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nb module

Numba-compiled functions.

Provides an arsenal of Numba-compiled functions that are used by accessors and in many other parts of the backtesting pipeline, such as technical indicators. These only accept NumPy arrays and other Numba-compatible types.

>>> import numpy as np
>>> import vectorbt as vbt

>>> # vectorbt.signals.nb.pos_rank_nb
>>> vbt.signals.nb.pos_rank_nb(np.array([False, True, True, True, False])[:, None])[:, 0]
[-1  0  1  2 -1]

Note

vectorbt treats matrices as first-class citizens and expects input arrays to be 2-dim, unless function has suffix _1d or is meant to be input to another function. Data is processed along index (axis 0).

All functions passed as argument should be Numba-compiled.

Returned indices should be absolute.

between_partition_ranges_nb function

between_partition_ranges_nb(
    a
)

Create a record of type range_dt for each range between two partitions in a.

between_ranges_nb function

between_ranges_nb(
    a
)

Create a record of type range_dt for each range between two signals in a.

between_two_ranges_nb function

between_two_ranges_nb(
    a,
    b,
    from_other=False
)

Create a record of type range_dt for each range between two signals in a and b.

If from_other is False, returns ranges from each in a to the succeeding in b. Otherwise, returns ranges from each in b to the preceding in a.

When a and b overlap (two signals at the same time), the distance between overlapping signals is still considered and from_i would match to_i.

clean_enex_1d_nb function

clean_enex_1d_nb(
    entries,
    exits,
    entry_first
)

Clean entry and exit arrays by picking the first signal out of each.

Entry signal must be picked first. If both signals are present, selects none.

clean_enex_nb function

clean_enex_nb(
    entries,
    exits,
    entry_first
)

2-dim version of clean_enex_1d_nb().

first_choice_nb function

first_choice_nb(
    from_i,
    to_i,
    col,
    a
)

choice_func_nb that returns the index of the first signal in a.

generate_enex_nb function

generate_enex_nb(
    shape,
    entry_wait,
    exit_wait,
    entry_pick_first,
    exit_pick_first,
    entry_choice_func_nb,
    entry_args,
    exit_choice_func_nb,
    exit_args
)

Pick entry signals using entry_choice_func_nb and exit signals using exit_choice_func_nb one after another.

Args

shape : array
Target shape.
entry_wait : int

Number of ticks to wait before placing entries.

Note

Setting entry_wait to 0 or False assumes that both entry and exit can be processed within the same bar, and exit can be processed before entry.

exit_wait : int

Number of ticks to wait before placing exits.

Note

Setting exit_wait to 0 or False assumes that both entry and exit can be processed within the same bar, and entry can be processed before exit.

entry_pick_first : bool
Whether to pick the first entry out of all returned by entry_choice_func_nb.
exit_pick_first : bool

Whether to pick the first exit out of all returned by exit_choice_func_nb.

Setting it to False acts similarly to setting skip_until_exit to True in generate_ex_nb().

entry_choice_func_nb : callable

Entry choice function.

See choice_func_nb in generate_nb().

entry_args : tuple
Arguments unpacked and passed to entry_choice_func_nb.
exit_choice_func_nb : callable

Exit choice function.

See choice_func_nb in generate_nb().

exit_args : tuple
Arguments unpacked and passed to exit_choice_func_nb.

generate_ex_nb function

generate_ex_nb(
    entries,
    wait,
    until_next,
    skip_until_exit,
    pick_first,
    exit_choice_func_nb,
    *args
)

Pick exit signals using exit_choice_func_nb after each signal in entries.

Args

entries : array
Boolean array with entry signals.
wait : int

Number of ticks to wait before placing exits.

Note

Setting wait to 0 or False may result in two signals at one bar.

until_next : int

Whether to place signals up to the next entry signal.

Note

Setting it to False makes it difficult to tell which exit belongs to which entry.

skip_until_exit : bool

Whether to skip processing entry signals until the next exit.

Has only effect when until_next is disabled.

Note

Setting it to True makes it difficult to tell which exit belongs to which entry.

pick_first : bool
Whether to pick the first signal out of all returned by exit_choice_func_nb.
exit_choice_func_nb : callable

Exit choice function.

See choice_func_nb in generate_nb().

