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))