Auto Generate API Documentation (#2360)

* Started auto doc generation

* Created a markdown document

* Added test changed to md

* Created working version of new documentation

* Made requested changes

* Generate docs for all

Co-authored-by: minhhoang1023 <40023817+minhhoang1023@users.noreply.github.com>

docs/generate.py

@@ -0,0 +1,92 @@
+from typing import Callable, Any, Optional
+from inspect import signature
+import importlib
+import os
+
+from openbb_terminal.api import functions
+
+
+def all_functions() -> list[tuple[str, str, Callable[..., Any]]]:
+    """Uses the base api functions dictionary to get a list of all functions we have linked
+    in our API.
+
+    Returns
+    ----------
+    func_list: list[Tuple[str, str, Callable[..., Any]]]
+        A list of functions organized as (path_to_func, view/model, the_function)
+    """
+    func_list = []
+    for key, sub_dict in functions.items():
+        for sub_key, item_path in sub_dict.items():
+            full_path = item_path.split(".")
+            module_path = ".".join(full_path[:-1])
+            module = importlib.import_module(module_path)
+            target_function = getattr(module, full_path[-1])
+            func_list.append((key, sub_key, target_function))
+    return func_list
+
+
+def groupby(orig_list: list[Any], index: int) -> dict[Any, Any]:
+    """Groups a list of iterable by the index provided
+
+    Parameters
+    ----------
+    orig_list: list[Any]
+        A list of iterables
+    index: int
+        The index to groupby
+
+    Returns
+    ----------
+    grouped: dict[Any, Any]
+        Group information where keys are the groupby item and values are the iterables
+    """
+    grouped: dict[Any, Any] = {}
+    for item in orig_list:
+        if item[index] in grouped:
+            grouped[item[index]].append(item)
+        else:
+            grouped[item[index]] = [item]
+    return grouped
+
+
+def generate_documentation(
+    base: str, key: str, value: list[tuple[str, str, Callable[..., Any]]]
+):
+    models = list(filter(lambda x: x[1] == "model", value))
+    views = list(filter(lambda x: x[1] == "view", value))
+    model_type = Optional[tuple[str, str, Callable[..., Any]]]
+    model: model_type = models[0] if models else None
+    view: model_type = views[0] if views else None
+    for end in key.split("."):
+        base += f"/{end}"
+        if not os.path.isdir(base):
+            os.mkdir(base)
+    with open(f"{base}/_index.md", "w") as f:
+        f.write(f"# {key}\n\n")
+        if view:
+            f.write(
+                "To obtain charts, make sure to add `chart=True` as the last parameter\n\n"
+            )
+        if model:
+            f.write(f"## Get underlying data \n###{key}{signature(model[2])}\n\n")
+            m_docs = str(model[2].__doc__)[:-5]
+            f.write(f"{m_docs}\n")
+        if view:
+            if model:
+                f.write("\n")
+            v_docs = str(view[2].__doc__)[:-5]
+            temp = str(signature(view[2]))
+            # TODO: This breaks if there is a ')' inside the function arguments
+            idx = temp.find(")")
+            new_signature = temp[:idx] + ", chart=True" + temp[idx:]
+            f.write(f"## Getting charts \n###{key}{new_signature}\n\n")
+            f.write(f"{v_docs}\n")
+
+
+if __name__ == "__main__":
+    folder_path = os.path.realpath("./website/content/api")
+    funcs = all_functions()
+    grouped_funcs = groupby(funcs, 0)
+    for k, v in grouped_funcs.items():
+        generate_documentation(folder_path, k, v)

openbb_terminal/alternative/oss/github_view.py

@@ -128,7 +128,7 @@ def display_repo_summary(repo: str, export: str = "") -> None:
     repo : str
             Repository to display summary. Format: org/repo, e.g., openbb-finance/openbbterminal
     export : str
-    Export dataframe data to csv,json,xlsx file
+        Export dataframe data to csv,json,xlsx file
     """
     data = github_model.get_repo_summary(repo)
     if not data.empty:

openbb_terminal/cryptocurrency/defi/graph_model.py

@@ -13,6 +13,7 @@ from openbb_terminal.cryptocurrency.dataframe_helpers import (
 from openbb_terminal.decorators import log_start_end
 
 logger = logging.getLogger(__name__)
+# pylint: disable=unsupported-assignment-operation
 
