11 hot performing tokens after the crash sent markets reeling

Well, it finally happened.

After weeks of waiting, the $BTC market made up its mind, and as I’ve been suggesting for the last few weeks, it decided to head south.  You’re not likely to find a ton of optimism, at least for a little while, as Bitcoin set new lows for the year, breaking support it’d held since June.

Let’s look at the market charts.

Disclaimer: I wrote this on Sunday night after close, so if something crazy happened between then and publication ¯_(ツ)_/¯. 

Oof.

Things are beginning to level off for $BTC but that was quite a drop, one that seriously put a damper on what was a nice stretch for the Alt market.

The important thing to remember looking at these charts is that they skew positive. The 150 influencers and traders I follow believe in crypto so when these charts go negative, things are getting ugly.

Yesterday’s spike notwithstanding, the sentiment behind the Alts has majorly cooled in recent weeks. And, unfortunately, with a new lower low set and no convincing bullish momentum after, there isn’t a lot of hot air in the market at the moment.

This can be a good time to scout Alts that are weathering the storm, if not in price, then in sentiment. These are the times that test coins and their communities, so strong sentiment in a grim market can be a good thing.

Go forth, but stay vigilant.

The Top Alts

These coins/tokens had the highest power rating in my analysis this week. The power rating is a weighted calculation taking into account sentiment, volume and the number of unique users who mentioned a coin. Generally, I eliminate any coins mentioned by less than 3 individual people as I don’t feel it would be representative of a trend.

Warning Signals (New)

As I’ve been mentioning in recent weeks, high sentiment doesn’t always mean a bull trend is coming. It can also mean one has peaked.  So from this point forward, I’ll be noting any coins in the top Alts that have posted significant gains in the week prior.

This week, though, there’s nada. Everything was down.

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The Data

As always, here’s all the data I collected this week.

