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 v0.3.32b0
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
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

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	100.0
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	longonly
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"[+] {ticker}{received[ticker].shape} {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, lc_cols=True)

[i] Downloading: SPY, QQQ
[+] SPY(7295, 7) Friday January 14, 2022, NYSE: 11:33:25
[+] QQQ(5753, 7) Friday January 14, 2022, NYSE: 11:33:29
[*] Download Complete

In [7]:

assets = dl(asset_tickers, lc_cols=True)

[i] Downloading: AAPL, TSLA, TWTR
[+] AAPL(10362, 7) Friday January 14, 2022, NYSE: 11:33:32
[+] TSLA(2909, 7) Friday January 14, 2022, NYSE: 11:33:35
[+] TWTR(2062, 7) Friday January 14, 2022, NYSE: 11:33:38
[*] 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	85.152894	85.518636	84.622214	84.629387	65667300	0.0	0
2005-01-06	84.937719	85.446886	84.808634	85.059631	47814700	0.0	0
2005-01-07	85.317818	85.504276	84.715420	84.937737	55847700	0.0	0
...	...	...	...	...	...	...	...
2009-12-24	88.985024	89.310218	88.834321	89.215042	39677500	0.0	0
2009-12-28	89.548135	89.619516	89.088098	89.405365	87508500	0.0	0
2009-12-29	89.635392	89.651253	89.270538	89.278465	80572500	0.0	0
2009-12-30	89.016738	89.349865	88.969144	89.246750	73138400	0.0	0
2009-12-31	89.445032	89.468832	88.350468	88.390129	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	0.991788	0.996840	0.958412	0.968976	691992000	0.0	0.0
2005-01-04	0.976631	1.002352	0.964076	0.978928	1096810400	0.0	0.0
2005-01-05	0.986888	0.998984	0.980611	0.987501	680433600	0.0	0.0
2005-01-06	0.990103	0.993778	0.969588	0.988267	705555200	0.0	0.0
2005-01-07	0.995156	1.066042	0.991329	1.060224	2227450400	0.0	0.0
...	...	...	...	...	...	...	...
2009-12-24	6.232738	6.410335	6.226613	6.400842	500889200	0.0	0.0
2009-12-28	6.482907	6.551189	6.418297	6.479538	644565600	0.0	0.0
2009-12-29	6.510770	6.513525	6.391351	6.402680	445205600	0.0	0.0
2009-12-30	6.394413	6.491479	6.378491	6.480455	412084400	0.0	0.0
2009-12-31	6.526079	6.532816	6.447385	6.452590	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 can you define 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 0x152aed490>

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 0x153059730>

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      pnl  \
0       0          0  1.153356    86.487293   88.390129  0.019501  1.94527   

   entry_fees  exit_fees  
0    0.249377        0.0  

None

Run Time                     Friday January 14, 2022, NYSE: 11:33:41
Mode                                                            TEST
Strategy                                                Buy and Hold
Direction                                                   longonly
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                                                    100.0
End Value                                                  101.94527
Total Return [%]                                             1.94527
Benchmark Return [%]                                        2.455634
Max Gross Exposure [%]                                         100.0
Total Fees Paid                                             0.249377
Max Drawdown [%]                                           55.189454
Max Drawdown Duration                              562 days 00:00:00
Total Trades                                                       1
Total Closed Trades                                                0
Total Open Trades                                                  1
Open Trade PnL                                               1.94527
Sharpe Ratio                                                0.163325
Calmar Ratio                                                0.010149
Omega Ratio                                                 1.028442
Sortino Ratio                                               0.231223
Annualized Return [%]                                       0.560106
Annualized Volatility [%]                                  28.894472
Skew                                                        0.426806
Kurtosis                                                   14.956114
Tail Ratio                                                  0.881898
Common Sense Ratio                                          0.886838
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  102.687642     0.971399     6.45259  5.640078   

