diff options
Diffstat (limited to 'python/exploration')
| -rw-r--r-- | python/exploration/beta_trade.py | 135 |
1 files changed, 83 insertions, 52 deletions
diff --git a/python/exploration/beta_trade.py b/python/exploration/beta_trade.py index 4f10e61f..bc53c33d 100644 --- a/python/exploration/beta_trade.py +++ b/python/exploration/beta_trade.py @@ -2,58 +2,89 @@ import math import os import pandas as pd import feather -from index_data import index_returns +from index_data import index_returns, get_index_quotes from arch import arch_model from math import log, exp, sqrt import numpy as np from scipy.optimize import minimize_scalar +from scipy.optimize import minimize -returns = index_returns(index=['IG', 'HY'], tenor='5yr') -returns_hy = (returns. - xs('HY', level=1). - dropna(). - reset_index(level='series'). - groupby(level=['date']). - nth(-1)) -returns_hy = returns_hy.set_index('series', append=True) -returns_ig = returns.xs('IG', level=1).reset_index('tenor', drop=True) -# hy starts trading later than ig, so we line it up based on hy series -df = pd.merge(returns_hy, returns_ig, left_index=True, right_index=True, - suffixes=('_hy','_ig')) -returns = df[['price_return_hy', 'price_return_ig']] -returns.columns = ['hy', 'ig'] -returns = returns.reset_index('series', drop=True) -feather.write_dataframe(returns.reset_index(), - os.path.join(os.environ["DATA_DIR"], "index_returns.fth")) +import matplotlib.pyplot as plt -#returns = returns.groupby('date').nth(-1) -# three ways of computing the volatility -# 20 days simple moving average -vol_sma = returns.hy.rolling(20).std() * math.sqrt(252) -vol_ewma = returns.hy.ewm(span=20).std() * math.sqrt(252) -# GARCH(1,1) -# we scale returns by 10 to help with the fitting -scale = 10 -am = arch_model(scale * returns.hy.dropna()) -res = am.fit() -vol_garch = res.conditional_volatility * math.sqrt(252)/scale -vol = pd.concat([vol_sma, vol_ewma, vol_garch], axis=1, keys=['sma', 'ewma', 'garch']) +def calc_returns(): + returns = index_returns(index=['IG', 'HY'], tenor='5yr') + returns_hy = (returns. + xs('HY', level=1). + dropna(). + reset_index(level='series'). + groupby(level=['date']). + nth(-1)) + returns_hy = returns_hy.set_index('series', append=True) + returns_ig = returns.xs('IG', level=1).reset_index('tenor', drop=True) + # hy starts trading later than ig, so we line it up based on hy series + df = pd.merge(returns_hy, returns_ig, left_index=True, right_index=True, + suffixes=('_hy','_ig')) + returns = df[['price_return_hy', 'price_return_ig']] + returns.columns = ['hy', 'ig'] + #feather.write_dataframe(returns.reset_index(), + # os.path.join(os.environ["DATA_DIR"], "index_returns.fth")) + return returns.reset_index('series', drop=True) -## let's get the betas -beta_ewma = (returns. - ewm(span=20). - cov(). - groupby(level='date'). - apply(lambda df: df.values[0,1]/df.values[1,1])) +def calc_betas(): + returns = calc_returns() + beta_ewma = (returns. + ewm(span=20). + cov(). + groupby(level='date'). + apply(lambda df: df.values[0,1]/df.values[1,1])) -beta_ewma5 = (returns. - ewm(span=5). - cov(). - groupby(level='date'). - apply(lambda df: df.values[0,1]/df.values[1,1])) + beta_ewma5 = (returns. + ewm(span=5). + cov(). + groupby(level='date'). + apply(lambda df: df.values[0,1]/df.values[1,1])) -feather.write_dataframe(beta_ewma.to_frame('beta'), - os.path.join(os.environ['DATA_DIR'], "beta.fth")) + return (beta_ewma, beta_ewma5) + +def plot_betas(): + betas = calc_betas() + plt.plot(betas[0], label = 'EWMA20') + plt.plot(betas[1], label = 'EWMA5') + plt.xlabel('date') + plt.ylabel('beta') + plt.legend() + +def calc_realized_vol(): + + # three ways of computing the volatility + # 1) 20 days simple moving average + # 2) exponentially weighted moving average + # 3) GARCH(1,1), we scale returns by 10 to help with the fitting + returns = calc_returns() + vol_sma = pd.DataFrame() + vol_ewma = pd.DataFrame() + for index in returns: + vol_sma[index] = returns[index].rolling(20).std() * math.sqrt(252) + vol_ewma[index] = returns[index].ewm(span=20).std() * math.sqrt(252) + scale = 10 + am = arch_model(scale * returns.hy.dropna()) + res = am.fit() + vol_garch = res.conditional_volatility * math.sqrt(252)/scale + vol = pd.concat([vol_sma, vol_ewma, vol_garch], axis=1, keys=['sma', 'ewma', 'garch']) + + ## 2 standard deviation + vol.quantile(.95) + + #feather.write_dataframe(beta_ewma.to_frame('beta'), + # os.path.join(os.environ['DATA_DIR'], "beta.fth")) + +def spreads_ratio(): + df = get_index_quotes(series = list(range(22,29))) + df1 = pd.DataFrame() + for index in ['IG', 'HY']: + df1[index] = df.modelspread.xs((index, '5yr'), level=[1,4]).groupby('date').last() + df1['ratio'] = df1.HY/df1.IG + return df1 def loglik(beta, returns): x = (returns.hy - beta*returns.ig) @@ -61,13 +92,13 @@ def loglik(beta, returns): fit = model.fit(maxlag=1) return - fit.llf -r = [] -for beta in np.arange(3, 5, 0.01): - prog = minimize(loglik, np.array([0.1, 0.1, 0.1]), args=(returns, beta), - bounds=[(None, None), (1e-6, None), (None, None)], - method='L-BFGS-B') - r.append(prog.fun) +# r = [] +# for beta in np.arange(3, 5, 0.01): +# prog = minimize(loglik, np.array([0.1, 0.1, 0.1]), args=(returns, beta), +# bounds=[(None, None), (1e-6, None), (None, None)], +# method='L-BFGS-B') +# r.append(prog.fun) -r = [] -for beta in np.arange(3, 5, 0.01): - r.append(test(returns, beta)) +# r = [] +# for beta in np.arange(3, 5, 0.01): +# r.append(test(returns, beta)) |