jinfinigon

A java api for the Infinigon's Social Analytical service This api offers three iterable classes Timeline, Tweets, and Snaphots.

REQUIRED LIBRARIES

The only prequisit is that the API uses by default json-simple-1.1.1.jar and jinfinigon.jar. Down load both jars and put it somewhere in your CLASS_PATH.

Connecting.

All the iterators take an optional Proxy object.

Token

For anonymous users the API is throttled. This allows you to either test against our system or create pages that are fairly static but calling these too frequently will get you an error such as: callback({"detail": "Request was throttled.Expected available in 85250 seconds."}); For any serious use you will need to use our simple token-based HTTP Authentication scheme. By default the API checks for an environment variable INFINIGON_TOKEN. To use your own token strategy just override getToken() of com.infinigongroup.api.InfinigonIterable.

Using a Proxy:

Proxy proxy = new Proxy(Proxy.Type.HTTP, new InetSocketAddress("10.38.89.25", 8080));
TimeSeries timeline = new Timeline("AAPL", Timeline.M, proxy);
for (Object timepoint : timeline) {
	System.out.println(timepoint);
}

`com.infinigongroup.api.Timeline`

The iterable Timeline class yields timepoints of data for a given stream that enable you to build timeline charts for individual streams.

`Timeline` Data

{	"date":"2015-08-19 00:45Z",
	"sentiment":0.0,
	"tweets":2
}

`Timeline` Parameters

stream & resolution

The constructor requires stream and resolution where:

stream - is the stream id such as AAPL or FDA
resolution - Data is aggregated by minute, hour or day (for more see Resolutions ) so you can use:

code	resolution
`TimeSeries.M`	for minutes
`TimeSeries.H`	for hours
`TimeSeries.d`	for days

start & stop

Optionally you can give a date and time range by setting start and stop. stop parameter always defaults to now, while start's default value depends of the resolution on given.

Format	Default Start Value
`TimeSeries.M`	24 hours ago
`TimeSeries.H`	7 days ago
`TimeSeries.d`	30 days ago

You can specify the dates using java.utils.Date or you can also use a String in many formats (see [Date Formats] (http://realtime.infinigongroup.com/api/docs/#data_dates)). All times are by default UTC so you must be explicit and add the timezone. You can test your date and time values using:

Time Delta

For the start parameter you can also give a time delta, specifying a period of time before the given (or default) stop date.

Period Code | Period | Example | Description --- | --- | --- | --- | --- M | minutes | "30M" | starting thirty minutes ago H | hours | "8H" | starting eight hours ago d | days |"5d" | starting five days ago w | weeks | "2w" | starting fortnight ago m | months | "3m" | starting on the same date 3 months ago y | years | "1y" | starting a year ago

`Timeline` Examples

Reading minute timepoints for AAPL

TimeSeries timeline = new Timeline("AAPL", TimeSeries.M, proxy);
int i =0;
for (Object timepoint : timeline) {
	System.out.print(i++ + ". ");
	System.out.println(timepoint);
}

0. {"date":"2015-08-19 16:35Z","sentiment":0.67,"tweets":18}
1. {"date":"2015-08-19 16:36Z","sentiment":0.64,"tweets":39}
2. {"date":"2015-08-19 16:37Z","sentiment":0.67,"tweets":21}
3. {"date":"2015-08-19 16:38Z","sentiment":0.73,"tweets":37}
4. {"date":"2015-08-19 16:39Z","sentiment":0.63,"tweets":52}
5. {"date":"2015-08-19 16:40Z","sentiment":0.61,"tweets":28}
6. {"date":"2015-08-19 16:41Z","sentiment":0.66,"tweets":32}
7. {"date":"2015-08-19 16:42Z","sentiment":0.65,"tweets":37}
8. {"date":"2015-08-19 16:43Z","sentiment":0.63,"tweets":30}
9. {"date":"2015-08-19 16:44Z","sentiment":0.7,"tweets":44}

Reading hour of timepoints for AAPL

TimeSeries timeline = new Timeline("AAPL", TimeSeries.H, proxy);
int i =0;
for (Object timepoint : timeline) {
	System.out.print(i++ + ". ");
	System.out.println(timepoint);
}

0. {"date":"2015-08-10 17:00Z","sentiment":0.65,"tweets":2193}
1. {"date":"2015-08-10 18:00Z","sentiment":0.64,"tweets":2326}
2. {"date":"2015-08-10 19:00Z","sentiment":0.66,"tweets":2408}
3. {"date":"2015-08-10 20:00Z","sentiment":0.65,"tweets":2209}
4. {"date":"2015-08-10 21:00Z","sentiment":0.62,"tweets":1974}
5. {"date":"2015-08-10 22:00Z","sentiment":0.63,"tweets":1857}
6. {"date":"2015-08-10 23:00Z","sentiment":0.64,"tweets":1762}
7. {"date":"2015-08-11 00:00Z","sentiment":0.63,"tweets":1671}
8. {"date":"2015-08-11 01:00Z","sentiment":0.63,"tweets":1665}
9. {"date":"2015-08-11 02:00Z","sentiment":0.63,"tweets":1874}

