Features
Strategy optimization
Explore conditional parameter spaces with grid and random search
✅ Each analyzable VBT object (such as data, indicator, or portfolio) can now be split into items, which are multiple objects of the same type, each containing only one column or group. This makes it possible to use VBT objects as standalone parameters and process only a subset of information at a time, such as a symbol in a data instance or a parameter combination in an indicator.
@vbt.parameterized(merge_func="column_stack")
def get_signals(fast_sma, slow_sma):
entries = fast_sma.crossed_above(slow_sma)
exits = fast_sma.crossed_below(slow_sma)
return entries, exits
data = vbt.YFData.pull(["BTC-USD", "ETH-USD"])
sma = data.run("talib:sma", timeperiod=range(20, 50, 2))
fast_sma = sma.rename_levels({"sma_timeperiod": "fast"})
slow_sma = sma.rename_levels({"sma_timeperiod": "slow"})
entries, exits = get_signals(
vbt.Param(fast_sma, condition="__fast__ < __slow__"),
vbt.Param(slow_sma)
)
entries.columnsMultiIndex([(20, 22, 'BTC-USD'),
(20, 22, 'ETH-USD'),
(20, 24, 'BTC-USD'),
(20, 24, 'ETH-USD'),
(20, 26, 'BTC-USD'),
(20, 26, 'ETH-USD'),
...
(44, 46, 'BTC-USD'),
(44, 46, 'ETH-USD'),
(44, 48, 'BTC-USD'),
(44, 48, 'ETH-USD'),
(46, 48, 'BTC-USD'),
(46, 48, 'ETH-USD')],
names=['fast', 'slow', 'symbol'], length=210)✅ The parameterized decorator no longer needs to materialize parameter grids if you are only interested in a subset of all parameter combinations. This change enables the generation of random parameter combinations almost instantly, no matter how large the total number of possible combinations is.
@vbt.parameterized(merge_func="concat")
def test_combination(data, n, sl_stop, tsl_stop, tp_stop):
return data.run(
"from_random_signals",
n=n,
sl_stop=sl_stop,
tsl_stop=tsl_stop,
tp_stop=tp_stop,
seed=42,
).total_return
n = np.arange(10, 100)
sl_stop = np.arange(1, 1000) / 1000
tsl_stop = np.arange(1, 1000) / 1000
tp_stop = np.arange(1, 1000) / 1000
len(n) * len(sl_stop) * len(tsl_stop) * len(tp_stop)89730269910test_combination(
vbt.YFData.pull("BTC-USD"),
n=vbt.Param(n),
sl_stop=vbt.Param(sl_stop),
tsl_stop=vbt.Param(tsl_stop),
tp_stop=vbt.Param(tp_stop),
_random_subset=10,
_seed=42
)n sl_stop tsl_stop tp_stop
18 0.476 0.485 0.862 2.233997
21 0.530 0.697 0.017 0.091041
49 0.499 0.560 0.954 15.987541
50 0.535 0.200 0.477 2.182477
72 0.763 0.360 0.129 -0.040304
78 0.503 0.071 0.533 10.851910
79 0.656 0.388 0.930 11.854461
80 0.746 0.042 0.644 0.702603
87 0.274 0.539 0.434 2.072847
97 0.806 0.206 0.426 7.136395
dtype: float64✅ The parameterized decorator now supports splitting parameter combinations into "mono-chunks," merging the parameter values within each chunk into a single value, and running the entire chunk with a single function call. This means you are no longer limited to processing only one parameter combination at a time 🌪️ Keep in mind that your function must be adapted to handle multiple parameter values, and you should modify the merging function as needed.
@vbt.parameterized(
merge_func="concat",
mono_chunk_len=100,
chunk_len="auto",
engine="threadpool",
warmup=True
)
@njit(nogil=True)
def test_stops_nb(close, entries, exits, sl_stop, tp_stop):
sim_out = vbt.pf_nb.from_signals_nb(
target_shape=(close.shape[0], sl_stop.shape[1]),
group_lens=np.full(sl_stop.shape[1], 1),
close=close,
long_entries=entries,
short_entries=exits,
sl_stop=sl_stop,
tp_stop=tp_stop,
save_returns=True
)
return vbt.ret_nb.total_return_nb(sim_out.in_outputs.returns)
data = vbt.YFData.pull("BTC-USD", start="2020")
entries, exits = data.run("randnx", n=10, seed=42, hide_params=True, unpack=True)
sharpe_ratios = test_stops_nb(
vbt.to_2d_array(data.close),
vbt.to_2d_array(entries),
vbt.to_2d_array(exits),
sl_stop=vbt.Param(np.arange(0.01, 1.0, 0.01), mono_merge_func=np.column_stack),
tp_stop=vbt.Param(np.arange(0.01, 1.0, 0.01), mono_merge_func=np.column_stack)
)
sharpe_ratios.vbt.heatmap().show()✅ Parameters can depend on each other. For example, when testing a crossover of moving averages, it makes no sense to test a fast window that is longer than the slow window. By filtering out such cases, you only need to evaluate about half as many parameter combinations.
@vbt.parameterized(merge_func="column_stack")
def ma_crossover_signals(data, fast_window, slow_window):
fast_sma = data.run("sma", fast_window, short_name="fast_sma")
slow_sma = data.run("sma", slow_window, short_name="slow_sma")
entries = fast_sma.real_crossed_above(slow_sma.real)
exits = fast_sma.real_crossed_below(slow_sma.real)
return entries, exits
entries, exits = ma_crossover_signals(
vbt.YFData.pull("BTC-USD", start="one year ago UTC"),
vbt.Param(np.arange(5, 50), condition="slow_window - fast_window >= 5"),
vbt.Param(np.arange(5, 50))
)
entries.columnsMultiIndex([( 5, 10),
( 5, 11),
( 5, 12),
( 5, 13),
( 5, 14),
...
