def update_wsb_table():
current_date = datetime.datetime.now().strftime("%Y-%m-%d")
if utils.is_trading_day(current_date) is False:
print(
'Today is not a trading day. WSB Sentiment from yesterday not updated.'
)
return
date_nine_wsb_entries_ago = utils.get_wsb_date_n_entries_ago(9)
print('Nine entries ago was on: ' + date_nine_wsb_entries_ago)
#wsb_data = get_wsb_data(start_date=date_nine_wsb_entries_ago)
wsb_data = get_wsb_data()
print('Retrieved Historical WSB data.')
print('Analyzing Sentiment figures...')
sentiment_dict = utils.get_yesterday_wsb_sentiment()
print(sentiment_dict)
print('Retrieved Sentiment from yesterday.')
# Now calculate the recent 10d moving average.
bull_bear_series = wsb_data['Bull/Bear Ratio']
bull_bear_series = bull_bear_series.append(
pd.Series([sentiment_dict['Bull/Bear Ratio']],
index=[sentiment_dict['Date']]))
print(bull_bear_series)
ema_10 = pd.Series.ewm(bull_bear_series, span=10).mean()
ema_10_value = Decimal(str(ema_10.get(key=sentiment_dict['Date'])))
# Now, add the entry to the AWS database.
date = sentiment_dict['Date']
bull_comments = Decimal(str(sentiment_dict['Bull Comments']))
bear_comments = Decimal(str(sentiment_dict['Bear Comments']))
bull_bear_ratio = Decimal(str(sentiment_dict['Bull/Bear Ratio']))
add_to_wsb_table(date, bull_comments, bear_comments, bull_bear_ratio,
ema_10_value)
def validate(data_list, data_dict, open_time, close_time):
time_step = datetime.timedelta(minutes=1)
# stamp = open_time + time_step
if not utils.is_trading_day(open_time):
return
if len(data_list) <= 0:
print 'invalid day: %s' % open_time
return
tmp_time = open_time + time_step
while tmp_time <= close_time:
str_date = tmp_time.strftime('%H%M')
if str_date == '1016' or str_date == '1020':
tmp_time += 15 * time_step # 休息15分钟
elif str_date == '1131':
tmp_time += 120 * time_step # 午休
#if tmp_time not in data_dict:
# print 'invalid timestamp: %s' % tmp_time
tmp_time += time_step
def update_spy_table():
yesterday = utils.yesterday_date()
if utils.is_trading_day(yesterday) is False:
print('Yesterday was not a trading day. No SPY data to update.')
return
print('Getting SPY Data from yesterday...')
info = utils.get_yesterday_spy_data()
print(info)
print('Retrieved SPY data from yesterday.')
date = info['Date']
open = Decimal(str(info['Open']))
close = Decimal(str(info['Close']))
add_to_spy_table(date, open, close)
# time.sleep(1)
#
# calc.calc_sub_price(open_time, close_time)
# merge.merge(open_time, close_time)
#
begin = datetime.datetime(year=2017, month=1,day=1,hour=9,minute=0,second=0,microsecond=0)
end = datetime.datetime(year=2017, month=10,day=10,hour=1,minute=0,second=0,microsecond=0)
# begin = read_date("开盘")
# end = read_date("收盘")
delta = datetime.timedelta(hours=12)
delta1 = datetime.timedelta(days=4)
while begin < end:
time.sleep(1)
open_time, close_time = utils.get_open_close_time(begin)
if not utils.is_trading_day(open_time):
begin += delta
continue
res = recv_data.check_data(open_time, close_time, config.ACTUAL_GOODS)
if not res:
print "there is no data between %s - %s" %(open_time, close_time)
begin += delta
continue
time.sleep(1)
res = recv_data.check_data(open_time, close_time, config.FUTURES)
if not res:
cur = dbutil.get_cursor()
cur.execute("delete from lme_cu_1min where date >= '%s' and date <= '%s'" % (open_time, close_time))
minute=0,
second=0,
microsecond=0)
end = datetime.datetime(year=2016,
month=11,
day=17,
hour=1,
minute=0,
second=0,
microsecond=0)
delta = datetime.timedelta(hours=12)
while begin < end:
open_time, close_time = utils.get_open_close_time(begin)
seq = gen_time_sequence(open_time, close_time, 60)
sub_price_data_list, high, low, data_dict = get_price_sub_data(
open_time, close_time)
lens = len(sub_price_data_list)
if lens <= 0 and utils.is_trading_day(open_time):
print seq
for i in range(0, lens, 1):
expect = seq[i]
if expect not in data_dict:
print "缺少数据: %s " % expect
begin += delta