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.