Hello dear Hivers,

here are the:

$POB Statistics For The Last 7 Days, 2024-09-13 to 2024-09-20:

Who has bought how many $POB at which time:

Top $POB Buyers And $HIVE Sellers

The inside of the circle shows the buyers of $POB, ordered by $HIVE they have spent. The outside shows the recipients of that $HIVE (sellers of $POB):

Comulated Amount Of Bought $POB Per Person

Top 10 $POB buyers, how much they got and how much $HIVE they spend for this. Sorted by $HIVE, that was spent:

Top 20 $POB Buyers

Sorted by the $HIVE, they have spent:

Buyer(Descending)	Sold $HIVE	% Sold $HIVE	Bought $POB	Avg. Price	Number of Trades
@centtoken	67.19925	37.25 %	10412.39593	0.00638	43
@balina	32.10796	17.80 %	4949.62565	0.00647	40
@intacto	27.61334	15.31 %	4439.71173	0.00626	152
@mcbot	10.34930	5.74 %	1668.69084	0.00605	49
@god-0	8.24228	4.57 %	1310.00949	0.00625	22
@anadolu	8.14585	4.52 %	1303.33626	0.00625	10
@deepresearch	8.11355	4.50 %	1220.00002	0.00661	14
@vocup	5.62055	3.12 %	927.07072	0.00611	5
@pepe.voter	3.01690	1.67 %	470.41840	0.00644	24
@uscrypton	2.00000	1.11 %	317.47846	0.00630	2
@offgridlife	1.95981	1.09 %	302.00000	0.00649	14
@chaosmagic23	1.47150	0.82 %	227.51467	0.00640	15
@automaton	1.29631	0.72 %	200.00000	0.00646	4
@sbtofficial	0.96732	0.54 %	150.00000	0.00645	1
@yintercept	0.93850	0.52 %	151.52427	0.00627	32
@irivers	0.42062	0.23 %	66.55516	0.00644	8
@hivecurious	0.34020	0.19 %	54.00000	0.00630	1
@richardcrill	0.30000	0.17 %	46.87354	0.00640	1
@doombot75	0.12061	0.07 %	18.58572	0.00649	2
@god0	0.11305	0.06 %	18.08847	0.00625	1
others	0.07529	0.04 %	12.02719	0.00943	3
Sum:	180.41219	100 %	28265.90652	0.00663	443

Comulated Amount Of Sold $POB Per Person

Top 10 $POB Sellers, how much they sold and how much $HIVE they got for this, sorted by $HIVE:

Top 20 $POB Sellers

Sorted by the $HIVE, they have got:

Seller(Descending)	Earned $HIVE	% Earned $HIVE	Sold $POB	Avg. Price	Number of Trades
@intacto	43.48509	24.10 %	6727.25917	0.00648	89
@kryptodenno	39.19525	21.73 %	6039.52490	0.00649	2
@mcbot	21.34326	11.83 %	3463.01954	0.00626	40
@cursedellie	12.95992	7.18 %	1999.52347	0.00647	3
@uscrypton	12.81081	7.10 %	2026.54495	0.00638	8
@pob.voter	9.32968	5.17 %	1495.83246	0.00625	20
@psyberx	7.15354	3.97 %	1161.92461	0.00610	4
@centtoken	6.50000	3.60 %	1000.00000	0.00650	2
@anadolu	4.12543	2.29 %	615.73547	0.00670	1
@xlety	3.83405	2.13 %	608.61768	0.00630	1
@quduus1	2.22091	1.23 %	354.45810	0.00627	3
@balina	1.41396	0.78 %	218.58572	0.00648	3
@cryptofiloz	1.29417	0.72 %	199.21298	0.00650	1
@notconvinced	1.08927	0.60 %	172.89954	0.00630	3
@god0	1.02865	0.57 %	160.65554	0.00643	3
@mypathtofire	0.95439	0.53 %	156.07293	0.00609	4
@valentin86	0.94948	0.53 %	151.89951	0.00625	3
@davideownzall	0.90590	0.50 %	141.54675	0.00640	1
@helios.publisher	0.85716	0.48 %	137.30781	0.00624	6
@valchiz	0.67912	0.38 %	108.65424	0.00625	1
others	8.28217	4.59 %	1326.63118	0.00634	245
Sum:	180.41221	100 %	28265.90655	0.00635	443

Price Of The $POB

$POB Summarize Metrics

Request	Received Hive	Received HIVE %	Sold $POB	Avg. Price
sell	111.13420	61.60%	17586.45062	0.00625
buy	69.27799	38.40%	10679.45591	0.00645
sum:	180.41219	100%	28265.90653	0.00635

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Comparison With Other Tokens

$HIVE/Token

This figure shows the value of $HIVE compared to some tokens. Be aware of the nonlinear (root square) y-axes.

US-Dollar/Token

Value of $USD compared to some token. Be aware of the nonlinear (root square) y-axes.

Origin

Table Of Token Prices in $HIVE and $USD

Average value of the prices of the token. Hive and US-Dollar compared to the token:

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Links:

How I Have Set Up Elasticsearch And Kibana On My Raspberry Pi To Monitor Token Activities and here: Do You Want To See Statistics Of Your Favorite HIVE Token? or on github.

@advertisingbot2/posts?filter=stats
@achimmertens

https://github.com/achimmertens

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My last Week

Last Friday was the funeral of my Father in law.
Saturday my brother and me mowed weeds at our flat. We were visited from this nice guy:

In the evening we had BBQ with our family at home.
Sunday I cooked Paela in the garden, also with my family, so we had a good and lazy weekend.
Tuesday I was in Bonn with my "Green Code Team" and we had very good discussions about how to safe some CO2 with intelligent software solutions.
Here is a book hint:

In the night I slept here:

The next day I went to Würzburg for a meeting with my new team.
I waited more that one hour for my train, which set me out in the middle of nowhere (Bad Breisig). The following train also ended up in nothing. A woman next to me said "I go on by car. Who wants to join me?" so I went with her to Koblenz. There I got by luck a train, which was also too late. I arrived 3 hours to late for my first visit in the Company. 4 hours later I went back. So I sat 13 hours of the day in trains. Germany has been in better conditions (not only with the trains).

The next two weeks there will be no statistics!

Regards,

Achim Mertens