*args : callable
Arguments passed to exit_choice_func_nb.

generate_nb function

generate_nb(
    shape,
    pick_first,
    choice_func_nb,
    *args
)

Create a boolean matrix of shape and pick signals using choice_func_nb.

Args

shape : array
Target shape.
pick_first : bool
Whether to pick the first signal out of all returned by choice_func_nb.
choice_func_nb : callable

Choice function.

choice_func_nb should accept index of the start of the range from_i, index of the end of the range to_i, index of the column col, and *args. It should return an array of indices from [from_i, to_i) (can be empty).

*args
Arguments passed to choice_func_nb.

Usage

>>> from numba import njit
>>> import numpy as np
>>> from vectorbt.signals.nb import generate_nb

>>> @njit
... def choice_func_nb(from_i, to_i, col):
...     return np.array([from_i + col])

>>> generate_nb((5, 3), choice_func_nb)
[[ True False False]
 [False  True False]
 [False False  True]
 [False False False]
 [False False False]]

generate_ohlc_stop_enex_nb function

generate_ohlc_stop_enex_nb(
    entries,
    open,
    high,
    low,
    close,
    stop_price_out,
    stop_type_out,
    sl_stop,
    sl_trail,
    tp_stop,
    reverse,
    is_open_safe,
    entry_wait,
    exit_wait,
    pick_first,
    flex_2d
)

Generate one after another using generate_enex_nb() and ohlc_stop_choice_nb().

Returns two arrays: new entries and exits.

Note

Has the same logic as calling generate_ohlc_stop_ex_nb() with skip_until_exit=True, but removes all entries that come before the next exit.

generate_ohlc_stop_ex_nb function

generate_ohlc_stop_ex_nb(
    entries,
    open,
    high,
    low,
    close,
    stop_price_out,
    stop_type_out,
    sl_stop,
    sl_trail,
    tp_stop,
    reverse,
    is_open_safe,
    wait,
    until_next,
    skip_until_exit,
    pick_first,
    flex_2d
)

Generate using generate_ex_nb() and ohlc_stop_choice_nb().

Usage

  • Generate trailing stop loss and take profit signals for 10%. Illustrates how exit signal can be generated within the same bar as entry.
>>> import numpy as np
>>> from vectorbt.signals.nb import generate_ohlc_stop_ex_nb

>>> entries = np.asarray([True, False, True, False, False])[:, None]
>>> entry_price = np.asarray([10, 11, 12, 11, 10])[:, None]
>>> high_price = entry_price + 1
>>> low_price = entry_price - 1
>>> close_price = entry_price
>>> stop_price_out = np.full_like(entries, np.nan, dtype=np.float64)
>>> stop_type_out = np.full_like(entries, -1, dtype=np.int64)

>>> generate_ohlc_stop_ex_nb(
...     entries=entries,
...     open=entry_price,
...     high=high_price,
...     low=low_price,
...     close=close_price,
...     stop_price_out=stop_price_out,
...     stop_type_out=stop_type_out,
...     sl_stop=0.1,
...     sl_trail=True,
...     tp_stop=0.1,
...     reverse=False,
...     is_open_safe=True,
...     wait=1,
...     until_next=True,
...     skip_until_exit=False,
...     pick_first=True,
...     flex_2d=True
... )
array([[ True],
       [False],
       [False],
       [ True],
       [False]])

>>> stop_price_out
array([[ 9. ],  << trailing SL from 10 (entry_price)
       [ nan],
       [ nan],
       [11.7],  << trailing SL from 13 (high_price)
       [ nan]])

>>> stop_type_out
array([[ 1],
       [-1],
       [-1],
       [ 1],
       [-1]])

Note that if is_open_safe was False, the first exit would be executed at the second bar. This is because we don't know whether the entry price comes before the high and low price at the first bar, and so the trailing stop isn't triggered for the low price of 9.0.

generate_rand_by_prob_nb function

generate_rand_by_prob_nb(
    shape,
    prob,
    pick_first,
    flex_2d,
    seed=None
)

Create a boolean matrix of shape and pick signals randomly by probability prob.

prob should be a 2-dim array of shape shape. Specify seed to make output deterministic.

