def caifuziyou(stocklist):
days0to15_data = bt.get_stock_data(bt.get_data(20), bt.get_data(0),
*stocklist)
days0to180_data = bt.get_stock_data(bt.get_data(180), bt.get_data(30),
*stocklist)
kelly_data = {}
for i in days0to15_data:
try:
x1 = days0to15_data[i]['Adj Close'].tolist()[-1]
x2 = max(days0to180_data[i]['Adj Close'].tolist())
if x2 / x1 >= 2: kelly_data[i] = x1 / x2
except:
pass
return kelly_data
def get_rjump(self, z=None, shell=None):
if all_or_none(z, shell):
z = self.z
shell = self.shell
rjump = {'K': get_data(z, 'rjump1'),
'L1': 1.17,
'L2': 1.39,
'L3': get_data(z, 'rjump4'),
'M1': 1.16,
'M2': 1.207,
'M3': 1.158,
'M4': 1.895,
'M5': 1.895,
'N': 2.0}
return rjump[shell]
def caifuziyou(stocklist):
days0to100_data = bt.get_stock_data(bt.get_data(100), bt.get_data(0),
*stocklist)
days0to5_data = bt.get_stock_data(bt.get_data(5), bt.get_data(0),
*stocklist)
days0to15_data = bt.get_stock_data(bt.get_data(15), bt.get_data(0),
*stocklist)
days0to30_data = bt.get_stock_data(bt.get_data(30), bt.get_data(0),
*stocklist)
kelly_data = {}
probability_rate = np.array([0.3, 0.3, 0.5, 0.8])
for i in days0to100_data:
try:
'''
get p
'''
days0to100_data[i]["daily"] = days0to100_data[i][
'Adj Close'].pct_change()
days = 10
dt = 1.0000 / days
mu = days0to100_data[i]["daily"].mean()
sigma = days0to100_data[i]["daily"].std()
startprice = days0to100_data[i]['Adj Close'].tolist()[-1]
temp1, temp2, temp3, temp4 = 0.0, 0.0, 0.0, 0.0
bsum = 0
for j in range(1000):
pricelist = bt.perdict10days(startprice,
mu,
dt,
sigma,
days=10)
if bt.incornot(list(pricelist)) >= 0.03:
temp1 += 1
bsum += bt.incornot(list(pricelist))
b = bsum / temp1
temp1 = float(temp1) / 1000
if bt.incornot(days0to5_data[i]['Adj Close'].tolist()) > 0.03:
temp2 = 1
if bt.incornot(days0to15_data[i]['Adj Close'].tolist()) > 0.06:
temp3 = 1
if bt.incornot(days0to30_data[i]['Adj Close'].tolist()) > 0.07:
temp4 = 1
p = sum(probability_rate * np.array([temp1, temp2, temp3, temp4
])) / sum(probability_rate)
kelly_data[i] = [i, startprice, p, b * 9]
except:
pass
l = []
for i in kelly_data:
kelly_data[i].append(
bt.kelly_caculation(kelly_data[i][-2], kelly_data[i][-1]))
l.append(kelly_data[i])
l.sort(key=lambda x: x[-3], reverse=True)
return l
def get_effyld(self, el=None, exciter='e'):
"""EFFective Fluorescence YieLD for all lines.
3/91 r.a. waldo
NEED TO FIND REFS FOR THIS
"""
