Backtesting with vectorbt

Use the vectorbt library for high-performance portfolio backtesting with built-in metrics, portfolio simulation, and visualization.

vectorbtportfolioperformance

Backtesting Libraries — vectorbt

1. Dependency Installation

[1]

!pip install pandas numpy vectorbt plotly

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Collecting vectorbt
  Downloading vectorbt-1.0.0-py3-none-any.whl.metadata (14 kB)
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Collecting dateparser (from vectorbt)
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Collecting schedule (from vectorbt)
  Downloading schedule-1.2.2-py3-none-any.whl.metadata (3.8 kB)
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Collecting mypy_extensions (from vectorbt)
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2. Library Imports

[2]

import warnings
warnings.filterwarnings("ignore")

import pandas as pd
import numpy as np
import vectorbt as vbt
import plotly.graph_objects as go
from plotly.subplots import make_subplots

3. What Is vectorbt?

vectorbt is a Python library for high-performance portfolio backtesting built on top of numpy and pandas. It is designed to evaluate thousands of parameter combinations simultaneously using vectorized array operations, making it the standard tool for parameter optimization and strategy screening.

Key features:

| Feature | Description | |---| | Vectorized computation | Entire backtest runs as array operations — no Python loops | | Parameter sweeps | Test all combinations of parameters in one call | | Built-in indicators | SMA, EMA, RSI, MACD, Bollinger Bands, and 100+ others | | Portfolio simulation | Fees, slippage, position sizing, and cash management in one API | | Performance analytics | Sharpe, Sortino, Calmar, drawdown, trade statistics built in | | Interactive plotting | Plotly-based charts for equity curves, trade markers, heatmaps |

When to use vectorbt vs event-driven:

vectorbt: Parameter optimization, signal screening, fast iteration during research.
Event-driven (Notebook 67): Final performance validation, realistic execution modeling, live system development.

4. Data Generation

[3]

def generate_data(periods: int) -> pd.DataFrame:
    start_date     = pd.to_datetime("2024-01-01 00:00:00+00:00")
    datetime_index = pd.date_range(start_date, periods=periods, freq="1min", tz="UTC")
    price_data = []; last_close = 42000
    volatility_scale = 0.005; wick_scale = 0.002

    for _ in range(periods):
        open_price  = last_close + np.random.normal(0, last_close * volatility_scale * 0.1)
        close_price = open_price + np.random.normal(0, last_close * volatility_scale)
        body_high   = max(open_price, close_price)
        body_low    = min(open_price, close_price)
        high_price  = max(body_high + abs(np.random.normal(0, last_close * wick_scale)),
                          open_price, close_price)
        low_price   = min(body_low  - abs(np.random.normal(0, last_close * wick_scale)),
                          open_price, close_price)
        if high_price < low_price:
            high_price, low_price = low_price, high_price
        price_data.append({
            "open":  max(1, int(open_price)),
            "high":  max(1, int(high_price)),
            "low":   max(1, int(low_price)),
            "close": max(1, int(close_price)),
        })
        last_close = close_price

    df = pd.DataFrame(price_data, index=datetime_index)
    df.index.name = "datetime"
    df["volume"]   = np.random.uniform(100.0, 500.0, periods)
    df["datetime"] = df.index.to_series()
    return df.reset_index(drop=True)

df = generate_data(500)

# Convert to indexed Series for vectorbt compatibility
close_series = pd.Series(
    df["close"].values,
    index=pd.DatetimeIndex(df["datetime"]),
    name="close",
)

display(df.head())
print(f"Series length: {len(close_series)}")

	open	high	low	close	volume	datetime
0	41983	42045	41972	41981	214.866516	2024-01-01 00:00:00+00:00
1	41986	42535	41820	42442	122.914521	2024-01-01 00:01:00+00:00
2	42425	43150	42363	43082	422.954606	2024-01-01 00:02:00+00:00
3	43097	43157	43002	43115	163.932242	2024-01-01 00:03:00+00:00
4	43097	43209	42713	42927	329.700926	2024-01-01 00:04:00+00:00

