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Description:

openbacktest 7.0.0

Open Backtest

Open source & beginner friendly crypto trading backtest library

French page

pip install open-backtest

Wanna contact me ? 👋
https://discord.gg/wfpGXvjj9t
Wanna support my work ? 💰

paypal: sh4ft.me@gmail.com
usdt (ERC20): 0x17B516E9cA55C330B6b2bd2830042cAf5C7ecD7a
btc: 34vo6zxSFYS5QJM6dpr4JLHVEo5vZ5owZH
eth: 0xF7f87bc828707354AAfae235dE584F27bDCc9569

thanks if you do it 💖
What is it ? 📈
Passionate about the world of crypto and about development I decided to create a python library because I found very
annoying for beginners to just run a simple backtest. Open Backtest got created to give apprentice but also confirmed
programmers a powerful and easy to use backtesting tool
How does it work ? 🔧
Open Backtest is currently made with a core engine that use different classes, it can run a backtest with binance data
and it can handle different timeframes. The library can also download and save data as a csv file to be able to load
it to save a considerable amount of time. The wallet class will handle orders and the data handler will summarize and
calculate all required data to analyze the backtest but also to plot graphs.
Requirements :

Pandas
Numpy
Plotly
Python-binance

All requirements will be downloaded and installed with Open Backtest installation
Doc 📝
How to run a backtest ?
We will see here a simple example
# ------------------------------------------------------------------
# Let's show you how to run a very simple strategy !
# First of all, let's see all our imports
# ------------------------------------------------------------------

# The library Technical Analyse is already included when installing open backtest
# it allow to add a lot of indicators very useful for trading strategy
from ta import trend, momentum

# Let's import here 4 classes of Open Backtest we will later see how to use it
from OpenBacktest.ObtEngine import Engine, Container, Pair, Report

# Python Binance is also included with Open Backtest it allow us to get the market data
from binance.client import Client

# ------------------------------------------------------------------
# The let's initialise our classes
# ------------------------------------------------------------------

# First of all we are here creating our container, it will contain all of our market pairs
container = Container()

# Let's add our market pair with the container.add_main_pair() method

# The parameter of the method is a Pair class with 5
# parameters, the parameters are quite self-explanatory but just to clarify, name is just a recognizable name for you
# that will be used later to get the data of a pair if you have multiple dataframes but we will see it later !
# At the moment just don't take care about the name it's not important,
# the path is the location of files that already exist or
# the location of futures files that will be saved, this parameter is optional.

# We register here our main pair ! The data will be get for the pair Ethereum - Usdt from the 01 january 2021 to now
# with candles of 1 hour
container.add_main_pair(
Pair(market_pair="ETHUSDT", start="01 january 2021", timeframe=Client.KLINE_INTERVAL_1HOUR, name="ETHUSDT",
path=""))

# Let's now initialise our engine with our container
engine = Engine(container)

# This line is not required ! it's used to save our data as csv files to be able to just have to load it
# for the next backtest
container.save_all(default_path="")

# We are here enriching our dataframe with technical indicators using TA lib more information here
# https://technical-analysis-library-in-python.readthedocs.io/en/latest/

# Let's add to our dataframe 2 EMA
engine.main_dataframe()["EMA3"] = trend.ema_indicator(engine.main_dataframe()['close'], 3)
engine.main_dataframe()["EMA100"] = trend.ema_indicator(engine.main_dataframe()['close'], 100)

# We will now set a strategy that will return a report class. The engine
# will call this function with the main dataframe and each index
def strategy(dataframe, index):
# first, there's our buy condition
if dataframe["EMA3"][index] >= dataframe["EMA100"][index]:
# we return a report with the order_type and the amount in percent of our coin wallet we want to buy
return Report("buy", percent_amount=100)
# then, there's our sell condition
if dataframe["EMA3"][index] <= dataframe["EMA100"][index]:
# we return a report with the order_type and the amount in percent of our token wallet we want to sell
return Report("sell", percent_amount=100)

# ------------------------------------------------------------------
# The let's run our backtest !
# ------------------------------------------------------------------

# This function is used to register our strategy
engine.register_strategy(strategy)

# This function is used to run the backtest, first parameter is the coin name, second is the token name, third
# is your initial coin balance 4th is your initial token balance 5th is your taker fees in percent
engine.run_strategy(coin_name="USDT", token_name="ETH", coin_balance=1000, token_balance=0, taker=0.075)

# We use this function to summarize and display the result of our backtest
engine.wallet.get_data_handler().display_wallet()

# And we finally use it to plot graphs of price and balance evolution, you can use the parameter size=... to set the
# points size and the parameter tradeline=False to disable trade lines
engine.wallet.get_data_handler().plot_wallet()

# -----------------------------------------------------------------------------------------------------------------
# And that's finish ! Hope you like and that it wasn't hard ! If you have any question dm me on discord: Shaft#3796
# -----------------------------------------------------------------------------------------------------------------

Next part is coming soon