Package for Santiment API access with python

Project description

sanpy

Python client library for the Santiment API — access on-chain, social, development, and price metrics for 3000+ crypto assets. Results are returned as pandas DataFrames with datetime indexing.

For full API documentation and metric definitions, see Santiment Academy.

Installation
- Extra packages
CLI
Configuration
Fetching data
SQL queries (Santiment Queries)
Metric discovery
Batching queries
Transforms and aggregation
Include incomplete data
Rate limit tools
Assets discovery
Non-standard metrics
Extras
Development

Installation

Install from PyPI:

pip install sanpy

Upgrade to the latest version:

pip install --upgrade sanpy

Extra packages

There are a few utilities in the san/extras/ directory for backtesting and event studies. To install their dependencies:

pip install 'sanpy[extras]'

CLI

Sanpy includes a command-line interface for quick data access without writing Python.

san --help
san --version

Configuration

san config set-key                  # Prompt for API key with hidden input (recommended)
san config set-key <your-api-key>   # Store API key (exposed to shell history — prefer the prompt form)
san config show                     # Show current config
san config path                     # Config file location
san config clear                    # Remove stored API key

The CLI reads the API key from (in order): --api-key flag, SANPY_APIKEY env var, stored config file.

Discovery

san metrics                   # List all metrics
san metrics --slug bitcoin    # Metrics for a specific asset
san projects                  # List all projects

Fetching data

san get price_usd --slug bitcoin --from 2024-01-01 --to 2024-01-05
san get price_usd --slug bitcoin --from 2024-01-01 --to 2024-01-05 -f json
san get price_usd --slug bitcoin --aggregation avg

san get-many price_usd --slugs bitcoin,ethereum --from 2024-01-01 --to 2024-01-05

Rate limits & complexity

san rate-limit                # API calls remaining
san api-calls                 # API call history
san complexity price_usd      # Query complexity check

The discovery (metrics, projects), data (get, get-many), and diagnostic (rate-limit, api-calls, complexity) commands registered in san/cli.py support --format (table, json, csv) and --api-key options. The config subcommands (set-key, show, path, clear) do not accept these flags.

Configuration

Some metrics require a paid SanAPI plan to access real-time or full historical data. You can provide an API key in two ways:

Environment variable

If the SANPY_APIKEY environment variable is set when you import san, it is loaded automatically:

export SANPY_APIKEY="my_api_key"

import san
san.ApiConfig.api_key  # 'my_api_key'

Programmatic

import san
san.ApiConfig.api_key = "my_api_key"

Configure retries, timeouts, and connection pooling

By default, sanpy reuses HTTP sessions and retries transient GraphQL transport failures.

san.ApiConfig.request_timeout controls (connect_timeout, read_timeout) and defaults to (3.05, 30)
san.ApiConfig.request_retry_count controls how many retries are attempted and defaults to 3
san.ApiConfig.request_backoff_factor controls exponential backoff between retries and defaults to 0.5
san.ApiConfig.pool_connections controls how many connection pools are cached and defaults to 10
san.ApiConfig.pool_maxsize controls how many reusable connections are kept per pool and defaults to 10

Retries apply to connection/read failures and HTTP 408, 500, 502, 503, and 504. HTTP 429 responses are surfaced to callers as rate-limit errors instead of being retried automatically.

To disable retries:

import san

san.ApiConfig.request_retry_count = 0

To customize retry and timeout behavior:

import san

san.ApiConfig.request_timeout = (2, 20)
san.ApiConfig.request_retry_count = 5
san.ApiConfig.request_backoff_factor = 0.25
san.ApiConfig.pool_connections = 20
san.ApiConfig.pool_maxsize = 50

Obtaining an API key

Log in to Sanbase.
Go to the Account page.
Under API Keys, click Generate new api key.

Fetching data

The library provides two main functions for fetching timeseries data:

san.get(metric, slug=..., ...) — fetch data for a single metric/asset pair.
san.get_many(metric, slugs=[...], ...) — fetch data for a single metric across multiple assets. This counts as 1 API call.