Reading day of timepoints for AAPL

TimeSeries timeline = new Timeline("AAPL", TimeSeries.d, proxy);
int i =0;
for (Object timepoint : timeline) {
	System.out.print(i++ + ". ");
	System.out.println(timepoint);
}

0. {"date":"2015-07-22 00:00Z","sentiment":0.62,"tweets":26528}
1. {"date":"2015-07-23 00:00Z","sentiment":0.64,"tweets":22045}
2. {"date":"2015-07-24 00:00Z","sentiment":0.65,"tweets":24992}
3. {"date":"2015-07-25 00:00Z","sentiment":0.63,"tweets":29631}
4. {"date":"2015-07-26 00:00Z","sentiment":0.42,"tweets":1436}
5. {"date":"2015-07-27 00:00Z","sentiment":0.61,"tweets":11123}
6. {"date":"2015-07-28 00:00Z","sentiment":0.65,"tweets":26182}
7. {"date":"2015-07-29 00:00Z","sentiment":0.66,"tweets":24080}
8. {"date":"2015-07-30 00:00Z","sentiment":0.66,"tweets":42599}
9. {"date":"2015-07-31 00:00Z","sentiment":0.63,"tweets":52068

Reading last 4 days of day of timepoints for AAPL

TimeSeries timeline = new Timeline("AAPL", TimeSeries.d, proxy).start("4d");
int i =0;
for (Object timepoint : timeline) {
	System.out.print(i++ + ". ");
	System.out.println(timepoint);
}

0. {"date":"2015-08-17 00:00Z","sentiment":0.65,"tweets":41620}
1. {"date":"2015-08-18 00:00Z","sentiment":0.66,"tweets":41506}
2. {"date":"2015-08-19 00:00Z","sentiment":0.66,"tweets":42823}
3. {"date":"2015-08-20 00:00Z","sentiment":0.68,"tweets":28244}

Reading minute data from a given time of day for AAPL

TimeSeries timeline = new Timeline("AAPL", TimeSeries.M).start("2015-08-19 12:34 EST").stop("2015-08-19 12:41 EST");
int i =0;
for (Object timepoint : timeline) {
	System.out.print(i++ + ". ");
	System.out.println(timepoint);
}

0. {"date":"2015-08-19 17:34Z","sentiment":0.66,"tweets":32}
1. {"date":"2015-08-19 17:35Z","sentiment":0.61,"tweets":36}
2. {"date":"2015-08-19 17:36Z","sentiment":0.74,"tweets":31}
3. {"date":"2015-08-19 17:37Z","sentiment":0.68,"tweets":41}
4. {"date":"2015-08-19 17:38Z","sentiment":0.72,"tweets":36}
5. {"date":"2015-08-19 17:39Z","sentiment":0.53,"tweets":19}
6. {"date":"2015-08-19 17:40Z","sentiment":0.72,"tweets":32}
7. {"date":"2015-08-19 17:41Z","sentiment":0.6,"tweets":35}

`com.infinigongroup.api.Tweets`

You can use the Tweets iterator to request tweets from any stream for a given period in time.

`Tweet` Data

 { 	"postedTime":"2015-08-21T00:42:08.000Z",
 	"author":"DeltaBravo33",
 	"text":"Instagram : by airbus.driver - #Qantas #QantasAirways #melbourneairport #melbourne #boeing #boeing737 #b737 #737 #m\u2026 http:\/\/t.co\/7TgGML73aA",
 	"avatar":"https:\/\/pbs.twimg.com\/profile_images\/589500634149343232\/wcMsP73m_normal.jpg"
 }

`Tweet` Parameters

stream

The constructor requires stream and resolution where:

stream - is the stream id such as AAPL or FDA

start & stop

As with Timeline you can give a date and time range by setting start and stop. stop parameter always defaults to now, while start's default value is the start of the current minute.

You can specify the dates using java.utils.Date or you can also use a String in many formats (see [Date Formats] (http://realtime.infinigongroup.com/api/docs/#data_dates)). All times are by default UTC so you must be explicit and add the timezone. You can test your date and time values using:

Time Delta

For the start parameter you can also give a time delta, specifying a period of time before the given (or default) stop date.

