def eval_impl(self) -> None:
self.counter = 0
self.evaluation = 0
for rt in self.matrix[EvaluatorMatrixTypes.REAL_TIME]:
if check_valid_eval_note(
self.matrix[EvaluatorMatrixTypes.REAL_TIME][rt]):
self.evaluation += self.matrix[
EvaluatorMatrixTypes.REAL_TIME][rt]
self.counter += 1
for ta in self.matrix[EvaluatorMatrixTypes.TA]:
if self.matrix[EvaluatorMatrixTypes.TA][ta]:
for ta_time_frame in self.matrix[EvaluatorMatrixTypes.TA][ta]:
if check_valid_eval_note(self.matrix[
EvaluatorMatrixTypes.TA][ta][ta_time_frame]):
self.evaluation += self.matrix[
EvaluatorMatrixTypes.TA][ta][ta_time_frame]
self.counter += 1
for social in self.matrix[EvaluatorMatrixTypes.SOCIAL]:
if check_valid_eval_note(
self.matrix[EvaluatorMatrixTypes.SOCIAL][social]):
self.evaluation += self.matrix[
EvaluatorMatrixTypes.SOCIAL][social]
self.counter += 1
self.finalize()
def calc_evaluator_divergence(self, matrix_type, evaluator_name, time_frame=None):
if time_frame is not None:
if check_valid_eval_note(self.matrix[matrix_type][evaluator_name][time_frame]):
return self._calc_eval_note_divergence(self.matrix[matrix_type][evaluator_name][time_frame])
else:
return START_PENDING_EVAL_NOTE
elif isinstance(self.matrix[matrix_type][evaluator_name], dict):
local_divergence_average = 0
local_divergence_counter = 0
for time_frame_iteration in self.matrix[matrix_type][evaluator_name]:
if check_valid_eval_note(self.matrix[matrix_type][evaluator_name][time_frame_iteration]):
result = self._calc_eval_note_divergence(self.matrix[matrix_type][evaluator_name]
[time_frame_iteration])
if result is not START_PENDING_EVAL_NOTE:
local_divergence_average += result
local_divergence_counter += 1
if local_divergence_counter > 0:
return local_divergence_average / local_divergence_counter
else:
return START_PENDING_EVAL_NOTE
else:
if check_valid_eval_note(self.matrix[matrix_type][evaluator_name]):
return self._calc_eval_note_divergence(self.matrix[matrix_type][evaluator_name])
else:
return START_PENDING_EVAL_NOTE
def eval_impl(self) -> None:
self.social_counter = 0
self.instant_counter = 0
self.instant_evaluation = 0
self.social_evaluation = 0
# TODO : This is an example
if InstantFluctuationsEvaluator.get_name() in self.matrix[EvaluatorMatrixTypes.REAL_TIME]:
if check_valid_eval_note(self.matrix[EvaluatorMatrixTypes.REAL_TIME][
InstantFluctuationsEvaluator.get_name()]):
self.instant_evaluation += self.matrix[EvaluatorMatrixTypes.REAL_TIME][
InstantFluctuationsEvaluator.get_name()]
self.inc_instant_counter()
if MediumNewsEvaluator.get_name() in self.matrix[EvaluatorMatrixTypes.SOCIAL]:
if check_valid_eval_note(self.matrix[EvaluatorMatrixTypes.SOCIAL][
MediumNewsEvaluator.get_name()]):
self.social_evaluation += self.matrix[EvaluatorMatrixTypes.SOCIAL][MediumNewsEvaluator.get_name()]
self.inc_social_counter()
if RedditForumEvaluator.get_name() in self.matrix[EvaluatorMatrixTypes.SOCIAL]:
if check_valid_eval_note(self.matrix[EvaluatorMatrixTypes.SOCIAL][
RedditForumEvaluator.get_name()]):
self.social_evaluation += \
self.matrix[EvaluatorMatrixTypes.SOCIAL][RedditForumEvaluator.get_name()]
self.inc_social_counter()
self.finalize()
async def eval_impl(self) -> None:
self.social_counter = 0
self.instant_counter = 0
self.instant_evaluation = 0
self.social_evaluation = 0
# TODO : This is an example
if InstantFluctuationsEvaluator.get_name() in self.matrix[
EvaluatorMatrixTypes.REAL_TIME]:
if check_valid_eval_note(
self.matrix[EvaluatorMatrixTypes.REAL_TIME][
InstantFluctuationsEvaluator.get_name()],
