def caculate_model(sid, nday):
s = stock("測試資料/台股/{0}d.csv".format(sid))
if s.complate_data(sid):
# 如果有新的資料，再重新laod 一次
s = stock("測試資料/台股/{0}d.csv".format(sid))
tmp12 = (mdates.num2date(s.date[-1]).strftime("%Y/%m/%d"))
print(tmp12)
def caculate_model(sid, nday):
s = stock("測試資料/台股/{0}d.csv".format(sid))
if s.complate_data(sid) :
# 如果有新的資料，再重新laod 一次
s = stock("測試資料/台股/{0}d.csv".format(sid))
tmp12 = (mdates.num2date(s.date[-1]).strftime("%Y/%m/%d"))
print(tmp12)
def test_get_exp_date(self):
# test accuracy of months returned from db
stock_obj = stock('SVU', datetime.strptime('2016-10-12', '%Y-%m-%d'))
for month in ['November2016', 'October2016']:
month_check = stock_obj.exp_dates['Expiration_Date'].str.contains(month).any()
assert month_check == True
# test expiraiton within current month
stock_obj = stock('SVU', datetime.strptime('2016-10-07', '%Y-%m-%d'))
assert stock_obj.expiration == 'November2016'
# test expiration next month
stock_obj = stock('SVU', datetime.strptime('2016-10-19', '%Y-%m-%d'))
assert stock_obj.expiration == 'October2016'
def buildDf(name, rebuild=False):
try:
if (rebuild == False):
df = pd.read_csv(str(name) + ".csv")
return df
else:
raise Exception
except:
#ZF is the zip file with all the stocks and etfs broken up into the two folders "Stocks/ for stock name or ETFs/ for etf name
#I then add the columns I think might be useful such as day of week, moving averages, targetColumn which is the cross over and the
#updown for other testing.
zf = zipfile.ZipFile('Data.zip')
df = pd.read_csv(zf.open("ETFs/" + name + '.us.txt'))
targetStock = stock(df)
print("adding day col")
df = targetStock.addDayCol(df)
print("adding MA10 col")
df = targetStock.addMaCol(df, 'MA10', 10)
print("adding MA50 col")
#df = targetStock.addMaCol(df,'MA20',20)
df = targetStock.addMaCol(df, 'MA50', 50)
print("adding Target col")
df = targetStock.addTargetCol(df)
print("adding UpDown col")
df = targetStock.addUpDownCol(df)
print("adding crossUpCrossDownCols")
df = targetStock.addCrossUpCrossDownCols(df)
#df = targetStock.addMaCol(df,'M,A100',100)
df.to_csv(path_or_buf=str(name) + ".csv")
return df
def valueStocks(ticker, sheetClass, cellnumber):
'''
Check When to buy when to sell.
Find the risk-rewards
Check how managment is doing
'''
TMK = stock(ticker)
###################### Current price to book ratio #################
TMK.bookValuePerShare()
TMK.pricePerBookValue()
print ("Current Price: ", TMK.trade_history["Close"][-1])
print ("Book value: %0.2f" %(TMK.trading["book value per share"][0]))
print ("Price - book value: %0.2f " %(TMK.trading["price-book value"][0]))
#print ("Sell at: %0.2f" % (TMK.trading["book value per share"][0]*2))
print ("Percent return: %0.2f%%" %(100*(TMK.trading["book value per share"][0]-TMK.trade_history["Close"][-1])/TMK.trade_history["Close"][-1]) )
msg = "%s: \nCurrent Price:\t\t%0.2f\nBook value:\t\t%0.2f\nPercentage to BV:\t%0.2f\n\n"%(ticker, TMK.trade_history["Close"][-1], TMK.trading["book value per share"][0], float(TMK.trading["book value per share"][0]-TMK.trade_history["Close"][-1])/TMK.trade_history["Close"][-1])
sheetClass.append(range_name="Example!A" + str(cellnumber), item=str(ticker))
sheetClass.append(range_name="Example!C" + str(cellnumber), item="%0.2f" %TMK.trading["book value per share"][0])
sheetClass.append(range_name="Example!H" + str(cellnumber), item="%0.2f" %TMK.trade_history["Close"][-1])
return msg
def main():
robin = stock('/home/dosx/.tokens/info.txt')
for symbol in robin.watchlist():
print(symbol)
prices = robin.history(symbol)
print(f"SMA: {robin.SMA(prices)}")
print(f"EMA: {robin.EMA(prices)}")
def __init__(self, filename, nday=40, BIAS_std1=0.15, BIAS_std2=1):
# for traning data, 自動找出最佳的 乖離率
print(filename, nday)
self.nday = nday
self.BIAS_std1 = BIAS_std1
self.BIAS_std2 = BIAS_std2
self.s = stock(filename, adj_fix=False)
_, self.list_ma = self.s.feature_MA(nday)
self.list_ma_slope = np.array(self.s.feature_MA_slope(nday)) # 均線斜率
self.list_BIAS = self.s.feature_BIAS(nday)
self.BIAS_mean = self.list_BIAS[nday:].mean()
self.BIAS_std = self.list_BIAS[nday:].std()
self.list_line1 = (self.BIAS_mean + self.BIAS_std1 *
self.BIAS_std) * self.list_ma + self.list_ma
self.list_line2 = (self.BIAS_mean - self.BIAS_std1 *
self.BIAS_std) * self.list_ma + self.list_ma
self.list_line3 = (self.BIAS_mean + self.BIAS_std2 *
self.BIAS_std) * self.list_ma + self.list_ma
self.list_line4 = (self.BIAS_mean - self.BIAS_std2 *
self.BIAS_std) * self.list_ma + self.list_ma
# 均線向下時，放寬
self.ma_slope_down = self.list_ma_slope[nday:].mean(
) - self.BIAS_std1 * self.list_ma_slope[nday:].std()
if self.ma_slope_down > 0:
self.ma_slope_down = 0
# 均線向上時，放寬
self.ma_slope_up = self.list_ma_slope[nday:].mean(
) + self.BIAS_std1 * self.list_ma_slope[nday:].std()
def __init__(self, filename, nday=40, BIAS_std1=0.15, BIAS_std2=1):
# for traning data, 自動找出最佳的 乖離率
print(filename, nday)
self.nday = nday
self.BIAS_std1 = BIAS_std1
self.BIAS_std2 = BIAS_std2
self.s = stock(filename, adj_fix = False)
_, self.list_ma = self.s.feature_MA(nday)
self.list_ma_slope = np.array(self.s.feature_MA_slope(nday)) # 均線斜率
self.list_BIAS = self.s.feature_BIAS(nday)
