Package for Santiment API access with python

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sanpy

PyPI version

Python client for cryptocurrency data from Santiment API. This library provides utilities for accessing the GraphQL Santiment API endpoint and convert the result to pandas dataframe.

More documentation regarding the API and definitions of metrics can be found on Santiment Academy

Table of contents

Installation

To install the latest sanpy from PyPI:

pip install sanpy

Upgrade to latest version

pip install --upgrade sanpy

Install extra packages

There are few scripts under extras directory related to backtesting and event studies. To install their dependencies use:

pip install sanpy[extras]

Restricted metrics

In order to access real-time data or historical data for some of the metrics, you'll need to set the API key, generated from an account with a paid API plan.

Configuration

You can provide an API key which gives access to the restricted metrics in two different ways:

Read the API key from the environment

During loading of the san module, if the SANPY_APIKEY exists, its content is read and set as the API key.

export SANPY_APIKEY="my_apikey"

import san
>>> san.ApiConfig.api_key
'my_apikey'

Manually configure an API key

import san
san.ApiConfig.api_key = "my_apikey"

How to obtain an API key

To obtain an API key you should log in to sanbase and go to the Account page - https://app.santiment.net/account. There is an API Keys section and a Generate new api key button.

Getting the data

Using the provided functions

The library provides the get and get_many functions that are used to fetch data. get is used to fetch timeseries data for a single metric/asset pair. get_many is used to fetch timeseries data for a single metric, but many assets. This is counted as 1 API call.

The first argument to the functions is the metric name.

The rest of the parameters are::

  • slug - (for get) The project identificator, as seen in the Available projects section
  • slugs - (for get_many) A list of projects' identificators, as seen in the Available projects section
  • selector - Allow for more flexible selection of the target. Some metrics are computed on blockchain addresses, for others you can provide a list of slugs, labels, amount of top holders. etc.
  • from_date - A date or datetime in ISO8601 format specifying the start of the queried period. Defaults to datetime.utcnow() - 365 days
  • to_date - A date or datetime in ISO86091 format specifying the end of the queried period. Defaults to datetime.utcnow()
  • interval - The interval between the data points in the timeseries. Defaults to '1d' It is represented in two different ways:
    • a fixed range: an integer followed by one of: s, m, h, d or w
    • a function, providing some semantic or a dynamic range: toStartOfMonth, toStartOfDay, toStartOfWeek, toMonday..

The returned result for time-series data is transformed into pandas DataFrame and is indexed by datetime. For get, the value column is named value. For get_many, there is one column per asset queried. The asset slugs are used for the column names.

For backwards compatibility, fetching the metric by providing "metric/slug" as the first instead of using a separate 'slug'/'selector' continues to work, but it is not the recommended approach.

For non-metric related data like getting the list of available assets, the data is fetched by providing a string in the format query/argument and additional parameters.

The examples below contain some of the described scenarios.

Fetch metric by providing metric as first argument and slug as named parameter:

import san
san.get(
  "price_usd",
  slug="bitcoin",
  from_date="2022-01-01",
  to_date="2022-01-05",
  interval="1d"
)

datetime                   value
2022-01-01 00:00:00+00:00  47686.811509
2022-01-02 00:00:00+00:00  47345.220564
2022-01-03 00:00:00+00:00  46458.116959
2022-01-04 00:00:00+00:00  45928.661063
2022-01-05 00:00:00+00:00  43569.003348

Fetch prices for multiple assets:

import san
san.get_many(
  "price_usd",
  slugs=["bitcoin", "ethereum", "tether"],
  from_date="2022-01-01",
  to_date="2022-01-05",
  interval="1d"
)

datetime                   bitcoin       ethereum     tether                                            
2022-01-01 00:00:00+00:00  47686.811509  3769.696916  1.000500
2022-01-02 00:00:00+00:00  47345.220564  3829.565045  1.000460
2022-01-03 00:00:00+00:00  46458.116959  3761.380274  1.000165
2022-01-04 00:00:00+00:00  45928.661063  3795.890130  1.000208
2022-01-05 00:00:00+00:00  43569.003348  3550.386882  1.000122