# need to check this xray may actually be accelerating voltage...?
# var was xenergy in RAW code
if el is None:
el = self
# Coster-Kronig Coefficients
ck12 = get_data(el.z, 'c12')
ck13 = get_data(el.z, 'c13')
ck23 = get_data(el.z, 'c23')
if el.shell[0] != 'L':
effyld = el.omega
elif el.shell[0] == 'L':
rj1 = self.get_rjump(el.z, 'L1')
rj2 = self.get_rjump(el.z, 'L2')
rj3 = self.get_rjump(el.z, 'L3')
l1 = get_data(el.z, 'Lb3')
l2 = get_data(el.z, 'Lb1')
l3 = get_data(el.z, 'La1')
if el.shell == 'L1':
effyld = el.omega
elif el.shell == 'L2':
if el.xray < l1 and el.xray > l2:
effyld = el.omega
elif el.xray > l1:
el1 = el
el1.shell = 'L2' # 3
omegaL2 = self.get_omega(el1)
if exciter == 'e':
effyld = omegaL2*(1.0 + ck12)
else:
effyld = omegaL2*(1.0 + ck12*(rj1 - 1.0) *
rj2/(rj2 - 1.0))
elif el.shell == 'L3':
if el.xray < l2 and el.xray > l3:
effyld = el.omega
elif el.xray < l1 and el.xray > l2:
effyld = el.omega*(1.0 + ck23*(rj2 - 1.0) *
rj3/(rj3 - 1.0))
elif el.xray > l1:
if exciter[0] == 'e':
effyld = el.omega*(1.0 + 0.5*ck13 +
0.5*(1.0 + ck12)*ck23)
else:
effyld = el.omega*(
1.0 +
ck13*(rj1 - 1.0)*rj2*rj3/(rj3 - 1.0) +
ck23*(rj2 - 1.0)*rj3/(rj3 - 1.0) +
ck12*(rj1 - 1.0)*rj2*rj3/(rj3 - 1.0))
elif el.shell[0] == 'N':
effyld = 0.0
return effyld
def user_input(self, volt):
z, name = get_options('Please enter Element Symbol:',
'els', count=True)
# make sure the selected line is valid
while True:
line = get_options('Please enter Element X-Ray Line '
'(Ka, Lg2, etc.):', 'lines')
xray = get_data(z, line)
if xray > 0:
break
user_alert('Invalid line for this element; try again:')
opt = self.get_opt()
if volt is None:
volt = get_nums('Accelerating voltage for this element?:',
50, 0)
return name, line, z, volt, opt
def get_omega(self, z=None, shell=None):
"""Fluorescence Yields"""
if all_or_none(z, shell):
z = self.z
shell = self.shell
omega = 0.0
if shell == 'K': # Bambynek etal., Rev. Mod. Physics. 1972 pg 757
# Could updat to Bambynek 1984
d = (0.015 + 0.0327*z - 0.64e-6*z**3)**4
omega = d/(1.0 + d)
elif shell == 'M4' or shell == 'M5': # ???? REF??
omega = 0.68e-9*(z - 13)**4
elif shell == 'N1': # ??? Ref??
omega = 1.0e-9*(z - 13)**4
elif shell == 'M2' or shell == 'M3' or shell[0] == 'N': # 5
omega = 0.0
else: # Krause, J. Phys. Chem. Ref. Data Vol. 8, No. 2, 1979, p. 307.
