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Indicators

Hurst exponent

  • Estimating the Hurst exponent provides insight into whether your data is a pure white-noise random process or exhibits underlying trends. VBT offers five (!) different implementations.
Plot rolling Hurst exponent by method
>>> data = vbt.YFData.pull("BTC-USD", start="12 months ago")
>>> hurst = vbt.HURST.run(data.close, method=["standard", "logrs", "rs", "dma", "dsod"])
>>> fig = vbt.make_subplots(specs=[[dict(secondary_y=True)]])
>>> data.plot(plot_volume=False, ohlc_trace_kwargs=dict(opacity=0.3), fig=fig)
>>> fig = hurst.hurst.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))
>>> fig = fig.select_range(start=hurst.param_defaults["window"])
>>> fig.show()

Smart Money Concepts

Plot previous high and low
>>> data = vbt.YFData.pull("BTC-USD", start="6 months ago")
>>> phl = vbt.smc("previous_high_low").run(  # (1)!
...     data.open,
...     data.high,
...     data.low,
...     data.close,
...     data.volume,
...     time_frame=vbt.Default("7D")
... )
>>> fig = data.plot()
>>> phl.previous_high.rename("previous_high").vbt.plot(fig=fig)
>>> phl.previous_low.rename("previous_low").vbt.plot(fig=fig)
>>> (phl.broken_high == 1).rename("broken_high").vbt.signals.plot_as_markers(
...     y=phl.previous_high, 
...     trace_kwargs=dict(marker=dict(color="limegreen")),
...     fig=fig
... )
>>> (phl.broken_low == 1).rename("broken_low").vbt.signals.plot_as_markers(
...     y=phl.previous_low, 
...     trace_kwargs=dict(marker=dict(color="orangered")),
...     fig=fig
... )
>>> fig.show()
  1. Each SMC indicator requires OHLCV.

Signal unraveling

  • 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.
For each signal, create a separate position with own stop orders
>>> data = vbt.YFData.pull("BTC-USD")
>>> fast_sma = data.run("talib_func:sma", timeperiod=20)  # (1)!
>>> 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")  # (2)!
>>> pf = vbt.PF.from_signals(
...     data, 
...     long_entries=entries, 
...     short_entries=exits, 
...     size=100,  # (3)!
...     size_type="value",
...     init_cash="auto",  # (4)!
...     tp_stop=0.2, 
...     sl_stop=0.1, 
...     group_by=vbt.ExceptLevel("signal"),  # (5)!
...     cash_sharing=True
... )
>>> pf.positions.returns.to_pd(ignore_index=True).vbt.barplot(
...     trace_kwargs=dict(marker=dict(colorscale="Spectral"))
... ).show()  # (6)!
  1. Run a TA-Lib function faster without building an indicator: "talib_func:sma" vs "talib:sma".
  2. Place each pair of entry->exit and exit->entry signals in a separate column.
  3. Order $100 worth of the asset.
  4. Simulate with infinite cash.
  5. Combine all columns (positions) under the same asset into a single portfolio.
  6. Show position returns by signal index.

Lightweight TA-Lib

  • TA-Lib functions wrapped with the indicator factory are very powerful because, unlike the official TA-Lib implementation, they can broadcast, handle DataFrames, skip missing values, and even resample to a different timeframe. Although TA-Lib functions are very fast, wrapping them with the indicator factory adds some overhead. To keep both the speed of TA-Lib and the power of VBT, the added features have been separated into a lightweight function that you can call just like a regular TA-Lib function.
Run and plot an RSI resampled to the monthly timeframe
>>> data = vbt.YFData.pull("BTC-USD")
>>> run_rsi = vbt.talib_func("rsi")
>>> rsi = run_rsi(data.close, timeperiod=12, timeframe="M")  # (1)!
>>> rsi
Date
2014-09-17 00:00:00+00:00          NaN
2014-09-18 00:00:00+00:00          NaN
2014-09-19 00:00:00+00:00          NaN
2014-09-20 00:00:00+00:00          NaN
2014-09-21 00:00:00+00:00          NaN
                                   ...
2024-01-18 00:00:00+00:00    64.210811
2024-01-19 00:00:00+00:00    64.210811
2024-01-20 00:00:00+00:00    64.210811
2024-01-21 00:00:00+00:00    64.210811
2024-01-22 00:00:00+00:00    64.210811
Freq: D, Name: Close, Length: 3415, dtype: float64

>>> plot_rsi = vbt.talib_plot_func("rsi")
>>> plot_rsi(rsi).show()
  1. Parameters are applied to the target timeframe.