self.BIAS_mean = self.list_BIAS[nday:].mean()
self.BIAS_std = self.list_BIAS[nday:].std()
self.list_line1 = (self.BIAS_mean + self.BIAS_std1 *
self.BIAS_std) * self.list_ma + self.list_ma
self.list_line2 = (self.BIAS_mean - self.BIAS_std1 *
self.BIAS_std) * self.list_ma + self.list_ma
self.list_line3 = (self.BIAS_mean + self.BIAS_std2 *
self.BIAS_std) * self.list_ma + self.list_ma
self.list_line4 = (self.BIAS_mean - self.BIAS_std2 *
self.BIAS_std) * self.list_ma + self.list_ma
# 均線向下時，放寬
self.ma_slope_down = self.list_ma_slope[nday:].mean() - self.BIAS_std1 * self.list_ma_slope[nday:].std()
if self.ma_slope_down > 0:
self.ma_slope_down = 0
# 均線向上時，放寬
self.ma_slope_up = self.list_ma_slope[nday:].mean() + self.BIAS_std1 * self.list_ma_slope[nday:].std()
def test():
symbol = 'INTC'
name = 'Intel Corporation'
pcp = 20.5
cp = 20.35
s1 = stock(symbol, name, pcp, cp)
print(s1.getChangePercent())
def __init__(self, wl):
'''
:param wl: [[code, '字段', '目标方向', '目标', '有效性'],
[2078, 'price', '低'，7， True]]
监控字段包括：价格、pe
'''
self.__stock = stock.stock()
self.__watchList = wl
def test():
nday = 20
s = stock("測試資料/台股/0056d.csv")
x = s.feature_diff()[nday:] * 100
a, b = tools.normfun_ex(x)
plt.plot(a, b, color='g', linewidth=1)
plt.hist(x, 60, normed=1, facecolor='blue', alpha=0.5)
plt.show()
def test():
nday = 20
s = stock("測試資料/台股/0056d.csv")
x = s.feature_diff()[nday:]*100
a, b = tools.normfun_ex(x)
plt.plot(a, b, color='g', linewidth=1)
plt.hist(x, 60, normed=1, facecolor='blue', alpha=0.5)
plt.show()
def test_K():
# K, D無常態分佈
nday = 10
s = stock("price_data.csv")
x = s.feature_K()
x = np.array(x[nday:])
a, b = tools.normfun_ex(x)
plt.plot(a, b, color='g', linewidth=1)
plt.hist(x, 60, normed=1, facecolor='blue', alpha=0.5)
plt.show()
def test_K():
# K, D無常態分佈
nday = 10
s = stock("price_data.csv")
x = s.feature_K()
x = np.array(x[nday:])
a, b = tools.normfun_ex(x)
plt.plot(a, b, color='g', linewidth=1)
plt.hist(x, 60, normed=1, facecolor='blue', alpha=0.5)
plt.show()
def stockProcess():
global strategy
print ""
print("DIGITAL STOCK TRADER")
print(
"////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////BALANCE: %s"
% variables.balance)
print ""
sleep = '''
sleep %s;
''' % (variables.sleepTime)
while variables.balance >= 0:
variables.hourMin = int(time.strftime("%H%M"))
variables.day = datetime.datetime.now().strftime("%A")
if variables.forceCycle or (
variables.hourMin > variables.startTime
) and (variables.hourMin < variables.endTime
) and variables.day != "Saturday" and variables.day != "Sunday":
for strategyName in variables.strategy:
strategy = getattr(
__import__("strategies", fromlist=[strategyName]),
strategyName)
temp = variables.balance
#BUYSTOCK: Buys the stock once certain criteria are met. Finviz provides the initial batch
buyList = csvReader.csvReader(strategy.buyStrategy)
for row in buyList:
order = stock.stock(row[1])
buyStock(order)
#SELLSTOCK: Sells stock in portfolio once certian criteria are met
for order in reversed(variables.portfolio):
sellStock(order)
if temp != variables.balance:
print(
"///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////BALANCE: %s"
% variables.balance)
print ""
subprocess.call(['sh', '-c', sleep])
reloadVariables.reloadVariables()
def __init__(self, filename, nday=40, BIAS_std1=0.15, BIAS_std2=1):
# for traning data, 自動找出最佳的 乖離率
self.nday = nday
self.BIAS_std1 = BIAS_std1
self.BIAS_std2 = BIAS_std2
self.s = stock(filename)
# self.s = stock_5day(filename)
self.list_ma = self.s.feature_MA(nday)
self.list_ma_slope = np.array(self.s.feature_MA_slope(nday)) # 均線斜率
self.list_BIAS = self.s.feature_BIAS(nday)
self.BIAS_mean = self.list_BIAS[nday:].mean()
self.BIAS_std = self.list_BIAS[nday:].std()
def fa(x):
if x > 0:
return True
else:
return False
def fb(x):
if x > 0:
return False
else:
return True
x1 = np.array(list(filter(fa, self.list_BIAS)))
x2 = np.array(list(filter(fb, self.list_BIAS)))
self.list_line1 = (tools.list_percentage(x1, BIAS_std1) +
1) * self.list_ma
self.list_line3 = (tools.list_percentage(x1, BIAS_std2) +
1) * self.list_ma
self.list_line2 = (tools.list_percentage(x2, (1 - BIAS_std1)) +
1) * self.list_ma
self.list_line4 = (tools.list_percentage(x2, (1 - BIAS_std2)) +
1) * self.list_ma
print(tools.list_percentage(self.list_BIAS, 0.1),
tools.list_percentage(self.list_BIAS, 0.5),
tools.list_percentage(self.list_BIAS, 0.6),
tools.list_percentage(self.list_BIAS, 0.85))
# 均線向下時，放寬
self.ma_slope_down = self.list_ma_slope[nday:].mean(
) - 0.15 * self.list_ma_slope[nday:].std()
if self.ma_slope_down > 0:
self.ma_slope_down = 0
# 均線向上時，放寬
self.ma_slope_up = self.list_ma_slope[nday:].mean(
) + 0.15 * self.list_ma_slope[nday:].std()
def main():
while True:
print("你可以进行如下操作：")
print("c:收银系统 i:库存系统 s:进货系统 t:今日销量 g:修改商品信息 e:退出")
op = input("请输入你的选择：")
if op == 'c':
csr.cashier()
# break
elif op == 'i':
inventory.inventory()
# break
elif op == 's':
stock.stock()
# break
elif op == 't':
selltoday.selltoday()
# break
elif op == 'g':
change.change()
elif op == 'e':
exit()
else:
print("操作不正确，请重新输入")
def get_all_stocks(self):
result = []
from stock import stock
high_url = 'https://www.nseindia.com/content/CM_52_wk_High_low.csv'
import csv
import urllib2
