def select_best_to_sell(self,
tester=None,
fund_list=None,
date=datetime.datetime(2019, 1, 2)):
print("sell select started:", datetime.datetime.now())
if tester is None:
tester = self.tester
if fund_list is None:
fund_in_db = db_operator.sql_query(
sql="SELECT distinct fund_code from Fund").fund_code.values
fund_list = data_operator.get_funds_list()
fund_list = fund_list[fund_list.ID.isin(fund_in_db)]
best_fund = ["", 0]
# test_all_funds_data = data_operator.load_funds(fund_list.ID.values,duration=[date-datetime.timedelta(days = 365*3), date])
my_strategy = Strategy(tester)
t1 = datetime.datetime.now()
for fund_code in fund_list.ID.values:
# all_fund_for_today = test_all_funds_data[test_all_funds_data.fund_code == fund_code]
best_prob = my_strategy.if_sell(fund_code,
date,
True,
all_data_fund=None)
if best_prob > best_fund[1]:
best_fund[0] = fund_code
best_fund[1] = best_prob
print(datetime.datetime.now() - t1, "used.")
print("-", best_fund, date, datetime.datetime.now())
return best_fund
def verify_funds(self,
duration=[
datetime.datetime(2015, 1, 31),
(datetime.datetime(2012, 1, 31) +
datetime.timedelta(365 * 8))
]):
fund_in_db = db_operator.sql_query(
sql="SELECT distinct fund_code from Fund").fund_code.values
fund_list = data_operator.get_funds_list()
fund_list = fund_list[fund_list.ID.isin(fund_in_db)]
principal_fund = 1000
self.tester = User(username="******",
password="******",
my_cash=principal_fund)
predict_results = pd.DataFrame({
"fund_code": [],
"fund_name": [],
"principal": [],
"init_asset": [],
"result": [],
"gain_rate": [],
"yearly_gain_rate": []
})
for i in range(len(fund_list)):
res = self.verify_one_fund(fund_code=fund_list.ID.iloc[i],
duration=duration)
predict_results.append([[
fund_list.ID.iloc[i], principal_fund, res[0], res[1], res[2],
res[3]
]])
print("fund_code:" + str(fund_list.ID.iloc[i]),
"fund_name:" + fund_list.Name.iloc[i], "init:" + str(res[0]),
"result:" + str(res[1]), "gain_rate:" + str(res[2]),
"yearly_gain_rate:" + str(res[3]))
predict_results.to_csv(config["output_path"])
def save_latest_price(self, today=datetime.datetime.today(), timedelta=10):
today_date = datetime.datetime(today.year, today.month, today.day)
fund_list = data_operator.get_funds_list().ID.values
today_fund = data_operator.get_funds_multi_thread(
fund_list=fund_list,
duration=[today_date - datetime.timedelta(timedelta), today_date],
entry_per_page=20)
data_operator.update_fund(today_fund)
def verify_funds_multithread(self,
duration=[
datetime.datetime(2015, 1, 31),
(datetime.datetime(2012, 1, 31) +
datetime.timedelta(365 * 8))
],
principal=3000):
print("job started: ", datetime.datetime.now())
fund_in_db = db_operator.sql_query(
sql="SELECT distinct fund_code from Fund").fund_code.values
fund_list = data_operator.get_funds_list()
fund_list = fund_list[fund_list.ID.isin(fund_in_db)]
predict_result = {}
predict_results = pd.DataFrame({
"fund_code": [],
"fund_name": [],
"principal": [],
"init_asset": [],
"result": [],
"gain_rate": [],
"yearly_gain_rate": []
})
with ThreadPoolExecutor(config["threads"]) as executor:
for i in range(len(fund_list)):
# tester = User(username="******"+fund_list.ID.iloc[i], password="******", my_cash=3000)
print(fund_list.ID.iloc[i], "started @",
datetime.datetime.now())
r1 = executor.submit(self.verify_one_fund, None,
fund_list.ID.iloc[i], duration, principal)
predict_result[fund_list.ID.iloc[i]] = r1
print(fund_list.ID.iloc[i], "ended @", datetime.datetime.now())
for (x, y) in predict_result.items():
res = y.result()
new_record = {}
new_record["fund_code"] = x
new_record["fund_name"] = fund_list[fund_list.ID == x].Name.iloc[0]
new_record["principal"] = principal
new_record["init_asset"] = res[0]
new_record["result"] = res[1]
new_record["gain_rate"] = res[2]
new_record["yearly_gain_rate"] = res[3]
