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_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 scan(stock):
# pot1 = get_stock_history_by_date(stock, '2015-10-08')
# pot2 = get_stock_history_by_date(stock, '2015-10-09')
pot3 = get_stock_history_by_date(stock, '2015-10-12')
pot4 = get_stock_history_by_date(stock, '2015-10-13')
pot5 = get_stock_history_by_date(stock, '2015-10-14')
pot6 = get_stock_history_by_date(stock, '2015-10-15')
if pot3 and pot4 and pot5 and pot6:
if 'rong_all_balance' in pot6 and pot6['close'] != 0.0:
if i(pot3['rong_all_balance']) > i(pot4['rong_all_balance']) > i(pot5['rong_all_balance']) > i(pot6['rong_all_balance']):
r = str(rate(i(pot3['rong_all_balance']), i(pot6['rong_all_balance'])))[:5]
print '{} {}'.format(stock, r)
def find_rate():
rank = []
for stock in stocks:
try:
first_open = get_stock_history_by_date(stock, '2015-07-09')['low']
last_close = get_stock_history_by_date(stock, '2015-07-24')['close']
r = rate(last_close, first_open)
rank.append((stock, r))
except:
continue
ranked = sorted(rank, key=lambda r: r[1])
for rn, rr in ranked:
name = get_stock_name_from_mongo(rn)
print u'{} {} {}'.format(name, rn, rr)
def find_last_week_up_most():
rank = []
for stock in stocks:
if not stock.startswith('002'):
continue
try:
first_open = get_stock_history_by_date(stock, '2015-09-30')['close']
last_close = get_stock_history_by_date(stock, '2015-11-06')['close']
r = rate(last_close, first_open)
rank.append((stock, r))
except:
continue
ranked = sorted(rank, key=lambda r: r[1])
for rn, rr in ranked:
print u'{} {}'.format(rn, rr)
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_rate():
rate = util.rate()
return json.dumps({'avg_rate': rate})
# poll_stats.py
# Jonah Smith
# Storytelling with Streaming Data, Spring 2016
#
# This file, in an infinite loop, uses the functions in the util file to
# calculate entropy and rate based on the state of the Redis db. It takes no
# input, and emits a JSON string with the entropy and rate to stdout. These
# messages are monitored by find-anomalies.py to, maybe not surprisingly, find
# anomalies.
import json
from sys import stdout
from time import sleep
# util has our functions for calculating the entropy and rate.
import util
# Repeat the entropy and rate calculations indefinitely.
while 1:
# Use our utility functions to calculate entropy and rate.
entropy = util.entropy()
rate = util.rate()
# Dump the entropy and rate to stdout and flush the stdout so we don't end
# up with a buffer.
print(json.dumps({'entropy': entropy, 'rate': rate}))
stdout.flush()
# Rest of one second. This will give us a nice smooth function for the rate
# and entropy values.
sleep(1)
def get_rate():
rate = util.rate()
return json.dumps({"avg_rate": rate})