 UNI_URL = "https://api.thegraph.com/subgraphs/name/uniswap/uniswap-v2"
 

openbb_terminal/cryptocurrency/due_diligence/coinpaprika_model.py

@@ -14,6 +14,7 @@ from openbb_terminal.decorators import log_start_end
 from openbb_terminal.rich_config import console
 
 logger = logging.getLogger(__name__)
+# pylint: disable=unsupported-assignment-operation
 
 
 @log_start_end(log=logger)

openbb_terminal/cryptocurrency/due_diligence/glassnode_model.py

@@ -10,6 +10,7 @@ from openbb_terminal.decorators import log_start_end
 from openbb_terminal.rich_config import console
 
 logger = logging.getLogger(__name__)
+# pylint: disable=unsupported-assignment-operation
 
 api_url = "https://api.glassnode.com/v1/metrics/"
 

openbb_terminal/cryptocurrency/due_diligence/messari_model.py

@@ -25,6 +25,7 @@ from openbb_terminal.rich_config import console
 logger = logging.getLogger(__name__)
 
 INTERVALS_TIMESERIES = ["5m", "15m", "30m", "1h", "1d", "1w"]
+# pylint: disable=unsupported-assignment-operation
 
 
 @log_start_end(log=logger)

openbb_terminal/cryptocurrency/onchain/ethplorer_view.py

@@ -14,6 +14,7 @@ from openbb_terminal.helper_funcs import export_data, print_rich_table
 from openbb_terminal.rich_config import console
 
 logger = logging.getLogger(__name__)
+# pylint: disable=unsupported-assignment-operation
 
 
 @log_start_end(log=logger)

openbb_terminal/cryptocurrency/overview/pycoingecko_model.py

@@ -20,6 +20,7 @@ from openbb_terminal.cryptocurrency.discovery.pycoingecko_model import get_coins
 from openbb_terminal.decorators import log_start_end
 
 logger = logging.getLogger(__name__)
+# pylint: disable=unsupported-assignment-operation
 
 HOLD_COINS = ["ethereum", "bitcoin"]
 

openbb_terminal/economy/finviz_model.py

@@ -14,6 +14,7 @@ from openbb_terminal.decorators import log_start_end
 from openbb_terminal.helper_funcs import get_user_agent
 
 logger = logging.getLogger(__name__)
+# pylint: disable=unsupported-assignment-operation
 
 GROUPS = {
     "sector": "Sector",

openbb_terminal/forex/polygon_model.py

@@ -8,6 +8,8 @@ from openbb_terminal.config_terminal import API_POLYGON_KEY as api_key
 from openbb_terminal.helper_funcs import get_user_agent
 from openbb_terminal.rich_config import console
 
+# pylint: disable=unsupported-assignment-operation
+
 
 def get_historical(
     fx_pair: str,

openbb_terminal/stocks/dark_pool_shorts/stockgrid_model.py

@@ -11,6 +11,7 @@ import requests
 from openbb_terminal.decorators import log_start_end
 
 logger = logging.getLogger(__name__)
+# pylint: disable=unsupported-assignment-operation
 
 
 @log_start_end(log=logger)

openbb_terminal/stocks/options/nasdaq_model.py

@@ -14,6 +14,7 @@ from openbb_terminal.rich_config import console
 from openbb_terminal.stocks.options.op_helpers import get_dte_from_expiration as get_dte
 
 logger = logging.getLogger(__name__)
+# pylint: disable=unsupported-assignment-operation
 
 
 @log_start_end(log=logger)

openbb_terminal/stocks/stocks_helper.py

@@ -36,6 +36,7 @@ logger = logging.getLogger(__name__)
 
 # pylint: disable=no-member,too-many-branches,C0302,R0913
 # pylint: disable=inconsistent-return-statements
+# pylint: disable=unsupported-assignment-operation
 
 INTERVALS = [1, 5, 15, 30, 60]
 SOURCES = ["YahooFinance", "AlphaVantage", "IEXCloud", "EODHD"]

openbb_terminal/stocks/tradinghours/bursa_model.py

@@ -11,6 +11,7 @@ import pytz
 from openbb_terminal.decorators import log_start_end
 
 logger = logging.getLogger(__name__)
+# pylint: disable=no-member
 
 
 @log_start_end(log=logger)

website/content/api/alt/covid/case_slopes/_index.md

@@ -0,0 +1,17 @@
+# alt.covid.case_slopes
+
+## Get underlying data 
+###alt.covid.case_slopes(days_back: int = 30, threshold: int = 10000) -> pandas.core.frame.DataFrame
+
+Load cases and find slope over period
+
+    Parameters
+    ----------
+    days_back: int
+        Number of historical days to consider
+    threshold: int
+        Threshold for total number of cases
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe containing slopes

