def true_range(high_ts, low_ts, close_ts):
"""Calculate true range."""
prev_close = generic_nb.fshift_nb(close_ts, 1)
tr1 = high_ts - low_ts
tr2 = np.abs(high_ts - prev_close)
tr3 = np.abs(low_ts - prev_close)
tr = nanmax_cube_nb(np.stack((tr1, tr2, tr3)))
return tr
def atr_caching_nb(close_ts, high_ts, low_ts, windows, ewms):
"""Caching function for `vectorbt.indicators.basic.ATR`."""
# Calculate TR here instead of re-calculating it for each param in atr_apply_nb
tr0 = high_ts - low_ts
tr1 = np.abs(high_ts - generic_nb.fshift_nb(close_ts, 1))
tr2 = np.abs(low_ts - generic_nb.fshift_nb(close_ts, 1))
tr = nanmax_cube_nb(np.stack((tr0, tr1, tr2)))
cache_dict = dict()
for i in range(windows.shape[0]):
h = hash((windows[i], ewms[i]))
if h not in cache_dict:
if ewms[i]:
atr = generic_nb.ewm_mean_nb(tr, windows[i])
else:
atr = generic_nb.rolling_mean_nb(tr, windows[i])
cache_dict[h] = atr
return tr, cache_dict
def true_range_nb(high, low, close):
"""Calculate true range."""
prev_close = generic_nb.fshift_nb(close, 1)
tr1 = high - low
tr2 = np.abs(high - prev_close)
tr3 = np.abs(low - prev_close)
tr = np.empty(prev_close.shape, dtype=np.float_)
for col in range(tr.shape[1]):
for i in range(tr.shape[0]):
tr[i, col] = max(tr1[i, col], tr2[i, col], tr3[i, col])
return tr
def obv_custom_func_nb(close_ts, volume_ts):
"""Custom calculation function for `vectorbt.indicators.basic.OBV`."""
obv = generic_nb.set_by_mask_mult_nb(volume_ts, close_ts < generic_nb.fshift_nb(close_ts, 1), -volume_ts)
obv = generic_nb.cumsum_nb(obv)
return obv
def obv_custom_nb(close: tp.Array2d, volume_ts: tp.Array2d) -> tp.Array2d:
"""Custom calculation function for `vectorbt.indicators.basic.OBV`."""
obv = generic_nb.set_by_mask_mult_nb(
volume_ts, close < generic_nb.fshift_nb(close, 1), -volume_ts)
obv = generic_nb.nancumsum_nb(obv)
return obv
