Portfolio¶
Asset weighting¶
- Asset weighting lets you fine-tune the influence of individual assets or strategies within your portfolio, giving you enhanced control over your portfolio's overall performance. The key benefit is that these weights are not limited to returns—they are consistently applied to all time series and metrics, including orders, cash flows, and more. This comprehensive approach ensures that every aspect of your portfolio stays precisely aligned.
Maximize Sharpe of a random portfolio
>>> data = vbt.YFData.pull(["AAPL", "MSFT", "GOOG"], start="2020")
>>> pf = data.run("from_random_signals", n=vbt.Default(50), seed=42, group_by=True)
>>> pf.get_sharpe_ratio(group_by=False) # (1)!
symbol
AAPL 1.401012
MSFT 0.456162
GOOG 0.852490
Name: sharpe_ratio, dtype: float64
>>> pf.sharpe_ratio # (2)!
1.2132857343006869
>>> prices = pf.get_value(group_by=False)
>>> weights = vbt.pypfopt_optimize(prices=prices) # (3)!
>>> weights
{'AAPL': 0.85232, 'MSFT': 0.0, 'GOOG': 0.14768}
>>> weighted_pf = pf.apply_weights(weights, rescale=True) # (4)!
>>> weighted_pf.weights
symbol
AAPL 2.55696
MSFT 0.00000
GOOG 0.44304
dtype: float64
>>> weighted_pf.get_sharpe_ratio(group_by=True) # (5)!
1.426112580298898
- Sharpe ratio for each individual asset.
- Sharpe ratio for the combined portfolio. The goal is to maximize this value.
- Maximize Sharpe based on equity (that is, portfolio value) development.
- Rescale the weights for multiplication and create a new portfolio with the weights applied.
- Sharpe ratio for the optimized portfolio.
Position views¶
- Position views let you analyze your portfolio by focusing on either long or short positions, providing a clear and distinct perspective for each investment strategy.
Separate long and short positions of a basic SMA crossover portfolio
>>> data = vbt.YFData.pull("BTC-USD")
>>> fast_sma = data.run("talib_func:sma", timeperiod=20)
>>> slow_sma = data.run("talib_func:sma", timeperiod=50)
>>> long_entries = fast_sma.vbt.crossed_above(slow_sma)
>>> short_entries = fast_sma.vbt.crossed_below(slow_sma)
>>> pf = vbt.PF.from_signals(
... data,
... long_entries=long_entries,
... short_entries=short_entries,
... fees=0.01,
... fixed_fees=1.0
... )
>>> long_pf = pf.long_view
>>> short_pf = pf.short_view
>>> fig = vbt.make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.01)
>>> fig = long_pf.assets.vbt.plot_against(
... 0,
... trace_kwargs=dict(name="Long position", line_shape="hv", line_color="mediumseagreen"),
... other_trace_kwargs=dict(visible=False),
... add_trace_kwargs=dict(row=1, col=1),
... fig=fig
... )
>>> fig = short_pf.assets.vbt.plot_against(
... 0,
... trace_kwargs=dict(name="Short position", line_shape="hv", line_color="coral"),
... other_trace_kwargs=dict(visible=False),
... add_trace_kwargs=dict(row=2, col=1),
... fig=fig
... )
>>> fig.show()
Index records¶
- How can you backtest time- and asset-anchored queries such as "Order X units of asset Y on date Z"? Typically, you would need to build a full array and set each detail manually. Now, there is a simpler way: with preparers and redesigned smart indexing, you can provide all information in a compressed record format! Behind the scenes, the record array is translated into a set of index dictionaries—one for each argument.
Define a basic signal strategy using records
>>> data = vbt.YFData.pull(["BTC-USD", "ETH-USD"], missing_index="drop")
>>> records = [
... dict(date="2022", symbol="BTC-USD", long_entry=True), # (1)!
... dict(date="2022", symbol="ETH-USD", short_entry=True),
... dict(row=-1, exit=True),
... ]
>>> pf = vbt.PF.from_signals(data, records=records) # (2)!
