def rsi_caching_nb(ts, windows, ewms):
"""Caching function for `vectorbt.indicators.basic.RSI`."""
delta = generic_nb.diff_nb(ts) # otherwise ewma will be all NaN
up, down = delta.copy(), delta.copy()
up = generic_nb.set_by_mask_nb(up, up < 0, 0)
down = np.abs(generic_nb.set_by_mask_nb(down, down > 0, 0))
# Cache
cache_dict = dict()
for i in range(windows.shape[0]):
h = hash((windows[i], ewms[i]))
if h not in cache_dict:
roll_up = ma_nb(up, windows[i], ewms[i])
roll_down = ma_nb(down, windows[i], ewms[i])
cache_dict[h] = roll_up, roll_down
return cache_dict
def rsi_cache_nb(close, windows, ewms, adjust):
"""Caching function for `vectorbt.indicators.basic.RSI`."""
delta = generic_nb.diff_nb(close)
up, down = delta.copy(), delta.copy()
up = generic_nb.set_by_mask_nb(up, up < 0, 0)
down = np.abs(generic_nb.set_by_mask_nb(down, down > 0, 0))
# Cache
cache_dict = dict()
for i in range(len(windows)):
h = hash((windows[i], ewms[i]))
if h not in cache_dict:
roll_up = ma_nb(up, windows[i], ewms[i], adjust=adjust)
roll_down = ma_nb(down, windows[i], ewms[i], adjust=adjust)
cache_dict[h] = roll_up, roll_down
return cache_dict
def rsi_caching_nb(ts, windows, ewms):
"""Caching function for `vectorbt.indicators.basic.RSI`."""
delta = generic_nb.diff_nb(ts)[1:, :] # otherwise ewma will be all NaN
up, down = delta.copy(), delta.copy()
up = generic_nb.set_by_mask_nb(up, up < 0, 0)
down = np.abs(generic_nb.set_by_mask_nb(down, down > 0, 0))
# Cache
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]:
roll_up = generic_nb.ewm_mean_nb(up, windows[i])
roll_down = generic_nb.ewm_mean_nb(down, windows[i])
else:
roll_up = generic_nb.rolling_mean_nb(up, windows[i])
roll_down = generic_nb.rolling_mean_nb(down, windows[i])
roll_up = generic_nb.prepend_nb(roll_up, 1,
np.nan) # bring to old shape
roll_down = generic_nb.prepend_nb(roll_down, 1, np.nan)
cache_dict[h] = roll_up, roll_down
return cache_dict