Eric Bidelman has documented some of the common workflows possible with headless Chrome over in https://developers.google.com/web/updates/2017/04/headless-chrome.
If you're looking at this in 2016 and beyond, I strongly recommend investigating real headless Chrome: https://chromium.googlesource.com/chromium/src/+/lkgr/headless/README.md
Windows and Mac users might find using Justin Ribeiro's Docker setup useful here while full support for these platforms is being worked out.
| /** | |
| * Created by andy hulstkamp | |
| */ | |
| var webpage = require("webpage"), | |
| fs = require("fs"); | |
| var debug = false, | |
| pageIndex = 0, | |
| allLinks = [], |
| #cloud-config | |
| script: | | |
| <powershell> | |
| Try { | |
| (new-object Net.WebClient).DownloadString("http://psget.net/GetPsGet.ps1") | iex | |
| import-module PsGet | |
| Install-Module PowerYaml | |
| $userdata = (Get-Yaml (Invoke-RestMethod http://instance-data/latest/user-data)) | |
| } | |
| Catch |
| # install 7-zip, curl and vim | |
| # (Windows 2012 comes with .NET 4.5 out-of-the-box) | |
| # Then use the EC2 tools to create a new AMI from the result, and you have a system | |
| # that will execute user-data as a PowerShell script after the instance fires up! | |
| # This has been tested on Windows 2012 64bits AMIs provided by Amazon (eu-west-1 ami-a1867dd6) | |
| # | |
| # Inject this as user-data of a Windows 2012 AMI, like this (edit the adminPassword to your needs): | |
| # | |
| # <powershell> | |
| # Set-ExecutionPolicy Unrestricted |
| $name = split-path (whoami) -leaf | |
| "Hello, $name!" |
| $name = split-path (whoami) -leaf | |
| "Hello, $name!" |
| """ | |
| This file defines a User model and a Flask application, and implements authentication using encryption. | |
| For more information, visit http://tecladocode.com/blog/learn-python-password-encryption-with-flask | |
| """ | |
| from flask import Flask, request, jsonify | |
| from werkzeug.security import generate_password_hash, check_password_hash | |
| import sqlite3 |
| """ | |
| Simple Moving Average implemented using KiteConnect Python library. -- [https://kite.trade](kite.trade) | |
| Rainmatter (c) 2016 | |
| License | |
| ------- | |
| This GIST is licensed under the MIT License | |
| """ | |
| from kiteconnect import KiteConnect |
| create database price_history_db | |
| CREATE CONTINUOUS QUERY "cq_1m" ON "price_history_db" BEGIN SELECT MIN(price) as low, MAX(price) as high, FIRST(price) as open, LAST(price) as close, SUM(size) as volume INTO "price_1m" FROM "trade" GROUP BY time(1m), symbol END | |
| CREATE CONTINUOUS QUERY "cq_5m" ON "price_history_db" BEGIN SELECT MIN(low) as low, MAX(high) as high, FIRST(open) as open, LAST(close) as close, SUM(volume) as volume INTO "price_5m" FROM "price_1m" GROUP BY time(5m), symbol END | |
| CREATE CONTINUOUS QUERY "cq_15m" ON "price_history_db" BEGIN SELECT MIN(low) as low, MAX(high) as high, FIRST(open) as open, LAST(close) as close, SUM(volume) as volume INTO "price_15m" FROM "price_5m" GROUP BY time(15m), symbol END | |
| CREATE CONTINUOUS QUERY "cq_1h" ON "price_history_db" BEGIN SELECT MIN(low) as low, MAX(high) as high, FIRST(open) as open, LAST(close) as close, SUM(volume) as volume INTO "price_1h" FROM "price_15m" GROUP BY time(1h), symbol END | |
| CREATE CONTINUOUS QUERY "cq_6h" ON "price_history_db" BEGIN SELECT |