Common parameters:

Parameter	Description	Default
`slug` / `slugs`	Project identifier(s), as listed in Assets discovery	—
`selector`	Dict for flexible targeting (address, label, holdersCount, etc.)	—
`from_date`	Start of the period (ISO 8601 string)	365 days ago
`to_date`	End of the period (ISO 8601 string)	now
`interval`	Data point spacing — fixed (`1d`, `1h`, `5m`, `1w`) or semantic (`toStartOfMonth`, `toStartOfWeek`)	`'1d'`

The returned result is a pandas.DataFrame indexed by datetime. For get, the value column is named value. For get_many, each column is named after the asset slug.

Single asset

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

Using default parameters (last 1 year of data with 1 day interval):

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

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

Using selectors

The selector parameter enables querying by organization, contract address, label, and more:

import san

# Development activity by GitHub organization
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

# Transactions for a specific contract address
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

# Amount held by top N holders
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

# DEX trade volume (requires PRO API key)
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

Legacy metric/slug format

The legacy format still works for backwards compatibility:

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

Prefer the explicit style with separate slug/selector for new code.

Non-timeseries endpoints

Some endpoints return tabular metadata rather than metric timeseries, for example:

san.get("projects/all")

Raw GraphQL queries

For queries not covered by built-in functions, you can execute arbitrary GraphQL against the Santiment API:

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

SQL queries (Santiment Queries)

Santiment Queries lets you execute SQL queries against a ClickHouse database hosted by Santiment. See the documentation for available tables and query syntax. Requires an API key.

Basic query returning 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

Use set_index to set a column as the DataFrame index:

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

Parameterized queries

Use {{key}} placeholders in the query and pass a parameters dict:

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

Metric discovery

Available metrics

san.available_metrics()

Available metrics for a slug

san.available_metrics_for_slug("santiment")

Metric/asset availability since

Fetch the earliest datetime for which a metric is available for a given slug:

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

Versioned metrics

Make sure the version of sanpy is 0.12.6 or newer

Some metrics support multiple versions (e.g., "1.0" and "2.0").

Check which versions are available:

import san

san.available_metric_versions("social_dominance_total")
# ['1.0', '2.0']

Pass version to san.get or san.get_many to select a specific version:

san.get(
    "social_dominance_total",
    slug="bitcoin",
    from_date="2026-01-01",
    to_date="2026-01-10",
    interval="1d",
    version="2.0"
)

san.get_many(
    "social_dominance_total",
    slugs=["bitcoin", "ethereum"],
    from_date="2026-01-01",
    to_date="2026-01-10",
    interval="1d",
    version="2.0"
)

Metric metadata

Fetch metadata for a metric, including access restrictions and available slugs:

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

Example result:

{
    "availableSlugs": ["0chain", "0x", "0xbtc", ...],
    "defaultAggregation": "AVG",
    "humanReadableName": "NVT (Using Circulation)",
    "isAccessible": True,
    "isRestricted": True,
    "restrictedFrom": "2020-03-21T08:44:14Z",
    "restrictedTo": "2020-06-17T08:44:14Z"
}

availableSlugs — All slugs that have data for this metric.
defaultAggregation — Aggregation function used when querying large intervals.
humanReadableName — Human-readable metric name.
isAccessible — Whether the metric is accessible with your current API plan. For example, circulation_1d requires a PRO plan.
isRestricted — Whether time restrictions apply to your current plan (Free if no API key is configured).
restrictedFrom / restrictedTo — The available date range for your current plan.

Metric complexity

Each API request has a complexity that depends on the date range, interval, metric, and subscription plan. The maximum complexity per request is 50,000.

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

If a request exceeds the limit, break it into smaller date ranges or upgrade your plan.

Batching queries

Two batch classes let you execute multiple queries efficiently:

Note: Each query in a batch still counts as a separate API call. To fetch a metric for multiple assets in a single API call, use san.get_many instead.

AsyncBatch executes queries concurrently in separate HTTP requests. Concurrency is controlled by the max_workers parameter (default: 10). Because each query runs in its own request, you avoid complexity accumulation. Supports both get and get_many.

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(
    "transaction_volume",
    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, trx_volume, daa_many] = batch.execute(max_workers=10)

Note: The older Batch class is deprecated. It combines all queries into a single GraphQL document, which causes complexity to accumulate and requests to be rejected for larger batches. Use AsyncBatch instead — the get, get_many, and execute methods share the same interface, so switching only requires changing the import.