Fetch development activity of a specific Github organization:

import san
san.get(
  "dev_activity",
  selector={"organization": "google"},
  from_date="2022-01-01",
  to_date="2022-01-05",
  interval="1d"
)

datetime                    value     
2022-01-01 00:00:00+00:00   176.0
2022-01-02 00:00:00+00:00   129.0
2022-01-03 00:00:00+00:00   562.0
2022-01-04 00:00:00+00:00  1381.0
2022-01-05 00:00:00+00:00  1334.0

Fetch a metric for a contract address, not a slug:

import san
san.get(
  "contract_transactions_count",
  selector={"contractAddress": "0x00000000219ab540356cBB839Cbe05303d7705Fa"},
  from_date="2022-01-01",
  to_date="2022-01-05",
  interval="1d"
)

datetime                   value     
2022-01-01 00:00:00+00:00   90.0
2022-01-02 00:00:00+00:00  339.0
2022-01-03 00:00:00+00:00  486.0
2022-01-04 00:00:00+00:00  314.0
2022-01-05 00:00:00+00:00  328.0

Fetch top holders metric and specify the number of top holders to be counted:

import san
san.get(
  "amount_in_top_holders",
  selector={"slug": "santiment", "holdersCount": 10},
  from_date="2022-01-01",
  to_date="2022-01-05",
  interval="1d"
)

datetime                   value
2022-01-01 00:00:00+00:00  7.391186e+07
2022-01-02 00:00:00+00:00  7.391438e+07
2022-01-03 00:00:00+00:00  7.391984e+07
2022-01-04 00:00:00+00:00  7.391984e+07
2022-01-05 00:00:00+00:00  7.391984e+07

Fetch trade volume of a given DEX for a given slug

import san
# This requires Santiment API PRO apikey configured
san.get(
  "total_trade_volume_by_dex",
  selector={"slug": "ethereum", "label": "decentralized_exchange", "owner": "UniswapV2"},
  from_date="2022-01-01",
  to_date="2022-01-05",
  interval="1d"
)

datetime                    value
2022-01-01 00:00:00+00:00   96882.176846
2022-01-02 00:00:00+00:00   85184.970249
2022-01-03 00:00:00+00:00  107489.846163
2022-01-04 00:00:00+00:00  105204.677503
2022-01-05 00:00:00+00:00  174178.848916

Fetch metric by providing metric/slug as first argument and no slug as named parameter:

import san

san.get(
    "daily_active_addresses/bitcoin",
    from_date="2018-06-01",
    to_date="2018-06-05",
    interval="1d"
)

datetime                   value      
2018-06-01 00:00:00+00:00  692508.0
2018-06-02 00:00:00+00:00  521887.0
2018-06-03 00:00:00+00:00  531464.0
2018-06-04 00:00:00+00:00  702902.0
2018-06-05 00:00:00+00:00  655695.0

Fetch non-timeseries data:

import san
san.get("projects/all")

                name             slug ticker   totalSupply
0             0chain           0chain    ZCN     400000000
1                 0x               0x    ZRX    1000000000
2          0xBitcoin            0xbtc  0xBTC      20999984
...

Execute an arbitrary GraphQL request

Some of the available queries in the Santiment API do not have a dedicated sanpy function. Alternatively, if the returned format needs to be parsed differently, this approach can be used, too. They can be fetched by providing the raw GraphQL query.

Fetching data for many slugs at the same time. Note that this is also available as san.get_many

import san
import pandas as pd

result = san.graphql.execute_gql("""
{
  getMetric(metric: "price_usd") {
    timeseriesDataPerSlug(
      selector: {slugs: ["ethereum", "bitcoin"]}
      from: "2022-05-05T00:00:00Z"
      to: "2022-05-08T00:00:00Z"
      interval: "1d") {
        datetime
        data{
          value
          slug
        }
    }
  }
}
""")

data = result['getMetric']['timeseriesDataPerSlug']
rows = []
for datetime_point in data:
    row = {'datetime': datetime_point['datetime']}
    for slug_data in datetime_point['data']:
        row[slug_data['slug']] = slug_data['value']
    rows.append(row)

df = pd.DataFrame(rows)
df.set_index('datetime', inplace=True)

datetime              bitcoin       ethereum                
2022-05-05T00:00:00Z  36575.142133  2749.213042
2022-05-06T00:00:00Z  36040.922350  2694.979684
2022-05-07T00:00:00Z  35501.954144  2636.092958

