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@@ -0,0 +1 @@ +/sanpy-0.11.6.tar.gz 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 +[](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 +[](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 +[](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 @@ -0,0 +1 @@ +dc1f4bd69685b835a8a7bb619a675f00 sanpy-0.11.6.tar.gz |