code stringlengths 3 6.57k |
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calculate_max_pain(chain: pd.DataFrame) |
np.array(chain.index) |
if ("OI_call" not in chain.columns) |
or ("OI_put" not in chain.columns) |
print("Incorrect columns. Unable to parse max pain") |
loss.append(get_loss_at_strike(price_at_exp, chain) |
idxmin() |
vol(other_args: List[str]) |
parse_known_args_and_warn(parser, other_args) |
print(e, "\n") |
voi(other_args: List[str]) |
volume (considering open interest) |
parse_known_args_and_warn(parser, other_args) |
print(e, "\n") |
oi(other_args: List[str]) |
parse_known_args_and_warn(parser, other_args) |
print(e, "\n") |
_version.get_versions() |
AgentHelper(object) |
ComputationDataProcessor (CDP) |
__init__(self, name, communicator, sample_interval) |
isinstance(communicator, AgentCommunicator) |
unpack_exps(self, exp_seqs) |
concat_lists(lists) |
extract_key(seq, k) |
e.val(k) |
extract_key(exp_seq, k) |
extract_key(exp_seq, k) |
extract_key(exp_seq[:-1], k) |
dict() |
dict() |
first(second) |
states(next_states) |
extract_key(exp_seq[:1], k) |
extract_key(exp_seq[1:2], k) |
extract_key(exp_seq[1:], "alive") |
ret.keys() |
keys() |
concat_lists(ret[k][kk]) |
len(exp_seqs) |
predict(self, inputs, states=dict() |
data (if necessary) |
inputs(dict) |
dict(inputs=inputs, states=states) |
self.comm.put_prediction_data(data, 1) |
self.comm.get_prediction_return() |
add_experience(self, e) |
self.store_data() |
_store_data(self, alive, data) |
data(dict) |
isinstance(data, dict) |
Experience(data) |
self.add_experience(t) |
self.learn() |
sample_experiences(self) |
self.learn() |
learn(self) |
store_data() |
self.sample_experiences() |
self.unpack_exps(exp_seqs) |
self.comm.put_training_data(data, size) |
self.comm.get_training_return() |
OnlineHelper(AgentHelper) |
learn() |
__init__(self, name, communicator, sample_interval=5) |
super(OnlineHelper, self) |
__init__(name, communicator, sample_interval) |
NoReplacementQueue() |
exp_replay() |
add_experience(self, e) |
self.exp_queue.add(e) |
sample_experiences(self) |
self.exp_queue.sample() |
ExpReplayHelper(AgentHelper) |
learn() |
super(ExpReplayHelper, self) |
ReplayBuffer(buffer_capacity) |
exp_replay() |
add_experience(self, e) |
self.replay_buffer.add(e) |
sample_experiences(self) |
self.replay_buffer.sample(self.num_experiences, self.num_seqs) |
Agent(Process) |
run() |
not (only do testing) |
__init__(self, num_games, actrep, learning) |
super(Agent, self) |
__init__() |
Value('i', 0) |
set_env(self, env_class, *args, **kwargs) |
env_class(*args, **kwargs) |
isinstance(helper, AgentHelper) |
_make_zero_states(self, prop) |
np.zeros(prop["shape"]) |
astype(dtype) |
predict(self, alg_name, inputs, states=dict() |
_pack_data() |
_unpack_data() |
inputs.items() |
states.items() |
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