Signally references tweets with $crypto hashtags against a database of prices, giving us a ‘price_then’ for each tweet. Signally considers ‘a call’ the first time a user mentions a particular asset within a given time period and calculates a theoretical ROI based on that data point. From this signally can create many interesting analytics. You can click on the links in the table to explore the data further.
# | User | Calls | ROI | |
1 |
|
@CryptoTony__ | 833 | 199,025.4% |
2 |
|
@BITCOINTRAPPER | 820 | 256,164.1% |
3 |
|
@CoinDesk | 284 | 3,470.3% |
4 |
|
@Hayess5178 | 907 | 2,763.4% |
5 |
|
@Phoenix_Ash3s | 605 | 3,081.2% |
6 |
|
@cryptomeowmeow | 97 | 5,613.9% |
7 |
|
@CryptoNewton | 352 | 1,698.1% |
8 |
|
@BigCheds | 1067 | 2,905.7% |
9 |
|
@BackedProtocol | 75 | 12,372.1% |
10 |
|
@AlexjFerraro | 632 | 2,380.5% |
11 |
|
@binance | 457 | 1,053.4% |
12 |
|
@devchart | 94 | 4,306.7% |
13 |
|
@CryptoLoveChris | 19 | 49,490.0% |
14 |
|
@eliz883 | 2058 | 1,462.5% |
15 |
|
@TradySlim | 512 | 2,631.1% |
16 |
|
@PrimeXBT | 190 | 11,081.3% |
17 |
|
@EliNoto | 8 | 43,222.8% |
18 |
|
@TheBirbNest | 109 | 6,241.3% |
19 |
|
@jadler0 | 1 | 167,270.6% |
20 |
|
@damskotrades | 453 | 2,409.6% |