  • VBT implements or integrates more than 500 indicators, making it hard to keep track of them all. To make indicators easier to find, several new methods are available for globally searching for indicators.
List all moving average indicators
>>> vbt.IF.list_indicators("*ma")
[
    'vbt:MA',
    'talib:DEMA',
    'talib:EMA',
    'talib:KAMA',
    'talib:MA',
    ...
    'technical:ZEMA',
    'technical:ZLEMA',
    'technical:ZLHMA',
    'technical:ZLMA'
]

>>> vbt.indicator("technical:ZLMA")  # (1)!
vectorbtpro.indicators.factory.technical.ZLMA
  1. Same as vbt.IF.get_indicator.

Indicators for ML

  • Want to feed indicators as features to a machine-learning model? There is no need to run them individually: you can tell VBT to run all indicators from an indicator package on the given data instance. The data instance will recognize the input names of each indicator and supply the required data. You can also easily change the defaults for each indicator.
Run all talib indicators on entire BTC-USD history
>>> data = vbt.YFData.pull("BTC-USD")
>>> features = data.run("talib", mavp=vbt.run_arg_dict(periods=14))
>>> features.shape
(3046, 175)

Signal detection

  • 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.
Detect sudden changes in the bandwidth of a Bollinger Bands indicator
>>> 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()

Pivot detection

  • 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.
Plot the last pivot value
>>> 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()

Technical indicators

  • VBT integrates most of the indicators and consensus classes from freqtrade's technical library.
Compute and plot the summary consensus with a one-liner
>>> vbt.YFData.pull("BTC-USD").run("sumcon", smooth=100).plot().show()

Renko chart

  • Unlike regular charts, a Renko chart is built using price movements. Each "brick" appears when the price changes by a specified amount. Because the output has irregular time intervals, only one column can be processed at once. As with everything, VBT's implementation can translate a huge number of data points very fast thanks to Numba.
Resample closing price into a Renko format
>>> data = vbt.YFData.pull("BTC-USD", start="2021", end="2022")
>>> renko_ohlc = data.close.vbt.to_renko_ohlc(1000, reset_index=True)  # (1)!
>>> renko_ohlc.vbt.ohlcv.plot().show()
  1. Bitcoin is very volatile, so the brick size is set to 1000.

Rolling OLS

  • Rolling regressions are models for analyzing changing relationships among variables over time. In VBT, this is implemented as an indicator that takes two time series and returns the slope, intercept, prediction, error, and the z-score of the error at each time step. This indicator can be used for cointegration tests, such as determining optimal rebalancing timings in pairs trading, and is also (literally) 1000x faster than the statsmodels equivalent RollingOLS 🔥
Determine the spread between BTC and ETH
>>> data = vbt.YFData.pull(
...     ["BTC-USD", "ETH-USD"], 
...     start="2022", 
...     end="2023",
...     missing_index="drop"
... )
>>> ols = vbt.OLS.run(
...     data.get("Close", "BTC-USD"), 
...     data.get("Close", "ETH-USD")
... )
>>> ols.plot_zscore().show()

TA-Lib time frames

  • Comparing indicators on different time frames involves many nuances. Now, all TA-Lib indicators support a parameter that resamples the input arrays to a target time frame, calculates the indicator, and then resamples the output arrays back to the original time frame. This makes parameterized MTF analysis easier than ever!

Tutorial

Learn more in the MTF analysis tutorial.

Run SMA on multiple time frames and display the whole thing as a heatmap
>>> h1_data = vbt.BinanceData.pull(
...     "BTCUSDT", 
...     start="3 months ago UTC", 
...     timeframe="1h"
... )
>>> mtf_sma = vbt.talib("SMA").run(
...     h1_data.close, 
...     timeperiod=14, 
...     timeframe=["1d", "4h", "1h"], 
...     skipna=True
... )
>>> mtf_sma.real.vbt.ts_heatmap().show()

1D-native indicators

  • Previously, custom indicators could only be created by accepting two-dimensional input arrays, which forced users to adapt all functions accordingly. Now, the indicator factory can split each input array along columns and pass one column at a time, making it much easier to design indicators that are meant to be run natively on one-dimensional data (such as TA-Lib!).
Create a TA-Lib powered STOCHRSI indicator
>>> import talib

>>> params = dict(
...     rsi_period=14, 
...     fastk_period=5, 
...     slowk_period=3, 
...     slowk_matype=0, 
...     slowd_period=3, 
...     slowd_matype=0
... )

>>> def stochrsi_1d(close, *args):
...     rsi = talib.RSI(close, args[0])
...     k, d = talib.STOCH(rsi, rsi, rsi, *args[1:])
...     return rsi, k, d

>>> STOCHRSI = vbt.IF(
...     input_names=["close"], 
...     param_names=list(params.keys()),
...     output_names=["rsi", "k", "d"]
... ).with_apply_func(stochrsi_1d, takes_1d=True, **params)

