Backtesting with vectorbt

Data Acquisition (yfinance)
Statistical and Technical Analysis (pandas_ta)
Backtesting Analysis and Results (vectorbt)

Initializations

In [1]:

import asyncio
import itertools
from datetime import datetime

from IPython import display

import numpy as np
import pandas as pd
import pandas_ta as ta
import vectorbt as vbt

import plotly.graph_objects as go

print("Package Versions:")
print(f"Numpy v{np.__version__}")
print(f"Pandas v{pd.__version__}")
print(f"vectorbt v{vbt.__version__}")
print(f"\nPandas TA v{ta.version}\nTo install the Latest Version:\n$ pip install -U git+https://github.com/twopirllc/pandas-ta\n")

%matplotlib inline

Package Versions:
Numpy v1.20.3
Pandas v1.3.0
vectorbt v0.23.1

Pandas TA v0.3.54b0
To install the Latest Version:
$ pip install -U git+https://github.com/twopirllc/pandas-ta

vectorbt Theme and Portfolio Settings

In [2]:

cheight, cwidth = 500, 1000 # Adjust as needed for Chart Height and Width
vbt.settings.set_theme("dark") # Options: "light" (Default), "dark" (my fav), "seaborn"

# Must be set
vbt.settings.portfolio["freq"] = "1D" # Daily

# Predefine vectorbt Portfolio settings
vbt.settings.portfolio["init_cash"] = 100_000
vbt.settings.portfolio["fees"] = 0.0025 # 0.25%
vbt.settings.portfolio["slippage"] = 0.0025 # 0.25%
# vbt.settings.portfolio["size"] = 100
# vbt.settings.portfolio["accumulate"] = False
vbt.settings.portfolio["allow_partial"] = False
vbt.settings.portfolio["signal_direction"] = "both"

pf_settings = pd.DataFrame(vbt.settings.portfolio.items(), columns=["Option", "Value"])
pf_settings.set_index("Option", inplace=True)

print(f"Portfolio Settings [Initial]")
pf_settings

Out [2]:

Portfolio Settings [Initial]

	Value
Option
call_seq	default
init_cash	100000
size	inf
size_type	amount
fees	0.0025
fixed_fees	0.0
slippage	0.0025
reject_prob	0.0
min_size	0.0
max_size	inf
size_granularity	NaN
lock_cash	False
allow_partial	False
raise_reject	False
val_price	inf
accumulate	False
sl_stop	NaN
sl_trail	False
tp_stop	NaN
stop_entry_price	close
stop_exit_price	stoplimit
stop_conflict_mode	exit
upon_stop_exit	close
upon_stop_update	override
use_stops	None
log	False
upon_long_conflict	ignore
upon_short_conflict	ignore
upon_dir_conflict	ignore
upon_opposite_entry	reversereduce
signal_direction	both
order_direction	both
cash_sharing	False
call_pre_segment	False
call_post_segment	False
ffill_val_price	True
update_value	False
fill_pos_record	True
row_wise	False
flexible	False
use_numba	True
seed	None
freq	1D
attach_call_seq	False
fillna_close	True
trades_type	exittrades
stats	{'filters': {'has_year_freq': {'filter_func': ...
plots	{'subplots': ['orders', 'trade_pnl', 'cum_retu...

Helper Methods

In [3]:

def combine_stats(pf: vbt.portfolio.base.Portfolio, ticker: str, strategy: str, mode: int = 0):
    header = pd.Series({
        "Run Time": ta.get_time(full=False, to_string=True),
        "Mode": "LIVE" if mode else "TEST",
        "Strategy": strategy,
        "Direction": vbt.settings.portfolio["signal_direction"],
        "Symbol": ticker.upper(),
        "Fees [%]": 100 * vbt.settings.portfolio["fees"],
        "Slippage [%]": 100 * vbt.settings.portfolio["slippage"],
        "Accumulate": vbt.settings.portfolio["accumulate"],
    })
    rstats = pf.returns_stats().dropna(axis=0).T
    stats = pf.stats().dropna(axis=0).T
    joint = pd.concat([header, stats, rstats])
    return joint[~joint.index.duplicated(keep="first")]