predict_results.append(new_record, ignore_index=True)
print("fund_code:" + new_record["fund_code"],
"fund_name:" + new_record["fund_name"],
"init:" + str(res[0]), "result:" + str(res[1]),
"gain_rate:" + str(res[2]),
"yearly_gain_rate:" + str(res[3]))
if os.path.exists(config["output_path"]):
os.remove(config["output_path"])
predict_results.to_csv(config["output_path"])
def verify_funds_multiprocesses(self,
duration=[
datetime.datetime(2015, 1, 31),
datetime.datetime(2020, 1, 28)
],
principal=3000):
p = Pool(config["ps_num"])
print("job started: ", datetime.datetime.now())
fund_in_db = db_operator.sql_query(
sql="SELECT distinct fund_code from Fund").fund_code.values
fund_list = data_operator.get_funds_list()
fund_list = fund_list[fund_list.ID.isin(fund_in_db)]
predict_result = {}
predict_results = pd.DataFrame({
"fund_code": [],
"fund_name": [],
"principal": [],
"init_asset": [],
"result": [],
"gain_rate": [],
"yearly_gain_rate": []
})
for i in range(len(fund_list)):
a = p.apply_async(func=self.verify_one_fund,
args=(None, fund_list.ID.iloc[i], duration,
principal))
predict_result[fund_list.ID.iloc[i]] = a
p.close()
p.join()
print("***** All jobs were thrown *****")
for (x, y) in predict_result.items():
res = y.get()
new_record = {}
new_record["fund_code"] = x
new_record["fund_name"] = fund_list[fund_list.ID == x].Name.iloc[0]
new_record["principal"] = principal
new_record["init_asset"] = res[0]
new_record["result"] = res[1]
new_record["gain_rate"] = res[2]
new_record["yearly_gain_rate"] = res[3]
predict_results.append(new_record, ignore_index=True)
print("fund_code:" + new_record["fund_code"],
"fund_name:" + new_record["fund_name"],
"init:" + str(res[0]), "result:" + str(res[1]),
"gain_rate:" + str(res[2]),
"yearly_gain_rate:" + str(res[3]))
if os.path.exists(config["output_path"]):
os.remove(config["output_path"])
predict_results.to_csv(config["output_path"])
def verify_comp(self,
duration=[
datetime.datetime(2015, 1, 31),
datetime.datetime(2020, 1, 28)
],
principal=3000):
fund_in_db = db_operator.sql_query(
sql="SELECT distinct fund_code from Fund").fund_code.values
fund_list = data_operator.get_funds_list()
fund_list = fund_list[fund_list.ID.isin(fund_in_db)]
tester = User(username="******" +
str(datetime.datetime.now().microsecond),
password="******",
my_cash=principal)
tester_operator = Operator(user=tester)
asset_before = tester_operator.get_asset_value(date=duration[0],
contains_cash=True,
realtimeprice=False)
for i in range((duration[1] - duration[0]).days + 1):
today = duration[0] + datetime.timedelta(days=i)
fund_sell_prob = self.select_best_to_sell(tester=tester,
fund_list=fund_list,
date=today)
fund_buy_prob = self.select_best_to_buy(tester=tester,
fund_list=fund_list,
date=today)
if fund_sell_prob[1] > 0.7:
tester_operator.sell_at_date(fund_code=fund_sell_prob[0],
date=today,
amount=100,
if_value=True)
if fund_buy_prob[1] > 0.7:
tester_operator.buy_at_date(fund_code=fund_buy_prob[0],
date=today,
amount=100,
if_value=True)
print(tester_operator.user.asset)
asset_after = tester_operator.get_asset_value(duration[1],
contains_cash=True,
realtimeprice=False)
print(tester.asset, tester.my_cash)
gain = asset_after - asset_before
gain_rate = (asset_after - asset_before) / asset_before
yearly_gain_rate = gain_rate / ((duration[1] - duration[0]).days / 365)
return [asset_before, asset_after, gain, gain_rate, yearly_gain_rate]
def get_funds_to_buy(self,
fund_list=data_operator.get_funds_list().ID.values):
# real_time_price = data_operator.get_realtime_price(fund_list)
today = datetime.datetime.today()
self.save_latest_price()
my_strategy = Strategy(user=self.user)
prob_to_buy = my_strategy.if_buy_accelerated(fund_list=fund_list,
today_date=today,
if_verify=False,
all_data_fund=None)
