def __init__(self):
self.insdustry_quote = base.load_industryquote_xlsx()
self.nav = base.load_nav_csv()
self.records = base.load_records_csv()
self.current_date = time
self.trading_day_df = base.load_trading_day_csv()
self.quote = base.load_quote_csv()
def top_nav_portcode(n):
df = base.load_nav_csv()
nav = df.groupby(
['PortCode'])['Nav'].apply(lambda x: x.iloc[-1]).reset_index(name='Q')
nav = nav.sort_values(by=['Q'], ascending=False)
names = nav.head(n)['PortCode'].tolist()
return names
def check_portcodes_detial(portcodes):
df = base.load_nav_csv()
tmp1 = df[df['PortCode'] == portcodes[0]][['NavDate', 'Nav']]
for p in portcodes[1:]:
tmp2 = df[df['PortCode'] == p][['NavDate', 'Nav']]
tmp1 = pd.merge(tmp1, tmp2, on=['NavDate'], how='left')
tmp1.reset_index(['NavDate'])
portcodes.insert(0, 'NavDate')
tmp1.columns = portcodes
return tmp1
def test_Update_IR_rank():
records = base.load_records_csv()
industry_quote = base.load_industryquote_xlsx()
nav = base.load_nav_csv()
quote = base.load_quote_csv()
IR_rank = base.load_irweek_csv()
date = '2017-01-06'
df_records = records[records.Updated <= date]
df_ind_quote = industry_quote[industry_quote.TradingDay <= date]
df_nav = nav[nav.NavDate <= date]
df_quote = quote[quote.TradingDay <= date]
Update_IR_rank(date, df_records, df_ind_quote, df_nav, df_quote, IR_rank)
def __init__(self, train_window, test_window):
self.portcodes = ['ZH010630']
data = base.load_certain_states_csv()
self.states = data.drop(data.index[:40]).round(0)
self.nav = base.load_nav_csv()
self.count = 1
self.phase = 'Train'
self.train_window = train_window
self.test_window = test_window
self.done = False
print("################## INIT ENV ####################")
print("PortCodes: ", self.portcodes)
print("Start Date: ", self.states.iloc[0, 0])
print("################################################")
def __init__(self, train_window, test_window):
self.portcodes = ['ZH010630']
self.port_num = len(self.portcodes)
s1 = base.load_states_csv()
s1.iloc[:, 1:] = preprocessing.scale(s1.iloc[:, 1:])
self.s1 = s1.drop(s1.index[:40])
self.nav = base.load_nav_csv()
self.count = 1
self.action_space = np.zeros(self.port_num + 1, )
self.observation_space = np.zeros(2, )
self.current_reward = 0
self.phase = 'Train'
self.train_window = train_window
self.test_window = test_window
self.done = False
print("################## INIT ENV ####################")
print("PortCodes: ", self.portcodes)
print("Start Date: ", self.s1.iloc[0, 0])
print("################################################")
import numpy as np
import pandas as pd
import base
quote = base.load_quote_csv()
industry = base.load_industry_csv()
records = base.load_records_csv()
nav = base.load_nav_csv()
industry_quote = base.load_industryquote_xlsx()
# get year-month info
nav_copy = nav.copy()
nav_copy.NavDate = nav_copy.NavDate.apply(lambda x: str(x)[:7])
# monthly nav ratio
nav_month_ratio = nav_copy.groupby([
'PortCode', 'NavDate'
]).apply(lambda x: (x.Nav.values[-1] - x.Nav.values[0]) / x.Nav.values[0])
nav_month_ratio = pd.DataFrame(nav_month_ratio)
nav_month_ratio.columns = ['Ratio']
# For each portcode and every month, get the secucodes.
records_month = records.copy()
records_month.Updated = records_month.Updated.apply(lambda x: str(x)[:7])
records_month = records_month.groupby(['PortCode',
'Updated']).apply(lambda x: x.SecuCode)
records_month = pd.DataFrame(records_month)
# get monthly ratio
quote_copy = quote.copy()
quote_copy.TradingDay = quote_copy.TradingDay.apply(lambda x: str(x)[:7])
quote_copy = quote_copy.groupby(['SecuCode', 'TradingDay']).apply(
def check_portcode_detial(portcode):
df = base.load_nav_csv()
df = df[df['PortCode'] == portcode]
return df