Period Code | Period | Example | Description --- | --- | --- | --- | --- M | minutes | "30M" | starting thirty minutes ago H | hours | "8H" | starting eight hours ago d | days |"5d" | starting five days ago w | weeks | "2w" | starting fortnight ago m | months | "3m" | starting on the same date 3 months ago y | years | "1y" | starting a year ago

`Tweet` Examples

Reading minute tweets for BA

TimeSeries tweets = new Tweets("FB", TimeSeries.M, proxy);
int i =0;
for (Object tweet : tweets) {
	System.out.print(i++ + ". ");
	System.out.println(tweet);
}

0. {"postedTime":"2015-08-21T00:42:00.000Z","author":"DeltaBravo33","text":"Instagram : by world_aviation99 - Good night!! Lufthansa Cargo Boeing 777F landing at Frankfurt intl. #Boeing #avpo\u2026 http:\/\/t.co\/iZWmjWSuUz","avatar":"https:\/\/pbs.twimg.com\/profile_images\/589500634149343232\/wcMsP73m_normal.jpg"}
1. {"postedTime":"2015-08-21T00:42:05.000Z","author":"kevinagipavuc","text":"RT @josephuhanokov: ? ??????? ?????? ??????????? ?? ?????????? Boeing ??? ????????? ???????? ?? ???????","avatar":"https:\/\/pbs.twimg.com\/profile_images\/523784246427537408\/OqWKikgU_normal.png"}
2. {"postedTime":"2015-08-21T00:42:08.000Z","author":"DeltaBravo33","text":"Instagram : by world_aviation99 - Good night!! Lufthansa Cargo Boeing 777F landing at Frankfurt intl. #Boeing #avpo\u2026 http:\/\/t.co\/0FZsSYizdK","avatar":"https:\/\/pbs.twimg.com\/profile_images\/589500634149343232\/wcMsP73m_normal.jpg"}
3. {"postedTime":"2015-08-21T00:42:08.000Z","author":"DeltaBravo33","text":"Instagram : by airbus.driver - #Qantas #QantasAirways #melbourneairport #melbourne #boeing #boeing737 #b737 #737 #m\u2026 http:\/\/t.co\/7TgGML73aA","avatar":"https:\/\/pbs.twimg.com\/profile_images\/589500634149343232\/wcMsP73m_normal.jpg"}

`com.infinigongroup.api.Snapshot`

Snapshot - Returns aggregation data for a selection of streams. Use this API to generate grids, maps, heat trees and clouds for groups of streams.

`Snapshot` Data

{
			"description": "Chevron Corporation",
			"sentiment": 0.558823529411764,
			"tags": [
				"Equities",
				"Energy",
				"SP500",
				"Oil & Gas",
				"DJ30"
			],
			"timestamp": {
				"$date": 1440164723564
			},
			"symbol": "CVX",
			"clout": 1292,
			"words": [
				[
					"chevron",
					18
				],
				[
					"oil",
					3
				],
				[
					"morningword",
					3
				],
				[
					"slick",
					3
				],
				[
					"xom",
					3
				],
				[
					"cvx",
					3
				],
				[
					"negra",
					2
				],
				[
					"loma",
					2
				],
				[
					"superpozo",
					2
				],
				[
					"negro",
					2
				]
			],
			"activity": 34,
			"change_5": 14,  
			"variance": -67,
			"change_3": 69,
			"change_10": 8
		}

`Snapshot` Fields

`last_request`

Gives you the timestamp in java.util.Date  of the last update.

`Snapshot` Parameters

`since`

Use this parameter to retrieve streams that have had activity since some given date. See the documentation on start. See above documentation on `start`.

`streams`

You can specify one or more streams, comma delimited, the default is all streams with available data.

for (Object snapshot : new Snapshots().streams("AAPL", "GOOG")) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}

0. {"clout":9278,"sentiment":0.6392857143,"symbol":"AAPL","change_10":7,"activity":6537,"change_3":18,"variance":-94,"change_5":18,"words":[["iphone",118.0],["ipad",86.0],["apple",68.0],["gameinsight",36.0],["full",29.0],["steve",27.0],["jobs",27.0],["ipadgames",27.0],["read",19.0],["16gb",17.0]],"description":"Apple Inc","tags":["Personal Computers","Equities","SP500","Technology","PWTRADEWATCHLIST"],"timestamp":{"$date":1440282041210}}
1. {"clout":5976,"sentiment":0.7231638418,"symbol":"GOOG","change_10":13,"activity":3351,"change_3":34,"variance":-92,"change_5":20,"words":[["google",105.0],["play",22.0],["servers",19.0],["allaboutgoogle",19.0],["datacenter",19.0],["loses",18.0],["disponible",18.0],["data",18.0],["app",15.0],["consecutive",15.0]],"description":"Google Inc.","tags":["Equities","SP500","Technology","Internet Information Providers","PWTRADEWATCHLIST"],"timestamp":{"$date":1440282020217}}

`tags`

Instead of specifying streams you can specify category or groups of streams that we call tags. The notation is a little more developed that just a comma delimited list, so I'm going to show you some examples:

{DJ30}{Energy}* Streams that are in the energy sector and belong to the DJ30.
<DJ30> Streams that are NOT in the DJ30.
{Energy}<DJ30>* Streams that are in the energy sector and DO NOT belong to the DJ30.
{Energy}<DJ30>*{SP500}| (Streams that are in energy sector but do not belong to the DJ30) OR ( those that belong to the SP500).

