automatic import of python-sanpy

author: CoprDistGit <infra@openeuler.org> 2023-05-15 07:06:36 +0000
committer: CoprDistGit <infra@openeuler.org> 2023-05-15 07:06:36 +0000
commit: 81d7dbe4efae5a859df93bc386d10722601dc9b3 (patch)
tree: 535a843505f613e1d27682fa6740d11a7bdd0df6 /python-sanpy.spec
parent: 65dec8eba1836a13443cdcce6b73dcfa643238b4 (diff)
1 files changed, 2612 insertions, 0 deletions
diff --git a/python-sanpy.spec b/python-sanpy.spec
new file mode 100644
index 0000000..7eaf97b
--- /dev/null
+++ b/python-sanpy.spec
@@ -0,0 +1,2612 @@
+%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
+[![PyPI version](https://badge.fury.io/py/sanpy.svg)](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 V<sub>i</sub> with the average of the last "moving_average_base" values.
+- "consecutive_differences" - Replace every value V<sub>i</sub> with the value V<sub>i</sub> - V<sub>i-1</sub> 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 V<sub>i</sub> with the percent change of V<sub>i-1</sub> and V<sub>i</sub> ( (V<sub>i</sub> / V<sub>i-1</sub> - 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
+[![PyPI version](https://badge.fury.io/py/sanpy.svg)](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 V<sub>i</sub> with the average of the last "moving_average_base" values.
+- "consecutive_differences" - Replace every value V<sub>i</sub> with the value V<sub>i</sub> - V<sub>i-1</sub> 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 V<sub>i</sub> with the percent change of V<sub>i-1</sub> and V<sub>i</sub> ( (V<sub>i</sub> / V<sub>i-1</sub> - 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
+[![PyPI version](https://badge.fury.io/py/sanpy.svg)](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 V<sub>i</sub> with the average of the last "moving_average_base" values.
+- "consecutive_differences" - Replace every value V<sub>i</sub> with the value V<sub>i</sub> - V<sub>i-1</sub> 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 V<sub>i</sub> with the percent change of V<sub>i-1</sub> and V<sub>i</sub> ( (V<sub>i</sub> / V<sub>i-1</sub> - 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 <Python_Bot@openeuler.org> - 0.11.6-1
+- Package Spec generated
author	CoprDistGit <infra@openeuler.org>	2023-05-15 07:06:36 +0000
committer	CoprDistGit <infra@openeuler.org>	2023-05-15 07:06:36 +0000
commit	81d7dbe4efae5a859df93bc386d10722601dc9b3 (patch)
tree	535a843505f613e1d27682fa6740d11a7bdd0df6 /python-sanpy.spec
parent	65dec8eba1836a13443cdcce6b73dcfa643238b4 (diff)