def fill_wave_type_number_dicts(self):
for wave_type in WAVEST.get_waves_types_for_processing([]):
wave_type_number_list = []
for tick in self.tick_list:
wave_type_number_list.append(
tick.get_wave_number_for_wave_type(wave_type))
self._wave_type_number_min_dict[wave_type] = np.min(
wave_type_number_list)
self._wave_type_number_max_dict[wave_type] = np.max(
wave_type_number_list)
def __init_by_period_and_aggregation__(self,
period=PRD.ALL,
aggregation=1):
self._period = period
self._aggregation = aggregation
self._period_list = [PRD.INTRADAY, PRD.DAILY
] if period == PRD.ALL else [period]
self._wave_types = WAVEST.get_waves_types_for_processing(
self._period_list)
self._fibonacci_wave_data_dict = self.__get_fibonacci_wave_data_dict__(
)
def __fill_waves_into_index_wave_type_number_dict__(self):
self._daily_index_wave_type_number_dict = {}
self._intraday_index_wave_type_number_dict = {}
for wave_type in self._wave_types:
period, direction = WAVEST.get_period_and_direction_for_wave_type(
wave_type)
for index in self._index_list:
key = '{}_{}'.format(index, wave_type)
df = self.__get_df_wave_for_period_and_direction__(
period, direction)
self._index_wave_type_number_dict[key] =\
self.__get_key_number_dict_for_index_and_wave_type__(df, index, period)
def __init__(self, access_layer_wave: AccessLayer4Wave,
index_config: IndexConfiguration):
self._access_layer_wave = access_layer_wave
self._index_config = index_config
self._aggregation = self.__get_aggregation__()
self._period = WPDT.get_period_for_wave_period_key(
self.wave_peak_date_type)
self._wave_types = WAVEST.get_waves_types_for_period(self._period)
self._index_list = index_config.index_list
self._data_last_fetched_time_stamp = 0
self._index_wave_type_number_dict = {
} # contains itself a dictionary {wave_period_key: number, ..}
self._df_wave = {}
self._tick_key_list_for_retrospection = []
def load_data(self, period=PRD.ALL, aggregation=1): # with group by data frames
self._period_list = [PRD.INTRADAY, PRD.DAILY] if period==PRD.ALL else [period]
self._wave_types = WAVEST.get_waves_types_for_processing(self._period_list)
self._period = self._period_list[0]
self._aggregation = aggregation
self._seconds_unit = self.__get_seconds_for_unit__()
for period in self._period_list:
if period == PRD.DAILY:
wave_period_key = WPDT.DAILY_DATE
else:
wave_period_key = WPDT.INTRADAY_TS
self._df_wave_dict[period] = self._access_layer_wave.get_grouped_by_for_wave_peak_plotting(
wave_period_key, aggregation)
self.__fill_waves_into_index_wave_type_number_dict__()
self._data_last_fetched_time_stamp = MyDate.time_stamp_now()
def __plot_wave_peaks__(self):
index = self.sys_config.index_config.get_index_for_symbol(self.symbol)
period = self.sys_config.period
aggregation = self.sys_config.period_aggregation
wave_tick_list = WaveTickList(self.pdh.pattern_data.df)
wave_data_handler = self.sys_config.fibonacci_wave_data_handler
wave_data_handler.fill_wave_type_number_dict_for_ticks_in_wave_tick_list(wave_tick_list, index, period)
for tick in wave_tick_list.tick_list:
for wave_type in WAVEST.get_waves_types_for_processing([period]):
color = wave_data_handler.get_color_for_wave_type_and_period(wave_type, period)
# if tick.date_str == '2019-03-07':
# print(color)
number_waves = tick.get_wave_number_for_wave_type(wave_type)
if number_waves >= 1: # thresholds were calculated in a pre-process
