def get_vf_func_approx(
self, t: int, features: Sequence[Callable[[NonTerminal[float]],
float]],
reg_coeff: float) -> LinearFunctionApprox[NonTerminal[float]]:
return LinearFunctionApprox.create(feature_functions=features,
regularization_coeff=reg_coeff,
direct_solve=True)
def get_linear_model() -> LinearFunctionApprox[Triple]:
ffs = feature_functions()
ag = adam_gradient()
return LinearFunctionApprox.create(feature_functions=ffs,
adam_gradient=ag,
regularization_coeff=0.,
direct_solve=True)
def least_squares_tdq(
transitions: Iterable[TransitionStep[S, A]],
feature_functions: Sequence[Callable[[Tuple[NonTerminal[S], A]],
float]],
target_policy: DeterministicPolicy[S, A], γ: float,
ε: float) -> LinearFunctionApprox[Tuple[NonTerminal[S], A]]:
'''transitions is a finite iterable'''
num_features: int = len(feature_functions)
a_inv: np.ndarray = np.eye(num_features) / ε
b_vec: np.ndarray = np.zeros(num_features)
for tr in transitions:
phi1: np.ndarray = np.array(
[f((tr.state, tr.action)) for f in feature_functions])
if isinstance(tr.next_state, NonTerminal):
phi2 = phi1 - γ * np.array([
f((tr.next_state, target_policy.action_for(
tr.next_state.state))) for f in feature_functions
])
else:
phi2 = phi1
temp: np.ndarray = a_inv.T.dot(phi2)
a_inv = a_inv - np.outer(a_inv.dot(phi1), temp) / (1 + phi1.dot(temp))
b_vec += phi1 * tr.reward
opt_wts: np.ndarray = a_inv.dot(b_vec)
return LinearFunctionApprox.create(feature_functions=feature_functions,
weights=Weights.create(opt_wts))
def linear_func_approx(
self, features: Sequence[Callable[[Tuple[float, float]], float]],
reg: float) -> LinearFunctionApprox[Tuple[float, float]]:
return LinearFunctionApprox.create(
feature_functions=features,
adam_gradient=self.adam_gradient(),
regularization_coeff=reg,
)
def fitted_lspi_put_option(
expiry: float, num_steps: int, num_paths: int, spot_price: float,
spot_price_frac: float, rate: float, vol: float, strike: float,
training_iters: int) -> LinearFunctionApprox[Tuple[float, float]]:
num_laguerre: int = 4
epsilon: float = 1e-3
ident: np.ndarray = np.eye(num_laguerre)
features: List[Callable[[Tuple[float, float]], float]] = [lambda _: 1.]
features += [(lambda t_s, i=i: np.exp(-t_s[1] / (2 * strike)) * lagval(
t_s[1] / strike, ident[i])) for i in range(num_laguerre)]
features += [
lambda t_s: np.cos(-t_s[0] * np.pi / (2 * expiry)),
lambda t_s: np.log(expiry - t_s[0])
if t_s[0] != expiry else 0., lambda t_s: (t_s[0] / expiry)**2
]
training_data: Sequence[TrainingDataType] = training_sim_data(
expiry=expiry,
num_steps=num_steps,
num_paths=num_paths,
spot_price=spot_price,
spot_price_frac=spot_price_frac,
rate=rate,
vol=vol)
dt: float = expiry / num_steps
gamma: float = np.exp(-rate * dt)
num_features: int = len(features)
states: Sequence[Tuple[float,
float]] = [(i * dt, s) for i, s, _ in training_data]
next_states: Sequence[Tuple[float, float]] = \
[((i + 1) * dt, s1) for i, _, s1 in training_data]
feature_vals: np.ndarray = np.array([[f(x) for f in features]
for x in states])
next_feature_vals: np.ndarray = np.array([[f(x) for f in features]
for x in next_states])
non_terminal: np.ndarray = np.array(
[i < num_steps - 1 for i, _, _ in training_data])
exer: np.ndarray = np.array([max(strike - s1, 0) for _, s1 in next_states])
wts: np.ndarray = np.zeros(num_features)
for _ in range(training_iters):
a_inv: np.ndarray = np.eye(num_features) / epsilon
b_vec: np.ndarray = np.zeros(num_features)
cont: np.ndarray = np.dot(next_feature_vals, wts)
cont_cond: np.ndarray = non_terminal * (cont > exer)
for i in range(len(training_data)):
phi1: np.ndarray = feature_vals[i]
