Backtesting.py

Backtest trading strategies with Python.

Original Project website + Documentation

Installation

$ git clone https://github.com/chancsc/backtesting.py.git
$ pip install yfinance
$ pip install pandas seaborn
$ pip install tabulate
$ pip install bokeh


May need these (install 1 by 1 and try run the command e.g.: python  bss_1.py CPNG 3 10 
$ sudo apt-get install libblas-dev
$ sudo apt-get install libhdf5-dev
$ sudo apt-get install libhdf5-serial-dev
$ sudo apt-get install libatlas-base-dev

To start

Call the most basic program to generate chart with 10 & 20 days MA (moving average), buy/sell indicators etc. By default, the script will pull data from 1 Jan {current year} --> {today}.

python check_bss.py TSLA

Back-test single stock

Run single check, NOT to open browser

python check_bss.py MSFT --browser=0

Run single check, NOT open browser, NOT display table in console

python check_bss.py AAPL --browser=0 --table=0

Run singgle check, NOT to open browser, NOT display table, year 2022

python check_bss.py AAPL --browser=0 --table=0 --year=2022

Download stock data to csv

Download YTD data for single stock

python get_stock_data_SA.py --stock=IQ

Download specific date range data for single stock

python get_stock_data_SA.py --stock=IQ --sdate=01/01/2022 --edate=01/01/2023

Batch mode

Batch mode, default retrieve current year data, back-test based on stock_list_us.json

python check_bss_batch.py

Batch mode, for year 2021 & export data to log

python check_bss_batch.py --year=2021 > PL2021-5-10.txt 2>&1

Batch mode, for year 2022, specify stocklist file, output to log

python check_bss_batch.py --year=2022 --file=stock_list_test.json > output.txt 2>&1

Cronjob setup

$ crontab -e

Scheduled to check US stock list on every Tue - Sat morning (SG Time, 6 am)

0 6 * * 2-6 ~/code/backtesting.py/run_check_bss_batch.sh

Scheduled to check US stock list on every Mon - Fri (SG Time 7:30 pm)

30 17 * * 1-5 ~/code/backtesting.py/run_check_bss_batch_asia.sh

Note on chart

10 days MA above 20 days MA --> Bullish (not neccessaary, depend on stock)

20 day MA above 10 days MA —> Bearish (not neccessaary, depend on stock)

Arrow up or down doesn’t represent bullish or bearish

Green & Red parallel short bar doesn’t represent bullish or bearish

python check_bss.py TSLA

Enhancement

Added new table output in the console for ease of reference. Size negative value = short the stock

+--------+---------------+--------------+--------------+-------------+
|   Size |   Entry Price |   Exit Price | Entry Time   | Exit Time   |
+========+===============+==============+==============+=============+
|    -52 |       190.997 |       193.13 | 2023-03-07   | 2023-03-29  |
+--------+---------------+--------------+--------------+-------------+
|     51 |       193.516 |       186.32 | 2023-03-29   | 2023-04-17  |
+--------+---------------+--------------+--------------+-------------+
|    -51 |       185.947 |       165.65 | 2023-04-17   | 2023-05-16  |
+--------+---------------+--------------+--------------+-------------+
|     63 |       165.981 |       279.56 | 2023-05-16   | 2023-07-20  |
+--------+---------------+--------------+--------------+-------------+

Read this user guide for more details usage

Usage (from orginal author)

from backtesting import Backtest, Strategy
from backtesting.lib import crossover

from backtesting.test import SMA, GOOG


class SmaCross(Strategy):
    def init(self):
        price = self.data.Close
        self.ma1 = self.I(SMA, price, 10)
        self.ma2 = self.I(SMA, price, 20)

    def next(self):
        if crossover(self.ma1, self.ma2):
            self.buy()
        elif crossover(self.ma2, self.ma1):
            self.sell()


bt = Backtest(GOOG, SmaCross, commission=.002,
              exclusive_orders=True)
stats = bt.run()
bt.plot()

Results in:

Start                     2004-08-19 00:00:00
End                       2013-03-01 00:00:00
Duration                   3116 days 00:00:00
Exposure Time [%]                       94.27
Equity Final [$]                     68935.12
Equity Peak [$]                      68991.22
Return [%]                             589.35
Buy & Hold Return [%]                  703.46
Return (Ann.) [%]                       25.42
Volatility (Ann.) [%]                   38.43
Sharpe Ratio                             0.66
Sortino Ratio                            1.30
Calmar Ratio                             0.77
Max. Drawdown [%]                      -33.08
Avg. Drawdown [%]                       -5.58
Max. Drawdown Duration      688 days 00:00:00
Avg. Drawdown Duration       41 days 00:00:00
# Trades                                   93
Win Rate [%]                            53.76
Best Trade [%]                          57.12
Worst Trade [%]                        -16.63
Avg. Trade [%]                           1.96
Max. Trade Duration         121 days 00:00:00
Avg. Trade Duration          32 days 00:00:00
Profit Factor                            2.13
Expectancy [%]                           6.91
SQN                                      1.78
Kelly Criterion                        0.6134
_strategy              SmaCross(n1=10, n2=20)
_equity_curve                          Equ...
_trades                       Size  EntryB...
dtype: object

Find more usage examples in the documentation.

Features

• Simple, well-documented API
• Blazing fast execution
• Built-in optimizer
• Library of composable base strategies and utilities
• Indicator-library-agnostic
• Supports any financial instrument with candlestick data
• Detailed results
• Interactive visualizations

Bugs

Before reporting bugs or posting to the discussion board, please read contributing guidelines, particularly the section about crafting useful bug reports and ```-fencing your code. We thank you!

Alternatives

See alternatives.md for a list of alternative Python backtesting frameworks and related packages.