if shell == 'M1':
shell = 'L3'
shell = shell.lower()
omega = get_data(z, shell)
return omega
import seaborn as sns
import basictools as bt
import stockplay as sp
import csv
sns.set(style="whitegrid")
capset = set()
with open('gt10blist') as f:
for i in f.readlines():
if "-" not in i and "." not in i:
capset.add(i.replace("\n", ""))
l = sp.caifuziyou(list(capset))
filename_tod = "ND" + bt.get_data(0) + ".csv"
filename_yes = "ND" + bt.get_data(1) + ".csv"
set_tod = set([])
set_yes = set([])
with open(filename_tod, 'w') as fd:
for t in l:
if t[-1] != 0:
set_tod.add(t[0])
writer = csv.writer(fd)
writer.writerow(t)
with open(filename_yes) as fd:
for i in fd.readlines():
if len(i) > 10:
set_yes.add(i.replace("\n", "").split(",")[0])
temp1 = []
for i in l:
if i[0] not in set_yes and i[0] in set_tod:
temp1.append([i[0], i[1], i[2]])
temp1.sort(key=lambda x: x[-1], reverse=True)
for i in temp1:
"*****@*****.**"
]
url = "https://en.wikipedia.org/wiki/List_of_S%26P_500_companies"
content = pd.read_html(url)
stocklist = content[0]['Symbol'].tolist()
print('sp500 index data got')
cur_date = str(datetime.date.today())
if not stocklist: exit()
print("starting polling date from yahoo")
earningdate = bt.get_next_event(*stocklist)
msg = ""
print("finding the delta less then 10days")
for i in earningdate:
if 0 < bt.get_date_delta(earningdate[i], cur_date) <= 10:
msg += i + ","
str1 = "caifu" + bt.get_data(0) + ".csv"
list1 = msg[:-1].split(",")
print list1
l = sp.caifuziyou(list1)
with open(str1, 'a') as fd:
for t in l:
if t[-1] != 0:
writer = csv.writer(fd)
writer.writerow([bt.get_date_delta(earningdate[i], cur_date)] +
t)
l = op.caifuziyou(list1)
with open(str1, 'a') as fd:
for t in l:
if t[-1] != 0:
writer = csv.writer(fd)
writer.writerow([bt.get_date_delta(earningdate[i], cur_date)] +
import copy
import csv
parent_list = ["Gavin", "Owen", "Chao", "Maggie", "Vivian", "Nina"]
child_list = ["Angelina", "Audrey", "Nora", "Ethan", "Edwin", "Annie"]
arrange_list = []
n = 0
while (n <= 9):
temp = copy.deepcopy(child_list)
for i in range(len(parent_list)):
index_k = random.randint(0, len(temp) - 1)
loop = 0
while (i == child_list.index(temp[index_k])):
index_k = random.randint(0, len(temp) - 1)
loop += 1
if loop == 5: break
if loop == 5:
for _ in range(1, len(parent_list)):
arrange_list.pop()
else:
arrange_list.append(
(parent_list[i], temp[index_k], str(bt.get_data(-4 - n * 7)),
"Week " + str(n + 11)))
temp.pop(index_k)
if loop == 5: pass
else: n += 1
with open("arrangement.csv", 'a') as fd:
for t in arrange_list:
print t
writer = csv.writer(fd)
writer.writerow(list(t))
content = pd.read_html(url)
stocklist = content[0]['Symbol'].tolist()
with open('/var/www/html/2.html', 'w') as file:
file.write('<html> \n')
file.write(' <head> \n')
file.write(' <title>Stock Data</title> \n')
file.write(' </head> \n')
file.write(' <body> \n')
for i in stocklist:
file.write(' <H1> ' + i + ' </H1> \n')