def earliest_common_index(d: dict):
    """Returns index of the earliest common index of all DataFrames in the dict"""
    min_date = None
    for df in d.values():
        if min_date is None:
            min_date = df.index[0]
        elif min_date < df.index[0]:
            min_date = df.index[0]
    return min_date

def dl(tickers: list, same_start: bool = False, **kwargs):
    if isinstance(tickers, str):
        tickers = [tickers]
    
    if not isinstance(tickers, list) or len(tickers) == 0:
        print("Must be a non-empty list of tickers or symbols")
        return

    if "limit" in kwargs and kwargs["limit"] and len(tickers) > kwargs["limit"]:
        from itertools import islice            
        tickers = list(islice(tickers, kwargs["limit"]))
        print(f"[!] Too many assets to compare. Using the first {kwargs['limit']}: {', '.join(tickers)}")

    print(f"[i] Downloading: {', '.join(tickers)}")

    received = {}
    if len(tickers):
        _df = pd.DataFrame()
        for ticker in tickers:
            received[ticker] = _df.ta.ticker(ticker, **kwargs)
            print(f"[+] {ta.get_time(full=False, to_string=True)}")
    
    if same_start and len(tickers) > 1:
        earliestci = earliest_common_index(received)
        print(f"[i] Earliest Common Date: {earliestci}")
        result = {ticker:df[df.index > earliestci].copy() for ticker,df in received.items()}
    else:
        result = received
    print(f"[*] Download Complete\n")
    return result

def dtmask(df: pd.DataFrame, start: datetime, end: datetime):
    return df.loc[(df.index >= start) & (df.index <= end), :].copy()

def show_data(d: dict):
    [print(f"{t}[{df.index[0]} - {df.index[-1]}]: {df.shape} {df.ta.time_range:.2f} years") for t,df in d.items()]
    
def trade_table(pf: vbt.portfolio.base.Portfolio, k: int = 1, total_fees: bool = False):
    if not isinstance(pf, vbt.portfolio.base.Portfolio): return
    k = int(k) if isinstance(k, int) and k > 0 else 1

    df = pf.trades.records[["status", "direction", "size", "entry_price", "exit_price", "return", "pnl", "entry_fees", "exit_fees"]]
    if total_fees:
        df["total_fees"] = df["entry_fees"] + df["exit_fees"]

    print(f"\nLast {k} of {df.shape[0]} Trades\n{df.tail(k)}\n")

Data Acquisition

Specify Symbols for Benchmarks and Assets

In [4]:

benchmark_tickers = ["SPY", "QQQ"]
asset_tickers = ["AAPL", "TSLA", "TWTR"]
all_tickers = benchmark_tickers + asset_tickers

print("Tickers by index #")
print("="*100)
print(f"Benchmarks: {', '.join([f'{k}: {v}' for k,v in enumerate(benchmark_tickers)])}")
print(f"    Assets: {', '.join([f'{k}: {v}' for k,v in enumerate(asset_tickers)])}")
print(f"       All: {', '.join([f'{k}: {v}' for k,v in enumerate(all_tickers)])}")

Tickers by index #
====================================================================================================
Benchmarks: 0: SPY, 1: QQQ
    Assets: 0: AAPL, 1: TSLA, 2: TWTR
       All: 0: SPY, 1: QQQ, 2: AAPL, 3: TSLA, 4: TWTR

In [5]:

benchmark = benchmark_tickers[0] # Change index for different benchmark
asset = asset_tickers[2] # Change index for different symbol
print(f"Selected Benchmark | Asset: {benchmark} | {asset}")

Selected Benchmark | Asset: SPY | TWTR

In [6]:

benchmarks = dl(benchmark_tickers, timed=True)

[i] Downloading: SPY, QQQ
[+] yf | SPY(7328, 7): 3297.1998 ms (3.2972 s)
[+] Friday March 4, 2022, NYSE: 4:13:28
[+] yf | QQQ(5786, 7): 3182.5192 ms (3.1825 s)
[+] Friday March 4, 2022, NYSE: 4:13:31
[*] Download Complete

In [7]:

assets = dl(asset_tickers, timed=True)