website/content/api/alt/covid/global_cases/_index.md

@@ -0,0 +1,16 @@
+# alt.covid.global_cases
+
+## Get underlying data 
+###alt.covid.global_cases(country: str) -> pandas.core.frame.DataFrame
+
+Get historical cases for given country
+
+    Parameters
+    ----------
+    country: str
+        Country to search for
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe of historical cases

website/content/api/alt/covid/global_deaths/_index.md

@@ -0,0 +1,16 @@
+# alt.covid.global_deaths
+
+## Get underlying data 
+###alt.covid.global_deaths(country: str) -> pandas.core.frame.DataFrame
+
+Get historical deaths for given country
+
+    Parameters
+    ----------
+    country: str
+        Country to search for
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe of historical deaths

website/content/api/alt/covid/ov/_index.md

@@ -0,0 +1,33 @@
+# alt.covid.ov
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###alt.covid.ov(country, limit: int = 100) -> pandas.core.frame.DataFrame
+
+Get historical cases and deaths by country
+
+    Parameters
+    ----------
+    country: str
+        Country to get data for
+    limit: int
+        Number of raw data to show
+
+## Getting charts 
+###alt.covid.ov(country, raw: bool = False, limit: int = 10, export: str = '', plot: bool = True, chart=True) -> None
+
+Show historical cases and deaths by country
+
+    Parameters
+    ----------
+    country: str
+        Country to get data for
+    raw: bool
+        Flag to display raw data
+    limit: int
+        Number of raw data to show
+    export: str
+        Format to export data
+    plot: bool
+        Flag to display historical plot

website/content/api/alt/covid/slopes/_index.md

@@ -0,0 +1,41 @@
+# alt.covid.slopes
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###alt.covid.slopes(days_back: int = 30, limit: int = 50, threshold: int = 10000, ascend: bool = False) -> pandas.core.frame.DataFrame
+
+Load cases and find slope over period
+
+    Parameters
+    ----------
+    days_back: int
+        Number of historical days to consider
+    limit: int
+        Number of rows to show
+    threshold: int
+        Threshold for total number of cases
+    ascend: bool
+        Flag to sort in ascending order
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe containing slopes
+
+## Getting charts 
+###alt.covid.slopes(days_back: int = 30, limit: int = 10, threshold: int = 10000, ascend: bool = False, export: str = '', chart=True) -> None
+
+
+
+    Parameters
+    ----------
+    days_back: int
+        Number of historical days to get slope for
+    limit: int
+        Number to show in table
+    ascend: bool
+        Flag to sort in ascending order
+    threshold: int
+        Threshold for total cases over period
+    export : str
+        Format to export data

website/content/api/alt/covid/stat/_index.md

@@ -0,0 +1,37 @@
+# alt.covid.stat
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###alt.covid.stat(country, stat: str = 'cases', limit: int = 10) -> pandas.core.frame.DataFrame
+
+Show historical cases and deaths by country
+
+    Parameters
+    ----------
+    country: str
+        Country to get data for
+    stat: str
+        Statistic to get.  Either "cases", "deaths" or "rates"
+    limit: int
+        Number of raw data to show
+
+## Getting charts 
+###alt.covid.stat(country, stat: str = 'cases', raw: bool = False, limit: int = 10, export: str = '', plot: bool = True, chart=True) -> None
+
+Show historical cases and deaths by country
+
+    Parameters
+    ----------
+    country: str
+        Country to get data for
+    stat: str
+        Statistic to get.  Either "cases", "deaths" or "rates"
+    raw: bool
+        Flag to display raw data
+    limit: int
+        Number of raw data to show
+    export: str
+        Format to export data
+    plot : bool
+        Flag to plot data

website/content/api/alt/oss/_make_request/_index.md

@@ -0,0 +1,15 @@
+# alt.oss._make_request
+
+## Get underlying data 
+###alt.oss._make_request(url: str) -> Optional[bs4.BeautifulSoup]
+
+Helper method to scrap
+
+    Parameters
+    ----------
+    url : str
+        url to scrape
+
+    Returns
+    -------
+        BeautifulSoup object

website/content/api/alt/oss/_retry_session/_index.md

@@ -0,0 +1,21 @@
+# alt.oss._retry_session
+
+## Get underlying data 
+###alt.oss._retry_session(url: str, retries: int = 3, backoff_factor: float = 1.0) -> requests.sessions.Session
+
+Helper methods that retries to make request
+
+
+    Parameters
+    ----------
+    url: str
+        Url to mount a session
+    retries: int
+        How many retries
+    backoff_factor: float
+        Backoff schema - time periods between retry
+
+    Returns
+    -------
+    requests.Session
+        Mounted session