req = urllib2.Request(high_url,
headers={'User-Agent': "Magic Browser"})
response = urllib2.urlopen(req)
cr = csv.reader(response)
high_dict = {}
for row in cr:
if len(row) < 2: continue
if row[1] != 'EQ': continue
#print row
high_dict[row[0]] = row[2]
import datetime
for i in range(5):
d = datetime.datetime.now() - datetime.timedelta(days=i)
try:
ltp_url = 'https://www.nseindia.com/archives/equities/mkt/MA' + d.strftime(
"%d") + d.strftime("%m") + d.strftime("%y") + '.csv'
#print ltp_url
req = urllib2.Request(ltp_url,
headers={'User-Agent': "Magic Browser"})
response = urllib2.urlopen(req)
break
except urllib2.HTTPError:
continue
cr = csv.reader(response)
ltp_dict = {}
import industry
for row in cr:
if len(row) != 6: continue
if row[2] != 'EQ': continue
if row[1] not in industry.watchlist: continue
if row[1] not in high_dict: continue
s = stock(name=row[1],
high52=int(float(high_dict[row[1]])),
ltp=int(float(row[3])),
tradevalue=int(float(row[4])))
result.append(s)
return result
def method1():
filename = 'sample.txt'
infile = open(filename, 'r')
lines = infile.readlines()
l = lines[0].strip('\r\n').split()
m = int(l[0]) # amount of money
k = int(l[1]) # number of different stocks
d = int(l[2]) # number of remaining days
data = readFile(filename)
stock_list = []
for stock_name in data:
newStock = stock(stock_name, map(float,data[stock_name][1:]), int(data[stock_name][0]), 'NONE')
stock_list.append(newStock)
stock_list = analyze_stock_list(stock_list)
def __init__(self, filename, nday, Bias1, Bias2,
Bias3, Bias4, ma_slope_down, ma_slope_up):
# for test data, 手動帶入所有參數
self.nday = nday
self.s = stock(filename)
self.list_ma = self.s.feature_MA(nday)
self.list_ma_slope = np.array(self.s.feature_MA_slope(nday)) # 均線斜率
self.list_line1 = (Bias1) * self.list_ma + self.list_ma
self.list_line2 = (Bias2) * self.list_ma + self.list_ma
self.list_line3 = (Bias3) * self.list_ma + self.list_ma
self.list_line4 = (Bias4) * self.list_ma + self.list_ma
self.ma_slope_down = ma_slope_down
self.ma_slope_up = ma_slope_up
def solve(self):
"""solve the problem with a relaxed optimality constraint."""
while self.get_num_parts_left() > 0:
if len(self.stocks) == 0:
self.stocks.append(stock(5600))#add another stock if we run out
for s in self.stocks:
p = self.get_largest_fitting_part(s)
if p != None:
p.cut += 1
s.cut(p)
else:
self.patterns.append(s)
self.stocks.remove(s)
#add all of the partially cut stocks to the patterns
self.patterns.extend(self.stocks)
def __init__(self, filename, nday, Bias1, Bias2, Bias3, Bias4,
ma_slope_down, ma_slope_up):
# for test data, 手動帶入所有參數
self.nday = nday
self.s = stock(filename)
self.list_ma = self.s.feature_MA(nday)
self.list_ma_slope = np.array(self.s.feature_MA_slope(nday)) # 均線斜率
self.list_line1 = (Bias1) * self.list_ma + self.list_ma
self.list_line2 = (Bias2) * self.list_ma + self.list_ma
self.list_line3 = (Bias3) * self.list_ma + self.list_ma
self.list_line4 = (Bias4) * self.list_ma + self.list_ma
self.ma_slope_down = ma_slope_down
self.ma_slope_up = ma_slope_up
def get():
n = body.args['stock']
print '\nLoading', n, '\n'
x = stock.stock(n)
nums = x.getPrices()
fin = x.getFin()
return jsonify({
'n': x.getName(),
'p': nums[0],
'diff': nums[1],
'op': nums[2],
'cap': fin[0],
'eps': fin[1],
'pe': fin[2],
'de': fin[3],
'pb': fin[4]
})
def stockProcess():
global strategy
print ""
print ("DIGITAL STOCK TRADER")
print("////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////BALANCE: %s" % variables.balance)
print ""
sleep = '''
sleep %s;
''' % (variables.sleepTime)
while variables.balance >= 0:
variables.hourMin = int(time.strftime("%H%M"))
variables.day = datetime.datetime.now().strftime("%A")
if variables.forceCycle or (variables.hourMin > variables.startTime) and (variables.hourMin < variables.endTime) and variables.day != "Saturday" and variables.day != "Sunday":
for strategyName in variables.strategy:
strategy = getattr(__import__("strategies", fromlist=[strategyName]), strategyName)
temp = variables.balance
#BUYSTOCK: Buys the stock once certain criteria are met. Finviz provides the initial batch
buyList = csvReader.csvReader(strategy.buyStrategy)
for row in buyList:
order = stock.stock(row[1])
buyStock(order)
#SELLSTOCK: Sells stock in portfolio once certian criteria are met
for order in reversed(variables.portfolio):
sellStock(order)
if temp != variables.balance:
print("///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////BALANCE: %s" % variables.balance)
print ""
subprocess.call(['sh','-c',sleep])
reloadVariables.reloadVariables()
def __init__(self, filename, nday=40, BIAS_std1=0.15, BIAS_std2=1):
# for traning data, 自動找出最佳的 乖離率
self.nday = nday
self.BIAS_std1 = BIAS_std1
self.BIAS_std2 = BIAS_std2
self.s = stock(filename)
# self.s = stock_5day(filename)
self.list_ma = self.s.feature_MA(nday)
self.list_ma_slope = np.array(self.s.feature_MA_slope(nday)) # 均線斜率
self.list_BIAS = self.s.feature_BIAS(nday)
self.BIAS_mean = self.list_BIAS[nday:].mean()
self.BIAS_std = self.list_BIAS[nday:].std()
def fa(x):
if x >0:
return True
else:
return False
def fb(x):
if x >0:
return False
else:
return True
x1 = np.array(list(filter(fa,self.list_BIAS)))
