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.

Metadata

Release files for quantex 0.6.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for quantex 0.6.1
File	Size	Uploaded
quantex-0.6.1.tar.gz	10.1 kB	Sep 14, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for quantex 0.6.1
File	Interpreter	ABI	Platform	Reset
quantex-0.6.1-py3-none-any.whl 11.3 kB Sep 14, 2026	Python 3	none	any	Details

Total release size: 21.4 kB

Release files / quantex-0.6.1.tar.gz

Download URL	quantex-0.6.1.tar.gz
Size	10.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`a80373f92e23384d62bdd579119d1865f151376e0b6652b921e6216c6da1fd28`
BLAKE2b-256 checksum How to use checksums	`3a5a042971cb49696cdc8976e505cb8e05c736c2f28711d6a1d6ef206559f4bd`
Upload date	Sep 14, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/2.4.1 CPython/3.14.6 Windows/11`

Release files / quantex-0.6.1-py3-none-any.whl

Download URL	quantex-0.6.1-py3-none-any.whl
Size	11.3 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`d1aab9a19058b80b07f006b08bd539c43de4e3cdabda3257d24a088a55f0533a`
BLAKE2b-256 checksum How to use checksums	`58c08f8e51061d81e5c3b215a76e0b7a91dd0ce4076aac63385a0219903a1eac`
Upload date	Sep 14, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`poetry/2.4.1 CPython/3.14.6 Windows/11`