(42, 48),
(42, 49),
(43, 48),
(43, 49),
(44, 49)],
names=['fast_window', 'slow_window'], length=820)✅ While grid search tests every possible combination of hyperparameters, random search selects and tests random combinations of hyperparameters. This is especially useful when there is a huge number of parameter combinations. Random search has also been shown to find equal or better values than grid search with fewer function evaluations. The indicator factory, parameterized decorator, and any method that performs broadcasting now support random search out of the box.
data = vbt.YFData.pull("BTC-USD", start="2020")
stop_values = np.arange(1, 100) / 100
pf = vbt.PF.from_random_signals(
data,
n=100,
seed=42,
sl_stop=vbt.Param(stop_values),
tsl_stop=vbt.Param(stop_values),
tp_stop=vbt.Param(stop_values),
broadcast_kwargs=dict(random_subset=1000, seed=42)
)
pf.total_return.sort_values(ascending=False)sl_stop tsl_stop tp_stop
0.02 0.22 0.55 1.093685
0.93 0.60 0.99 1.091450
0.88 0.68 0.99 1.091450
0.79 0.69 0.99 1.091450
0.62 0.99 1.091450
...
0.08 0.84 0.14 -0.466277
0.14 0.09 0.12 -0.502329
0.80 0.11 0.11 -0.509395
0.19 0.11 0.09 -0.529528
0.29 0.11 0.06 -0.563992
Name: total_return, Length: 1000, dtype: float64✅ There is a special decorator that allows any Python function to accept multiple parameter combinations, even if the function itself supports only one. The decorator wraps the function, gains access to its arguments, identifies all arguments acting as parameters, builds a grid from them, and calls the underlying function on each parameter combination from that grid. The execution can be easily parallelized. Once all outputs are ready, it merges them into a single object. Use cases are endless: from running indicators that cannot be wrapped with the indicator factory, to parameterizing entire pipelines! 🪄
@vbt.parameterized(merge_func="column_stack")
def sma(close, window):
return close.rolling(window).mean()
data = vbt.YFData.pull("BTC-USD")
sma(data.close, vbt.Param(range(20, 50)))window 20 21 22 \
Date
2014-09-17 00:00:00+00:00 NaN NaN NaN
2014-09-18 00:00:00+00:00 NaN NaN NaN
2014-09-19 00:00:00+00:00 NaN NaN NaN
... ... ... ...
2024-03-07 00:00:00+00:00 57657.135156 57395.376488 57147.339134
2024-03-08 00:00:00+00:00 58488.990039 58163.942708 57891.045455
2024-03-09 00:00:00+00:00 59297.836523 58956.156064 58624.648793
...
window 48 49
Date
2014-09-17 00:00:00+00:00 NaN NaN
2014-09-18 00:00:00+00:00 NaN NaN
2014-09-19 00:00:00+00:00 NaN NaN
... ... ...
2024-03-07 00:00:00+00:00 49928.186686 49758.599330
2024-03-08 00:00:00+00:00 50483.072266 50303.123565
2024-03-09 00:00:00+00:00 51040.440837 50846.672353
[3462 rows x 30 columns]✅ The broadcasting mechanism has been completely refactored and now supports parameters. Many parameters in VBT, such as SL and TP, are array-like and can be provided per row, per column, or even per element. Internally, even a scalar is treated as a regular time series and is broadcast along with other proper time series. Previously, to test multiple parameter combinations, you had to tile other time series so that all shapes matched perfectly. With this feature, the tiling procedure is performed automatically!
def steep_slope(close, up_th):
r = vbt.broadcast(dict(close=close, up_th=up_th))
return r["close"].pct_change() >= r["up_th"]
data = vbt.YFData.pull("BTC-USD", start="2020", end="2022")
fig = data.plot(plot_volume=False)
sma = vbt.talib("SMA").run(data.close, timeperiod=50).real
sma.rename("SMA").vbt.plot(fig=fig)
mask = steep_slope(sma, vbt.Param([0.005, 0.01, 0.015]))
def plot_mask_ranges(column, color):
mask.vbt.ranges.plot_shapes(
column=column,
plot_close=False,
add_shape_kwargs=dict(fillcolor=color),
fig=fig
)
plot_mask_ranges(0.005, "orangered")
plot_mask_ranges(0.010, "orange")
plot_mask_ranges(0.015, "yellow")
fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False)
fig.show()✅ There is a new module for working with parameters.
from itertools import combinations
window_space = np.arange(100)
fastk_windows, slowk_windows = list(zip(*combinations(window_space, 2)))
window_type_space = list(vbt.enums.WType)
param_product = vbt.combine_params(
dict(
fast_window=vbt.Param(fastk_windows, level=0),
slow_window=vbt.Param(slowk_windows, level=0),
signal_window=vbt.Param(window_space, level=1),
macd_wtype=vbt.Param(window_type_space, level=2),
signal_wtype=vbt.Param(window_type_space, level=2),
),
random_subset=10_000,
seed=42,
build_index=False
)
pd.DataFrame(param_product) fast_window slow_window signal_window macd_wtype signal_wtype
0 0 1 0 2 2
1 0 3 5 1 1
2 0 3 55 4 4
3 0 3 80 2 2
4 0 4 17 2 2
... ... ... ... ... ...
9995 97 99 14 0 0
9996 97 99 35 2 2
9997 98 99 6 2 2
9998 98 99 44 4 4
9999 98 99 78 4 4
[10000 rows x 5 columns]Copyright © 2021–2026 Oleg Polakow. All rights reserved.
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