An event-driven backtesting framework to test trading strategies based on fundamental analysis.
Example tests are in the /examples folder.
package main
import (
"github.com/mamady/gobacktest/pkg/backtest"
"github.com/mamady/gobacktest/pkg/data"
"github.com/mamady/gobacktest/pkg/strategy"
)
func main() {
// we need a new blanc backtester
test := backtest.New()
// define the symbols to be tested and load them into the backtest
symbols := []string{"TEST.DE"}
test.SetSymbols(symbols)
// create a data provider and load the data into the backtest
data := &data.BarEventFromCSVFile{FileDir: "../testdata/test/"}
data.Load(symbols)
test.SetData(data)
// set the portfolio with initial cash and a default size and risk manager
portfolio := &backtest.Portfolio{}
portfolio.SetInitialCash(10000)
sizeManager := &backtest.Size{DefaultSize: 100, DefaultValue: 1000}
portfolio.SetSizeManager(sizeManager)
riskManager := &backtest.Risk{}
portfolio.SetRiskManager(riskManager)
test.SetPortfolio(portfolio)
// create a strategy provider and load it into the backtest
strategy := &strategy.Basic{}
test.SetStrategy(strategy)
// create an execution provider and load it into the backtest
exchange := &backtest.Exchange{}
test.SetExchange(exchange)
// choose a statistic and load into the backtest
statistic := &backtest.Statistic{}
test.SetStatistic(statistic)
// run the backtest
test.Run()
// print the result of the test
test.Stats().TotalEquityReturn()
}The internal calculations use the github.com/shopspring/decimal package for arbitrary-precision fixed-point decimals.
Make sure to install it into your $GOPATH with
go get github.com/shopspring/decimal
These are the basic components of an event-driven framework.
- BackTester - general test case, bundles the following elements into a single test
- EventHandler - the different types of events, which travel through this system - data event, signal event, order event and fill event
- DataHandler - interface to a set of data, e.g historical quotes, fundamental data, dividends etc.
- StrategyHandler - generates a buy/sell signal based on the data
- PortfolioHandler - generates orders and manages profit & loss
- (SizeHandler) - manages the size of an order
- (RiskHandler) - manages the risk allocation of a portfolio
- ExecutionHandler - sends orders to the broker and receives the “fills” or signals that the stock has been bought or sold
- StatisticHandler - tracks all events during the backtests and calculates useful statistics like equity return, drawdown or sharp ratio etc., could be used to replay the complete backtest for later reference
- (ComplianceHandler) - tracks and documents all trades to the portfolio for compliance reasons
An overviev of the infrastructure of a complete backtesting and trading environment. Taken from the production roadmap of QuantRocket.
- General
- API gateway
- configuration loader
- logging service
- cron service
- Data
- database backup and download service
- securities master services
- historical market data service
- fundamental data service
- earnings data service
- dividend data service
- real-time market data service
- exchange calendar service
- Strategy
- performance analysis service - tearsheet
- Portfolio
- account and portfolio service
- risk management service
- Execution
- trading platform gateway service
- order management and trade ledger service
- backtesting and trading engine
These links to articles are a good starting point to understand the intentions and basic functions of an event-driven backtesting framework.
- Initial idea via a blog post Python For Finance: Algorithmic Trading by Karlijn Willems @willems_karlijn.
- Very good explanation of the internals of a backtesting system by Michael Halls-Moore @mhallsmoore in the blog post series Event-Driven-Backtesting-with-Python.
- QuantConnect
- Quantopian
- QuantRocket - in development, available Q2/2018
- Quandl - financial data
- QSTrader - open-source backtesting framework from QuantStart
- Quantocracy - forum for quant news
- QuantStart - articels and tutorials about quant finance