x2 = np.array(list(filter(fb,self.list_BIAS)))
self.list_line1 = (tools.list_percentage(x1,BIAS_std1)+1)* self.list_ma
self.list_line3 = (tools.list_percentage(x1,BIAS_std2)+1)* self.list_ma
self.list_line2 = (tools.list_percentage(x2,(1-BIAS_std1))+1)* self.list_ma
self.list_line4 = (tools.list_percentage(x2,(1-BIAS_std2))+1)* self.list_ma
print(tools.list_percentage(self.list_BIAS,0.1), tools.list_percentage(self.list_BIAS,0.5), tools.list_percentage(self.list_BIAS,0.6), tools.list_percentage(self.list_BIAS,0.85))
# 均線向下時，放寬
self.ma_slope_down = self.list_ma_slope[nday:].mean() - 0.15 * self.list_ma_slope[nday:].std()
if self.ma_slope_down > 0:
self.ma_slope_down = 0
# 均線向上時，放寬
self.ma_slope_up = self.list_ma_slope[nday:].mean() + 0.15 * self.list_ma_slope[nday:].std()
def netAssetValue(self):
self.stocks = []
current_positions = []
for item in self.portfolio_:
stock_obj = stock(item['ticker'])
stock_obj.get_history()
time.sleep(2)
current_positions.append(stock_obj.get_current())
stock_obj.driver.terminate()
self.stocks.append(stock_obj)
original_positions = [item['entry_price'] for item in self.portfolio_]
self.netValue = 0
for index, pair in enumerate(zip(current_positions,
original_positions)):
running_net = (pair[0] -
pair[1]) * self.portfolio_[index]['quantity']
self.stocks[index].delta = running_net
self.netValue += running_net
def test_BIAS():
#計算乖離率, 有常態分佈
nday = 50
f_name = "測試資料/台股/HYGd.csv"
# s = stock_daytrade(f_name)
s = stock(f_name)
c = np.array(s.close)
o = np.array(s.opened)
x = s.feature_BIAS(nday)
x = x[nday:]
# x = tools.time_filter(x, s.date,9*60,11*60)
a, b = tools.normfun_ex(x)
plt.plot(a, b, color='g', linewidth=1)
plt.hist(x, 50, normed=1, facecolor='blue', alpha=0.5)
plt.show()
def fa(x):
if x > 0:
return True
else:
return False
def fb(x):
if x > 0:
return False
else:
return True
x1 = np.array(list(filter(fa, x)))
x2 = np.array(list(filter(fb, x)))
print(x1)
a, b = tools.normfun_ex(x1)
plt.plot(a, b, color='g', linewidth=1)
plt.hist(x1, 50, normed=1, facecolor='blue', alpha=0.5)
plt.show()
a, b = tools.normfun_ex(x2)
plt.plot(a, b, color='g', linewidth=1)
plt.hist(x2, 50, normed=1, facecolor='blue', alpha=0.5)
plt.show()
def test_BIAS():
#計算乖離率, 有常態分佈
nday = 50
f_name = "測試資料/台股/HYGd.csv"
# s = stock_daytrade(f_name)
s = stock(f_name)
c = np.array(s.close)
o = np.array(s.opened)
x = s.feature_BIAS(nday)
x = x[nday:]
# x = tools.time_filter(x, s.date,9*60,11*60)
a, b = tools.normfun_ex(x)
plt.plot(a, b, color='g', linewidth=1)
plt.hist(x, 50, normed=1, facecolor='blue', alpha=0.5)
plt.show()
def fa(x):
if x >0:
return True
else:
return False
def fb(x):
if x >0:
return False
else:
return True
x1 = np.array(list(filter(fa,x)))
x2 = np.array(list(filter(fb,x)))
print(x1)
a, b = tools.normfun_ex(x1)
plt.plot(a, b, color='g', linewidth=1)
plt.hist(x1, 50, normed=1, facecolor='blue', alpha=0.5)
plt.show()
a, b = tools.normfun_ex(x2)
plt.plot(a, b, color='g', linewidth=1)
plt.hist(x2, 50, normed=1, facecolor='blue', alpha=0.5)
plt.show()
# -*- coding: utf-8 -*-
import numpy as np
import sys
import tools
from stock import stock
from stock import stock_5day
from datetime import datetime
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
if __name__ == '__main__':
sid = sys.argv[1]
#s = stock("測試資料/台股/{0}.csv".format(sid))
s = stock("測試資料/台股/{0}.csv".format(sid), adj_fix=True) # yahoo 的還原資料
# nday = int(sys.argv[2]) #設定主軸
period = 12
date, data = s.feature_Annualized(1, period)
date2, data2 = s.feature_Annualized(2, period)
date3, data3 = s.feature_Annualized(3, period)
date4, data4 = s.feature_Annualized(4, period)
date5, data5 = s.feature_Annualized(5, period)
date6, data6 = s.feature_Annualized(6, period)
date7, data7 = s.feature_Annualized(7, period)
date8, data8 = s.feature_Annualized(8, period)
date9, data9 = s.feature_Annualized(9, period)
date10, data10 = s.feature_Annualized(10, period)
#print("{0} {1} {2} {3} {4} {5} {6} {7} {8} {9} {10} {11} {12} {13} {14}".format(
# round(np.average(data),2),round(np.std(data),2),round(np.min(data),2),
import numpy as np import matplotlib.pyplot as plt def wsbMomentum(ticker,n_days=400,fEMA_period=22,sEMA_period=11,ema_IS=13, fast_ema_IS=12, slow_ema_IS=26, `signal_IS=9): ''' Weekly Impulse: 0, red; 1, green; 2, neutral after red. Daily Impulse: 0, red; 1, green; 2, neutral after red. Daily Price: 0, above value; 1, in the value zone; 2, below value - between the two ema MACD-H bullish divergece: Perfection: ''' score = 0 # Data stock_daily = stock(ticker, days=n_days) stock_weekly = stock(ticker, days=n_days, period="weekly") # Weekly Impulse stock_weekly.Impulse_System(plot_data=False) score += (stock_weekly.impulse_data[-1] + 1) # Daily Impulse stock_daily.Impulse_System(plot_data=False) score += (stock_daily.impulse_data[-1] + 1) # Daily Price fast_ema = stock_daily.EMA(period=fEMA_period, plot_data = False) slow_ema = stock_daily.EMA(period=sEMA_period, plot_data = False)
async def on_message(message):
if message.author == client.user:
return
if message.content.lower() == 'hi PieDE'.lower() or message.content.lower(
) == 'hello PieDE'.lower():
author = message.author.name
output = "OMG, " + author + " HI HI HI HI!"