>>> pf.orders.readable
Order Id Column Signal Index Creation Index
0 0 BTC-USD 2022-01-01 00:00:00+00:00 2022-01-01 00:00:00+00:00 \
1 1 BTC-USD 2023-04-25 00:00:00+00:00 2023-04-25 00:00:00+00:00
2 0 ETH-USD 2022-01-01 00:00:00+00:00 2022-01-01 00:00:00+00:00
3 1 ETH-USD 2023-04-25 00:00:00+00:00 2023-04-25 00:00:00+00:00
Fill Index Size Price Fees Side Type
0 2022-01-01 00:00:00+00:00 0.002097 47686.812500 0.0 Buy Market \
1 2023-04-25 00:00:00+00:00 0.002097 27534.675781 0.0 Sell Market
2 2022-01-01 00:00:00+00:00 0.026527 3769.697021 0.0 Sell Market
3 2023-04-25 00:00:00+00:00 0.026527 1834.759644 0.0 Buy Market
Stop Type
0 None
1 None
2 None
3 None
- Every broadcastable argument is supported. Rows can be specified using "row", "index", "date", or "datetime". Columns can be specified with "col", "column", or "symbol". If you do not provide a row or column, the entire row or column will be set, respectively. If neither is provided, the whole array will be set. Rows and columns can be given as integer positions, labels, datetimes, or even complex indexers!
- Arguments not used in records can still be provided as usual. Arguments used in records can also be provided to serve as defaults.
Portfolio preparers¶
- When you pass an argument to a simulation method such as
Portfolio.from_signals, it goes through a complex preparation pipeline to convert it into a format suitable for Numba. This pipeline usually involves enum mapping, broadcasting, data type checks, template substitution, and many other steps. To make VBT more transparent, this pipeline has been moved to a separate class, giving you full control over the arguments that reach the Numba functions! You can even extend the preparers to automatically prepare arguments for your own simulators.
Get a prepared argument, modify it, and use in a new simulation
>>> data = vbt.YFData.pull("BTC-USD", end="2017-01")
>>> prep_result = vbt.PF.from_holding(
... data,
... stop_ladder="uniform",
... tp_stop=vbt.Param([
... [0.1, 0.2, 0.3, 0.4, 0.5],
... [0.4, 0.5, 0.6],
... ], keys=["tp_ladder_1", "tp_ladder_2"]),
... return_prep_result=True # (1)!
... )
>>> prep_result.target_args["tp_stop"] # (2)!
array([[0.1, 0.4],
[0.2, 0.5],
[0.3, 0.6],
[0.4, nan],
[0.5, nan]])
>>> new_tp_stop = prep_result.target_args["tp_stop"] + 0.1
>>> new_prep_result = prep_result.replace(target_args=dict(tp_stop=new_tp_stop), nested_=True) # (3)!
>>> new_prep_result.target_args["tp_stop"]
array([[0.2, 0.5],
[0.3, 0.6],
[0.4, 0.7],
[0.5, nan],
[0.6, nan]])
>>> pf = vbt.PF.from_signals(new_prep_result) # (4)!
>>> pf.total_return
tp_stop
tp_ladder_1 0.4
tp_ladder_2 0.6
Name: total_return, dtype: float64
>>> sim_out = new_prep_result.target_func(**new_prep_result.target_args) # (5)!
>>> pf = vbt.PF(order_records=sim_out, **new_prep_result.pf_args)
>>> pf.total_return
tp_stop
tp_ladder_1 0.4
tp_ladder_2 0.6
Name: total_return, dtype: float64
- Returns the result of the preparation.
- Contains two attributes: target arguments as
target_argsand portfolio arguments aspf_args. - Replace the argument. Since it is inside another dictionary (
target_args), you need to enablenested_. The result is a new instance ofPFPrepResult. - Pass the new preparation result as the first argument to the base simulation method.
- Or simulate manually!
Stop laddering¶
- Stop laddering is a technique for incrementally moving out of a position. Instead of providing a single stop value to close a position, you can provide an array of stop values, with each one removing a certain amount of the position when triggered. You can control this amount by choosing a different ladder mode. Thanks to a new broadcasting feature that allows arrays to broadcast along just one axis, the stop values do not need to have the same shape as the data. You can even provide stop arrays of different shapes as parameters!
