Grademark

Toolkit for algo trading and backtesting in JavaScript and TypeScript.

This API builds on Data-Forge and is best used from Data-Forge Notebook (making it easy to plot charts and visualize).

Check out the release notes to see updates and breaking changes.

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Please see what this looks like in the Grademark first example and the unit tests in this repo.

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First example

From the Grademark first example here's some example output. Click to see the first example as a notebook.

Analysis of a sequence of trades looks like this:

Here's a chart that visualizes the equity curve for the example strategy:

Here's another chart, this one is a visualization of the drawdown for the example strategy:

Pre-requisites

• Make sure your data is sorted in forward chronological order.

Data format

Your data needs to be loaded into memory in the following format:

interface IBar {
    time: Date;
    open: number;
    high: number;
    low: number;
    close: number;
}

const data: IBar[] = ... load your data ...

Features

• Define a trading strategy with entry and exit rules.
• Backtest a trading strategy on a single financial instrument.
• Apply custom indicators to your input data series.
• Specify lookback period.
• Built-in intrabar stop loss.
• Compute and plot equity curve and drawdown charts.
• Throughly covered by automated tests.
• Calculation of risk and rmultiples.
• Intrabar profit target.
• Intrabar trailing stop loss.
• Conditional buy on price level (intrabar).
• Monte carlo simulation.
• Multiple parameter optimization based on permutations of parameters (using grid search and hill-climb algorithms).
• Walk forward optimization and backtesting.
• Plot a chart of trailing stop loss.
• Short selling.

Data-Forge Notebook comes with example JavaScript notebooks that demonstrate many of these features.

If you need help with new features please reach out!

Maybe coming later

• Support for precise decimal numbers.
• Fees.
• Slippage.
• Position sizing.
• Testing multiple instruments / portfolio simulation / ranking instruments.
• Market filters.

Complete examples

For a ready to go example please see the repo grademark-first-example.

Usage

Instructions here are for JavaScript, but this library is written in TypeScript and so it can also be used from TypeScript.

Installation

npm install --save grademark

Import modules

const dataForge = require('data-forge');
require('data-forge-fs'); // For file loading capability.
require('data-forge-indicators'); // For the moving average indicator.
require('data-forge-plot'); // For rendering charts.
const { backtest, analyze, computeEquityCurve, computeDrawdown } = require('grademark');

Load your data

Use Data-Forge to load and prep your data, make sure your data is sorted in forward chronological order.

This example loads a CSV file, but feel free to load your data from REST API, database or wherever you want!

let inputSeries = dataForge.readFileSync("STW.csv")
    .parseCSV()
    .parseDates("date", "D/MM/YYYY")
    .parseFloats(["open", "high", "low", "close", "volume"])
    .setIndex("date") // Index so we can later merge on date.
    .renameSeries({ date: "time" });

The example data file is available in the example repo.

Add indicators

Add whatever indicators and signals you want to your data.

const movingAverage = inputSeries
    .deflate(bar => bar.close)          // Extract closing price series.
    .sma(30);                           // 30 day moving average.

inputSeries = inputSeries
    .withSeries("sma", movingAverage)   // Integrate moving average into data, indexed on date.
    .skip(30)                           // Skip blank sma entries.

Create a strategy

This is a very simple and very naive mean reversion strategy:

const strategy = {
    entryRule: (enterPosition, args) => {
        if (args.bar.close < args.bar.sma) { // Buy when price is below average.
            enterPosition({ direction: "long" }); // Long is default, pass in "short" to short sell.
        }
    },

    exitRule: (exitPosition, args) => {
        if (args.bar.close > args.bar.sma) {
            exitPosition(); // Sell when price is above average.
        }
    },

    stopLoss: args => { // Optional intrabar stop loss.
        return args.entryPrice * (5/100); // Stop out on 5% loss from entry price.
    },
};

Running a backtest

Backtest your strategy, then compute and print metrics:

const trades = backtest(strategy, inputSeries)
console.log("Made " + trades.length + " trades!");

const startingCapital = 10000;
const analysis = analyze(startingCapital, trades);
console.log(analysis);

Visualizing the results

Use Data-Forge Plot to visualize the equity curve and drawdown chart from your trading strategy:

computeEquityCurve(trades)
    .plot()
    .renderImage("output/my-equity-curve.png");

computeDrawdown(trades)
    .plot()
    .renderImage("output/my-drawdown.png");

Advanced backtesting

We are only just getting started in this example to learn more please follow my blog and YouTube channel.

Resources

• Data-Forge
• Data-Forge Notebook

Support the developer

Click here to support the developer.