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 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
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)
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
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)