Hello dear Hivers,

here are the:

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

Who has bought how many $LEO at which time:

Top $LEO Buyers And $HIVE Sellers

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

Comulated Amount Of Bought $LEO Per Person

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

Top 20 $LEO Buyers

Sorted by the $HIVE, they have spent:

Buyer(Descending)	Sold $HIVE	% Sold $HIVE	Bought $LEO	Avg. Price	Number of Trades
@konvik	6529.18884	41.27 %	36667.86818	0.17579	87
@jelly-cz	2575.95692	16.28 %	15383.77097	0.16688	96
@all.coin.hive	2414.69204	15.26 %	14470.46587	0.16758	50
@god0	663.83703	4.20 %	3936.62997	0.16812	36
@jelly13	530.70350	3.35 %	3312.47804	0.15820	6
@mcbot	371.12871	2.35 %	2215.20600	0.16563	62
@rubilu	340.42754	2.15 %	2007.39697	0.16958	2
@onealfa	336.38957	2.13 %	1994.64999	0.16935	10
@sbi-tokens	319.22799	2.02 %	1866.83604	0.17060	2
@god-0	308.90885	1.95 %	1864.52500	0.16473	33
@mightpossibly	270.70128	1.71 %	1620.62699	0.16700	15
@taskmaster4450le	215.61753	1.36 %	1200.89601	0.17914	12
@enginemaker	201.47554	1.27 %	1185.03597	0.16945	8
@balina	94.63077	0.60 %	563.40500	0.16769	147
@alexicp	85.73540	0.54 %	514.54702	0.16677	9
@leotaker	80.14487	0.51 %	476.65900	0.16850	24
@master-lamps	66.57820	0.42 %	396.87099	0.16615	6
@ganjafarmer	52.00424	0.33 %	306.88600	0.16928	13
@bradleyarrow	51.45675	0.33 %	300.00000	0.17429	3
@topbooster	30.57870	0.19 %	180.00000	0.16984	4
others	279.37022	1.77 %	1640.92900	0.17604	152
Sum:	15818.75449	100 %	92105.68301	0.17299	777

Comulated Amount Of Sold $LEO Per Person

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

Top 20 $LEO Sellers

Sorted by the $HIVE, they have got:

Seller(Descending)	Earned $HIVE	% Earned $HIVE	Sold $LEO	Avg. Price	Number of Trades
@shortsegments	5992.87614	37.88 %	33785.93515	0.17273	72
@konvik	2148.32536	13.58 %	12785.46799	0.16758	54
@all.coin.hive	1864.75649	11.79 %	10933.14400	0.17127	47
@bartosz546	1112.40583	7.03 %	6875.75693	0.16309	19
@god0	958.30164	6.06 %	5674.36497	0.17157	27
@phoenixonhive	570.42418	3.61 %	3500.99999	0.16261	13
@xawi	453.70058	2.87 %	2493.31201	0.18197	2
@uscrypton	309.24632	1.95 %	1843.62900	0.16775	14
@keychain.swap	280.97957	1.78 %	1666.01899	0.16782	32
@kryptodenno	244.25018	1.54 %	1467.48199	0.16644	16
@enginemaker	198.44706	1.25 %	1176.14301	0.17280	8
@sepracore	169.30762	1.07 %	1014.50000	0.17004	31
@smallbites	91.99500	0.58 %	500.00000	0.18399	1
@lalai	91.99489	0.58 %	500.00000	0.18399	1
@leotaker	81.27054	0.51 %	479.21001	0.17396	29
@tomhall	77.24725	0.49 %	466.79700	0.16586	9
@bitrocker2020	65.30588	0.41 %	393.33800	0.16600	11
@ganjafarmer	64.01579	0.40 %	366.53999	0.17504	8
@master-lamps	63.73756	0.40 %	365.10799	0.17070	14
@jphamer1	62.77828	0.40 %	373.47700	0.16741	11
others	917.38840	5.80 %	5444.45899	0.16895	358
Sum:	15818.75456	100 %	92105.68301	0.16925	777

Price Of The $LEO

$LEO Summarize Metrics

Request	Received Hive	Received HIVE %	Sold $LEO	Avg. Price
sell	8221.44127	51.97%	49183.56183	0.16677
buy	7597.31321	48.03%	42922.12116	0.17316
sum:	15818.75448	100%	92105.68299	0.16997

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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

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