CoinSentiment (of 100)VolumeInfluencersPower Rating
AION769681.5
BNB68141080.5
XMR6914880.5
OST884380
SC933379.75
BTX756477.75
DGB883377.25
ORM786377.25
SUQA6910477
ubq784477
DOGE6213876.75
XYO6916376.75
FTM699476.5
DRGN669576
LINK6313576
SRN7811275.75
dev933275.5
tomo6911375.5
XBI777273.5
XLM48722773.5
VET5041972.75
ada49191272.25
PIVX783372.25
GNT863272
DCR636471.75
XTL647371.25
BCH431523271
HOT765271
BCC5014770.75
STORM4914870.5
zec44391670.5
ethusd606470.25
XLR665370.25
GVT615469.75
LUX615469.75
NDX764269.75
rvn44191169.75
SCRL892269.5
XEM892269.5
XSN892269.5
PHR679269.25
SENT645369.25
BTC383418069
BWK4622569
XEL872268.5
ICX40331368.25
ALQO783268
HXRO644368
XRP37873168
ETH37512567.5
KLKS676267.5
ZIL507567.25
PPC629266.75
EOS38161266.25
GIN4514466.25
PHX4914366.25
MTH665266
PASC4611466
ZRX38141265.75
NEO4110965.5
HKN506465.25
OMG486565.25
MANA447664.75
NPXS3910964.5
QKC4011664.5
CND782264
CV782264
BBK693263.5
WSX693263.5
PAI7838163
VIA399663
aapl407662.75
RPM624262.75
SS895162.75
pk3844362.25
BLZ504361
rto933160.75
RYO933160.75
ACM874160.5
SNM633260.5
EVX864160
dero7512159.75
xhv22471659.75
BCO474359.5
BIS692259.5
MSP474359.5
tube474359.5
XCP692259.5
ZNZ692259.5
BAT20622259
dgtx972158.75
SHA777158.75
EMC2863157.25
XNK863157.25
AURA642257
ENG642257
SCT756156.75
zer775156.75
WAVES414356.5
ECC922156.25
spy1917756.25
BSV453355.75
TFD453355.75
TRX1816955.75
LNS754154.5
ETC227754.25
MOD6116153.75
DEC6410153.5
GO396253.5
CHX6016153.25
VLD686153.25
xby783153.25
ZPT783153.25
GFY199653
XTZ414252.25
ETI646151.25
GLPM665151.25
AMB1001150
APPC502250
DADI1110750
EVE502250
grin502250
IRD502250
KEY991149.5
LTCUSD991149.5
VEY991149.5
ARO782149.25
CMX782149.25
fed782149.25
LTC912649.25
QLC782149.25
VTHO782149.25
WTI782149.25
MCO615148.75
XZC693148.75
CLOAK971148.5
dibx971148.5
DIG971148.5
MEDX971148.5
STQ971148.5
FLASH961148
LIFE961148
NEXO961148
ENJ4816147.25
BB941147
FUEL941147
MONK941147
SPHTX941147
TNT941147
SPX96545.75
mft412245.5
ONT412245.5
veo412245.5
DBIX692144.75
POE117344.75
TEL692144.75
BTS891144.5
CLOUT891144.5
DBX891144.5
DERO��_��__891144.5
frnd891144.5
MSR197244.5
STR891144.5
XMRUSD891144.5
xrn891144.5
bcn223344.25
wabi186243
fun183342.25
BCPT418141.5
WAN74441.5
TRAC223240
ren503139.25
ETCUSD781139
GXX781139
LSK781139
QSP781139
qtum781139
rain781139
SMART781139
TPAY781139
XAU781139
XLQ781139
XVG113338.75
LEND115238.5
DASH73336.75
hts453136.75
kubo453136.75
LOKI73336.75
DXY222236
GBTC222236
SKY222236
bxy502135.25
CMT502135.25
kbg202235
BTO691134.5
CAN691134.5
CECIL691134.5
IOTA691134.5
MDA691134.5
PRL113234.5
steem691134.5
nuls44233.75
IOST412130.75
BCO��_�s112230.5
WTC112230.5
XRPUSD112230.5
UKG215128.75
VIBE72228.5
gdxj62228
MUE223125.25
WINGS223125.25
ARK501125
block501125
BURST501125
BZX501125
card501125
CURV501125
DCO501125
hyg501125
KRW501125
MET501125
nflx501125
NXE501125
Q501125
RDD501125
rdn501125
REP501125
RYA501125
TKY501125
VZ501125
WAX02225
XRR501125
IOTX213124.75
nxt213124.75
APH65121.25
STRAT222121.25
ABS202120.25
gs202120.25
LTHN113119.75
HYDRO112115.75
OSTK112115.75
TVIX102115.25
YZY102115.25
ZEL72113.75
BTC’221111
btcz221111
CAL221111
CATO221111
CNN221111
CUX221111
DJI221111
DJIA221111
DNR221111
ETN221111
EXC221111
FSY221111
GE221111
gld221111
hit221111
KLKS��_��__221111
LEE221111
MBOX221111
MWAT221111
NEBL221111
NLX221111
OAX221111
OCE221111
oil221111
POT221111
REQ221111
THC221111
TLRY221111
UP221111
URA221111
UUU221111
vix221111
WSC221111
XAP221111
XTC221111
ZEN221111
ZINC221111
rupx02110.25
AMD11115.5
arch11115.5
eem11115.5
k11115.5
MNM11115.5
MTC11115.5
OMX11115.5
PART11115.5
PAY11115.5
XRP��_��__11115.5
ZCL11115.5
CTR7113.5
DB7113.5
LQD7113.5
PAL7113.5
ADC4112
BLUE4112
DASH��_��__4112
dot4112
POA4112
DENT3111.5
NVDA3111.5
ZRXUSD3111.5
APIS2111
KCS2111
nix2111
VIB2111
BTG0110
CVC0110
GFY0110
mas0110
METM0110

 

Methodology

Each week, I scrape the Twitter accounts of some of the crypto world’s favorite influencers, traders and TA folk. I run a sentiment analysis to see which coins they are mentioning positively, neutrally and which they are mentioning in a negative light.

I also take in data on unique influencers, retweets, favorites, volume and strength of feeling in sentiment.

I calculate power by through a weighted formula that takes into consideration volume, number of unique influencers and sentiment. The higher the score, the better, the lower the worse.

This isn’t financial advice, just my own way of trying to make sense of what’s out there. And as with most things, the more data I collect, hopefully, the more interesting it’ll be. This is a work in progress. Please leave suggestions on how to make it better. I imagine if I keep up with it, I’ll be able to expand a bunch of the analysis, but I wanted to start somewhere

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