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 v1.21.1
Pandas v1.3.0
vectorbt v0.20.0

Pandas TA v0.3.17b0
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
lock_cash	False
allow_partial	False
raise_reject	False
close_first	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
stop_exit_mode	close
stop_update_mode	override
use_stops	None
log	False
conflict_mode	ignore
signal_direction	longonly
order_direction	all
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
use_numba	True
seed	None
freq	1D
fillna_close	True
stats	{'filters': {'has_year_freq': {'filter_func': ...
plot	{'subplots': ['orders', 'trade_returns', 'cum_...

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(7181, 7) Wednesday August 4, 2021, NYSE: 9:43:24
[+] QQQ(5639, 7) Wednesday August 4, 2021, NYSE: 9:43:27
[*] Download Complete

In [7]:

assets = dl(asset_tickers, lc_cols=True)

[i] Downloading: AAPL, TSLA, TWTR
[+] AAPL(10248, 7) Wednesday August 4, 2021, NYSE: 9:43:30
[+] TSLA(2795, 7) Wednesday August 4, 2021, NYSE: 9:43:32
[+] TWTR(1948, 7) Wednesday August 4, 2021, NYSE: 9:43:35
[*] 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	87.761711	87.906107	86.563257	86.852043	55748000	0.0	0
2005-01-04	86.967560	87.025318	85.509199	85.790764	69167600	0.0	0
2005-01-05	85.725790	86.093993	85.191540	85.198761	65667300	0.0	0
2005-01-06	85.509188	86.021780	85.379234	85.631920	47814700	0.0	0
2005-01-07	85.891825	86.079536	85.285374	85.509186	55847700	0.0	0
...	...	...	...	...	...	...	...
2009-12-24	89.583749	89.911131	89.432032	89.815315	39677500	0.0	0
2009-12-28	90.150658	90.222520	89.687526	90.006927	87508500	0.0	0
2009-12-29	90.238441	90.254408	89.871132	89.879112	80572500	0.0	0
2009-12-30	89.615662	89.951030	89.567748	89.847221	73138400	0.0	0
2009-12-31	90.046848	90.070809	88.944921	88.984848	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	0.994723	0.999791	0.961248	0.971844	691992000	0.0	0.0
2005-01-04	0.979521	1.005318	0.966930	0.981825	1096810400	0.0	0.0
2005-01-05	0.989809	1.001940	0.983514	0.990424	680433600	0.0	0.0
2005-01-06	0.993034	0.996720	0.972458	0.991192	705555200	0.0	0.0
2005-01-07	0.998101	1.069197	0.994263	1.063362	2227450400	0.0	0.0
...	...	...	...	...	...	...	...
2009-12-24	6.251186	6.429309	6.245044	6.419788	500889200	0.0	0.0
2009-12-28	6.502094	6.570578	6.437294	6.498715	644565600	0.0	0.0
2009-12-29	6.530040	6.532804	6.410268	6.421631	445205600	0.0	0.0
2009-12-30	6.413338	6.510692	6.397369	6.499636	412084400	0.0	0.0
2009-12-31	6.545397	6.552154	6.466470	6.471691	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)

Out [15]:

<AxesSubplot:title={'center':'SPY Trends'}, xlabel='date'>

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)

Out [16]:

<AxesSubplot:title={'center':'AAPL Trends'}, xlabel='date'>

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  1.145648    87.069173   88.984848  0.019502  1.945312   

   entry_fees  exit_fees  
0    0.249377        0.0  

None

/Users/kj/Documents/GitHub/pandas-ta/env/lib/python3.9/site-packages/vectorbt/portfolio/trades.py:408: RuntimeWarning: invalid value encountered in true_divide
  return self.wrapper.wrap_reduced(win_count / total_count, group_by=group_by, **wrap_kwargs)
/Users/kj/Documents/GitHub/pandas-ta/env/lib/python3.9/site-packages/vectorbt/portfolio/trades.py:447: RuntimeWarning: invalid value encountered in true_divide
  profit_factor = total_win / np.abs(total_loss)
/Users/kj/Documents/GitHub/pandas-ta/env/lib/python3.9/site-packages/vectorbt/portfolio/trades.py:408: RuntimeWarning: invalid value encountered in true_divide
  return self.wrapper.wrap_reduced(win_count / total_count, group_by=group_by, **wrap_kwargs)

Run Time                     Wednesday August 4, 2021, NYSE: 9:43:35
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                                                 101.945312
Total Return [%]                                            1.945312
Benchmark Return [%]                                        2.455676
Max Gross Exposure [%]                                         100.0
Total Fees Paid                                             0.249377
Max Drawdown [%]                                           55.189433
Max Drawdown Duration                              562 days 00:00:00
Total Trades                                                       1
Total Closed Trades                                                0
Total Open Trades                                                  1
Open Trade P&L                                              1.945312
Sharpe Ratio                                                0.163326
Calmar Ratio                                                0.010149
Omega Ratio                                                 1.028442
Sortino Ratio                                               0.231223
Annualized Return [%]                                       0.560118
Annualized Volatility [%]                                  28.894487
Skew                                                        0.426802
Kurtosis                                                   14.956002
Tail Ratio                                                  0.881919
Common Sense Ratio                                          0.886859
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  102.384595     0.974274    6.471691  5.640079   

         pnl  entry_fees  exit_fees  
0  562.60143    0.249377        0.0  

None

/Users/kj/Documents/GitHub/pandas-ta/env/lib/python3.9/site-packages/vectorbt/portfolio/trades.py:408: RuntimeWarning: invalid value encountered in true_divide
  return self.wrapper.wrap_reduced(win_count / total_count, group_by=group_by, **wrap_kwargs)
/Users/kj/Documents/GitHub/pandas-ta/env/lib/python3.9/site-packages/vectorbt/portfolio/trades.py:447: RuntimeWarning: invalid value encountered in true_divide
  profit_factor = total_win / np.abs(total_loss)

