def get_price(self):
price = get_stock_price(self.stock)
# today, yesterday, current, high, low
today = float(price[1])
yesterday = float(price[2])
current = float(price[3])
high = float(price[4])
low = float(price[5])
turnover = int(float(price[9])/100000000.0)
name = price[0].split('"')[-1]
r = str(rate(current, yesterday))[:4]
if not self.last_price:
self.forward = '--'
elif current > self.last_price:
self.forward = '/\\'
elif current < self.last_price:
self.forward = '\/'
else:
pass
self.last_price = current
return u'{} {} ({}% {}) {} {}'.format(self.name, self.stock, r, current, self.forward, turnover)
def get_priceMom(rebalDate, codes):
past = ut.get_recentBday(rebalDate - datetime.timedelta(365))
recent = ut.get_recentBday(rebalDate - datetime.timedelta(30))
rebalDate = ut.get_recentBday(rebalDate)
prices = ut.get_stock_price(codes, past, rebalDate )
mom_12M = (prices.loc[rebalDate, :] / prices.loc[past, :]) - 1
mom_1M = (prices.loc[rebalDate, :] / prices.loc[recent, :]) - 1
momData = (mom_12M - mom_1M).dropna()
return momData
def get_price(self):
price = get_stock_price(self.stock)
# today, yesterday, current, high, low
today = float(price[1])
yesterday = float(price[2])
current = float(price[3])
high = float(price[4])
low = float(price[5])
volume = float(price[8])
name = price[0].split('"')[-1]
if today == '0.00':
return u'{}(0.00)'.format(self.stock)
r = rate(current, yesterday)
if not self.available and r < 9.9:
self.reopen = True
if r > 9.9:
self.available = False
else:
self.available = True
if len(self.five_price) == 5:
self.five_price.pop(0)
self.five_price.append(r)
if len(self.five_price) > 1:
forward = self.five_price[-1] - self.five_price[-2]
else:
forward = '0.00'
r = str(r)[:4]
forward = str(forward)[:4]
buy = int(price[10]) + int(price[12]) + int(price[14]) + int(price[16]) + int(price[18])
sell = int(price[20]) + int(price[22]) + int(price[24]) + int(price[26]) + int(price[28])
if sell == 0:
bs = float(buy) / float(1)
if volume > 0:
buy_rate = buy /volume
else:
bs = float(buy) / float(sell)
if bs > 1:
bs = str(bs)[:4]
return u'{}({} {})({})'.format(self.stock, r, forward, bs)
else:
return u''
def get_priceMom(codes, rebalDate):
yearAgo = rebalDate - datetime.timedelta(days=365)
monthAgo = rebalDate - datetime.timedelta(days=30)
yearAgo = util.get_recentBday(yearAgo)
monthAgo = util.get_recentBday(monthAgo)
rebalDate = util.get_recentBday(rebalDate)
prices = util.get_stock_price(codes, yearAgo, rebalDate)
mom_12M = (prices.loc[rebalDate, :] / prices.loc[yearAgo, :]) - 1
mom_1M = (prices.loc[rebalDate, :] / prices.loc[monthAgo, :]) - 1
momData = (mom_12M - mom_1M).dropna()
return momData
def find():
count = 0
rank = []
for stock in get_all_stock():
price = get_stock_price(stock)
# today, yesterday, current, high, low
today = float(price[1])
yesterday = float(price[2])
current = float(price[3])
high = float(price[4])
low = float(price[5])
volume = float(price[8])
name = price[0].split('"')[-1]
if today == '0.00':
continue
r = rate(current, yesterday)
buy = int(price[10]) + int(price[12]) + int(price[14]) + int(price[16]) + int(price[18])
sell = int(price[20]) + int(price[22]) + int(price[24]) + int(price[26]) + int(price[28])
if sell == 0:
bs = float(buy) / float(1)
else:
bs = float(buy) / float(sell)
if volume > 0:
buy_rate = buy / volume
###############
if r > 9.9:
count += 1
else:
if name.find(u'中') == 0:
print u'{} {} {}'.format(stock, name, r)
print count
def get_backtest_history(dollar_inv, rebalData, roundup=False, tradeCost=0.01):
dollar_ongoing = dollar_inv
basket_history = {}
rebal_date = list(set(rebalData.date))
rebal_date.sort()
tradeCost_history = {}
turnover_history = {}
weight_history = {}
inv_money_list = [dollar_inv]
inv_money_date = [rebal_date[0]]
for i in tqdm(range(len(rebal_date))):
if i < len(rebal_date) - 1:
rebalDate = rebal_date[i] # 리밸런싱 시점
stock_list_i = rebalData[rebalData.date ==
rebal_date[i]].code # 기간별 종목 리스트
