Euronext Brussels(BEL20) INDEX analysis in python

Euronext Brussels(BEL20) INDEX is a european index. It consists of minimum 10 and maximum 20 companines.

df = yf.download('^BFX',

start='1985-01-01',

end='2021-08-13',

progress=False)

df = df.loc[:, ['Adj Close']]

df.rename(columns={'Adj Close':'adj_close'}, inplace=True)

df[['simple_rtn','log_rtn']].tail(20)

Log return of BEL20

def realized_volatility(x):

 return np.sqrt(np.sum(x**2))

df_rv = df.groupby(pd.Grouper(freq='M')).apply(realized_volatility)

df_rv.rename(columns={'log_rtn': 'rv'}, inplace=True)

df_rv.rv = df_rv.rv * np.sqrt(12)

fig, ax = plt.subplots(2, 1, sharex=True)

ax[0].plot(df)

ax[1].plot(df_rv)

[<matplotlib.lines.Line2D at 0x7f2509bbc510>,
 <matplotlib.lines.Line2D at 0x7f2509c550d0>,
 <matplotlib.lines.Line2D at 0x7f2509c552d0>]

fig, ax = plt.subplots(3, 1, figsize=(24, 20), sharex=True)

df.adj_close.plot(ax=ax[0])

ax[0].set(title = 'BFX time series',

ylabel = 'Stock price ($)')

df.simple_rtn.plot(ax=ax[1])

ax[1].set(ylabel = 'Simple returns (%)')

df.log_rtn.plot(ax=ax[2])

ax[2].set(xlabel = 'Date',

ylabel = 'Log returns (%)')

[Text(0, 0.5, 'Log returns (%)'), Text(0.5, 0, 'Date')]

import cufflinks as cf

from plotly.offline import iplot, init_notebook_mode

init_notebook_mode()

df_rolling = df[['simple_rtn']].rolling(window=21) \

.agg(['mean', 'std'])

df_rolling.columns = df_rolling.columns.droplevel()

df_outliers = df.join(df_rolling)

def indentify_outliers(row, n_sigmas=3):

   x = row['simple_rtn']

   mu = row['mean']

   sigma = row['std']

   if (x > mu + 3 * sigma) | (x < mu - 3 * sigma):

    return 1

   else:

    return 0

df_outliers['outlier'] = df_outliers.apply(indentify_outliers,

axis=1)

outliers = df_outliers.loc[df_outliers['outlier'] == 1,

['simple_rtn']]

fig, ax = plt.subplots()

ax.plot(df_outliers.index, df_outliers.simple_rtn,

color='blue', label='Normal')

ax.scatter(outliers.index, outliers.simple_rtn,

color='red', label='Anomaly')

ax.set_title("BFX returns")

ax.legend(loc='lower right')

<matplotlib.legend.Legend at 0x7f2505f4a690>

r_range = np.linspace(min(df.log_rtn), max(df.log_rtn), num=1000)

mu = df.log_rtn.mean()

sigma = df.log_rtn.std()

norm_pdf = scs.norm.pdf(r_range, loc=mu, scale=sigma)

fig, ax = plt.subplots(1, 2, figsize=(16, 8))

# histogram

sns.distplot(df.log_rtn, kde=False, norm_hist=True, ax=ax[0])

ax[0].set_title('Distribution of BFX returns', fontsize=16)

ax[0].plot(r_range, norm_pdf, 'g', lw=2,

label=f'N({mu:.2f}, {sigma**2:.4f})')

ax[0].legend(loc='upper left');

Distribution of BEL 20

df.log_rtn.plot(title='Daily BFX returns')

<matplotlib.axes._subplots.AxesSubplot at 0x7f2505ec8a90>

df = yf.download(['^BFX', '^VIX'],

start='1985-01-01',

end='2021-08-13',

progress=False)

df.tail()

Data of BEL 20 AND VIX

df = df[['Adj Close']]

df.tail()

Adj close of BEL20 and VIX

df.columns = df.columns.droplevel(0)

df = df.rename(columns={'^BFX': 'bfx', '^VIX': 'vix'})

df['log_rtn'] = np.log(df.bfx / df.bfx.shift(1))

df['vol_rtn'] = np.log(df.vix / df.vix.shift(1))

df.dropna(how='any', axis=0, inplace=True)

corr_coeff = df.log_rtn.corr(df.vol_rtn)

ax = sns.regplot(x='log_rtn', y='vol_rtn', data=df,

line_kws={'color': 'red'})

ax.set(title=f'BFX vs. VIX ($\\rho$ = {corr_coeff:.2f})',

ylabel='VIX log returns',

xlabel='BFX log returns')

[Text(0, 0.5, 'VIX log returns'),
 Text(0.5, 0, 'BFX log returns'),
 Text(0.5, 1.0, 'BFX vs. VIX ($\\rho$ = -0.40)')]

More New BEL20