Features
Trading signals
Detect, unravel, and analyze pivots, crossovers, and trading signals
✅ To backtest each signal individually, you can now "unravel" each signal, or each pair of entry and exit signals, into its own column. This creates a wide, two-dimensional mask that, when backtested, returns performance metrics for each signal rather than for the entire column.
data = vbt.YFData.pull("BTC-USD")
fast_sma = data.run("talib_func:sma", timeperiod=20)
slow_sma = data.run("talib_func:sma", timeperiod=50)
entries = fast_sma.vbt.crossed_above(slow_sma)
exits = fast_sma.vbt.crossed_below(slow_sma)
entries, exits = entries.vbt.signals.unravel_between(exits, relation="anychain")
pf = vbt.PF.from_signals(
data,
long_entries=entries,
short_entries=exits,
size=100,
size_type="value",
init_cash="auto",
tp_stop=0.2,
sl_stop=0.1,
group_by=vbt.ExceptLevel("signal"),
cash_sharing=True
)
pf.positions.returns.to_pd(ignore_index=True).vbt.barplot(
trace_kwargs=dict(marker=dict(colorscale="Spectral"))
).show() ✅ VBT includes an indicator that uses a robust peak detection algorithm based on z-scores. This indicator can be used to identify outbreaks and outliers in any time series data.
data = vbt.YFData.pull("BTC-USD")
fig = vbt.make_subplots(rows=2, cols=1, shared_xaxes=True)
bbands = data.run("bbands")
bbands.loc["2022"].plot(add_trace_kwargs=dict(row=1, col=1), fig=fig)
sigdet = vbt.SIGDET.run(bbands.bandwidth, factor=5)
sigdet.loc["2022"].plot(add_trace_kwargs=dict(row=2, col=1), fig=fig)
fig.show()✅ The pivot detection indicator is a tool for finding when the price trend is reversing. By identifying support and resistance areas, it helps spot significant price changes while filtering out short-term fluctuations and reducing noise. It works simply: a peak is registered when the price jumps above one threshold, and a valley is recorded when the price falls below another. Another advantage is that, unlike the regular Zig Zag indicator, which tends to look ahead, our indicator only returns confirmed pivot points and is safe to use in backtesting.
data = vbt.YFData.pull("BTC-USD", start="2020", end="2023")
fig = data.plot(plot_volume=False)
pivot_info = data.run("pivotinfo", up_th=1.0, down_th=0.5)
pivot_info.plot(fig=fig, conf_value_trace_kwargs=dict(visible=False))
fig.show()✅ Crossovers are now robust to NaNs.
data = vbt.YFData.pull("BTC-USD", start="2022-01", end="2022-03")
fast_sma = vbt.talib("SMA").run(data.close, vbt.Default(5)).real
slow_sma = vbt.talib("SMA").run(data.close, vbt.Default(10)).real
np.random.seed(42)
fast_sma.iloc[np.random.choice(np.arange(len(fast_sma)), 5)] = np.nan
slow_sma.iloc[np.random.choice(np.arange(len(slow_sma)), 5)] = np.nan
crossed_above = fast_sma.vbt.crossed_above(slow_sma, skipna=True)
crossed_below = fast_sma.vbt.crossed_below(slow_sma, skipna=True)
fig = fast_sma.rename("Fast SMA").vbt.lineplot()
slow_sma.rename("Slow SMA").vbt.lineplot(fig=fig)
crossed_above.vbt.signals.plot_as_entries(fast_sma, fig=fig)
crossed_below.vbt.signals.plot_as_exits(fast_sma, fig=fig)
fig.show()Copyright © 2021–2026 Oleg Polakow. All rights reserved.
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