Snapshots of streams that are in the energy sector and belong to the DJ30.

for (Object snapshot : new Snapshots().tags("{DJ30}{Energy}*")) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}

0. {"clout":1557,"sentiment":0.575,"symbol":"XOM","change_10":-42,"activity":155,"change_3":-47,"variance":-72,"change_5":-39,"words":[["mobil",30.0],["baru",6.0],["ini",6.0],["drive",6.0],["coba",6.0],["mau",6.0],["test",6.0],["iims",6.0],["pameran",6.0],["bisa",6.0]],"description":"Exxon Mobil Corporation","tags":["Equities","Energy","SP500","Oil & Gas","DJ30"],"timestamp":{"$date":1440282187760}}
1. {"clout":1356,"sentiment":0.5666666667,"symbol":"CVX","change_10":15,"activity":30,"change_3":-23,"variance":25,"change_5":15,"words":[["chevron",26.0],["etsy",7.0],["blue",4.0],["via",2.0],["pnnfi0xrsb",2.0],["yellow",2.0],["female",2.0],["help",2.0],["transfer",2.0],["white",2.0]],"description":"Chevron Corporation","tags":["Equities","Energy","SP500","Oil & Gas","DJ30"],"timestamp":{"$date":1440282211473}}

Snapshots of streams that are in the energy sector and DO NOT belong to the DJ30.

for (Object snapshot : new Snapshots().tags("{Energy}<DJ30>*")) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}

0. {"clout":98,"sentiment":0.0,"symbol":"SGY","change_10":3500,"activity":2,"change_3":-100,"variance":0,"change_5":-100,"words":[],"description":"Stone Energy Corp.","tags":["Equities","Energy","Independent Oil & Gas","AUTOGEN","NYSE"],"timestamp":{"$date":1440282083093}}
1. {"clout":93,"sentiment":0.25,"symbol":"E","change_10":-26,"activity":4,"change_3":-100,"variance":-13,"change_5":-100,"words":[],"description":"Eni S.P.A.","tags":["Major Integrated Oil & Gas","AUTOGEN","Equities","Energy","PWTRADEWATCHLIST","NYSE"],"timestamp":{"$date":1440282247729}}

As you can see we are using Reverse Polish Notation.

Sets of tags you want are defined using comma delimited tags in braces {}
Sets of tags you don't want are defined using comma delimited tags in braces angle brackets <>
You can union (or OR) your sets using the | pipe symbol.
You can intersect (or AND) your sets using the * star symbol.

Just remember in Reverse Polish Notation

operandA operandB operator = operandA operator operandB
operandA operandB operator1 operandC operator2 = (operandA operator1 operandB) operator2 operandC
operandA operandB operator1 operandC operandD operator2 operator3 = (operandA operator1 operandB) operator3 (operandC operator2 operandD)

For more on RPN see http://en.wikipedia.org/wiki/Reverse_Polish_notation.

`words`

Just as with `tags` you can filter by words in the word cloud using our RPN. The setting is character case insensitive. For instance 
'{buy}<best buy>*{fda}{approval}*|`

Returns the data for streams whose word cloud contains buy but not best buy, or any streams whose word cloud contains fda and approval.

for (Object snapshot : new Snapshots().words("{buy}<best buy>*{fda}{approval}*|")) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}