xy = wave_tick_list.get_xy_parameters_for_wave_peak(tick, wave_type, period, aggregation)
self.__add_xy_values_as_polygon__(xy, color, closed=True, fill=False, alpha=1, line_width=1)
def __get_data_for_heatmap_figure__(self,
index: str,
for_mood_graph=False):
x_data = self._wave_handler.tick_key_list_for_retrospection
if self._wave_handler.period_for_retrospection == PRD.INTRADAY:
x_data = [
str(MyDate.get_date_time_from_epoch_seconds(key))
for key in x_data
]
y_data = WAVEST.get_waves_types_for_processing(
[self._wave_handler.period_for_retrospection])
y_data = y_data[::-1] # we want them in this order
z_data = [
self._wave_handler.get_waves_number_list_for_wave_type_and_index(
wt, index) for wt in y_data
]
# print('__get_data_for_heatmap_figure__: {}: \n{}\n{}\n{}'.format(index, x_data, y_data, z_data))
if for_mood_graph:
return self.__get_heatmap_data_for_mood_graph__(
x_data, y_data, z_data)
return [x_data, y_data, z_data]
def __get_wave_peak_shape_list__(self, detector: PatternDetector):
return_list = []
symbol = detector.sys_config.runtime_config.actual_ticker
index = detector.sys_config.index_config.get_index_for_symbol(symbol)
period = detector.sys_config.period
# print('__get_wave_peak_shape_list__: symbol={}, index={}'.format(symbol, index))
wave_tick_list = WaveTickList(detector.pdh.pattern_data.df)
self._fibonacci_wave_data_handler.fill_wave_type_number_dict_for_ticks_in_wave_tick_list(
wave_tick_list, index, period)
for tick in wave_tick_list.tick_list:
for wave_type in WAVEST.get_waves_types_for_processing([PRD.DAILY
]):
number_waves = tick.get_wave_number_for_wave_type(wave_type)
if number_waves >= 1: # in the pre-process we have already used thresholds
color = self._fibonacci_wave_data_handler.get_color_for_wave_type_and_period(
wave_type, period)
shape = DashInterface.get_wave_peak_shape(
detector.sys_config, wave_tick_list, tick, wave_type,
color)
if shape is not None:
return_list.append(shape)
return return_list
fibonacci_wave_data_handler = FibonacciWaveDataHandler(StockDatabase())
fibonacci_wave_data_handler.load_data(period=PRD.DAILY, aggregation=1)
test_case = FWDHTC.HEAT_MAP
if test_case == FWDHTC.RELOAD:
fibonacci_wave_data_handler.reload_data_when_outdated()
elif test_case == FWDHTC.HEAT_MAP:
period = PRD.DAILY
aggregation = 1
index = INDICES.Q_FSE
fibonacci_wave_data_handler.init_tick_key_list_for_retrospection(
100, period=period, aggregation=aggregation)
x_data = fibonacci_wave_data_handler.tick_key_list_for_retrospection
y_data = WAVEST.get_waves_types_for_processing(
[fibonacci_wave_data_handler.period_for_retrospection])
z_data = [
fibonacci_wave_data_handler.
get_waves_number_list_for_wave_type_and_index(wt, index)
for wt in y_data
]
# print('__get_data_for_heatmap_figure__: {}: \n{}\n{}\n{}'.format(index, x_data, y_data, z_data))
for index_z, z_data_list in enumerate(z_data):
z_data_list_with_numbers = []
for index, key in enumerate(x_data):
if z_data_list[index] > 0:
z_data_list_with_numbers.append('{}: {}'.format(
key, z_data_list[index]))
print('{}: {}'.format(y_data[index_z], z_data_list_with_numbers))
def __get_df_wave_for_wave_type__(self, show_period: str, wave_type: str) -> pd.DataFrame:
period, direction = WAVEST.get_period_and_direction_for_wave_type(wave_type)
df_for_period = self._df_wave_dict[show_period]
df = df_for_period[df_for_period[DC.PERIOD] == period]
return df[df[DC.WAVE_TYPE] == direction]