[i] Downloading: AAPL, TSLA, TWTR
[+] yf | AAPL(10395, 7): 2928.4840 ms (2.9285 s)
[+] Friday March 4, 2022, NYSE: 4:13:34
[+] yf | TSLA(2942, 7): 2660.0862 ms (2.6601 s)
[+] Friday March 4, 2022, NYSE: 4:13:37
[+] yf | TWTR(2095, 7): 2686.0984 ms (2.6861 s)
[+] Friday March 4, 2022, NYSE: 4:13:40
[*] Download Complete

Define Testing Dates and Ranges

In [8]:

start_date = datetime(2005, 1, 1) # Adjust as needed
end_date = datetime(2010, 1, 1)   # Adjust as needed

Select and Benchmark and Asset to Backtest

In [9]:

print("Available Data:")
print("="*100)
print(f"Benchmarks: {', '.join(benchmarks.keys())}")
print(f"Assets: {', '.join(assets.keys())}")

Available Data:
====================================================================================================
Benchmarks: SPY, QQQ
Assets: AAPL, TSLA, TWTR

In [10]:

benchmark_name = "SPY" # Select a Benchmark
asset_name = "AAPL" # Select an Asset

benchmarkdf = benchmarks[benchmark_name]
assetdf     = assets[asset_name]

# Set True if you want to constrain Data between start_date & end_date
common_range = True
if common_range:
    crs = f" from {start_date} to {end_date}"
    benchmarkdf = dtmask(benchmarkdf, start_date, end_date)
    assetdf = dtmask(assetdf, start_date, end_date)

# Update DataFrame names
benchmarkdf.name = benchmark_name
assetdf.name = asset_name
print(f"Analysis of: {benchmarkdf.name} and {assetdf.name}{crs if common_range else ''}")

Analysis of: SPY and AAPL from 2005-01-01 00:00:00 to 2010-01-01 00:00:00

Sanity Check

In [11]:

benchmarkdf

Out [11]:

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2005-01-03	87.175188	87.318618	85.984743	86.271599	55748000	0.0	0
2005-01-04	86.386323	86.443696	84.937709	85.217392	69167600	0.0	0
2005-01-05	85.152863	85.518605	84.622183	84.629356	65667300	0.0	0
2005-01-06	84.937742	85.446909	84.808657	85.059654	47814700	0.0	0
2005-01-07	85.317803	85.504260	84.715405	84.937721	55847700	0.0	0
...	...	...	...	...	...	...	...
2009-12-24	88.984994	89.310187	88.834291	89.215012	39677500	0.0	0
2009-12-28	89.548150	89.619532	89.088114	89.405380	87508500	0.0	0
2009-12-29	89.635408	89.651268	89.270553	89.278481	80572500	0.0	0
2009-12-30	89.016730	89.349857	88.969136	89.246742	73138400	0.0	0
2009-12-31	89.445055	89.468855	88.350491	88.390152	90637900	0.0	0

1259 rows × 7 columns

In [12]:

assetdf

Out [12]:

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2005-01-03	0.990526	0.995572	0.957192	0.967743	691992000	0.0	0.0
2005-01-04	0.975388	1.001076	0.962849	0.977682	1096810400	0.0	0.0
2005-01-05	0.985632	0.997713	0.979364	0.986245	680433600	0.0	0.0
2005-01-06	0.988843	0.992513	0.968354	0.987009	705555200	0.0	0.0
2005-01-07	0.993889	1.064685	0.990067	1.058874	2227450400	0.0	0.0
...	...	...	...	...	...	...	...
2009-12-24	6.224808	6.402180	6.218692	6.392699	500889200	0.0	0.0
2009-12-28	6.474656	6.542852	6.410129	6.471292	644565600	0.0	0.0
2009-12-29	6.502484	6.505237	6.383217	6.394532	445205600	0.0	0.0
2009-12-30	6.386275	6.483218	6.370373	6.472208	412084400	0.0	0.0
2009-12-31	6.517776	6.524504	6.439182	6.444380	352410800	0.0	0.0

1259 rows × 7 columns

Creating Trading Signals for vectorbt

vectorbt can create a Backtest using vbt.Portfolio.from_signals(*args, **kwargs) based on trends that you create with Pandas TA.

