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.
The module can be accessed directly via vbt.nb.
>>> import numpy as np
>>> import vectorbt as vbt
>>> # vectorbt.generic.nb.rolling_mean_1d_nb
>>> vbt.nb.rolling_mean_1d_nb(np.array([1, 2, 3, 4]), 2)
array([nan, 1.5, 2.5, 3.5])
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).
Rolling functions with minp=None have min_periods set to the window size.
All functions passed as argument should be Numba-compiled.
ol_bshift_1d_nb function¶
_bshift_1d_nb(
arr,
n,
fill_value
)
ol_bshift_nb function¶
_bshift_nb(
arr,
n,
fill_value
)
ol_fshift_1d_nb function¶
_fshift_1d_nb(
arr,
n,
fill_value
)
ol_fshift_nb function¶
_fshift_nb(
arr,
n,
fill_value
)
ol_nancumprod_nb function¶
_nancumprod_nb(
arr
)
ol_nancumsum_nb function¶
_nancumsum_nb(
arr
)
ol_nanprod_nb function¶
_nanprod_nb(
arr
)
ol_nansum_nb function¶
_nansum_nb(
arr
)
ol_set_by_mask_1d_nb function¶
_set_by_mask_1d_nb(
arr,
mask,
value
)
ol_set_by_mask_mult_1d_nb function¶
_set_by_mask_mult_1d_nb(
arr,
mask,
values
)
ol_set_by_mask_mult_nb function¶
_set_by_mask_mult_nb(
arr,
mask,
values
)
ol_set_by_mask_nb function¶
_set_by_mask_nb(
arr,
mask,
value
)
any_squeeze_nb function¶
any_squeeze_nb(
col,
group,
a
)
Return any (ignores NaNs) of a group.
apply_and_reduce_nb function¶
apply_and_reduce_nb(
a,
apply_func_nb,
apply_args,
reduce_func_nb,
reduce_args
)
Apply apply_func_nb on each column and reduce into a single value using reduce_func_nb.
apply_func_nb should accept index of the column, the column itself, and *apply_args. Should return an array.
reduce_func_nb should accept index of the column, the array of results from apply_func_nb for that column, and *reduce_args. Should return a single value.
apply_nb function¶
apply_nb(
a,
apply_func_nb,
*args
)
Apply function on each column.
apply_func_nb should accept index of the column, the array, and *args. Should return a single value or an array of shape a.shape[1].
applymap_nb function¶
applymap_nb(
a,
map_func_nb,
*args
)
Map non-NA elements element-wise using map_func_nb.
map_func_nb should accept index of the row, index of the column, the element itself, and *args. Should return a single value.
argmax_reduce_nb function¶
argmax_reduce_nb(
col,
a
)
Return position of max.
argmin_reduce_nb function¶
argmin_reduce_nb(
col,
a
)
Return position of min.
bfill_1d_nb function¶
bfill_1d_nb(
a
)
Fill NaNs by propagating first valid observation backward.
Numba equivalent to pd.Series(a).fillna(method='bfill').
Warning
This operation looks ahead.
bfill_nb function¶
bfill_nb(
a
)
2-dim version of bfill_1d_nb().
bshift_1d_nb function¶
bshift_1d_nb(
arr,
n=1,
fill_value=nan
)
Shift backward by n positions.
Numba equivalent to pd.Series(arr).shift(-n).
Warning
This operation looks ahead.
bshift_nb function¶
bshift_nb(
arr,
n=1,
fill_value=nan
)
2-dim version of bshift_1d_nb().
count_reduce_nb function¶
count_reduce_nb(
col,
a
)
Return count (ignores NaNs).
crossed_above_1d_nb function¶
crossed_above_1d_nb(
arr1,
arr2,
wait=0
)
Get the crossover of the first array going above the second array.
crossed_above_nb function¶
crossed_above_nb(
arr1,
arr2,
wait=0
)
2-dim version of crossed_above_1d_nb().
dd_decline_duration_nb function¶
dd_decline_duration_nb(
start_idx_arr,
valley_idx_arr
)
Return the duration of the peak-to-valley phase of each drawdown record.
dd_drawdown_nb function¶
dd_drawdown_nb(
peak_val_arr,
valley_val_arr
)
Return the drawdown of each drawdown record.