website/content/api/alt/oss/github_data/_index.md

@@ -0,0 +1,16 @@
+# alt.oss.github_data
+
+## Get underlying data 
+###alt.oss.github_data(url: str, **kwargs)
+
+Get repository stats
+
+    Parameters
+    ----------
+    url: str
+        github api endpoint
+    params: dict
+        params to pass to api endpoint
+    Returns
+    -------
+    dict with data

website/content/api/alt/oss/history/_index.md

@@ -0,0 +1,31 @@
+# alt.oss.history
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###alt.oss.history(repo: str)
+
+Get repository star history
+
+    Parameters
+    ----------
+    repo : str
+            Repo to search for Format: org/repo, e.g., openbb-finance/openbbterminal
+
+    Returns
+    -------
+    pd.DataFrame - Columns: Date, Stars
+
+## Getting charts 
+###alt.oss.history(repo: str, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Display repo summary [Source: https://api.github.com]
+
+    Parameters
+    ----------
+    repo : str
+            Repository to display star history. Format: org/repo, e.g., openbb-finance/openbbterminal
+    export : str
+            Export dataframe data to csv,json,xlsx file
+    external_axes : Optional[List[plt.Axes]], optional
+            External axes (1 axis is expected in the list), by default None

website/content/api/alt/oss/repo_summary/_index.md

@@ -0,0 +1,29 @@
+# alt.oss.repo_summary
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###alt.oss.repo_summary(repo: str)
+
+Get repository summary
+
+    Parameters
+    ----------
+    repo : str
+            Repo to search for Format: org/repo, e.g., openbb-finance/openbbterminal
+
+    Returns
+    -------
+    pd.DataFrame - Columns: Metric, Value
+
+## Getting charts 
+###alt.oss.repo_summary(repo: str, export: str = '', chart=True) -> None
+
+Display repo summary [Source: https://api.github.com]
+
+    Parameters
+    ----------
+    repo : str
+            Repository to display summary. Format: org/repo, e.g., openbb-finance/openbbterminal
+    export : str
+        Export dataframe data to csv,json,xlsx file

website/content/api/alt/oss/ross/_index.md

@@ -0,0 +1,40 @@
+# alt.oss.ross
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###alt.oss.ross() -> pandas.core.frame.DataFrame
+
+Get startups from ROSS index [Source: https://runacap.com/]
+
+    Parameters
+    ----------
+
+    Returns
+    -------
+    pandas.DataFrame:
+        list of startups
+
+## Getting charts 
+###alt.oss.ross(limit: int = 10, sortby: str = 'Stars AGR [%]', descend: bool = False, show_chart: bool = False, show_growth: bool = True, chart_type: str = 'stars', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Display list of startups from ross index [Source: https://runacap.com/]
+
+    Parameters
+    ----------
+    limit: int
+        Number of startups to search
+    sortby: str
+        Key by which to sort data. Default: Stars AGR [%]
+    descend: bool
+        Flag to sort data descending
+    show_chart: bool
+        Flag to show chart with startups
+    show_growth: bool
+        Flag to show growth line chart
+    chart_type: str
+        Chart type {stars,forks}
+    export : str
+        Export dataframe data to csv,json,xlsx file
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/alt/oss/search/_index.md

@@ -0,0 +1,18 @@
+# alt.oss.search
+
+## Get underlying data 
+###alt.oss.search(sortby: str = 'stars', page: int = 1, categories: str = '') -> pandas.core.frame.DataFrame
+
+Get repos sorted by stars or forks. Can be filtered by categories
+
+    Parameters
+    ----------
+    sortby : str
+            Sort repos by {stars, forks}
+    categories : str
+            Check for repo categories. If more than one separate with a comma: e.g., finance,investment. Default: None
+    page : int
+            Page number to get repos
+    Returns
+    -------
+    pd.DataFrame with list of repos

website/content/api/alt/oss/search_repos/_index.md

@@ -0,0 +1,18 @@
+# alt.oss.search_repos
+
+## Get underlying data 
+###alt.oss.search_repos(sortby: str = 'stars', page: int = 1, categories: str = '') -> pandas.core.frame.DataFrame
+
+Get repos sorted by stars or forks. Can be filtered by categories
+
+    Parameters
+    ----------
+    sortby : str
+            Sort repos by {stars, forks}
+    categories : str
+            Check for repo categories. If more than one separate with a comma: e.g., finance,investment. Default: None
+    page : int
+            Page number to get repos
+    Returns
+    -------
+    pd.DataFrame with list of repos

