diff options
Diffstat (limited to 'python/rmbs')
| -rw-r--r-- | python/rmbs/CRT_data.py | 40 | ||||
| -rw-r--r-- | python/rmbs/marketing.py | 60 |
2 files changed, 100 insertions, 0 deletions
diff --git a/python/rmbs/CRT_data.py b/python/rmbs/CRT_data.py new file mode 100644 index 00000000..a4084575 --- /dev/null +++ b/python/rmbs/CRT_data.py @@ -0,0 +1,40 @@ +from glob import iglob +import os +import pandas as pd +import datetime +from db import dbengine +import .load_globeop_report as load_globeop + +import .bbg_helpers + +def get_CRT_notional(): + + BBG_IP = ['192.168.9.65'] + bbgstartdate = pd.datetime(2011, 1, 1) + path = '/home/serenitas/edwin/CRT/' + hist_securities = pd.read_csv(path + 'all_cusips.csv', header=None) + hist_securities = hist_securities + ' Mtge' + hist_fields = ['MTG_HIST_FACT'] + orig_ntl_field = ['MTG_ORIG_AMT'] + + with bbg_helpers.init_bbg_session(BBG_IP) as session: + hist_data = bbg_helpers.retrieve_data(session, hist_securities[0].tolist(), hist_fields) + + with bbg_helpers.init_bbg_session(BBG_IP) as session: + orig_ntl = bbg_helpers.retrieve_data(session, hist_securities[0].tolist(), orig_ntl_field) + + hist_data1 = pd.DataFrame() + for cusip in hist_data: + new_df= pd.DataFrame() + new_df['notional'] = hist_data[cusip]['MTG_HIST_FACT']['Factor'] * orig_ntl[cusip]['MTG_ORIG_AMT'] + new_df['date'] = hist_data[cusip]['MTG_HIST_FACT']['Payment Date'] + new_df['cusip'] = cusip + hist_data1 = hist_data1.append(new_df) + + hist_data1.to_hdf(path + 'crt_notional.hdf','notionals') + +def calc_CRT_notional(): + path = '/home/serenitas/edwin/CRT/' + hist_data1 = pd.read_hdf(path + 'crt_notional.hdf') + hist_data1 = hist_data1.set_index('date') + return hist_data1.groupby(pd.TimeGrouper('M')).sum()
\ No newline at end of file diff --git a/python/rmbs/marketing.py b/python/rmbs/marketing.py new file mode 100644 index 00000000..120e00bd --- /dev/null +++ b/python/rmbs/marketing.py @@ -0,0 +1,60 @@ +import pandas as pd +import datetime +from db import dbengine +from yieldcurve import YC + +def ver_one(): + date = (datetime.date.today() + pd.offsets.BMonthEnd(-1)).date() + df = pd.read_sql_query("select * from risk_positions(%s, 'Subprime') where price >0 and length(identifier) = 9" + , dbengine('dawndb'), params = [date], index_col=['identifier']) + v1_temp = {} + for cusip in df.index: + v1_temp[cusip] = pd.read_sql_query("select * from priced_percentiles where date(timestamp) = %s and cusip = %s and model_version = 1" + , dbengine('dawndb'), params = [date, cusip]) + v1 = pd.concat(v1_temp) + v1 = v1.set_index('cusip') + v1 = v1.merge(df, left_index=True, right_index=True) + yc = YC(evaluation_date=date) + v1 = v1.replace('M_MTG_GOOD','M_MTG_LMG') + + v1['return'] = v1.apply(lambda df: (df.pv/(df.price/100)) ** (1/df.moddur) - 1 + yc.zero_rate(df.moddur).rate, axis = 1) + + port_ret = v1.groupby('percentile').apply(wavg, 'return', 'usd_market_value') + strat_ret = v1.groupby(['percentile','strategy']).apply(wavg, 'return', 'usd_market_value').unstack(-1) + port_ret = port_ret.rename('Portfolio') + strat_ret = strat_ret.join(port_ret) + + return strat_ret + +def plot_strat(): + + import seaborn as sns + + strat_ret = ver_one() + strat_ret = strat_ret.rename(columns={ \ + 'M_MTG_FP': 'First Pay',\ + 'M_MTG_IO': 'Interest Only',\ + 'M_MTG_LMG': 'Long Duration Par',\ + 'M_MTG_PR': 'Pro-Rata Pay',\ + 'M_MTG_RW': 'R&W Dependent',\ + 'M_MTG_SD': 'Stepdown',\ + 'M_MTG_THRU': 'Pass-through' + }) + + sns.set(style="darkgrid") + ax = strat_ret.plot(figsize = [10, 3.5]) + ax.set_ylim([-.2,.6]) + + vals = ax.get_yticks() + ax.set_yticklabels(['{:3.0f}%'.format(x*100) for x in vals]) + ax.set_ylabel('return') + + ax.legend(loc='upper center', bbox_to_anchor=(0.5, -.05), shadow=True, ncol=3) + +def wavg(group, avg_name, weight_name): + d = group[avg_name] + w = group[weight_name] + try: + return (d * w).sum() / w.sum() + except ZeroDivisionError: + return d.mean() |