0. {"clout":2972,"sentiment":0.6,"symbol":"NDAQ","change_10":104,"activity":65,"change_3":119,"variance":18,"change_5":138,"words":[["nasdaq",65.0],["inc",35.0],["corp",6.0],["rating",4.0],["zacks",4.0],["buy",4.0],["stock",4.0],["upgraded",3.0],["sells",3.0],["lifted",2.0]],"description":"Nasdaq OMX Group Inc","tags":["Diversified Investments","SP500","Financial","Equities"],"timestamp":{"$date":1440282934625}}
1. {"clout":936,"sentiment":0.3,"symbol":"NOK","change_10":-3,"activity":96,"change_3":8,"variance":-72,"change_5":-3,"words":[["nokia",28.0],["business",9.0],["german",9.0],["car",9.0],["sale",9.0],["consortium",9.0],["maps",9.0],["confirms",9.0],["buy",4.0],["smoothblink2konga",4.0]],"description":"Nokia Corporation","tags":["Equities","Communication Equipment","Technology"],"timestamp":{"$date":1440282989083}}
2. {"clout":1412,"sentiment":0.3684210526,"symbol":"BBY","change_10":10,"activity":38,"change_3":44,"variance":0,"change_5":21,"words":[["buy",32.0],["best",32.0],["time",2.0]],"description":"Best Buy Co. Inc.","tags":["Services","Equities","SP500","PWTRADEWATCHLIST","Electronics Stores"],"timestamp":{"$date":1440283001402}}
3. {"clout":1246,"sentiment":0.4545454545,"symbol":"URBN","change_10":73,"activity":33,"change_3":74,"variance":31,"change_5":26,"words":[["outfitters",20.0],["urban",20.0],["swags",5.0],["fleeks",5.0],["sensible",5.0],["dad",5.0],["buy",5.0],["sir",5.0],["amp",5.0],["dankcharnley",5.0]],"description":"Urban Outfitters Inc","tags":["Services","Equities","SP500","PWTRADEWATCHLIST","Apparel Stores"],"timestamp":{"$date":1440283019757}}
4. {"clout":4853,"sentiment":0.5784313725,"symbol":"TGT","change_10":138,"activity":102,"change_3":427,"variance":164,"change_5":356,"words":[["target",99.0],["best",57.0],["price",55.0],["offer",12.0],["freeshipping",10.0],["get",9.0],["clothing",9.0],["amp",7.0],["mossimo",7.0],["buy",7.0]],"description":"Target Corp.","tags":["PWTRADEW","Equities","Morgan Stanley Watchlist","Variety Stores","Discount","Services","SP500"],"timestamp":{"$date":1440283019889}}
5. {"clout":35092,"sentiment":0.6952054795,"symbol":"SFS","change_10":9,"activity":878,"change_3":35,"variance":-15,"change_5":23,"words":[["smart",819.0],["full",126.0],["android",115.0],["phone",114.0],["core",93.0],["unlocked",86.0],["dual",73.0],["read",67.0],["apple",64.0],["mobile",58.0]],"description":"Smart","tags":["NYSE","Equities"],"timestamp":{"$date":1440283021015}}
6. {"clout":4033,"sentiment":0.48,"symbol":"KRFT","change_10":-16,"activity":199,"change_3":7,"variance":-50,"change_5":-1,"words":[["oreo",70.0],["nabisco",7.0],["food_you_love",4.0],["cookies",3.0],["vie",3.0],["xkingpunk",3.0],["nini_oreo",2.0],["itsfoodporn",2.0],["cheesecake",2.0],["vsak3jq71a",2.0]],"description":"Kraft Foods Group Inc","tags":["Equities","Food - Major Diversified","SP500","Consumer Goods","Morgan Stanley Watc"],"timestamp":{"$date":1440283021271}}
7. {"clout":8726,"sentiment":0.6940298507,"symbol":"AAPL","change_10":2,"activity":6438,"change_3":-1,"variance":-94,"change_5":0,"words":[["iphone",114.0],["ipad",102.0],["apple",56.0],["full",38.0],["gameinsight",32.0],["ipadgames",31.0],["read",23.0],["16gb",18.0],["ebay",17.0],["case",15.0]],"description":"Apple Inc","tags":["Personal Computers","Equities","SP500","Technology","PWTRADEWATCHLIST"],"timestamp":{"$date":1440283021303}}
8. {"clout":9035,"sentiment":0.7012448133,"symbol":"BITCOIN","change_10":-33,"activity":242,"change_3":-37,"variance":-13,"change_5":-33,"words":[["bitcoin",235.0],["free",52.0],["freebitcoin",44.0],["satoshis",41.0],["earned",31.0],["robotcoingame",31.0],["total",31.0],["robot",31.0],["left",30.0],["btc",22.0]],"description":"Bitcoin","tags":["Bitcoin Information"],"timestamp":{"$date":1440283021514}}
9. {"clout":4475,"sentiment":0.3583333333,"symbol":"NFLX","change_10":20,"activity":1164,"change_3":73,"variance":-86,"change_5":42,"words":[["netflix",116.0],["chill",31.0],["watch",17.0],["amp",11.0],["bensetters",6.0],["multiple",6.0],["let",6.0],["times",6.0],["netfixandchill",5.0],["like",4.0]],"description":"NetFlix Inc.","tags":["Equities","Music & Video Stores","Level2","PWTRADEWATCHLIST","Services","SP500"],"timestamp":{"$date":1440283021636}}

`change_3`

Use this parameter to filter by **percent change** in the last **3 minutes**.

 for (Object snapshot : new Snapshots().change_3(180)) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}