Fetching a specific set of fields for a project:

import san
import pandas as pd

result = san.graphql.execute_gql("""{
  projectBySlug(slug: "santiment") {
    slug
    name
    ticker
    infrastructure
    mainContractAddress
    twitterLink
  }
}""")

pd.DataFrame(result["projectBySlug"], index=[0])

  infrastructure                         mainContractAddress       name       slug ticker                        twitterLink
0            ETH  0x7c5a0ce9267ed19b22f8cae653f198e3e8daf098  Santiment  santiment    SAN  https://twitter.com/santimentfeed

Execute SQL queries and get the result

One of the Santiment products is Santiment Queries. It allows you to execute SQL queries on a database hosted by Santiment. Explore the documentation in order to get familiar with the available data and how to write SQL queries.

In order to execute a query you need to provide your API key.

Executing a query and getting the result as a pandas DataFrame:

import san
san.execute_sql(query="SELECT * FROM daily_metrics_v2 LIMIT 5")

   metric_id  asset_id                    dt  value           computed_at
0         10      1369  2015-07-17T00:00:00Z    0.0  2020-10-21T08:48:42Z
1         10      1369  2015-07-18T00:00:00Z    0.0  2020-10-21T08:48:42Z
2         10      1369  2015-07-19T00:00:00Z    0.0  2020-10-21T08:48:42Z
3         10      1369  2015-07-20T00:00:00Z    0.0  2020-10-21T08:48:42Z
4         10      1369  2015-07-21T00:00:00Z    0.0  2020-10-21T08:48:42Z

In order to change the index to one of the columns, provide the set_index parameter:

import san
san.execute_sql(query="SELECT * FROM daily_metrics_v2 LIMIT 5", set_index="dt")

dt                    metric_id  asset_id  value           computed_at
2015-07-17T00:00:00Z         10      1369    0.0  2020-10-21T08:48:42Z
2015-07-18T00:00:00Z         10      1369    0.0  2020-10-21T08:48:42Z
2015-07-19T00:00:00Z         10      1369    0.0  2020-10-21T08:48:42Z
2015-07-20T00:00:00Z         10      1369    0.0  2020-10-21T08:48:42Z
2015-07-21T00:00:00Z         10      1369    0.0  2020-10-21T08:48:42Z

The queries can be parametrized. In the query the parameters are named parameters, surrounded by two curly brackets {{key}}. The parameters is a dictionary. The query can be a multiline string:

san.execute_sql(query="""
  SELECT
    get_metric_name(metric_id) AS metric,
    get_asset_name(asset_id) AS asset,
    dt,
    argMax(value, computed_at)
  FROM daily_metrics_v2
  WHERE
    asset_id = get_asset_id({{slug}}) AND
    metric_id = get_metric_id({{metric}}) AND
    dt >= now() - INTERVAL {{last_n_days}} DAY
  GROUP BY dt, metric_id, asset_id
  ORDER BY dt ASC
""",
parameters={'slug': 'bitcoin', 'metric': 'daily_active_addresses', 'last_n_days': 7},
set_index="dt")

dt                                         metric   asset  value                     
2023-03-22T00:00:00Z  daily_active_addresses  bitcoin                    941446.0
2023-03-23T00:00:00Z  daily_active_addresses  bitcoin                    913215.0
2023-03-24T00:00:00Z  daily_active_addresses  bitcoin                    884271.0
2023-03-25T00:00:00Z  daily_active_addresses  bitcoin                    906851.0
2023-03-26T00:00:00Z  daily_active_addresses  bitcoin                    835596.0
2023-03-27T00:00:00Z  daily_active_addresses  bitcoin                   1052637.0
2023-03-28T00:00:00Z  daily_active_addresses  bitcoin                    311566.0

Available metrics

Getting all of the metrics as a list is done using the following code:

san.available_metrics()

Available Metrics for Slug

Getting all of the metrics for a given slug is achieved with the following code:

san.available_metrics_for_slug("santiment")