file.write(" <img alt='no image1' src='" + i +
"10days.png'></img> \n")
file.write(' </body> \n')
file.write('<html> \n')
while (1):
data1 = bt.get_stock_data(bt.get_data(95), bt.get_data(1), *stocklist)
for i in data1:
data1[i]["daily"] = data1[i]['Adj Close'].pct_change()
days = 10
dt = 1.0000 / days
mu = data1[i]["daily"].mean()
sigma = data1[i]["daily"].std()
if data1[i]['Adj Close'].tolist() != []:
startprice = data1[i]['Adj Close'].tolist()[-1]
else:
startprice = 0 - 100.00
for j in range(100):
plt.plot(bt.perdict10days(startprice, mu, dt, sigma, days=10))
str1 = "/var/www/html/" + i + "10days.png"
print(str1)
plt.savefig(str1)
import yfinance as yf
import os
import time
sns.set(style="whitegrid")
if __name__ == "__main__":
url = "https://en.wikipedia.org/wiki/List_of_S%26P_500_companies"
content = pd.read_html(url)
stocklist = content[0]['Symbol'].tolist()
with open('/var/www/html/2.html', 'w') as file:
file.write('<html> \n')
file.write(' <head> \n')
file.write(' <title>Stock Data</title> \n')
file.write(' </head> \n')
file.write(' <body> \n')
for i in stocklist:
file.write(' <H1> ' + i + " " + bt.get_data(0) +
' </H1> \n')
file.write(" <img alt='no image1' src='" + i +
"10days.png'></img> \n")
file.write(' </body> \n')
file.write('<html> \n')
while (1):
try:
data1 = bt.get_stock_data(bt.get_data(95), bt.get_data(1),
*stocklist)
for i in data1:
data1[i]["daily"] = data1[i]['Adj Close'].pct_change()
days = 10
dt = 1.0000 / days
mu = data1[i]["daily"].mean()
sigma = data1[i]["daily"].std()
def caifuziyou(stocklist):
earningdate = bt.get_next_event(*stocklist)
latest_option_date = {}
optiondata_call = {}
optiondata_put = {}
for i in earningdate:
temp = yf.Ticker(i)
if temp.options:
for j in sorted(temp.options):
if 3 < bt.get_date_delta(j, earningdate[i]) < 35:
latest_option_date[i] = j
break
if i in latest_option_date:
optiondata_call[i] = temp.option_chain(
latest_option_date[i]).calls
optiondata_put[i] = temp.option_chain(
latest_option_date[i]).puts
else:
pass
'''
check the stock flipping more than 2%
'''
days0to100_data = bt.get_stock_data(bt.get_data(100), bt.get_data(0),
*stocklist)
days0to30_data = bt.get_stock_data(bt.get_data(20), bt.get_data(0),
*stocklist)
days30to60_data = bt.get_stock_data(bt.get_data(90), bt.get_data(0),
*stocklist)
days60to90_data = bt.get_stock_data(bt.get_data(180), bt.get_data(91),
*stocklist)
kelly_data = {}
probability_rate = np.array([0.2, 0.2, 1, 0.2])
for i in latest_option_date:
'''
get p
'''
days0to100_data[i]["daily"] = days0to100_data[i][
'Adj Close'].pct_change()
days = 10
dt = 1.0000 / days
mu = days0to100_data[i]["daily"].mean()
sigma = days0to100_data[i]["daily"].std()
startprice = days0to100_data[i]['Adj Close'].tolist()[-1]
temp1, temp2, temp3, temp4 = 0.0, 0.0, 0.0, 0.0
for j in range(1000):
pricelist = bt.perdict10days(startprice, mu, dt, sigma, days=10)
if bt.incornot(list(pricelist)) > 0.02: temp1 += 1
temp1 = float(temp1) / 1000