0. {"clout":228,"sentiment":0.25,"symbol":"TSX:WJA","change_10":115,"activity":4,"change_3":614,"variance":1,"change_5":329,"words":[["kfll",2.0],["boeing",2.0],["aviationlovers",2.0],["avgeek",2.0],["livery",2.0],["tail",2.0],["plaid",2.0],["avporn",2.0],["westjet",2.0],["aviation",2.0]],"description":null,"tags":[],"timestamp":{"$date":1440282543164}}
1. {"clout":47,"sentiment":0.0,"symbol":"BGS","change_10":35,"activity":1,"change_3":349,"variance":167,"change_5":170,"words":[],"description":"B&G Foods Inc.","tags":["Equities","Consumer Goods","Processed & Packaged Goods"],"timestamp":{"$date":1440282543998}}
2. {"clout":47,"sentiment":0.0,"symbol":"PGR","change_10":260,"activity":1,"change_3":1100,"variance":10,"change_5":620,"words":[],"description":"Progressive Corp.","tags":["Property & Casualty Insurance","SP500","Financial","Equities"],"timestamp":{"$date":1440282604505}}
3. {"clout":91,"sentiment":0.0,"symbol":"SKYNEWS","change_10":300,"activity":1,"change_3":1234,"variance":482,"change_5":700,"words":[],"description":"Sky News","tags":["Publication"],"timestamp":{"$date":1440282615756}}
4. {"clout":44,"sentiment":0.0,"symbol":"KMB","change_10":30,"activity":1,"change_3":333,"variance":-55,"change_5":160,"words":[],"description":"Kimberly-Clark Corporation","tags":["Equities","SP500","Consumer Goods","Personal Products"],"timestamp":{"$date":1440282643536}}
5. {"clout":17,"sentiment":0.0,"symbol":"PNR","change_10":19,"activity":1,"change_3":294,"variance":238,"change_5":137,"words":[],"description":"Pentair Ltd.","tags":["Diversified Machinery","SP500","Equities","Industrial Goods"],"timestamp":{"$date":1440282729034}}
6. {"clout":46,"sentiment":0.0,"symbol":"FERGUSONTRACKER","change_10":129,"activity":20,"change_3":662,"variance":-93,"change_5":358,"words":[],"description":"Ferguson Situation","tags":["News"],"timestamp":{"$date":1440282745912}}

`change_5`

Use this parameter to filter by **percent change** in the last **5 minutes**.

 for (Object snapshot : new Snapshots().change_5(180)) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}

0. {"clout":55,"sentiment":0.0,"symbol":"UFC:WANDERLEISILVA","change_10":275,"activity":2,"change_3":-100,"variance":46,"change_5":275,"words":[],"description":null,"tags":[],"timestamp":{"$date":1440282676824}}
1. {"clout":46,"sentiment":0.0,"symbol":"FERGUSONTRACKER","change_10":129,"activity":20,"change_3":662,"variance":-93,"change_5":358,"words":[],"description":"Ferguson Situation","tags":["News"],"timestamp":{"$date":1440282745912}}
2. {"clout":27,"sentiment":0.0,"symbol":"TSX:BMO","change_10":312,"activity":1,"change_3":-100,"variance":302,"change_5":723,"words":[],"description":null,"tags":[],"timestamp":{"$date":1440282746616}}
3. {"clout":58,"sentiment":0.0,"symbol":"GT","change_10":66,"activity":1,"change_3":-100,"variance":-70,"change_5":232,"words":[],"description":"Goodyear Tire & Rubber Co","tags":["Equities","Rubber & Plastics","SP500","Consumer Goods"],"timestamp":{"$date":1440282766247}}
4. {"clout":55,"sentiment":0.0,"symbol":"TG","change_10":66,"activity":1,"change_3":452,"variance":200,"change_5":232,"words":[],"description":"Tredegar Corp.","tags":["Equities","Rubber & Plastics","Basic Materials","AUTOGEN","NYSE"],"timestamp":{"$date":1440282793676}}
5. {"clout":125,"sentiment":0.0,"symbol":"TSX:TRI","change_10":152,"activity":3,"change_3":459,"variance":201,"change_5":235,"words":[],"description":null,"tags":[],"timestamp":{"$date":1440282837295}}
6. {"clout":43,"sentiment":0.0,"symbol":"TSX:GMM.U","change_10":162,"activity":1,"change_3":773,"variance":-17,"change_5":424,"words":[],"description":null,"tags":[],"timestamp":{"$date":1440282857421}}
7. {"clout":124,"sentiment":0.0,"symbol":"DDS","change_10":224,"activity":4,"change_3":170,"variance":79,"change_5":386,"words":[["people",2.0],["dillard",2.0],["mall",2.0],["lot",2.0],["department",2.0],["rocking",2.0],["blast",2.0],["saw",2.0],["store",2.0],["cool",2.0]],"description":"Dillard's Inc","tags":["Equities","NYSE","Services","Department Stores"],"timestamp":{"$date":1440282859798}}
8. {"clout":78,"sentiment":0.0,"symbol":"FXI","change_10":295,"activity":2,"change_3":558,"variance":-64,"change_5":690,"words":[],"description":"FTSE China 25 Index Fund Ishares","tags":["Amex","Equities"],"timestamp":{"$date":1440282882266}}

