Statistics For The $POB Token For Week 34 including my diary

in #hive-1503292 months ago

Hello dear Hivers,

here are the:

$POB Statistics For The Last 7 Days, 2024-08-16 to 2024-08-23:

Who has bought how many $POB at which time:

01_BoughtPobByTime.png

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):
02_TopTenPobBuyers.png

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:
03_CommulatedAmountOfBoughtPob.png

Top 20 $POB Buyers

Sorted by the $HIVE, they have spent:

Buyer(Descending)Sold $HIVE% Sold $HIVEBought $POBAvg. PriceNumber of Trades
@anadolu86.0761635.54 %12118.172490.0072248
@szati54.0478322.32 %7885.666120.0068432
@balina37.1112015.32 %5497.855770.00672200
@justclickindiva18.531517.65 %2622.040840.0070620
@intacto15.917476.57 %2364.398800.00671575
@sbi-tokens10.324994.26 %1586.019650.006511
@pepe.voter6.534862.70 %993.767510.0067013
@vocup3.040061.26 %456.996310.006643
@coffeebuds2.698261.11 %390.922350.0069015
@yintercept2.148580.89 %322.749790.0067658
@god02.114410.87 %314.540600.006709
@deepresearch1.036390.43 %150.000000.006919
@god-01.029910.43 %153.892750.006715
@mcbot0.524870.22 %78.019150.006754
@chaosmagic230.349500.14 %50.000000.006991
@richardcrill0.296100.12 %42.863270.006916
@he-index0.198920.08 %28.489000.0069851
@irivers0.119500.05 %16.740720.007094
@buynburn0.081840.03 %12.000000.006821
@baconface0.000020.00 %0.003760.006551
others00.00 %00.000000
Sum:242.18238100 %35085.138880.007181056

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:
04_CommulatedAmountOfSoldPob.png

Top 20 $POB Sellers

Sorted by the $HIVE, they have got:

Seller(Descending)Earned $HIVE% Earned $HIVESold $POBAvg. PriceNumber of Trades
@mcbot49.8903620.60 %7312.707160.0067978
@god027.1995411.23 %3815.515810.0069515
@jhelbich25.1708610.39 %3416.704740.007372
@cursedellie23.687689.78 %3476.840330.0068520
@bhattg16.555446.84 %2349.973290.006845
@gwajnberg13.753405.68 %2037.540770.006752
@crazyphantombr10.987584.54 %1687.612910.006523
@bradleyarrow9.862374.07 %1450.091950.006828
@balina8.180943.38 %1162.835810.0069826
@anadolu7.000002.89 %1000.000000.007001
@intacto6.990242.89 %992.186970.0070389
@sbi-tokens5.422562.24 %827.742160.0065612
@uscrypton4.369561.80 %624.223180.007002
@taskmaster4450le4.090301.69 %620.755180.0066013
@thisismylife3.512021.45 %516.040940.006813
@badbitch2.089190.86 %307.161560.006816
@pob.voter1.625280.67 %242.533920.006723
@kei21.467570.61 %213.617600.006872
@chacald.dcymt1.297810.54 %191.981400.006766
@helios.publisher1.158470.48 %174.747400.006679
others17.871217.38 %2664.325740.00685751
Sum:242.18238100 %35085.138820.006841056

Price Of The $POB

05_PriceOfPob.png

$POB Summarize Metrics

grafik.png

RequestReceived HiveReceived HIVE %Sold $POBAvg. Price
sell142.3820158.79%20985.714430.00672
buy99.8003741.21%14099.424420.00699
sum:242.18238000000002100%35085.138850.00685

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.

01_HivePerToken.png

US-Dollar/Token

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

02_USDPerToken.png

coinMarketCapChart.png
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:

03_TableOfTokenPrices.png


grafik.png

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.

https://peakd.com/@advertisingbot2/posts?filter=stats
https://peakd.com/@achimmertens

https://github.com/achimmertens


My last Week

I am still struggeling, yes fighting, with the technical issues aof finetuning an Large Language Model. There are lots of videos on youtube, how to finetune an LLM, but they don't work on my side. Either the resources in the collab jupyter notebook are not enough (10% before the end), or my harddisk is to small (I have now a bigger one) or the dependencies of the libraries are meshed. The fight is still ongoing. And am only satisfied, when I am able to give my model some trainingdata and after learning that, it is able to generate usable answers for my charity- and green code questions.

Regards,

Achim Mertens