def parse_option_data_by_symbol(self, symbol):
df = pd.read_hdf(self.option_data_file, symbol)
if df is not None:
print '{} data exists, parse it to object...'.format(symbol)
equity = Equity()
equity.symbol = symbol
equity.tradeTime = self.trade_date
self.equity_records.append(equity)
for index, row in df.iterrows():
equity.lastPrice = row['Underlying_Price']
option = Option()
option.symbol = symbol
option.tradeTime = self.trade_date,
option.expirationDate = row['Expiry']
option.optionType = row['Type']
option.strikePrice = row['Strike']
option.bidPrice = row['Bid']
option.lastPrice = row['Last']
option.priceChange = row['Change']
option.volatility = row['ImpliedVolatility']
option.theoretical = row['TheoreticalValue']
option.delta = row['Delta']
option.gamma = row['Gamma']
option.rho = row['Rho']
option.theta = row['Theta']
option.vega = row['Vega']
option.openInterest = row['Open Int']
option.volume = row['Volume']
self.option_records.append(option)
else:
print 'Missing data for {} ...'.format(symbol)
def combine_records(symbol, records):
equity = Equity()
equity.symbol = symbol
equity.tradeTime = records[-1][0]
equity.openPrice = records[0][1]
equity.highPrice = max(map(lambda x:x[2], records))
equity.lowPrice = min(map(lambda x:x[3],records))
equity.lastPrice = records[-1][4]
volume = sum(filter(lambda y: y is not None, map(lambda x: x[5], records)))
if volume != 0:
equity.volume = volume
return equity
def load_equity_data_by_symbol(self, symbol):
file_path = os.path.join(self.daily_path, 'equity_data',
'{}.json'.format(symbol))
with open(file_path) as fs:
json_data = json.load(fs)
equity = Equity.loads(json_data['data'][0])
return equity
def parse_record(self, content, symbol):
json_data = json.loads(content)
dic_data = json_data['Time Series (1min)']
for key, value in dic_data.iteritems():
time = key
open = value['1. open']
high = value['2. high']
low = value['3. low']
close = value['4. close']
# volume = value['5. volume']
equity = Equity(symbol, time, open, high, low, close, None, None)
yield equity
def load_log(self, symbol, date):
file_name = '%s%s.log' % (symbol, date.strftime('%Y%m%d'))
path = PathMgr.get_data_path('quantopian_daily_min/%s' % file_name)
content = read_file_to_string(path)
lines = content.split('\n')
filtered_lines = filter(lambda x: len(x) > 100, lines)
lines = map(lambda x: string_fetch(x, 'PRINT ', ''), filtered_lines)
close_list_str = ','.join(lines)
prices_list = map(float, close_list_str.split(','))
datetimes = TradeTime.generate_datetimes(date, date)
equities = map(lambda x, y: Equity(symbol, x, y, y, y, y), datetimes,
prices_list)
EquityMinDAO().insert(equities)
def parse_line(self, line, symbol):
record = line.split(',')
time = datetime.datetime.strptime('%s %s' % (record[0], record[1]),
'%m/%d/%Y %H:%M')
open_price = float(record[2])
high_price = float(record[3])
low_price = float(record[4])
close_price = float(record[5])
volume = float(record[6])
if 9.5 * 60 <= time.hour * 60 + time.minute <= 16 * 60:
return Equity(symbol, time, open_price, high_price, low_price,
close_price, close_price - open_price, volume)
else:
return None
def add_missing_data_in_real_time(
self,
symbol='SVXY',
):
us_dt = datetime.datetime.now(tz=pytz.timezone('US/Eastern'))
now = datetime.datetime(us_dt.year, us_dt.month, us_dt.day, us_dt.hour,
us_dt.minute, us_dt.second)
if TradeTime.is_trade_day(now.date()):
default_start_time = datetime.datetime(now.year, now.month,
now.day, 9, 30, 0)
start_time = max(
default_start_time,
datetime.datetime(now.year, now.month, now.day, now.hour - 1,
now.minute, 0))
if now > start_time:
if TradeTime.is_half_trade_day(now.date()):
default_end_time = datetime.datetime(
now.year, now.month, now.day, 13, 0, 0)
else:
default_end_time = datetime.datetime(
now.year, now.month, now.day, 16, 0, 0)
end_time = min(now, default_end_time)
if end_time > start_time:
minutes_count = range((end_time - start_time).seconds /
60 + 1)
trade_minutes = map(
lambda x: start_time + datetime.timedelta(minutes=x),
minutes_count)
# print trade_minutes
rows = self.get_time_and_price(symbol, start_time,
end_time)
# print rows
j = 0
missing_records = []
for i, time in enumerate(trade_minutes):
if rows[j][0] > time:
if j > 0:
price = rows[j - 1][1]
else:
price = rows[0][1]
missing_records.append(
Equity(symbol, time, price, price, price,
price, 0, 0))
else:
j = j + 1
if len(missing_records) > 0:
self.insert(missing_records)
return len(missing_records)
def add_missing_data(self, symbol='SVXY', validate_date=None):
if validate_date is None:
validate_date = TradeTime.get_latest_trade_date()
start_time = datetime.datetime.fromordinal(validate_date.toordinal())
end_time = start_time + datetime.timedelta(days=1)
rows = self.get_time_and_price(symbol, start_time, end_time)
j = 0
missing_records = []
for i, time in enumerate(TradeTime.get_all_trade_min(validate_date)):
if j >= len(rows) or rows[j][0] > time:
if j > 0:
price = rows[j - 1][1]
else:
price = rows[0][1]
missing_records.append(
Equity(symbol, time, price, price, price, price, 0, 0))
else:
j = j + 1
if len(missing_records) > 0:
self.insert(missing_records)
return len(missing_records)
def parse_line(self, line, code):
time = datetime.datetime.strptime(line[0:19], '%Y-%m-%d %H:%M:%S')
price = float(line[21:])
return Equity(code, time, price, price, price, price, price, None)