self.evaluator_types_matrix.get_evaluator_eval_type(
InstantFluctuationsEvaluator.get_name())):
self.instant_evaluation += self.matrix[
EvaluatorMatrixTypes.REAL_TIME][
InstantFluctuationsEvaluator.get_name()]
self.inc_instant_counter()
if RedditForumEvaluator.get_name() in self.matrix[
EvaluatorMatrixTypes.SOCIAL]:
if check_valid_eval_note(
self.matrix[EvaluatorMatrixTypes.SOCIAL][
RedditForumEvaluator.get_name()],
self.evaluator_types_matrix.get_evaluator_eval_type(
RedditForumEvaluator.get_name())):
self.social_evaluation += \
self.matrix[EvaluatorMatrixTypes.SOCIAL][RedditForumEvaluator.get_name()]
self.inc_social_counter()
self.finalize()
async def eval_impl(self) -> None:
# TODO : temp counter without relevance
self.social_counter = 0
self.rt_counter = 0
# relevance counters
self.ta_relevance_counter = 0
# eval note total with relevance factor
self.ta_evaluation = 0
self.social_evaluation = 0
self.rt_evaluation = 0
# example
# if RSIMomentumEvaluator.get_name() in self.matrix[EvaluatorMatrixTypes.TA]:
# self.divergence_evaluation = self.divergence_evaluator_analyser.calc_evaluator_divergence(
# EvaluatorMatrixTypes.TA,
# RSIMomentumEvaluator.get_name())
for rt in self.matrix[EvaluatorMatrixTypes.REAL_TIME]:
if check_valid_eval_note(
self.matrix[EvaluatorMatrixTypes.REAL_TIME][rt],
self.evaluator_types_matrix.get_evaluator_eval_type(rt)):
self.rt_evaluation += self.matrix[
EvaluatorMatrixTypes.REAL_TIME][rt]
self.inc_rt_counter()
for ta in self.matrix[EvaluatorMatrixTypes.TA]:
if self.matrix[EvaluatorMatrixTypes.TA][ta]:
for ta_time_frame in self.matrix[EvaluatorMatrixTypes.TA][ta]:
if check_valid_eval_note(
self.matrix[
EvaluatorMatrixTypes.TA][ta][ta_time_frame],
self.evaluator_types_matrix.
get_evaluator_eval_type(ta)):
time_frame_relevance = TimeFramesRelevance[
ta_time_frame]
self.ta_evaluation += self.matrix[
EvaluatorMatrixTypes.
TA][ta][ta_time_frame] * time_frame_relevance
self.inc_ta_counter(time_frame_relevance)
for social in self.matrix[EvaluatorMatrixTypes.SOCIAL]:
eval_note = self.matrix[EvaluatorMatrixTypes.SOCIAL][social]
if check_valid_eval_note(
eval_note,
self.evaluator_types_matrix.get_evaluator_eval_type(
social)):
self.social_evaluation += eval_note
self.inc_social_counter()
self.finalize()
def get_eval_note(matrix, matrix_type, evaluator_name, time_frame=None):
if matrix_type in matrix and evaluator_name in matrix[matrix_type]:
if time_frame is not None:
if isinstance(matrix[matrix_type][evaluator_name], dict) \
and time_frame in matrix[matrix_type][evaluator_name]:
eval_note = matrix[matrix_type][evaluator_name][time_frame]
if check_valid_eval_note(eval_note):
return eval_note
else:
eval_note = matrix[matrix_type][evaluator_name]
if check_valid_eval_note(eval_note):
return eval_note
return None
def get_eval_note(matrix: MatrixType,
matrix_type: EvaluatorMatrixTypes,
evaluator_name: str,
time_frame: TimeFrames = None) -> MatrixValueType:
if matrix_type in matrix and evaluator_name in matrix[matrix_type]:
if time_frame is not None:
if isinstance(matrix[matrix_type][evaluator_name], dict) \
and time_frame in matrix[matrix_type][evaluator_name]:
eval_note = matrix[matrix_type][evaluator_name][time_frame]
if check_valid_eval_note(eval_note):
return eval_note
else:
eval_note = matrix[matrix_type][evaluator_name]
if check_valid_eval_note(eval_note):
return eval_note
return None
def set_final_eval(self):
strategy = self.trading_mode.get_strategy_instances_by_classes(
