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polakowo
GitHub Repository: polakowo/vectorbt
 Path: blob/master/tests/notebooks/indicators.ipynb
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Kernel: Python 3

indicators

import vectorbt as vbt

import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from numba import njit
import itertools
import talib
import ta

# Disable caching for performance testing
vbt.settings.caching['enabled'] = False

close = pd.DataFrame({
    'a': [1., 2., 3., 4., 5.],
    'b': [5., 4., 3., 2., 1.],
    'c': [1., 2., 3., 2., 1.]
}, index=pd.DatetimeIndex([
    datetime(2018, 1, 1),
    datetime(2018, 1, 2),
    datetime(2018, 1, 3),
    datetime(2018, 1, 4),
    datetime(2018, 1, 5)
]))
np.random.seed(42)
high = close * np.random.uniform(1, 1.1, size=close.shape)
low = close * np.random.uniform(0.9, 1, size=close.shape)
volume = close * 0 + np.random.randint(1, 10, size=close.shape).astype(float)

big_close = pd.DataFrame(np.random.randint(10, size=(1000, 1000)).astype(float))
big_close.index = [datetime(2018, 1, 1) + timedelta(days=i) for i in range(1000)]
big_high = big_close * np.random.uniform(1, 1.1, size=big_close.shape)
big_low = big_close * np.random.uniform(0.9, 1, size=big_close.shape)
big_volume = big_close * 0 + np.random.randint(10, 100, size=big_close.shape).astype(float)

close_ts = pd.Series([1, 2, 3, 4, 3, 2, 1], index=pd.DatetimeIndex([
    datetime(2018, 1, 1),
    datetime(2018, 1, 2),
    datetime(2018, 1, 3),
    datetime(2018, 1, 4),
    datetime(2018, 1, 5),
    datetime(2018, 1, 6),
    datetime(2018, 1, 7)
]))
high_ts = close_ts * 1.1
low_ts = close_ts * 0.9
volume_ts = pd.Series([4, 3, 2, 1, 2, 3, 4], index=close_ts.index)

IndicatorFactory

def apply_func(i, ts, p, a, b=100):
    return ts * p[i] + a + b

@njit
def apply_func_nb(i, ts, p, a, b):
    return ts * p[i] + a + b # numba doesn't support **kwargs

# Custom function can be anything that takes time series, params and other arguments, and returns outputs
def custom_func(ts, p, *args, **kwargs):
    return vbt.base.combine_fns.apply_and_concat_one(len(p), apply_func, ts, p, *args, **kwargs)

@njit
def custom_func_nb(ts, p, *args):
    return vbt.base.combine_fns.apply_and_concat_one_nb(len(p), apply_func_nb, ts, p, *args)

F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['out'])
print(F.from_custom_func(custom_func, var_args=True)
      .run(close, [0, 1], 10, b=100).out)
print(F.from_custom_func(custom_func_nb, var_args=True)
      .run(close, [0, 1], 10, 100).out)
custom_p 0 1 a b c a b c 2018-01-01 110.0 110.0 110.0 111.0 115.0 111.0 2018-01-02 110.0 110.0 110.0 112.0 114.0 112.0 2018-01-03 110.0 110.0 110.0 113.0 113.0 113.0 2018-01-04 110.0 110.0 110.0 114.0 112.0 112.0 2018-01-05 110.0 110.0 110.0 115.0 111.0 111.0 custom_p 0 1 a b c a b c 2018-01-01 110.0 110.0 110.0 111.0 115.0 111.0 2018-01-02 110.0 110.0 110.0 112.0 114.0 112.0 2018-01-03 110.0 110.0 110.0 113.0 113.0 113.0 2018-01-04 110.0 110.0 110.0 114.0 112.0 112.0 2018-01-05 110.0 110.0 110.0 115.0 111.0 111.0 
# Apply function is performed on each parameter individually, and each output is then stacked for you
# Apply functions are less customizable than custom functions, but are simpler to write
def apply_func(ts, p, a, b=100):
    return ts * p + a + b

@njit
def apply_func_nb(ts, p, a, b):
    return ts * p + a + b  # numba doesn't support **kwargs
     
F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['out'])
print(F.from_apply_func(apply_func, var_args=True)
      .run(close, [0, 1], 10, b=100).out)
print(F.from_apply_func(apply_func_nb, var_args=True)
      .run(close, [0, 1], 10, 100).out)
custom_p 0 1 a b c a b c 2018-01-01 110.0 110.0 110.0 111.0 115.0 111.0 2018-01-02 110.0 110.0 110.0 112.0 114.0 112.0 2018-01-03 110.0 110.0 110.0 113.0 113.0 113.0 2018-01-04 110.0 110.0 110.0 114.0 112.0 112.0 2018-01-05 110.0 110.0 110.0 115.0 111.0 111.0 custom_p 0 1 a b c a b c 2018-01-01 110.0 110.0 110.0 111.0 115.0 111.0 2018-01-02 110.0 110.0 110.0 112.0 114.0 112.0 2018-01-03 110.0 110.0 110.0 113.0 113.0 113.0 2018-01-04 110.0 110.0 110.0 114.0 112.0 112.0 2018-01-05 110.0 110.0 110.0 115.0 111.0 111.0 
# test *args
F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['out'])
print(F.from_apply_func(lambda ts, p, a: ts * p + a, var_args=True)
      .run(close, [0, 1, 2], 3).out) 
print(F.from_apply_func(njit(lambda ts, p, a: ts * p + a), var_args=True)
      .run(close, [0, 1, 2], 3).out)
custom_p 0 1 2 a b c a b c a b c 2018-01-01 3.0 3.0 3.0 4.0 8.0 4.0 5.0 13.0 5.0 2018-01-02 3.0 3.0 3.0 5.0 7.0 5.0 7.0 11.0 7.0 2018-01-03 3.0 3.0 3.0 6.0 6.0 6.0 9.0 9.0 9.0 2018-01-04 3.0 3.0 3.0 7.0 5.0 5.0 11.0 7.0 7.0 2018-01-05 3.0 3.0 3.0 8.0 4.0 4.0 13.0 5.0 5.0 custom_p 0 1 2 a b c a b c a b c 2018-01-01 3.0 3.0 3.0 4.0 8.0 4.0 5.0 13.0 5.0 2018-01-02 3.0 3.0 3.0 5.0 7.0 5.0 7.0 11.0 7.0 2018-01-03 3.0 3.0 3.0 6.0 6.0 6.0 9.0 9.0 9.0 2018-01-04 3.0 3.0 3.0 7.0 5.0 5.0 11.0 7.0 7.0 2018-01-05 3.0 3.0 3.0 8.0 4.0 4.0 13.0 5.0 5.0 
# test **kwargs
# Numba doesn't support kwargs out of the box
F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['out'])
print(F.from_apply_func(lambda ts, p, a=1: ts * p + a)
      .run(close, [0, 1, 2], a=3).out)
custom_p 0 1 2 a b c a b c a b c 2018-01-01 3.0 3.0 3.0 4.0 8.0 4.0 5.0 13.0 5.0 2018-01-02 3.0 3.0 3.0 5.0 7.0 5.0 7.0 11.0 7.0 2018-01-03 3.0 3.0 3.0 6.0 6.0 6.0 9.0 9.0 9.0 2018-01-04 3.0 3.0 3.0 7.0 5.0 5.0 11.0 7.0 7.0 2018-01-05 3.0 3.0 3.0 8.0 4.0 4.0 13.0 5.0 5.0 
# test no inputs
F = vbt.IndicatorFactory(param_names=['p'], output_names=['out'])
print(F.from_apply_func(lambda p: np.full((3, 3), p))
      .run([0, 1]).out)
print(F.from_apply_func(njit(lambda p: np.full((3, 3), p)))
      .run([0, 1]).out)
custom_p 0 1 0 1 2 0 1 2 0 0 0 0 1 1 1 1 0 0 0 1 1 1 2 0 0 0 1 1 1 custom_p 0 1 0 1 2 0 1 2 0 0 0 0 1 1 1 1 0 0 0 1 1 1 2 0 0 0 1 1 1 
# test no inputs with input_shape, input_index and input_columns
F = vbt.IndicatorFactory(param_names=['p'], output_names=['out'])
print(F.from_apply_func(lambda input_shape, p: np.full(input_shape, p), require_input_shape=True)
      .run((5,), 0).out)
print(F.from_apply_func(njit(lambda input_shape, p: np.full(input_shape, p)), require_input_shape=True)
      .run((5,), 0).out)

print(F.from_apply_func(lambda input_shape, p: np.full(input_shape, p), require_input_shape=True)
      .run((5,), [0, 1]).out)
print(F.from_apply_func(njit(lambda input_shape, p: np.full(input_shape, p)), require_input_shape=True)
      .run((5,), [0, 1]).out)