if bt.incornot(days0to30_data[i]['Adj Close'].tolist()) > 0.03:
temp2 = 1
if bt.incornot(days30to60_data[i]['Adj Close'].tolist()) > 0.1:
temp3 = 1
if bt.incornot(days60to90_data[i]['Adj Close'].tolist()) > 0.1:
temp4 = 1
p = sum(probability_rate *
np.array([temp1, temp2, temp3, temp4])) / sum(probability_rate)
if p >= 0.6: corp = "call"
elif p <= 0.2: corp = "put"
else: corp = "hold"
if corp == "call": kelly_data[i] = [corp, i, latest_option_date[i], p]
elif corp == "put" and 1 - p > 0.6:
kelly_data[i] = [corp, i, latest_option_date[i], 1 - p]
else:
kelly_data[i] = [corp, i, -1, -1]
'''
get b
'''
strikelist_call = optiondata_call[i]["strike"].tolist()
strikelist_put = optiondata_put[i]["strike"].tolist()
target = bt.closestprice(strikelist_call, startprice)
if optiondata_call[i].loc[lambda df: df['strike'] == target][[
"bid", "ask"
]].sum(axis=1).tolist() != []:
option_call_price = optiondata_call[i].loc[
lambda df: df['strike'] == target][["bid", "ask"]].sum(
axis=1).tolist()[0] / 2
else:
option_call_price = 1000
if optiondata_put[i].loc[lambda df: df['strike'] == target][[
"bid", "ask"
]].sum(axis=1).tolist() != []:
option_put_price = optiondata_put[i].loc[
lambda df: df['strike'] == target][["bid", "ask"]].sum(
axis=1).tolist()[0] / 2
else:
option_put_price = 1000
if option_call_price == 0 or not option_call_price:
option_call_price = 1000
if option_put_price == 0 or not option_put_price:
option_put_price = 1000
b_call = (startprice * 0.03 - 0) / option_call_price
b_put = (startprice * 0.02 - 0) / option_put_price
if kelly_data[i][0] == "call":
kelly_data[i].append(b_call)
kelly_data[i].append(option_call_price)
kelly_data[i].append(target)
if kelly_data[i][0] == "put":
kelly_data[i].append(b_put)
kelly_data[i].append(option_put_price)
kelly_data[i].append(target)
for i in kelly_data:
if kelly_data[i][0] != "hold":
kelly_data[i].append(
bt.kelly_caculation(kelly_data[i][-4], kelly_data[i][-3]))
else:
kelly_data[i] = []
l = []
for i in kelly_data:
if kelly_data[i] == [] or kelly_data[i][-1] == 0: continue
l.append(kelly_data[i])
l.sort(key=lambda x: x[-5], reverse=True)
return l
def Mac(el1, el2):
"""Calculation of Mass the Absorption Coefficient of an impinging X-Ray
from el1 on el2.
This calc needs to be checked. See above refs"""
if el1.z == 5 and el2.z == 40:
""" B xray interacting with Zr ??? weird"""
xmu = 8270.0
iflag = 2
ie = ""
cutoff = 0
xray = el1.xray
z = el2.z
mass = el2.mass
# calculate 'c' parameter
if xray > get_data(z, 'K'):
if z < 6:
c = -2.87536e-4 + 1.808599e-3 * z
else:
c = (5.253e-3 +
1.33257e-3 * z +
-7.5937e-5 * z**2 +
1.69357e-6 * z**3 +
-1.3975e-8 * z**4)
elif xray < get_data(z, 'L3'):
c = (-9.24e-5 +
1.41478e-4 * z +
-5.24999e-6 * z**2 +
9.85296e-8 * z**3 +
-9.07306e-10 * z**4 +
3.19254e-12 * z**5)
if xray > get_data(z, 'L1'):
c = c
elif xray > get_data(z, 'L2'):
c = c * 0.858
else:
c = c*(0.8933 - z*8.29e-3 + z**2*6.38e-5)
elif xray > get_data(z, 'M1'):