Test two TP ladders
>>> data = vbt.YFData.pull("BTC-USD", end="2017-01")
>>> pf = vbt.PF.from_holding(
... data,
... stop_ladder="uniform",
... tp_stop=vbt.Param([
... [0.1, 0.2, 0.3, 0.4, 0.5],
... [0.4, 0.5, 0.6],
... ], keys=["tp_ladder_1", "tp_ladder_2"])
... )
>>> pf.trades.plot(column="tp_ladder_1").show()
Staticization¶
- One major limitation of Numba is that functions passed as arguments (that is, callbacks) make the main function uncacheable, forcing it to be recompiled in every new runtime, again and again. This especially affects the performance of simulator functions, as they can take up to a minute to compile. Thankfully, there is a new trick available: "staticization". Here is how it works. First, the source code of a function is annotated with a special syntax. The annotated code is then extracted (also called "cutting"
), modified into a cacheable version by removing any callbacks from the arguments, and saved to a Python file. Once the function is called again, the cacheable version is executed. Sound complicated? Take a look below!
Define the signal function in a Python file, here signal_func_nb.py
from vectorbtpro import *
@njit
def signal_func_nb(c, fast_sma, slow_sma): # (1)!
long = vbt.pf_nb.iter_crossed_above_nb(c, fast_sma, slow_sma)
short = vbt.pf_nb.iter_crossed_below_nb(c, fast_sma, slow_sma)
return long, False, short, False
- Make sure the function is named exactly the same as the callback argument.
Run a staticized simulation. Running the script again won't re-compile.
>>> data = vbt.YFData.pull("BTC-USD")
>>> pf = vbt.PF.from_signals(
... data,
... signal_func_nb="signal_func_nb.py", # (1)!
... signal_args=(vbt.Rep("fast_sma"), vbt.Rep("slow_sma")),
... broadcast_named_args=dict(
... fast_sma=data.run("sma", 20, hide_params=True, unpack=True),
... slow_sma=data.run("sma", 50, hide_params=True, unpack=True)
... ),
... staticized=True # (2)!
... )
- Path to the module where the function is located.
- Handles all the magic
Position info¶
- Dynamic signal functions now have access to the current position information, such as (open) P&L.
Enter randomly, exit randomly but only if in profit
>>> @njit
... def signal_func_nb(c, entries, exits):
... is_entry = vbt.pf_nb.select_nb(c, entries)
... is_exit = vbt.pf_nb.select_nb(c, exits)
... if is_entry:
... return True, False, False, False
... if is_exit:
... pos_info = c.last_pos_info[c.col]
... if pos_info["status"] == vbt.pf_enums.TradeStatus.Open:
... if pos_info["pnl"] >= 0:
... return False, True, False, False
... return False, False, False, False
>>> data = vbt.YFData.pull("BTC-USD")
>>> entries, exits = data.run("RANDNX", n=10, unpack=True)
>>> pf = vbt.Portfolio.from_signals(
... data,
... signal_func_nb=signal_func_nb,
... signal_args=(vbt.Rep("entries"), vbt.Rep("exits")),
... broadcast_named_args=dict(entries=entries, exits=exits),
... jitted=False # (1)!
... )
>>> pf.trades.readable[["Entry Index", "Exit Index", "PnL"]]
Entry Index Exit Index PnL
0 2014-11-01 00:00:00+00:00 2016-01-08 00:00:00+00:00 39.134739
1 2016-03-27 00:00:00+00:00 2016-09-07 00:00:00+00:00 61.220063
2 2016-12-24 00:00:00+00:00 2016-12-31 00:00:00+00:00 14.471414
3 2017-03-16 00:00:00+00:00 2017-08-05 00:00:00+00:00 373.492028
4 2017-09-12 00:00:00+00:00 2018-05-05 00:00:00+00:00 815.699284
5 2019-02-15 00:00:00+00:00 2019-11-10 00:00:00+00:00 2107.383227
6 2019-12-04 00:00:00+00:00 2019-12-10 00:00:00+00:00 12.630214
7 2020-07-12 00:00:00+00:00 2021-11-14 00:00:00+00:00 21346.035444
8 2022-01-15 00:00:00+00:00 2023-03-06 00:00:00+00:00 -11925.133817
- Disable Numba during testing to avoid compilation.
Time stops¶
- Joining other stop orders, time stop orders can close a position either after a certain period of time or on a specific date.
Enter randomly, exit before the end of the month
>>> data = vbt.YFData.pull("BTC-USD", start="2022-01", end="2022-04")
>>> entries = vbt.pd_acc.signals.generate_random(data.symbol_wrapper, n=10)
>>> pf = vbt.PF.from_signals(data, entries, dt_stop="M") # (1)!