await message.channel.send(output)
if message.content.startswith('$'):
symbol = message.content.replace('$', '')
output = stock.stock(symbol)
await message.channel.send(output)
if message.content.startswith('alert '):
symbol = message.content.replace('alert ', '')
output = stock.alert(symbol)
await message.channel.send(output)
if message.content == '!kitty' or message.content == '!cat':
kitty.getCatLink()
await message.channel.send(file=discord.File('img/temp-cat.jpg'))
#WORK IN PROGRESS
if message.content.startswith('!t2s'):
translate = message.content[5:]
t2s.synthesize_ssml(translate)
channel = message.author.voice.channel
vc = await channel.connect()
vc.play(discord.FFmpegPCMAudio('output.mp3'),
after=lambda e: print(audio_complete_msg, e))
if message.content.startswith('!sax'):
channel = message.author.voice.channel
vc = await channel.connect()
await message.channel.send(file=discord.File('preload/saxgif.gif'))
time.sleep(0.5)
vc.play(discord.FFmpegPCMAudio('preload/sax.mp3'),
after=lambda e: print(audio_complete_msg, e))
time.sleep(8)
await message.channel.purge(limit=1, check=is_me)
guild = message.guild
voice_client = guild.voice_client
await voice_client.disconnect()
if message.content.startswith('!play'):
channel = message.author.voice.channel
await message.delete()
await message.channel.send("Playing...")
url = ""
url = message.content[6:]
if url != "":
ytaudio.createAudio(url)
vc = await channel.connect()
vc.play(discord.FFmpegPCMAudio('temp-audio.mp3'),
after=lambda e: print(audio_complete_msg, e))
if message.content.startswith('!code'):
wf = open("userUploadPie.py", "w")
wf.write(message.content[5:])
wf.close()
output = convertPiethon.convert()
for i in output:
await message.channel.send(i)
if message.content == '!leave':
guild = message.guild
voice_client = guild.voice_client
await voice_client.disconnect()
import config_fund
Configs = config_fund.Configs
def get_datetime_cnyes_date(tradeDate):
excel_date = (tradeDate/86400) + 25569.333333333333 # to excel date
a = xlrd.xldate_as_tuple(excel_date,0)
day_str = "{0}-{1}-{2}".format(a[0],a[1],a[2])
#print(day_str)
dt = datetime.strptime(day_str,'%Y-%m-%d')
return dt
for config in Configs:
print ("\r\n == ",config)
s = stock(config['file_path'])
last_date = s.date[-1]
#print(last_date)
#配息
URL = "http://fund.cnyes.com/api/v1/fund/{0}/dividend?page=".format(config['cnyes_id'])
with urllib.request.urlopen("{0}1".format(URL)) as url:
data = json.loads(url.read().decode())
last_page = data['items']['last_page']
dividend_date = []
dividend_val = []
for page in range(1,last_page+1):
print (page, "/", last_page)
with urllib.request.urlopen("{0}{1}".format(URL,page)) as url:
return parts_left
"""return the part that minimizes stocksize - part size, return none if no part will fit"""
def get_largest_fitting_part(self, stock):
largest = None
for p in self.parts:
if p.remaining() > 0 and p.length < stock.length:
if largest is None or p.length > largest.length:
largest = p
return largest
def get_waste(self):
sum_waste = 0
for p in c.patterns:
sum_waste += p.get_waste()
percent_waste = sum_waste / (5600*len(self.patterns)) * 100
return percent_waste, sum_waste
if __name__ == "__main__":
stocks = [stock(5600)] #for now assume that we have an unlimited quantitiy of stock
parts = [part(1380, 22), part(1520, 25), part(1560, 12), part(1710, 14) , part(1820,18), part(1880, 18),
part(1930, 20), part(2000, 10), part(2050, 12), part(2100, 14), part(2140, 16), part(2150, 18), part(2200, 20)]
c = cutter(stocks, parts)
c.solve()
p_waste, s_waste = c.get_waste()
print "percent waste: " , p_waste
print "total waste: " , s_waste
print "number of patterns: " , len(c.patterns)
def decorated_stock(message):
stock(message)
def insertData(self, cr, uid, com, tallyData):
a = tallyData
pool = self.getPoolObj(cr)
account, acc_type = self.getAccountObj(pool)
uomObj, prodObj, prodCatObj, currencyObj = self.getStockObj(pool)
godownObj = self.getStockLocationObj(pool)
empObj = self.getEmployeeObj(pool)
if a.has_key('TALLYMESSAGE'):
l = len(a['TALLYMESSAGE'])
dic = {}
if l <= 0:
pass
elif l < 2: #for single record [TALLYMESSAGE] is dictionary.
