータ
silver_df = data.DataReader('SI=F', 'yahoo', start, end) 
silver_open = silver_df['Open']
silver_close = silver_df['Close']
silver_high = silver_df['High']
silver_low = silver_df['Low']

#金銀比価のデータ
ratio_df = pd.DataFrame()
ratio_df['Open'] = gold_open / silver_open
ratio_df['High'] = gold_high / silver_high
ratio_df['Low'] = gold_low / silver_low
ratio_df['Close'] = gold_close / silver_close
ratio_df.dropna(inplace=True)

#グラフ作成用のモジュールをインポート
#conda install -c conda-forge cufflinks-py
import cufflinks as cf
import plotly.offline as plyo
plyo.init_notebook_mode(connected=True)

#グラフ用のデータ
qf = cf.QuantFig(
    ratio_df,
    title='Gold to Silver ratio',
    legend='top',
    name='Gold/Silver'
)

#グラフの表示と保存
plyo.iplot(
    qf.iplot(asFigure=True),
    image='png',
    filename='goldsilver'
)

#相関を調べるデータ
compare_df = pd.DataFrame()
compare_df['Gold'] = gold_close[-1500:]
compare_df['Silver'] = silver_close[-1500:]
#欠損データの削除
compare_df.dropna(inplace=True)

#終値の日次変化を計算
rets = np.log(compare_df / compare_df.shift(1))
rets.dropna(inplace=True)

#回帰直線を作成
reg = np.polyfit(rets['Gold'], rets['Silver'], deg=1)

#相関図を作成
ax = rets.plot(kind='scatter', x='Gold', y='Silver', figsize=(20,10), fontsize=18)
ax.plot(rets['Gold'], np.polyval(reg, rets['Gold']), 'r', lw=2)
plt.xlabel('Gold price change', fontsize=18)
plt.ylabel('Silver price change', fontsize=18)
plt.savefig("goldsilver_scatter.png")

#相関係数を表示
rets.corr()

#移動平均を計算
ratio_df['SMA1'] = ratio_df['Close'].rolling(window=50).mean()
ratio_df['SMA2'] = ratio_df['Close'].rolling(window=200).mean()

#チャートに使用するデータ
ratio_date = ratio_df.index[-1500:]
ratio_close = ratio_df['Close'][-1500:]
ratio_sma1 = ratio_df['SMA1'][-1500:]
ratio_sma2 = ratio_df['SMA2'][-1500:]

#チャートの表示と保存
plt.figure(figsize=(20, 10))
plt.plot(ratio_date, ratio_close, lw=2, label = 'Close', color='g')
plt.plot(ratio_date, ratio_sma1, lw=2, label = 'SMA50', color='b')
plt.plot(ratio_date, ratio_sma2, lw=2, label = 'SMA200', color='r')
plt.legend(loc=1, fontsize=18)
plt.xlabel('year', fontsize=18)
plt.ylabel('ratio', fontsize=18)
plt.tick_params(labelsize=18)
plt.savefig("goldsilver_ratio.png")