dd_recovery_duration_nb function¶
dd_recovery_duration_nb(
valley_idx_arr,
end_idx_arr
)
Return the duration of the valley-to-recovery phase of each drawdown record.
dd_recovery_duration_ratio_nb function¶
dd_recovery_duration_ratio_nb(
start_idx_arr,
valley_idx_arr,
end_idx_arr
)
Return the ratio of the recovery duration to the decline duration of each drawdown record.
dd_recovery_return_nb function¶
dd_recovery_return_nb(
valley_val_arr,
end_val_arr
)
Return the recovery return of each drawdown record.
describe_reduce_nb function¶
describe_reduce_nb(
col,
a,
perc,
ddof
)
Return descriptive statistics (ignores NaNs).
Numba equivalent to pd.Series(a).describe(perc).
diff_1d_nb function¶
diff_1d_nb(
a,
n=1
)
Return the 1-th discrete difference.
Numba equivalent to pd.Series(a).diff().
diff_nb function¶
diff_nb(
a,
n=1
)
2-dim version of diff_1d_nb().
ewm_mean_1d_nb function¶
ewm_mean_1d_nb(
a,
span,
minp=0,
adjust=False
)
Return exponential weighted average.
Numba equivalent to pd.Series(a).ewm(span=span, min_periods=minp, adjust=adjust).mean().
Adaptation of pd._libs.window.aggregations.window_aggregations.ewma with default arguments.
ewm_mean_nb function¶
ewm_mean_nb(
a,
span,
minp=0,
adjust=False
)
2-dim version of ewm_mean_1d_nb().
ewm_std_1d_nb function¶
ewm_std_1d_nb(
a,
span,
minp=0,
adjust=False,
ddof=0
)
Return exponential weighted standard deviation.
Numba equivalent to pd.Series(a).ewm(span=span, min_periods=minp).std(ddof=ddof).
Adaptation of pd._libs.window.aggregations.window_aggregations.ewmcov with default arguments.
ewm_std_nb function¶
ewm_std_nb(
a,
span,
minp=0,
adjust=False,
ddof=0
)
2-dim version of ewm_std_1d_nb().
expanding_apply_nb function¶
expanding_apply_nb(
a,
minp,
apply_func_nb,
*args
)
Expanding version of rolling_apply_nb().
expanding_matrix_apply_nb function¶
expanding_matrix_apply_nb(
a,
minp,
apply_func_nb,
*args
)
Expanding version of rolling_matrix_apply_nb().
expanding_max_1d_nb function¶
expanding_max_1d_nb(
a,
minp=1
)
Return expanding max.
Numba equivalent to pd.Series(a).expanding(min_periods=minp).max().
expanding_max_nb function¶
expanding_max_nb(
a,
minp=1
)
2-dim version of expanding_max_1d_nb().
expanding_mean_1d_nb function¶
expanding_mean_1d_nb(
a,
minp=1
)
Return expanding mean.
Numba equivalent to pd.Series(a).expanding(min_periods=minp).mean().
expanding_mean_nb function¶
expanding_mean_nb(
a,
minp=1
)
2-dim version of expanding_mean_1d_nb().
expanding_min_1d_nb function¶
expanding_min_1d_nb(
a,
minp=1
)
Return expanding min.
Numba equivalent to pd.Series(a).expanding(min_periods=minp).min().
expanding_min_nb function¶
expanding_min_nb(
a,
minp=1
)
2-dim version of expanding_min_1d_nb().
expanding_std_1d_nb function¶
expanding_std_1d_nb(
a,
minp=1,
ddof=0
)
Return expanding standard deviation.
Numba equivalent to pd.Series(a).expanding(min_periods=minp).std(ddof=ddof).
expanding_std_nb function¶
expanding_std_nb(
a,
minp=1,
ddof=0
)
2-dim version of expanding_std_1d_nb().
ffill_1d_nb function¶
ffill_1d_nb(
a
)
Fill NaNs by propagating last valid observation forward.
Numba equivalent to pd.Series(a).fillna(method='ffill').
ffill_nb function¶
ffill_nb(
a
)
2-dim version of ffill_1d_nb().
fillna_1d_nb function¶
fillna_1d_nb(
a,
value
)
Replace NaNs with value.