website/content/api/alt/oss/stars_history/_index.md

@@ -0,0 +1,15 @@
+# alt.oss.stars_history
+
+## Get underlying data 
+###alt.oss.stars_history(repo: str)
+
+Get repository star history
+
+    Parameters
+    ----------
+    repo : str
+            Repo to search for Format: org/repo, e.g., openbb-finance/openbbterminal
+
+    Returns
+    -------
+    pd.DataFrame - Columns: Date, Stars

website/content/api/alt/oss/summary/_index.md

@@ -0,0 +1,29 @@
+# alt.oss.summary
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###alt.oss.summary(repo: str) -> pandas.core.frame.DataFrame
+
+Get repository summary
+
+    Parameters
+    ----------
+    repo : str
+            Repo to search for Format: org/repo, e.g., openbb-finance/openbbterminal
+
+    Returns
+    -------
+    pd.DataFrame - Columns: Metric, Value
+
+## Getting charts 
+###alt.oss.summary(repo: str, export: str = '', chart=True) -> None
+
+Display repo summary [Source: https://api.github.com]
+
+    Parameters
+    ----------
+    repo : str
+            Repository to display summary. Format: org/repo, e.g., openbb-finance/openbbterminal
+    export : str
+        Export dataframe data to csv,json,xlsx file

website/content/api/alt/oss/top/_index.md

@@ -0,0 +1,38 @@
+# alt.oss.top
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###alt.oss.top(sortby: str, limit: int = 50, categories: str = '') -> pandas.core.frame.DataFrame
+
+Get repos sorted by stars or forks. Can be filtered by categories
+
+    Parameters
+    ----------
+    sortby : str
+            Sort repos by {stars, forks}
+    categories : str
+            Check for repo categories. If more than one separate with a comma: e.g., finance,investment. Default: None
+    limit : int
+            Number of repos to search for
+    Returns
+    -------
+    pd.DataFrame with list of repos
+
+## Getting charts 
+###alt.oss.top(sortby: str, categories: str = '', limit: int = 10, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Display repo summary [Source: https://api.github.com]
+
+    Parameters
+    ----------
+    sortby : str
+        Sort repos by {stars, forks}
+    categories : str
+        Check for repo categories. If more than one separate with a comma: e.g., finance,investment. Default: None
+    limit : int
+        Number of repos to look at
+    export : str
+        Export dataframe data to csv,json,xlsx file
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/alt/oss/top_repos/_index.md

@@ -0,0 +1,38 @@
+# alt.oss.top_repos
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###alt.oss.top_repos(sortby: str, top: int, categories: str) -> pandas.core.frame.DataFrame
+
+Get repos sorted by stars or forks. Can be filtered by categories
+
+    Parameters
+    ----------
+    sortby : str
+            Sort repos by {stars, forks}
+    categories : str
+            Check for repo categories. If more than one separate with a comma: e.g., finance,investment. Default: None
+    top : int
+            Number of repos to search for
+    Returns
+    -------
+    pd.DataFrame with list of repos
+
+## Getting charts 
+###alt.oss.top_repos(sortby: str, categories: str, limit: int, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Display repo summary [Source: https://api.github.com]
+
+    Parameters
+    ----------
+    sortby : str
+            Sort repos by {stars, forks}
+    categories : str
+            Check for repo categories. If more than one separate with a comma: e.g., finance,investment. Default: None
+    limit : int
+    Number of repos to look at
+    export : str
+    Export dataframe data to csv,json,xlsx file
+    external_axes : Optional[List[plt.Axes]], optional
+    External axes (1 axis is expected in the list), by default None

website/content/api/common/behavioural_analysis/sentiment/_index.md

@@ -0,0 +1,38 @@
+# common.behavioural_analysis.sentiment
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.behavioural_analysis.sentiment(post_data: List[str]) -> float
+
+Find the sentiment of a post and related comments
+
+    Parameters
+    ----------
+    post_data: List[str]
+        A post and its comments in string form
+
+    Returns
+    -------
+    float
+        A number in the range [-1, 1] representing sentiment
+
+## Getting charts 
+###common.behavioural_analysis.sentiment(symbol: str, n_tweets: int = 15, n_days_past: int = 2, compare: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Plot sentiments from symbol
+
+    Parameters
+    ----------
+    symbol: str
+        Stock ticker symbol to get sentiment for
+    n_tweets: int
+        Number of tweets to get per hour
+    n_days_past: int
+        Number of days to extract tweets for
+    compare: bool
+        Show corresponding change in stock price
+    export: str
+        Format to export tweet dataframe
+    external_axes: Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/common/behavioural_analysis/sentiment_stats/_index.md