print(F.from_apply_func(lambda input_shape, p: np.full(input_shape, p), require_input_shape=True)
      .run((5, 3), [0, 1], input_index=close.index, input_columns=close.columns).out)
print(F.from_apply_func(njit(lambda input_shape, p: np.full(input_shape, p)), require_input_shape=True)
      .run((5, 3), [0, 1], input_index=close.index, input_columns=close.columns).out)
0 0 1 0 2 0 3 0 4 0 dtype: int64 0 0 1 0 2 0 3 0 4 0 dtype: int64 custom_p 0 1 0 0 1 1 0 1 2 0 1 3 0 1 4 0 1 custom_p 0 1 0 0 1 1 0 1 2 0 1 3 0 1 4 0 1 custom_p 0 1 a b c a b c 2018-01-01 0 0 0 1 1 1 2018-01-02 0 0 0 1 1 1 2018-01-03 0 0 0 1 1 1 2018-01-04 0 0 0 1 1 1 2018-01-05 0 0 0 1 1 1 custom_p 0 1 a b c a b c 2018-01-01 0 0 0 1 1 1 2018-01-02 0 0 0 1 1 1 2018-01-03 0 0 0 1 1 1 2018-01-04 0 0 0 1 1 1 2018-01-05 0 0 0 1 1 1 
# test multiple inputs
F = vbt.IndicatorFactory(input_names=['ts1', 'ts2'], param_names=['p'], output_names=['out'])
print(F.from_apply_func(lambda ts1, ts2, p: ts1 * ts2 * p)
      .run(close, high, [0, 1]).out)
print(F.from_apply_func(njit(lambda ts1, ts2, p: ts1 * ts2 * p))
      .run(close, high, [0, 1]).out)
custom_p 0 1 a b c a b c 2018-01-01 0.0 0.0 0.0 1.037454 27.376786 1.073199 2018-01-02 0.0 0.0 0.0 4.239463 16.249630 4.062398 2018-01-03 0.0 0.0 0.0 9.052275 9.779559 9.541004 2018-01-04 0.0 0.0 0.0 17.132916 4.008234 4.387964 2018-01-05 0.0 0.0 0.0 27.081107 1.021234 1.018182 custom_p 0 1 a b c a b c 2018-01-01 0.0 0.0 0.0 1.037454 27.376786 1.073199 2018-01-02 0.0 0.0 0.0 4.239463 16.249630 4.062398 2018-01-03 0.0 0.0 0.0 9.052275 9.779559 9.541004 2018-01-04 0.0 0.0 0.0 17.132916 4.008234 4.387964 2018-01-05 0.0 0.0 0.0 27.081107 1.021234 1.018182 
# test no params
F = vbt.IndicatorFactory(input_names=['ts'], output_names=['out'])
print(F.from_apply_func(lambda ts: ts)
      .run(close).out)
print(F.from_apply_func(njit(lambda ts: ts))
      .run(close).out)
a b c 2018-01-01 1.0 5.0 1.0 2018-01-02 2.0 4.0 2.0 2018-01-03 3.0 3.0 3.0 2018-01-04 4.0 2.0 2.0 2018-01-05 5.0 1.0 1.0 a b c 2018-01-01 1.0 5.0 1.0 2018-01-02 2.0 4.0 2.0 2018-01-03 3.0 3.0 3.0 2018-01-04 4.0 2.0 2.0 2018-01-05 5.0 1.0 1.0 
# test no inputs and no params
F = vbt.IndicatorFactory(output_names=['out'])
print(F.from_apply_func(lambda: np.full((3, 3), 1))
      .run().out)
print(F.from_apply_func(njit(lambda: np.full((3, 3), 1)))
      .run().out)
0 1 2 0 1 1 1 1 1 1 1 2 1 1 1 0 1 2 0 1 1 1 1 1 1 1 2 1 1 1 
# test multiple params
F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])
print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2))
      .run(close, np.asarray([0, 1]), np.asarray([2, 3])).out) 
print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)))
      .run(close, np.asarray([0, 1]), np.asarray([2, 3])).out)
custom_p1 0 1 custom_p2 2 3 a b c a b c 2018-01-01 2.0 10.0 2.0 4.0 20.0 4.0 2018-01-02 4.0 8.0 4.0 8.0 16.0 8.0 2018-01-03 6.0 6.0 6.0 12.0 12.0 12.0 2018-01-04 8.0 4.0 4.0 16.0 8.0 8.0 2018-01-05 10.0 2.0 2.0 20.0 4.0 4.0 custom_p1 0 1 custom_p2 2 3 a b c a b c 2018-01-01 2.0 10.0 2.0 4.0 20.0 4.0 2018-01-02 4.0 8.0 4.0 8.0 16.0 8.0 2018-01-03 6.0 6.0 6.0 12.0 12.0 12.0 2018-01-04 8.0 4.0 4.0 16.0 8.0 8.0 2018-01-05 10.0 2.0 2.0 20.0 4.0 4.0 
# test param_settings array_like
F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])
print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2), 
                        param_settings={'p1': {'is_array_like': True}})
      .run(close, np.asarray([0, 1, 2]), np.asarray([2, 3])).out) 
print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)), 
                        param_settings={'p1': {'is_array_like': True}})
      .run(close, np.asarray([0, 1, 2]), np.asarray([2, 3])).out)
custom_p1 array_0 array_1 custom_p2 2 3 a b c a b c 2018-01-01 2.0 15.0 4.0 3.0 20.0 5.0 2018-01-02 4.0 12.0 8.0 6.0 16.0 10.0 2018-01-03 6.0 9.0 12.0 9.0 12.0 15.0 2018-01-04 8.0 6.0 8.0 12.0 8.0 10.0 2018-01-05 10.0 3.0 4.0 15.0 4.0 5.0 custom_p1 array_0 array_1 custom_p2 2 3 a b c a b c 2018-01-01 2.0 15.0 4.0 3.0 20.0 5.0 2018-01-02 4.0 12.0 8.0 6.0 16.0 10.0 2018-01-03 6.0 9.0 12.0 9.0 12.0 15.0 2018-01-04 8.0 6.0 8.0 12.0 8.0 10.0 2018-01-05 10.0 3.0 4.0 15.0 4.0 5.0 
# test param_settings bc_to_input
F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])
print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2), 
                        param_settings={'p1': {'is_array_like': True, 'bc_to_input': True}})
      .run(close, np.asarray([0, 1, 2]), np.asarray([2, 3])).out) 
print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)), 
                        param_settings={'p1': {'is_array_like': True, 'bc_to_input': True}})
      .run(close, np.asarray([0, 1, 2]), np.asarray([2, 3])).out)
custom_p1 array_0 array_1 custom_p2 2 3 a b c a b c 2018-01-01 2.0 15.0 4.0 3.0 20.0 5.0 2018-01-02 4.0 12.0 8.0 6.0 16.0 10.0 2018-01-03 6.0 9.0 12.0 9.0 12.0 15.0 2018-01-04 8.0 6.0 8.0 12.0 8.0 10.0 2018-01-05 10.0 3.0 4.0 15.0 4.0 5.0 custom_p1 array_0 array_1 custom_p2 2 3 a b c a b c 2018-01-01 2.0 15.0 4.0 3.0 20.0 5.0 2018-01-02 4.0 12.0 8.0 6.0 16.0 10.0 2018-01-03 6.0 9.0 12.0 9.0 12.0 15.0 2018-01-04 8.0 6.0 8.0 12.0 8.0 10.0 2018-01-05 10.0 3.0 4.0 15.0 4.0 5.0 
# test param product
F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])
print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2))
      .run(close, [0, 1], [2, 3], param_product=True).out) 
print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)))
      .run(close, [0, 1], [2, 3], param_product=True).out)
custom_p1 0 1 \ custom_p2 2 3 2 3 a b c a b c a b c a b 2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0 3.0 15.0 3.0 4.0 20.0 2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0 6.0 12.0 6.0 8.0 16.0 2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0 9.0 9.0 9.0 12.0 12.0 2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0 12.0 6.0 6.0 16.0 8.0 2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0 15.0 3.0 3.0 20.0 4.0 custom_p1 custom_p2 c 2018-01-01 4.0 2018-01-02 8.0 2018-01-03 12.0 2018-01-04 8.0 2018-01-05 4.0 custom_p1 0 1 \ custom_p2 2 3 2 3 a b c a b c a b c a b 2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0 3.0 15.0 3.0 4.0 20.0 2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0 6.0 12.0 6.0 8.0 16.0 2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0 9.0 9.0 9.0 12.0 12.0 2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0 12.0 6.0 6.0 16.0 8.0 2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0 15.0 3.0 3.0 20.0 4.0 custom_p1 custom_p2 c 2018-01-01 4.0 2018-01-02 8.0 2018-01-03 12.0 2018-01-04 8.0 2018-01-05 4.0 
# test default params
F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])
print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2), p2=2)
      .run(close, [0, 1]).out)
print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)), p2=2)
      .run(close, [0, 1]).out)
custom_p1 0 1 a b c a b c 2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0 2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0 2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0 2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0 2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0 custom_p1 0 1 a b c a b c 2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0 2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0 2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0 2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0 2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0 
# test hide_params
F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])
print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2), hide_params=['p2'])
      .run(close, [0, 1], 2).out)
print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)), hide_params=['p2'])
      .run(close, [0, 1], 2).out)
custom_p1 0 1 a b c a b c 2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0 2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0 2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0 2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0 2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0 custom_p1 0 1 a b c a b c 2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0 2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0 2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0 2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0 2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0 
# test hide_default
F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])
print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2), p2=2)
      .run(close, [0, 1], hide_default=False).out)
print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)), p2=2)
      .run(close, [0, 1], hide_default=False).out)
print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2), p2=2)
      .run(close, [0, 1], hide_default=True).out)
print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)), p2=2)
      .run(close, [0, 1], hide_default=True).out)
custom_p1 0 1 custom_p2 2 2 a b c a b c 2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0 2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0 2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0 2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0 2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0 custom_p1 0 1 custom_p2 2 2 a b c a b c 2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0 2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0 2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0 2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0 2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0 custom_p1 0 1 a b c a b c 2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0 2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0 2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0 2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0 2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0 custom_p1 0 1 a b c a b c 2018-01-01 2.0 10.0 2.0 3.0 15.0 3.0 2018-01-02 4.0 8.0 4.0 6.0 12.0 6.0 2018-01-03 6.0 6.0 6.0 9.0 9.0 9.0 2018-01-04 8.0 4.0 4.0 12.0 6.0 6.0 2018-01-05 10.0 2.0 2.0 15.0 3.0 3.0 
# test multiple outputs
F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['o1', 'o2'])
print(F.from_apply_func(lambda ts, p: (ts * p, ts * p ** 2))
      .run(close, [0, 1]).o1)
print(F.from_apply_func(lambda ts, p: (ts * p, ts * p ** 2))
      .run(close, [0, 1]).o2)
print(F.from_apply_func(njit(lambda ts, p: (ts * p, ts * p ** 2)))
      .run(close, [0, 1]).o1)
print(F.from_apply_func(njit(lambda ts, p: (ts * p, ts * p ** 2)))
      .run(close, [0, 1]).o2)
custom_p 0 1 a b c a b c 2018-01-01 0.0 0.0 0.0 1.0 5.0 1.0 2018-01-02 0.0 0.0 0.0 2.0 4.0 2.0 2018-01-03 0.0 0.0 0.0 3.0 3.0 3.0 2018-01-04 0.0 0.0 0.0 4.0 2.0 2.0 2018-01-05 0.0 0.0 0.0 5.0 1.0 1.0 custom_p 0 1 a b c a b c 2018-01-01 0.0 0.0 0.0 1.0 5.0 1.0 2018-01-02 0.0 0.0 0.0 2.0 4.0 2.0 2018-01-03 0.0 0.0 0.0 3.0 3.0 3.0 2018-01-04 0.0 0.0 0.0 4.0 2.0 2.0 2018-01-05 0.0 0.0 0.0 5.0 1.0 1.0 custom_p 0 1 a b c a b c 2018-01-01 0.0 0.0 0.0 1.0 5.0 1.0 2018-01-02 0.0 0.0 0.0 2.0 4.0 2.0 2018-01-03 0.0 0.0 0.0 3.0 3.0 3.0 2018-01-04 0.0 0.0 0.0 4.0 2.0 2.0 2018-01-05 0.0 0.0 0.0 5.0 1.0 1.0 custom_p 0 1 a b c a b c 2018-01-01 0.0 0.0 0.0 1.0 5.0 1.0 2018-01-02 0.0 0.0 0.0 2.0 4.0 2.0 2018-01-03 0.0 0.0 0.0 3.0 3.0 3.0 2018-01-04 0.0 0.0 0.0 4.0 2.0 2.0 2018-01-05 0.0 0.0 0.0 5.0 1.0 1.0 
# test in-place outputs
def apply_func(ts, ts_out, p):
    ts_out[:, 0] = p
    return ts * p