          pnl  entry_fees  exit_fees  
0  562.601299    0.249377        0.0  

None

Run Time                     Friday January 14, 2022, NYSE: 11:33:43
Mode                                                            TEST
Strategy                                                Buy and Hold
Direction                                                   longonly
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                                                    100.0
End Value                                                 662.601299
Total Return [%]                                          562.601299
Benchmark Return [%]                                      565.918447
Max Gross Exposure [%]                                         100.0
Total Fees Paid                                             0.249377
Max Drawdown [%]                                           60.866739
Max Drawdown Duration                              456 days 00:00:00
Total Trades                                                       1
Total Closed Trades                                                0
Total Open Trades                                                  1
Open Trade PnL                                            562.601299
Sharpe Ratio                                                1.329387
Calmar Ratio                                                1.199638
Omega Ratio                                                 1.209774
Sortino Ratio                                               1.982725
Annualized Return [%]                                      73.018048
Annualized Volatility [%]                                  51.088456
Skew                                                         -0.0376
Kurtosis                                                    3.435277
Tail Ratio                                                  1.037886
Common Sense Ratio                                           1.79573
Value at Risk                                              -0.041246
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 3 Trades
   status  direction      size  entry_price  exit_price    return        pnl  \
0       1          0  1.150758    86.682513   92.979295  0.067460   6.729207   
1       1          0  1.105106    96.337411  111.226828  0.149168  15.880933   
2       0          0  1.694383    72.182249   88.390129  0.222041  27.156597   

   entry_fees  exit_fees  
0    0.249377   0.267492  
1    0.266158   0.307294  
2    0.305761   0.000000

Run Time                      Friday January 14, 2022, NYSE: 11:33:45
Mode                                                             TEST
Strategy                                                Long Strategy
Direction                                                    longonly
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                                                     100.0
End Value                                                  149.766737
Total Return [%]                                            49.766737
Benchmark Return [%]                                         2.455634
Max Gross Exposure [%]                                          100.0
Total Fees Paid                                               1.39608
Max Drawdown [%]                                            10.131828
Max Drawdown Duration                               450 days 00:00:00
Total Trades                                                        3
Total Closed Trades                                                 2
Total Open Trades                                                   1
Open Trade PnL                                              27.156597
Win Rate [%]                                                    100.0
Best Trade [%]                                               14.91685
Worst Trade [%]                                               6.74603
Avg Winning Trade [%]                                        10.83144
Avg Winning Trade Duration                          264 days 00:00:00
Profit Factor                                                     inf
Expectancy                                                   11.30507
Sharpe Ratio                                                 1.009581
Calmar Ratio                                                 1.226135
Omega Ratio                                                  1.224227
Sortino Ratio                                                1.431913
Annualized Return [%]                                        12.42299
Annualized Volatility [%]                                    12.35747
Skew                                                        -0.412931
Kurtosis                                                     6.272027
Tail Ratio                                                   1.032312
Common Sense Ratio                                           1.160556
Value at Risk                                               -0.010296
Alpha                                                        0.122837
Beta                                                         0.182611
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 4 Trades
   status  direction       size  entry_price  exit_price    return        pnl  \
0       1          0  60.807495     1.640433    1.767255  0.072117   7.193685   
1       1          0  45.984334     2.325278    3.693643  0.582002  62.231413   
2       1          0  29.344183     5.759322    3.874154 -0.331506 -56.025416   
3       0          0  30.839251     3.667952    6.452590  0.756681  85.593376   

   entry_fees  exit_fees  
0    0.249377   0.268656  
1    0.267316   0.424624  
2    0.422506   0.284210  
3    0.282792   0.000000

Run Time                      Friday January 14, 2022, NYSE: 11:33:46
Mode                                                             TEST
Strategy                                                Long Strategy
Direction                                                    longonly
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                                                     100.0
End Value                                                  198.993058
Total Return [%]                                            98.993058
Benchmark Return [%]                                       565.918447
Max Gross Exposure [%]                                          100.0
Total Fees Paid                                              2.199481
Max Drawdown [%]                                            59.898126
Max Drawdown Duration                               506 days 00:00:00
Total Trades                                                        4
Total Closed Trades                                                 3
Total Open Trades                                                   1
Open Trade PnL                                              85.593376
Win Rate [%]                                                66.666667
Best Trade [%]                                              58.200249
Worst Trade [%]                                            -33.150625
Avg Winning Trade [%]                                       32.705959
Avg Losing Trade [%]                                       -33.150625
Avg Winning Trade Duration                          266 days 12:00:00
Avg Losing Trade Duration                            89 days 00:00:00
Profit Factor                                                1.239171
Expectancy                                                   4.466561
Sharpe Ratio                                                 0.739016
Calmar Ratio                                                  0.36859
Omega Ratio                                                  1.140899
Sortino Ratio                                                 1.08289
Annualized Return [%]                                       22.077855
Annualized Volatility [%]                                   35.543975
Skew                                                         0.024379
Kurtosis                                                     4.672308
Tail Ratio                                                   1.095477
Common Sense Ratio                                           1.337335
Value at Risk                                               -0.029217
Alpha                                                       -0.063652
Beta                                                         0.482566
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()

In [ ]:

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