def _create_storage(
cls,
freq=_default_freq,
storage_start=_default_storage_start,
storage_end=_default_storage_end,
constraints=_default_constraints,
min_inventory=None,
max_inventory=None,
max_injection_rate=None,
max_withdrawal_rate=None,
injection_cost=_constant_injection_cost,
withdrawal_cost=_constant_withdrawal_cost,
cmdty_consumed_inject=_constant_cmdty_consumed_inject,
cmdty_consumed_withdraw=_constant_cmdty_consumed_withdraw,
terminal_storage_npv=_default_terminal_npv_calc,
inventory_loss=_constant_inventory_loss,
inventory_cost=_constant_inventory_cost):
return cs.CmdtyStorage(freq,
storage_start,
storage_end,
injection_cost,
withdrawal_cost,
constraints=constraints,
min_inventory=min_inventory,
max_inventory=max_inventory,
max_injection_rate=max_injection_rate,
max_withdrawal_rate=max_withdrawal_rate,
cmdty_consumed_inject=cmdty_consumed_inject,
cmdty_consumed_withdraw=cmdty_consumed_withdraw,
terminal_storage_npv=terminal_storage_npv,
inventory_loss=inventory_loss,
inventory_cost=inventory_cost)
def test_intrinsic_value_runs(self):
ratchets = [
(date(2019, 8, 28),
[
(0.0, -150.0, 255.2),
(2000.0, -200.0, 175.0),
]),
(date(2019, 9, 10),
[
(0.0, -170.5, 235.8),
(700.0, -180.2, 200.77),
(1800.0, -190.5, 174.45),
])
]
storage_start = date(2019, 8, 28)
storage_end = date(2019, 9, 25)
constant_injection_cost = 0.015
constant_pcnt_consumed_inject = 0.0001
constant_withdrawal_cost = 0.02
constant_pcnt_consumed_withdraw = 0.000088
constant_pcnt_inventory_loss = 0.001;
constant_pcnt_inventory_cost = 0.002;
def terminal_npv_calc(price, inventory):
return price * inventory - 15.4 # Some arbitrary calculation
cmdty_storage = cs.CmdtyStorage('D', storage_start, storage_end, constant_injection_cost, constant_withdrawal_cost,
ratchets, ratchet_interp=cs.RatchetInterp.LINEAR,
cmdty_consumed_inject=constant_pcnt_consumed_inject, cmdty_consumed_withdraw=constant_pcnt_consumed_withdraw,
terminal_storage_npv=terminal_npv_calc,
inventory_loss=constant_pcnt_inventory_loss, inventory_cost=constant_pcnt_inventory_cost)
inventory = 650.0
val_date = date(2019, 9, 2)
forward_curve = utils.create_piecewise_flat_series([58.89, 61.41, 59.89, 59.89], [val_date, date(2019, 9, 12), date(2019, 9, 18), storage_end], freq='D')
# TODO test with proper interest rate curve
flat_interest_rate = 0.03
interest_rate_curve = pd.Series(index = pd.period_range(val_date, storage_end + timedelta(days=60), freq='D'))
interest_rate_curve[:] = flat_interest_rate
twentieth_of_next_month = lambda period: period.asfreq('M').asfreq('D', 'end') + 20
intrinsic_results = cs.intrinsic_value(cmdty_storage, val_date, inventory, forward_curve, settlement_rule=twentieth_of_next_month,
interest_rates=interest_rate_curve, num_inventory_grid_points=100)
def test_storage_value_date_equals_storage_end_returns_zero_npv_empty_profile(
self):
storage_start = date(2019, 8, 28)
storage_end = date(2019, 9, 25)
cmdty_storage = cs.CmdtyStorage('D',
storage_start,
storage_end,
injection_cost=0.1,
withdrawal_cost=0.2,
min_inventory=0,
max_inventory=1000,
max_injection_rate=2.5,
max_withdrawal_rate=3.6)
inventory = 0.0
val_date = date(2019, 9, 25)
forward_curve = utils.create_piecewise_flat_series(
[58.89, 61.41, 70.89, 70.89],
[storage_start,
date(2019, 9, 12),
date(2019, 9, 18), storage_end],
freq='D')
flat_interest_rate = 0.03
interest_rate_curve = pd.Series(index=pd.period_range(
val_date, storage_end + timedelta(days=60), freq='D'))
interest_rate_curve[:] = flat_interest_rate
twentieth_of_next_month = lambda period: period.asfreq('M').asfreq(
'D', 'end') + 20
intrinsic_results = cs.intrinsic_value(
cmdty_storage,
val_date,
inventory,
forward_curve,
settlement_rule=twentieth_of_next_month,
interest_rates=interest_rate_curve,
num_inventory_grid_points=100)
self.assertEqual(0.0, intrinsic_results.npv)
self.assertEqual(0, len(intrinsic_results.profile))
def test_trinomial_value_runs(self):
constraints = [(date(2019, 8, 28), [