F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['out'], in_output_names=['ts_out'])
print(F.from_apply_func(apply_func)
      .run(close, [0, 1]).ts_out)
print(F.from_apply_func(njit(apply_func))
      .run(close, [0, 1]).ts_out)

print(F.from_apply_func(apply_func, in_output_settings={'ts_out': {'dtype': np.int64}})
      .run(close, [0, 1]).ts_out)
print(F.from_apply_func(njit(apply_func), in_output_settings={'ts_out': {'dtype': np.int64}})
      .run(close, [0, 1]).ts_out)

print(F.from_apply_func(apply_func, ts_out=-1)
      .run(close, [0, 1]).ts_out)
print(F.from_apply_func(njit(apply_func), ts_out=-1)
      .run(close, [0, 1]).ts_out)
custom_p 0 1 a b c a b c 2018-01-01 0.0 0.0 0.0 1.0 0.0 0.0 2018-01-02 0.0 0.0 0.0 1.0 0.0 0.0 2018-01-03 0.0 0.0 0.0 1.0 2.0 3.0 2018-01-04 0.0 5.0 5.0 1.0 3.0 2.0 2018-01-05 0.0 1.0 2.0 1.0 2.0 1.0 custom_p 0 1 a b c a b c 2018-01-01 0.0 0.0 0.0 1.0 0.0 0.0 2018-01-02 0.0 0.0 0.0 1.0 0.0 0.0 2018-01-03 0.0 0.0 0.0 1.0 2.0 3.0 2018-01-04 0.0 5.0 5.0 1.0 3.0 2.0 2018-01-05 0.0 1.0 2.0 1.0 2.0 1.0 custom_p 0 1 \ a b c a 2018-01-01 0 0 0 1 2018-01-02 0 0 0 1 2018-01-03 0 0 0 1 2018-01-04 0 4617315517961601024 4617315517961601024 1 2018-01-05 0 4607182418800017408 4611686018427387904 1 custom_p b c 2018-01-01 0 0 2018-01-02 0 0 2018-01-03 4611686018427387904 4613937818241073152 2018-01-04 4613937818241073152 4611686018427387904 2018-01-05 4611686018427387904 4607182418800017408 custom_p 0 1 \ a b c a 2018-01-01 0 4616189618054758400 4618441417868443648 1 2018-01-02 0 4618441417868443648 4616189618054758400 1 2018-01-03 0 4616189618054758400 4611686018427387904 1 2018-01-04 0 4624633867356078080 4624633867356078080 1 2018-01-05 0 4613937818241073152 4618441417868443648 1 custom_p b c 2018-01-01 4621819117588971520 4621819117588971520 2018-01-02 4611686018427387904 4616189618054758400 2018-01-03 4618441417868443648 4621256167635550208 2018-01-04 4621256167635550208 4618441417868443648 2018-01-05 4618441417868443648 4613937818241073152 custom_p 0 1 a b c a b c 2018-01-01 0 -1 -1 1 -1 -1 2018-01-02 0 -1 -1 1 -1 -1 2018-01-03 0 -1 -1 1 -1 -1 2018-01-04 0 -1 -1 1 -1 -1 2018-01-05 0 -1 -1 1 -1 -1 custom_p 0 1 a b c a b c 2018-01-01 0 -1 -1 1 -1 -1 2018-01-02 0 -1 -1 1 -1 -1 2018-01-03 0 -1 -1 1 -1 -1 2018-01-04 0 -1 -1 1 -1 -1 2018-01-05 0 -1 -1 1 -1 -1 
# test kwargs_to_args
F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['out'])
print(F.from_apply_func(lambda ts, p, a, kw: ts * p + a + kw, kwargs_to_args=['kw'], var_args=True)
      .run(close, [0, 1, 2], 3, kw=10).out) 
print(F.from_apply_func(njit(lambda ts, p, a, kw: ts * p + a + kw), kwargs_to_args=['kw'], var_args=True)
      .run(close, [0, 1, 2], 3, kw=10).out)
custom_p 0 1 2 a b c a b c a b c 2018-01-01 13.0 13.0 13.0 14.0 18.0 14.0 15.0 23.0 15.0 2018-01-02 13.0 13.0 13.0 15.0 17.0 15.0 17.0 21.0 17.0 2018-01-03 13.0 13.0 13.0 16.0 16.0 16.0 19.0 19.0 19.0 2018-01-04 13.0 13.0 13.0 17.0 15.0 15.0 21.0 17.0 17.0 2018-01-05 13.0 13.0 13.0 18.0 14.0 14.0 23.0 15.0 15.0 custom_p 0 1 2 a b c a b c a b c 2018-01-01 13.0 13.0 13.0 14.0 18.0 14.0 15.0 23.0 15.0 2018-01-02 13.0 13.0 13.0 15.0 17.0 15.0 17.0 21.0 17.0 2018-01-03 13.0 13.0 13.0 16.0 16.0 16.0 19.0 19.0 19.0 2018-01-04 13.0 13.0 13.0 17.0 15.0 15.0 21.0 17.0 17.0 2018-01-05 13.0 13.0 13.0 18.0 14.0 14.0 23.0 15.0 15.0 
# test caching func
F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p'], output_names=['out'])
print(F.from_apply_func(lambda ts, param, c: ts * param + c, cache_func=lambda ts, params: 100)
      .run(close, [0, 1]).out)
print(F.from_apply_func(njit(lambda ts, param, c: ts * param + c), cache_func=njit(lambda ts, params: 100))
      .run(close, [0, 1]).out)
custom_p 0 1 a b c a b c 2018-01-01 100.0 100.0 100.0 101.0 105.0 101.0 2018-01-02 100.0 100.0 100.0 102.0 104.0 102.0 2018-01-03 100.0 100.0 100.0 103.0 103.0 103.0 2018-01-04 100.0 100.0 100.0 104.0 102.0 102.0 2018-01-05 100.0 100.0 100.0 105.0 101.0 101.0 custom_p 0 1 a b c a b c 2018-01-01 100.0 100.0 100.0 101.0 105.0 101.0 2018-01-02 100.0 100.0 100.0 102.0 104.0 102.0 2018-01-03 100.0 100.0 100.0 103.0 103.0 103.0 2018-01-04 100.0 100.0 100.0 104.0 102.0 102.0 2018-01-05 100.0 100.0 100.0 105.0 101.0 101.0 
# test run_combs
F = vbt.IndicatorFactory(input_names=['ts'], param_names=['p1', 'p2'], output_names=['out'])
print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2))
      .run_combs(close, [0, 1, 2], [3, 4, 5], short_names=['i1', 'i2'])[0].out)
print(F.from_apply_func(lambda ts, p1, p2: ts * (p1 + p2))
      .run_combs(close, [0, 1, 2], [3, 4, 5], short_names=['i1', 'i2'])[1].out)
print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)))
      .run_combs(close, [0, 1, 2], [3, 4, 5], short_names=['i1', 'i2'])[0].out)
print(F.from_apply_func(njit(lambda ts, p1, p2: ts * (p1 + p2)))
      .run_combs(close, [0, 1, 2], [3, 4, 5], short_names=['i1', 'i2'])[1].out)
i1_p1 0 1 i1_p2 3 4 a b c a b c a b c 2018-01-01 3.0 15.0 3.0 3.0 15.0 3.0 5.0 25.0 5.0 2018-01-02 6.0 12.0 6.0 6.0 12.0 6.0 10.0 20.0 10.0 2018-01-03 9.0 9.0 9.0 9.0 9.0 9.0 15.0 15.0 15.0 2018-01-04 12.0 6.0 6.0 12.0 6.0 6.0 20.0 10.0 10.0 2018-01-05 15.0 3.0 3.0 15.0 3.0 3.0 25.0 5.0 5.0 i2_p1 1 2 i2_p2 4 5 a b c a b c a b c 2018-01-01 5.0 25.0 5.0 7.0 35.0 7.0 7.0 35.0 7.0 2018-01-02 10.0 20.0 10.0 14.0 28.0 14.0 14.0 28.0 14.0 2018-01-03 15.0 15.0 15.0 21.0 21.0 21.0 21.0 21.0 21.0 2018-01-04 20.0 10.0 10.0 28.0 14.0 14.0 28.0 14.0 14.0 2018-01-05 25.0 5.0 5.0 35.0 7.0 7.0 35.0 7.0 7.0 i1_p1 0 1 i1_p2 3 4 a b c a b c a b c 2018-01-01 3.0 15.0 3.0 3.0 15.0 3.0 5.0 25.0 5.0 2018-01-02 6.0 12.0 6.0 6.0 12.0 6.0 10.0 20.0 10.0 2018-01-03 9.0 9.0 9.0 9.0 9.0 9.0 15.0 15.0 15.0 2018-01-04 12.0 6.0 6.0 12.0 6.0 6.0 20.0 10.0 10.0 2018-01-05 15.0 3.0 3.0 15.0 3.0 3.0 25.0 5.0 5.0 i2_p1 1 2 i2_p2 4 5 a b c a b c a b c 2018-01-01 5.0 25.0 5.0 7.0 35.0 7.0 7.0 35.0 7.0 2018-01-02 10.0 20.0 10.0 14.0 28.0 14.0 14.0 28.0 14.0 2018-01-03 15.0 15.0 15.0 21.0 21.0 21.0 21.0 21.0 21.0 2018-01-04 20.0 10.0 10.0 28.0 14.0 14.0 28.0 14.0 14.0 2018-01-05 25.0 5.0 5.0 35.0 7.0 7.0 35.0 7.0 7.0 
from collections import namedtuple

TestEnum = namedtuple('TestEnum', ['Hello', 'World'])(0, 1)
# test attr_settings
F = vbt.IndicatorFactory(
    input_names=['ts'], output_names=['o1', 'o2'], in_output_names=['ts_out'],
    attr_settings={
        'ts': {'dtype': None}, 
        'o1': {'dtype': np.float64}, 
        'o2': {'dtype': np.bool_}, 
        'ts_out': {'dtype': TestEnum}
    }
)
dir(F.from_apply_func(lambda ts, ts_out: (ts + ts_out, ts + ts_out)).run(close))
['__annotations__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_config', '_iloc', '_in_output_names', '_indexing_kwargs', '_input_mapper', '_input_names', '_level_names', '_loc', '_metrics', '_o1', '_o2', '_output_flags', '_output_names', '_param_names', '_run', '_run_combs', '_short_name', '_ts', '_ts_out', '_wrapper', 'apply_func', 'build_metrics_doc', 'config', 'copy', 'custom_func', 'deep_getattr', 'dumps', 'iloc', 'in_output_names', 'indexing_func', 'indexing_kwargs', 'input_names', 'level_names', 'load', 'loads', 'loc', 'metrics', 'o1', 'o1_above', 'o1_below', 'o1_equal', 'o1_stats', 'o2', 'o2_and', 'o2_or', 'o2_stats', 'o2_xor', 'output_flags', 'output_names', 'override_metrics_doc', 'param_names', 'post_resolve_attr', 'pre_resolve_attr', 'regroup', 'resolve_attr', 'resolve_self', 'run', 'run_combs', 'save', 'select_one', 'select_one_from_obj', 'self_aliases', 'short_name', 'stats', 'stats_defaults', 'to_doc', 'ts', 'ts_above', 'ts_below', 'ts_equal', 'ts_out', 'ts_out_readable', 'ts_out_stats', 'ts_stats', 'update_config', 'wrapper', 'writeable_attrs', 'xs']
CustomInd = vbt.IndicatorFactory(
    input_names=['ts1', 'ts2'],
    param_names=['p1', 'p2'],
    output_names=['o1', 'o2']
).from_apply_func(lambda ts1, ts2, p1, p2: (ts1 * p1, ts2 * p2))

dir(CustomInd) # you can list here all of the available tools
['__annotations__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_in_output_names', '_input_names', '_metrics', '_output_flags', '_output_names', '_param_names', '_run', '_run_combs', 'apply_func', 'build_metrics_doc', 'config', 'copy', 'custom_func', 'deep_getattr', 'dumps', 'iloc', 'in_output_names', 'indexing_func', 'indexing_kwargs', 'input_names', 'level_names', 'load', 'loads', 'loc', 'metrics', 'o1', 'o1_above', 'o1_below', 'o1_equal', 'o1_stats', 'o2', 'o2_above', 'o2_below', 'o2_equal', 'o2_stats', 'output_flags', 'output_names', 'override_metrics_doc', 'p1_list', 'p1_loc', 'p2_list', 'p2_loc', 'param_names', 'post_resolve_attr', 'pre_resolve_attr', 'regroup', 'resolve_attr', 'resolve_self', 'run', 'run_combs', 'save', 'select_one', 'select_one_from_obj', 'self_aliases', 'short_name', 'stats', 'stats_defaults', 'to_doc', 'ts1', 'ts1_above', 'ts1_below', 'ts1_equal', 'ts1_stats', 'ts2', 'ts2_above', 'ts2_below', 'ts2_equal', 'ts2_stats', 'tuple_loc', 'update_config', 'wrapper', 'writeable_attrs', 'xs']
custom_ind = CustomInd.run(close, high, [1, 2], [3, 4])
big_custom_ind = CustomInd.run(big_close, big_high, [1, 2], [3, 4])

print(custom_ind.wrapper.index) # subclasses ArrayWrapper
print(custom_ind.wrapper.columns)
print(custom_ind.wrapper.ndim)
print(custom_ind.wrapper.shape)
print(custom_ind.wrapper.freq)
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05'], dtype='datetime64[ns]', freq=None) MultiIndex([(1, 3, 'a'), (1, 3, 'b'), (1, 3, 'c'), (2, 4, 'a'), (2, 4, 'b'), (2, 4, 'c')], names=['custom_p1', 'custom_p2', None]) 2 (5, 6) 1 days 00:00:00 
# not changed during indexing
print(custom_ind.short_name)
print(custom_ind.level_names)
print(custom_ind.input_names)
print(custom_ind.param_names)
print(custom_ind.output_names)
print(custom_ind.output_flags)
print(custom_ind.p1_list)
print(custom_ind.p2_list)
custom ('custom_p1', 'custom_p2') ('ts1', 'ts2') ('p1', 'p2') ('o1', 'o2') {} [1, 2] [3, 4]