k = a['TALLYMESSAGE'].keys()[0]
dic = a['TALLYMESSAGE'][k]
if (k == 'GROUP' or k == 'LEDGER'):
if dic.has_key('NAME') and dic['NAME']:
obj_ledgers = ledgers.ledgers()
obj_ledgers.insertRecords(cr, uid, dic, com, account,
acc_type)
elif (k == 'UNIT'):
obj_stock = stock.stock()
obj_stock.insertUnits(cr, uid, uomObj, dic)
elif (k == 'STOCKGROUP'):
obj_stock = stock.stock()
obj_stock.insertStockGroups(cr, uid, prodCatObj, dic)
elif (k == 'STOCKITEM'):
obj_stock = stock.stock()
obj_stock.insertStockItems(cr, uid, prodObj, uomObj,
prodCatObj, dic, com)
elif (k == 'CURRENCY'):
obj_stock = stock.stock()
obj_stock.insertCurrencies(cr, uid, currencyObj, dic, com)
elif (k == 'GODOWN'):
obj_stock = stock.stock()
obj_stock.insertGodowns(cr, uid, godownObj, dic, com)
elif (k == 'COSTCENTRE'):
obj_employee = employee.employee()
obj_employee.insertEmployees(cr, uid, empObj, dic, com)
else: #for multiple records [TALLYMESSAGE] is list of dictionaries.
for i in a['TALLYMESSAGE']:
k = i.keys()[0]
dic = i[k]
if (k == 'GROUP' or k == 'LEDGER'):
if dic.has_key('NAME') and dic['NAME']:
obj_ledgers = ledgers.ledgers()
obj_ledgers.insertRecords(cr, uid, dic, com,
account, acc_type)
elif (k == 'UNIT'):
obj_stock = stock.stock()
obj_stock.insertUnits(cr, uid, uomObj, dic)
elif (k == 'STOCKGROUP'):
obj_stock = stock.stock()
obj_stock.insertStockGroups(cr, uid, prodCatObj, dic)
elif (k == 'STOCKITEM'):
obj_stock = stock.stock()
obj_stock.insertStockItems(cr, uid, prodObj, uomObj,
prodCatObj, dic, com)
elif (k == 'CURRENCY'):
obj_stock = stock.stock()
obj_stock.insertCurrencies(cr, uid, currencyObj, dic,
com)
elif (k == 'GODOWN'):
obj_stock = stock.stock()
obj_stock.insertGodowns(cr, uid, godownObj, dic, com)
elif (k == 'COSTCENTRE'):
obj_employee = employee.employee()
obj_employee.insertEmployees(cr, uid, empObj, dic, com)
tickers = []
invested = []
average_price = []
for line in text:
tempLine = line.split("\n")
temp = tempLine[0].split(":")
tickers.append(temp[0])
invested.append(float(temp[1]))
average_price.append(float(temp[2]))
plot_handels = []
total_percentage = []
total_returns = []
for i in xrange(0, len(tickers)):
print "Loading data from ", tickers[i]
scraped_data = stock(tickers[i])
return_investment = []
percent_return = []
for j in xrange(0, len(scraped_data.historic["date"])):
[return_investment,
percent_return] = scraped_data.investment_return(average_price[i])
if not i:
total_percentage.append(percent_return[j])
total_returns.append(return_investment[j])
else:
total_percentage[j] = total_percentage[j] + percent_return[j]
total_returns[j] = total_returns[j] + return_investment[j]
def main():
# Extract the data from g sheets
test = Gsheet(MAINSPREADSHEET_ID)
raw = test.get_values()
# convert the data to list
temp = [[c for c in r] for r in raw]
# convert all the string date into datetime class
for i in range(1, len(temp)):
temp[i][0] = datetime.datetime.strptime(temp[i][0], "%m/%d/%Y")
# convert list into numpy
data = np.array(temp) #.astype(str)
# Convert numpy into pandas
Book = pd.DataFrame(data=data[1:, 1:],
index=data[1:, 0],
columns=data[0, 1:])
# Filter all Ticker and remove repetitve
Investment_ticker = list(set(Book['Ticker'].tolist()))
# Get the stock data
# Book.index[0]+datetime.timedelta(15)
#print (Investment_ticker)
set_data = {}
print("Loading stock history")
for ticker in Investment_ticker:
print(ticker)
temp_ETF = stock(ticker,
from_date=Book.index[0],
to_date=datetime.datetime.today())
# Convert all the dataa tonumber of shares
Investment_data(temp_ETF, Book)
# Add the stock to the panel
#temp_ETF.plot_line(day_value="Close")
set_data[ticker] = temp_ETF.trade_history
# Check the size of data. Must be equal at all times
for ticker in Investment_ticker:
date_index = set_data[ticker].index
L = len(set_data[ticker])
try:
if L < prev_L:
for idx, val in enumerate(prev_dateI):
if val > date_index[0]:
i = idx
break
temp_col = list(set_data[ticker].columns.values)
temp_prev_data = pd.DataFrame(data=np.zeros(
(len(prev_dateI[:i - 1]), len(temp_col))),
index=prev_dateI[:i - 1],
columns=temp_col)
set_data[ticker] = temp_prev_data.append(set_data[ticker])
continue
except UnboundLocalError:
pass
prev_L = L
prev_dateI = date_index
# Convert data to Pandas
total_invest_data = pd.Panel(set_data)
# set up total_invest data structure
total_invest = total_invest_data[Investment_ticker[0]]["Close Investment"]
for ticker in Investment_ticker[1:]:
total_invest = total_invest_data[ticker][
"Close Investment"] + total_invest
# Sum all the money invested
Principal = []
for i in range(0, len(Book.index)):
if "Purchase" in Book.iloc[i]["Transaction"]:
if i == 0:
Principal.append(float(Book.iloc[i]["Amount"].split("$")[1]))
continue
Principal.append(
float(Book.iloc[i]["Amount"].split("$")[1]) + Principal[i - 1])
elif "Sale" in Book.iloc[i]["Transaction"]:
if i == 0:
Principal.append(float(Book.iloc[i]["Amount"].split("$")[1]))
