adding cache fot njit decorators - the result is a 3x speed improveme…

…nt in with pytest

jesse/indicators/bandpass.py

@@ -46,7 +46,7 @@ def bandpass(candles: np.ndarray, period: int = 20, bandwidth: float = 0.3,  sou
         return BandPass(bp[-1], bp_normalized[-1], signal[-1], trigger[-1])
 
 
-@njit
+@njit(cache=True)
 def bp_fast(source, hp, alpha, beta):  # Function is compiled to machine code when called the first time
 
     bp = np.copy(hp)

jesse/indicators/cg.py

@@ -26,7 +26,7 @@ def cg(candles: np.ndarray, period: int = 10, source_type: str = "close", sequen
     return same_length(candles, res) if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def go_fast(source, period):  # Function is compiled to machine code when called the first time
     res = np.full_like(source, fill_value=np.nan)
     for i in range(source.size):

jesse/indicators/correlation_cycle.py

@@ -39,7 +39,7 @@ def correlation_cycle(candles: np.ndarray, period: int = 20, threshold: int = 9,
         return CC(realPart[-1], imagPart[-1], angle[-1], state[-1])
 
 
-@njit
+@njit(cache=True)
 def go_fast(source, period):  # Function is compiled to machine code when called the first time
     # Correlation Cycle Function
     PIx2 = 4.0 * np.arcsin(1.0)

jesse/indicators/cwma.py

@@ -31,7 +31,7 @@ def cwma(candles: np.ndarray, period: int = 14, source_type: str = "close", sequ
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def vpwma_fast(source, period):
     newseries = np.copy(source)
     for j in range(period + 1, source.shape[0]):

jesse/indicators/damiani_volatmeter.py

@@ -42,7 +42,7 @@ def damiani_volatmeter(candles: np.ndarray, vis_atr: int = 13, vis_std: int = 20
         return DamianiVolatmeter(vol[-1], t[-1])
 
 
-@njit
+@njit(cache=True)
 def damiani_volatmeter_fast(source, sed_std, atrvis, atrsed, vis_std,
                             threshold):  # Function is compiled to machine code when called the first time
     lag_s = 0.5

jesse/indicators/edcf.py

@@ -31,7 +31,7 @@ def edcf(candles: np.ndarray, period: int = 15, source_type: str = "hl2", sequen
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def edcf_fast(source, period):
     newseries = np.full_like(source, np.nan)
 

jesse/indicators/efi.py

@@ -31,7 +31,7 @@ def efi(candles: np.ndarray, period: int = 13, source_type: str = "close", seque
     return res_with_nan if sequential else res_with_nan[-1]
 
 
-@njit
+@njit(cache=True)
 def efi_fast(source, volume):
     dif = np.zeros(source.size - 1)
     for i in range(1, source.size):

jesse/indicators/emd.py

@@ -39,7 +39,7 @@ def emd(candles: np.ndarray, period: int = 20, delta=0.5, fraction=0.1, sequenti
         return EMD(avg_peak[-1], mean[-1], avg_valley[-1])
 
 
-@njit
+@njit(cache=True)
 def bp_fast(price, period, delta):
     # bandpass filter
     beta = np.cos(2 * np.pi / period)
@@ -55,7 +55,7 @@ def bp_fast(price, period, delta):
     return bp
 
 
-@njit
+@njit(cache=True)
 def peak_valley_fast(bp, price):
     peak = np.copy(bp)
     valley = np.copy(bp)

jesse/indicators/epma.py

@@ -32,7 +32,7 @@ def epma(candles: np.ndarray, period: int = 11, offset: int = 4, source_type: st
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def epma_fast(source, period, offset):
     newseries = np.copy(source)
     for j in range(period + offset + 1 , source.shape[0]):

jesse/indicators/frama.py

@@ -36,7 +36,7 @@ def frama(candles: np.ndarray, window: int = 10, FC: int = 1, SC: int = 300, seq
         return res[-1]
 
 
-@njit
+@njit(cache=True)
 def frame_fast(candles, n, SC, FC):
     w = np.log(2.0 / (SC + 1))
 

jesse/indicators/gauss.py

@@ -36,7 +36,7 @@ def gauss(candles: np.ndarray, period: int = 14, poles: int = 4, source_type: st
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def gauss_fast(source, period, poles):
     N = source.size
     source = source[~np.isnan(source)]

jesse/indicators/heikin_ashi_candles.py

@@ -24,7 +24,7 @@ def heikin_ashi_candles(candles: np.ndarray, sequential: bool = False) -> HA:
     else:
         return HA(open[-1], close[-1], high[-1], low[-1])
 