Trend Creation

A Trend is the result of some calculation or condition of one or more indicators. For simplicity, a Trend is either True or 1 and No Trend is False or 0. Using the Hello World of Trends, the Golden/Death Cross, it's Trend is Long when long = ma(close, 50) > ma(close, 200) and Short when short = ma(close, 50) < ma(close, 200) .

In [13]:

# Example Long Trends for the selected Asset
# * Uncomment others for exploration or replace them with your own TA Trend Strategy
def trends(df: pd.DataFrame, mamode: str = "sma", fast: int = 50, slow: int = 200):
    return ta.ma(mamode, df.Close, length=fast) > ta.ma(mamode, df.Close, length=slow) # SMA(fast) > SMA(slow) "Golden/Death Cross"
#     return ta.increasing(ta.ma(mamode, df.Close, length=fast)) # Increasing MA(fast)
#     return ta.macd(df.Close, fast, slow).iloc[:,1] > 0 # MACD Histogram is positive

Display Trends

In [14]:

trend_kwargs = {"mamode": "ema", "fast": 20, "slow": 50}

In [15]:

benchmark_trends = trends(benchmarkdf, **trend_kwargs)
benchmark_trends.copy().astype(int).plot(figsize=(16, 1), kind="area", color=["limegreen"], alpha=0.9, title=f"{benchmarkdf.name} Trends", grid=True).axhline(0, color="black")

Out [15]:

<matplotlib.lines.Line2D at 0x1560f43d0>

In [16]:

asset_trends = trends(assetdf, **trend_kwargs)
asset_trends.copy().astype(int).plot(figsize=(16, 1), kind="area", color=["limegreen"], alpha=0.98, title=f"{assetdf.name} Trends", grid=True).axhline(0, color="black")

Out [16]:

<matplotlib.lines.Line2D at 0x1564f0af0>

Trend Signals

Given a Trend, Trend Signals returns the Trend, Trades, Entries and Exits as boolean integers. When asbool=True, it returns Trends, Entries and Exits as boolean values which is helpful when combined with the vectorbt backtesting package.

In [17]:

# trade_offset = 0 for Live Signals (close is last price)
# trade_offset = 1 for Backtesting
LIVE = 0

benchmark_signals = assetdf.ta.tsignals(benchmark_trends, asbool=True, trade_offset=LIVE, append=True)
benchmark_signals.tail()

Out [17]:

	TS_Trends	TS_Trades	TS_Entries	TS_Exits
Date
2009-12-24	True	0	False	False
2009-12-28	True	0	False	False
2009-12-29	True	0	False	False
2009-12-30	True	0	False	False
2009-12-31	True	0	False	False

In [18]:

asset_signals = assetdf.ta.tsignals(asset_trends, asbool=True, trade_offset=LIVE, append=True)
asset_signals.tail()

Out [18]:

	TS_Trends	TS_Trades	TS_Entries	TS_Exits
Date
2009-12-24	True	0	False	False
2009-12-28	True	0	False	False
2009-12-29	True	0	False	False
2009-12-30	True	0	False	False
2009-12-31	True	0	False	False

In [ ]:

Creating vectorbt Portfolios

vectorbt Portfolio Options

Buy 'N Hold Portfolios with their Single Trade and Performance Statistics

In [19]:

# Benchmark Buy and Hold (BnH) Strategy
benchmarkpf_bnh = vbt.Portfolio.from_holding(benchmarkdf.Close)
print(trade_table(benchmarkpf_bnh))
combine_stats(benchmarkpf_bnh, benchmarkdf.name, "Buy and Hold", LIVE)

Out [19]:

