def run_projection(model_iters, metrics, params):
dfs = []
for model_iter, metric, param in zip(model_iters, metrics, params):
crv_aggregator = create_curve_aggregator(metric)
proj_mgr = ProjectionManager(crv_aggregator, model_iter, **param)
proj_mgr.run()
dfs.append( crv_aggregator.to_dataframe())
return pd.concat(dfs, axis=1)
def main():
from lib.utils import dcf_act_act
from Managers.MarketDataManager import MARKET_DATA_MANAGER
MARKET_DATA_MANAGER.reset()
MARKET_DATA_MANAGER.setup(dt.date(2014, 4, 15))
# Setup a bond
asset1 = AssBondFixRate(
face=100,
coupon_rate=0.05,
frequency=4,
issue_date=dt.date(2014, 4, 15),
expiration_date=dt.date(2015, 4, 15),
dcf=dcf_act_act,
pricing_model=None,
name='Sample Bond 1',
)
# or a "float rate bond" but provided with a scenario generator gives you flat rate
# a LIBOR 3M +50 bps floater
asset2 = AssBondFloater(rate_index_name='LIBOR_3M', spread=0.005)
pricing_date = dt.date(2014, 4, 15)
# create a portfolio
port = AssPortfolioModelBase([asset1, asset2], [1, 1])
port.create_iterator(dt.date(2014, 4, 15))
print(port)
metrics = ['Date', 'CF_In:Interest', 'CF_In:Principal']
crv_aggregator = create_curve_aggregator(metrics)
params = {'pricing date': pricing_date, 'periods': 60, 'frequency': 'MS'}
proj_mgr = ProjectionManager(crv_aggregator, port, **params)
proj_mgr.run()
df = crv_aggregator.to_dataframe()
print(df)
def run_bond(self, asset):
from Managers.MarketDataManager import MARKET_DATA_MANAGER
MARKET_DATA_MANAGER.reset()
MARKET_DATA_MANAGER.setup(dt.date(2014,4,15))
credit_model = NoDefaultCreditModel()
# Setup Asset Model
pricing_date = dt.date(2014, 4, 15)
model = AssModelBase(asset, credit_model)
model_iter = model.create_iterator(pricing_date)
metrics = ['Date', 'CF_In:Interest', 'CF_In:Principal']
crv_aggregator = create_curve_aggregator(metrics)
params = {'pricing date': pricing_date, 'periods': 60, 'frequency': 'MS'}
proj_mgr = ProjectionManager(crv_aggregator, model_iter, **params)
proj_mgr.run()
df = crv_aggregator.to_dataframe()
#df.to_csv('test_1')
#print(df)
return df
def run_va_model(self):
raw_input = {"Acct Value": 1344581.6,
"Attained Age": 52.8,
"ID": "000001",
"Issue Age": 45.1,
"Issue Date": dt.date(2005, 6, 22),
"Initial Date": dt.date(2013, 2, 1),
"Maturity Age": 90,
"Population": 1,
"Riders": dict({}),
"ROP Amount": 1038872.0,
"Gender": "F",
"RPB": 1038872.0,
"Free Withdrawal Rate": 0.1,
"Asset Names": ["Fund A", "Fund B"],
"Asset Values": [1344581.6/2, 1344581.6/2]}
# For now, we assume the init_date is month begin
step_per_year = 12
periods = 360
init_date = dt.date(2013, 2, 1)
pricing_date = init_date
# Set up the investment index
#credit_rider = isr.InsCreditRateFixed(credit_rate)
# set up the mutual fund return index
init_df = [ pd.TimeSeries(data=[100], index=[init_date], name='stock A'),
pd.TimeSeries(data=[100], index=[init_date], name='stock B')
]
eq_index = [ip.IndexProvider(init_df[0], 'stock A'), ip.IndexProvider(init_df[1], 'stock B')]
# no vol, otherwise randomness will break my test
sim_engine = EqBSEngine(np.array([0.02, 0.02]), np.array([0.0, 0.0]), corr=np.array([[1., 0.3], [0.3, 1.]]))
