This GraphGist will begin to explore how stock option data can be modeled as a graph, some simple Cypher queries for calculating payout at expiration for an options contract and a very basic look at finding profitable options trades given a specific forecast. Please note that some of the concepts here have been simplified and are only meant as an educational overview of exploring Neo4j and graph data modeling.
Amazon Web Services global infractructure is steadily expanding and now serves thousands of customers in over 190 countries. Certain services are only available in some regions and compute prices vary across the globe. Wouldn’t it be nice if you could slice and dice through the entire AWS domain of services, data centres and prices all in one spot to optimise your AWS bill? , enter the AWS Global Infrastructure Graph!
At the time of writing the AWS global infrastructure graph consists of 5 continents, with 10 regions, and 21 availability zones, offering 32 services. The data shown here is current as of 21 January 2014.
disclaimer AWS consumers beware! The prices and services listed in the graph are correct as of January 21st 2014, please refer to the AWS price calculator for the latest prices and service offering’s per region - http://calculator.s3.amazonaws.com/calc5.html
Most Finance portfolio management’s would be using RDBMS. But the same could be built with neo4j in a much succint manner. The analysis that can be done over it is interesting too. Consider the system has the data for x members, we can get analysis as follows:
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How much percentage people spend money on a particular liability, say loans.
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How much percentage does a particular asset contribute to average earnings.
| #!/bin/bash | |
| user="CHANGEME" | |
| pages=$(curl -I https://api.github.com/users/$user/starred | sed -nr 's/^Link:.*page=([0-9]+).*/\1/p') | |
| for page in $(seq 0 $pages); do | |
| curl "https://api.github.com/users/$user/starred?page=$page&per_page=100" | jq -r '.[].html_url' | | |
| while read rp; do | |
| git clone $rp | |
| done |
| //usr/bin/env go run $0 "$@"; exit | |
| package main | |
| import ( | |
| "fmt" | |
| "os" | |
| ) | |
| func main() { | |
| fmt.Println("Hello world!") |
This gist contains lists of modules available in
in AWS Lambda.
| package main | |
| import ( | |
| "fmt" | |
| "math" | |
| "runtime" | |
| "strings" | |
| ) | |
| const ( |
| #!/usr/bin/env php | |
| <?php | |
| /* | |
| * Filter to fill the IP gaps in a MaxMind GeoLite tables. | |
| * | |
| * For every missing range in the file it puts a dummy one. | |
| */ | |
| $types = [ | |
| 'asnum' => [0, 0, 1, "%s,%s,\"-\"\n"], | |
| 'blocks' => [2, 0, 1, "\"%s\",\"%s\",\"1\"\n"], |
| package main | |
| // The aim of this example is to illustrate backpressure using golang channels and go routines. | |
| // | |
| // This is the basis for a simple data processing service which could either be reading from | |
| // some internal queue or a socket of some sort. | |
| import ( | |
| "fmt" | |
| "math/rand" |