Merge pull request #176 from thrasibule/fix_examples

Fix examples

examples/scripts/Download_USD_LIBOR.py

@@ -22,7 +22,10 @@
 # December 2011. The data is stored in a pandas DataFrame.
 
 import os
-import urllib
+try:
+    import urllib.request as urllib
+except ImportError:
+    import urllib
 import numpy as np
 
 from pandas.io.parsers import read_csv
@@ -105,4 +108,4 @@ def good_row(z):
     df_libor = df_libor.rename(columns=columns_dic)
     good_rows = df_libor.apply(good_row, axis=1)
     df_libor_good = df_libor[good_rows]
-    df_libor_good.save(os.path.join('..', 'data', 'df_libor.pkl'))
+    df_libor_good.to_pickle(os.path.join('..', 'data', 'df_libor.pkl'))

examples/scripts/Libor_to_ZC.py

@@ -34,7 +34,7 @@
 
 if __name__ == '__main__':
 
-    df_libor = pd.load(os.path.join('..', 'data', 'df_libor.pkl'))
+    df_libor = pd.read_pickle(os.path.join('..', 'data', 'df_libor.pkl'))
 
     dtObs = df_libor.index
 

examples/scripts/OptionQuotes.py

@@ -204,23 +204,21 @@ def impvol(cp, strike, premium):
             except:
                 vol = np.nan
             return vol/np.sqrt(timeToMaturity)
-        
+
         # implied bid/ask vol for all options
-
-        df_call['IVBid'] = [impvol('C', strike, price) for strike, price
-                            in zip(df_call['Strike'], df_call['PBid'])]
-        df_call['IVAsk'] = [impvol('C', strike, price) for strike, price
-                            in zip(df_call['Strike'], df_call['PAsk'])]
-
-        df_call['IVMid'] = (df_call['IVBid'] + df_call['IVAsk'])/2
-
-        df_put['IVBid'] = [impvol('P', strike, price) for strike, price
-                           in zip(df_put['Strike'], df_put['PBid'])]
-        df_put['IVAsk'] = [impvol('P', strike, price) for strike, price
-                           in zip(df_put['Strike'], df_put['PAsk'])]
-
-        df_put['IVMid'] = (df_put['IVBid'] + df_put['IVAsk'])/2
-
+
+        df_call = df_call.assign(IVBid = [impvol('C', strike, price) for strike, price
+                                          in zip(df_call['Strike'], df_call['PBid'])],
+                                 IVAsk = [impvol('C', strike, price) for strike, price
+                                          in zip(df_call['Strike'], df_call['PBid'])])
+        df_call = df_call.assign(IVMid = (df_call.IVBid + df_call.IVAsk)/2)
+
+        df_put = df_put.assign(IVBid = [impvol('P', strike, price) for strike, price
+                                        in zip(df_put['Strike'], df_put['PBid'])],
+                               IVAsk = [impvol('P', strike, price) for strike, price
+                                        in zip(df_put['Strike'], df_put['PAsk'])])
+        df_put = df_put.assign(IVMid = (df_put['IVBid'] + df_put['IVAsk'])/2)
+
         f_call = interp1d(df_call['Strike'].values, df_call['IVMid'].values)
         f_put = interp1d(df_put['Strike'].values, df_put['IVMid'].values)
 
@@ -229,10 +227,12 @@ def impvol(cp, strike, premium):
 
         # Quick Delta, computed with ATM vol
         rv = norm()
-        df_call['QuickDelta'] = [rv.cdf(np.log(Fwd/strike)/(atmVol*np.sqrt(timeToMaturity))) \
-        for strike in df_call['Strike']]
-        df_put['QuickDelta'] = [rv.cdf(np.log(Fwd/strike)/(atmVol*np.sqrt(timeToMaturity))) \
-        for strike in df_put['Strike']]
+        df_call = (df_call.
+                   assign(QuickDelta=
+                          rv.cdf(np.log(Fwd/df_call.Strike.values) / (atmVol*np.sqrt(timeToMaturity)))))
+        df_put = (df_put.
+                  assign(QuickDelta=
+                         rv.cdf(np.log(Fwd/df_put.Strike.values)/(atmVol*np.sqrt(timeToMaturity)))))
 
         # keep data within QD range
 
@@ -282,5 +282,4 @@ def impvol(cp, strike, premium):
 
     # save a csv file and pickled data frame
     df_final.to_csv('../data/df_options_SPX_24jan2011.csv', index=False)
-    df_final.save('../data/df_options_SPX_24jan2011.pkl')
-
+    df_final.to_pickle('../data/df_options_SPX_24jan2011.pkl')

examples/scripts/SPX_Options.py

@@ -138,6 +138,6 @@ def read_SPX_file(option_data_file):
 
     # save a csv file and pickled data frame
     df_SPX.to_csv('../data/df_SPX_24jan2011.csv', index=False)
-    df_SPX.save('../data/df_SPX_24jan2011.pkl')
+    df_SPX.to_pickle('../data/df_SPX_24jan2011.pkl')
     print('File saved')
 

examples/scripts/USD_Deposit_Swap.py

@@ -40,20 +40,16 @@
     dt_obs = datetime.datetime(2011, 12, 20)
 