Fetch timeseries metric

import san

san.get(
    "daily_active_addresses",
    slug="santiment",
    from_date="2018-06-01",
    to_date="2018-06-05",
    interval="1d"
)

Using the defaults params (last 1 year of data with 1 day interval):

san.get("daily_active_addresses", slug="santiment")
san.get("price_usd", slug="santiment")

Fetching metadata for a metric

Fetching the metadata for an on-chain metric.

san.metadata(
    "nvt",
    arr=["availableSlugs", "defaultAggregation", "humanReadableName", "isAccessible", "isRestricted", "restrictedFrom", "restrictedTo"]
)

Example result:

{"availableSlugs": ["0chain", "0x", "0xbtc", "0xcert", "1sg", ...],
"defaultAggregation": "AVG", "humanReadableName": "NVT (Using Circulation)", "isAccessible": True, "isRestricted": True, "restrictedFrom": "2020-03-21T08:44:14Z", "restrictedTo": "2020-06-17T08:44:14Z"}
  • availableSlugs - A list of all slugs available for this metric.
  • defaultAggregation - If big interval are queried, all values that fall into this interval will be aggregated with this aggregation.
  • humanReadableName - A name of the metric suitable for showing to users.
  • isAccessible - True if the metric is accessible. If API key is configured, c hecks the API plan subscriptions. False if the metric is not accessible. For example circulation_1d requires PRO plan subscription in order to be accessible at all.
  • isRestricted - True if time restrictions apply to the metric and your current plan (Free if no API key is configured). Check restrictedFrom and restrictedTo.
  • restrictedFrom - The first datetime available of that metric for your current plan.
  • restrictedTo - The last datetime available of that metric and your current plan.

Batching multiple queries

Multiple queries can be executed in a batch to speed up the performance.

There are two batch classes provided - Batch and AsyncBatch.

Note: Batching improves the performance and the developer experience, but every query put inside the batch is still counted as one separate API call. To fetch a metric for multiple assets at a time take a look at san.get_many

  • AsyncBatch is the recommended batch class. It executes all the queries in separate HTTP requests. The benefit of using AsyncBatch over looping and executing every API call is that the queries can be executed concurrently. Putting multiple API calls in separate HTTP calls also allows to fetch more data, otherwise you might run into Complexity issues. The concurrency is controlled by the max_workers optional parameter to the execute function. By default the max_workers value is 10. It also supports get_many function to fetch data for many assets.

  • Batch combines all the provided queries in a single GraphQL document and executes them in a single HTTP request. This batching technique should be used when lightweight queries that don't fetch a lot of data are used. The reason is that the complexity of each query is accumulated and the batch can be rejected.

Note: If you have been using Batch() and want to switch to the newer AsyncBatch() you only need to change the batch initialization. The functions for adding queries and executing the batch, as well as the format of the response, are the same.

from san import Batch

batch = Batch()

batch.get(
    "daily_active_addresses",
    slug="santiment",
    from_date="2018-06-01",
    to_date="2018-06-05",
    interval="1d"
)

batch.get(
    "transaction_volume",
    slug="santiment",
    from_date="2018-06-01",
    to_date="2018-06-05",
    interval="1d"
)

[daa, trx_volume] = batch.execute()

from san import AsyncBatch

batch = AsyncBatch()

batch.get(
    "daily_active_addresses",
    slug="santiment",
    from_date="2018-06-01",
    to_date="2018-06-05",
    interval="1d"
)
batch.get_many(
    "daily_active_addresses",
    slugs=["bitcoin", "ethereum"],
    from_date="2018-06-01",
    to_date="2018-06-05",
    interval="1d"
)
[daa, daa_many] = batch.execute(max_workers=10)

Rate Limit Tools

There are two functions, which can help you in handling the rate limits:

  • is_rate_limit_exception - Returns whether the exception caught is because of rate limitation
  • rate_limit_time_left - Returns the time left before the rate limit expires
  • api_calls_made - Returns the API calls for each day in which it was used
  • api_calls_remaining - Returns the API calls remaining for the month, hour and minute

