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 v0.3.48b0
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(7324, 7): 3052.9428 ms (3.0529 s)
[+] Monday February 28, 2022, NYSE: 6:52:24
[+] yf | QQQ(5782, 7): 3106.3748 ms (3.1064 s)
[+] Monday February 28, 2022, NYSE: 6:52:27
[*] Download Complete

In [7]:

assets = dl(asset_tickers, timed=True)

[i] Downloading: AAPL, TSLA, TWTR
[+] yf | AAPL(10391, 7): 2772.3595 ms (2.7724 s)
[+] Monday February 28, 2022, NYSE: 6:52:30
[+] yf | TSLA(2938, 7): 2678.0592 ms (2.6781 s)
[+] Monday February 28, 2022, NYSE: 6:52:32
[+] yf | TWTR(2091, 7): 2632.2783 ms (2.6323 s)
[+] Monday February 28, 2022, NYSE: 6:52:35
[*] 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	87.175203	87.318634	85.984758	86.271614	55748000	0.0	0
2005-01-04	86.386354	86.443727	84.937739	85.217422	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	85.317788	85.504245	84.715390	84.937706	55847700	0.0	0
...	...	...	...	...	...	...	...
2009-12-24	88.985001	89.310195	88.834298	89.215019	39677500	0.0	0
2009-12-28	89.548142	89.619524	89.088106	89.405373	87508500	0.0	0
2009-12-29	89.635408	89.651268	89.270553	89.278481	80572500	0.0	0
2009-12-30	89.016723	89.349850	88.969129	89.246735	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	6.224807	6.402178	6.218690	6.392697	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	6.505238	6.383218	6.394533	445205600	0.0	0.0
2009-12-30	6.386275	6.483218	6.370374	6.472208	412084400	0.0	0.0
2009-12-31	6.517775	6.524503	6.439181	6.444379	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": "sma", "fast": 50, "slow": 200}

In [15]:

benchmark_trends = trends(benchmarkdf, **trend_kwargs)
benchmark_trends.copy().astype(int).plot(figsize=(16, 1), kind="area", color=["green"], alpha=0.45, title=f"{benchmarkdf.name} Trends", grid=True).axhline(0, color="black")

Out [15]:

<matplotlib.lines.Line2D at 0x159f8feb0>

In [16]:

asset_trends = trends(assetdf, **trend_kwargs)
asset_trends.copy().astype(int).plot(figsize=(16, 1), kind="area", color=["green"], alpha=0.45, title=f"{assetdf.name} Trends", grid=True).axhline(0, color="black")

Out [16]:

<matplotlib.lines.Line2D at 0x15a53cc10>

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  1153.355827    86.487293   88.390152  0.019502   

           pnl  entry_fees  exit_fees  
0  1945.296804  249.376559        0.0  

None

Run Time                     Monday February 28, 2022, NYSE: 6:52:38
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                                              101945.296804
Total Return [%]                                            1.945297
Benchmark Return [%]                                         2.45566
Max Gross Exposure [%]                                         100.0
Total Fees Paid                                           249.376559
Max Drawdown [%]                                           55.189437
Max Drawdown Duration                              562 days 00:00:00
Total Trades                                                       1
Total Closed Trades                                                0
Total Open Trades                                                  1
Open Trade PnL                                           1945.296804
Sharpe Ratio                                                0.163326
Calmar Ratio                                                0.010149
Omega Ratio                                                 1.028442
Sortino Ratio                                               0.231223
Annualized Return [%]                                       0.560114
Annualized Volatility [%]                                  28.894478
Skew                                                        0.426805
Kurtosis                                                   14.956085
Tail Ratio                                                  0.881923
Common Sense Ratio                                          0.886862
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    6.444379  5.640077   

             pnl  entry_fees  exit_fees  
0  562601.208633  249.376559        0.0  

None

Run Time                     Monday February 28, 2022, NYSE: 6:52:40
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                                              662601.208633
Total Return [%]                                          562.601209
Benchmark Return [%]                                      565.918356
Max Gross Exposure [%]                                         100.0
Total Fees Paid                                           249.376559
Max Drawdown [%]                                           60.866749
Max Drawdown Duration                              456 days 00:00:00
Total Trades                                                       1
Total Closed Trades                                                0
Total Open Trades                                                  1
Open Trade PnL                                         562601.208633
Sharpe Ratio                                                1.329387
Calmar Ratio                                                1.199638
Omega Ratio                                                 1.209774
Sortino Ratio                                               1.982724
Annualized Return [%]                                      73.018041
Annualized Volatility [%]                                  51.088461
Skew                                                       -0.037601
Kurtosis                                                    3.435281
Tail Ratio                                                  1.037875
Common Sense Ratio                                          1.795712
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 5 Trades
   status  direction         size  entry_price  exit_price    return  \
0       1          0  1150.758533    86.682497   92.979310  0.067461   
1       1          1  1145.019089    92.979310   96.337426 -0.041207   
2       1          0  1059.681526    96.337426  111.226836  0.149168   
3       1          1  1054.396331   111.226836   72.182241  0.346913   
4       0          0  2186.974521    72.182241   88.390152  0.222042   