Pandas indexing

print(custom_ind._ts1)
print(custom_ind.ts1)

print(custom_ind.ts1.iloc[:, 0])
print(custom_ind.iloc[:, 0].ts1)

print(custom_ind.ts1.iloc[:, [0]])
print(custom_ind.iloc[:, [0]].ts1)

print(custom_ind.ts1.iloc[:2, :])
print(custom_ind.iloc[:2, :].ts1)
[[1. 5. 1.] [2. 4. 2.] [3. 3. 3.] [4. 2. 2.] [5. 1. 1.]] custom_p1 1 2 custom_p2 3 4 a b c a b c 2018-01-01 1.0 5.0 1.0 1.0 5.0 1.0 2018-01-02 2.0 4.0 2.0 2.0 4.0 2.0 2018-01-03 3.0 3.0 3.0 3.0 3.0 3.0 2018-01-04 4.0 2.0 2.0 4.0 2.0 2.0 2018-01-05 5.0 1.0 1.0 5.0 1.0 1.0 2018-01-01 1.0 2018-01-02 2.0 2018-01-03 3.0 2018-01-04 4.0 2018-01-05 5.0 Name: (1, 3, a), dtype: float64 2018-01-01 1.0 2018-01-02 2.0 2018-01-03 3.0 2018-01-04 4.0 2018-01-05 5.0 Name: (1, 3, a), dtype: float64 custom_p1 1 custom_p2 3 a 2018-01-01 1.0 2018-01-02 2.0 2018-01-03 3.0 2018-01-04 4.0 2018-01-05 5.0 custom_p1 1 custom_p2 3 a 2018-01-01 1.0 2018-01-02 2.0 2018-01-03 3.0 2018-01-04 4.0 2018-01-05 5.0 custom_p1 1 2 custom_p2 3 4 a b c a b c 2018-01-01 1.0 5.0 1.0 1.0 5.0 1.0 2018-01-02 2.0 4.0 2.0 2.0 4.0 2.0 custom_p1 1 2 custom_p2 3 4 a b c a b c 2018-01-01 1.0 5.0 1.0 1.0 5.0 1.0 2018-01-02 2.0 4.0 2.0 2.0 4.0 2.0 
print(custom_ind.o1.iloc[:, 0])
%timeit big_custom_ind.o1.iloc[:, 0] # benchmark, 1 column

print(custom_ind.iloc[:, 0].o1) # performed on the object itself
%timeit big_custom_ind.iloc[:, 0] # slower since it forwards the operation to each dataframe
2018-01-01 1.0 2018-01-02 2.0 2018-01-03 3.0 2018-01-04 4.0 2018-01-05 5.0 Name: (1, 3, a), dtype: float64 120 µs ± 540 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) 2018-01-01 1.0 2018-01-02 2.0 2018-01-03 3.0 2018-01-04 4.0 2018-01-05 5.0 Name: (1, 3, a), dtype: float64 1.33 ms ± 40.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) 
print(custom_ind.o1.iloc[:, np.arange(3)])
%timeit big_custom_ind.o1.iloc[:, np.arange(1000)] # 1000 columns

print(custom_ind.iloc[:, np.arange(3)].o1)
%timeit big_custom_ind.iloc[:, np.arange(1000)]
custom_p1 1 custom_p2 3 a b c 2018-01-01 1.0 5.0 1.0 2018-01-02 2.0 4.0 2.0 2018-01-03 3.0 3.0 3.0 2018-01-04 4.0 2.0 2.0 2018-01-05 5.0 1.0 1.0 910 µs ± 59.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) custom_p1 1 custom_p2 3 a b c 2018-01-01 1.0 5.0 1.0 2018-01-02 2.0 4.0 2.0 2018-01-03 3.0 3.0 3.0 2018-01-04 4.0 2.0 2.0 2018-01-05 5.0 1.0 1.0 18 ms ± 759 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 
print(custom_ind.o1.loc[:, (1, 3, 'a')])
%timeit big_custom_ind.o1.loc[:, (1, 3, 0)] # 1 column

print(custom_ind.loc[:, (1, 3, 'a')].o1)
%timeit big_custom_ind.loc[:, (1, 3, 0)]
2018-01-01 1.0 2018-01-02 2.0 2018-01-03 3.0 2018-01-04 4.0 2018-01-05 5.0 Name: (1, 3, a), dtype: float64 147 µs ± 270 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) 2018-01-01 1.0 2018-01-02 2.0 2018-01-03 3.0 2018-01-04 4.0 2018-01-05 5.0 Name: (1, 3, a), dtype: float64 1.34 ms ± 4.28 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) 
print(custom_ind.o1.loc[:, (1, 3)])
%timeit big_custom_ind.o1.loc[:, 1] # 1000 columns

print(custom_ind.loc[:, (1, 3)].o1)
%timeit big_custom_ind.loc[:, 1]
a b c 2018-01-01 1.0 5.0 1.0 2018-01-02 2.0 4.0 2.0 2018-01-03 3.0 3.0 3.0 2018-01-04 4.0 2.0 2.0 2018-01-05 5.0 1.0 1.0 169 µs ± 1.54 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) a b c 2018-01-01 1.0 5.0 1.0 2018-01-02 2.0 4.0 2.0 2018-01-03 3.0 3.0 3.0 2018-01-04 4.0 2.0 2.0 2018-01-05 5.0 1.0 1.0 6.1 ms ± 75.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 
print(custom_ind.o1.xs(1, axis=1, level=0))
%timeit big_custom_ind.o1.xs(1, axis=1, level=0) # 1000 columns

print(custom_ind.xs(1, axis=1, level=0).o1)
%timeit big_custom_ind.xs(1, axis=1, level=0)
custom_p2 3 a b c 2018-01-01 1.0 5.0 1.0 2018-01-02 2.0 4.0 2.0 2018-01-03 3.0 3.0 3.0 2018-01-04 4.0 2.0 2.0 2018-01-05 5.0 1.0 1.0 161 µs ± 371 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) custom_p2 3 a b c 2018-01-01 1.0 5.0 1.0 2018-01-02 2.0 4.0 2.0 2018-01-03 3.0 3.0 3.0 2018-01-04 4.0 2.0 2.0 2018-01-05 5.0 1.0 1.0 6.2 ms ± 119 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Parameter indexing

# Indexing by parameter
print(custom_ind._p1_mapper)
print(custom_ind.p1_loc[2].o1)
print(custom_ind.p1_loc[1:2].o1)
print(custom_ind.p1_loc[[1, 1, 1]].o1)
Index([1, 1, 1, 2, 2, 2], dtype='int64', name='custom_p1') custom_p2 4 a b c 2018-01-01 2.0 10.0 2.0 2018-01-02 4.0 8.0 4.0 2018-01-03 6.0 6.0 6.0 2018-01-04 8.0 4.0 4.0 2018-01-05 10.0 2.0 2.0 custom_p1 1 2 custom_p2 3 4 a b c a b c 2018-01-01 1.0 5.0 1.0 2.0 10.0 2.0 2018-01-02 2.0 4.0 2.0 4.0 8.0 4.0 2018-01-03 3.0 3.0 3.0 6.0 6.0 6.0 2018-01-04 4.0 2.0 2.0 8.0 4.0 4.0 2018-01-05 5.0 1.0 1.0 10.0 2.0 2.0 custom_p1 1 custom_p2 3 a b c a b c a b c 2018-01-01 1.0 5.0 1.0 1.0 5.0 1.0 1.0 5.0 1.0 2018-01-02 2.0 4.0 2.0 2.0 4.0 2.0 2.0 4.0 2.0 2018-01-03 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 2018-01-04 4.0 2.0 2.0 4.0 2.0 2.0 4.0 2.0 2.0 2018-01-05 5.0 1.0 1.0 5.0 1.0 1.0 5.0 1.0 1.0 
%timeit big_custom_ind.p1_loc[1] # 1000 columns
%timeit big_custom_ind.p1_loc[np.full(10, 1)] # 10000 columns
18.2 ms ± 1.45 ms per loop (mean ± std. dev. of 7 runs, 100 loops each) 122 ms ± 18.2 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) 
print(custom_ind._tuple_mapper)
print(custom_ind.tuple_loc[(1, 3)].o1)
print(custom_ind.tuple_loc[(1, 3):(2, 4)].o1)
[(1, 3), (1, 3), (1, 3), (2, 4), (2, 4), (2, 4)] a b c 2018-01-01 1.0 5.0 1.0 2018-01-02 2.0 4.0 2.0 2018-01-03 3.0 3.0 3.0 2018-01-04 4.0 2.0 2.0 2018-01-05 5.0 1.0 1.0 custom_p1 1 2 custom_p2 3 4 a b c a b c 2018-01-01 1.0 5.0 1.0 2.0 10.0 2.0 2018-01-02 2.0 4.0 2.0 4.0 8.0 4.0 2018-01-03 3.0 3.0 3.0 6.0 6.0 6.0 2018-01-04 4.0 2.0 2.0 8.0 4.0 4.0 2018-01-05 5.0 1.0 1.0 10.0 2.0 2.0 
%timeit big_custom_ind.tuple_loc[(1, 3)]
%timeit big_custom_ind.tuple_loc[[(1, 3)] * 10]
22.5 ms ± 5.03 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) 172 ms ± 10.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Comparison methods

print(custom_ind.o1 > 2)
%timeit big_custom_ind.o1.values > 2 # don't even try pandas

print(custom_ind.o1_above(2))
%timeit big_custom_ind.o1_above(2) # slower than numpy because of constructing dataframe
custom_p1 1 2 custom_p2 3 4 a b c a b c 2018-01-01 False True False False True False 2018-01-02 False True False True True True 2018-01-03 True True True True True True 2018-01-04 True False False True True True 2018-01-05 True False False True False False 485 µs ± 9.08 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) custom_p1 1 2 custom_p2 3 4 a b c a b c 2018-01-01 False True False False True False 2018-01-02 False True False True True True 2018-01-03 True True True True True True 2018-01-04 True False False True True True 2018-01-05 True False False True False False 3.32 ms ± 6.22 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 
print(pd.concat((custom_ind.o1 > 2, custom_ind.o1 > 3), axis=1))
%timeit np.hstack((big_custom_ind.o1.values > 2, big_custom_ind.o1.values > 3))

print(custom_ind.o1_above([2, 3]))
%timeit big_custom_ind.o1_above([2, 3])
custom_p1 1 2 1 \ custom_p2 3 4 3 a b c a b c a b c 2018-01-01 False True False False True False False True False 2018-01-02 False True False True True True False True False 2018-01-03 True True True True True True False False False 2018-01-04 True False False True True True True False False 2018-01-05 True False False True False False True False False custom_p1 2 custom_p2 4 a b c 2018-01-01 False True False 2018-01-02 True True True 2018-01-03 True True True 2018-01-04 True True True 2018-01-05 True False False 1.37 ms ± 45.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) custom_o1_above 2 3 \ custom_p1 1 2 1 custom_p2 3 4 3 a b c a b c a b 2018-01-01 False True False False True False False True 2018-01-02 False True False True True True False True 2018-01-03 True True True True True True False False 2018-01-04 True False False True True True True False 2018-01-05 True False False True False False True False custom_o1_above custom_p1 2 custom_p2 4 c a b c 2018-01-01 False False True False 2018-01-02 False True True True 2018-01-03 False True True True 2018-01-04 False True True True 2018-01-05 False True False False 8.31 ms ± 85.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