Example:

import time
import san

try:
  san.get(
    "price_usd",
    slug="santiment",
    from_date="utc_now-30d",
    to_date="utc_now",
    interval="1d"
  )
except Exception as e:
  if san.is_rate_limit_exception(e):
    rate_limit_seconds = san.rate_limit_time_left(e)
    print(f"Will sleep for {rate_limit_seconds}")
    time.sleep(rate_limit_seconds)

...

calls_by_day = san.api_calls_made()
calls_remaining = san.api_calls_remaining()

Metric Complexity

Fetch the complexity of a metric. The complexity depends on the from/to/interval parameters, as well as the metric and the subscription plan. A request might have a maximum complexity of 50000. If a request has a higher complexity there are a few ways to solve the issue:

  • Break down the request into multiple requests with smaller from-to ranges.
  • Upgrade to a higher subscription plan.

More about the complexity can be found on Santiment Academy

san.metric_complexity(
    metric="price_usd",
    from_date="2020-01-01",
    to_date="2020-02-20",
    interval="1d"
)

Include Incomplete Data Flag

Daily metrics have one value per day. For the current day, the latest computed value will not include a full day of data. For example, computing daily_active_addresses at 08:00 includes data for one third of the day. To reduce confusion, the current day value for metrics that have this behaviour is excluded. To force fetching the current day value, the includeIncompleteData flag must be used.

san.get(
  "daily_active_addresses/bitcoin",
  from_date="utc_now-3d",
  to_date="utc_now",
  interval="1d",
  include_incomplete_data=True
)

Metric/Asset pair available cince

Fetch the first datetime for which a metric is available for a given slug.

san.available_metric_for_slug_since(metric="daily_active_addresses", slug="santiment")

Transform the result

Example usage:

san.get(
  "price_usd",
  slug="santiment",
  from_date="2020-06-01",
  to_date="2021-06-05",
  interval="1d",
  transform={"type": "moving_average", "moving_average_base": 100},
  aggregation="LAST"
)

Where the parameters, that are not mentioned, are optional:

transform - Apply a transformation on the data. The supported transformations are:

  • "moving_average" - Replace every value Vi with the average of the last "moving_average_base" values.
  • "consecutive_differences" - Replace every value Vi with the value Vi - Vi-1 where i is the position in the list. Automatically fetches some extra data needed in order to compute the first value.
  • "percent_change" - Replace every value Vi with the percent change of Vi-1 and Vi ( (Vi / Vi-1 - 1) * 100) where i is the position in the list. Automatically fetches some extra data needed in order to compute the first value.

aggregation - the aggregation which is used for the query results.

Available projects

Returns a DataFrame with all the projects available in the Santiment API. Not all metrics will be available for each of the projects.

slug is the unique identifier of a project, used in the metrics fetching.

san.get("projects/all")

Example result:

                 name             slug ticker   totalSupply
0              0chain           0chain    ZCN     400000000
1                  0x               0x    ZRX    1000000000
2           0xBitcoin            0xbtc  0xBTC      20999984
3     0xcert Protocol           0xcert    ZXC     500000000
4              1World           1world    1WO      37219453
5        AB-Chain RTB     ab-chain-rtb    RTB      27857813
6             Abulaba          abulaba    AAA     397000000
7                 AC3              ac3    AC3    80235326.0
...

Non-standard metrics

Here is a list of metrics that are not part of the returned list of metrics found above. This is due to having different response format and semantics.

Other Price metrics

Marketcap, Price USD, Price BTC and Trading Volume

san.get(
    "prices",
    slug="santiment",
    from_date="2018-06-01",
    to_date="2018-06-05",
    interval="1d"
)

Open, High, Close, Low Prices, Volume, Marketcap

Note: this query cannot be batched!

san.get(
    "ohlcv",
    slug="santiment",
    from_date="2018-06-01",
    to_date="2018-06-05",
    interval="1d"
)

Example result:

datetime                        openPriceUsd  closePriceUsd  highPriceUsd  lowPriceUsd   volume  marketcap
2018-06-01 00:00:00+00:00       1.24380        1.27668       1.26599       1.19099       852857  7.736268e+07
2018-06-02 00:00:00+00:00       1.26136        1.30779       1.27612       1.20958      1242520  7.864724e+07
2018-06-03 00:00:00+00:00       1.28270        1.28357       1.24625       1.21872      1032910  7.844339e+07
2018-06-04 00:00:00+00:00       1.23276        1.24910       1.18528       1.18010       617451  7.604326e+07

Mining Pools Distribution

Returns distribution of miners between mining pools. What part of the miners are using top3, top10 and all the other pools. Currently only ETH is supported.