            pnl  entry_fees   exit_fees  
0   6729.243061  249.376559  267.491837  
1  -4387.034807  266.157713  275.770480  
2  15228.152450  255.217477  294.662558  
3  40685.013050  293.192919  190.271725  
4  35051.636542  394.651805    0.000000

Run Time                      Monday February 28, 2022, NYSE: 6:52:43
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                                               193307.010295
Total Return [%]                                             93.30701
Benchmark Return [%]                                          2.45566
Max Gross Exposure [%]                                          100.0
Total Fees Paid                                           2486.793074
Max Drawdown [%]                                            15.563698
Max Drawdown Duration                               152 days 00:00:00
Total Trades                                                        5
Total Closed Trades                                                 4
Total Open Trades                                                   1
Open Trade PnL                                           35051.636542
Win Rate [%]                                                     75.0
Best Trade [%]                                              34.691333
Worst Trade [%]                                              -4.12071
Avg Winning Trade [%]                                       18.784746
Avg Losing Trade [%]                                         -4.12071
Avg Winning Trade Duration                          299 days 08:00:00
Avg Losing Trade Duration                            25 days 00:00:00
Profit Factor                                               14.278986
Expectancy                                               14563.843438
Sharpe Ratio                                                  1.12802
Calmar Ratio                                                   1.3529
Omega Ratio                                                  1.199806
Sortino Ratio                                                1.653531
Annualized Return [%]                                       21.056133
Annualized Volatility [%]                                   18.452612
Skew                                                        -0.067298
Kurtosis                                                     4.864552
Tail Ratio                                                   1.074076
Common Sense Ratio                                           1.300235
Value at Risk                                               -0.015183
Alpha                                                        0.244574
Beta                                                         -0.22021
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 7 Trades
   status  direction          size  entry_price  exit_price    return  \
2       1          0  31274.957324     2.322318    3.688943  0.582004   
3       1          1  31118.972499     3.688943    5.751992 -0.565650   
4       1          0   8696.749673     5.751992    3.869225 -0.331506   
5       1          1   8653.374363     3.869225    3.663286  0.048358   
6       0          0   9580.725397     3.663286    6.444379  0.756680   

            pnl  entry_fees   exit_fees  
2  42271.144392  181.575988  288.428861  
3 -64934.425122  286.990314  447.490175  
4 -16583.130130  125.059079   84.124212  
5   1619.117766   83.704640   79.249460  
6  26557.150475   87.742340    0.000000

Run Time                      Monday February 28, 2022, NYSE: 6:52:44
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                                                61741.828711
Total Return [%]                                           -38.258171
Benchmark Return [%]                                       565.918356
Max Gross Exposure [%]                                          100.0
Total Fees Paid                                           2801.436059
Max Drawdown [%]                                            83.338162
Max Drawdown Duration                               506 days 00:00:00
Total Trades                                                        7
Total Closed Trades                                                 6
Total Open Trades                                                   1
Open Trade PnL                                           26557.150475
Win Rate [%]                                                     50.0
Best Trade [%]                                               58.20035
Worst Trade [%]                                            -56.564997
Avg Winning Trade [%]                                       23.415944
Avg Losing Trade [%]                                       -40.623387
Avg Winning Trade Duration                          231 days 00:00:00
Avg Losing Trade Duration                            68 days 08:00:00
Profit Factor                                                0.440762
Expectancy                                              -10802.553627
Sharpe Ratio                                                -0.071063
Calmar Ratio                                                -0.156551
Omega Ratio                                                  0.988973
Sortino Ratio                                               -0.099878
Annualized Return [%]                                      -13.046644
Annualized Volatility [%]                                   46.262956
Skew                                                          0.15088
Kurtosis                                                     4.766459
Tail Ratio                                                   0.945769
Common Sense Ratio                                           0.822378
Value at Risk                                               -0.039647
Alpha                                                       -0.137602
Beta                                                         0.169161
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.

1.3 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

1.3 MiB

Raw Blame History