See rand_by_prob_choice_nb().

generate_rand_enex_by_prob_nb function

generate_rand_enex_by_prob_nb(
    shape,
    entry_prob,
    exit_prob,
    entry_wait,
    exit_wait,
    entry_pick_first,
    exit_pick_first,
    flex_2d,
    seed=None
)

Pick entries by probability entry_prob and exits by probability exit_prob one after another.

entry_prob and exit_prob should be 2-dim arrays of shape shape. Specify seed to make output deterministic.

generate_rand_enex_nb function

generate_rand_enex_nb(
    shape,
    n,
    entry_wait,
    exit_wait,
    seed=None
)

Pick a number of entries and the same number of exits one after another.

Respects entry_wait and exit_wait constraints through a number of tricks. Tries to mimic a uniform distribution as much as possible.

The idea is the following: with constraints, there is some fixed amount of total space required between first entry and last exit. Upscale this space in a way that distribution of entries and exit is similar to a uniform distribution. This means randomizing the position of first entry, last exit, and all signals between them.

n uses flexible indexing. Specify seed to make output deterministic.

generate_rand_ex_by_prob_nb function

generate_rand_ex_by_prob_nb(
    entries,
    prob,
    wait,
    until_next,
    skip_until_exit,
    flex_2d,
    seed=None
)

Pick an exit after each entry in entries by probability prob.

prob should be a 2-dim array of shape shape. Specify seed to make output deterministic.

generate_rand_ex_nb function

generate_rand_ex_nb(
    entries,
    wait,
    until_next,
    skip_until_exit,
    seed=None
)

Pick an exit after each entry in entries.

Specify seed to make output deterministic.

generate_rand_nb function

generate_rand_nb(
    shape,
    n,
    seed=None
)

Create a boolean matrix of shape and pick a number of signals randomly.

Specify seed to make output deterministic.

See rand_choice_nb().

generate_stop_enex_nb function

generate_stop_enex_nb(
    entries,
    ts,
    stop,
    trailing,
    entry_wait,
    exit_wait,
    pick_first,
    flex_2d
)

Generate one after another using generate_enex_nb() and stop_choice_nb().

Returns two arrays: new entries and exits.

Note

Has the same logic as calling generate_stop_ex_nb() with skip_until_exit=True, but removes all entries that come before the next exit.

generate_stop_ex_nb function

generate_stop_ex_nb(
    entries,
    ts,
    stop,
    trailing,
    wait,
    until_next,
    skip_until_exit,
    pick_first,
    flex_2d
)

Generate using generate_ex_nb() and stop_choice_nb().

Usage

  • Generate trailing stop loss and take profit signals for 10%.
>>> import numpy as np
>>> from vectorbt.signals.nb import generate_stop_ex_nb

>>> entries = np.asarray([False, True, False, False, False])[:, None]
>>> ts = np.asarray([1, 2, 3, 2, 1])[:, None]

>>> generate_stop_ex_nb(entries, ts, -0.1, True, 1, True, True)
array([[False],
       [False],
       [False],
       [ True],
       [False]])

>>> generate_stop_ex_nb(entries, ts, 0.1, False, 1, True, True)
array([[False],
       [False],
       [ True],
       [False],
       [False]])

norm_avg_index_1d_nb function

norm_avg_index_1d_nb(
    a
)

Get mean index normalized to (-1, 1).

norm_avg_index_nb function

norm_avg_index_nb(
    a
)

2-dim version of norm_avg_index_1d_nb().

nth_index_1d_nb function

nth_index_1d_nb(
    a,
    n
)

Get the index of the n-th True value.

Note

n starts with 0 and can be negative.

nth_index_nb function

nth_index_nb(
    a,
    n
)

2-dim version of nth_index_1d_nb().

ohlc_stop_choice_nb function

ohlc_stop_choice_nb(
    from_i,
    to_i,
    col,
    open,
    high,
    low,
    close,
    stop_price_out,
    stop_type_out,
    sl_stop,
    sl_trail,
    tp_stop,
    reverse,
    is_open_safe,
    wait,
    pick_first,
    temp_idx_arr,
    flex_2d
)

choice_func_nb that returns the indices of the stop price being hit within OHLC.

Compared to stop_choice_nb(), takes into account the whole bar, can check for both (trailing) stop loss and take profit simultaneously, and tracks hit price and stop type.

Note

We don't have intra-candle data. If there was a huge price fluctuation in both directions, we can't determine whether SL was triggered before TP and vice versa. So some assumptions need to be made: 1) trailing stop can only be based on previous close/high, and 2) we pessimistically assume that SL comes before TP.

