Patterns and event studies

✅ Each simulation starts and ends at specific points, usually matching the first and last rows of your data. You can set a different simulation range beforehand, and now, you can also adjust this range during the simulation. This flexibility lets you stop the simulation when further processing is unnecessary. Additionally, the date range is saved in the portfolio object, so all metrics and subplots recognize it. Processing only the relevant dates speeds up execution and adds a new dimension to your analysis: isolated time windows 🔬

@njit
def post_segment_func_nb(ctx):
    value = vbt.pf_nb.get_group_value_nb(ctx, ctx.group)
    if value <= 0:
        vbt.pf_nb.stop_group_sim_nb(ctx, ctx.group)  

pf = vbt.PF.from_random_signals(
    "BTC-USD",
    n=10,
    seed=42,
    sim_start="auto",  
    post_segment_func_nb=post_segment_func_nb,
    leverage=10,
)
pf.plot_value()  

✅ Patterns are distinctive formations created by price movements on a chart and are central to technical analysis. There are now new dedicated functions and classes for detecting patterns of any complexity in any type of time series data. The idea is simple: fit a pattern to align with the scale and period of your selected data window, then compute the element-wise distance between them to get a single similarity score. You can adjust the threshold for this score to decide above which value a data window should be marked as "matched." Thanks to Numba, this operation can be performed hundreds of thousands of times per second! 🔎

data = vbt.YFData.pull("BTC-USD")
data.hlc3.vbt.find_pattern(
    pattern=[5, 1, 3, 1, 2, 1],
    window=100,
    max_window=700,
).loc["2017":"2019"].plot().show()

✅ There are cleaner ways to analyze events and their impact on price than conventional backtesting. Meet projections! 👋 Not only can they help you assess event performance visually and quantitatively, but they can also project events into the future to support trading. This is done by extracting the price range after each event, collecting all these price ranges into a multidimensional array, and then deriving confidence intervals and other useful statistics from that array. When combined with patterns, these tools are a quantitative analyst's dream! 🌟

data = vbt.YFData.pull("ETH-USD")
pattern_ranges = data.hlc3.vbt.find_pattern(
    pattern=data.close.iloc[-7:],
    rescale_mode="rebase"
)
delta_ranges = pattern_ranges.with_delta(7)
fig = data.iloc[-7:].plot(plot_volume=False)
delta_ranges.plot_projections(fig=fig)
fig.show()

✅ Previously, OHLC data was used for simulation, but only the close price was analyzed. Now, most classes let you track all OHLC data for more accurate quantitative and qualitative analysis.

data = vbt.YFData.pull("BTC-USD", start="2020-01", end="2020-03")
pf = vbt.PF.from_random_signals(
    open=data.open,
    high=data.high,
    low=data.low,
    close=data.close,
    n=10,
    seed=42
)
pf.trades.plot().show()