Heads up: This is a framework in development, with only basic functionality.

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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

Example tests are in the /examples folder.

package main

import (
    "github.com/dirkolbrich/gobacktest/pkg/backtest"
    "github.com/dirkolbrich/gobacktest/pkg/data"
    "github.com/dirkolbrich/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()
}

Dependencies

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

Basic components

These are the basic components of an event-driven framework.

1. BackTester - general test case, bundles the following elements into a single test
2. EventHandler - the different types of events, which travel through this system - data event, signal event, order event and fill event
3. DataHandler - interface to a set of data, e.g historical quotes, fundamental data, dividends etc.
4. StrategyHandler - generates a buy/sell signal based on the data
5. PortfolioHandler - generates orders and manages profit & loss
   • (SizeHandler) - manages the size of an order
   • (RiskHandler) - manages the risk allocation of a portfolio
6. ExecutionHandler - sends orders to the broker and receives the “fills” or signals that the stock has been bought or sold
7. 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

Infrastructure example

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

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Resources

Articles

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.

Other backtesting frameworks

• QuantConnect
• Quantopian
• QuantRocket - in development, available Q2/2018
• Quandl - financial data
• QSTrader - open-source backtesting framework from QuantStart

General information on Quantitative Finance

• Quantocracy - forum for quant news
• QuantStart - articels and tutorials about quant finance