if z < 30:
c = (1.889757e-2 +
-1.8517159e-3 * z +
6.9602789e-5 * z**2 +
-1.1641145e-6 * z**3 +
7.2773258e-9 * z**4)
else:
c = (3.0039e-3 +
-1.73663566e-4 * z +
4.0424792e-6 * z**2 +
-4.0585911e-8 * z**3 +
1.497763e-10 * z**4)
elif xray > get_data(z, 'M5'):
c1 = (7.7708e-5 +
-7.83544e-6 * z +
2.209365e-7 * z**2 +
-1.29086e-9 * z**3)
c2 = (1.406 +
0.0162 * z +
-6.561e-4 * z**2 +
4.865e-6 * z**3)
c3 = (0.584 +
0.01955 * z +
-1.285e-4 * z**2)
c4 = (1.082 +
1.366e-3 * z)
c5 = (1.6442 +
-0.0480 * z +
4.0664e-4 * z**2)
if xray > get_data(z, 'M2'):
c = c1*c2*c3
elif xray > get_data(z, 'M3'):
c = c1*c2*c4
elif xray > get_data(z, 'M4'):
c = c1*c2*0.95
else: # M5
ie = 'M5'
c = c1*c2*c5
else:
c = 1.08*(4.3156e-3 +
-1.4653e-4 * z +
1.707073e-6 * z**2 +
-6.69827e-9 * z**3)
if xray < get_data(z, 'N1'):
cutoff = ((0.252*z - 31.1812)*z + 1042.)/1000.0
ie = 'N1'
# calculate 'n' parameter
if xray > get_data(z, 'K'):
if z < 6:
n = (3.34745 +
0.02652873 * z +
-0.01273815 * z**2)
else:
n = (3.112 +
-0.0121 * z)
elif xray > get_data(z, 'L3'):
n = (2.7575 +
1.889e-3 * z +
-4.982e-5 * z**2)
elif xray > get_data(z, 'M1'):
n = (0.5385 +
0.084597 * z +
-1.08246e-3 * z**2 +
4.4509e-6 * z**3)
elif xray > get_data(z, 'M5'):
n = 3.0 - 0.004*z
else:
n = 0.3736 + 0.02401*z
# calculate 'a' parameter
if xray > get_data(z, 'K'):
if z < 6:
a = (24.4545 +
155.6055 * z +
-14.15422 * z**2)
else:
a = (47.0 * z +
6.52 * z**2 +
-0.152624 * z**3)
elif xray > get_data(z, 'L3'):
a = (17.8096 * z +
0.067429 * z**2 +
0.01253775 * z**3 +
-1.16286e-4 * z**4)
elif xray > get_data(z, 'M1'):
a = (10.2575657 * z +
-0.822863477 * z**2 +
2.63199611e-2 * z**3 +
-1.8641019e-4 * z**4)
elif xray > get_data(z, 'M5'):
a = (4.62 * z +
-0.04 * z**2)
else:
a = (19.64 * z +
-0.61239 * z**2 +
5.39309e-3 * z**3)
# calculate 'b' parameter
if xray > get_data(z, 'K'):
if z < 6:
b = -103. + 18.2*z
else:
b = 0.
elif xray > get_data(z, 'L3'):
b = 0.
elif xray > get_data(z, 'M1'):
if z < 61:
b = (5.654 * z +
-0.536839169 * z**2 +
0.018972278 * z**3 +
-1.683474e-4 * z**4)
else:
b = (-1232.4022 * z +
51.114164 * z**2 +
-0.699473097 * z**3 +
3.1779619e-3 * z**4)
elif xray > get_data(z, 'M5'):
b = (2.51 +
(-0.052) * z +
3.78e-4 * z**2) * get_data(z, 'M4') * 1000.
else:
b = -113. + 4.5*z
if xray > get_data(z, 'N1'):
qq = (-xray*1000.+b)/a
if qq > 88.:
qq = 88.
elif qq < -88.:
qq = -88.
xmu = c * z**4 / mass * ((12.397 / xray)**n) * (1. - np.exp(qq))
if xmu < 0.0:
mess = '!!! negative mac !!!'
else:
xmu = (((12.397 / xray)**n) * c * z**4 / mass * (xray - cutoff) /
(1.08 * get_data(z, 'N1')))
if xray < 1.1 * cutoff:
mess = '!!! Ec<1.1xcutoff !!!'
elif xray - cutoff < 0.02 and xray - cutoff > -0.005:
mess = '!!! close to edge !!!'
else:
mess = 'Good!!'
if ie == 'M5':
mess += '!!!M4>Ec>M5 edge & Zab<70!!!'
if el2.mass >= 70:
mess = 'Good!!'
if ie == 'N1':
mess += '!!! Ec below M5 edge !!!'
if xmu < 0.0 and ie == 'N1':
mess = '!!!neg. mac & Ec<M5edge!!!'