`change_10`

Use this parameter to filter by **percent change** in the last **10 minutes**.

     for (Object snapshot : new Snapshots().change_3(300).change_5(180).change_10(300)) {
            System.out.print(i++ + ". ");
            System.out.println(snapshot);
            if (i == 20) break;
        }

0. {"clout":55,"sentiment":0.0,"symbol":"CFN","change_10":586,"activity":1,"change_3":2186,"variance":101,"change_5":1272,"words":[],"description":"CareFusion Corporation","tags":["SAC List","Health Care","Medical\/Dental Instruments","Equities"],"timestamp":{"$date":1440282914323}}
1. {"clout":51,"sentiment":0.0,"symbol":"CCE","change_10":555,"activity":1,"change_3":2082,"variance":50,"change_5":1210,"words":[],"description":"Coca-Cola Enterprises","tags":["Beverages Non-Alcoholic","SP500","Consumer Goods","Equities"],"timestamp":{"$date":1440283054172}}
2. {"clout":83,"sentiment":0.0,"symbol":"MTB","change_10":300,"activity":2,"change_3":567,"variance":-97,"change_5":300,"words":[],"description":"M&T Bank Corporation","tags":["Equities","SP500","Financial","Regional - Northeast Banks"],"timestamp":{"$date":1440283056919}}
3. {"clout":48,"sentiment":0.0,"symbol":"TSX:TOG","change_10":2780,"activity":1,"change_3":9501,"variance":0,"change_5":5660,"words":[],"description":null,"tags":[],"timestamp":{"$date":1440283057520}}
4. {"clout":48,"sentiment":0.0,"symbol":"TSX:TMC","change_10":2780,"activity":1,"change_3":9501,"variance":0,"change_5":5660,"words":[],"description":null,"tags":[],"timestamp":{"$date":1440283057680}}
5. {"clout":98,"sentiment":0.0,"symbol":"TPVG","change_10":4700,"activity":2,"change_3":15900,"variance":0,"change_5":9501,"words":[],"description":"Triplepoint Venture Growth Bdc","tags":["NYSE","Equities"],"timestamp":{"$date":1440283057704}}
6. {"clout":48,"sentiment":0.0,"symbol":"TSX:SGY","change_10":1958,"activity":1,"change_3":6758,"variance":0,"change_5":4015,"words":[],"description":null,"tags":[],"timestamp":{"$date":1440283062709}}
7. {"clout":1639,"sentiment":0.6666666667,"symbol":"ZAGG","change_10":516,"activity":36,"change_3":414,"variance":1279,"change_5":550,"words":[["zagg",35.0],["ipad",33.0],["mini",33.0],["full",33.0],["apple",33.0],["gray",33.0],["retina",33.0],["space",33.0],["2nd",33.0],["book",33.0]],"description":"Zagg Inc.","tags":["Equities","NASDAQ","Specialty Retail","AUTOGEN","Consumer Non-Durables"],"timestamp":{"$date":1440283074787}}
8. {"clout":148,"sentiment":0.3333333333,"symbol":"WHLR","change_10":5301,"activity":3,"change_3":17900,"variance":0,"change_5":10700,"words":[["real",2.0],["estate",2.0],["wheeler",2.0],["whlr",2.0],["trust",2.0],["investment",2.0]],"description":"Wheeler Real Estate Investment","tags":["Equities","NASDAQ","REIT - Diversified","AUTOGEN","Real Estate"],"timestamp":{"$date":1440283099475}}
9. {"clout":148,"sentiment":0.3333333333,"symbol":"WHLRP","change_10":5301,"activity":3,"change_3":17900,"variance":0,"change_5":10700,"words":[["real",2.0],["estate",2.0],["wheeler",2.0],["whlr",2.0],["trust",2.0],["investment",2.0]],"description":"Wheeler Real Estate Investment","tags":["Nasdaq","Equities"],"timestamp":{"$date":1440283099601}}

`variance`

Use this parameter to filter by **variance** for the hour. So if you used a value of 30 the Snapshot iterator would return data for streams that have 30% or more tweets than the average for that stream for the given hour for that day. This is useful for consumer companies such as Mac Donald's that typically have a higher rate at lunch times and in the evenings.

for (Object snapshot : new Snapshots().variance(3000)) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}

