%global _empty_manifest_terminate_build 0
Name: python-sanpy
Version: 0.11.6
Release: 1
Summary: Package for Santiment API access with python
License: MIT
URL: https://github.com/santiment/sanpy
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ae/59/073aadc1791953312c1a383261a3c5d219bf901db0c567529976fdfdf7b2/sanpy-0.11.6.tar.gz
BuildArch: noarch
Requires: python3-pandas
Requires: python3-requests
Requires: python3-iso8601
Requires: python3-setuptools
Requires: python3-numpy
Requires: python3-matplotlib
Requires: python3-scipy
Requires: python3-mlfinlab
%description
[](https://badge.fury.io/py/sanpy)
Python client for cryptocurrency data from [Santiment API](https://api.santiment.net/).
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
- [sanpy](#sanpy)
- [Table of contents](#table-of-contents)
- [Installation](#installation)
- [Upgrade to latest version](#upgrade-to-latest-version)
- [Install extra packages](#install-extra-packages)
- [Restricted metrics](#restricted-metrics)
- [Configuration](#configuration)
- [Read the API key from the environment](#read-the-api-key-from-the-environment)
- [Manually configure an API key](#manually-configure-an-api-key)
- [How to obtain an API key](#how-to-obtain-an-api-key)
- [Getting the data](#getting-the-data)
- [Using the provided functions](#using-the-provided-functions)
- [Execute an arbitrary GraphQL request](#execute-an-arbitrary-graphql-request)
- [Execute SQL queries and get the result](#execute-sql-queries-and-get-the-result)
- [Available metrics](#available-metrics)
- [Available Metrics for Slug](#available-metrics-for-slug)
- [Fetch timeseries metric](#fetch-timeseries-metric)
- [Fetching metadata for a metric](#fetching-metadata-for-a-metric)
- [Batching multiple queries](#batching-multiple-queries)
- [Rate Limit Tools](#rate-limit-tools)
- [Metric Complexity](#metric-complexity)
- [Include Incomplete Data Flag](#include-incomplete-data-flag)
- [Metric/Asset pair available cince](#metricasset-pair-available-cince)
- [Transform the result](#transform-the-result)
- [Available projects](#available-projects)
- [Non-standard metrics](#non-standard-metrics)
- [Other Price metrics](#other-price-metrics)
- [Marketcap, Price USD, Price BTC and Trading Volume](#marketcap-price-usd-price-btc-and-trading-volume)
- [Open, High, Close, Low Prices, Volume, Marketcap](#open-high-close-low-prices-volume-marketcap)
- [Mining Pools Distribution](#mining-pools-distribution)
- [Historical Balance](#historical-balance)
- [Ethereum Top Transactions](#ethereum-top-transactions)
- [Ethereum Spent Over Time](#ethereum-spent-over-time)
- [Token Top Transactions](#token-top-transactions)
- [Top Transfers](#top-transfers)
- [Emerging Trends](#emerging-trends)
- [Top Social Gainers Losers](#top-social-gainers-losers)
- [Extras](#extras)
- [Development](#development)
- [Running tests](#running-tests)
- [Running integration tests](#running-integration-tests)
## Installation
To install the latest [sanpy from PyPI](https://pypi.org/project/sanpy/):
```bash
pip install sanpy
```
## Upgrade to latest version
```bash
pip install --upgrade sanpy
```
## Install extra packages
There are few scripts under [extras](/san/extras) directory related to backtesting and event studies. To install their dependencies use:
```bash
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](#configuration), 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.
```shell
export SANPY_APIKEY="my_apikey"
```
```python
import san
>>> san.ApiConfig.api_key
'my_apikey'
```
### Manually configure an API key
```python
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](https://app.santiment.net/login)
and go to the `Account` page - [https://app.santiment.net/account](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](#available-projects)
- `slugs` - (for `get_many`) A list of projects' identificators, as seen in [the Available projects section](#available-projects)
- `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:
```python
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:
```python
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:
```python
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:
```python
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:
```python
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
```python
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:
```python
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:
```python
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](https://api.santiment.net) 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`
```python
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:
```python
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](https://academy.santiment.net/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:
```python
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:
```python
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:
```python
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:
```python
san.available_metrics()
```
## Available Metrics for Slug
Getting all of the metrics for a given slug is achieved with the following code:
```python
san.available_metrics_for_slug("santiment")
```
## Fetch timeseries metric
```python
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):
```python
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.