Premium metric

san.get(
    "mining_pools_distribution",
    slug="ethereum",
    from_date="2019-06-01",
    to_date="2019-06-05",
    interval="1d"
)

Example result:

datetime                      other     top10      top3
2019-06-01 00:00:00+00:00  0.129237  0.249906  0.620857
2019-06-02 00:00:00+00:00  0.127432  0.251903  0.620666
2019-06-03 00:00:00+00:00  0.122058  0.249603  0.628339
2019-06-04 00:00:00+00:00  0.127726  0.254982  0.617293
2019-06-05 00:00:00+00:00  0.120436  0.265842  0.613722

Historical Balance

Historical balance for erc20 token or eth address. Returns the historical balance for a given address in the given interval.

san.get(
    "historical_balance",
    slug="santiment",
    address="0x1f3df0b8390bb8e9e322972c5e75583e87608ec2",
    from_date="2019-04-18",
    to_date="2019-04-23",
    interval="1d"
)

Example result:

datetime                     balance
2019-04-18 00:00:00+00:00  382338.33
2019-04-19 00:00:00+00:00  382338.33
2019-04-20 00:00:00+00:00  382338.33
2019-04-21 00:00:00+00:00  215664.33
2019-04-22 00:00:00+00:00  215664.33

Ethereum Top Transactions

Top ETH transactions for project's team wallets.

Available transaction types:

  • ALL
  • IN
  • OUT
san.get(
    "eth_top_transactions",
    slug="santiment",
    from_date="2019-04-18",
    to_date="2019-04-30",
    limit=5,
    transaction_type="ALL"
)

Example result:

The result is shortened for convenience

datetime                           fromAddress  fromAddressInExchange           toAddress  toAddressInExchange              trxHash      trxValue
2019-04-29 21:33:31+00:00  0xe76fe52a251c8f...                  False  0x45d6275d9496b...                False  0x776cd57382456a...        100.00
2019-04-29 21:21:18+00:00  0xe76fe52a251c8f...                  False  0x468bdccdc334f...                False  0x848414fb5c382f...         40.95
2019-04-19 14:14:52+00:00  0x1f3df0b8390bb8...                  False  0xd69bc0585e05e...                False  0x590512e1f1fbcf...         19.48
2019-04-19 14:09:58+00:00  0x1f3df0b8390bb8...                  False  0x723fb5c14eaff...                False  0x78e0720b9e72d1...         15.15

Ethereum Spent Over Time

ETH spent for each interval from the project's team wallet and time period

san.get(
    "eth_spent_over_time",
    slug="santiment",
    from_date="2019-04-18",
    to_date="2019-04-23",
    interval="1d"
)

Example result:

datetime                    ethSpent
2019-04-18 00:00:00+00:00   0.000000
2019-04-19 00:00:00+00:00  34.630284
2019-04-20 00:00:00+00:00   0.000000
2019-04-21 00:00:00+00:00   0.000158
2019-04-22 00:00:00+00:00   0.000000

Token Top Transactions

Top transactions for the token of a given project

san.get(
    "token_top_transactions",
    slug="santiment",
    from_date="2019-04-18",
    to_date="2019-04-30",
    limit=5
)

Example result:

The result is shortened for convenience

datetime                           fromAddress  fromAddressInExchange           toAddress  toAddressInExchange              trxHash      trxValue
2019-04-21 13:51:59+00:00  0x1f3df0b8390bb8...                  False  0x5eaae5e949952...                False  0xdbced935b09dd0...  166674.00000
2019-04-28 07:43:38+00:00  0x0a920bfdf7f977...                  False  0x868074aab18ea...                False  0x5f2214d34bcdc3...   33181.82279
2019-04-28 07:53:32+00:00  0x868074aab18ea3...                  False  0x876eabf441b2e...                 True  0x90bd286da38a2b...   33181.82279
2019-04-26 14:38:45+00:00  0x876eabf441b2ee...                   True  0x76af586d041d6...                False  0xe45b86f415e930...   28999.64023
2019-04-30 15:17:28+00:00  0x876eabf441b2ee...                   True  0x1f4a90043cf2d...                False  0xc85892b9ef8c64...   20544.42975