TA-Lib

ts = pd.DataFrame({
    'a': [1, 2, 3, 4, np.nan],
    'b': [np.nan, 4, 3, 2, 1],
    'c': [1, 2, np.nan, 2, 1]
}, index=pd.DatetimeIndex([
    datetime(2018, 1, 1),
    datetime(2018, 1, 2),
    datetime(2018, 1, 3),
    datetime(2018, 1, 4),
    datetime(2018, 1, 5)
]))

SMA = vbt.talib('SMA')

print(SMA.run(close['a'], 2).real)
print(SMA.run(close, 2).real)
print(SMA.run(close, [2, 3]).real)
2018-01-01 NaN 2018-01-02 1.5 2018-01-03 2.5 2018-01-04 3.5 2018-01-05 4.5 Name: (2, a), dtype: float64 sma_timeperiod 2 a b c 2018-01-01 NaN NaN NaN 2018-01-02 1.5 4.5 1.5 2018-01-03 2.5 3.5 2.5 2018-01-04 3.5 2.5 2.5 2018-01-05 4.5 1.5 1.5 sma_timeperiod 2 3 a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 1.5 4.5 1.5 NaN NaN NaN 2018-01-03 2.5 3.5 2.5 2.0 4.0 2.000000 2018-01-04 3.5 2.5 2.5 3.0 3.0 2.333333 2018-01-05 4.5 1.5 1.5 4.0 2.0 2.000000 
%timeit SMA.run(big_close)
%timeit SMA.run(big_close, np.arange(2, 10))
%timeit SMA.run(big_close, np.full(10, 2))
%timeit SMA.run(big_close, np.full(10, 2), run_unique=True)
13 ms ± 226 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 80.1 ms ± 1.22 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) 99 ms ± 2.78 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) 22.3 ms ± 572 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 
comb = itertools.combinations(np.arange(2, 20), 2)
fast_windows, slow_windows = np.asarray(list(comb)).transpose()
print(fast_windows)
print(slow_windows)
[ 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 12 12 12 12 12 12 12 13 13 13 13 13 13 14 14 14 14 14 15 15 15 15 16 16 16 17 17 18] [ 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 6 7 8 9 10 11 12 13 14 15 16 17 18 19 7 8 9 10 11 12 13 14 15 16 17 18 19 8 9 10 11 12 13 14 15 16 17 18 19 9 10 11 12 13 14 15 16 17 18 19 10 11 12 13 14 15 16 17 18 19 11 12 13 14 15 16 17 18 19 12 13 14 15 16 17 18 19 13 14 15 16 17 18 19 14 15 16 17 18 19 15 16 17 18 19 16 17 18 19 17 18 19 18 19 19] 
%timeit SMA.run(big_close, fast_windows), SMA.run(big_close, slow_windows) # individual caching
%timeit SMA.run_combs(big_close, np.arange(2, 20)) # mutual caching
4.04 s ± 652 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) 1.51 s ± 287 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) 
%timeit vbt.MA.run(big_close, fast_windows), vbt.MA.run(big_close, slow_windows) # the same using Numba
%timeit vbt.MA.run_combs(big_close, np.arange(2, 20))
1.27 s ± 40.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) 1.2 s ± 5.87 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) 
sma1, sma2 = SMA.run_combs(close, [2, 3, 4])
print(sma1.real_crossed_above(sma2))
print(sma1.real_crossed_below(sma2))
/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/base/accessors.py:667: RuntimeWarning: invalid value encountered in greater result = combine_func(inputs[0], inputs[1], *args, **kwargs) /Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/base/accessors.py:667: RuntimeWarning: invalid value encountered in less result = combine_func(inputs[0], inputs[1], *args, **kwargs)
sma_1_timeperiod 2 3 \ sma_2_timeperiod 3 4 4 a b c a b c a b 2018-01-01 False False False False False False False False 2018-01-02 False False False False False False False False 2018-01-03 True False True False False False False False 2018-01-04 False False False True False True True False 2018-01-05 False False False False False False False False sma_1_timeperiod sma_2_timeperiod c 2018-01-01 False 2018-01-02 False 2018-01-03 False 2018-01-04 True 2018-01-05 False sma_1_timeperiod 2 3 \ sma_2_timeperiod 3 4 4 a b c a b c a b 2018-01-01 False False False False False False False False 2018-01-02 False False False False False False False False 2018-01-03 False True False False False False False False 2018-01-04 False False False False True False False True 2018-01-05 False False True False False True False False sma_1_timeperiod sma_2_timeperiod c 2018-01-01 False 2018-01-02 False 2018-01-03 False 2018-01-04 False 2018-01-05 False 
dir(vbt.talib('BBANDS'))
['__annotations__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_in_output_names', '_input_names', '_metrics', '_output_flags', '_output_names', '_param_names', '_run', '_run_combs', 'apply_func', 'build_metrics_doc', 'close', 'close_above', 'close_below', 'close_equal', 'close_stats', 'config', 'copy', 'custom_func', 'deep_getattr', 'dumps', 'iloc', 'in_output_names', 'indexing_func', 'indexing_kwargs', 'input_names', 'level_names', 'load', 'loads', 'loc', 'lowerband', 'lowerband_above', 'lowerband_below', 'lowerband_equal', 'lowerband_stats', 'matype_list', 'matype_loc', 'metrics', 'middleband', 'middleband_above', 'middleband_below', 'middleband_equal', 'middleband_stats', 'nbdevdn_list', 'nbdevdn_loc', 'nbdevup_list', 'nbdevup_loc', 'output_flags', 'output_names', 'override_metrics_doc', 'param_names', 'post_resolve_attr', 'pre_resolve_attr', 'regroup', 'resolve_attr', 'resolve_self', 'run', 'run_combs', 'save', 'select_one', 'select_one_from_obj', 'self_aliases', 'short_name', 'stats', 'stats_defaults', 'timeperiod_list', 'timeperiod_loc', 'to_doc', 'tuple_loc', 'update_config', 'upperband', 'upperband_above', 'upperband_below', 'upperband_equal', 'upperband_stats', 'wrapper', 'writeable_attrs', 'xs']

MA

print(close.rolling(2).mean())
print(close.ewm(span=3, min_periods=3).mean())
print(vbt.talib('SMA').run(close, timeperiod=2).real)
print(vbt.MA.run(close, [2, 3], ewm=[False, True]).ma)  # adjust=False
a b c 2018-01-01 NaN NaN NaN 2018-01-02 1.5 4.5 1.5 2018-01-03 2.5 3.5 2.5 2018-01-04 3.5 2.5 2.5 2018-01-05 4.5 1.5 1.5 a b c 2018-01-01 NaN NaN NaN 2018-01-02 NaN NaN NaN 2018-01-03 2.428571 3.571429 2.428571 2018-01-04 3.266667 2.733333 2.200000 2018-01-05 4.161290 1.838710 1.580645 sma_timeperiod 2 a b c 2018-01-01 NaN NaN NaN 2018-01-02 1.5 4.5 1.5 2018-01-03 2.5 3.5 2.5 2018-01-04 3.5 2.5 2.5 2018-01-05 4.5 1.5 1.5 ma_window 2 3 ma_ewm False True a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 1.5 4.5 1.5 NaN NaN NaN 2018-01-03 2.5 3.5 2.5 2.2500 3.7500 2.2500 2018-01-04 3.5 2.5 2.5 3.1250 2.8750 2.1250 2018-01-05 4.5 1.5 1.5 4.0625 1.9375 1.5625 
# One window
%timeit big_close.rolling(2).mean() # pandas
%timeit vbt.talib('SMA').run(big_close, timeperiod=2)
%timeit vbt.MA.run(big_close, 2, return_cache=True) # cache only
%timeit vbt.MA.run(big_close, 2) # with pre+postprocessing and still beats pandas

print(vbt.MA.run(big_close, 2).ma.shape)
49 ms ± 143 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 12.6 ms ± 181 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 7.39 ms ± 58.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 12.5 ms ± 391 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) (1000, 1000) 
# Multiple windows
%timeit pd.concat([big_close.rolling(i).mean() for i in np.arange(2, 10)])
%timeit vbt.talib('SMA').run(big_close, np.arange(2, 10))
%timeit vbt.MA.run(big_close, np.arange(2, 10))
%timeit vbt.MA.run(big_close, np.arange(2, 10), run_unique=True)
%timeit vbt.MA.run(big_close, np.arange(2, 10), return_cache=True) # cache only
cache = vbt.MA.run(big_close, np.arange(2, 10), return_cache=True)
%timeit vbt.MA.run(big_close, np.arange(2, 10), use_cache=cache) # using cache

print(vbt.MA.run(big_close, np.arange(2, 10)).ma.shape)
406 ms ± 1.24 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) 68.9 ms ± 347 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 64.6 ms ± 181 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 64.3 ms ± 266 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 50.1 ms ± 355 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 37.9 ms ± 3.98 ms per loop (mean ± std. dev. of 7 runs, 100 loops each) (1000, 8000) 
# One window repeated
%timeit pd.concat([big_close.rolling(i).mean() for i in np.full(10, 2)])
%timeit vbt.talib('SMA').run(big_close, np.full(10, 2))
%timeit vbt.MA.run(big_close, np.full(10, 2))
%timeit vbt.MA.run(big_close, np.full(10, 2), run_unique=True)  # slower for large inputs
%timeit vbt.MA.run(big_close, np.full(10, 2), return_cache=True)

print(vbt.MA.run(big_close, np.full(10, 2)).ma.shape)
517 ms ± 6.51 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) 88.9 ms ± 3.68 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) 25.7 ms ± 61.7 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 26.2 ms ± 188 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 6.61 ms ± 346 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) (1000, 10000) 
%timeit pd.concat([big_close.iloc[:, :10].rolling(i).mean() for i in np.full(100, 2)])
%timeit vbt.talib('SMA').run(big_close.iloc[:, :10], np.full(100, 2))
%timeit vbt.MA.run(big_close.iloc[:, :10], np.full(100, 2))
%timeit vbt.MA.run(big_close.iloc[:, :10], np.full(100, 2), run_unique=True)  # faster for smaller inputs
%timeit vbt.MA.run(big_close.iloc[:, :10], np.full(100, 2), return_cache=True)

print(vbt.MA.run(big_close.iloc[:, :10], np.full(100, 2)).ma.shape)
71.6 ms ± 158 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 10.7 ms ± 127 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 4.51 ms ± 57.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 4.35 ms ± 36 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 613 µs ± 2.28 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) (1000, 1000) 
ma = vbt.MA.run(close, [2, 3], ewm=[False, True])

print(ma.ma)
ma_window 2 3 ma_ewm False True a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 1.5 4.5 1.5 NaN NaN NaN 2018-01-03 2.5 3.5 2.5 2.2500 3.7500 2.2500 2018-01-04 3.5 2.5 2.5 3.1250 2.8750 2.1250 2018-01-05 4.5 1.5 1.5 4.0625 1.9375 1.5625 
ma[(2, False, 'a')].plot().show_svg()
Image in a Jupyter notebook