```python
san.metadata(
"nvt",
arr=["availableSlugs", "defaultAggregation", "humanReadableName", "isAccessible", "isRestricted", "restrictedFrom", "restrictedTo"]
)
```
Example result:
```python
{"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](https://academy.santiment.net/for-developers/#graphql-api-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](https://academy.santiment.net/for-developers/#graphql-api-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.
```python
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()
```
```python
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:
```python
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]()
```python
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.
```python
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.
```python
san.available_metric_for_slug_since(metric="daily_active_addresses", slug="santiment")
```
## Transform the result
Example usage:
```python
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.
```python
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
```python
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!
```python
san.get(
"ohlcv",
slug="santiment",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)
```
Example result:
```python
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](#premium-metrics)
```python
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.
```python
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
```python
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
```python
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
```python
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``.
```python
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
```
```python
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.
```python
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.
```python
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](/examples/extras) folder.
## Development
It is recommended to use [pipenv](https://github.com/pypa/pipenv) for managing your local environment.
Setup project:
```bash
pipenv install
```
Install main dependencies:
```bash
pipenv run pip install -e .
```
Install extra dependencies:
```bash
pipenv run pip install -e '.[extras]'
```
## Running tests
```bash
python setup.py test
```
## Running integration tests
```bash
python setup.py nosetests -a integration
```
%package -n python3-sanpy
Summary: Package for Santiment API access with python
Provides: python-sanpy
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-sanpy
[](https://badge.fury.io/py/sanpy)
Python client for cryptocurrency data from [Santiment API](https://api.santiment.net/).
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
- [sanpy](#sanpy)
- [Table of contents](#table-of-contents)
- [Installation](#installation)
- [Upgrade to latest version](#upgrade-to-latest-version)
- [Install extra packages](#install-extra-packages)
- [Restricted metrics](#restricted-metrics)
- [Configuration](#configuration)
- [Read the API key from the environment](#read-the-api-key-from-the-environment)
- [Manually configure an API key](#manually-configure-an-api-key)
- [How to obtain an API key](#how-to-obtain-an-api-key)
- [Getting the data](#getting-the-data)
- [Using the provided functions](#using-the-provided-functions)
- [Execute an arbitrary GraphQL request](#execute-an-arbitrary-graphql-request)
- [Execute SQL queries and get the result](#execute-sql-queries-and-get-the-result)
- [Available metrics](#available-metrics)
- [Available Metrics for Slug](#available-metrics-for-slug)
- [Fetch timeseries metric](#fetch-timeseries-metric)
- [Fetching metadata for a metric](#fetching-metadata-for-a-metric)
- [Batching multiple queries](#batching-multiple-queries)
- [Rate Limit Tools](#rate-limit-tools)
- [Metric Complexity](#metric-complexity)
- [Include Incomplete Data Flag](#include-incomplete-data-flag)
- [Metric/Asset pair available cince](#metricasset-pair-available-cince)
- [Transform the result](#transform-the-result)
- [Available projects](#available-projects)
- [Non-standard metrics](#non-standard-metrics)
- [Other Price metrics](#other-price-metrics)
- [Marketcap, Price USD, Price BTC and Trading Volume](#marketcap-price-usd-price-btc-and-trading-volume)
- [Open, High, Close, Low Prices, Volume, Marketcap](#open-high-close-low-prices-volume-marketcap)
- [Mining Pools Distribution](#mining-pools-distribution)
- [Historical Balance](#historical-balance)
- [Ethereum Top Transactions](#ethereum-top-transactions)
- [Ethereum Spent Over Time](#ethereum-spent-over-time)
- [Token Top Transactions](#token-top-transactions)
- [Top Transfers](#top-transfers)
- [Emerging Trends](#emerging-trends)
- [Top Social Gainers Losers](#top-social-gainers-losers)
- [Extras](#extras)
- [Development](#development)
- [Running tests](#running-tests)
- [Running integration tests](#running-integration-tests)
## Installation
To install the latest [sanpy from PyPI](https://pypi.org/project/sanpy/):
```bash
pip install sanpy
```
## Upgrade to latest version
```bash
pip install --upgrade sanpy
```
## Install extra packages
There are few scripts under [extras](/san/extras) directory related to backtesting and event studies. To install their dependencies use:
```bash
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](#configuration), 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.