     # extract a yield curve from data frame
-    libor_rates = pd.load(os.path.join('..', 'data', 'df_libor.pkl'))
-    df_libor = pd.DataFrame(libor_rates.xs(dt_obs), columns=['Rate'])
-
+    libor_rates = pd.read_pickle(os.path.join('..', 'data', 'df_libor.pkl'))
+    df_libor = pd.DataFrame({'Rate': libor_rates.loc[dt_obs]})
     # add maturity column
     df_libor['Maturity'] = [maturities_dic[k] for k in df_libor.index]
 
-    # add maturity column
-    df_libor['Maturity'] = [col_mat_dic[k] for k in df_libor.index]
-
     # ... and sort by increasing maturity
-    df_libor = df_libor.sort_index(by='Maturity')
+    df_libor = df_libor.sort_values('Maturity')
 
     print(df_libor)
-    
+
     pl.plot(df_libor['Maturity'], df_libor['Rate'])
     pl.xlabel('Maturity (Yr)')
     pl.ylabel('Deposit/Libor Rate')

examples/scripts/make_zero_coupon.py

@@ -23,7 +23,7 @@
 from quantlib.time.api import (ModifiedFollowing, Actual360,
                                Thirty360, Semiannual, ActualActual)
 
-from quantlib.time.api import ISDA
+from quantlib.time.api import ISDA, pydate_from_qldate
 from quantlib.currency.api import USDCurrency
 from quantlib.quotes import SimpleQuote
 
@@ -35,19 +35,6 @@
 import matplotlib.pyplot as plt
 import pandas
 
-
-def QLDateTodate(dt):
-    """
-    Converts a QL Date to a datetime
-    """
-
-    return datetime.datetime(dt.year, dt.month, dt.day)
-
-
-def dateToDate(dt):
-    return Date(dt.day, dt.month, dt.year)
-
-
 def get_term_structure(df_libor, dtObs):
 
     settings = Settings()
@@ -56,7 +43,7 @@ def get_term_structure(df_libor, dtObs):
     calendar = TARGET()
 
     # must be a business day
-    eval_date = calendar.adjust(dateToDate(dtObs))
+    eval_date = calendar.adjust(Date.from_datetime(dtObs))
     settings.evaluation_date = eval_date
 
     settlement_days = 2
@@ -114,28 +101,27 @@ def get_term_structure(df_libor, dtObs):
     ts_day_counter = ActualActual(ISDA)
     tolerance = 1.0e-15
 
-    ts = PiecewiseYieldCurve(BootstrapTrait.Discount,
-                             Interpolator.LogLinear,
-                             settlement_date,
-                             rate_helpers,
-                             ts_day_counter,
-                             tolerance)
-
+    ts = PiecewiseYieldCurve.from_reference_date(BootstrapTrait.Discount,
+                                                 Interpolator.LogLinear,
+                                                 settlement_date,
+                                                 rate_helpers,
+                                                 ts_day_counter,
+                                                 tolerance)
+    ts.extrapolation = True
     return ts
 
-
 def zero_curve(ts, dtObs):
     dtMax = ts.max_date
 
     calendar = TARGET()
     days = range(10, 365 * 20, 30)
-    dtMat = [min(dtMax, calendar.advance(dateToDate(dtObs), d, Days))
+    dtMat = [min(dtMax, calendar.advance(Date.from_datetime(dtObs), d, Days))
              for d in days]
     # largest dtMat < dtMax, yet QL run time error
 
-    df = np.array([ts.discount(dt, extrapolate=True) for dt in dtMat])
-    dtMat = [QLDateTodate(dt) for dt in dtMat]
-    dtToday = QLDateTodate(dtObs)
+    df = np.array([ts.discount(dt) for dt in dtMat])
+    dtMat = [pydate_from_qldate(dt) for dt in dtMat]
+    dtToday = dtObs.date()
     dt = np.array([(d - dtToday).days / 365.0 for d in dtMat])
     zc = -np.log(df) / dt
     return (dtMat, zc)

examples/scripts/stovol_calibration.py

@@ -385,17 +385,17 @@ def batesdoubleexp_calibration(df_option, ival=None):
 
 # Calibration
 # -----------
-# 
+#
 # Finally, the calibration is performed by first loading the option data and calling the calibration routine.
 
 # Plot Actual vs. Fitted Implied Volatility
 # -----------------------------------------
-# 
+#
 # We display 4 graphs in one plot, and show the bid/ask market volatility with the fitted volatility
 # for selected maturities.
 
 def calibration_subplot(ax, group, i, model_name):
-    group = group.sort_index(by='Strike')
+    group = group.sort_values(by='Strike')
     dtExpiry = group.get_value(group.index[0], 'dtExpiry')
     K = group['Strike']
     VB = group['IVBid']
@@ -440,7 +440,7 @@ def calibration_plot(df_calibration, model_name):
         plt.show(block=False)
 
 if __name__ == '__main__':
-    df_options = pandas.load('../data/df_options_SPX_24jan2011.pkl')
+    df_options = pandas.read_pickle('../data/df_options_SPX_24jan2011.pkl')
 
     df_heston_cal = heston_calibration(df_options)
     calibration_plot(df_heston_cal, 'Heston')