Series length: 500

5. Single-Parameter Backtest

[4]

# --- Compute moving averages using vectorbt's built-in indicator engine ---
fast_ma = vbt.MA.run(close_series, window=10)
slow_ma = vbt.MA.run(close_series, window=30)

# --- Generate entry and exit signals ---
entries = fast_ma.ma_crossed_above(slow_ma)   # Fast MA crosses above slow MA
exits   = fast_ma.ma_crossed_below(slow_ma)   # Fast MA crosses below slow MA

# --- Build and run portfolio ---
portfolio = vbt.Portfolio.from_signals(
    close_series,
    entries,
    exits,
    init_cash   = 10_000,
    fees        = 0.001,        # 0.1% per trade (round-trip = 0.2%)
    freq        = "1min",
)

print("--- vectorbt Portfolio Statistics ---")
print(portfolio.stats())

--- vectorbt Portfolio Statistics ---
Start                         2024-01-01 00:00:00+00:00
End                           2024-01-01 08:19:00+00:00
Period                                  0 days 08:20:00
Start Value                                     10000.0
End Value                                    9407.42885
Total Return [%]                              -5.925711
Benchmark Return [%]                           2.012815
Max Gross Exposure [%]                            100.0
Total Fees Paid                              200.497049
Max Drawdown [%]                              13.034522
Max Drawdown Duration                   0 days 04:18:00
Total Trades                                         10
Total Closed Trades                                  10
Total Open Trades                                     0
Open Trade PnL                                      0.0
Win Rate [%]                                       20.0
Best Trade [%]                                 4.164551
Worst Trade [%]                                -2.68185
Avg Winning Trade [%]                          3.832015
Avg Losing Trade [%]                          -1.687851
Avg Winning Trade Duration              0 days 01:07:30
Avg Losing Trade Duration               0 days 00:13:00
Profit Factor                                  0.565981
Expectancy                                   -59.257115
Sharpe Ratio                                  -23.85127
Calmar Ratio                                  -7.671935
Omega Ratio                                    0.888054
Sortino Ratio                                -32.249531
dtype: object

Explanation:

vbt.MA.run(close_series, window=10): Computes the moving average using vectorbt's indicator pipeline. The result is a vectorbt indicator object that supports crossover detection directly.
ma_crossed_above(slow_ma): Returns a boolean Series that is True only on the exact candle where the fast MA crosses above the slow MA — equivalent to (fast > slow) & (fast.shift(1) <= slow.shift(1)).
vbt.Portfolio.from_signals: The core portfolio simulator. It processes the entry and exit boolean arrays, manages cash, applies fees, and tracks position state — all in a single vectorized pass. The freq parameter is used for annualizing performance metrics.
portfolio.stats(): Returns a comprehensive dictionary of performance metrics including total return, Sharpe ratio, max drawdown, win rate, and trade count.

6. Parameter Optimization

[5]

# --- Multi-parameter sweep: test all combinations of fast and slow windows ---
fast_windows = [5, 10, 15, 20]
slow_windows = [20, 30, 40, 50]

fast_ma_multi = vbt.MA.run(close_series, window=fast_windows, short_name="fast")
slow_ma_multi = vbt.MA.run(close_series, window=slow_windows, short_name="slow")

entries_multi = fast_ma_multi.ma_crossed_above(slow_ma_multi)
exits_multi   = fast_ma_multi.ma_crossed_below(slow_ma_multi)

portfolio_multi = vbt.Portfolio.from_signals(
    close_series,
    entries_multi,
    exits_multi,
    init_cash = 10_000,
    fees      = 0.001,
    freq      = "1min",
)

# Extract total return for each parameter combination
returns_matrix = portfolio_multi.total_return()
print("--- Total Return Matrix (Fast Window × Slow Window) ---")
display(returns_matrix.unstack())

--- Total Return Matrix (Fast Window × Slow Window) ---

slow_window	20	30	40	50
fast_window
5	-0.139453	NaN	NaN	NaN
10	NaN	-0.059257	NaN	NaN
15	NaN	NaN	-0.055753	NaN
20	NaN	NaN	NaN	-0.048005

Explanation: vectorbt evaluates all 16 parameter combinations (4 fast × 4 slow) simultaneously in a single function call — no outer loop is required. The result is a matrix where each cell contains the total return for that fast/slow window pair. This capability is what makes vectorbt the standard tool for strategy parameter optimization — equivalent analysis in a loop-based framework would require 16 separate backtest runs.