user_alert(mess + '\nemiter=%s %s; absorber=%s; mu=%.4g'
% (el1.name, el1.line, el2.name, xmu))
if xmu <= 0.0:
xmu1 = xmu
xmu = get_nums('MAC is negative; Enter a value for this MAC :')
user_alert('emiter=%s %s; absorber=%s;'
'mu changed from %.4g to %.4g'
% (el1.name, el1.line, el2.name, xmu1, xmu))
return xmu
stocklist.append(i.replace("\n", ""))
print stocklist
if __name__ == "__main__":
with open('/var/www/html/nsdq.html', 'w') as file:
file.write('<html> \n')
file.write(' <head> \n')
file.write(' <title> NYSE Stock Data</title> \n')
file.write(' </head> \n')
file.write(' <body> \n')
for i in stocklist:
file.write(' <H1> ' + i + ' </H1> \n')
file.write(" <img alt='no image1' src='" + i +
".png'></img> \n")
file.write(' </body> \n')
file.write('<html> \n')
while (1):
data1 = bt.get_stock_data(bt.get_data(365), bt.get_data(0), *stocklist)
for i in stocklist:
try:
data1[i].plot(subplots=False, figsize=(10, 4))
str1 = "/var/www/html/" + i + ".png"
plt.savefig(str1)
plt.clf()
plt.cla()
plt.close()
except:
plt.clf()
plt.cla()
plt.close()
time.sleep(86400)
sns.set(style="whitegrid")
import stockplay as sp
import pandas as pd
import csv
str1='JWN ,NTAP ,SNPS ,CPRT ,TJX ,COST ,MDT ,LB ,PGR ,A ,KEYS ,HRL ,ROST ,NVDA ,ADSK ,HPE ,PVH ,BBY ,TTWO ,DG ,DE ,INTU ,ULTA ,DLTR ,RCL ,HPQ ,ADI ,AZO ,DXC '
list1=str1.replace(",","").split(" ")
l=op.caifuziyou(list1)
with open(r'ymh1.csv','a') as fd:
for t in l:
if t[-1]!=0:
writer=csv.writer(fd)
writer.writerow([bt.get_data(0)]+t)
l=sp.caifuziyou(list1)
with open(r'ymh1.csv','a') as fd:
for t in l:
if t[-1]!=0:
writer=csv.writer(fd)
writer.writerow([bt.get_data(0)]+t)
'''
url = "https://en.wikipedia.org/wiki/List_of_S%26P_500_companies"
content = pd.read_html(url)
stocklist = content[0]['Symbol'].tolist()
=======
str1="CPB 17 ,JWN 11 ,NTAP 10 ,SNPS 3 ,CPRT 3 ,TJX 4 ,COST 11 ,MDT 4 ,LB 3 ,PGR 3 ,A 4 ,KEYS 9 ,HRL 4 ,ROST 4 ,KSS 2 ,NVDA 4 ,ADSK 10 ,HPE 4 ,PVH 10 ,BBY 4 ,TTWO 3 ,DG 11 ,DE 5 ,AAP 2 ,INTU 4 ,GPS 11 ,ULTA 11 ,DLTR 11 ,LOW 3 ,HPQ 10 ,TIF 16 ,WMT 2 ,HD 2 ,ADI 3 ,AZO 9 ,DXC 11 ,"
l1=[]
import seaborn as sns
import pandas as pd
import basictools as bt
import stockplay as sp
import csv
sns.set(style="whitegrid")
url = "https://en.wikipedia.org/wiki/List_of_S%26P_500_companies"
content = pd.read_html(url)
stocklist = content[0]['Symbol'].tolist()+['pdd',"sqqq","tqqq","pltr","qqq","vldr"]
l=sp.caifuziyou(stocklist)
filename_tod=bt.get_data(0)+".csv"
filename_yes=bt.get_data(1)+".csv"
set_tod=set([])
set_yes=set([])
with open(filename_tod, 'w') as fd:
for t in l:
if t[-1] != 0 :
set_tod.add(t[0])
writer = csv.writer(fd)
writer.writerow(t)
with open(filename_yes) as fd:
for i in fd.readlines():
if len(i) > 10:
set_yes.add(i.replace("\n", "").split(",")[0])
temp1=[]
for i in l:
if i[0] not in set_yes and i[0] in set_tod:
temp1.append([i[0],i[1],i[2]])