event-driven backtesting framework written in golang
Heads up: This is a framework in development, with only basic functionality.
gobacktest - Fundamental stock analysis backtesting
An event-driven backtesting framework to test stock trading strategies based on fundamental analysis. Preferably this package will be the core of a backend service exposed via a REST API.
Usage
Basic example:
package main
import (
"github.com/dirkolbrich/gobacktest""github.com/dirkolbrich/gobacktest/data""github.com/dirkolbrich/gobacktest/strategy"
)
funcmain() {
// initiate a new backtestertest:= gobacktest.New()
// define and load symbolssymbols:= []string{"TEST.DE"}
test.SetSymbols(symbols)
// create a data provider and load the data into the backtestdata:= &data.BarEventFromCSVFile{FileDir: "../testdata/test/"}
data.Load(symbols)
test.SetData(data)
// choose a strategystrategy:= strategy.BuyAndHold()
// create an asset and append it to the strategy
strategy.SetChildren(gobacktest.NewAsset("TEST.DE"))
The goal is to create a flexible backtesting system, which could be used as a backend or as a stand alone within a Jupyter notebook.
If you are interested in financial stuff, algorithmic trading or quantitative finance and writing code in Go, feel welcome.
I would appreciate contributors for basic algos, tearsheet and graph integration, documentation or just in general discussion about the structure of the framework from an end user perspective.
Cheers.
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gobacktest is an event-driven backtesting framework written in Golang.
event-driven backtesting framework written in golang
Heads up: This is a framework in development, with only basic functionality.
gobacktest - Fundamental stock analysis backtesting
An event-driven backtesting framework to test stock trading strategies based on fundamental analysis. Preferably this package will be the core of a backend service exposed via a REST API.
Usage
Basic example:
The goal is to create a flexible backtesting system, which could be used as a backend or as a stand alone within a Jupyter notebook.
If you are interested in financial stuff, algorithmic trading or quantitative finance and writing code in Go, feel welcome.
I would appreciate contributors for basic algos, tearsheet and graph integration, documentation or just in general discussion about the structure of the framework from an end user perspective.
Cheers.