def normalize(ticker: Ticker, calc_price: bool = False) -> dict:
# convert named tuples to dicts so we send usable keys
new_ticks = []
for tick in ticker.ticks:
td = tick._asdict()
if td['tickType'] in (48, 77):
td['type'] = 'trade'
new_ticks.append(td)
ticker.ticks = new_ticks
# some contracts don't have volume so we may want to calculate
# a midpoint price based on data we can acquire (such as bid / ask)
if calc_price:
ticker.ticks.append({'type': 'trade', 'price': ticker.marketPrice()})
# serialize for transport
data = asdict(ticker)
# add time stamps for downstream latency measurements
data['brokerd_ts'] = time.time()
if ticker.rtTime:
data['broker_ts'] = data['rtTime_s'] = float(ticker.rtTime) / 1000.
return data
def normalize(ticker: Ticker, calc_price: bool = False) -> dict:
# convert named tuples to dicts so we send usable keys
new_ticks = []
for tick in ticker.ticks:
td = tick._asdict()
td['type'] = tick_types.get(td['tickType'], 'n/a')
new_ticks.append(td)
ticker.ticks = new_ticks
# some contracts don't have volume so we may want to calculate
# a midpoint price based on data we can acquire (such as bid / ask)
if calc_price:
ticker.ticks.append({'type': 'trade', 'price': ticker.marketPrice()})
# serialize for transport
data = asdict(ticker)
# add time stamps for downstream latency measurements
data['brokerd_ts'] = time.time()
# stupid stupid shit...don't even care any more..
# leave it until we do a proper latency study
# if ticker.rtTime is not None:
# data['broker_ts'] = data['rtTime_s'] = float(
# ticker.rtTime.timestamp) / 1000.
data.pop('rtTime')
return data