w_ = rebalData[rebalData.date == rebal_date[i]].weight
weightData = pd.concat([stock_list_i, w_],
axis=1).set_index('code')
if i == 0:
weightData['money'] = weightData[
'weight'] * dollar_inv # 1기인 경우 초기 투자금으로 시작
else:
weightData['money'] = weightData[
'weight'] * dollar_ongoing # 1기 이후는 투자금의 매 기간별 마지막 시점에서의 금액 재투자
stock_price_ = util.get_stock_price(
stock_list_i, util.get_recentBday(rebalDate),
util.get_recentBday(
rebalDate)).transpose() # 해당 일자의 바스켓의 주가 추출
weightData = pd.merge(
weightData,
stock_price_,
how='inner',
left_on=weightData.index,
right_on=stock_price_.index).set_index('key_0')
weightData.columns = ['weight', 'money', 'price']
weightData['n_stocks'] = weightData['money'] / weightData['price']
if roundup == True: # 초기 투입자금으로 투자가능한 종목별 주식수 (정수로 내림)
weightData.n_stocks = np.floor(weightData.n_stocks)
else:
weightData.n_stocks = weightData.n_stocks
basket_price = util.get_basket_history(
weightData.index.values, weightData.n_stocks.values,
util.get_recentBday(rebal_date[i]),
util.get_recentBday(rebal_date[i + 1]))
dollar_ongoing = basket_price.iloc[-1, :].values[
0] # 투자금의 매 기간별 마지막 시점에서의 포트의 가치(금액)
# 투자기간 마지막 시점 (다음리밸) 에서의 가격
last_price = util.get_stock_price(
weightData.index.values,
util.get_recentBday(rebal_date[i + 1]),
util.get_recentBday(rebal_date[i + 1])).transpose()
weightData = pd.merge(weightData,
last_price,
how='inner',
left_on=weightData.index,
right_on=last_price.index).set_index('key_0')
weightData.columns = [
'weight', 'money', 'price', 'n_stocks', 'last_price'
]
weightData['last_weight'] = (
weightData.last_price * weightData.n_stocks) / (
weightData.last_price *
weightData.n_stocks).sum() # 투자기간의 마지막 시점에서의 비중
new_weight = rebalData[rebalData.date == rebal_date[
i + 1]].set_index('code')['weight'] # 새로운 비중
new_weight = pd.Series(new_weight, name='new_weight')
weightData = pd.concat([weightData, new_weight], axis=1)
weightData['new_weight'] = weightData['new_weight'].fillna(0)
weightData['weight'] = weightData['weight'].fillna(0)
tradingCost = np.abs(weightData.new_weight - weightData.last_weight
).sum() * tradeCost * dollar_ongoing
dollar_ongoing -= tradingCost
basket_history[rebalDate] = basket_price
inv_money_list += list(basket_price.values.squeeze()[1:-1])
inv_money_list += [dollar_ongoing]
inv_money_date += list(basket_price.index[1:])
tradeCost_history[''.join([
x for x in str(rebal_date[i + 1])[:10] if x != '-'
])] = tradingCost
turnover_history[''.join([
x for x in str(rebal_date[i + 1])[:10] if x != '-'
])] = tradingCost / dollar_ongoing
weight_history[''.join([
x for x in str(rebal_date[i + 1])[:10] if x != '-'
])] = weightData
elif i == len(rebal_date) - 1:
rebalDate = rebal_date[i] # 리밸런싱 시점
#print(rebalDate)
stock_list_i = rebalData[rebalData.date ==
rebal_date[i]].code # 기간별 종목 리스트
lastDay = datetime.datetime.today() - datetime.timedelta(2)
w_ = rebalData[rebalData.date == rebal_date[i]].weight
weightData = pd.concat([stock_list_i, w_],
axis=1).set_index('code')
if i == 0:
weightData['money'] = weightData[
'weight'] * dollar_inv # 1기인 경우 초기 투자금으로 시작
else:
weightData['money'] = weightData[
'weight'] * dollar_ongoing # 1기 이후는 투자금의 매 기간별 마지막 시점에서의 금액 재투자
stock_price_ = util.get_stock_price(
stock_list_i, util.get_recentBday(rebalDate),
util.get_recentBday(
rebalDate)).transpose() # 해당 일자의 바스켓의 주가 추출
weightData = pd.merge(
weightData,
stock_price_,
how='inner',
left_on=weightData.index,
right_on=stock_price_.index).set_index('key_0')
weightData.columns = ['weight', 'money', 'price']
weightData['n_stocks'] = weightData['money'] / weightData['price']
if roundup == True: # 초기 투입자금으로 투자가능한 종목별 주식수 (정수로 내림)
weightData.n_stocks = np.floor(weightData.n_stocks)
else:
weightData.n_stocks = weightData.n_stocks
basket_price = util.get_basket_history(
weightData.index.values, weightData.n_stocks.values,
util.get_recentBday(rebal_date[i]),
util.get_recentBday(lastDay))
dollar_ongoing = basket_price.iloc[-1, :].values[
0] # 투자금의 매 기간별 마지막 시점에서의 포트의 가치(금액)
#print(inv_money_list)