Run Time                     Wednesday August 4, 2021, NYSE: 9:43:37
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                                                  662.60143
Total Return [%]                                           562.60143
Benchmark Return [%]                                      565.918578
Max Gross Exposure [%]                                         100.0
Total Fees Paid                                             0.249377
Max Drawdown [%]                                           60.866748
Max Drawdown Duration                              457 days 00:00:00
Total Trades                                                       1
Total Closed Trades                                                0
Total Open Trades                                                  1
Open Trade P&L                                             562.60143
Sharpe Ratio                                                1.329387
Calmar Ratio                                                1.199638
Omega Ratio                                                 1.209774
Sortino Ratio                                               1.982724
Annualized Return [%]                                      73.018058
Annualized Volatility [%]                                  51.088459
Skew                                                         -0.0376
Kurtosis                                                    3.435275
Tail Ratio                                                  1.037861
Common Sense Ratio                                          1.795687
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 3 Trades
   status  direction      size  entry_price  exit_price    return        pnl  \
0       1          0  1.143068    87.265716   93.604879  0.067460   6.729224   
1       1          0  1.097720    96.985603  111.975205  0.149169  15.880938   
2       0          0  1.683060    72.667880   88.984848  0.222042  27.156674   

   entry_fees  exit_fees  
0    0.249377   0.267492  
1    0.266158   0.307294  
2    0.305761   0.000000

/Users/kj/Documents/GitHub/pandas-ta/env/lib/python3.9/site-packages/vectorbt/portfolio/trades.py:447: RuntimeWarning: divide by zero encountered in true_divide
  profit_factor = total_win / np.abs(total_loss)

Run Time                      Wednesday August 4, 2021, NYSE: 9:43:40
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                                                  149.766837
Total Return [%]                                            49.766837
Benchmark Return [%]                                         2.455676
Max Gross Exposure [%]                                          100.0
Total Fees Paid                                              1.396081
Max Drawdown [%]                                            10.131748
Max Drawdown Duration                               451 days 00:00:00
Total Trades                                                        3
Total Closed Trades                                                 2
Total Open Trades                                                   1
Open Trade P&L                                              27.156674
Win Rate [%]                                                    100.0
Best Trade [%]                                              14.916852
Worst Trade [%]                                              6.746047
Avg Winning Trade [%]                                        10.83145
Avg Winning Trade Duration                          264 days 00:00:00
Profit Factor                                                     inf
Expectancy                                                  11.305081
Sharpe Ratio                                                 1.009579
Calmar Ratio                                                 1.226147
Omega Ratio                                                  1.224227
Sortino Ratio                                                1.431908
Annualized Return [%]                                       12.423011
Annualized Volatility [%]                                   12.357522
Skew                                                        -0.412955
Kurtosis                                                      6.27214
Tail Ratio                                                   1.032279
Common Sense Ratio                                           1.160519
Value at Risk                                               -0.010296
Alpha                                                        0.122838
Beta                                                         0.182612
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  60.628049     1.645288    1.772486  0.072117   7.193697   
1       1          0  45.848650     2.332160    3.704574  0.582002  62.231414   
2       1          0  29.257588     5.776368    3.885621 -0.331506 -56.025411   
3       0          0  30.748240     3.678809    6.471691  0.756681  85.593399   

   entry_fees  exit_fees  
0    0.249377   0.268656  
1    0.267316   0.424624  
2    0.422507   0.284210  
3    0.282792   0.000000

Run Time                      Wednesday August 4, 2021, NYSE: 9:43:41
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                                                  198.993099
Total Return [%]                                            98.993099
Benchmark Return [%]                                       565.918578
Max Gross Exposure [%]                                          100.0
Total Fees Paid                                              2.199481
Max Drawdown [%]                                            59.898137
Max Drawdown Duration                               506 days 00:00:00
Total Trades                                                        4
Total Closed Trades                                                 3
Total Open Trades                                                   1
Open Trade P&L                                              85.593399
Win Rate [%]                                                66.666667
Best Trade [%]                                              58.200243
Worst Trade [%]                                             -33.15062
Avg Winning Trade [%]                                       32.705962
Avg Losing Trade [%]                                        -33.15062
Avg Winning Trade Duration                          266 days 12:00:00
Avg Losing Trade Duration                            89 days 00:00:00
Profit Factor                                                1.239172
Expectancy                                                   4.466566
Sharpe Ratio                                                 0.739016
Calmar Ratio                                                  0.36859
Omega Ratio                                                  1.140899
Sortino Ratio                                                 1.08289
Annualized Return [%]                                       22.077862
Annualized Volatility [%]                                   35.543979
Skew                                                         0.024374
Kurtosis                                                     4.672291
Tail Ratio                                                   1.095481
Common Sense Ratio                                           1.337339
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 Unescape Escape

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