Last 1 of 1 Trades
   status  direction         size  entry_price  exit_price    return  \
0       0          0  1153.356031    86.487278   88.390152  0.019502   

           pnl  entry_fees  exit_fees  
0  1945.314835  249.376559        0.0  

None

Run Time                     Friday March 4, 2022, NYSE: 4:13:43
Mode                                                        TEST
Strategy                                            Buy and Hold
Direction                                                   both
Symbol                                                       SPY
Fees [%]                                                    0.25
Slippage [%]                                                0.25
Accumulate                                                 False
Start                                        2005-01-03 00:00:00
End                                          2009-12-31 00:00:00
Period                                        1259 days 00:00:00
Start Value                                             100000.0
End Value                                          101945.314835
Total Return [%]                                        1.945315
Benchmark Return [%]                                    2.455679
Max Gross Exposure [%]                                     100.0
Total Fees Paid                                       249.376559
Max Drawdown [%]                                       55.189444
Max Drawdown Duration                          562 days 00:00:00
Total Trades                                                   1
Total Closed Trades                                            0
Total Open Trades                                              1
Open Trade PnL                                       1945.314835
Sharpe Ratio                                            0.163326
Calmar Ratio                                            0.010149
Omega Ratio                                             1.028442
Sortino Ratio                                           0.231223
Annualized Return [%]                                   0.560119
Annualized Volatility [%]                              28.894491
Skew                                                    0.426808
Kurtosis                                               14.956064
Tail Ratio                                              0.881911
Common Sense Ratio                                      0.886851
Value at Risk                                           -0.02229
Alpha                                                  -0.001443
Beta                                                    1.000002
dtype: object

In [20]:

# Asset Buy and Hold (BnH) Strategy
assetpf_bnh = vbt.Portfolio.from_holding(assetdf.Close)
print(trade_table(assetpf_bnh))
combine_stats(assetpf_bnh, assetdf.name, "Buy and Hold", LIVE)

Out [20]:

Last 1 of 1 Trades
   status  direction           size  entry_price  exit_price    return  \
0       0          0  102818.467805     0.970163     6.44438  5.640078   

             pnl  entry_fees  exit_fees  
0  562601.306688  249.376559        0.0  

None

Run Time                     Friday March 4, 2022, NYSE: 4:13:45
Mode                                                        TEST
Strategy                                            Buy and Hold
Direction                                                   both
Symbol                                                      AAPL
Fees [%]                                                    0.25
Slippage [%]                                                0.25
Accumulate                                                 False
Start                                        2005-01-03 00:00:00
End                                          2009-12-31 00:00:00
Period                                        1259 days 00:00:00
Start Value                                             100000.0
End Value                                          662601.306688
Total Return [%]                                      562.601307
Benchmark Return [%]                                  565.918454
Max Gross Exposure [%]                                     100.0
Total Fees Paid                                       249.376559
Max Drawdown [%]                                       60.866741
Max Drawdown Duration                          456 days 00:00:00
Total Trades                                                   1
Total Closed Trades                                            0
Total Open Trades                                              1
Open Trade PnL                                     562601.306688
Sharpe Ratio                                            1.329387
Calmar Ratio                                            1.199638
Omega Ratio                                             1.209774
Sortino Ratio                                           1.982725
Annualized Return [%]                                  73.018049
Annualized Volatility [%]                              51.088448
Skew                                                   -0.037599
Kurtosis                                                3.435268
Tail Ratio                                              1.037877
Common Sense Ratio                                      1.795715
Value at Risk                                          -0.041247
Alpha                                                   -0.00145
Beta                                                     1.00001
dtype: object

Signal Portfolios with their Last 'k' Trades and Performance Statistics

In [21]:

# Benchmark Portfolio from Trade Signals
benchmarkpf_signals = vbt.Portfolio.from_signals(
    benchmarkdf.Close,
    entries=benchmark_signals.TS_Entries,
    exits=benchmark_signals.TS_Exits,
)
trade_table(benchmarkpf_signals, k=5)
combine_stats(benchmarkpf_signals, benchmarkdf.name, "Long Strategy", LIVE)

Out [21]:

Last 5 of 15 Trades
    status  direction         size  entry_price  exit_price    return  \
10       1          0   795.303054   115.222213  109.017225 -0.058718   
11       1          1   791.336455   109.017225  105.611285  0.026320   
12       1          0   838.303190   105.611285  102.026354 -0.038860   
13       1          1   834.122127   102.026354   63.908901  0.369538   
14       0          0  1822.478518    63.908901   88.390152  0.380565   

             pnl  entry_fees   exit_fees  
10  -5380.691483  229.091444  216.754330  
11   2270.635665  215.673261  208.935151  
12  -3440.418062  221.335694  213.822544  
13  31448.585118  212.756098  133.269571  
14  44325.372865  291.181497    0.000000

