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 v0.3.63b0
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(7367, 7): 3810.4675 ms (3.8105 s)
[+] Sunday May 1, 2022, NYSE: 14:17:10
[+] yf | QQQ(5825, 7): 3371.7531 ms (3.3718 s)
[+] Sunday May 1, 2022, NYSE: 14:17:14
[*] Download Complete

In [7]:

assets = dl(asset_tickers, timed=True)

[i] Downloading: AAPL, TSLA, TWTR
[+] yf | AAPL(10434, 7): 3390.6886 ms (3.3907 s)
[+] Sunday May 1, 2022, NYSE: 14:17:17
[+] yf | TSLA(2981, 7): 3219.1295 ms (3.2191 s)
[+] Sunday May 1, 2022, NYSE: 14:17:20
[+] yf | TWTR(2134, 7): 3016.2403 ms (3.0162 s)
[+] Sunday May 1, 2022, NYSE: 14:17:23
[*] 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	86.905223	87.048210	85.718465	86.004433	55748000	0.0	0
2005-01-04	86.118795	86.175989	84.674667	84.953484	69167600	0.0	0
2005-01-05	84.889173	85.253781	84.360136	84.367287	65667300	0.0	0
2005-01-06	84.674677	85.182267	84.545992	84.796211	47814700	0.0	0
2005-01-07	85.053580	85.239459	84.453047	84.674675	55847700	0.0	0
...	...	...	...	...	...	...	...
2009-12-24	88.709400	89.033586	88.559164	88.938705	39677500	0.0	0
2009-12-28	89.270845	89.342005	88.812233	89.128517	87508500	0.0	0
2009-12-29	89.357828	89.373640	88.994104	89.002007	80572500	0.0	0
2009-12-30	88.741052	89.073148	88.693606	88.970352	73138400	0.0	0
2009-12-31	89.168022	89.191748	88.076849	88.116386	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	0.962850	0.977682	1096810400	0.0	0.0
2005-01-05	0.985632	0.997713	0.979363	0.986244	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	6.224809	6.402180	6.218692	6.392699	500889200	0.0	0.0
2009-12-28	6.474657	6.542852	6.410130	6.471292	644565600	0.0	0.0
2009-12-29	6.502485	6.505237	6.383218	6.394533	445205600	0.0	0.0
2009-12-30	6.386275	6.483218	6.370373	6.472208	412084400	0.0	0.0
2009-12-31	6.517777	6.524505	6.439183	6.444382	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 0x156c5ab50>

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 0x157201400>

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]:

tsignals

	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]:

tsignals

	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  1156.938842    86.219444   88.116386  0.019501   

           pnl  entry_fees  exit_fees  
0  1945.270079  249.376559        0.0  

None

Run Time                     Sunday May 1, 2022, NYSE: 14:17:27
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                                         101945.270079
Total Return [%]                                        1.94527
Benchmark Return [%]                                   2.455634
Max Gross Exposure [%]                                    100.0
Total Fees Paid                                      249.376559
Max Drawdown [%]                                      55.189427
Max Drawdown Duration                         562 days 00:00:00
Total Trades                                                  1
Total Closed Trades                                           0
Total Open Trades                                             1
Open Trade PnL                                      1945.270079
Sharpe Ratio                                           0.163325
Calmar Ratio                                           0.010149
Omega Ratio                                            1.028442
Sortino Ratio                                          0.231223
Annualized Return [%]                                  0.560106
Annualized Volatility [%]                             28.894497
Skew                                                   0.426797
Kurtosis                                              14.955984
Tail Ratio                                             0.881903
Common Sense Ratio                                     0.886842
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  102818.493136     0.970162    6.444382  5.640081   

             pnl  entry_fees  exit_fees  
0  562601.617014  249.376559        0.0  

None

Run Time                     Sunday May 1, 2022, NYSE: 14:17:28
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                                         662601.617014
Total Return [%]                                     562.601617
Benchmark Return [%]                                 565.918766
Max Gross Exposure [%]                                    100.0
Total Fees Paid                                      249.376559
Max Drawdown [%]                                      60.866735
Max Drawdown Duration                         456 days 00:00:00
Total Trades                                                  1
Total Closed Trades                                           0
Total Open Trades                                             1
Open Trade PnL                                    562601.617014
Sharpe Ratio                                           1.329387
Calmar Ratio                                           1.199638
Omega Ratio                                            1.209774
Sortino Ratio                                          1.982725
Annualized Return [%]                                 73.018072
Annualized Volatility [%]                             51.088458
Skew                                                  -0.037599
Kurtosis                                                3.43526
Tail Ratio                                              1.03789
Common Sense Ratio                                     1.795738
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   797.770311   114.865419  108.679631 -0.058718   
11       1          1   793.791407   108.679631  105.284168  0.026321   
12       1          0   840.904944   105.284168  101.710388 -0.038859   
13       1          1   836.710904   101.710388   63.711012  0.369538   
14       0          0  1828.133472    63.711012   88.116386  0.380564   