MSTD

print(close.rolling(2).std(ddof=0))
print(close.ewm(span=3, min_periods=3).std(ddof=0))
print(vbt.talib('STDDEV').run(close, timeperiod=2).real)  
print(vbt.MSTD.run(close, [2, 3], ewm=[False, True]).mstd)  # adjust=False, ddof=0
a b c 2018-01-01 NaN NaN NaN 2018-01-02 0.5 0.5 0.5 2018-01-03 0.5 0.5 0.5 2018-01-04 0.5 0.5 0.5 2018-01-05 0.5 0.5 0.5 a b c 2018-01-01 NaN NaN NaN 2018-01-02 NaN NaN NaN 2018-01-03 0.963624 0.963624 0.963624 2018-01-04 1.177164 1.177164 0.686607 2018-01-05 1.345243 1.345243 0.881714 stddev_timeperiod 2 a b c 2018-01-01 NaN NaN NaN 2018-01-02 0.5 0.5 0.5 2018-01-03 0.5 0.5 0.5 2018-01-04 0.5 0.5 0.5 2018-01-05 0.5 0.5 0.5 mstd_window 2 3 mstd_ewm False True a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 0.5 0.5 0.5 NaN NaN NaN 2018-01-03 0.5 0.5 0.5 1.048809 1.048809 1.048809 2018-01-04 0.5 0.5 0.5 1.300183 1.300183 0.740013 2018-01-05 0.5 0.5 0.5 1.469294 1.469294 0.864326 
# One window
%timeit big_close.rolling(2).std()
%timeit vbt.talib('STDDEV').run(big_close, timeperiod=2)
%timeit vbt.MSTD.run(big_close, 2)

print(vbt.MSTD.run(big_close, 2).mstd.shape)
65.2 ms ± 486 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 13.7 ms ± 281 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 11.4 ms ± 669 µs per loop (mean ± std. dev. of 7 runs, 1 loop each) (1000, 1000) 
# Multiple windows
%timeit pd.concat([big_close.rolling(i).std() for i in np.arange(2, 10)])
%timeit vbt.talib('STDDEV').run(big_close, timeperiod=np.arange(2, 10))
%timeit vbt.MSTD.run(big_close, np.arange(2, 10))

print(vbt.MSTD.run(big_close, np.arange(2, 10)).mstd.shape)
534 ms ± 1.43 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) 80.3 ms ± 1.18 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) 81.8 ms ± 973 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) (1000, 8000) 
# One window repeated
%timeit vbt.talib('STDDEV').run(big_close, timeperiod=np.full(10, 2))
%timeit vbt.MSTD.run(big_close, window=np.full(10, 2))

print(vbt.MSTD.run(big_close, window=np.full(10, 2)).close.shape)
98 ms ± 317 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 27.8 ms ± 44.6 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) (1000, 10000) 
mstd = vbt.MSTD.run(close, [2, 3], [False, True])

print(mstd.mstd)
mstd_window 2 3 mstd_ewm False True a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 0.5 0.5 0.5 NaN NaN NaN 2018-01-03 0.5 0.5 0.5 1.048809 1.048809 1.048809 2018-01-04 0.5 0.5 0.5 1.300183 1.300183 0.740013 2018-01-05 0.5 0.5 0.5 1.469294 1.469294 0.864326 
mstd[(2, False, 'a')].plot().show_svg()
Image in a Jupyter notebook

BBANDS

print(vbt.ta('BollingerBands').run(close['a'], window=2, window_dev=2).bollinger_hband)
print(vbt.ta('BollingerBands').run(close['a'], window=2, window_dev=2).bollinger_mavg)
print(vbt.ta('BollingerBands').run(close['a'], window=2, window_dev=2).bollinger_lband)

print(vbt.talib('BBANDS').run(close, timeperiod=2, nbdevup=2, nbdevdn=2).upperband)
print(vbt.talib('BBANDS').run(close, timeperiod=2, nbdevup=2, nbdevdn=2).middleband)
print(vbt.talib('BBANDS').run(close, timeperiod=2, nbdevup=2, nbdevdn=2).lowerband)

print(vbt.BBANDS.run(close, window=2, ewm=False, alpha=2).upper)
print(vbt.BBANDS.run(close, window=2, ewm=False, alpha=2).middle)
print(vbt.BBANDS.run(close, window=2, ewm=False, alpha=2).lower)
2018-01-01 NaN 2018-01-02 2.5 2018-01-03 3.5 2018-01-04 4.5 2018-01-05 5.5 Name: (2, 2, a), dtype: float64 2018-01-01 NaN 2018-01-02 1.5 2018-01-03 2.5 2018-01-04 3.5 2018-01-05 4.5 Name: (2, 2, a), dtype: float64 2018-01-01 NaN 2018-01-02 0.5 2018-01-03 1.5 2018-01-04 2.5 2018-01-05 3.5 Name: (2, 2, a), dtype: float64 bbands_timeperiod 2 bbands_nbdevup 2 bbands_nbdevdn 2 a b c 2018-01-01 NaN NaN NaN 2018-01-02 2.5 5.5 2.5 2018-01-03 3.5 4.5 3.5 2018-01-04 4.5 3.5 3.5 2018-01-05 5.5 2.5 2.5 bbands_timeperiod 2 bbands_nbdevup 2 bbands_nbdevdn 2 a b c 2018-01-01 NaN NaN NaN 2018-01-02 1.5 4.5 1.5 2018-01-03 2.5 3.5 2.5 2018-01-04 3.5 2.5 2.5 2018-01-05 4.5 1.5 1.5 bbands_timeperiod 2 bbands_nbdevup 2 bbands_nbdevdn 2 a b c 2018-01-01 NaN NaN NaN 2018-01-02 0.5 3.5 0.5 2018-01-03 1.5 2.5 1.5 2018-01-04 2.5 1.5 1.5 2018-01-05 3.5 0.5 0.5 bb_window 2 bb_ewm False bb_alpha 2 a b c 2018-01-01 NaN NaN NaN 2018-01-02 2.5 5.5 2.5 2018-01-03 3.5 4.5 3.5 2018-01-04 4.5 3.5 3.5 2018-01-05 5.5 2.5 2.5 bb_window 2 bb_ewm False bb_alpha 2 a b c 2018-01-01 NaN NaN NaN 2018-01-02 1.5 4.5 1.5 2018-01-03 2.5 3.5 2.5 2018-01-04 3.5 2.5 2.5 2018-01-05 4.5 1.5 1.5 bb_window 2 bb_ewm False bb_alpha 2 a b c 2018-01-01 NaN NaN NaN 2018-01-02 0.5 3.5 0.5 2018-01-03 1.5 2.5 1.5 2018-01-04 2.5 1.5 1.5 2018-01-05 3.5 0.5 0.5 
# One window
%timeit vbt.talib('BBANDS').run(big_close, timeperiod=2)
%timeit vbt.BBANDS.run(big_close, window=2)

print(vbt.BBANDS.run(big_close).close.shape)
23 ms ± 367 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 21.6 ms ± 412 µs per loop (mean ± std. dev. of 7 runs, 1 loop each) (1000, 1000) 
# Multiple windows
%timeit vbt.talib('BBANDS').run(big_close, timeperiod=np.arange(2, 10))
%timeit vbt.BBANDS.run(big_close, window=np.arange(2, 10))

print(vbt.BBANDS.run(big_close, window=np.arange(2, 10)).close.shape)
192 ms ± 1.9 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) 185 ms ± 1.42 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) (1000, 8000) 
# One window repeated
%timeit vbt.talib('BBANDS').run(big_close, timeperiod=np.full(10, 2))
%timeit vbt.BBANDS.run(big_close, window=np.full(10, 2))

print(vbt.BBANDS.run(big_close, window=np.full(10, 2)).close.shape)
215 ms ± 1.51 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) 100 ms ± 1.3 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) (1000, 10000) 
bb = vbt.BBANDS.run(close, window=2, alpha=[1., 2.], ewm=False)

print(bb.middle)
print()
print(bb.upper)
print()
print(bb.lower)
print()
print(bb.percent_b)
print()
print(bb.bandwidth)
bb_window 2 bb_ewm False bb_alpha 1.0 2.0 a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 1.5 4.5 1.5 1.5 4.5 1.5 2018-01-03 2.5 3.5 2.5 2.5 3.5 2.5 2018-01-04 3.5 2.5 2.5 3.5 2.5 2.5 2018-01-05 4.5 1.5 1.5 4.5 1.5 1.5 bb_window 2 bb_ewm False bb_alpha 1.0 2.0 a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 2.0 5.0 2.0 2.5 5.5 2.5 2018-01-03 3.0 4.0 3.0 3.5 4.5 3.5 2018-01-04 4.0 3.0 3.0 4.5 3.5 3.5 2018-01-05 5.0 2.0 2.0 5.5 2.5 2.5 bb_window 2 bb_ewm False bb_alpha 1.0 2.0 a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 1.0 4.0 1.0 0.5 3.5 0.5 2018-01-03 2.0 3.0 2.0 1.5 2.5 1.5 2018-01-04 3.0 2.0 2.0 2.5 1.5 1.5 2018-01-05 4.0 1.0 1.0 3.5 0.5 0.5 bb_window 2 bb_ewm False bb_alpha 1.0 2.0 a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 1.0 0.0 1.0 0.75 0.25 0.75 2018-01-03 1.0 0.0 1.0 0.75 0.25 0.75 2018-01-04 1.0 0.0 0.0 0.75 0.25 0.25 2018-01-05 1.0 0.0 0.0 0.75 0.25 0.25 bb_window 2 bb_ewm False bb_alpha 1.0 2.0 a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 0.666667 0.222222 0.666667 1.333333 0.444444 1.333333 2018-01-03 0.400000 0.285714 0.400000 0.800000 0.571429 0.800000 2018-01-04 0.285714 0.400000 0.400000 0.571429 0.800000 0.800000 2018-01-05 0.222222 0.666667 0.666667 0.444444 1.333333 1.333333 
print(bb.close_below(bb.upper) & bb.close_above(bb.lower)) # price between bands
bb_window 2 bb_ewm False bb_alpha 1.0 2.0 a b c a b c 2018-01-01 False False False False False False 2018-01-02 False False False True True True 2018-01-03 False False False True True True 2018-01-04 False False False True True True 2018-01-05 False False False True True True
/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/base/accessors.py:667: RuntimeWarning: invalid value encountered in less /Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/base/accessors.py:667: RuntimeWarning: invalid value encountered in greater 
bb[(2, False, 1., 'a')].plot().show_svg()
Image in a Jupyter notebook