>>> pf.orders.readable[["Fill Index", "Side", "Stop Type"]]
Fill Index Side Stop Type
0 2022-01-19 00:00:00+00:00 Buy None
1 2022-01-31 00:00:00+00:00 Sell DT
2 2022-02-25 00:00:00+00:00 Buy None
3 2022-02-28 00:00:00+00:00 Sell DT
4 2022-03-11 00:00:00+00:00 Buy None
5 2022-03-31 00:00:00+00:00 Sell DT
- Use
dt_stopfor datetime-based stops andtd_stopfor timedelta-based stops. Datetime-based stops can be periods ("D"), timestamps ("2023-01-01"), and even specific times ("18:00").
Target size to signals¶
- Target size can be converted to signals using a special signal function, giving access to stop and limit order functionality. This is especially useful, for example, in portfolio optimization.
Perform the Mean-Variance Optimization with SL and TP
>>> data = vbt.YFData.pull(
... ["SPY", "TLT", "XLF", "XLE", "XLU", "XLK", "XLB", "XLP", "XLY", "XLI", "XLV"],
... start="2022",
... end="2023",
... missing_index="drop"
... )
>>> pfo = vbt.PFO.from_riskfolio(data.returns, every="M")
>>> pf = pfo.simulate(
... data,
... pf_method="from_signals",
... sl_stop=0.05,
... tp_stop=0.1,
... stop_exit_price="close" # (1)!
... )
>>> pf.plot_allocations().show()
- Otherwise, user and stop signals will occur at different times within the same bar, making it impossible to establish the correct order of execution.
Target price¶
- Limit and stop orders can now be defined using a target price instead of a delta.
Set the SL to the previous low in a random portfolio
>>> data = vbt.YFData.pull("BTC-USD")
>>> pf = vbt.PF.from_random_signals(
... data,
... n=100,
... sl_stop=data.low.vbt.ago(1),
... delta_format="target"
... )
>>> sl_orders = pf.orders.stop_type_sl
>>> signal_index = pf.wrapper.index[sl_orders.signal_idx.values]
>>> hit_index = pf.wrapper.index[sl_orders.idx.values]
>>> hit_after = hit_index - signal_index
>>> hit_after
TimedeltaIndex([ '7 days', '3 days', '1 days', '5 days', '4 days',
'1 days', '28 days', '1 days', '1 days', '1 days',
'1 days', '1 days', '13 days', '10 days', '5 days',
'1 days', '3 days', '4 days', '1 days', '9 days',
'5 days', '1 days', '1 days', '2 days', '1 days',
'1 days', '1 days', '3 days', '1 days', '1 days',
'1 days', '1 days', '1 days', '2 days', '2 days',
'1 days', '12 days', '3 days', '1 days', '1 days',
'1 days', '1 days', '1 days', '3 days', '1 days',
'1 days', '1 days', '4 days', '1 days', '1 days',
'2 days', '6 days', '11 days', '1 days', '2 days',
'1 days', '1 days', '1 days', '1 days', '1 days',
'4 days', '10 days', '1 days', '1 days', '1 days',
'2 days', '3 days', '1 days'],
dtype='timedelta64[ns]', name='Date', freq=None)
Leverage¶
- Leverage is now an integral part of portfolio simulation. Supports two leverage modes:
lazy(enables leverage only if there is not enough cash) andeager(enables leverage while using only part of the available cash). Allows setting leverage per order, and can also determine the optimal leverage value automatically to fulfill any order requirement!
Explore how leverage affects the equity curve in a random portfolio
>>> data = vbt.YFData.pull("BTC-USD", start="2020", end="2022")
>>> pf = vbt.PF.from_random_signals(
... data,
... n=100,
... leverage=vbt.Param([0.5, 1, 2, 3]),
... )
>>> pf.value.vbt.plot().show()
Order delays¶
- By default, VBT executes every order at the end of the current bar. Previously, if you wanted to delay execution to the next bar, you had to manually shift all order-related arrays by one bar, which made the process error-prone. Now, you can simply specify how many bars in the past should be used to take order information from. In addition, the price argument now supports "nextopen" and "nextclose" as options, providing a one-line solution.