self.symbol)[HighFrequencyStrategiesEvaluator]
strategy_eval = strategy.get_eval_note()
if check_valid_eval_note(strategy_eval):
self.final_eval = strategy_eval
def _check_matrix_divergence(self):
for matrix_type in self.matrix:
for evaluator_name in self.matrix[matrix_type]:
if isinstance(self.matrix[matrix_type][evaluator_name], dict):
for time_frame in self.matrix[matrix_type][evaluator_name]:
if check_valid_eval_note(
self.matrix[matrix_type][evaluator_name][time_frame]):
if self._calc_eval_note_divergence(self.matrix[matrix_type][evaluator_name][time_frame]) \
is not START_PENDING_EVAL_NOTE:
self._log_divergence(matrix_type,
evaluator_name,
self.matrix[matrix_type][evaluator_name][time_frame],
time_frame)
else:
if check_valid_eval_note(self.matrix[matrix_type][evaluator_name]):
if self._calc_eval_note_divergence(self.matrix[matrix_type][evaluator_name]) \
is not START_PENDING_EVAL_NOTE:
self._log_divergence(matrix_type,
evaluator_name,
self.matrix[matrix_type][evaluator_name])
def eval_impl(self) -> None:
# TODO : temp counter without relevance
self.social_counter = 0
self.rt_counter = 0
# relevance counters
self.ta_relevance_counter = 0
# eval note total with relevance factor
self.ta_evaluation = 0
self.social_evaluation = 0
self.rt_evaluation = 0
# example
# if RSIMomentumEvaluator.get_name() in self.matrix[EvaluatorMatrixTypes.TA]:
# self.divergence_evaluation = self.divergence_evaluator_analyser.calc_evaluator_divergence(
# EvaluatorMatrixTypes.TA,
# RSIMomentumEvaluator.get_name())
for rt in self.matrix[EvaluatorMatrixTypes.REAL_TIME]:
if check_valid_eval_note(self.matrix[EvaluatorMatrixTypes.REAL_TIME][rt]):
self.rt_evaluation += self.matrix[EvaluatorMatrixTypes.REAL_TIME][rt]
self.inc_rt_counter()
for ta in self.matrix[EvaluatorMatrixTypes.TA]:
if self.matrix[EvaluatorMatrixTypes.TA][ta]:
for ta_time_frame in self.matrix[EvaluatorMatrixTypes.TA][ta]:
if check_valid_eval_note(self.matrix[EvaluatorMatrixTypes.TA][ta][ta_time_frame]):
time_frame_relevance = TimeFramesRelevance[ta_time_frame]
self.ta_evaluation += self.matrix[EvaluatorMatrixTypes.TA][ta][
ta_time_frame] * time_frame_relevance
self.inc_ta_counter(time_frame_relevance)
for social in self.matrix[EvaluatorMatrixTypes.SOCIAL]:
eval_note = self.matrix[EvaluatorMatrixTypes.SOCIAL][social]
if check_valid_eval_note(eval_note):
self.social_evaluation += eval_note
self.inc_social_counter()
self.finalize()
def set_final_eval(self):
strategies_analysis_note_counter = 0
# Strategies analysis
for evaluated_strategies in self.symbol_evaluator.get_strategies_eval_list(self.exchange):
strategy_eval = evaluated_strategies.get_eval_note()
if check_valid_eval_note(strategy_eval):
self.final_eval += strategy_eval * evaluated_strategies.get_pertinence()
strategies_analysis_note_counter += evaluated_strategies.get_pertinence()
if strategies_analysis_note_counter > 0:
self.final_eval /= strategies_analysis_note_counter
else:
self.final_eval = INIT_EVAL_NOTE
def _prepare(self):
strategies_analysis_note_counter = 0
# Strategies analysis
for evaluated_strategies in self.symbol_evaluator.get_strategies_eval_list(self.exchange):
strategy_eval = evaluated_strategies.get_eval_note()
if check_valid_eval_note(strategy_eval):
self.final_eval += strategy_eval * evaluated_strategies.get_pertinence()
strategies_analysis_note_counter += evaluated_strategies.get_pertinence()
if strategies_analysis_note_counter > 0:
self.final_eval /= strategies_analysis_note_counter
else:
self.final_eval = INIT_EVAL_NOTE
