def main():
findbillion_database = class_findbillion_database(dataset_path)
#
est_eps = class_est_eps(findbillion_database)
est_yield = class_est_yield(findbillion_database)
#-----------------------------------------
# ground truth of dividend_cash
fs_dividendpolicy = class_fs_dividendpolicy(findbillion_database)
dividend_cash = fs_dividendpolicy.get_Dividend_Cash(stockid, year_est)
#-----------------------------------------
# methed 1: average cash dividend
dividend_cash_avg = est_yield.get_average_5y_cash_dividend(
stockid, year_stat)
error_method1 = (dividend_cash_avg - dividend_cash) / dividend_cash * 100
print("Average cash dividend: ")
print("cash dividend (true): {:2.2f}".format(dividend_cash))
print("cash dividend (predict): {:2.2f}".format(dividend_cash_avg))
print("error of predict: {:2.2f}%".format(error_method1))
#--------------------------------------------------
# methed 2: average dividend payout ratio
print("----------------------------------------")
eps_est_last4q = est_eps.est_last_4q_eps_by_netincome_ratio(
stockid, year_est, 4)
est_dividend_cash = est_yield.est_cash_dividend_by_dividend_payout_ratio(
stockid, year_stat, eps_est_last4q)
error_method2 = (est_dividend_cash - dividend_cash) / dividend_cash * 100
print("Average cash dividend payout ratio: ")
print("cash dividend (true): {:2.2f}".format(dividend_cash))
print("cash dividend (predict): {:2.2f}".format(est_dividend_cash))
print("error of predict: {:2.2f}%".format(error_method2))
#--------------------------------------------------
# methed 3: estimate dividend payout ratio by linear regression
print("----------------------------------------")
eps_est = est_eps.est_last_4q_eps_by_netincome_ratio(stockid, year_est, 4)
est_dividend_cash = est_yield.est_cash_dividend_by_linear_regression_5y(
stockid, year_stat, eps_est)
error_method3 = (est_dividend_cash - dividend_cash) / dividend_cash * 100
print("Estimate cash dividend payout ratio by linear regression: ")
print("cash dividend (true): {:2.2f}".format(dividend_cash))
print("cash dividend (predict): {:2.2f}".format(est_dividend_cash))
print("error of predict: {:2.2f}%".format(error_method3))
def main():
year = 2019
quarter = 4
stockid = 2330
findbillion_database = class_findbillion_database(dataset_path)
financial_ratio = class_financial_ratio(findbillion_database)
est_eps = class_est_eps(findbillion_database)
eps_true = financial_ratio.get_eps_last_4q(stockid, year, quarter)
eps_pred = est_eps.est_last_4q_eps_by_netincome_ratio(
stockid, year, quarter)
err_percentage = (eps_pred - eps_true) / eps_true * 100
print("eps (true): {:2.2f}".format(eps_true))
print("eps (predict): {:2.2f}".format(eps_pred))
print("error of predict: {:2.2f}%".format(err_percentage))
def main():
findbillion_database = class_findbillion_database(dataset_path)
for year in year_list:
print('----------------------------')
print(year)
# eps ground truth
financial_ratio = class_financial_ratio(findbillion_database)
eps_true = financial_ratio.get_eps_last_4q(stockid, year, quarter)
# estimated eps
est_eps = class_est_eps(findbillion_database)
eps_pred = est_eps.est_last_4q_eps_by_netincome_ratio(
stockid, year, quarter)
# error
err_percentage = (eps_pred - eps_true) / eps_true * 100
print("eps (true): {:2.2f}".format(eps_true))
print("eps (predict): {:2.2f}".format(eps_pred))
print("error of predict: {:2.2f}%".format(err_percentage))
dataset_path = r'../../data'
save_path = r'../../~result'
num_cpu = 4
debug = 0
#year_stat_list = [2012, 2013, 2014, 2015, 2016, 2017] # 2012~2017
#year_est_list = [2013, 2014, 2015, 2016, 2017, 2018] # 2013~2018
#hold_year = 1
year_stat_list = [2012, 2013, 2014, 2015] # 2012~2017
year_est_list = [2013, 2014, 2015, 2016] # 2013~2018
hold_year = 3
num_stock_sel = 10
findbillion_database = class_findbillion_database(dataset_path)
yield_buyin = 0.0625
yield_buyin_target = 0.05
np.random.seed(7)
def processing_roe_15pa(stockid, year_stat):
print(format(stockid) + '...')
financial_ratio = class_financial_ratio(findbillion_database)
roe_avg = financial_ratio.get_ROE_avg(stockid, year_stat, 4, 5)
if roe_avg is not None and roe_avg > 0.15:
return stockid
else:
return None