Numba equivalent to pd.Series(a).fillna(value).
fillna_nb function¶
fillna_nb(
a,
value
)
2-dim version of fillna_1d_nb().
filter_nb function¶
filter_nb(
a,
filter_func_nb,
*args
)
Filter non-NA elements elementwise using filter_func_nb. The filtered out elements will become NA.
filter_func_nb should accept index of the row, index of the column, the element itself, and *args. Should return a bool.
find_ranges_nb function¶
find_ranges_nb(
ts,
gap_value
)
Find ranges and store their information as records to an array.
Usage
- Find ranges in time series:
>>> import numpy as np
>>> import pandas as pd
>>> from vectorbt.generic.nb import find_ranges_nb
>>> ts = np.asarray([
... [np.nan, np.nan, np.nan, np.nan],
... [ 2, np.nan, np.nan, np.nan],
... [ 3, 3, np.nan, np.nan],
... [np.nan, 4, 4, np.nan],
... [ 5, np.nan, 5, 5],
... [ 6, 6, np.nan, 6]
... ])
>>> records = find_ranges_nb(ts, np.nan)
>>> pd.DataFrame.from_records(records)
id col start_idx end_idx
0 0 0 1 3
1 1 0 4 6
2 2 1 2 4
3 3 1 5 6
4 4 2 3 5
5 5 3 4 6
flat_reduce_grouped_nb function¶
flat_reduce_grouped_nb(
a,
group_lens,
in_c_order,
reduce_func_nb,
*args
)
Same as reduce_grouped_nb() but passes flattened array.
flat_reduce_grouped_to_array_nb function¶
flat_reduce_grouped_to_array_nb(
a,
group_lens,
in_c_order,
reduce_func_nb,
*args
)
Same as reduce_grouped_to_array_nb() but passes flattened 1D array.
flatten_forder_nb function¶
flatten_forder_nb(
a
)
Flatten a in F order.
flatten_grouped_nb function¶
flatten_grouped_nb(
a,
group_lens,
in_c_order
)
Flatten each group of columns.
flatten_uniform_grouped_nb function¶
flatten_uniform_grouped_nb(
a,
group_lens,
in_c_order
)
Flatten each group of columns of the same length.
fshift_1d_nb function¶
fshift_1d_nb(
arr,
n=1,
fill_value=nan
)
Shift forward by n positions.
Numba equivalent to pd.Series(arr).shift(n).
fshift_nb function¶
fshift_nb(
arr,
n=1,
fill_value=nan
)
2-dim version of fshift_1d_nb().
get_drawdowns_nb function¶
get_drawdowns_nb(
ts
)
Fill drawdown records by analyzing a time series.
Usage
>>> import numpy as np
>>> import pandas as pd
>>> from vectorbt.generic.nb import get_drawdowns_nb
>>> ts = np.asarray([
... [1, 5, 1, 3],
... [2, 4, 2, 2],
... [3, 3, 3, 1],
... [4, 2, 2, 2],
... [5, 1, 1, 3]
... ])
>>> records = get_drawdowns_nb(ts)
>>> pd.DataFrame.from_records(records)
id col peak_idx start_idx valley_idx end_idx peak_val valley_val \
0 0 1 0 1 4 4 5.0 1.0
1 1 2 2 3 4 4 3.0 1.0
2 2 3 0 1 2 4 3.0 1.0
end_val status
0 1.0 0
1 1.0 0
2 3.0 1
groupby_apply_nb function¶
groupby_apply_nb(
a,
groups,
apply_func_nb,
*args
)
Provide group-by calculations.
groups should be a dictionary, where each key is an index that points to an element in the new array where a group-by result will be stored, while the value should be an array of indices in a to apply apply_func_nb on.
apply_func_nb should accept indices of the group, index of the column, the array, and *args. Should return a single value.
groupby_matrix_apply_nb function¶
groupby_matrix_apply_nb(
a,
groups,
apply_func_nb,
*args
)
groupby_apply_nb() with apply_func_nb being applied on all columns at once.
apply_func_nb should accept indices of the group, the 2-dim array, and *args. Should return a single value or an array of shape a.shape[1].