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+# common.behavioural_analysis.sentiment_stats
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.behavioural_analysis.sentiment_stats(ticker: str) -> Dict
+
+Get sentiment stats [Source: finnhub]
+
+    Parameters
+    ----------
+    ticker : str
+        Ticker to get sentiment stats
+
+    Returns
+    -------
+    Dict
+        Get sentiment stats
+
+## Getting charts 
+###common.behavioural_analysis.sentiment_stats(ticker: str, export: str = '', chart=True)
+
+
+    Sentiment stats which displays buzz, news score, articles last week, articles weekly average,
+    bullish vs bearish percentages, sector average bullish percentage, and sector average news score
+
+    Parameters
+    ----------
+    ticker : str
+        Ticker to get sentiment stats
+    export : str
+        Format to export data

website/content/api/common/news/_index.md

@@ -0,0 +1,36 @@
+# common.news
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.news(term: str = '', sources: str = 'bloomberg.com') -> pandas.core.frame.DataFrame
+
+Get news for a given term and source. [Source: Feedparser]
+
+    Parameters
+    ----------
+    term : str
+        term to search on the news articles
+    sources: str
+        sources to exclusively show news from
+
+    Returns
+    ----------
+    articles : dict
+        term to search on the news articles
+
+## Getting charts 
+###common.news(term: str, sources: str = '', limit: int = 5, export: str = '', chart=True)
+
+Display news for a given term and source. [Source: Feedparser]
+
+    Parameters
+    ----------
+    term : str
+        term to search on the news articles
+    sources : str
+        sources to exclusively show news from
+    limit : int
+        number of articles to display
+    export : str
+        Export dataframe data to csv,json,xlsx file

website/content/api/common/qa/bw/_index.md

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+# common.qa.bw
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.qa.bw(data: pandas.core.frame.DataFrame, target: str, symbol: str = '', yearly: bool = True, external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None) -> None
+
+Show box and whisker plots
+
+    Parameters
+    ----------
+    symbol : str
+        Name of dataset
+    data : pd.DataFrame
+        Dataframe to look at
+    target : str
+        Data column to look at
+    yearly : bool
+        Flag to indicate yearly accumulation
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None
+
+## Getting charts 
+###common.qa.bw(data: pandas.core.frame.DataFrame, target: str, symbol: str = '', yearly: bool = True, external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Show box and whisker plots
+
+    Parameters
+    ----------
+    symbol : str
+        Name of dataset
+    data : pd.DataFrame
+        Dataframe to look at
+    target : str
+        Data column to look at
+    yearly : bool
+        Flag to indicate yearly accumulation
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/common/qa/calculate_adjusted_var/_index.md

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+# common.qa.calculate_adjusted_var
+
+## Get underlying data 
+###common.qa.calculate_adjusted_var(kurtosis: float, skew: float, ndp: float, std: float, mean: float) -> float
+
+Calculates VaR, which is adjusted for skew and kurtosis (Cornish-Fischer-Expansion)
+
+    Parameters
+    ----------
+    kurtosis: float
+        kurtosis of data
+    skew: float
+        skew of data
+    ndp: float
+        normal distribution percentage number (99% -> -2.326)
+    std: float
+        standard deviation of data
+    mean: float
+        mean of data
+
+    Returns
+    -------
+    float
+        Real adjusted VaR

website/content/api/common/qa/decompose/_index.md

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+# common.qa.decompose
+
+## Get underlying data 
+###common.qa.decompose(data: pandas.core.frame.DataFrame, multiplicative: bool = False) -> Tuple[Any, pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]
+
+Perform seasonal decomposition
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Dataframe of targeted data
+    multiplicative : bool
+        Boolean to indicate multiplication instead of addition
+
+    Returns
+    -------
+    result: Any
+        Result of statsmodels seasonal_decompose
+    cycle: pd.DataFrame
+        Filtered cycle
+    trend: pd.DataFrame
+        Filtered Trend