Compare orders without and with a delay in a random portfolio
>>> pf = vbt.PF.from_random_signals(
... vbt.YFData.pull("BTC-USD", start="2021-01", end="2021-02"),
... n=3,
... price=vbt.Param(["close", "nextopen"])
... )
>>> fig = pf.orders["close"].plot(
... buy_trace_kwargs=dict(name="Buy (close)", marker=dict(symbol="triangle-up-open")),
... sell_trace_kwargs=dict(name="Buy (close)", marker=dict(symbol="triangle-down-open"))
... )
>>> pf.orders["nextopen"].plot(
... plot_ohlc=False,
... plot_close=False,
... buy_trace_kwargs=dict(name="Buy (nextopen)"),
... sell_trace_kwargs=dict(name="Sell (nextopen)"),
... fig=fig
... )
>>> fig.show()
Signal callbacks¶
- Want to customize your simulation based on signals, or even generate signals dynamically according to the current backtesting environment? Two new callbacks now bring simulator flexibility to the next level: one lets you generate or override signals for each asset at every bar, and another allows you to compute user-defined metrics for the entire group at the end of each bar. Both accept a "context" that contains information about the current simulation state, enabling trading decisions to be made in a way similar to event-driven backtesters.
Backtest SMA crossover iteratively
>>> InOutputs = namedtuple("InOutputs", ["fast_sma", "slow_sma"])
>>> def initialize_in_outputs(target_shape):
... return InOutputs(
... fast_sma=np.full(target_shape, np.nan),
... slow_sma=np.full(target_shape, np.nan)
... )
>>> @njit
... def signal_func_nb(c, fast_window, slow_window):
... fast_sma = c.in_outputs.fast_sma
... slow_sma = c.in_outputs.slow_sma
... fast_start_i = c.i - fast_window + 1
... slow_start_i = c.i - slow_window + 1
... if fast_start_i >= 0 and slow_start_i >= 0:
... fast_sma[c.i, c.col] = np.nanmean(c.close[fast_start_i : c.i + 1])
... slow_sma[c.i, c.col] = np.nanmean(c.close[slow_start_i : c.i + 1])
... is_entry = vbt.pf_nb.iter_crossed_above_nb(c, fast_sma, slow_sma)
... is_exit = vbt.pf_nb.iter_crossed_below_nb(c, fast_sma, slow_sma)
... return is_entry, is_exit, False, False
... return False, False, False, False
>>> pf = vbt.PF.from_signals(
... vbt.YFData.pull("BTC-USD"),
... signal_func_nb=signal_func_nb,
... signal_args=(50, 200),
... in_outputs=vbt.RepFunc(initialize_in_outputs),
... )
>>> fig = pf.get_in_output("fast_sma").vbt.plot()
>>> pf.get_in_output("slow_sma").vbt.plot(fig=fig)
>>> pf.orders.plot(plot_ohlc=False, plot_close=False, fig=fig)
>>> fig.show()
Limit orders¶
- Long-awaited support for limit orders is now available for signal-based simulation! Includes time-in-force (TIF) orders such as DAY, GTC, GTD, LOO, and FOK
You can also reverse a limit order or create it using a delta for easier testing.
Explore how limit delta affects number of orders in a random portfolio
>>> pf = vbt.PF.from_random_signals(
... vbt.YFData.pull("BTC-USD"),
... n=100,
... order_type="limit",
... limit_delta=vbt.Param(np.arange(0.001, 0.1, 0.001)), # (1)!
... )
>>> pf.orders.count().vbt.plot(
... xaxis_title="Limit delta",
... yaxis_title="Order count"
... ).show()
- Limit delta is the distance between the close (or any specified price) and the target limit price, expressed as a percentage. The higher the delta, the lower the chance it will eventually be hit.
Delta formats¶
- Previously, stop orders could only be provided as percentages. While this worked for single values, it often required extra transformations for arrays. For example, setting SL to ATR meant you needed to know the entry price. More generally, to lock in a specific dollar amount of a trade, you might want to use a fixed price trailing stop. To address this, VBT now offers multiple stop value formats ("delta formats") to choose from.
Use ATR as SL
>>> data = vbt.YFData.pull("BTC-USD")
>>> atr = vbt.talib("ATR").run(data.high, data.low, data.close).real
>>> pf = vbt.PF.from_holding(
... data.loc["2022-01-01":"2022-01-07"],
... sl_stop=atr.loc["2022-01-01":"2022-01-07"],
... delta_format="absolute"
... )
>>> pf.orders.plot().show()
Bar skipping¶
- Simulation based on orders and signals can now (partially) skip bars that do not define any orders, often resulting in significant speedups for strategies with sparsely distributed orders.