max_reduce_nb function¶
max_reduce_nb(
col,
a
)
Return max (ignores NaNs).
max_squeeze_nb function¶
max_squeeze_nb(
col,
group,
a
)
Return max (ignores NaNs) of a group.
mean_reduce_nb function¶
mean_reduce_nb(
col,
a
)
Return mean (ignores NaNs).
median_reduce_nb function¶
median_reduce_nb(
col,
a
)
Return median (ignores NaNs).
min_reduce_nb function¶
min_reduce_nb(
col,
a
)
Return min (ignores NaNs).
min_squeeze_nb function¶
min_squeeze_nb(
col,
group,
a
)
Return min (ignores NaNs) of a group.
nancnt_nb function¶
nancnt_nb(
a
)
Compute count while ignoring NaNs.
nancumprod_nb function¶
nancumprod_nb(
arr
)
Numba equivalent of np.nancumprod along axis 0.
nancumsum_nb function¶
nancumsum_nb(
arr
)
Numba equivalent of np.nancumsum along axis 0.
nanmax_nb function¶
nanmax_nb(
a
)
Numba-equivalent of np.nanmax along axis 0.
nanmean_nb function¶
nanmean_nb(
a
)
Numba-equivalent of np.nanmean along axis 0.
nanmedian_nb function¶
nanmedian_nb(
a
)
Numba-equivalent of np.nanmedian along axis 0.
nanmin_nb function¶
nanmin_nb(
a
)
Numba-equivalent of np.nanmin along axis 0.
nanprod_nb function¶
nanprod_nb(
arr
)
Numba equivalent of np.nanprod along axis 0.
nanstd_1d_nb function¶
nanstd_1d_nb(
a,
ddof=0
)
Numba-equivalent of np.nanstd.
nanstd_nb function¶
nanstd_nb(
a,
ddof=0
)
2-dim version of nanstd_1d_nb().
nansum_nb function¶
nansum_nb(
arr
)
Numba equivalent of np.nansum along axis 0.
nth_index_reduce_nb function¶
nth_index_reduce_nb(
col,
a,
n
)
Return index of n-th element.
nth_reduce_nb function¶
nth_reduce_nb(
col,
a,
n
)
Return n-th element.
pct_change_1d_nb function¶
pct_change_1d_nb(
a,
n=1
)
Return the percentage change.
Numba equivalent to pd.Series(a).pct_change().
pct_change_nb function¶
pct_change_nb(
a,
n=1
)
2-dim version of pct_change_1d_nb().
range_coverage_nb function¶
range_coverage_nb(
start_idx_arr,
end_idx_arr,
status_arr,
col_map,
index_lens,
overlapping=False,
normalize=False
)
Get coverage of range records.
Set overlapping to True to get the number of overlapping steps. Set normalize to True to get the number of steps in relation either to the total number of steps (when overlapping=False) or to the number of covered steps (when overlapping=True).
range_duration_nb function¶
range_duration_nb(
start_idx_arr,
end_idx_arr,
status_arr
)
Get duration of each duration record.
ranges_to_mask_nb function¶
ranges_to_mask_nb(
start_idx_arr,
end_idx_arr,
status_arr,
col_map,
index_len
)
Convert ranges to 2-dim mask.
reduce_grouped_nb function¶
reduce_grouped_nb(
a,
group_lens,
reduce_func_nb,
*args
)
Reduce each group of columns into a single value using reduce_func_nb.
reduce_func_nb should accept index of the group, the array of row values, and *args. Should return a single value.
reduce_grouped_to_array_nb function¶
reduce_grouped_to_array_nb(
a,
group_lens,
reduce_func_nb,
*args
)
Reduce each group of columns into an array of values using reduce_func_nb.
reduce_func_nb same as for reduce_grouped_nb() but should return an array.
Note
Output of reduce_func_nb should be strictly homogeneous.
reduce_nb function¶
reduce_nb(
a,
reduce_func_nb,
*args
)
Reduce each column into a single value using reduce_func_nb.
reduce_func_nb should accept index of the column, the array, and *args. Should return a single value.
reduce_to_array_nb function¶
reduce_to_array_nb(
a,
reduce_func_nb,
*args
)
Reduce each column into an array of values using reduce_func_nb.
reduce_func_nb same as for reduce_nb() but should return an array.