```shell
export SANPY_APIKEY="my_apikey"
```
```python
import san
>>> san.ApiConfig.api_key
'my_apikey'
```
### Manually configure an API key
```python
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](https://app.santiment.net/login)
and go to the `Account` page - [https://app.santiment.net/account](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](#available-projects)
- `slugs` - (for `get_many`) A list of projects' identificators, as seen in [the Available projects section](#available-projects)
- `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:
```python
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:
```python
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:
```python
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:
```python
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:
```python
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
```python
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:
```python
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:
```python
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](https://api.santiment.net) 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`
```python
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:
```python
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](https://academy.santiment.net/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:
```python
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:
```python
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:
```python
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:
```python
san.available_metrics()
```
## Available Metrics for Slug
Getting all of the metrics for a given slug is achieved with the following code:
```python
san.available_metrics_for_slug("santiment")
```
## Fetch timeseries metric
```python
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):
```python
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.
```python
san.metadata(
"nvt",
arr=["availableSlugs", "defaultAggregation", "humanReadableName", "isAccessible", "isRestricted", "restrictedFrom", "restrictedTo"]
)
```
Example result:
```python
{"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](https://academy.santiment.net/for-developers/#graphql-api-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](https://academy.santiment.net/for-developers/#graphql-api-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.
```python
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()
```
```python
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:
```python
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]()
```python
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.
```python
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.
```python
san.available_metric_for_slug_since(metric="daily_active_addresses", slug="santiment")
```
## Transform the result
Example usage:
```python
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.
```python
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
```python
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!
```python
san.get(
"ohlcv",
slug="santiment",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)
```
Example result:
```python
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](#premium-metrics)
```python
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.
```python
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
```python
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
```python
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
```python
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``.
```python
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
```
```python
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.
```python
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.
```python
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](/examples/extras) folder.
## Development
It is recommended to use [pipenv](https://github.com/pypa/pipenv) for managing your local environment.
Setup project:
```bash
pipenv install
```
Install main dependencies:
```bash
pipenv run pip install -e .
```
Install extra dependencies:
```bash
pipenv run pip install -e '.[extras]'
```
## Running tests
```bash
python setup.py test
```
## Running integration tests
```bash
python setup.py nosetests -a integration
```
%package help
Summary: Development documents and examples for sanpy
Provides: python3-sanpy-doc
%description help
[](https://badge.fury.io/py/sanpy)
Python client for cryptocurrency data from [Santiment API](https://api.santiment.net/).