>>> data = vbt.YFData.pull("BTC-USD", start="2022-01", end="2022-06")
>>> stochrsi = STOCHRSI.run(data.close)
>>> fig = stochrsi.k.rename("%K").vbt.plot()
>>> stochrsi.d.rename("%D").vbt.plot(fig=fig)
>>> fig.show()

Parallelizable indicators

  • Processing parameter combinations with the indicator factory can be distributed across multiple threads, processes, or even in the cloud. This is a huge help when working with slow indicators 🐌
Benchmark a serial and multithreaded rolling min-max indicator
>>> @njit
... def minmax_nb(close, window):
...     return (
...         vbt.nb.rolling_min_nb(close, window),
...         vbt.nb.rolling_max_nb(close, window)
...     )

>>> MINMAX = vbt.IF(
...     class_name="MINMAX",
...     input_names=["close"], 
...     param_names=["window"], 
...     output_names=["min", "max"]
... ).with_apply_func(minmax_nb, window=14)

>>> data = vbt.YFData.pull("BTC-USD")

>>> %%timeit
>>> minmax = MINMAX.run(
...     data.close, 
...     np.arange(2, 200),
...     jitted_loop=True
... )
420 ms ± 2.05 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

>>> %%timeit
>>> minmax = MINMAX.run(
...     data.close, 
...     np.arange(2, 200),
...     jitted_loop=True,
...     jitted_warmup=True,  # (1)!
...     execute_kwargs=dict(engine="threadpool", n_chunks="auto")  # (2)!
... )
120 ms ± 355 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
  1. Run one parameter combination to compile the indicator before running others in a multithreaded fashion.
  2. One Numba loop per thread, with the same number of threads as there are cores.

TA-Lib plotting

  • Every TA-Lib indicator is fully capable of plotting itself, based entirely on output flags!
>>> data = vbt.YFData.pull("BTC-USD", start="2020", end="2021")

>>> vbt.talib("MACD").run(data.close).plot().show()

Indicator expressions

  • Indicators can now be created from expressions. An indicator expression is a regular string representing Python code enhanced with various extensions. The indicator factory automatically derives all required information, such as inputs, parameters, outputs, NumPy, VBT, and TA-Lib functions, and even complex indicators, thanks to a unique format and built-in matching mechanism. Designing indicators has never been easier!
Build a MACD indicator from an expression
>>> data = vbt.YFData.pull("BTC-USD", start="2020", end="2021")

>>> expr = """
... MACD:
... fast_ema = @talib_ema(close, @p_fast_w)
... slow_ema = @talib_ema(close, @p_slow_w)
... macd = fast_ema - slow_ema
... signal = @talib_ema(macd, @p_signal_w)
... macd, signal
... """
>>> MACD = vbt.IF.from_expr(expr, fast_w=12, slow_w=26, signal_w=9)  # (1)!
>>> macd = MACD.run(data.close)
>>> fig = macd.macd.rename("MACD").vbt.plot()
>>> macd.signal.rename("Signal").vbt.plot(fig=fig)
>>> fig.show()
  1. No need to manually set input_names, param_names, or other information.

WorldQuant Alphas

Run the first alpha
>>> data = vbt.YFData.pull(["BTC-USD", "ETH-USD", "XRP-USD"], missing_index="drop")

>>> vbt.wqa101(1).run(data.close).out
symbol                      BTC-USD   ETH-USD   XRP-USD
Date                                                   
2017-11-09 00:00:00+00:00  0.166667  0.166667  0.166667
2017-11-10 00:00:00+00:00  0.166667  0.166667  0.166667
2017-11-11 00:00:00+00:00  0.166667  0.166667  0.166667
2017-11-12 00:00:00+00:00  0.166667  0.166667  0.166667
2017-11-13 00:00:00+00:00  0.166667  0.166667  0.166667
...                             ...       ...       ...
2023-01-31 00:00:00+00:00  0.166667  0.166667  0.166667
2023-02-01 00:00:00+00:00  0.000000  0.000000  0.500000
2023-02-02 00:00:00+00:00  0.000000  0.000000  0.500000
2023-02-03 00:00:00+00:00  0.000000  0.500000  0.000000
2023-02-04 00:00:00+00:00 -0.166667  0.333333  0.333333

[1914 rows x 3 columns]

Robust crossovers

  • Crossovers are now robust to NaNs.
Remove a bunch of data points and plot the crossovers
>>> 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
>>> 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, dropna=True)
>>> crossed_below = fast_sma.vbt.crossed_below(slow_sma, dropna=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()

And many more...

  • Look forward to more killer features being added every week!