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Ingest sample Market Orders Data feed from PubNub to Postgres with TimescaleDB extension installed and enabled for time series analysis.
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README.md

stream-sequelize-node

Ingest sample Market Orders Data feed from PubNub to Postgres with TimescaleDB extension installed and enabled for time series analysis.

Installation

Clone or download repository

git clone https://github.com/sfrechette/stream-sequelize-node.git 
cd stream-sequilize-node

Install the dependencies with the following command:

npm install

Configuration

Create an .env file and add the following with your configuration values

DB_SERVER_USER_NAME = ""    
DB_SERVER_USER_PASSWORD = ""    
DB_SERVER_HOST = "" 
DB_SERVER_PORT = ""     

Usage

node ingest.js

Prerequesites

Running this application assumes that you already have an instance of Postgres running and have installed the TimescaleDB extension. If not please refer the following documentation:

Getting started, installing and setting up
https://docs.timescale.com/v0.9/getting-started

Database schema

Next you need to execute the following SQL code snippets in your Postgres environment in order to create the database, TimescaleDB extension, schema definition, and hypertable.

create database market_orders;
\c market_orders
create extension if not exists timescaledb cascade;
create table orders (
	order_time          bigint not null, 
	trade_type          text not null,
	symbol              text not null, 
	order_quantity      double precision not null, 
	bid_price           double precision not null
);
create index on orders(order_time desc);
create index on orders(trade_type, order_time desc);
create index on orders(symbol, order_time desc);
create index on orders(order_time desc, order_quantity);
create index on orders(order_time desc, bid_price);
-- 86400000000 is in usecs and is equal to 1 day
select create_hypertable('orders', 'order_time', chunk_time_interval => 86400000000);

Once completed, you can now run the application to start ingesting data in your database.

Sample queries

Assuming that you have been ingesting data for a while (you can modify the time interval), run some queries to test and validate how Timescale performs nicely in fetching results while data is also being ingested in the database.

-- Average bid price by 5 minute intervals for Google and day trade type 
select 	time_bucket('5 minutes', to_timestamp(order_time)) as five_min,
        avg(bid_price) as avg_bid_price
from 	orders
where	symbol = 'Google' and trade_type = 'day'
group by five_min
order by five_min limit 20;
-- Min and Max bid price by 2 minute intervals for all symbols 
select 	time_bucket('2 minutes', to_timestamp(order_time)) as two_min,
        symbol, 
        min(bid_price) as min_bid_price,
        max(bid_price) as max_bid_price 
from 	orders
group by two_min, symbol
order by two_min desc limit 20;
-- Orders with order quantity greater than 975...
select 	order_time,
        trade_type, 
        order_quantity, 
        bid_price
from 	orders
where	order_quantity > 975 and 
        symbol = 'Bespin Gas' and 
        trade_type = 'market' limit 20;

Links

TimescaleDB
https://www.timescale.com/

Data feed
Market Orders - artificial data stream that provides the latest market orders for a fictitious marketplace
https://www.pubnub.com/developers/realtime-data-streams/financial-securities-market-orders/

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