Benchmark bar skipping in a buy-and-hold portfolio
>>> data = vbt.BinanceData.pull("BTCUSDT", start="one month ago UTC", timeframe="minute")
>>> size = data.symbol_wrapper.fill(np.nan)
>>> size[0] = np.inf
>>> %%timeit
>>> vbt.PF.from_orders(data, size, ffill_val_price=True) # (1)!
5.92 ms ± 300 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)
- When enabled (default), this argument forces the simulation to process every bar.
>>> %%timeit
>>> vbt.PF.from_orders(data, size, ffill_val_price=False)
2.75 ms ± 16 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
Signal contexts¶
- Signal generation functions have been redesigned to operate on contexts. This allows you to design more complex signal strategies with less namespace pollution.
Tutorial
Learn more in the Signal development tutorial.
Entry at the first bar of the week, exit at the last bar of the week
>>> @njit
... def entry_place_func_nb(c, index):
... for i in range(c.from_i, c.to_i): # (1)!
... if i == 0:
... return i - c.from_i # (2)!
... else:
... index_before = index[i - 1]
... index_now = index[i]
... index_next_week = vbt.dt_nb.future_weekday_nb(index_before, 0)
... if index_now >= index_next_week: # (3)!
... return i - c.from_i
... return -1
>>> @njit
... def exit_place_func_nb(c, index):
... for i in range(c.from_i, c.to_i):
... if i == len(index) - 1:
... return i - c.from_i
... else:
... index_now = index[i]
... index_after = index[i + 1]
... index_next_week = vbt.dt_nb.future_weekday_nb(index_now, 0)
... if index_after >= index_next_week: # (4)!
... return i - c.from_i
... return -1
>>> data = vbt.YFData.pull("BTC-USD", start="2020-01-01", end="2020-01-14")
>>> entries, exits = vbt.pd_acc.signals.generate_both(
... data.symbol_wrapper.shape,
... entry_place_func_nb=entry_place_func_nb,
... entry_place_args=(data.index.vbt.to_ns(),),
... exit_place_func_nb=exit_place_func_nb,
... exit_place_args=(data.index.vbt.to_ns(),),
... wrapper=data.symbol_wrapper
... )
>>> pd.concat((
... entries.rename("Entries"),
... exits.rename("Exits")
... ), axis=1).to_period("W")
Entries Exits
Date
2020-01-06/2020-01-12 True False
2020-01-06/2020-01-12 False False
2020-01-06/2020-01-12 False False
2020-01-06/2020-01-12 False False
2020-01-06/2020-01-12 False False
2020-01-06/2020-01-12 False False
2020-01-06/2020-01-12 False True
2020-01-13/2020-01-19 True False
- Iterate over the bars in the current period segment.
- If a signal should be placed, return an index relative to the segment.
- Place a signal if the current bar crosses Monday.
- Place a signal if the next bar crosses Monday.
Pre-computation¶
- There is a tradeoff between memory usage and execution speed: a dataset with 1000 columns is usually processed much faster than processing a 1-column dataset 1000 times. However, the first dataset also requires 1000 times more memory than the second. That's why, during the simulation phase, VBT primarily generates orders, while other portfolio attributes such as balances, equity, and returns are reconstructed later during the analysis phase if the user needs them. For cases where performance is the main concern, arguments are now available that let you pre-compute these attributes during simulation!
Benchmark a random portfolio with 1000 columns without and with pre-computation
>>> data = vbt.YFData.pull("BTC-USD")
>>> %%timeit # (1)!
>>> for n in range(1000):
... pf = vbt.PF.from_random_signals(data, n=n, save_returns=False)
... pf.sharpe_ratio
15 s ± 829 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
- Good for RAM, bad for performance.
>>> %%timeit
>>> pf = vbt.PF.from_random_signals(data, n=np.arange(1000).tolist(), save_returns=False)
>>> pf.sharpe_ratio
855 ms ± 6.26 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
>>> %%timeit
>>> pf = vbt.PF.from_random_signals(data, n=np.arange(1000).tolist(), save_returns=True)
>>> pf.sharpe_ratio
593 ms ± 7.07 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
Cash deposits¶
- Cash can be deposited or withdrawn at any time.