continue
Principal.append(Principal[i - 1] -
float(Book.iloc[i]["Amount"].split("$")[1]))
#Principal.append(Principal[-1])
weekFormatter = DateFormatter("%b %d '%y") #%H:%M:%S
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
ax.xaxis.set_major_formatter(weekFormatter)
plt.plot(total_invest, 'g', Book.index, Principal, '-k')
#plt.plot(Book.index,Principal)
#fig.subplots_adjust()
ax.xaxis_date()
ax.autoscale_view()
plt.title("Total")
plt.legend(labels=["Investment", "Principal"])
plt.setp(plt.gca().get_xticklabels(),
rotation=45,
horizontalalignment='right')
plt.grid()
#plt.show()
# Create Month to month ROI and year to year
#print (Book.index)
#print (Principal)
#print (total_invest )
month_principal = []
month_date = []
for i, d in enumerate(Book.index):
try:
if d.month > prev_date.month and d.year == prev_date.year or d.year != prev_date.year:
# all shenanigans
#print (i, d, d.month)
#print (Principal[i])
if d.day == 1:
k = i
#print ("Month", d.month, Book.index[i] ,Principal[i])
else:
k = i - 1
#print ("Prev month", d.month,Book.index[i-1], Principal[i-1])
if d.month > prev_date.month + 1:
dummy = prev_date.month + 1
while dummy < d.month:
#print (datetime.datetime(d.year,dummy,1), Principal[k])
month_date.append(datetime.datetime(d.year, dummy, 1))
month_principal.append(Principal[k])
#print ("miss month", dummy, Principal[k])
dummy = dummy + 1
#Principal[k]
#print (datetime.datetime(d.year,d.month,1), Principal[k])
month_date.append(datetime.datetime(d.year, d.month, 1))
month_principal.append(Principal[k])
prev_date = d
except UnboundLocalError:
prev_date = d
continue
# if the month already pass it and not investment has being made then it updates to the current month
if datetime.datetime.today().month > Book.index[-1].month:
print(Book.index[-1], Principal[-1])
d_dummy = Book.index[-1]
month_date.append(datetime.datetime(d_dummy.year, d_dummy.month + 1,
1))
month_principal.append(Principal[-1])
# Investment
total_invest_month = []
j = 0
for index, d in enumerate(total_invest.index):
if j >= len(month_date):
break
if month_date[j].month == d.month and month_date[j].year == d.year:
#print (d, index, total_invest[index] )
j = j + 1
total_invest_month.append(total_invest[index])
percent_month = []
ROI = []
for i in range(0, len(month_date)):
#print (total_invest_month[i], month_principal[i])
percent_month.append((total_invest_month[i] - month_principal[i]) /
month_principal[i] * 100.0)
ROI.append(total_invest_month[i] - month_principal[i])
#percent_month=100.0*(total_invest_month - month_principal)/month_principal
weekFormatter = DateFormatter("%b %d '%y") #%H:%M:%S
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
ax.xaxis.set_major_formatter(weekFormatter)
plt.plot(month_date, ROI)
#fig.subplots_adjust()
ax.xaxis_date()
ax.autoscale_view()
plt.title("Total")
plt.legend(labels=["Investment", "Principal"])
plt.setp(plt.gca().get_xticklabels(),
rotation=45,
horizontalalignment='right')
plt.grid()
ax2 = ax.twinx()
ax2.plot(month_date, percent_month, 'g*-')
plt.show()
def getChart():
n = body.args['stock']
x = stock.stock(n)
c = x.renderChart()
return jsonify({'n': n, 'chart': c[0], 'perf': c[1]})
from sklearn.model_selection import train_test_split
from stock import stock
import matplotlib.pyplot as plt
# learning model
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
def non_shuffling_train_test_split(X, y, test_size=0.2):
i = int((1 - test_size) * X.shape[0]) + 1
X_train, X_test = np.split(X, [i])
y_train, y_test = np.split(y, [i])
return X_train, X_test, y_train, y_test
stock_TPE = stock('price_data.csv')
all_days = len(stock_TPE.close)
K0 = np.array(stock_TPE.feature_K())
K1 = np.roll(K0, 1)
K2 = np.roll(K0, 2)
K3 = np.roll(K0, 3)
K4 = np.roll(K0, 4)
K5 = np.roll(K0, 5)
K6 = np.roll(K0, 6)
K7 = np.roll(K0, 7)
K8 = np.roll(K0, 8)
K9 = np.roll(K0, 9)
B5 = stock_TPE.feature_BIAS(5)
B10 = stock_TPE.feature_BIAS(10)
B15 = stock_TPE.feature_BIAS(15)
def fetchP():
n = body.args['stock']
x = stock.stock(n)
nums = x.getPrices()
return jsonify({'n': n, 'p': nums[0], 'diff': nums[1]})
import datetime
import stock
import matplotlib.pyplot as plot
tickers = "AAPL","SBUX","GRPN","EC","T","IBM","INTC","NFLX","GOOG","OSTK","CMG","TWX"
how_long = 90
time_between = "d"
ending = datetime.date.today()
time_span = datetime.timedelta(days=how_long)
starting = ending - time_span
s = ([])
j = 0
for ticker in tickers:
s.append(stock.stock(ticker,starting,ending,time_between))
print ticker + "\nNormalized Standard Deviation:\n" + repr(100*s[j].std_dev/s[j].price_avg) + \
"\nMaximum Return:\n" + repr(max(s[j].returns)*100) + \
"\nLinear Deviation:\n" + repr(s[j].r_squared) + \
"\nInitial Investment:\n" + repr(s[j].price_init) + "\n"
j += 1
plot.show()
#stdev = list(stdev)
#which = stdev.index(max(stdev))
#print s[which].ticker
#print stdev[which]
#print s[which].r_squared