Note
Output of reduce_func_nb should be strictly homogeneous.
rolling_apply_nb function¶
rolling_apply_nb(
a,
window,
minp,
apply_func_nb,
*args
)
Provide rolling window calculations.
apply_func_nb should accept index of the row, index of the column, the array, and *args. Should return a single value.
rolling_matrix_apply_nb function¶
rolling_matrix_apply_nb(
a,
window,
minp,
apply_func_nb,
*args
)
rolling_apply_nb() with apply_func_nb being applied on all columns at once.
apply_func_nb should accept index of the row, the 2-dim array, and *args. Should return a single value or an array of shape a.shape[1].
rolling_max_1d_nb function¶
rolling_max_1d_nb(
a,
window,
minp=None
)
Return rolling max.
Numba equivalent to pd.Series(a).rolling(window, min_periods=minp).max().
rolling_max_nb function¶
rolling_max_nb(
a,
window,
minp=None
)
2-dim version of rolling_max_1d_nb().
rolling_mean_1d_nb function¶
rolling_mean_1d_nb(
a,
window,
minp=None
)
Return rolling mean.
Numba equivalent to pd.Series(a).rolling(window, min_periods=minp).mean().
rolling_mean_nb function¶
rolling_mean_nb(
a,
window,
minp=None
)
2-dim version of rolling_mean_1d_nb().
rolling_min_1d_nb function¶
rolling_min_1d_nb(
a,
window,
minp=None
)
Return rolling min.
Numba equivalent to pd.Series(a).rolling(window, min_periods=minp).min().
rolling_min_nb function¶
rolling_min_nb(
a,
window,
minp=None
)
2-dim version of rolling_min_1d_nb().
rolling_std_1d_nb function¶
rolling_std_1d_nb(
a,
window,
minp=None,
ddof=0
)
Return rolling standard deviation.
Numba equivalent to pd.Series(a).rolling(window, min_periods=minp).std(ddof=ddof).
rolling_std_nb function¶
rolling_std_nb(
a,
window,
minp=None,
ddof=0
)
2-dim version of rolling_std_1d_nb().
row_apply_nb function¶
row_apply_nb(
a,
apply_func_nb,
*args
)
Apply function on each row.
apply_func_nb should accept index of the row, the array, and *args. Should return a single value or an array of shape a.shape[1].
set_by_mask_1d_nb function¶
set_by_mask_1d_nb(
arr,
mask,
value
)
Set each element to a value by boolean mask.
set_by_mask_mult_1d_nb function¶
set_by_mask_mult_1d_nb(
arr,
mask,
values
)
Set each element in one array to the corresponding element in another by boolean mask.
values must be of the same shape as in the array.
set_by_mask_mult_nb function¶
set_by_mask_mult_nb(
arr,
mask,
values
)
2-dim version of set_by_mask_mult_1d_nb().
set_by_mask_nb function¶
set_by_mask_nb(
arr,
mask,
value
)
2-dim version of set_by_mask_1d_nb().
shuffle_1d_nb function¶
shuffle_1d_nb(
a,
seed=None
)
Shuffle each column in a.
Specify seed to make output deterministic.
shuffle_nb function¶
shuffle_nb(
a,
seed=None
)
2-dim version of shuffle_1d_nb().
squeeze_grouped_nb function¶
squeeze_grouped_nb(
a,
group_lens,
squeeze_func_nb,
*args
)
Squeeze each group of columns into a single column using squeeze_func_nb.
squeeze_func_nb should accept index of the row, index of the group, the array, and *args. Should return a single value.
std_reduce_nb function¶
std_reduce_nb(
col,
a,
ddof
)
Return std (ignores NaNs).
sum_reduce_nb function¶
sum_reduce_nb(
col,
a
)
Return sum (ignores NaNs).
sum_squeeze_nb function¶
sum_squeeze_nb(
col,
group,
a
)
Return sum (ignores NaNs) of a group.
value_counts_nb function¶
value_counts_nb(
codes,
n_uniques,
group_lens
)
Return value counts per column/group.