             pnl  entry_fees   exit_fees  
10  -5380.682196  229.090553  216.753458  
11   2270.682765  215.672393  208.934170  
12  -3440.366007  221.334944  213.821921  
13  31448.467750  212.755477  133.269246  
14  44325.101312  291.180584    0.000000

Run Time                      Sunday May 1, 2022, NYSE: 14:17:31
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                                          161088.515438
Total Return [%]                                       61.088515
Benchmark Return [%]                                    2.455634
Max Gross Exposure [%]                                     100.0
Total Fees Paid                                      6786.593906
Max Drawdown [%]                                       21.642411
Max Drawdown Duration                          411 days 00:00:00
Total Trades                                                  15
Total Closed Trades                                           14
Total Open Trades                                              1
Open Trade PnL                                      44325.101312
Win Rate [%]                                           35.714286
Best Trade [%]                                          36.95377
Worst Trade [%]                                        -5.871785
Avg Winning Trade [%]                                  10.694623
Avg Losing Trade [%]                                   -3.476782
Avg Winning Trade Duration                     136 days 14:24:00
Avg Losing Trade Duration                       37 days 18:40:00
Profit Factor                                           1.564774
Expectancy                                           1197.386723
Sharpe Ratio                                             0.76285
Calmar Ratio                                            0.684927
Omega Ratio                                             1.124145
Sortino Ratio                                           1.111297
Annualized Return [%]                                  14.823462
Annualized Volatility [%]                              21.015086
Skew                                                    0.013949
Kurtosis                                                4.710068
Tail Ratio                                              1.023206
Common Sense Ratio                                      1.174881
Value at Risk                                          -0.017307
Alpha                                                   0.191159
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  29355.566042     5.320024    4.917979 -0.080383   
15       1          1  29209.154242     4.917979    3.043387  0.377124   
16       1          0  64956.830347     3.043387    2.652390 -0.133153   
17       1          1  64632.856130     2.652390    3.112366 -0.178853   
18       0          0  45253.946608     3.112366    6.444382  1.068073   

              pnl  entry_fees   exit_fees  
14  -12553.631451  390.430815  360.925126  
15   54173.875496  359.125001  222.236907  
16  -26322.911779  494.221947  430.727040  
17  -30661.105740  428.578776  502.902836  
18  150434.722694  352.117165    0.000000

Run Time                      Sunday May 1, 2022, NYSE: 14:17:32
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                                          291633.706004
Total Return [%]                                      191.633706
Benchmark Return [%]                                  565.918766
Max Gross Exposure [%]                                     100.0
Total Fees Paid                                     10631.091236
Max Drawdown [%]                                       46.106062
Max Drawdown Duration                          368 days 00:00:00
Total Trades                                                  19
Total Closed Trades                                           18
Total Open Trades                                              1
Open Trade PnL                                     150434.722694
Win Rate [%]                                           33.333333
Best Trade [%]                                         78.771666
Worst Trade [%]                                       -17.885338
Avg Winning Trade [%]                                  34.907989
Avg Losing Trade [%]                                  -10.451628
Avg Winning Trade Duration                     124 days 12:00:00
Avg Losing Trade Duration                       21 days 20:00:00
Profit Factor                                             1.2815
Expectancy                                           2288.832406
Sharpe Ratio                                            0.926132
Calmar Ratio                                             0.78913
Omega Ratio                                             1.141239
Sortino Ratio                                           1.339789
Annualized Return [%]                                  36.383655
Annualized Volatility [%]                              43.942062
Skew                                                   -0.036486
Kurtosis                                                1.988356
Tail Ratio                                              0.968328
Common Sense Ratio                                      1.320641
Value at Risk                                          -0.038211
Alpha                                                   0.353838
Beta                                                    0.152646
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.

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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

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