QuantEx Documentation
QuantEx is a Python backtesting library for OHLCV data. It is designed to be easy to use and extend, and to provide a simple interface for backtesting trading strategies.
Getting Started
To get started with QuantEx, you can install it using pip:
pip install quantex
QuantEx is designed to be used by writing your strategy as event-driven code. An example buy and hold strategy is shown below:
from quantex.strategy import Strategy
class BuyAndHold(Strategy):
def init(self):
pass
def next(self):
if self.broker.is_closed():
self.broker.buy(amount=50)
This strategy will buy 50 shares of the stock when there are no open positions. To run this strategy, we must first get some data. Currently, QuantEx supports Yahoo Finance and pandas DataFrames as data sources.
To run the strategy with the data, we can use the Backtester class:
from quantex.backtester import Backtester
from quantex.datasource import YahooDataSource
bt = Backtester(BuyAndHold)
source = YahooDataSource("NVDA", start="2020-01-01", end="2025-12-31")
bt.add_data(source, "NVDA")
result = bt.run()
The result object contains information about the strategy, such as the total return, annualized return, and max drawdown.
If you would like to include commissions in your strategy, you can use the Commission class:
from quantex.commission import Commission, ApplicationType
from quantex.backtester import Backtester
from quantex.datasource import YahooDataSource
bt = Backtester(BuyAndHold, commission=Commission(ApplicationType.PER_ORDER, fixed=1))
source = YahooDataSource("NVDA", start="2020-01-01", end="2025-12-31")
bt.add_data(source, "NVDA")
result = bt.run()
This will charge a fixed commission of 1 dollar (or whatever the currency price is quoted in) per order.
Documentation
The documentation for QuantEx can be found here.
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Metadata
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Release files for quantex 0.6.7
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