DCA $10 into Bitcoin each month
>>> data = vbt.YFData.pull("BTC-USD")
>>> cash_deposits = data.symbol_wrapper.fill(0.0)
>>> month_start_mask = ~data.index.tz_convert(None).to_period("M").duplicated()
>>> cash_deposits[month_start_mask] = 10
>>> pf = vbt.PF.from_orders(
... data.close,
... init_cash=0,
... cash_deposits=cash_deposits
... )
>>> pf.input_value # (1)!
1020.0
>>> pf.final_value
20674.328828315127
- Total invested.
Cash earnings¶
- Cash can be continuously earned or spent depending on the current position.
Backtest Apple without and with dividend reinvestment
>>> data = vbt.YFData.pull("AAPL", start="2010")
>>> pf_kept = vbt.PF.from_holding( # (1)!
... data.close,
... cash_dividends=data.get("Dividends")
... )
>>> pf_kept.cash.iloc[-1] # (2)!
93.9182408043298
>>> pf_kept.assets.iloc[-1] # (3)!
15.37212731743495
>>> pf_reinvested = vbt.PF.from_orders( # (4)!
... data.close,
... cash_dividends=data.get("Dividends")
... )
>>> pf_reinvested.cash.iloc[-1]
0.0
>>> pf_reinvested.assets.iloc[-1]
18.203284859405468
>>> fig = pf_kept.value.rename("Value (kept)").vbt.plot()
>>> pf_reinvested.value.rename("Value (reinvested)").vbt.plot(fig=fig)
>>> fig.show()
- Keep dividends as cash.
- Final cash balance.
- Final number of shares in the portfolio.
- Reinvest dividends at the next bar.
In-place outputs¶
- The portfolio can now accept and return any user-defined arrays filled during simulation, such as signals. In-place output arrays can broadcast together with regular arrays using templates and broadcastable named arguments. Additionally, VBT will (semi-)automatically determine how to correctly wrap and index each array, for example, whenever you select a column from the entire portfolio.
Track the debt of a random portfolio during the simulation
>>> data = vbt.YFData.pull(["BTC-USD", "ETH-USD"], missing_index="drop")
>>> size = data.symbol_wrapper.fill(np.nan)
>>> rand_indices = np.random.choice(np.arange(len(size)), 10)
>>> size.iloc[rand_indices[0::2]] = -np.inf
>>> size.iloc[rand_indices[1::2]] = np.inf
>>> @njit
... def post_segment_func_nb(c):
... for col in range(c.from_col, c.to_col):
... col_debt = c.last_debt[col]
... c.in_outputs.debt[c.i, col] = col_debt
... if col_debt > c.in_outputs.max_debt[col]:
... c.in_outputs.max_debt[col] = col_debt
>>> pf = vbt.PF.from_def_order_func(
... data.close,
... size=size,
... post_segment_func_nb=post_segment_func_nb,
... in_outputs=dict(
... debt=vbt.RepEval("np.empty_like(close)"),
... max_debt=vbt.RepEval("np.full(close.shape[1], 0.)")
... ) # (1)!
... )
>>> pf.get_in_output("debt") # (2)!
symbol BTC-USD ETH-USD
Date
2017-11-09 00:00:00+00:00 0.000000 0.000000
2017-11-10 00:00:00+00:00 0.000000 0.000000
2017-11-11 00:00:00+00:00 0.000000 0.000000
2017-11-12 00:00:00+00:00 0.000000 0.000000
2017-11-13 00:00:00+00:00 0.000000 0.000000
... ... ...
2023-02-08 00:00:00+00:00 43.746892 25.054571
2023-02-09 00:00:00+00:00 43.746892 25.054571
2023-02-10 00:00:00+00:00 43.746892 25.054571
2023-02-11 00:00:00+00:00 43.746892 25.054571
2023-02-12 00:00:00+00:00 43.746892 25.054571
[1922 rows x 2 columns]
>>> pf.get_in_output("max_debt") # (3)!
symbol
BTC-USD 75.890464
ETH-USD 25.926328
Name: max_debt, dtype: float64
- Instruct the portfolio class to wait until all arrays are broadcast and create a new floating array of the final shape.
- The portfolio instance knows how to properly wrap a custom NumPy array as a pandas object.
- The same applies to reduced NumPy arrays.