simulator = ScenarioGenerator(eq_index, sim_engine, **{'max_time_step': 5. / BDAYS_PER_YEAR})
MARKET_DATA_MANAGER.reset()
MARKET_DATA_MANAGER.setup(init_date)
MARKET_DATA_MANAGER.index_table[ 'stock A'] = eq_index[0]
MARKET_DATA_MANAGER.index_table[ 'stock B'] = eq_index[1]
MARKET_DATA_MANAGER.scen_gen_table['stock A']=simulator
MARKET_DATA_MANAGER.scen_gen_table['stock B']=simulator
fund_info = {'Fund A':
{
'Allocations': {
'stock A': 1,
'stock B': 0,
},
'Management Fee': 0.01,
'Description': 'blah blah',
},
'Fund B':
{
'Allocations': {
'stock A': 0,
'stock B': 1,
},
'Management Fee': 0.01,
'Description': 'blah blah',
},
}
credit_rider = isr.InsCreditRateMutualFunds(fund_info=fund_info)
# Set up non-rider fees
annual_fee_rate = 0.01
annual_booking_fee = 100
mgmt_fee = mif.InsFeeProp(annual_fee_rate, fee_name="Mgmt Fee")
booking_fee = mif.InsFeeConst(annual_booking_fee, fee_name="Booking Fee")
fees = [mgmt_fee, booking_fee]
# Set up rider
db_rider_fee_rate = 0.005
db_rider = mir.InsRiderDB(extract_strict(raw_input, "ROP Amount"), db_rider_fee_rate, rider_name="UWL")
riders = [db_rider]
# Setup investment index
inv_index = credit_rider.inv_index(init_date, periods, step_per_year)
# Setup iteration
product = InsProduct(riders, fees, inv_index)
acct = InsAcct(raw_input, product)
acct_iter = acct.acct_iterator()
# Setup lapse function and lapse model
xs = [0]
ys = [0.0, 0.1]
shock_func = linear_comp_bounded(1, 0, floor=0.5, cap=1.5)
lapse_model = LapseDynamic(InsStepFunc(xs, ys), shock_func, rider_name='UWL')
# Setup surrender charge
xs = [0]
ys = [100, 100]
fixed_charge_func = InsStepFunc(xs, ys)
xs = [0, 1, 2]
ys = [0.0, 0.3, 0.2, 0.0]
pct_charge_func = InsStepFunc(xs, ys)
surrender_charge = SurrenderCharge(fixed_charge_func, pct_charge_func)
# Setup mortality function and mortality model
xs = [x for x in range(0, 100)]
ys = [0.01] * 100
ys.append(float('inf'))
mort_model = InsMortModel(InsStepFunc(xs, ys))
# Setup VA Model
model = InsModelVA(acct, lapse_model, mort_model, surrender_charge)
model_iter = model.create_iterator(pricing_date)
# model iterator to evolve the model_iter to move forward
metrics = ['Account Value',
'Active Population',
'Benefit Base.UWL',
'Rider Fee.UWL',
'Benefit.UWL',
'Fee.Mgmt Fee',
'Fee.Booking Fee',
'Date',
'Attained Age',
'Anniv Flag',
'Death',
'Lapse',
'Paid Benefit.UWL',
'Surrender Charge',
]
crv_aggregator = create_curve_aggregator(metrics)
params = {'pricing date': init_date, 'periods': 60, 'frequency': 'MS'}
proj_mgr = ProjectionManager(crv_aggregator, model_iter, **params)
proj_mgr.run()
df = crv_aggregator.to_dataframe()
# df[['Rider Fee.UWL', 'Fee.Mgmt Fee', 'Fee.Booking Fee', 'Surrender Charge']].plot(kind='bar', stacked=True)
return df
'Account Value',
'Active Population',
'Benefit Base.UWL',
'Rider Fee.UWL',
'Benefit.UWL',
'Fee.Mgmt Fee',
'Fee.Booking Fee',
'Date',
'Attained Age',
'Anniv Flag',
'Death',
'Lapse',
'Paid Benefit.UWL',
'Surrender Charge',
]
crv_aggregator = create_curve_aggregator(metrics)