-@njit
+@njit(cache=True)
 def ha_fast(source):
 
     # index consts to facilitate reading the code

jesse/indicators/high_pass.py

@@ -33,7 +33,7 @@ def high_pass(candles: np.ndarray, period: int = 48, source_type: str = "close",
         return None if np.isnan(hpf[-1]) else hpf[-1]
 
 
-@njit
+@njit(cache=True)
 def high_pass_fast(source, period):  # Function is compiled to machine code when called the first time
     k = 1
     alpha = 1 + (np.sin(2 * np.pi * k / period) - 1) / np.cos(2 * np.pi * k / period)

jesse/indicators/high_pass_2_pole.py

@@ -34,7 +34,7 @@ def high_pass_2_pole(candles: np.ndarray, period: int = 48, source_type: str = "
         return None if np.isnan(hpf[-1]) else hpf[-1]
 
 
-@njit
+@njit(cache=True)
 def high_pass_2_pole_fast(source, period, K=0.707):  # Function is compiled to machine code when called the first time
     alpha = 1 + (np.sin(2 * np.pi * K / period) - 1) / np.cos(2 * np.pi * K / period)
     newseries = np.copy(source)

jesse/indicators/hurst_exponent.py

@@ -37,7 +37,7 @@ def hurst_exponent(candles: np.ndarray, min_chunksize: int = 8, max_chunksize: i
     return None if np.isnan(h) else h
 
 
-@njit
+@njit(cache=True)
 def hurst_rs(x, min_chunksize, max_chunksize, num_chunksize):
     """Estimate the Hurst exponent using R/S method.
     Estimates the Hurst (H) exponent using the R/S method from the time series.

jesse/indicators/hwma.py

@@ -37,7 +37,7 @@ def hwma(candles: np.ndarray, na: float = 0.2, nb: float = 0.1, nc: float = 0.1,
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def hwma_fast(source, na, nb, nc):
     last_a = last_v = 0
     last_f = source[0]

jesse/indicators/itrend.py

@@ -31,7 +31,7 @@ def itrend(candles: np.ndarray, alpha: float = 0.07, source_type: str = "hl2", s
         return ITREND(signal[-1], it[-1], trigger[-1])
 
 
-@njit
+@njit(cache=True)
 def itrend_fast(source, alpha):
     it = np.copy(source)
     for i in range(2, 7):

jesse/indicators/jma.py

@@ -28,7 +28,7 @@ def jma(candles: np.ndarray, period:int=7, phase:float=50, power:int=2, source_t
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def jma_helper(src, phaseRatio, beta, alpha):
     jma_val = np.copy(src)
 

jesse/indicators/lrsi.py

@@ -26,7 +26,7 @@ def lrsi(candles: np.ndarray, alpha: float = 0.2, sequential: bool = False) -> U
         return None if np.isnan(rsi[-1]) else rsi[-1]
 
 
-@njit
+@njit(cache=True)
 def lrsi_fast(alpha, candles):
     price = (candles[:, 3] + candles[:, 4]) / 2
     l0 = np.copy(price)

jesse/indicators/maaq.py

@@ -48,7 +48,7 @@ def maaq(candles: np.ndarray, period: int = 11, fast_period: int = 2, slow_perio
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def maaq_fast(source, temp, period):
     newseries = np.copy(source)
     for i in range(period, source.shape[0]):

jesse/indicators/mcginley_dynamic.py

@@ -33,7 +33,7 @@ def mcginley_dynamic(candles: np.ndarray, period: int = 10, k: float = 0.6, sour
     return mg if sequential else mg[-1]
 
 
-@njit
+@njit(cache=True)
 def md_fast(source, k, period):
     mg = np.full_like(source, np.nan)
     for i in range(source.size):

jesse/indicators/mwdx.py

@@ -34,7 +34,7 @@ def mwdx(candles: np.ndarray, factor: float = 0.2, source_type: str = "close", s
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def mwdx_fast(source, fac):
     newseries = np.copy(source)
     for i in range(1, source.shape[0]):

jesse/indicators/nma.py

@@ -29,7 +29,7 @@ def nma(candles: np.ndarray, period: int = 40, source_type: str = "close", seque
 
     return res if sequential else res[-1]
 
-@njit
+@njit(cache=True)
 def nma_fast(source, period):
     # Ensure source values are positive before taking log
     source = np.clip(source, a_min=1e-10, a_max=None)

jesse/indicators/pma.py

@@ -33,7 +33,7 @@ def pma(candles: np.ndarray, source_type: str = "hl2", sequential: bool = False)
         return PMA(predict[-1], trigger[-1])
 