Flexible attributes¶
- Portfolio attributes can now be partially or even fully computed from user-defined arrays. This gives you greater control over post-simulation analysis, such as overriding some simulation data, testing hyperparameters without re-simulating the entire portfolio, or avoiding repeated reconstruction when caching is disabled.
Compute the net exposure by caching its components
>>> data = vbt.YFData.pull("BTC-USD")
>>> pf = vbt.PF.from_random_signals(data.close, n=100)
>>> value = pf.get_value() # (1)!
>>> long_exposure = vbt.PF.get_gross_exposure( # (2)!
... asset_value=pf.get_asset_value(direction="longonly"),
... value=value,
... wrapper=pf.wrapper
... )
>>> short_exposure = vbt.PF.get_gross_exposure(
... asset_value=pf.get_asset_value(direction="shortonly"),
... value=value,
... wrapper=pf.wrapper
... )
>>> del value # (3)!
>>> net_exposure = vbt.PF.get_net_exposure(
... long_exposure=long_exposure,
... short_exposure=short_exposure,
... wrapper=pf.wrapper
... )
>>> del long_exposure
>>> del short_exposure
>>> net_exposure
Date
2014-09-17 00:00:00+00:00 1.0
2014-09-18 00:00:00+00:00 1.0
2014-09-19 00:00:00+00:00 1.0
2014-09-20 00:00:00+00:00 1.0
2014-09-21 00:00:00+00:00 1.0
...
2023-02-08 00:00:00+00:00 0.0
2023-02-09 00:00:00+00:00 0.0
2023-02-10 00:00:00+00:00 0.0
2023-02-11 00:00:00+00:00 0.0
2023-02-12 00:00:00+00:00 0.0
Freq: D, Length: 3071, dtype: float64
- Call the instance method to use the data stored in the portfolio.
- Call the class method to provide all the data explicitly.
- Delete the object as soon as it is no longer needed to free memory.
Shortcut properties¶
- In-place output arrays can be used to override regular portfolio attributes. The portfolio will automatically pick the pre-computed array and perform all future calculations using this array, avoiding redundant reconstruction.
Modify the returns from within the simulation
>>> data = vbt.YFData.pull("BTC-USD")
>>> size = data.symbol_wrapper.fill(np.nan)
>>> rand_indices = np.random.choice(np.arange(len(size)), 10)
>>> size.iloc[rand_indices[0::2]] = -np.inf
>>> size.iloc[rand_indices[1::2]] = np.inf
>>> @njit
... def post_segment_func_nb(c):
... for col in range(c.from_col, c.to_col):
... return_now = c.last_return[col]
... return_now = 0.5 * return_now if return_now > 0 else return_now
... c.in_outputs.returns[c.i, col] = return_now
>>> pf = vbt.PF.from_def_order_func(
... data.close,
... size=size,
... size_type="targetpercent",
... post_segment_func_nb=post_segment_func_nb,
... in_outputs=dict(
... returns=vbt.RepEval("np.empty_like(close)")
... )
... )
>>> pf.returns # (1)!
Date
2014-09-17 00:00:00+00:00 0.000000
2014-09-18 00:00:00+00:00 0.000000
2014-09-19 00:00:00+00:00 0.000000
2014-09-20 00:00:00+00:00 0.000000
2014-09-21 00:00:00+00:00 0.000000
...
2023-02-08 00:00:00+00:00 -0.015227
2023-02-09 00:00:00+00:00 -0.053320
2023-02-10 00:00:00+00:00 -0.008439
2023-02-11 00:00:00+00:00 0.005569 << modified
2023-02-12 00:00:00+00:00 0.001849 << modified
Freq: D, Length: 3071, dtype: float64
>>> pf.get_returns() # (2)!
Date
2014-09-17 00:00:00+00:00 0.000000
2014-09-18 00:00:00+00:00 0.000000
2014-09-19 00:00:00+00:00 0.000000
2014-09-20 00:00:00+00:00 0.000000
2014-09-21 00:00:00+00:00 0.000000
...
2023-02-08 00:00:00+00:00 -0.015227
2023-02-09 00:00:00+00:00 -0.053320
2023-02-10 00:00:00+00:00 -0.008439
2023-02-11 00:00:00+00:00 0.011138
2023-02-12 00:00:00+00:00 0.003697
Freq: D, Length: 3071, dtype: float64
- Pre-computed returns.
- Actual returns.
And many more...¶
- Look forward to more killer features being added every week!