Top Transfers

Top transfers for the token of a given project, address and transaction_type arguments can be added as well, in the form of a key-value pair. The transaction_type parameter can have one of these three values: ALL, OUT, IN.

san.get(
    "top_transfers",
    slug="santiment",
    from_date="utc_now-30d",
    to_date="utc_now",
)

The result is shortened for convenience

Example result:

                          fromAddress   toAddress     trxHash       trxValue
datetime                                                                                                                                                                                                                          
2021-06-17 00:16:26+00:00  0xa48df...  0x876ea...  0x62a56...  136114.069733
2021-06-17 00:10:05+00:00  0xbd3c2...  0x876ea...  0x732a5...  117339.779890
2021-06-19 21:36:03+00:00  0x59646...  0x0d45b...  0x5de31...  112336.882707
...

san.get(
    "top_transfers",
    slug="santiment",
    address="0x26e068650ae54b6c1b149e1b926634b07e137b9f",
    transaction_type="ALL",
    from_date="utc_now-30d",
    to_date="utc_now",
)

Example result:

                          fromAddress  toAddress    trxHash   trxValue
datetime                                                                                                                                                                                        
2021-06-13 09:14:01+00:00  0x26e06...  0xfd3d...  0x4af6...  69854.528
2021-06-13 09:13:01+00:00  0x876ea...  0x26e0...  0x18c1...  69854.528
2021-06-14 08:54:52+00:00  0x876ea...  0x26e0...  0xdceb...  59920.591
...

Emerging Trends

Emerging trends for a given period of time.

san.get(
    "emerging_trends",
    from_date="2019-07-01",
    to_date="2019-07-02",
    interval="1d",
    size=5
)

Example result:

datetime                        score    word
2019-07-01 00:00:00+00:00  375.160034    lnbc
2019-07-01 00:00:00+00:00  355.323281    dent
2019-07-01 00:00:00+00:00  268.653820    link
2019-07-01 00:00:00+00:00  231.721809  shorts
2019-07-01 00:00:00+00:00  206.812798     btt
2019-07-02 00:00:00+00:00  209.343752  bounce
2019-07-02 00:00:00+00:00  135.412811    vidt
2019-07-02 00:00:00+00:00  116.842801     bat
2019-07-02 00:00:00+00:00   98.517600  bottom
2019-07-02 00:00:00+00:00   89.309975   haiku

Top Social Gainers Losers

Top social gainers/losers returns the social volume changes for crypto projects.

san.get(
    "top_social_gainers_losers",
    from_date="2019-07-18",
    to_date="2019-07-30",
    size=5,
    time_window="2d",
    status="ALL"
)

Example result:

The result is shortened for convenience

datetime                              slug     change    status
2019-07-28 01:00:00+00:00     libra-credit  21.000000    GAINER
2019-07-28 01:00:00+00:00             aeon  -1.000000     LOSER
2019-07-28 01:00:00+00:00    thunder-token   5.000000  NEWCOMER
2019-07-28 02:00:00+00:00     libra-credit  43.000000    GAINER
...                                    ...        ...       ...
2019-07-30 07:00:00+00:00            storj  12.000000  NEWCOMER
2019-07-30 11:00:00+00:00            storj  21.000000    GAINER
2019-07-30 11:00:00+00:00            aergo  -1.000000     LOSER
2019-07-30 11:00:00+00:00            litex   8.000000  NEWCOMER

Extras

Take a look at the examples folder.

Development

It is recommended to use pipenv for managing your local environment.

Setup project:

pipenv install

Install main dependencies:

pipenv run pip install -e .

Install extra dependencies:

pipenv run pip install -e '.[extras]'

Running tests

python setup.py test

Running integration tests

python setup.py nosetests -a integration

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for santiment from gravatar.com santiment

Unverified details

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