 
-@njit
+@njit(cache=True)
 def pma_fast(source):
     predict = np.full_like(source, np.nan)
     trigger = np.full_like(source, np.nan)

jesse/indicators/reflex.py

@@ -37,7 +37,7 @@ def reflex(candles: np.ndarray, period: int = 20, source_type: str = "close", se
         return None if np.isnan(rf[-1]) else rf[-1]
 
 
-@njit
+@njit(cache=True)
 def reflex_fast(ssf, period):
     rf = np.full_like(ssf, 0)
     ms = np.full_like(ssf, 0)

jesse/indicators/rma.py

@@ -34,7 +34,7 @@ def rma(candles: np.ndarray, length: int = 14, source_type="close", sequential=F
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def rma_fast(source, _length):
     alpha = 1 / _length
     newseries = np.copy(source)

jesse/indicators/rsx.py

@@ -26,7 +26,7 @@ def rsx(candles: np.ndarray, period: int = 14, source_type: str = "close", seque
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def rsx_fast(source, period):
     # variables
     f0 = 0

jesse/indicators/sqwma.py

@@ -31,7 +31,7 @@ def sqwma(candles: np.ndarray, period: int = 14, source_type: str = "close", seq
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def sqwma_fast(source, period):
     newseries = np.copy(source)
     for j in range(period + 1, source.shape[0]):

jesse/indicators/srwma.py

@@ -31,7 +31,7 @@ def srwma(candles: np.ndarray, period: int = 14, source_type: str = "close", seq
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def srwma_fast(source, period):
     newseries = np.copy(source)
     for j in range(period + 1, source.shape[0]):

jesse/indicators/supersmoother.py

@@ -33,7 +33,7 @@ def supersmoother(candles: np.ndarray, period: int = 14, source_type: str = "clo
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def supersmoother_fast(source, period):
     a = np.exp(-1.414 * np.pi / period)
     b = 2 * a * np.cos(1.414 * np.pi / period)

jesse/indicators/supersmoother_3_pole.py

@@ -37,7 +37,7 @@ def supersmoother_3_pole(candles: np.ndarray, period: int = 14, source_type: str
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def supersmoother_fast(source, period):
     a = np.exp(-np.pi / period)
     b = 2 * a * np.cos(1.738 * np.pi / period)

jesse/indicators/supertrend.py

@@ -32,7 +32,7 @@ def supertrend(candles: np.ndarray, period: int = 10, factor: float = 3, sequent
         return SuperTrend(super_trend[-1], changed[-1])
 
 
-@njit
+@njit(cache=True)
 def supertrend_fast(candles, atr, factor, period):
     # Calculation of SuperTrend
     upper_basic = (candles[:, 3] + candles[:, 4]) / 2 + (factor * atr)

jesse/indicators/trendflex.py

@@ -38,7 +38,7 @@ def trendflex(candles: np.ndarray, period: int = 20, source_type: str = "close",
         return None if np.isnan(tf[-1]) else tf[-1]
 
 
-@njit
+@njit(cache=True)
 def trendflex_fast(ssf, period):
     tf = np.full_like(ssf, 0)
     ms = np.full_like(ssf, 0)

jesse/indicators/vi.py

@@ -28,7 +28,7 @@ def vi(candles: np.ndarray, period: int = 14, sequential: bool = False) -> VI:
         return VI(vpn_with_nan[-1], vmn_with_nan[-1])
 
 
-@njit
+@njit(cache=True)
 def vi_fast(candles, period):
     candles_close = candles[:, 2]
     candles_high = candles[:, 3]

jesse/indicators/vlma.py

@@ -52,7 +52,7 @@ def vlma(candles: np.ndarray, min_period: int = 5, max_period: int = 50, matype:
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def vlma_fast(source, a, b, c, d, min_period, max_period):
     newseries = np.copy(source)
     period = np.zeros_like(source)

jesse/indicators/voss.py

@@ -34,7 +34,7 @@ def voss(candles: np.ndarray, period: int = 20, predict: int = 3, bandwith: floa
         return VossFilter(voss_val[-1], filt[-1])
 
 
-@njit
+@njit(cache=True)
 def voss_fast(source, period, predict, bandwith):
     voss = np.full_like(source, 0)
     filt = np.full_like(source, 0)

jesse/indicators/vpwma.py

@@ -32,7 +32,7 @@ def vpwma(candles: np.ndarray, period: int = 14, power: float = 0.382, source_ty
     return res if sequential else res[-1]
 
 
-@njit
+@njit(cache=True)
 def vpwma_fast(source, period, power):
     newseries = np.copy(source)
     for j in range(period + 1, source.shape[0]):