RSI

print(vbt.ta('RSIIndicator').run(close=close['a'], window=2).rsi)  # alpha=1/n
print(vbt.ta('RSIIndicator').run(close=close['b'], window=2).rsi)
print(vbt.ta('RSIIndicator').run(close=close['c'], window=2).rsi)
print(vbt.talib('RSI').run(close, timeperiod=2).real)
print(vbt.RSI.run(close, window=[2, 2], ewm=[True, False]).rsi)  # span=n
2018-01-01 NaN 2018-01-02 100.0 2018-01-03 100.0 2018-01-04 100.0 2018-01-05 100.0 Name: (2, a), dtype: float64 2018-01-01 NaN 2018-01-02 0.0 2018-01-03 0.0 2018-01-04 0.0 2018-01-05 0.0 Name: (2, b), dtype: float64 2018-01-01 NaN 2018-01-02 100.000000 2018-01-03 100.000000 2018-01-04 42.857143 2018-01-05 20.000000 Name: (2, c), dtype: float64 rsi_timeperiod 2 a b c 2018-01-01 NaN NaN NaN 2018-01-02 NaN NaN NaN 2018-01-03 100.0 0.0 100.0 2018-01-04 100.0 0.0 50.0 2018-01-05 100.0 0.0 25.0 rsi_window 2 rsi_ewm True False a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 NaN NaN NaN NaN NaN NaN 2018-01-03 100.0 0.0 100.000000 100.0 0.0 100.0 2018-01-04 100.0 0.0 33.333333 100.0 0.0 50.0 2018-01-05 100.0 0.0 11.111111 100.0 0.0 0.0 
# One window
%timeit vbt.talib('RSI').run(big_close, timeperiod=2)
%timeit vbt.RSI.run(big_close, window=2)

print(vbt.RSI.run(big_close, window=2).rsi.shape)
19.4 ms ± 149 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 27.6 ms ± 922 µs per loop (mean ± std. dev. of 7 runs, 1 loop each) (1000, 1000) 
# Multiple windows
%timeit vbt.talib('RSI').run(big_close, timeperiod=np.arange(2, 10))
%timeit vbt.RSI.run(big_close, window=np.arange(2, 10))

print(vbt.RSI.run(big_close, window=np.arange(2, 10)).rsi.shape)
128 ms ± 253 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 139 ms ± 244 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) (1000, 8000) 
# One window repeated
%timeit vbt.talib('RSI').run(big_close, timeperiod=np.full(10, 2))
%timeit vbt.RSI.run(big_close, window=np.full(10, 2))

print(vbt.RSI.run(big_close, window=np.full(10, 2)).rsi.shape)
160 ms ± 214 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 53.6 ms ± 137 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) (1000, 10000) 
rsi = vbt.RSI.run(close, window=[2, 3], ewm=[False, True])

print(rsi.rsi)
rsi_window 2 3 rsi_ewm False True a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 NaN NaN NaN NaN NaN NaN 2018-01-03 100.0 0.0 100.0 NaN NaN NaN 2018-01-04 100.0 0.0 50.0 100.0 0.0 50.0 2018-01-05 100.0 0.0 0.0 100.0 0.0 25.0 
print(rsi.rsi_above(70))
rsi_window 2 3 rsi_ewm False True a b c a b c 2018-01-01 False False False False False False 2018-01-02 False False False False False False 2018-01-03 True False True False False False 2018-01-04 True False False True False False 2018-01-05 True False False True False False
/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/base/accessors.py:667: RuntimeWarning: invalid value encountered in greater 
rsi[(2, False, 'a')].plot().show_svg()
Image in a Jupyter notebook

STOCH

print(vbt.ta('StochasticOscillator').run(high=high['a'], low=low['a'], close=close['a'], window=2, smooth_window=3).stoch)
print(vbt.ta('StochasticOscillator').run(high=high['a'], low=low['a'], close=close['a'], window=2, smooth_window=3).stoch_signal)
print(vbt.talib('STOCHF').run(high, low, close, fastk_period=2, fastd_period=3).fastk)
print(vbt.talib('STOCHF').run(high, low, close, fastk_period=2, fastd_period=3).fastd)
print(vbt.STOCH.run(high, low, close, k_window=2, d_window=3).percent_k)
print(vbt.STOCH.run(high, low, close, k_window=2, d_window=3).percent_d)
2018-01-01 NaN 2018-01-02 90.033913 2018-01-03 98.459370 2018-01-04 81.624981 2018-01-05 74.524237 Name: (2, 3, a), dtype: float64 2018-01-01 NaN 2018-01-02 NaN 2018-01-03 NaN 2018-01-04 90.039421 2018-01-05 84.869529 Name: (2, 3, a), dtype: float64 stochf_fastk_period 2 stochf_fastd_period 3 a b c 2018-01-01 NaN NaN NaN 2018-01-02 NaN NaN NaN 2018-01-03 NaN NaN NaN 2018-01-04 81.624981 3.297837 11.941605 2018-01-05 74.524237 3.900804 7.395659 stochf_fastk_period 2 stochf_fastd_period 3 a b c 2018-01-01 NaN NaN NaN 2018-01-02 NaN NaN NaN 2018-01-03 NaN NaN NaN 2018-01-04 90.039421 12.025053 64.904657 2018-01-05 84.869529 7.952381 35.000612 stoch_k_window 2 stoch_d_window 3 a b c 2018-01-01 NaN NaN NaN 2018-01-02 90.033913 16.118819 97.107794 2018-01-03 98.459370 16.658503 85.664573 2018-01-04 81.624981 3.297837 11.941605 2018-01-05 74.524237 3.900804 7.395659 stoch_k_window 2 stoch_d_window 3 a b c 2018-01-01 NaN NaN NaN 2018-01-02 NaN NaN NaN 2018-01-03 NaN NaN NaN 2018-01-04 90.039421 12.025053 64.904657 2018-01-05 84.869529 7.952381 35.000612 
# One window
%timeit vbt.talib('STOCHF').run(big_high, big_low, big_close, fastk_period=2)
%timeit vbt.STOCH.run(big_high, big_low, big_close, k_window=2)

print(vbt.STOCH.run(big_high, big_low, big_close, k_window=2).percent_d.shape)
29.4 ms ± 199 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 26.6 ms ± 607 µs per loop (mean ± std. dev. of 7 runs, 1 loop each) (1000, 1000) 
# Multiple windows
%timeit vbt.talib('STOCHF').run(big_high, big_low, big_close, fastk_period=np.arange(2, 10))
%timeit vbt.STOCH.run(big_high, big_low, big_close, k_window=np.arange(2, 10))

print(vbt.STOCH.run(big_high, big_low, big_close, k_window=np.arange(2, 10)).percent_d.shape)
220 ms ± 234 µs per loop (mean ± std. dev. of 7 runs, 1 loop each) 283 ms ± 5.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) (1000, 8000) 
# One window repeated
%timeit vbt.talib('STOCHF').run(big_high, big_low, big_close, fastk_period=np.full(10, 2))
%timeit vbt.STOCH.run(big_high, big_low, big_close, k_window=np.full(10, 2))

print(vbt.STOCH.run(big_high, big_low, big_close, k_window=np.full(10, 2)).percent_d.shape)
264 ms ± 672 µs per loop (mean ± std. dev. of 7 runs, 1 loop each) 132 ms ± 519 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) (1000, 10000) 
stochastic = vbt.STOCH.run(high, low, close, k_window=[2, 4], d_window=2, d_ewm=[False, True])

print(stochastic.percent_k)
print(stochastic.percent_d)
stoch_k_window 2 4 \ stoch_d_window 2 2 stoch_d_ewm False True a b c a b 2018-01-01 NaN NaN NaN NaN NaN 2018-01-02 90.033913 16.118819 97.107794 NaN NaN 2018-01-03 98.459370 16.658503 85.664573 NaN NaN 2018-01-04 81.624981 3.297837 11.941605 91.582811 1.221173 2018-01-05 74.524237 3.900804 7.395659 88.208468 1.313450 stoch_k_window stoch_d_window stoch_d_ewm c 2018-01-01 NaN 2018-01-02 NaN 2018-01-03 NaN 2018-01-04 47.019332 2018-01-05 4.190157 stoch_k_window 2 4 \ stoch_d_window 2 2 stoch_d_ewm False True a b c a b 2018-01-01 NaN NaN NaN NaN NaN 2018-01-02 NaN NaN NaN NaN NaN 2018-01-03 94.246641 16.388661 91.386183 NaN NaN 2018-01-04 90.042175 9.978170 48.803089 NaN NaN 2018-01-05 78.074609 3.599321 9.668632 89.333249 1.282691 stoch_k_window stoch_d_window stoch_d_ewm c 2018-01-01 NaN 2018-01-02 NaN 2018-01-03 NaN 2018-01-04 NaN 2018-01-05 18.466549 
stochastic[(2, 2, False, 'a')].plot().show_svg()
Image in a Jupyter notebook

MACD

print(vbt.ta('MACD').run(close['a'], window_fast=2, window_slow=3, window_sign=2).macd)
print(vbt.ta('MACD').run(close['a'], window_fast=2, window_slow=3, window_sign=2).macd_signal)
print(vbt.ta('MACD').run(close['a'], window_fast=2, window_slow=3, window_sign=2).macd_diff)

print(vbt.talib('MACD').run(close, fastperiod=2, slowperiod=3, signalperiod=2).macd)  # uses sma
print(vbt.talib('MACD').run(close, fastperiod=2, slowperiod=3, signalperiod=2).macdsignal)
print(vbt.talib('MACD').run(close, fastperiod=2, slowperiod=3, signalperiod=2).macdhist)

print(vbt.MACD.run(close, fast_window=2, slow_window=3, signal_window=2, macd_ewm=True, signal_ewm=True).macd)
print(vbt.MACD.run(close, fast_window=2, slow_window=3, signal_window=2, macd_ewm=True, signal_ewm=True).signal)
print(vbt.MACD.run(close, fast_window=2, slow_window=3, signal_window=2, macd_ewm=True, signal_ewm=True).hist)
2018-01-01 NaN 2018-01-02 NaN 2018-01-03 0.305556 2018-01-04 0.393519 2018-01-05 0.443673 Name: (3, 2, 2, a), dtype: float64 2018-01-01 NaN 2018-01-02 NaN 2018-01-03 NaN 2018-01-04 0.364198 2018-01-05 0.417181 Name: (3, 2, 2, a), dtype: float64 2018-01-01 NaN 2018-01-02 NaN 2018-01-03 NaN 2018-01-04 0.029321 2018-01-05 0.026492 Name: (3, 2, 2, a), dtype: float64 macd_fastperiod 2 macd_slowperiod 3 macd_signalperiod 2 a b c 2018-01-01 NaN NaN NaN 2018-01-02 NaN NaN NaN 2018-01-03 NaN NaN NaN 2018-01-04 0.5 -0.5 0.166667 2018-01-05 0.5 -0.5 -0.111111 macd_fastperiod 2 macd_slowperiod 3 macd_signalperiod 2 a b c 2018-01-01 NaN NaN NaN 2018-01-02 NaN NaN NaN 2018-01-03 NaN NaN NaN 2018-01-04 0.5 -0.5 0.333333 2018-01-05 0.5 -0.5 0.037037 macd_fastperiod 2 macd_slowperiod 3 macd_signalperiod 2 a b c 2018-01-01 NaN NaN NaN 2018-01-02 NaN NaN NaN 2018-01-03 NaN NaN NaN 2018-01-04 0.0 0.0 -0.166667 2018-01-05 0.0 0.0 -0.148148 macd_fast_window 2 macd_slow_window 3 macd_signal_window 2 macd_macd_ewm True macd_signal_ewm True a b c 2018-01-01 NaN NaN NaN 2018-01-02 NaN NaN NaN 2018-01-03 0.305556 -0.305556 0.305556 2018-01-04 0.393519 -0.393519 0.060185 2018-01-05 0.443673 -0.443673 -0.167438 macd_fast_window 2 macd_slow_window 3 macd_signal_window 2 macd_macd_ewm True macd_signal_ewm True a b c 2018-01-01 NaN NaN NaN 2018-01-02 NaN NaN NaN 2018-01-03 NaN NaN NaN 2018-01-04 0.364198 -0.364198 0.141975 2018-01-05 0.417181 -0.417181 -0.064300 macd_fast_window 2 macd_slow_window 3 macd_signal_window 2 macd_macd_ewm True macd_signal_ewm True a b c 2018-01-01 NaN NaN NaN 2018-01-02 NaN NaN NaN 2018-01-03 NaN NaN NaN 2018-01-04 0.029321 -0.029321 -0.081790 2018-01-05 0.026492 -0.026492 -0.103138 
# One window
%timeit vbt.talib('MACD').run(big_close, fastperiod=2)
%timeit vbt.MACD.run(big_close, fast_window=2)

