def create_points_dataframe(market_name, delay_days,
volume_percent_filter=0, ascend_or_descend='ascend',
column_name='Percent-Change', sign_or_value='value', number_of_iterations=1):
daily_files_paths_list = path_finding_functions.get_all_daily_files_paths_in_specific_market(market_name)
advanced_utils.check_for_daily_gaps(daily_files_paths_list)
sample_daily_dataframe = stocks_analysis.all_companies_data_frame(daily_files_paths_list[-1])
initial_volume_filtered_dataframe = \
advanced_utils.volume_filtered_market_dataframe(sample_daily_dataframe, volume_percent_filter)
symbols_list = initial_volume_filtered_dataframe['Symbol'].tolist()
# split the symbols list, so each thread takes a sub-list to work on.
splitted_list_of_symbols = advanced_utils.splitted_symbols_list(symbols_list, NUMBER_OF_SUBLISTS)
companies_squared_dataframe = \
build_companies_squared_dataframe(symbols_list, splitted_list_of_symbols,
column_name, market_name, delay_days,
volume_percent_filter, ascend_or_descend,
sign_or_value, number_of_iterations)
sort_by_companies = ['TOPS', 'CSCO', 'SRNE'] # just to make sure i see all companies that were-
# included in the count, and not spreaded all over the sheet.
companies_squared_dataframe.sort_values(by=sort_by_companies, ascending=False, inplace=True)
file_path = path_finding_functions.\
set_points_file_path(market_name + "_" + ascend_or_descend + "_"
+ sign_or_value + "_" + "delay" + str(delay_days))
writer = pd.ExcelWriter(file_path, engine='xlsxwriter')
companies_squared_dataframe.to_excel(writer)
writer.save()
def get_last_daily_dataframe(market_name):
last_daily_path = path_finding_functions.get_last_daily_data_file_path(
market_name)
print("Last daily file's path: ", last_daily_path)
last_daily_dataframe = stocks_analysis.all_companies_data_frame(
last_daily_path)
return last_daily_dataframe
def __init__(self):
self.daily_data_file_path_currencies = scrape_API.EoddataExchange(
'currencies').set_daily_data_file_name()
if os.path.isfile(self.daily_data_file_path_currencies) is False:
try:
scrape_API.EoddataExchange('currencies').create_daily_data()
except:
print(
"no currency symbols data file found.\n initiating symbols data file creation."
)
scrape_API.EoddataExchange('currencies').create_symbols_file()
self.all_currencies_dataframe = stocks_analysis.all_companies_data_frame(
self.daily_data_file_path_currencies)
def __init__(self, exchange_name):
self.daily_data_file_path = scrape_API.EoddataExchange(
exchange_name).set_daily_data_file_name()
self.viable_data_column_names = VIABLE_DATA_COLUMN_NAMES
try:
if os.path.isfile(self.daily_data_file_path) is False:
scrape_API.EoddataExchange(exchange_name).create_daily_data()
print("Created daily data file of market: ", exchange_name)
except IndexError:
print(
"no " + exchange_name +
" symbols data file found.\n initiating symbols data file creation."
)
scrape_API.EoddataExchange(exchange_name).create_symbols_file()
print("Symbols data file created. Initializing daily data scrape.")
scrape_API.EoddataExchange(exchange_name).create_daily_data()
self.all_daily_dataframe = stocks_analysis.all_companies_data_frame(
self.daily_data_file_path)
def all_currencies_data_frame(data_file_path):
all_currencies_dataframe = stocks_analysis.all_companies_data_frame(
data_file_path)
return all_currencies_dataframe
def build_count_dict_from_daily_files(number_of_counted_days, daily_files_paths_list,
company_symbol, col_name, delay_days, updated_daily_dict_counter,
ascend_or_descend, volume_percent_filter_to_remove=0, sign_or_value='sign'):
ascend_count = 0
descend_count = 0
company_value_sign = 0
previous_company_values = 0
print(company_symbol, ascend_or_descend, sign_or_value)
# run over each daily file, focusing on a specific company (company_symbol):
for i in range(number_of_counted_days):
# focused_day_dataframe
today_dataframe = stocks_analysis.all_companies_data_frame(daily_files_paths_list[i])
# today_dataframe = advanced_utils.remove_companies_black_list_from_dataframe(today_dataframe)
volume_filtered_dataframe = advanced_utils.\
volume_filtered_market_dataframe(today_dataframe, volume_percent_filter_to_remove)
if sign_or_value == 'sign': # take its percent-change sign (+ or -)
company_value_sign = advanced_utils.\
get_company_value_sign_from_daily_dataframe(volume_filtered_dataframe,
company_symbol, col_name)
elif sign_or_value == 'value': # take its percent-change value.
try:
company_value_sign = \
volume_filtered_dataframe[volume_filtered_dataframe['Symbol'] ==
company_symbol]['Percent-Change'].tolist()[0]
except IndexError:
print("Index out of range. Company is not in volume_filtered_dataframe: ", company_symbol)
company_value_sign = 0
delayed_daily_dataframe = \
stocks_analysis.all_companies_data_frame(daily_files_paths_list[i + delay_days])
# delayed_daily_dataframe = advanced_utils.remove_companies_black_list_from_dataframe(delayed_daily_dataframe)
next_company_values = volume_filtered_dataframe[volume_filtered_dataframe['Symbol'] == company_symbol]
check_equals = check_equal_days_values(today_dataframe, delayed_daily_dataframe,
col_name, next_company_values, previous_company_values)
previous_company_values = next_company_values
if check_equals is True:
continue
if company_value_sign > 0:
ascend_count += 1
if ascend_or_descend == 'descend':
continue
elif company_value_sign < 0:
descend_count += 1
if ascend_or_descend == 'ascend':
continue
else:
continue
volume_filtered_delayed_dataframe = \
advanced_utils.volume_filtered_market_dataframe(delayed_daily_dataframe, volume_percent_filter_to_remove)
updated_daily_dict_counter = \
advanced_utils.add_day_to_counter_dict(volume_filtered_delayed_dataframe,
company_value_sign, col_name,
updated_daily_dict_counter, ascend_or_descend, sign_or_value)
return updated_daily_dict_counter, ascend_count, descend_count