def check_point():
stock_list_file = 'basic-no3.csv'
df = pd.read_csv(Path().joinpath(stock_list_file), converters={'code': lambda x: str(x)})
if df.empty:
print('no stock list, quit')
quit()
result_string = ''
tick_dir = Path().joinpath('..', '..', 'stockdata', 'day')
result_df = mylib2.hist_callback(df, tick_dir, 5, check_point_one_for_day,
0,
0,
20)
if not result_df.empty:
result_string2 = result_string + "\n\n meet checkpoint 1 \n" + result_df.to_string()
else:
result_string2 = result_string + '\n\n no meet checkpoint 1\n'
#print(result_string2)
tick_dir_week = Path().joinpath('..', '..', 'stockdata', 'week')
result_df2 = mylib2.hist_callback(df, tick_dir_week, 3, check_point_one_for_week,
0,
0,
20)
if not result_df2.empty:
result_df2 = result_df[result_df['code'].isin(result_df2['code'])]
result_string2 = "\n\n meet checkpoint 1 \n" + result_df2.to_string()
else:
result_string2 = '\n\n no week meet checkpoint 1\n'
print(result_string2)
def get_a_across_b(df, cross_above, cross_type, num_of_days_periods,
period_type, rank):
result_string = ''
tick_dir = getPath(period_type)
result_list = mylib2.hist_callback(df, tick_dir, num_of_days_periods,
mylib6.find_a_cross_b, cross_above,
cross_type, 30)
df_result = pd.DataFrame(result_list,
columns=[
'name', 'code', 'start_date', 'end_date',
'days', 'p_change', 'p1', 'p2', 'p3'
])
df_result.code = df_result.code.astype('str')
str_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
tmpfile = Path().joinpath(
'tmp', 'get_a_across_b-' + str(mylib2.head_offset) + '_' +
period_type + cross_type + '_' + str(num_of_days_periods) + '_days_' +
"-" + str_time + '.csv')
if rank:
df_result = add_columns_and_rank_stock(df_result)
df_result.set_index('code', inplace=True)
df_result.to_csv(tmpfile)
return df_result
def get_latest_n_periods_price_up(df, num_of_periods, type, period_type):
result_string = ''
tick_dir = Path().joinpath('..', '..', get_data_dir(), type)
result_df = mylib2.hist_callback(df, tick_dir, num_of_periods,
mylib2.find_price_go_up_for_n_period,
period_type, 0, 20)
return result_df
def get_w_shape(df, num_of_periods, type='day'):
result_string = ''
tick_dir = Path().joinpath('..', '..', get_data_dir(), type)
df_result = mylib2.hist_callback(df, tick_dir, num_of_periods,
mylib4.find_w_shape, 0, 0, num_of_periods)
df_result = fill_columns(df, df_result)
str_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
df_result = rank_stock(df_result)
tmpfile = Path().joinpath('tmp', 'w-shape-' + str_time + '.csv')
df_result.to_csv(tmpfile)
return df_result
def get_current_price_up_periods(df):
tick_dir = Path().joinpath('..', '..', get_data_dir(), 'day')
df_result = mylib2.hist_callback(df, tick_dir, 60,
mylib5.find_current_price_up_for_n_period,
60, 0, 60)
df_result = fill_columns(df, df_result)
str_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
tmpfile = Path().joinpath('tmp', 'get_no_touch_ma5-' + str_time + '.csv')
df_result = rank_stock(df_result)
df_result.sort_values('days', inplace=True, ascending=False)
df_result.to_csv(tmpfile)
return df_result
def get_history_high_price(df, event_to_now, num_of_days_periods, period_type):
tick_dir = getPath(period_type)
df_result = mylib2.hist_callback(df, tick_dir, num_of_days_periods,
mylib4.find_history_high_v2, event_to_now,
0, 60)
str_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
tmpfile = Path().joinpath(
'tmp', 'get_history_high_price-' + str_time + '_' +
str(num_of_days_periods) + '_days_' + '.csv')
df_result.set_index('code', inplace=True)
df_result.to_csv(tmpfile)
return df_result
def get_latest_sum_of_week_price_up(stock_list_file, num_of_weeks):
# stock_list_file = 'basic-no3.csv'
# num_of_weeks = 2
df = pd.read_csv(Path().joinpath(stock_list_file),