print(vbt.MACD.run(big_close, fast_window=2).macd.shape)
28.4 ms ± 571 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 23.1 ms ± 969 µs per loop (mean ± std. dev. of 7 runs, 1 loop each) (1000, 1000) 
# Multiple windows
%timeit vbt.talib('MACD').run(big_close, fastperiod=np.arange(2, 10))
%timeit vbt.MACD.run(big_close, fast_window=np.arange(2, 10))

print(vbt.MACD.run(big_close, fast_window=np.arange(2, 10)).macd.shape)
217 ms ± 2.93 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) 145 ms ± 364 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) (1000, 8000) 
# One window repeated
%timeit vbt.talib('MACD').run(big_close, fastperiod=np.full(10, 2))
%timeit vbt.MACD.run(big_close, fast_window=np.full(10, 2))

print(vbt.MACD.run(big_close, fast_window=np.full(10, 2)).macd.shape)
267 ms ± 1.33 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) 134 ms ± 6.54 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) (1000, 10000) 
macd = vbt.MACD.run(close, fast_window=2, slow_window=3, signal_window=[2, 3], macd_ewm=True, signal_ewm=True)

print(macd.macd)
print(macd.signal)
print(macd.hist)
macd_fast_window 2 macd_slow_window 3 macd_signal_window 2 3 macd_macd_ewm True True macd_signal_ewm True True a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 NaN NaN NaN NaN NaN NaN 2018-01-03 0.305556 -0.305556 0.305556 0.305556 -0.305556 0.305556 2018-01-04 0.393519 -0.393519 0.060185 0.393519 -0.393519 0.060185 2018-01-05 0.443673 -0.443673 -0.167438 0.443673 -0.443673 -0.167438 macd_fast_window 2 macd_slow_window 3 macd_signal_window 2 3 macd_macd_ewm True True macd_signal_ewm True True a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 NaN NaN NaN NaN NaN NaN 2018-01-03 NaN NaN NaN NaN NaN NaN 2018-01-04 0.364198 -0.364198 0.141975 NaN NaN NaN 2018-01-05 0.417181 -0.417181 -0.064300 0.396605 -0.396605 0.007716 macd_fast_window 2 macd_slow_window 3 macd_signal_window 2 3 macd_macd_ewm True True macd_signal_ewm True True a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 NaN NaN NaN NaN NaN NaN 2018-01-03 NaN NaN NaN NaN NaN NaN 2018-01-04 0.029321 -0.029321 -0.081790 NaN NaN NaN 2018-01-05 0.026492 -0.026492 -0.103138 0.047068 -0.047068 -0.175154 
macd[(2, 3, 2, True, True, 'a')].plot().show_svg()
/Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/indicators/basic.py:666: RuntimeWarning: invalid value encountered in greater /Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/indicators/basic.py:667: RuntimeWarning: invalid value encountered in greater /Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/indicators/basic.py:667: RuntimeWarning: invalid value encountered in less_equal /Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/indicators/basic.py:668: RuntimeWarning: invalid value encountered in less /Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/indicators/basic.py:669: RuntimeWarning: invalid value encountered in less /Users/olegpolakow/Documents/SourceTree/vectorbt/vectorbt/indicators/basic.py:669: RuntimeWarning: invalid value encountered in greater_equal
Image in a Jupyter notebook

ATR

print(vbt.ta('AverageTrueRange').run(high['a'], low['a'], close['a'], window=2).average_true_range)
print(vbt.ta('AverageTrueRange').run(high['b'], low['b'], close['b'], window=2).average_true_range)
print(vbt.ta('AverageTrueRange').run(high['c'], low['c'], close['c'], window=2).average_true_range)
print(vbt.talib('ATR').run(high, low, close, timeperiod=2).real)
print(vbt.ATR.run(high, low, close, window=[2, 3], ewm=[False, True]).atr)
2018-01-01 0.000000 2018-01-02 0.619423 2018-01-03 0.818424 2018-01-04 1.050826 2018-01-05 1.233524 Name: (2, a), dtype: float64 2018-01-01 0.000000 2018-01-02 1.053372 2018-01-03 1.132864 2018-01-04 1.087915 2018-01-05 1.064337 Name: (2, b), dtype: float64 2018-01-01 0.000000 2018-01-02 0.575961 2018-01-03 0.878148 2018-01-04 1.019107 2018-01-05 1.057231 Name: (2, c), dtype: float64 atr_timeperiod 2 a b c 2018-01-01 NaN NaN NaN 2018-01-02 NaN NaN NaN 2018-01-03 1.068578 1.247932 1.105767 2018-01-04 1.175904 1.145449 1.132916 2018-01-05 1.296063 1.093104 1.114135 atr_window 2 3 atr_ewm False True a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 0.619423 1.053372 0.575961 NaN NaN NaN 2018-01-03 1.068578 1.247932 1.105767 0.818424 1.132864 0.878148 2018-01-04 1.150327 1.127661 1.170200 1.050826 1.087915 1.019107 2018-01-05 1.349725 1.041862 1.127710 1.233524 1.064337 1.057231 
# One window
%timeit vbt.talib('ATR').run(big_high, big_low, big_close, timeperiod=2)
%timeit vbt.ATR.run(big_high, big_low, big_close, window=2)

print(vbt.ATR.run(big_high, big_low, big_close, window=2).atr.shape)
22.5 ms ± 470 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 33.5 ms ± 340 µs per loop (mean ± std. dev. of 7 runs, 1 loop each) (1000, 1000) 
# Multiple windows
%timeit vbt.talib('ATR').run(big_high, big_low, big_close, timeperiod=np.arange(2, 10))
%timeit vbt.ATR.run(big_high, big_low, big_close, window=np.arange(2, 10)) # rolling min/max very expensive

print(vbt.ATR.run(big_high, big_low, big_close, window=np.arange(2, 10)).atr.shape)
158 ms ± 695 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) 110 ms ± 1.14 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) (1000, 8000) 
# One window repeated
%timeit vbt.talib('ATR').run(big_high, big_low, big_close, timeperiod=np.full(10, 2))
%timeit vbt.ATR.run(big_high, big_low, big_close, window=np.full(10, 2))

print(vbt.ATR.run(big_high, big_low, big_close, window=np.full(10, 2)).atr.shape)
197 ms ± 1.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) 67.5 ms ± 1.89 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) (1000, 10000) 
atr = vbt.ATR.run(high, low, close, window=[2, 3], ewm=[False, True])

print(atr.tr)
print(atr.atr)
atr_window 2 3 atr_ewm False True a b c a b c 2018-01-01 0.119114 0.823236 0.120724 0.119114 0.823236 0.120724 2018-01-02 1.119732 1.283508 1.031199 1.119732 1.283508 1.031199 2018-01-03 1.017425 1.212357 1.180335 1.017425 1.212357 1.180335 2018-01-04 1.283229 1.042965 1.160065 1.283229 1.042965 1.160065 2018-01-05 1.416221 1.040759 1.095355 1.416221 1.040759 1.095355 atr_window 2 3 atr_ewm False True a b c a b c 2018-01-01 NaN NaN NaN NaN NaN NaN 2018-01-02 0.619423 1.053372 0.575961 NaN NaN NaN 2018-01-03 1.068578 1.247932 1.105767 0.818424 1.132864 0.878148 2018-01-04 1.150327 1.127661 1.170200 1.050826 1.087915 1.019107 2018-01-05 1.349725 1.041862 1.127710 1.233524 1.064337 1.057231 
atr[(2, False, 'a')].plot().show_svg()
Image in a Jupyter notebook

OBV

print(vbt.ta('OnBalanceVolumeIndicator').run(close['a'], volume['a']).on_balance_volume)
print(vbt.ta('OnBalanceVolumeIndicator').run(close['b'], volume['b']).on_balance_volume)
print(vbt.ta('OnBalanceVolumeIndicator').run(close['c'], volume['c']).on_balance_volume)
print(vbt.talib('OBV').run(close, volume).real)
print(vbt.OBV.run(close, volume).obv)
2018-01-01 3.0 2018-01-02 12.0 2018-01-03 16.0 2018-01-04 25.0 2018-01-05 29.0 Name: a, dtype: float64 2018-01-01 7.0 2018-01-02 0.0 2018-01-03 -9.0 2018-01-04 -14.0 2018-01-05 -21.0 Name: b, dtype: float64 2018-01-01 5.0 2018-01-02 7.0 2018-01-03 9.0 2018-01-04 7.0 2018-01-05 -1.0 Name: c, dtype: float64 a b c 2018-01-01 3.0 7.0 5.0 2018-01-02 12.0 0.0 7.0 2018-01-03 16.0 -9.0 9.0 2018-01-04 25.0 -14.0 7.0 2018-01-05 29.0 -21.0 -1.0 a b c 2018-01-01 3.0 7.0 5.0 2018-01-02 12.0 0.0 7.0 2018-01-03 16.0 -9.0 9.0 2018-01-04 25.0 -14.0 7.0 2018-01-05 29.0 -21.0 -1.0 
%timeit vbt.talib('OBV').run(big_close, big_volume)
%timeit vbt.OBV.run(big_close, big_volume)

print(vbt.OBV.run(big_close, big_volume).obv.shape)
19.3 ms ± 549 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 17.1 ms ± 1.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) (1000, 1000) 
obv = vbt.OBV.run(close, volume)

print(obv.obv)
a b c 2018-01-01 3.0 7.0 5.0 2018-01-02 12.0 0.0 7.0 2018-01-03 16.0 -9.0 9.0 2018-01-04 25.0 -14.0 7.0 2018-01-05 29.0 -21.0 -1.0 
print(obv.obv_above([0, 5]))
obv_above 0 5 a b c a b c 2018-01-01 True True True False True False 2018-01-02 True False True True False True 2018-01-03 True False True True False True 2018-01-04 True False True True False True 2018-01-05 True False False True False False 
obv['a'].plot().show_svg()
Image in a Jupyter notebook