params = {'pricing date': init_date, 'periods': 60, 'frequency': 'MS'}
proj_mgr = ProjectionManager(crv_aggregator, model_iter, **params)
proj_mgr.run()
df = crv_aggregator.to_dataframe()
df[[
'Rider Fee.UWL', 'Fee.Mgmt Fee', 'Fee.Booking Fee', 'Surrender Charge'
]].plot(kind='bar', stacked=True)
import os
if not os.path.isdir(OUTPUT_PATH):
os.makedirs(OUTPUT_PATH)
df.to_csv(OUTPUT_PATH + '/rider_rs.csv')
print('output saved to %s' % OUTPUT_PATH + '/rider_rs.csv')
def run_fa_model(self):
raw_input = {
"Acct Value": 1344581.6,
"Attained Age": 52.8,
"ID": "000001",
"Issue Age": 45.1,
"Issue Date": dt.date(2005, 6, 22),
"Initial Date": dt.date(2013, 2, 1),
"Maturity Age": 90,
"Population": 1,
"Riders": dict({}),
"ROP Amount": 1038872.0,
"Gender": "F",
"RPB": 1038872.0,
"Free Withdrawal Rate": 0.1,
"Asset Names": ["Credit Account"],
"Asset Values": [1344581.6]
}
# For now, we assume the init_date is month begin
step_per_year = 12
credit_rate = 0.03
periods = 360
init_date = dt.date(2013, 2, 1)
pricing_date = init_date
# Set up the investment index
credit_rider = isr.InsCreditRateFixed(credit_rate)
# Set up non-rider fees
annual_fee_rate = 0.01
annual_booking_fee = 100
mgmt_fee = mif.InsFeeProp(annual_fee_rate, fee_name="Mgmt Fee")
booking_fee = mif.InsFeeConst(annual_booking_fee,
fee_name="Booking Fee")
fees = [mgmt_fee, booking_fee]
# Set up rider
db_rider_fee_rate = 0.005
db_rider = mir.InsRiderDB(extract_strict(raw_input, "ROP Amount"),
db_rider_fee_rate,
rider_name="UWL")
riders = [db_rider]
# Setup investment index
inv_index = credit_rider.inv_index(init_date, periods, step_per_year)
# Setup iteration
product = InsProduct(riders, fees, inv_index)
acct = InsAcct(raw_input, product)
# Setup lapse function and lapse model
xs = [0]
ys = [0.0, 0.1]
lapse_model = LapseStatic(InsStepFunc(xs, ys))
# Setup surrender charge
xs = [0]
ys = [100, 100]
fixed_charge_func = InsStepFunc(xs, ys)
xs = [0, 1, 2]
ys = [0.0, 0.3, 0.2, 0.0]
pct_charge_func = InsStepFunc(xs, ys)
surrender_charge = SurrenderCharge(fixed_charge_func, pct_charge_func)
# Setup mortality function and mortality model
xs = [x for x in range(0, 100)]
ys = [0.01] * 100
ys.append(float('inf'))
mort_model = InsMortModel(InsStepFunc(xs, ys))
# Setup FA Model
model = InsModelFA(acct, lapse_model, mort_model, surrender_charge)
model_iter = model.create_iterator(pricing_date)
# model iterator to evolve the model_iter to move forward
metrics = [
'Account Value',
'Active Population',
'Benefit Base.UWL',
'Rider Fee.UWL',
'Benefit.UWL',
'Fee.Mgmt Fee',
'Fee.Booking Fee',
'Date',
'Attained Age',
'Anniv Flag',
'Death',
'Lapse',
'Paid Benefit.UWL',
'Surrender Charge',
]
crv_aggregator = create_curve_aggregator(metrics)
time_line = pd.date_range(init_date, periods=60, freq='MS').date
prev_date = init_date
for d in time_line:
if d != init_date:
year_frac = (d - prev_date).days / DAYS_PER_YEAR
model_iter.next(d, year_frac)
crv_aggregator.collect_element(model_iter)
prev_date = d
df = crv_aggregator.to_dataframe()
return df