Run Time                      Friday March 4, 2022, NYSE: 4:13:47
Mode                                                         TEST
Strategy                                            Long Strategy
Direction                                                    both
Symbol                                                        SPY
Fees [%]                                                     0.25
Slippage [%]                                                 0.25
Accumulate                                                  False
Start                                         2005-01-03 00:00:00
End                                           2009-12-31 00:00:00
Period                                         1259 days 00:00:00
Start Value                                              100000.0
End Value                                           161089.153138
Total Return [%]                                        61.089153
Benchmark Return [%]                                     2.455679
Max Gross Exposure [%]                                      100.0
Total Fees Paid                                       6786.610242
Max Drawdown [%]                                        21.642453
Max Drawdown Duration                           411 days 00:00:00
Total Trades                                                   15
Total Closed Trades                                            14
Total Open Trades                                               1
Open Trade PnL                                       44325.372865
Win Rate [%]                                            35.714286
Best Trade [%]                                            36.9538
Worst Trade [%]                                         -5.871773
Avg Winning Trade [%]                                   10.694638
Avg Losing Trade [%]                                    -3.476755
Avg Winning Trade Duration                      136 days 14:24:00
Avg Losing Trade Duration                        37 days 18:40:00
Profit Factor                                            1.564789
Expectancy                                            1197.412877
Sharpe Ratio                                             0.762856
Calmar Ratio                                             0.684931
Omega Ratio                                              1.124146
Sortino Ratio                                            1.111307
Annualized Return [%]                                   14.823594
Annualized Volatility [%]                               21.015065
Skew                                                      0.01395
Kurtosis                                                  4.71009
Tail Ratio                                               1.023224
Common Sense Ratio                                       1.174903
Value at Risk                                           -0.017307
Alpha                                                    0.191161
Beta                                                    -0.301324
dtype: object

In [22]:

# Asset Portfolio from Trade Signals
assetpf_signals = vbt.Portfolio.from_signals(
    assetdf.Close,
    entries=asset_signals.TS_Entries,
    exits=asset_signals.TS_Exits,
)
trade_table(assetpf_signals, k=5)
combine_stats(assetpf_signals, assetdf.name, "Long Strategy", LIVE)

Out [22]:

Last 5 of 19 Trades
    status  direction          size  entry_price  exit_price    return  \
14       1          0  29355.475854     5.320027    4.917979 -0.080384   
15       1          1  29209.064503     4.917979    3.043388  0.377124   
16       1          0  64956.602912     3.043388    2.652390 -0.133154   
17       1          1  64632.629830     2.652390    3.112366 -0.178853   
18       0          0  45253.806451     3.112366    6.444380  1.068073   

              pnl  entry_fees   exit_fees  
14  -12553.649294  390.429791  360.924052  
15   54173.687923  359.123932  222.236312  
16  -26322.882027  494.220411  430.725571  
17  -30660.967566  428.577314  502.901037  
18  150434.202886  352.116048    0.000000

Run Time                      Friday March 4, 2022, NYSE: 4:13:48
Mode                                                         TEST
Strategy                                            Long Strategy
Direction                                                    both
Symbol                                                       AAPL
Fees [%]                                                     0.25
Slippage [%]                                                 0.25
Accumulate                                                  False
Start                                         2005-01-03 00:00:00
End                                           2009-12-31 00:00:00
Period                                         1259 days 00:00:00
Start Value                                              100000.0
End Value                                           291632.738041
Total Return [%]                                       191.632738
Benchmark Return [%]                                   565.918454
Max Gross Exposure [%]                                      100.0
Total Fees Paid                                      10631.077665
Max Drawdown [%]                                        46.105984
Max Drawdown Duration                           368 days 00:00:00
Total Trades                                                   19
Total Closed Trades                                            18
Total Open Trades                                               1
Open Trade PnL                                      150434.202886
Win Rate [%]                                            33.333333
Best Trade [%]                                          78.771593
Worst Trade [%]                                        -17.885319
Avg Winning Trade [%]                                   34.907941
Avg Losing Trade [%]                                   -10.451635
Avg Winning Trade Duration                      124 days 12:00:00
Avg Losing Trade Duration                        21 days 20:00:00
Profit Factor                                            1.281497
Expectancy                                            2288.807509
Sharpe Ratio                                              0.92613
Calmar Ratio                                             0.789128
Omega Ratio                                              1.141238
Sortino Ratio                                            1.339786
Annualized Return [%]                                   36.383524
Annualized Volatility [%]                               43.942042
Skew                                                    -0.036484
Kurtosis                                                 1.988354
Tail Ratio                                               0.968337
Common Sense Ratio                                       1.320653
Value at Risk                                           -0.038211
Alpha                                                    0.353838
Beta                                                     0.152645
dtype: object