0. {"clout":729,"sentiment":0.125,"symbol":"URE","change_10":1687,"activity":16,"change_3":2133,"variance":9500,"change_5":2803,"words":[["ure",14.0],["someone",14.0],["like",14.0],["asks",14.0],["urpri",14.0],["birthday",14.0],["coliegestudent",14.0]],"description":"Ultra Real Estate Proshares","tags":["Amex","Equities"],"timestamp":{"$date":1440283328335}}
1. {"clout":8419,"sentiment":0.6889952153,"symbol":"BEAM","change_10":1537,"activity":209,"change_3":1519,"variance":22744,"change_5":1357,"words":[["vodka",208.0],["effen",208.0],["cent",201.0],["french",200.0],["trash",199.0],["montana",199.0],["throws",109.0],["iamakademiks",88.0],["throwing",82.0],["responds",82.0]],"description":"Beam Inc.","tags":["Equities","Beverages - Wineries & Distillers","SP500","Consumer Goods"],"timestamp":{"$date":1440283429252}}
2. {"clout":754,"sentiment":0.3529411765,"symbol":"WEATHER-SOYBEANS","change_10":281,"activity":17,"change_3":199,"variance":4917,"change_5":169,"words":[["tornado",12.0],["raiders",5.0],["minnesota",4.0],["watch",4.0],["vikings",3.0],["pregame",3.0],["field",3.0],["due",3.0],["lightning",3.0],["warning",3.0]],"description":"Soybeans US Crop","tags":["Commodities"],"timestamp":{"$date":1440283437478}}
3. {"clout":754,"sentiment":0.3529411765,"symbol":"WEATHER-CORN","change_10":284,"activity":17,"change_3":201,"variance":3257,"change_5":171,"words":[["tornado",12.0],["raiders",5.0],["minnesota",4.0],["watch",4.0],["vikings",3.0],["pregame",3.0],["field",3.0],["due",3.0],["lightning",3.0],["warning",3.0]],"description":"Corn","tags":["Commodities"],"timestamp":{"$date":1440283437576}}

`fields`

You can specify which fields you want to retrieve using a comma delimited list. By default you get them all. The available fields are:

activity
change_3
change_5
change_10
description
sentiment
variance
words
tags

for (Object snapshot : new Snapshots().variance(3000).fields("variance", "sentiment")) {
    System.out.print(i++ + ". ");
    System.out.println(snapshot);
    if (i == 20) break;
}

0. {"symbol":"URE","sentiment":0.125,"variance":9500}
1. {"symbol":"WEATHER-SOYBEANS","sentiment":0.2,"variance":4327}
2. {"symbol":"BEAM","sentiment":0.6723163842,"variance":19246}

`order`

You can use the order parameter to return the data sorted by a field, for example:

variance Returns the data in ascending order
-variance Returns the data in descending order

for (Object snapshot : new Snapshots().variance(3000).fields("variance", "sentiment").order("-sentiment")) {
	System.out.print(i++ + ". ");
	System.out.println(snapshot);
	if (i == 20) break;
}

0. {"symbol":"BEAM","sentiment":0.7006802721,"variance":15967}
1. {"symbol":"WEATHER-SOYBEANS","sentiment":0.2,"variance":4327}
2. {"symbol":"URE","sentiment":0.1,"variance":5900}

Replacing `json-simple-1.1.1.jar`.

Override protected Object parse(BufferedReader br) to call your new JSON parser.
Override protected int resultsSize() to return the size of the JSON result array.
Override protected Object resultsGet(int index) to return an element of the JSON result array.
Override protected Object responseGet(String key) to return an value of the JSON response object.

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.idea		.idea
dist/jinfinigon_jar		dist/jinfinigon_jar
src		src
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md

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canuckasaurus/java_api

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jinfinigon

REQUIRED LIBRARIES

Connecting.

Token

Using a Proxy:

com.infinigongroup.api.Timeline

Timeline Data

Timeline Parameters

stream & resolution

start & stop

Time Delta

Timeline Examples

Reading minute timepoints for AAPL

Reading hour of timepoints for AAPL

Reading day of timepoints for AAPL

Reading last 4 days of day of timepoints for AAPL

Reading minute data from a given time of day for AAPL

com.infinigongroup.api.Tweets

Tweet Data

Tweet Parameters

stream

start & stop

Time Delta

Tweet Examples

Reading minute tweets for BA

com.infinigongroup.api.Snapshot

Snapshot Data

Snapshot Fields

last_request

Snapshot Parameters

since

streams

tags

Snapshots of streams that are in the energy sector and belong to the DJ30.

Snapshots of streams that are in the energy sector and DO NOT belong to the DJ30.

words

change_3

change_5

change_10

variance

fields

order

Replacing json-simple-1.1.1.jar.

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`com.infinigongroup.api.Timeline`

`Timeline` Data

`Timeline` Parameters

`Timeline` Examples

`com.infinigongroup.api.Tweets`

`Tweet` Data

`Tweet` Parameters

`Tweet` Examples

`com.infinigongroup.api.Snapshot`

`Snapshot` Data

`Snapshot` Fields

`last_request`

`Snapshot` Parameters

`since`

`streams`

`tags`

`words`

`change_3`

`change_5`

`change_10`

`variance`

`fields`

`order`

Replacing `json-simple-1.1.1.jar`.

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