7. Visualization

[7]

buy_signals_mask  = entries[entries].index
sell_signals_mask = exits[exits].index

fig = make_subplots(
    rows=2, cols=1, shared_xaxes=True,
    subplot_titles=[
        "Price + MA Crossover + Entry/Exit Signals",
        "vectorbt Equity Curve",
    ],
    row_heights=[0.55, 0.45],
)

fig.add_trace(go.Candlestick(
    x=df["datetime"],
    open=df["open"], high=df["high"],
    low=df["low"],   close=df["close"],
    name="Price"), row=1, col=1)

fig.add_trace(go.Scatter(
    x=close_series.index, y=fast_ma.ma.values,
    mode="lines", name="Fast MA (10)",
    line=dict(color="blue", width=1)), row=1, col=1)

fig.add_trace(go.Scatter(
    x=close_series.index, y=slow_ma.ma.values,
    mode="lines", name="Slow MA (30)",
    line=dict(color="orange", width=1)), row=1, col=1)

buy_prices  = close_series.loc[buy_signals_mask]
sell_prices = close_series.loc[sell_signals_mask]

fig.add_trace(go.Scatter(
    x=buy_prices.index,
    y=buy_prices.values  * 0.999,
    mode="markers",
    marker=dict(symbol="triangle-up",   size=10, color="green"),
    name="Buy Signal"), row=1, col=1)

fig.add_trace(go.Scatter(
    x=sell_prices.index,
    y=sell_prices.values * 1.001,
    mode="markers",
    marker=dict(symbol="triangle-down", size=10, color="red"),
    name="Sell Signal"), row=1, col=1)

equity_values = portfolio.value()
fig.add_trace(go.Scatter(
    x=equity_values.index, y=equity_values.values,
    mode="lines", name="Portfolio Value ($)",
    line=dict(color="green", width=2)), row=2, col=1)

fig.update_layout(
    title_text="vectorbt Backtest — MA Crossover Strategy",
    xaxis_rangeslider_visible=False,
    height=800,
    yaxis=dict(autorange=True),
    xaxis2_title="Datetime",
    yaxis_title="Price",
    yaxis2_title="Portfolio Value ($)",
)
fig.show()

[ ]

Task

I will update the notebook to improve its formality, clarity, and consistency. Specifically, I will:

Refine the introductory markdown cells (e5741ce3, eefdc1e3) for a more formal and technical presentation.
Add comments to the dependency installation code (6a30bce5) to explain its purpose.
Modify the library imports markdown (5ffcb3ab) and add detailed comments to the import code (4554082d) for documentation purposes.
Rewrite the 'What Is vectorbt?' markdown section (b6da932e) to be concise, technically accurate, and formal.
Enhance the 'Data Generation' markdown introduction (bf923033) and add technical comments to the generate_data function and close_series conversion within the code cell (4c4965df).
Improve the 'Single-Parameter Backtest' markdown introduction (9a218c0b), add technical comments to the backtest code (c03c38b8), and rewrite its 'Explanation' markdown (598b7240).
Refine the 'Parameter Optimization' markdown introduction (2a617563), add technical comments to the multi-parameter sweep code (2426f084), and rewrite its 'Explanation' markdown (e6440eba).
Enhance the 'Visualization' markdown introduction (00861abe) and add comments to the plotting code (da05a445) to explain each component.

I will confirm that all changes adhere to the specified rules upon completion.