# prob_to_sell = my_strategy.if_sell_accelerated_2(fund_list=self.user.asset.my_fund.values, today_date=today, if_verify=True, all_data_fund=None)
if len(prob_to_buy) > 0:
for code in prob_to_buy.index:
print("buy: ", code, "prob: ", prob_to_buy[code])
# tester_operator.buy_at_date(fund_code=code, date=today, amount=30, if_value=True)
# if len(prob_to_sell)>0:
# for code in prob_to_sell.index:
# print("sell: ", code, "prob: ", prob_to_sell[code])
# # tester_operator.sell_fund_all(fund_code=code,date=today)
return prob_to_buy
import datetime
sys.setrecursionlimit(100000)
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from utils.getfund import data_operator
# from utils.readconfig import config
# from models.models import db_operator
from utils.operatefund import *
# import datetime
# from bs4 import BeautifulSoup
import pandas as pd
from multiprocessing import Process, Queue, Pool
from models.models import db_operator
from concurrent.futures import ThreadPoolExecutor
# funds = ["110013", "000011","040001","020022"]
# count = 0
fds_list = data_operator.get_funds_list("data/fund_data/all_fund.csv")
res = data_operator.get_realtime_price(fund_codes=fds_list.ID.values)
print(res)
# fund_in_db = db_operator.sql_query(sql = "SELECT distinct fund_code from Fund").fund_code.values
# ff = db_operator.sql_query("SELECT * from Fund where fund_code = '110013' Order by date DESC")
# print(ff)
# ff = db_operator.sql_query("SELECT * from Fund where fund_code = '110013' Order by date")
# ff = data_operator.load_funds(fund_codes=["110013","110031"],duration=[datetime.datetime(2018,1,2),datetime.datetime(2018,2,1)],orderby="date", asc = "DESC")
# ff = data_operator.load_fund(fund_code="110013", duration=[datetime.datetime(2018,1,1), datetime.datetime(2018,1,10)],orderby="price", asc="DESC")
# print(ff.dtypes, ff)
# print(fund_in_db)
# print("111",datetime.datetime.now())
# fund_data_3y = data_operator.load_funds(fund_codes=fund_in_db,duration=[datetime.datetime(2015,1,27), datetime.datetime(2018,1,25)])
# print("112",datetime.datetime.now())
# fund_data_2y = fund_data_3y[fund_data_3y.date>datetime.datetime(2015,1,27)+datetime.timedelta(days = 364)]
# print("113",datetime.datetime.now())
import datetime
sys.setrecursionlimit(100000)
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from utils.getfund import data_operator
# from utils.readconfig import config
# from models.models import db_operator
from utils.operatefund import *
# import datetime
# from bs4 import BeautifulSoup
import pandas as pd
from multiprocessing import Process, Queue, Pool
from models.models import db_operator
from concurrent.futures import ThreadPoolExecutor
# funds = ["110013", "000011","040001","020022"]
# count = 0
fds_list = data_operator.get_funds_list()
res = data_operator.get_realtime_price(fund_codes=fds_list.ID.values)
print(res)
# fund_in_db = db_operator.sql_query(sql = "SELECT distinct fund_code from Fund").fund_code.values
# ff = db_operator.sql_query("SELECT * from Fund where fund_code = '110013' Order by date DESC")
# print(ff)
# ff = db_operator.sql_query("SELECT * from Fund where fund_code = '110013' Order by date")
# ff = data_operator.load_funds(fund_codes=["110013","110031"],duration=[datetime.datetime(2018,1,2),datetime.datetime(2018,2,1)],orderby="date", asc = "DESC")
# ff = data_operator.load_fund(fund_code="110013", duration=[datetime.datetime(2018,1,1), datetime.datetime(2018,1,10)],orderby="price", asc="DESC")
# print(ff.dtypes, ff)
# print(fund_in_db)
# print("111",datetime.datetime.now())
# fund_data_3y = data_operator.load_funds(fund_codes=fund_in_db,duration=[datetime.datetime(2015,1,27), datetime.datetime(2018,1,25)])
# print("112",datetime.datetime.now())
# fund_data_2y = fund_data_3y[fund_data_3y.date>datetime.datetime(2015,1,27)+datetime.timedelta(days = 364)]
# print("113",datetime.datetime.now())