phi2: np.ndarray = phi1 - \
cont_cond[i] * gamma * next_feature_vals[i]
temp: np.ndarray = a_inv.T.dot(phi2)
a_inv -= np.outer(a_inv.dot(phi1), temp) / (1 + phi1.dot(temp))
b_vec += phi1 * (1 - cont_cond[i]) * exer[i] * gamma
wts = a_inv.dot(b_vec)
return LinearFunctionApprox.create(feature_functions=features,
weights=Weights.create(wts))
exponent: float = 0.5
ffs: Sequence[Callable[[InventoryState],
float]] = [(lambda x, s=s: float(x == s))
for s in nt_states]
mc_ag: AdamGradient = AdamGradient(learning_rate=0.05,
decay1=0.9,
decay2=0.999)
td_ag: AdamGradient = AdamGradient(learning_rate=0.003,
decay1=0.9,
decay2=0.999)
mc_func_approx: LinearFunctionApprox[
InventoryState] = LinearFunctionApprox.create(feature_functions=ffs,
adam_gradient=mc_ag)
td_func_approx: LinearFunctionApprox[
InventoryState] = LinearFunctionApprox.create(feature_functions=ffs,
adam_gradient=td_ag)
it_mc: Iterable[FunctionApprox[InventoryState]] = mc_prediction_learning_rate(
mrp=si_mrp,
start_state_distribution=Choose(set(nt_states)),
gamma=gamma,
tolerance=mc_episode_length_tol,
initial_func_approx=mc_func_approx,
)
it_td: Iterable[FunctionApprox[InventoryState]] = td_prediction_learning_rate(
mrp=si_mrp,
μ: float
σ: float
expiry: int
def __init__(self,
μ: float,
σ: float,
expiry: int,
expectation_samples: int = 10000):
self.μ = μ
self.σ = σ
super().__init__(sampler=lambda:
(np.random.randint(expiry + 1),
np.random.normal(loc=self.μ, scale=self.σ)),
expectation_samples=expectation_samples)
nt_states_distribution = InitialDistrib(strike, sigma, expiry_val)
ag = AdamGradient(learning_rate=0.5, decay1=0.9, decay2=0.999)
ffs = [lambda x: x[0], lambda x: x[1]]
lfa = LinearFunctionApprox.create(feature_functions=ffs,
adam_gradient=ag,
regularization_coeff=0.001,
direct_solve=True)
solution_2 = value_iteration(mdp, 1, lfa, nt_states_distribution, 100)
"""
for i in solution_2:
print(i)
"""
#This second method does not really work
mc_ag: AdamGradient = AdamGradient(
learning_rate=0.05,
decay1=0.9,
decay2=0.999
)
td_ag: AdamGradient = AdamGradient(
learning_rate=0.003,
decay1=0.9,
decay2=0.999
)
mc_func_approx: LinearFunctionApprox[InventoryState] = \
LinearFunctionApprox.create(
feature_functions=ffs,
adam_gradient=mc_ag
)
td_func_approx: LinearFunctionApprox[InventoryState] = \
LinearFunctionApprox.create(
feature_functions=ffs,
adam_gradient=td_ag
)
it_mc: Iterable[FunctionApprox[InventoryState]] = \
mc_prediction_learning_rate(
mrp=si_mrp,
start_state_distribution=Choose(set(nt_states)),
gamma=gamma,
episode_length_tolerance=mc_episode_length_tol,
initial_func_approx=mc_func_approx
init_price_stdev: float = 10.0
num_shares: int = 100
num_time_steps: int = 5
alpha: float = 0.03
beta: float = 0.05
price_diff = [lambda p_s: beta * p_s.shares for _ in range(num_time_steps)]
dynamics = [
lambda p_s: Gaussian(μ=p_s.price - alpha * p_s.shares, σ=0.)
for _ in range(num_time_steps)
]
ffs = [
lambda p_s: p_s.state.price * p_s.state.shares,
lambda p_s: float(p_s.state.shares * p_s.state.shares)
]
fa: FunctionApprox = LinearFunctionApprox.create(feature_functions=ffs)
init_price_distrib: Gaussian = Gaussian(μ=init_price_mean,
σ=init_price_stdev)
ooe: OptimalOrderExecution = OptimalOrderExecution(
shares=num_shares,
time_steps=num_time_steps,
avg_exec_price_diff=price_diff,
price_dynamics=dynamics,
utility_func=lambda x: x,
discount_factor=1,
func_approx=fa,
initial_price_distribution=init_price_distrib)
it_vf: Iterator[Tuple[ValueFunctionApprox[PriceAndShares],
DeterministicPolicy[PriceAndShares, int]]] = \
ooe.backward_induction_vf_and_pi()
