def _resync_dividend_policy(stock_id):
stock_data = read(str(stock_id))
if stock_data is None:
return
print(stock_data.df_performance.index)
price_measurement_processor = PriceMeasurementProcessor(stock_id)
df_prices = price_measurement_processor.get_data_frame()
# print('index = ', df_prices.index)
# print('type = ', type(df_prices.index))
dividend_policy_processor = DividendPolicyProcessor(stock_id)
year_indexes = stock_data.df_performance.index.tolist()
df_dividend_policy = dividend_policy_processor.get_data_frames(
{'year': year_indexes[0]}, {'year': year_indexes[-1]})
prices = []
shares = []
for year in stock_data.df_performance.index.tolist():
prices.append(df_prices.loc[str(year), '平均股價'] if str(year) in
df_prices.index else 0)
shares.append(df_dividend_policy.loc[str(year), '股息'] if str(year) in
df_dividend_policy.index else float(0))
print('prices = ', prices)
print('result_share = ', shares)
stock_data.df_performance = stock_data.df_performance.assign(
股息=shares).assign(平均股價=prices)
print('result = ', stock_data.df_performance)
store(stock_data)
def test_read_stock_data(self):
from stock_data import read
stock_data = read('1101')
self.assertIsNotNone(stock_data)
print(stock_data.stock_id)
print(stock_data.df_statement)
print(stock_data.df_performance)
print(stock_data.df_profit_matrix)
def sync_data(stock_id):
stock_data = read(str(stock_id))
df_statement = _sync_statement(
stock_id, None if stock_data is None else stock_data.df_statement)
df_performance = _sync_performance(
stock_id, df_statement,
None if stock_data is None else stock_data.df_performance)
df_profit_matrix = _sync_profit_matrix(
stock_id, None if stock_data is None else stock_data.df_profit_matrix)
return StockData(
stock_id, df_performance, df_statement, df_profit_matrix
) if df_statement is not None and df_performance is not None and df_profit_matrix is not None else None
def generate_predictions2(df_prices, stock_ids=[]):
error_stock_ids = []
df_predictions = None
for stock_id in stock_ids:
str_stock_id = str(stock_id)
# try:
# stock_data = get_stock_data(str_stock_id)
# except:
# stock_data = None
#
# if stock_data is None:
# continue
try:
stock_data = read(str_stock_id)
s_stock = get_predict_evaluate(stock_data,
float(df_prices.loc[str_stock_id]))
except Exception as e:
print("Unexpected error:", e)
traceback.print_tb(e.__traceback__)
print('Get error when get stock ', stock_id, ' stock_data = ',
stock_data)
error_stock_ids.append(stock_id)
s_stock = None
except:
print("Unexpected error:", sys.exc_info()[0])
error_stock_ids.append(stock_id)
s_stock = None
if s_stock is None:
continue
if df_predictions is None:
df_predictions = pd.DataFrame(columns=s_stock.index,
data=[s_stock.values])
df_predictions['股號'] = [str_stock_id]
df_predictions = df_predictions.set_index('股號')
print("first record")
print(df_predictions)
print("index = ", df_predictions.index)
else:
print('get index = ', df_predictions.index)
df_predictions.loc[str_stock_id] = s_stock.values
print('result = ', df_predictions)
print('err_id = ', error_stock_ids)
output_path = gen_output_path('data', 'evaluations.xlsx')
with pd.ExcelWriter(output_path) as writer:
df_predictions.to_excel(writer, sheet_name='predictions')
def test_predict_evaluation(self):
from stock_data import read
stock_data = read('2330')
self.assertIsNotNone(stock_data)
df = None
with open(gen_output_path('data', 'prices.xlsx'), 'rb') as file:
df = pd.read_excel(file)
file.close()
s_2330 = get_predict_evaluate(stock_data, float(df.loc['2330', '收盤價'])).rename('2330')
result = pd.concat([s_2330], axis=1)
print('result 1', result.T)
stock_data = read('6294')
s_6294 = get_predict_evaluate(stock_data, float(df.loc['6294', '收盤價'])).rename('6294')
self.assertIsNotNone(stock_data)
result.loc[:, '6294'] = s_6294
print('result 2', result.T)
result.loc[:, '6294'] = s_6294
print('result 3', result.T)
def get_stock_data(stock_id, sync_to_latest=False):
from stock_data import read
str_stock_id = str(stock_id)
stock_data = read(str_stock_id)
if stock_data is None or sync_to_latest:
try:
print('get stock_data for ', stock_id)
# stock_data = get_evaluate_performance(str_stock_id, 2014)
stock_data = sync_data(str_stock_id)
from stock_data import store
store(stock_data)
except (IndexError, KeyError, AttributeError) as e:
print("get exception: ", e, " for ", stock_id)
traceback.print_tb(e.__traceback__)
return None
return stock_data
def test_get_matrix_value(self):
from stock_data import read
stock_data = read('1102')
get_matrix_value(stock_data)