# 투자기간 마지막 시점 (다음리밸) 에서의 가격
last_price = util.get_stock_price(
weightData.index.values, util.get_recentBday(lastDay),
util.get_recentBday(lastDay)).transpose()
weightData = pd.merge(weightData,
last_price,
how='inner',
left_on=weightData.index,
right_on=last_price.index).set_index('key_0')
weightData.columns = [
'weight', 'money', 'price', 'n_stocks', 'last_price'
]
weightData['last_weight'] = (
weightData.last_price * weightData.n_stocks) / (
weightData.last_price *
weightData.n_stocks).sum() # 투자기간의 마지막 시점에서의 비중
weightData['new_weight'] = 0
tradingCost = 0
dollar_ongoing -= tradingCost
basket_history[rebalDate] = basket_price
inv_money_list += list(basket_price.values.squeeze()[1:-1])
inv_money_list += [dollar_ongoing]
inv_money_date += list(basket_price.index[1:])
tradeCost_history[''.join(
[x for x in str(lastDay)[:10] if x != '-'])] = tradingCost
turnover_history[''.join([
x for x in str(lastDay)[:10] if x != '-'
])] = tradingCost / dollar_ongoing
weight_history[''.join([x for x in str(lastDay)[:10]
if x != '-'])] = weightData
inv_money_history = pd.DataFrame(inv_money_list, index=inv_money_date)
return inv_money_history, tradeCost_history, turnover_history, weight_history
pd.read_excel('info.xlsx', sheet_name='market_M'))
sector = util.data_cleansing_ts(
pd.read_excel('info.xlsx', sheet_name='sector_M'))
risk_1 = util.data_cleansing_ts(
pd.read_excel('info.xlsx', sheet_name='risk_1_M'))
risk_2 = util.data_cleansing_ts(
pd.read_excel('info.xlsx', sheet_name='risk_2_M'))
inK200 = util.data_cleansing_ts(
pd.read_excel('info.xlsx', sheet_name='inK200_M'))
inKQ150 = util.data_cleansing_ts(
pd.read_excel('info.xlsx', sheet_name='inKQ150_M'))
# 종목 및 BM 가격 데이터
allFirm = market.columns.values
rPrice_firm = util.get_stock_price(
allFirm,
pd.to_datetime(market.index.values[0]) - datetime.timedelta(days=365),
market.index.values[-1])
#rPrice_bm = util.get_index_price(['I.001', 'I.101'], market.index.values[0], market.index.values[-1])
return_20D = rPrice_firm.pct_change(20)
##############################################################################
# 1. 투자 유니버스 구성
##############################################################################
def getUniverse(marketInfo,
volInfo,
riskInfo_1,
riskInfo_2,
rebalDate_,
tradeVolLimit=10):
__author__ = 'yu'
from pymongo import MongoClient
from util import get_all_stock, get_stock_price, boll_daily
c = MongoClient('121.199.5.143').stock
for stock in get_all_stock():
print stock
price = get_stock_price(stock)
# today, yesterday, current, high, low
today = float(price[1])
yesterday = float(price[2])
current = float(price[3])
high = float(price[4])
low = float(price[5])
name = price[0].split('"')[-1]
date = price[30]
volume = price[8]
c.history.update({
'date': date,
'stock': stock
}, {
'$set': {
'date': date,
'open': today,
'high': high,
'low': low,
mpl.style.use('bmh')
import pandas_datareader.data as web
import matplotlib.pylab as plt
from datetime import datetime
import statsmodels.api as sm
from pykalman import KalmanFilter
from math import sqrt
path = 'C:/Woojin/###. Git/Project_Q/III. Factor Exposed Pairs Trading'
os.chdir(path)
#import backtest_pipeline_ver2 as bt
import util
codes = pd.read_excel('firmCode.xlsx')['Code'].values
price = util.get_stock_price(codes, pd.to_datetime('2010-01-01'),
pd.to_datetime('2019-06-30'))
size = util.data_cleansing(pd.read_excel('firmSize.xlsx'))
#NOTE CRITICAL LEVEL HAS BEEN SET TO 5% FOR COINTEGRATION TEST
def find_cointegrated_pairs(dataframe, critial_level=0.05):
n = dataframe.shape[1] # the length of dateframe
pvalue_matrix = np.ones((n, n)) # initialize the matrix of p
keys = dataframe.columns # get the column names
pairs = [] # initilize the list for cointegration
for i in range(n):
for j in range(i + 1, n): # for j bigger than i
stock1 = dataframe[keys[i]] # obtain the price of "stock1"
stock2 = dataframe[keys[j]] # obtain the price of "stock2"
result = sm.tsa.stattools.coint(stock1,
stock2) # get conintegration