In [ ]:

Buy and Hold Plots

In [23]:

vbt.settings.set_theme("seaborn")

Benchmark

In [24]:

benchmarkpf_bnh.trades.plot(title=f"{benchmarkdf.name} | Trades", height=cheight, width=cwidth).show_png()

In [25]:

benchmarkpf_bnh.value().vbt.plot(title=f"{benchmarkdf.name} | Equity Curve", trace_kwargs=dict(name=u"\u00A4"), height=cheight // 2, width=cwidth).show_png()

In [26]:

benchmarkpf_bnh.drawdown().vbt.plot(title=f"{benchmarkdf.name} | Drawdown", trace_kwargs=dict(name="%"), height=cheight // 2, width=cwidth).show_png()

In [27]:

benchmarkpf_bnh.trades.plot_pnl(title=f"{benchmarkdf.name} | PnL", height=cheight // 2, width=cwidth).show_png()

In [28]:

benchmarkpf_bnh.asset_returns().vbt.plot(title=f"{benchmarkdf.name} | Asset Returns", trace_kwargs=dict(name="%"), height=cheight // 2, width=cwidth).show_png()

In [29]:

benchmarkpf_bnh.cash().vbt.plot(title=f"{benchmarkdf.name} | Cash", trace_kwargs=dict(name=u"\u00A4"), height=cheight // 2, width=cwidth).show_png()

In [30]:

total_assetfees = benchmarkpf_bnh.trades.records_readable["Entry Fees"] + benchmarkpf_bnh.trades.records_readable["Exit Fees"]
total_assetfees.vbt.plot(title=f"{benchmarkdf.name} | Total Fees", trace_kwargs=dict(name=u"\u00A4"), height=cheight // 2, width=cwidth).show_png()

In [ ]:

Asset

In [31]:

assetpf_bnh.trades.plot(title=f"{assetdf.name} | Trades", height=cheight, width=cwidth).show_png()

In [32]:

assetpf_bnh.value().vbt.plot(title=f"{assetdf.name} | Equity Curve", trace_kwargs=dict(name=u"\u00A4"), height=cheight // 2, width=cwidth).show_png()

In [33]:

assetpf_bnh.drawdown().vbt.plot(title=f"{assetdf.name} | Drawdown", trace_kwargs=dict(name="%"), height=cheight // 2, width=cwidth).show_png()

In [34]:

assetpf_bnh.trades.plot_pnl(title=f"{assetdf.name} | PnL", height=cheight // 2, width=cwidth).show_png()

In [35]:

assetpf_bnh.asset_returns().vbt.plot(title=f"{assetdf.name} | Asset Returns", trace_kwargs=dict(name="%"), height=cheight // 2, width=cwidth).show_png()

In [36]:

assetpf_bnh.cash().vbt.plot(title=f"{assetdf.name} | Cash", trace_kwargs=dict(name=u"\u00A4"), height=cheight // 2, width=cwidth).show_png()

In [37]:

total_assetfees = assetpf_bnh.trades.records_readable["Entry Fees"] + assetpf_bnh.trades.records_readable["Exit Fees"]
total_assetfees.vbt.plot(title=f"{assetdf.name} | Total Fees", trace_kwargs=dict(name=u"\u00A4"), height=cheight // 2, width=cwidth).show_png()

