def setup_parameters():
nb_periods = 126
mean, std = 6e-4, 1.04e-2
div_periods = np.array([53, 117, 181, 242])
div_periods = div_periods[div_periods <= nb_periods]
div_yield = 5e-3 * np.ones_like(div_periods)
rate, s0 = 4e-5, 64.94
omega = np.random.choice([-1, 1], nb_periods)
tree = BinomialTree(nb_periods,
mean,
std,
rate,
s0,
div_yield=div_yield,
div_periods=div_periods)
tree.generate()
return tree, omega, s0
def remove_min_node(self):
min_tree = self.get_min_tree()
if not min_tree:
return
self.trees.remove(min_tree)
for root_child in min_tree.root.children:
new_tree = BinomialTree(root=root_child)
self.trees.append(new_tree)
self._fix_heap()
def test_is_discounted_asset_price_martingale():
nb_periods = 126
mean, std = 6e-4, 1.04e-2
div_periods = np.array([53, 117, 181, 242])
div_periods = div_periods[div_periods <= nb_periods]
div_yield = 5e-3 * np.ones_like(div_periods)
rate, s0 = 4e-5, 64.94
tree = BinomialTree(nb_periods,
mean,
std,
rate,
s0,
div_yield=div_yield,
div_periods=div_periods)
omega = np.random.choice([-1, 1], nb_periods)
tree.generate()
assert np.isclose(
tree_paths.extract(tree.tree, omega)[:-1],
tree.calculate_expectation(omega)).all()
def test_binomial_tree_upper_population(self):
btree = BinomialTree(self.volatility, self.rates)
calculated_rates = [
ra * (math.e**(1 * (en + 1) * (self.volatility / 100)))
for en, ra in enumerate(self.rates[1:])
]
calculated_rates.insert(0, self.rates[0])
binomial_rates = [btree.root_node.value]
next = btree.root_node.up_child
while next is not None:
binomial_rates.append(next.value)
next = next.up_child
self.assertEqual(binomial_rates, calculated_rates)
def add_elem(self, key): tree = BinomialTree(initial_key=key) self.trees.append(tree) self._fix_heap()
def __init__(self, tree: BinomialTree, payoff_func, **option_params):
self.tree = tree
self.payoff_tree = tree.calculate_payoff_tree(payoff_func,
**option_params)
self.envelope_tree = tree.calculate_envelope_tree(self.payoff_tree)