website/content/api/common/qa/es/_index.md

@@ -0,0 +1,48 @@
+# common.qa.es
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.qa.es(data: pandas.core.frame.DataFrame, use_mean: bool = False, distribution: str = 'normal', percentile: Union[float, int] = 0.999, portfolio: bool = False) -> pandas.core.frame.DataFrame
+
+Gets Expected Shortfall for specified stock dataframe
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Data dataframe
+    use_mean: bool
+        If one should use the data mean for calculation
+    distribution: str
+        Type of distribution, options: laplace, student_t, normal
+    percentile: Union[float,int]
+        VaR percentile
+    portfolio: bool
+        If the data is a portfolio
+
+    Returns
+    -------
+    list
+        list of ES
+    list
+        list of historical ES
+
+## Getting charts 
+###common.qa.es(data: pandas.core.frame.DataFrame, symbol: str = '', use_mean: bool = False, distribution: str = 'normal', percentile: float = 0.999, portfolio: bool = False, chart=True) -> None
+
+Displays expected shortfall
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Data dataframe
+    use_mean:
+        if one should use the data mean return
+    symbol: str
+        name of the data
+    distribution: str
+        choose distribution to use: logistic, laplace, normal
+    percentile: int
+        es percentile
+    portfolio: bool
+        If the data is a portfolio

website/content/api/common/qa/kurtosis/_index.md

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+# common.qa.kurtosis
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.qa.kurtosis(data: pandas.core.frame.DataFrame, window: int = 14) -> pandas.core.frame.DataFrame
+
+Kurtosis Indicator
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Dataframe of targeted data
+    window: int
+        Length of window
+
+    Returns
+    -------
+    df_kurt : pd.DataFrame
+        Dataframe of rolling kurtosis
+
+## Getting charts 
+###common.qa.kurtosis(symbol: str, data: pandas.core.frame.DataFrame, target: str, window: int = 14, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+View rolling kurtosis
+
+    Parameters
+    ----------
+    symbol: str
+        Ticker
+    data: pd.DataFrame
+        Dataframe of stock prices
+    target: str
+        Column in data to look at
+    window: int
+        Length of window
+    export: str
+        Format to export data
+    external_axes: Optional[List[plt.Axes]], optional
+        External axes (2 axes are expected in the list), by default None

website/content/api/common/qa/normality/_index.md

@@ -0,0 +1,35 @@
+# common.qa.normality
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.qa.normality(data: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame
+
+
+    Look at the distribution of returns and generate statistics on the relation to the normal curve.
+    This function calculates skew and kurtosis (the third and fourth moments) and performs both
+    a Jarque-Bera and Shapiro Wilk test to determine if data is normally distributed.
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Dataframe of targeted data
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe containing statistics of normality
+
+## Getting charts 
+###common.qa.normality(data: pandas.core.frame.DataFrame, target: str, export: str = '', chart=True) -> None
+
+View normality statistics
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        DataFrame
+    target : str
+        Column in data to look at
+    export : str
+        Format to export data

website/content/api/common/qa/omega/_index.md

@@ -0,0 +1,29 @@
+# common.qa.omega
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.qa.omega(data: pandas.core.frame.DataFrame, threshold_start: float = 0, threshold_end: float = 1.5) -> pandas.core.frame.DataFrame
+
+Get the omega series
+    Parameters
+    ----------
+    data: pd.DataFrame
+        stock dataframe
+    threshold_start: float
+        annualized target return threshold start of plotted threshold range
+    threshold_end: float
+        annualized target return threshold end of plotted threshold range
+
+## Getting charts 
+###common.qa.omega(data: pandas.core.frame.DataFrame, threshold_start: float = 0, threshold_end: float = 1.5, chart=True) -> None
+
+Displays the omega ratio
+    Parameters
+    ----------
+    data: pd.DataFrame
+        stock dataframe
+    threshold_start: float
+        annualized target return threshold start of plotted threshold range
+    threshold_end: float
+        annualized target return threshold end of plotted threshold range

website/content/api/common/qa/quantile/_index.md

@@ -0,0 +1,46 @@
+# common.qa.quantile
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.qa.quantile(data: pandas.core.frame.DataFrame, window: int = 14, quantile_pct: float = 0.5) -> Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]
+
+Overlay Median & Quantile
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Dataframe of targeted data
+    window : int
+        Length of window
+    quantile_pct: float
+        Quantile to display
+
+    Returns
+    -------
+    df_med : pd.DataFrame
+        Dataframe of median prices over window
+    df_quantile : pd.DataFrame
+        Dataframe of gievn quantile prices over window
+
+## Getting charts 
+###common.qa.quantile(data: pandas.core.frame.DataFrame, target: str, symbol: str = '', window: int = 14, quantile: float = 0.5, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+View rolling quantile
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Dataframe
+    target: str
+        Column in data to look at
+    symbol : str
+        Stock ticker
+    window : int
+        Length of window
+    quantile: float
+        Quantile to get
+    export: str
+        Format to export data
+    external_axes: Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/common/qa/rolling/_index.md

