pip install sanpyOptionally you can provide an api key which gives access to some restricted metrics:
import san
san.ApiConfig.api_key = 'api-key-provided-by-sanbase'The data is fetched by providing a string in the format query/slug and additional parameters.
query: Available queries can be found in section: Available metricsslug: A list of projects with their slugs, names, etc. can be fetched like this:
import san
san.get("projects/all") name slug ticker
0 0x 0x ZRX
1 Achain achain ACT
2 AdEx adx-net ADX
...
Parameters:
from_date,to_date- A date or datetime in iso8601 format specifying the start and end datetime for the returned data for ex:2018-06-01interval- The interval of the returned data - an integer followed by one of:s,m,h,dorw
Default values for parameters:
from_date:datetime.now() - 365 daysto_date:datetime.now()interval:'1d'
The returned value for time-series data is in pandas DataFrame format indexed by datetime.
import san
daa = san.get(
"daily_active_addresses/santiment",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)
prices = san.get(
"prices/santiment",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)Using the defaults params:
daa = san.get("daily_active_addresses/santiment")
prices = san.get("prices/santiment")from san import Batch
batch = Batch()
batch.get(
"daily_active_addresses/santiment",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)
batch.get(
"daily_active_addresses/santiment",
from_date="2018-06-06",
to_date="2018-06-10",
interval="1d"
)
[daa1, daa2] = batch.execute()Below are described some available metrics and are given examples for fetching and for the returned format.
This metric includes the number of unique addresses that participated in the transfers of given token during the day.
daa = san.get(
"daily_active_addresses/santiment",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)Example result:
activeAddresses
datetime
2018-06-01 00:00:00+00:00 2
2018-06-02 00:00:00+00:00 4
2018-06-03 00:00:00+00:00 6
2018-06-04 00:00:00+00:00 6
2018-06-05 00:00:00+00:00 14
Each transaction has an equivalent burn rate record. The burn rate is calculated by multiplying the number of tokens moved by the number of blocks in which they appeared. Spikes in burn rate could indicate large transactions or movement of tokens that have been held for a long time.
burn_rate = san.get(
"burn_rate/santiment",
from_date="2018-05-01",
to_date="2018-05-02",
interval="1h"
)Example result:
burnRate
datetime
2018-05-01 11:00:00+00:00 3.009476e+06
2018-05-01 14:00:00+00:00 2.161845e+09
2018-05-01 17:00:00+00:00 7.263414e+05
2018-05-01 19:00:00+00:00 7.424445e+07
2018-05-01 21:00:00+00:00 6.987085e+07
2018-05-01 22:00:00+00:00 2.052304e+08
Total amount of tokens for a project that were transacted on the blockchain. This metric includes only on-chain volume, not volume in exchanges.
tv = san.get(
"transaction_volume/santiment",
from_date="2018-05-01",
to_date="2018-05-02",
interval="1h"
)Example result:
transactionVolume
datetime
2018-05-01 11:00:00+00:00 298.707310
2018-05-01 14:00:00+00:00 19356.439888
2018-05-01 17:00:00+00:00 1088.967586
2018-05-01 19:00:00+00:00 99.600000
2018-05-01 21:00:00+00:00 6177.411536
2018-05-01 22:00:00+00:00 41397.348795
2018-05-01 23:00:00+00:00 300.000000
Returns a list of github activity for a given slug and time interval.
An article explaining the github activity tracking
ga = san.get(
"github_activity/santiment",
from_date="2018-05-01",
to_date="2018-05-05",
interval="24h"
)Example result:
activity
datetime
2018-05-02 00:00:00+00:00 32
2018-05-03 00:00:00+00:00 9
2018-05-04 00:00:00+00:00 18
Fetch price history for a given slug in USD or BTC.
prices = san.get(
"prices/santiment",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)
prices = san.get(
"prices/santiment",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)
prices = san.get(
"prices/ethereum",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)Example result:
priceBtc priceUsd
datetime
2018-06-01 00:00:00+00:00 0.0001649780416666666 1.234634930555555
2018-06-02 00:00:00+00:00 0.00016521851041666669 1.2551352777777771
2018-06-03 00:00:00+00:00 0.000162902558303887 1.251881943462897
2018-06-04 00:00:00+00:00 0.0001600935277777778 1.2135782638888888
priceBtc priceUsd
datetime
2018-06-01 00:00:00+00:00 0.0001649780416666666 1.234634930555555
2018-06-02 00:00:00+00:00 0.00016521851041666669 1.2551352777777771
2018-06-03 00:00:00+00:00 0.000162902558303887 1.251881943462897
2018-06-04 00:00:00+00:00 0.0001600935277777778 1.2135782638888888
priceBtc priceUsd
datetime
2018-06-01 00:00:00+00:00 0.07708937311827956 576.862577060932
2018-06-02 00:00:00+00:00 0.0774746559139785 588.6194336917561
2018-06-03 00:00:00+00:00 0.07944145999999996 610.5163418181814
2018-06-04 00:00:00+00:00 0.07947329054545459 602.5116327272724
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