CDF para subas del CCL en Argentina en una ventana de tiempo

Author: gauss314

ccl_subas_quantiles.py

@@ -0,0 +1,40 @@
+import yfinance as yf
+import numpy as np
+import matplotlib.pyplot as plt
+
+plt.style.use('dark_background')
+
+tickers =['GGAL','GGAL.BA','YPF','YPFD.BA','PAM','PAMP.BA']
+data = yf.download(tickers, auto_adjust=True, start='2011-01-01')['Close']
+print('\n\n')
+
+ccl = data['YPFD.BA']/data['YPF']
+ccl += data['GGAL.BA']/data['GGAL'] * 10
+ccl += data['PAMP.BA']/data['PAM'] * 25
+ccl /= 3
+
+ruedas = 55
+subas_fw = ccl.pct_change(ruedas)*100
+fig, ax = plt.subplots(figsize=(15,10), nrows=2)
+
+ax[0].hist(subas_fw, bins=150, width=0.2, color='w', alpha=0.4)
+ax[0].set_title(f'CCL pctChange a {ruedas} Ruedas, Histograma', y=1, fontsize=16)
+
+serie = subas_fw.rolling(20).mean()
+ax[1].plot(serie, color='silver', lw=1, alpha=0.75)
+ax[1].fill_between(serie.index, 0, serie, where = serie < 0 , color='red', alpha=0.2)
+ax[1].fill_between(serie.index, 0, serie, where = serie > 0 , color='green', alpha=0.2)
+
+ax[1].set_title('CCL pctChange a {ruedas}  Ruedas, SMA mensual', y=1, fontsize=16)
+
+values = [5,10,15,20,25,30]
+targets = ((1 + np.array(values)/100)*ccl.iloc[-1]).round(2)
+
+for z in range(len(values)):
+    ax[0].axvline(values[z], color='w', ls='--', lw=1, alpha=0.35)
+    ax[1].plot(ccl.index, [values[z]]*len(ccl), 'w--', alpha=0.35)
+    sup_z = len(subas_fw.loc[subas_fw > values[z] ])/len(subas_fw)
+    print(f'Prob de suba {ruedas} ruedas > {values[z]}% (${targets[z]}): {round(sup_z*100,1)}%')
+
+plt.subplots_adjust(wspace=None, hspace=0.2)
+plt.show()