#print s[1:2].r_squared
ax2.plot(delta, 'blue')
ax3 = fig.add_subplot(223)
plt.ylabel('theta')
ax3.plot(theta, 'blue')
else:
ax2 = fig.add_subplot(222)
plt.ylabel('vega')
ax2.plot(vega, 'blue')
ax3 = fig.add_subplot(223)
plt.ylabel('volatility')
ax3.plot(volatility, 'blue')
ax4 = fig.add_subplot(224)
plt.ylabel('Price of underlying')
ax4.plot(underlying, 'red')
if __name__ == '__main__':
#pd.set_option("display.max_rows", None, "display.max_columns", None)
s1 = stock(100, .005, .05)
s1.gbm(100)
c1 = bsm(call=True, x=100, r=.05, t=45, stock=s1, time_stock=40)
c1.fill()
c2 = bsm(call=True, x=100, r=.05, t=30, stock=s1, time_stock=40)
c2.fill()
plt.xlabel('stock days')
plt.ylabel('vega')
plt.plot(c1.history['vega'], 'orange', label='t=45')
plt.plot(c2.history['vega'], 'grey', label='t=30')
plt.legend(loc="upper right")
plt.show()
def caculate_model(sid, nday):
print("")
s = stock("測試資料/台股/{0}d.csv".format(sid))
# if s.complate_data(sid) :
# 如果有新的資料，再重新laod 一次
# s = stock("測試資料/台股/{0}d.csv".format(sid))
#s.reduce_data_len(250*10) # 用五年內的資料
pulldown_date, pulldown = s.feature_pulldown(20*6) # 半年
print("半年高檔拉回標準：", round(tools.list_percentage(pulldown,0.4),3), "目前：", round(pulldown[-1],3))
tmp1= round(tools.list_percentage(pulldown,0.4),3)
tmp2= round(pulldown[-1],3)
BIAS = s.feature_BIAS(nday)
BIAS_low = list(filter(lambda x: x < 0, BIAS[nday:]))
print("主軸拉回標準：", round(tools.list_percentage(BIAS_low,0.4)*100,3), "目前：", round(BIAS[-1]*100,3))
tmp3 = round(tools.list_percentage(BIAS_low,0.4)*100,3)
tmp4 = round(BIAS[-1]*100,3)
#1日 K
list_K = s.feature_K()
currnt_K = list_K[-1]
print("1日K 高低檔數值：", round(tools.list_percentage(list_K,0.2),2), round(tools.list_percentage(list_K,0.8),2), "目前 K：", round(currnt_K, 2))
tmp5 = round(tools.list_percentage(list_K,0.2),2)
tmp6 = round(tools.list_percentage(list_K,0.8),2)
tmp7 = round(currnt_K, 2)
MA_slope = s.feature_MA(nday)
print("目前均線：", MA_slope[-1], " 均線漲跌：", round(MA_slope[-1] - MA_slope[-2],4))
tmp11 = round(MA_slope[-1] - MA_slope[-2],4)
tmp12 = (mdates.num2date(s.date[-1]).strftime("%Y/%m/%d"))
#中長 紅黑K統計
diff = s.feature_diff()[1:]*100
diff_up_list = list(filter(lambda x: x > 0, diff))
diff_down_list = list(filter(lambda x: x < 0, diff))
diff_current = diff[-1]
diff_up_std1 = tools.list_percentage(diff_up_list,0.55)
diff_up_std2 = tools.list_percentage(diff_up_list,0.75)
diff_down_std1 = tools.list_percentage(diff_down_list,1-0.55)
diff_down_std2 = tools.list_percentage(diff_down_list,1-0.75)
print("中紅標準：", round(diff_up_std1,2), "\t目前：", round(diff_current,2))
print("中黑標準：", round(diff_down_std1,2), "\t目前：", round(diff_current,2))
print("長紅標準：", round(diff_up_std2,2), "\t目前：", round(diff_current,2))
print("長黑標準：", round(diff_down_std2,2), "\t目前：", round(diff_current,2))
#5日 K
s5 = stock_5day("測試資料/台股/{0}d.csv".format(sid))
list_K = s5.feature_K()
currnt_K = list_K[-1]
print("5日K 高低檔數值：", round(tools.list_percentage(list_K,0.2),2), round(tools.list_percentage(list_K,0.8),2), "目前 K：", round(currnt_K, 2))
print(tmp12,",",s.close[-1],",",s.close[-2],",",s.close[-3],",",s.close[-4])
tmp8 = round(tools.list_percentage(list_K,0.2),2)
tmp9 = round(tools.list_percentage(list_K,0.8),2)
tmp10 = round(currnt_K, 2)
print("")
if tmp2<tmp1:
print("** 高檔拉回")
if tmp4<tmp3:
print("** 主軸拉回")
if tmp7<tmp5:
print("** 1日k 低檔")
if tmp10<tmp8:
print("** 5日k 低檔")
if diff_current > diff_up_std2:
print("** 長紅")
elif diff_current > diff_up_std1:
print("** 中紅")
if diff_current < diff_down_std2:
print("** 長黑")
elif diff_current < diff_down_std1:
print("** 中黑")
print("")
print(tmp12,tmp1,tmp2,tmp3,tmp4,tmp5,tmp6,tmp7,tmp8,tmp9,tmp10, tmp11)
# -*- coding: utf-8 -*-
import numpy as np
import sys
import tools
from stock import stock
from stock import stock_5day
from datetime import datetime
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
if __name__ == '__main__':
s = stock("測試資料/台股/{0}.csv".format(sys.argv[1]))
nday = int(sys.argv[2]) #設定主軸
period = 12
date, data = s.feature_Annualized(1, period)
date2, data2 = s.feature_Annualized(2, period)
date3, data3 = s.feature_Annualized(3, period)
date4, data4 = s.feature_Annualized(4, period)
date5, data5 = s.feature_Annualized(5, period)
date6, data6 = s.feature_Annualized(6, period)
date7, data7 = s.feature_Annualized(7, period)
date8, data8 = s.feature_Annualized(8, period)
date9, data9 = s.feature_Annualized(9, period)
date10, data10 = s.feature_Annualized(10, period)
#print("{0} {1} {2} {3} {4} {5} {6} {7} {8} {9} {10} {11} {12} {13} {14}".format(
# round(np.average(data),2),round(np.std(data),2),round(np.min(data),2),
# round(np.average(data3),2),round(np.std(data3),2),round(np.min(data3),2),
# round(np.average(data4),2),round(np.std(data4),2),round(np.min(data4),2),
def creatingStocks(symbol):
print("Creating Stocks")
for x in symbolStock:
stocks.append(stk.stock(x, Ticker(x)))
def add(self, i):
a = stock(i)
self.stock.append(a)