In [ ]:

Signal Plots

In [38]:

vbt.settings.set_theme("dark")

Benchmark

In [39]:

benchmarkpf_signals.trades.plot(title=f"{benchmarkdf.name} | Trades", height=cheight, width=cwidth).show_png()

In [40]:

benchmarkpf_signals.value().vbt.plot(title=f"{benchmarkdf.name} | Equity Curve", trace_kwargs=dict(name=u"\u00A4"), height=cheight // 2, width=cwidth).show_png()

In [41]:

benchmarkpf_signals.drawdown().vbt.plot(title=f"{benchmarkdf.name} | Drawdown", trace_kwargs=dict(name="%"), height=cheight // 2, width=cwidth).show_png()

In [42]:

benchmarkpf_signals.trades.plot_pnl(title=f"{benchmarkdf.name} | PnL", height=cheight // 2, width=cwidth).show_png()

In [43]:

benchmarkpf_signals.asset_returns().vbt.plot(title=f"{benchmarkdf.name} | Asset Returns", trace_kwargs=dict(name="%"), height=cheight // 2, width=cwidth).show_png()

In [44]:

benchmarkpf_signals.cash().vbt.plot(title=f"{benchmarkdf.name} | Cash", trace_kwargs=dict(name=u"\u00A4"), height=cheight // 2, width=cwidth).show_png()

In [45]:

total_assetfees = benchmarkpf_signals.trades.records_readable["Entry Fees"] + benchmarkpf_signals.trades.records_readable["Exit Fees"]
total_assetfees.vbt.plot(title=f"{benchmarkdf.name} | Total Fees", trace_kwargs=dict(name=u"\u00A4"), height=cheight // 2, width=cwidth).show_png()

In [ ]:

Asset

In [46]:

assetpf_signals.trades.plot(title=f"{assetdf.name} | Trades", height=cheight, width=cwidth).show_png()

In [47]:

assetpf_signals.value().vbt.plot(title=f"{assetdf.name} | Equity Curve", trace_kwargs=dict(name=u"\u00A4"), height=cheight // 2, width=cwidth).show_png()

In [48]:

assetpf_signals.drawdown().vbt.plot(title=f"{assetdf.name} | Drawdown", trace_kwargs=dict(name="%"), height=cheight // 2, width=cwidth).show_png()

In [49]:

assetpf_signals.trades.plot_pnl(title=f"{assetdf.name} | PnL", height=cheight // 2, width=cwidth).show_png()

In [50]:

assetpf_signals.asset_returns().vbt.plot(title=f"{assetdf.name} | Asset Returns", trace_kwargs=dict(name="%"), height=cheight // 2, width=cwidth).show_png()

In [51]:

assetpf_signals.cash().vbt.plot(title=f"{assetdf.name} | Cash", trace_kwargs=dict(name=u"\u00A4"), height=cheight // 2, width=cwidth).show_png()

In [52]:

total_assetfees = assetpf_signals.trades.records_readable["Entry Fees"] + assetpf_signals.trades.records_readable["Exit Fees"]
total_assetfees.vbt.plot(title=f"{assetdf.name} | Total Fees", trace_kwargs=dict(name=u"\u00A4"), height=cheight // 2, width=cwidth).show_png()

Disclaimer

All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, or individual’s trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Any opinions, news, research, analyses, prices, or other information offered is provided as general market commentary, and does not constitute investment advice. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from use of or reliance on such information.

1.4 MiB Raw Blame History

Backtesting with vectorbt

Initializations

vectorbt Theme and Portfolio Settings

Helper Methods

Data Acquisition

Specify Symbols for Benchmarks and Assets

Define Testing Dates and Ranges

Select and Benchmark and Asset to Backtest

Sanity Check

Creating Trading Signals for vectorbt

Trend Creation

Display Trends

Trend Signals

Creating vectorbt Portfolios

Buy 'N Hold Portfolios with their Single Trade and Performance Statistics

Signal Portfolios with their Last 'k' Trades and Performance Statistics

Buy and Hold Plots

Benchmark

Asset

Signal Plots

Benchmark

Asset

Disclaimer

1.4 MiB

Raw Blame History