converters={'code': lambda x: str(x)})
if df.empty:
print('no stock list, quit')
quit()
result_string = ''
tick_dir = Path().joinpath('..', '..', get_data_dir(), 'week')
result_df = mylib2.hist_callback(df, tick_dir, num_of_weeks,
mylib2.sum_of_latest_n_days_price_up, 0,
0, 20)
return result_df
def get_price_above(df, type, num_of_days_periods, period_type):
result_string = ''
tick_dir = getPath(period_type)
df_result = mylib2.hist_callback(df, tick_dir, num_of_days_periods,
mylib6.find_price_above, type, 0, 60)
str_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
tmpfile = Path().joinpath(
'tmp', 'get_price_above-' + period_type + '_' +
str(num_of_days_periods) + '_days_' + '.csv')
#df_result = rank_stock(df_result)
#pdb.set_trace()
df_result.set_index('code', inplace=True)
df_result.to_csv(tmpfile)
return df_result
def get_first_history_high_volume_in_n(df, num_of_days, num_of_days_periods=0):
result_string = ''
tick_dir = Path().joinpath('..', '..', get_data_dir(), 'day')
df_result = mylib2.hist_callback(df, tick_dir, num_of_days_periods,
mylib4.find_first_history_high_volume,
num_of_days, 0, 60)
df_result = fill_columns(df, df_result)
str_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
tmpfile = Path().joinpath(
'tmp', 'get_first_history_high_volume-' + str_time + '_' +
str(num_of_days_periods) + '_days_' + '.csv')
df_result = rank_stock(df_result)
df_result.to_csv(tmpfile)
return df_result
def get_price_continuous_down_in_n(df, num_of_days_down, price_down_sum,
num_of_days_periods, period_type):
result_string = ''
tick_dir = getPath(period_type)
df_result = mylib2.hist_callback(
df, tick_dir, num_of_days_periods,
mylib6.find_price_continuous_down_for_n_period, num_of_days_down,
price_down_sum, 60)
df_result = fill_columns(df, df_result)
str_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
tmpfile = Path().joinpath(
'tmp', 'get_price_continuous_down_in_n-' + period_type + str_time +
'_' + str(num_of_days_periods) + '_days_' + '.csv')
df_result = rank_stock(df_result)
df_result.to_csv(tmpfile)
return df_result
def get_price_up_with_percentage(df, percentage, num_of_days_periods,
period_type):
result_string = ''
tick_dir = getPath(period_type)
result_list = mylib2.hist_callback(df, tick_dir, num_of_days_periods,
mylib6.find_price_up_with_percentage,
percentage, 0, 60)
df_result = pd.DataFrame(
result_list,
columns=['name', 'code', 'start_date', 'end_date', 'days', 'p_change'])
df_result.code = df_result.code.astype('str')
str_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
tmpfile = Path().joinpath(
'tmp', 'get_price_up_with_percentage-' + period_type + '_' +
str(num_of_days_periods) + '_days_' + str_time + '.csv')
#df_result = rank_stock(df_result)
#pdb.set_trace()
df_result.set_index('code', inplace=True)
df_result.to_csv(tmpfile)
return df_result
def get_miminum_price_sum_in_n(df, is_price_up, min_price_range, min_day_range,
num_of_days_periods, period_type):
tick_dir = getPath(period_type)
result_list = mylib2.hist_callback(
df, tick_dir, num_of_days_periods,
mylib6.find_minimum_price_sum_for_n_period, is_price_up,
(min_price_range, min_day_range), 60)
df_result = pd.DataFrame(
result_list,
columns=['name', 'code', 'start_date', 'end_date', 'days', 'p_change'])
df_result.code = df_result.code.astype('str')
#df_result = fill_columns(df, df_result)
str_time = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
tmpfile = Path().joinpath(
'tmp', 'get_price_sum_in_n-' + period_type + str_time + '_' +
str(num_of_days_periods) + '_days_' + '.csv')
#df_result = rank_stock(df_result)
#pdb.set_trace()
df_result.sort_values('p_change', inplace=True, ascending=not is_price_up)
df_result.set_index('code', inplace=True)
df_result.to_csv(tmpfile)
return df_result