Args

col : int
Current column.
from_i : int
Index to start generation from (inclusive).
to_i : int
Index to run generation to (exclusive).
open : array of float
Entry price such as open or previous close.
high : array of float
High price.
low : array of float
Low price.
close : array of float
Close price.
stop_price_out : array of float
Array where hit price of each exit will be stored.
stop_type_out : array of int

Array where stop type of each exit will be stored.

0 for stop loss, 1 for take profit.

sl_stop : float or array_like

Percentage value for stop loss.

Can be per frame, column, row, or element-wise. Set to np.nan to disable.

sl_trail : bool or array_like

Whether sl_stop is trailing.

Can be per frame, column, row, or element-wise. Set to False to disable.

tp_stop : float or array_like

Percentage value for take profit.

Can be per frame, column, row, or element-wise. Set to np.nan to disable.

reverse : bool or array_like
Whether to do the opposite, i.e.: prices are followed downwards.
is_open_safe : bool

Whether entry price comes right at or before open.

If True and wait is 0, can use high/low at entry bar. Otherwise uses only close.

wait : int

Number of ticks to wait before placing exits.

Setting False or 0 may result in entry and exit signal at one bar.

Note

If wait is greater than 0, even with is_open_safe set to True, trailing stop won't update at bars that come before from_i.

pick_first : bool
Whether to stop as soon as the first exit signal is found.
temp_idx_arr : array of int
Empty integer array used to temporarily store indices.
flex_2d : bool
See flex_select_auto_nb().

part_pos_rank_nb function

part_pos_rank_nb(
    i,
    col,
    reset_i,
    prev_part_end_i,
    part_start_i,
    part_pos_temp
)

rank_func_nb that returns the rank of each partition by its position in the series.

partition_ranges_nb function

partition_ranges_nb(
    a
)

Create a record of type range_dt for each partition of signals in a.

rand_by_prob_choice_nb function

rand_by_prob_choice_nb(
    from_i,
    to_i,
    col,
    prob,
    pick_first,
    temp_idx_arr,
    flex_2d
)

choice_func_nb to randomly pick values from range [from_i, to_i) with probability prob.

prob uses flexible indexing.

rand_choice_nb function

rand_choice_nb(
    from_i,
    to_i,
    col,
    n
)

choice_func_nb to randomly pick n values from range [from_i, to_i).

n uses flexible indexing.

rand_enex_apply_nb function

rand_enex_apply_nb(
    input_shape,
    n,
    entry_wait,
    exit_wait
)

apply_func_nb that calls generate_rand_enex_nb().

rank_nb function

rank_nb(
    a,
    reset_by,
    after_false,
    rank_func_nb,
    *args
)

Rank each signal using rank_func_nb.

Applies rank_func_nb on each True value. Should accept index of the row, index of the column, index of the last reset signal, index of the end of the previous partition, index of the start of the current partition, and *args. Should return -1 for no rank, otherwise 0 or greater.

Setting after_false to True will disregard the first partition of True values if there is no False value before them.

sig_pos_rank_nb function

sig_pos_rank_nb(
    i,
    col,
    reset_i,
    prev_part_end_i,
    part_start_i,
    sig_pos_temp,
    allow_gaps
)

rank_func_nb that returns the rank of each signal by its position in the partition.

stop_choice_nb function

stop_choice_nb(
    from_i,
    to_i,
    col,
    ts,
    stop,
    trailing,
    wait,
    pick_first,
    temp_idx_arr,
    flex_2d
)

choice_func_nb that returns the indices of the stop being hit.

Args

from_i : int
Index to start generation from (inclusive).
to_i : int
Index to run generation to (exclusive).
col : int
Current column.
ts : array of float
2-dim time series array such as price.
stop : float or array_like

Stop value for stop loss.

Can be per frame, column, row, or element-wise. Set to np.nan to disable.

trailing : bool or array_like

Whether to use trailing stop.

Can be per frame, column, row, or element-wise. Set to False to disable.

wait : int

Number of ticks to wait before placing exits.

Setting False or 0 may result in two signals at one bar.

Note

If wait is greater than 0, trailing stop won't update at bars that come before from_i.

pick_first : bool
Whether to stop as soon as the first exit signal is found.
temp_idx_arr : array of int
Empty integer array used to temporarily store indices.
flex_2d : bool
See flex_select_auto_nb().