Multi-Timeframe SMA Cross - M1

The bot

Install (local)

Clone the repository

git clone [email protected]:quant-studio/qs-oanda-trading-bot-mk1.git

Open a console, cd into the directory, then install the dependencies:

npm install

Create env vars to save your Oanda API key & your Oanda Account Number (or hard-code them in app.js line 18 & 19):

OANDA_API_KEY=13a5f4284fa8598ac37c445e0ef0fa3a-cr45169bceer7a1318c0nde3292o15e9
OANDA_ACC=131-011-3347965-011

(invalid keys & acc number, just for the example. Replace with your owns.)

Test locally:

node test

Deployment on AWS Lambda

On your AWS console, open CodeStar, and filter to only show the templates for Web Services in NodeJS, and select the "AWS Lambda" template.

Select a name, then connect your github account.

Click Next until the end. Leave everything as default.

CodeStar will create a new github repository on your github account. It might take a few minutes.

Clone the github repository you just created with CodeStar.

Copy the following files into your repository:

• app.js
• bot.js
• core.js
• index.js
• oanda.js
• package.json
• test.js
• .gitignore
• buildspec.yml

Deploy

To deploy, just push to github. CodeStar will monitor your github repository and trigger a rebuild of your lambda function everytime there is a new commit to master.

git add --all && git commit -m "Base code" && git push

It takes a few minutes for your code to deploy & build on lambda.

You can follow the deployment and investigate any deployment issues by looking at your CodePipeline console. Open CodePipeline, locate your bot, and the UI is pretty self-explanatory:

Once deployed, you can setup & test your bot.

Configure your lambda function

By default, your lambda function has a 3 seconds timeout & barely any memory allocated. That won't work for this.

Go to your AWS console, then open AWS Lambda.

Look for your bot in the list:

When you are on your bot's lambda settings, scroll down until you see Basic Settings:

Even though AWS will charge you more when you use more memory, you also get charged for the execution time. Counter-intuitively, it's usually cheaper to allocate more memory, as it speeds up the execution time quite significantly.

Here are my settings:

Now you'll want to setup the env vars, to provide the Oanda API key & account number without having to hard-code them in your code and risk accidentally pushing them to github.

Scroll back up and locate Environment variables:

Create the OANDA_API_KEY and OANDA_ACC env vars, along with their values:

There is a link on your OANDA fxTrade account profile page titled “Manage API Access” (My Account -> My Services -> Manage API Access). From there, you can generate a personal access token to use with the OANDA API, as well as revoke a token you may currently have.

Don't forget to save, and you're ready to test.

Testing your AWS Lambda deployment

Scroll all the way up, locate the test UI and click "Select a test event":

Create a new test. Leave the parameters empty (we don't need any), and give it a neme, then click Create:

Now select & execute your test:

Schedule the execution of your code

In your AWS console, open CloudWatch.

Go to Events > Rules, then click Create Rule:

Select schedule then Cron expression.

If you want to execute your bot every hour Monday to Friday for example, you'd use:

0/60 * ? * MON-FRI *

Note: Check the official doc to understand the cron format.

On the right side, select your lambda function:

Leave everything as default and click Configure Details

On the next step, give your rule a name, a description, then click Create.

Checking the logs

To check the execution logs of your bot and make sure everythign is working as expected, in your AWS console open CloudWatch.

On the left click Logs then look for the log starting with /aws/lambda/awscodestar- + the name of your bot.

If you don't find a log group for your bot, that means your bot hasn't been executed yet.

Wait for it to execute or execute it manually or via a test to start creating logs.

Finding your bot's GET endpoint to trigger it manually

In your AWS console open API Gateway. Look for the name of your bot and go to Stages.

Click "Prod", and you'll find your endpoint's url:

Open that url in your browser to execute the bot.

The bot doesn't return anything as an HTTP response right now, you you should get {} as a response.

The Quant Studio Algorithm:

Requirements

• Nodejs V8.0.0 or more recent

Files

• MultiTimeframeSMACrossM1.js is your algorithm, including its datasources.
• simple-stats.js is a required dependency of your algorithm. Keep this file in the same directory as MultiTimeframeSMACrossM1.js.

Installation

npm install

Execute the demo:

node demo

How to use

Load your algorithm:

var MultiTimeframeSMACrossM1	= require("./MultiTimeframeSMACrossM1");

Execute your algorithm:

Each datasources will refresh its data and the algorithm will be executed, returning an array of objects. Each object represents a datapoint.

MultiTimeframeSMACrossM1.refresh({
	rights: {
	    "ebbfe4432a": {
	        "oanda_api_key": ""	// Use your own API key
	    },
	    "098f004b68": {
	        "oanda_api_key": ""	// Use your own API key
	    }
	}
}, function(response) {
	console.log(JSON.stringify(response, null, 4));
});

Output Schema

Your function MultiTimeframeSMACrossM1(dataset) outputs an array of objects, each containing the following keys:

• open: open (Candles)
• high: high (Candles)
• low: low (Candles)
• close: close (Candles)
• volume: volume (Candles)
• Date: Date (Candles)
• spread: spread (Candles)
• open: open (Candles)
• high: high (Candles)
• low: low (Candles)
• close: close (Candles)
• volume: volume (Candles)
• Date: Date (Candles)
• spread: spread (Candles)
• Output: Output (H1 to M1)
• _fastH1: H1 Fast (SMA)
• _slowH1: H1 Slow (SMA)
• fastM1: M1 Fast (SMA)
• slowM1: Slow M1 (SMA)
• lt: lt (_ < _)
• lt: lt (_ < _)
• fastH1: Fast (H1 to M1)
• slowH1: Slow (H1 to M1)
• buyLevel: Buy Level (Plus - Minus)
• sellLevel: Sell Level (Plus - Minus)
• RSI: RSI (RSI)
• RSI-1: RSI (RSI)
• rsiH1: H1 RSI (H1 to M1)
• medM1: Medium M1 (SMA)
• med2M1: Medium 2 M1 (SMA)
• lt: lt (_ < _)
• lt: lt (_ < _)
• Binary AND: Binary AND (AND (x4))
• sell: Sell (Pulse)
• superfastM1: Superfast M1 (SMA)
• superfastTriggerM1: Superfast Trigger M1 (SMA)
• RSITrigger: RSI Trigger (RSI)