Transforms and aggregation

Apply server-side transformations to the data:

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"
)

Supported transforms:

Type	Description
`moving_average`	Replace each value with the average of the last `moving_average_base` values
`consecutive_differences`	Replace each value with the difference from the previous value (Vi - V{i-1})
`percent_change`	Replace each value with the percent change from the previous value ((Vi / V{i-1} - 1) * 100)

aggregation controls how values within each interval are combined (e.g., LAST, AVG, SUM).

Include incomplete data

Daily metrics exclude the current (incomplete) day by default. For example, daily_active_addresses queried at 08:00 would only reflect one-third of the day's activity. To include the partial current-day value:

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

Rate limit tools

Four utility functions help you handle API rate limits:

san.is_rate_limit_exception(exception) — check if an exception was caused by rate limiting.
san.rate_limit_time_left(exception) — seconds until the rate limit resets.
san.api_calls_made() — API calls made per day.
san.api_calls_remaining() — remaining calls for the current 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"Rate limited. Sleeping for {rate_limit_seconds}s")
        time.sleep(rate_limit_seconds)

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

Assets discovery

Returns a DataFrame with all projects tracked by the Santiment API. The slug column is the unique identifier used in all metric queries.

san.get("projects/all")

                 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
...

Non-standard metrics

The following metrics have non-standard response formats and are not included in san.available_metrics().

Price metrics

Market Cap, Price USD, Price BTC, and Trading Volume:

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

OHLCV (Open, High, Low, Close, Volume, Market Cap):

This query cannot be batched and does not support the separate slug/selector argument format.

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

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

Historical balance

Returns the historical balance for an ERC-20 token or ETH address:

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

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 a project's team wallets. transaction_type can be ALL, IN, or OUT.

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

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 per interval from a project's team wallets:

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

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 a project's token:

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

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 token transfers for a project. Optionally filter by address and transaction_type (ALL, IN, OUT):

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

                          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
...

Filter by address and transaction type:

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

                          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

Trending words in crypto social media for a given period:

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

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

Extras

Backtesting and event study utilities are available in san/extras/. See the examples/extras folder for usage.

Development

It is recommended to use pipenv for managing the local development environment.