(0.0, -150.0, 255.2),
(2000.0, -200.0, 175.0),
]),
(date(2019, 9, 10), [
(0.0, -170.5, 235.8),
(700.0, -180.2, 200.77),
(1800.0, -190.5, 174.45),
])]
storage_start = date(2019, 8, 28)
storage_end = date(2019, 9, 25)
constant_injection_cost = 0.015
constant_pcnt_consumed_inject = 0.0001
constant_withdrawal_cost = 0.02
constant_pcnt_consumed_withdraw = 0.000088
constant_pcnt_inventory_loss = 0.001
constant_pcnt_inventory_cost = 0.002
def terminal_npv_calc(price, inventory):
return price * inventory - 15.4 # Some arbitrary calculation
cmdty_storage = cs.CmdtyStorage(
'D',
storage_start,
storage_end,
constant_injection_cost,
constant_withdrawal_cost,
constraints,
cmdty_consumed_inject=constant_pcnt_consumed_inject,
cmdty_consumed_withdraw=constant_pcnt_consumed_withdraw,
terminal_storage_npv=terminal_npv_calc,
inventory_loss=constant_pcnt_inventory_loss,
inventory_cost=constant_pcnt_inventory_cost)
inventory = 650.0
val_date = date(2019, 9, 2)
forward_curve = utils.create_piecewise_flat_series(
[58.89, 61.41, 59.89, 59.89],
[val_date,
date(2019, 9, 12),
date(2019, 9, 18), storage_end],
freq='D')
# TODO test with proper interest rate curve
flat_interest_rate = 0.03
interest_rate_curve = pd.Series(index=pd.period_range(
val_date, storage_end + timedelta(days=60), freq='D'))
interest_rate_curve[:] = flat_interest_rate
# Trinomial Tree parameters
mean_reversion = 14.5
spot_volatility = utils.create_piecewise_flat_series(
[1.35, 1.13, 1.24, 1.24],
[val_date,
date(2019, 9, 12),
date(2019, 9, 18), storage_end],
freq='D')
time_step = 1.0 / 365.0
twentieth_of_next_month = lambda period: period.asfreq('M').asfreq(
'D', 'end') + 20
trinomial_value = cs.trinomial_value(
cmdty_storage,
val_date,
inventory,
forward_curve,
spot_volatility,
mean_reversion,
time_step,
settlement_rule=twentieth_of_next_month,
interest_rates=interest_rate_curve,
num_inventory_grid_points=100)
self.assertTrue(isinstance(trinomial_value, float))
def test_trinomial_delta_deep_itm_equals_intrinsic_delta(self):
storage_start = '2019-12-01'
storage_end = '2020-04-01'
constant_injection_rate = 700.0
constant_withdrawal_rate = 700.0
constant_injection_cost = 1.23
constant_withdrawal_cost = 0.98
min_inventory = 0.0
max_inventory = 100000.0
cmdty_storage = cs.CmdtyStorage(
'D',
storage_start,
storage_end,
constant_injection_cost,
constant_withdrawal_cost,
min_inventory=min_inventory,
max_inventory=max_inventory,
max_injection_rate=constant_injection_rate,
max_withdrawal_rate=constant_withdrawal_rate)
inventory = 0.0
val_date = '2019-08-29'
low_price = 23.87
high_price = 150.32
num_days_at_high_price = 20
date_switch_high_price = '2020-03-12' # TODO calculate this from num_days_at_high_price
forward_curve = utils.create_piecewise_flat_series(
[low_price, high_price, high_price],
[val_date, date_switch_high_price, storage_end],
freq='D')
flat_interest_rate = 0.00
interest_rate_curve = pd.Series(
index=pd.period_range(val_date, '2020-06-01', freq='D'))
interest_rate_curve[:] = flat_interest_rate
# Trinomial Tree parameters
mean_reversion = 14.5
spot_volatility = pd.Series(
index=pd.period_range(val_date, '2020-06-01', freq='D'))
spot_volatility[:] = 1.15
time_step = 1.0 / 365.0
twentieth_of_next_month = lambda period: period.asfreq('M').asfreq(
'D', 'end') + 20
delta_fwd_contracts = [(storage_start, '2020-03-11'),
(date_switch_high_price, storage_end)]
trinomial_deltas = cs.trinomial_deltas(
cmdty_storage,
val_date,
inventory,
forward_curve,
spot_volatility,
mean_reversion,
time_step,
interest_rates=interest_rate_curve,
settlement_rule=twentieth_of_next_month,
fwd_contracts=delta_fwd_contracts,
num_inventory_grid_points=500)
withdraw_delta = trinomial_deltas[1]
expected_withdraw_delta = constant_withdrawal_rate * num_days_at_high_price
pcnt_error = (withdraw_delta -
expected_withdraw_delta) / expected_withdraw_delta
self.assertAlmostEqual(pcnt_error, 0.0, 3)
self.assertTrue(isinstance(trinomial_deltas, list))