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
- [sanpy](#sanpy)
- [Table of contents](#table-of-contents)
- [Installation](#installation)
- [Upgrade to latest version](#upgrade-to-latest-version)
- [Install extra packages](#install-extra-packages)
- [Restricted metrics](#restricted-metrics)
- [Configuration](#configuration)
- [Read the API key from the environment](#read-the-api-key-from-the-environment)
- [Manually configure an API key](#manually-configure-an-api-key)
- [How to obtain an API key](#how-to-obtain-an-api-key)
- [Getting the data](#getting-the-data)
- [Using the provided functions](#using-the-provided-functions)
- [Execute an arbitrary GraphQL request](#execute-an-arbitrary-graphql-request)
- [Execute SQL queries and get the result](#execute-sql-queries-and-get-the-result)
- [Available metrics](#available-metrics)
- [Available Metrics for Slug](#available-metrics-for-slug)
- [Fetch timeseries metric](#fetch-timeseries-metric)
- [Fetching metadata for a metric](#fetching-metadata-for-a-metric)
- [Batching multiple queries](#batching-multiple-queries)
- [Rate Limit Tools](#rate-limit-tools)
- [Metric Complexity](#metric-complexity)
- [Include Incomplete Data Flag](#include-incomplete-data-flag)
- [Metric/Asset pair available cince](#metricasset-pair-available-cince)
- [Transform the result](#transform-the-result)
- [Available projects](#available-projects)
- [Non-standard metrics](#non-standard-metrics)
- [Other Price metrics](#other-price-metrics)
- [Marketcap, Price USD, Price BTC and Trading Volume](#marketcap-price-usd-price-btc-and-trading-volume)
- [Open, High, Close, Low Prices, Volume, Marketcap](#open-high-close-low-prices-volume-marketcap)
- [Mining Pools Distribution](#mining-pools-distribution)
- [Historical Balance](#historical-balance)
- [Ethereum Top Transactions](#ethereum-top-transactions)
- [Ethereum Spent Over Time](#ethereum-spent-over-time)
- [Token Top Transactions](#token-top-transactions)
- [Top Transfers](#top-transfers)
- [Emerging Trends](#emerging-trends)
- [Top Social Gainers Losers](#top-social-gainers-losers)
- [Extras](#extras)
- [Development](#development)
- [Running tests](#running-tests)
- [Running integration tests](#running-integration-tests)
## Installation
To install the latest [sanpy from PyPI](https://pypi.org/project/sanpy/):
```bash
pip install sanpy
```
## Upgrade to latest version
```bash
pip install --upgrade sanpy
```
## Install extra packages
There are few scripts under [extras](/san/extras) directory related to backtesting and event studies. To install their dependencies use:
```bash
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](#configuration), 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.
```shell
export SANPY_APIKEY="my_apikey"
```
```python
import san
>>> san.ApiConfig.api_key
'my_apikey'
```
### Manually configure an API key
```python
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](https://app.santiment.net/login)
and go to the `Account` page - [https://app.santiment.net/account](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](#available-projects)
- `slugs` - (for `get_many`) A list of projects' identificators, as seen in [the Available projects section](#available-projects)
- `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:
```python
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:
```python
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:
```python
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:
```python
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:
```python
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
```python
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:
```python
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:
```python
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](https://api.santiment.net) 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`
```python
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:
```python
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](https://academy.santiment.net/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:
```python
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:
```python
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:
```python
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:
```python
san.available_metrics()
```
## Available Metrics for Slug
Getting all of the metrics for a given slug is achieved with the following code:
```python
san.available_metrics_for_slug("santiment")
```
## Fetch timeseries metric
```python
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):
```python
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.
```python
san.metadata(
"nvt",
arr=["availableSlugs", "defaultAggregation", "humanReadableName", "isAccessible", "isRestricted", "restrictedFrom", "restrictedTo"]
)
```
Example result:
```python
{"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](https://academy.santiment.net/for-developers/#graphql-api-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](https://academy.santiment.net/for-developers/#graphql-api-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.
```python
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()
```
```python
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:
```python
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]()
```python
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.
```python
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.
```python
san.available_metric_for_slug_since(metric="daily_active_addresses", slug="santiment")
```
## Transform the result
Example usage:
```python
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.
```python
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
```python
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!
```python
san.get(
"ohlcv",
slug="santiment",
from_date="2018-06-01",
to_date="2018-06-05",
interval="1d"
)
```
Example result:
```python
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](#premium-metrics)
```python
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.
```python
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
```python
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
```python
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
```python
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``.
```python
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
```
```python
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.
```python
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.
```python
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](/examples/extras) folder.
## Development
It is recommended to use [pipenv](https://github.com/pypa/pipenv) for managing your local environment.
Setup project:
```bash
pipenv install
```
Install main dependencies:
```bash
pipenv run pip install -e .
```
Install extra dependencies:
```bash
pipenv run pip install -e '.[extras]'
```
## Running tests
```bash
python setup.py test
```
## Running integration tests
```bash
python setup.py nosetests -a integration
```
%prep
%autosetup -n sanpy-0.11.6
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-sanpy -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 15 2023 Python_Bot - 0.11.6-1
- Package Spec generated