def compute(self, today, assets, out, data):
drawdowns = fmax.accumulate(data, axis=0) - data
drawdowns[isnan(drawdowns)] = NINF
drawdown_ends = nanargmax(drawdowns, axis=0)
# TODO: Accelerate this loop in Cython or Numba.
for i, end in enumerate(drawdown_ends):
peak = nanmax(data[:end + 1, i])
out[i] = (peak - data[end, i]) / data[end, i]
def compute(self, today, assets, out, data):
drawdowns = fmax.accumulate(data, axis=0) - data
drawdowns[isnan(drawdowns)] = NINF
drawdown_ends = nanargmax(drawdowns, axis=0)
# TODO: Accelerate this loop in Cython or Numba.
for i, end in enumerate(drawdown_ends):
peak = nanmax(data[:end + 1, i])
out[i] = (peak - data[end, i]) / data[end, i]
def compute(self, today, assets, out, highs, lows, closes):
high_to_low = highs[1:] - lows[1:]
high_to_prev_close = abs(highs[1:] - closes[:-1])
low_to_prev_close = abs(lows[1:] - closes[:-1])
out[:] = nanmax(
dstack((
high_to_low,
high_to_prev_close,
low_to_prev_close,
)), 2)
def compute(self, today, assets, out, highs, lows, closes):
high_to_low = highs[1:] - lows[1:]
high_to_prev_close = abs(highs[1:] - closes[:-1])
low_to_prev_close = abs(lows[1:] - closes[:-1])
out[:] = nanmax(
dstack((
high_to_low,
high_to_prev_close,
low_to_prev_close,
)),
2
)
def compute(self, today, assets, out, closes, lows, highs):
highest_highs = nanmax(highs, axis=0)
lowest_lows = nanmin(lows, axis=0)
today_closes = closes[-1]
evaluate(
'((tc - ll) / (hh - ll)) * 100',
local_dict={
'tc': today_closes,
'll': lowest_lows,
'hh': highest_highs,
},
global_dict={},
out=out,
)
def compute(self, today, assets, out, closes, lows, highs):
highest_highs = nanmax(highs, axis=0)
lowest_lows = nanmin(lows, axis=0)
today_closes = closes[-1]
evaluate(
'((tc - ll) / (hh - ll)) * 100',
local_dict={
'tc': today_closes,
'll': lowest_lows,
'hh': highest_highs,
},
global_dict={},
out=out,
)
def compute(self, today, assets, out, closes, lows, highs):
highest_highs = nanmax(highs, axis=0)
lowest_lows = nanmin(lows, axis=0)
today_closes = closes[-1]
evaluate(
"((tc - ll) / (hh - ll)) * 100",
local_dict={
"tc": today_closes,
"ll": lowest_lows,
"hh": highest_highs,
},
global_dict={},
out=out,
)
def highs(self, assets, dt):
"""
The high field's aggregation returns the largest high seen between
the market open and the current dt.
If there has been no data on or before the `dt` the high is `nan`.
Returns
-------
np.array with dtype=float64, in order of assets parameter.
"""
market_open, prev_dt, dt_value, entries = self._prelude(dt, "high")
highs = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
highs.append(np.NaN)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, "high")
entries[asset] = (dt_value, val)
highs.append(val)
continue
else:
try:
last_visited_dt, last_max = entries[asset]
if last_visited_dt == dt_value:
highs.append(last_max)
continue
elif last_visited_dt == prev_dt:
curr_val = self._minute_reader.get_value(
asset, dt, "high")
if pd.isnull(curr_val):
val = last_max
elif pd.isnull(last_max):
val = curr_val
else:
val = max(last_max, curr_val)
entries[asset] = (dt_value, val)
highs.append(val)
continue
else:
after_last = pd.Timestamp(last_visited_dt +
self._one_min,
tz="UTC")
window = self._minute_reader.load_raw_arrays(
["high"],
after_last,
dt,
[asset],
)[0].T
val = nanmax(np.append(window, last_max))
entries[asset] = (dt_value, val)
highs.append(val)
continue
except KeyError:
window = self._minute_reader.load_raw_arrays(
["high"],
market_open,
dt,
[asset],
)[0].T
val = nanmax(window)
entries[asset] = (dt_value, val)
highs.append(val)
continue
return np.array(highs)