# Set up the project
pipenv install

# Install the package in editable mode
pipenv run pip install -e .

# Install dev dependencies (ruff, pytest)
pipenv run pip install -e '.[dev]'

# Install extra dependencies (numpy, matplotlib, scipy, mlfinlab)
pipenv run pip install -e '.[extras]'

# Run tests (integration tests are excluded by default)
pipenv run pytest

# Run integration tests (requires SANPY_APIKEY)
pipenv run pytest -m integration

# Lint
pipenv run ruff check .

# Format
pipenv run ruff format .

Project details

Release history Release notifications | RSS feed

This version

0.14.0

Apr 20, 2026

0.13.0

Mar 26, 2026

0.12.5

Mar 5, 2026

0.12.4

Jan 20, 2026

0.12.3

May 29, 2025

0.12.2

Apr 16, 2025

0.12.1

Mar 28, 2025

0.12.0

Mar 21, 2024

0.11.8

Dec 15, 2023

0.11.6

Mar 29, 2023

0.11.5

Feb 1, 2023

0.11.4

Jan 28, 2023

0.11.3

Nov 4, 2022

0.11.2

Oct 27, 2022

0.11.1

Oct 27, 2022

0.11.0

Oct 27, 2022

0.10.2

Sep 29, 2022

0.10.1

Aug 30, 2022

0.10.0

Jun 15, 2022

0.9.1

Jan 13, 2022

0.8.12

Nov 26, 2021

0.8.11

Aug 24, 2021

0.8.10

Aug 24, 2021

0.8.9

Jul 15, 2021

0.8.8

Jul 7, 2021

0.8.7

May 7, 2021

0.8.6

Nov 23, 2020

0.8.5

Aug 24, 2020

0.8.4

Jul 24, 2020

0.8.3

Jul 9, 2020

0.8.2

Jul 2, 2020

0.8.0

Jun 25, 2020

0.7.19

Jun 18, 2020

0.7.18

Mar 25, 2020

0.7.16

Feb 20, 2020

0.7.15

Feb 4, 2020

0.7.14

Jan 27, 2020

0.7.13

Jan 21, 2020

0.7.11

Jan 7, 2020

0.7.10

Dec 19, 2019

0.7.9

Dec 10, 2019

0.7.8

Nov 29, 2019

0.7.5

Nov 4, 2019

0.7.4

Nov 1, 2019

0.7.1

Oct 7, 2019

0.7.0

Sep 18, 2019

0.6.1

Jul 22, 2019

0.6.0

Jul 22, 2019

0.5.3

Jul 9, 2019

0.5.2

Jun 28, 2019

0.5.1

Jun 20, 2019

0.5.0

May 7, 2019

0.4.2

May 2, 2019

0.4.1

Apr 8, 2019

0.3.4

Feb 11, 2019

0.3.3

Feb 7, 2019

0.3.2

Jan 25, 2019

0.3.1

Jan 14, 2019

0.3.0

Dec 20, 2018

0.2.1

Dec 11, 2018

0.2.0

Dec 4, 2018

0.1.6

Nov 13, 2018

0.1.5

Oct 26, 2018

0.1.4

Oct 25, 2018

0.1.2

Oct 19, 2018

0.1.1

Oct 18, 2018

0.1.0

Oct 11, 2018

0.0.20

Oct 10, 2018

0.0.19

Oct 9, 2018

0.0.18

Sep 21, 2018

0.0.17

Sep 20, 2018

0.0.16

Sep 19, 2018

0.0.13

Sep 11, 2018

0.0.12

Aug 15, 2018

0.0.9

Aug 15, 2018

0.0.6

Jul 6, 2018

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sanpy-0.14.0.tar.gz (67.7 kB view details)

Uploaded Apr 20, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

sanpy-0.14.0-py3-none-any.whl (71.3 kB view details)

Uploaded Apr 20, 2026 Python 3

File details

Details for the file sanpy-0.14.0.tar.gz.

File metadata

Download URL: sanpy-0.14.0.tar.gz
Upload date: Apr 20, 2026
Size: 67.7 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/6.2.0 CPython/3.9.14

File hashes

Hashes for sanpy-0.14.0.tar.gz
Algorithm	Hash digest
SHA256	`dd624ff321d3e4775dd2f6d64e76c011c9fac23390885f850065fa6bda44c80f`
MD5	`ae557880457ec2dbf05ab26cf3e58084`
BLAKE2b-256	`abe240ed0a263db3502a9a2b9f4c2043c6ad6f7d0be4657612cddf36e870d620`

See more details on using hashes here.

File details

Details for the file sanpy-0.14.0-py3-none-any.whl.

File metadata

Download URL: sanpy-0.14.0-py3-none-any.whl
Upload date: Apr 20, 2026
Size: 71.3 kB
Tags: Python 3
Uploaded using Trusted Publishing? No
Uploaded via: twine/6.2.0 CPython/3.9.14

File hashes

Hashes for sanpy-0.14.0-py3-none-any.whl
Algorithm	Hash digest
SHA256	`d1a86cb6ad7133f628bc94d36e663d21ae9d7829c9b195f7dc213565115a68d5`
MD5	`eb575ad026a7ceeb5987050efd700bb3`
BLAKE2b-256	`648dad71f8546ef56dd4b91f791ad5f8ced47bf406fb2afd20ebe9b2813b2b0e`

See more details on using hashes here.

sanpy 0.14.0

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Project description

sanpy

Table of contents

Installation

Extra packages

CLI

Configuration

Discovery

Fetching data

Rate limits & complexity

Configuration

Environment variable

Programmatic

Configure retries, timeouts, and connection pooling

Obtaining an API key

Fetching data

Single asset

Multiple assets

Using selectors

Legacy metric/slug format

Non-timeseries endpoints

Raw GraphQL queries

SQL queries (Santiment Queries)

Parameterized queries

Metric discovery

Available metrics

Available metrics for a slug

Metric/asset availability since

Versioned metrics

Metric metadata

Metric complexity

Batching queries

Transforms and aggregation

Include incomplete data

Rate limit tools

Assets discovery

Non-standard metrics

Price metrics

Historical balance

Ethereum top transactions

Ethereum spent over time

Token top transactions

Top transfers

Emerging trends

Extras

Development

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distribution

File details

File metadata

File hashes

File details

File metadata

File hashes