def make_daily_bar_data(cls, assets, calendar, sessions):
# Generate prices corresponding to uniform random returns with a slight
# positive tendency.
start = cls.INTERNATIONAL_PRICING_STARTING_PRICES[calendar.name]
closes = random_tick_prices(start, len(sessions))
opens = closes - 0.05
highs = closes + 0.10
lows = closes - 0.10
volumes = np.arange(10000, 10000 + len(closes))
base_frame = pd.DataFrame(
{
"close": closes,
"open": opens,
"high": highs,
"low": lows,
"volume": volumes,
},
index=sessions,
)
for asset in assets:
sid = asset.sid
yield sid, base_frame + sid
def make_daily_bar_data(cls, assets, calendar, sessions):
# Generate prices corresponding to uniform random returns with a slight
# positive tendency.
start = cls.INTERNATIONAL_PRICING_STARTING_PRICES[calendar.name]
closes = random_tick_prices(start, len(sessions))
opens = closes - 0.05
highs = closes + 0.10
lows = closes - 0.10
volumes = np.arange(10000, 10000 + len(closes))
base_frame = pd.DataFrame({
'close': closes,
'open': opens,
'high': highs,
'low': lows,
'volume': volumes,
}, index=sessions)
for asset in assets:
sid = asset.sid
yield sid, base_frame + sid