@@ -0,0 +1,42 @@
+# common.qa.rolling
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.qa.rolling(data: pandas.core.frame.DataFrame, window: int = 14) -> Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]
+
+Return rolling mean and standard deviation
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Dataframe of target data
+    window: int
+        Length of rolling window
+
+    Returns
+    -------
+    pd.DataFrame:
+        Dataframe of rolling mean
+    pd.DataFrame:
+        Dataframe of rolling standard deviation
+
+## Getting charts 
+###common.qa.rolling(data: pandas.core.frame.DataFrame, target: str, symbol: str = '', window: int = 14, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+View mean std deviation
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Dataframe
+    target: str
+        Column in data to look at
+    symbol : str
+        Stock ticker
+    window : int
+        Length of window
+    export: str
+        Format to export data
+    external_axes: Optional[List[plt.Axes]], optional
+        External axes (2 axes are expected in the list), by default None

website/content/api/common/qa/sharpe/_index.md

@@ -0,0 +1,34 @@
+# common.qa.sharpe
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.qa.sharpe(data: pandas.core.frame.DataFrame, rfr: float = 0, window: float = 252) -> pandas.core.frame.DataFrame
+
+Calculates the sharpe ratio
+    Parameters
+    ----------
+    data: pd.DataFrame
+        selected dataframe column
+    rfr: float
+        risk free rate
+    window: float
+        length of the rolling window
+
+    Returns
+    -------
+    sharpe: pd.DataFrame
+        sharpe ratio
+
+## Getting charts 
+###common.qa.sharpe(data: pandas.core.frame.DataFrame, rfr: float = 0, window: float = 252, chart=True) -> None
+
+Calculates the sharpe ratio
+    Parameters
+    ----------
+    data: pd.DataFrame
+        selected dataframe column
+    rfr: float
+        risk free rate
+    window: float
+        length of the rolling window

website/content/api/common/qa/skew/_index.md

@@ -0,0 +1,40 @@
+# common.qa.skew
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.qa.skew(data: pandas.core.frame.DataFrame, window: int = 14) -> pandas.core.frame.DataFrame
+
+Skewness Indicator
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Dataframe of targeted data
+    window : int
+        Length of window
+
+    Returns
+    -------
+    data_skew : pd.DataFrame
+        Dataframe of rolling skew
+
+## Getting charts 
+###common.qa.skew(symbol: str, data: pandas.core.frame.DataFrame, target: str, window: int = 14, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+View rolling skew
+
+    Parameters
+    ----------
+    symbol: str
+        Stock ticker
+    data: pd.DataFrame
+        Dataframe
+    target: str
+        Column in data to look at
+    window: int
+        Length of window
+    export: str
+        Format to export data
+    external_axes: Optional[List[plt.Axes]], optional
+        External axes (2 axes are expected in the list), by default None

website/content/api/common/qa/sortino/_index.md

@@ -0,0 +1,38 @@
+# common.qa.sortino
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.qa.sortino(data: pandas.core.frame.DataFrame, target_return: float = 0, window: float = 252, adjusted: bool = False) -> pandas.core.frame.DataFrame
+
+Calculates the sortino ratio
+    Parameters
+    ----------
+    data: pd.DataFrame
+        selected dataframe
+    target_return: float
+        target return of the asset
+    window: float
+        length of the rolling window
+    adjusted: bool
+        adjust the sortino ratio
+
+    Returns
+    -------
+    sortino: pd.DataFrame
+        sortino ratio
+
+## Getting charts 
+###common.qa.sortino(data: pandas.core.frame.DataFrame, target_return: float, window: float, adjusted: bool, chart=True) -> None
+
+Displays the sortino ratio
+    Parameters
+    ----------
+    data: pd.DataFrame
+        selected dataframe
+    target_return: float
+        target return of the asset
+    window: float
+        length of the rolling window
+    adjusted: bool
+        adjust the sortino ratio

website/content/api/common/qa/spread/_index.md

@@ -0,0 +1,44 @@
+# common.qa.spread
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.qa.spread(data: pandas.core.frame.DataFrame, window: int = 14) -> Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]
+
+Standard Deviation and Variance
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        DataFrame of targeted data
+    window: int
+        Length of window
+
+    Returns
+    -------
+    df_sd: pd.DataFrame
+        Dataframe of rolling standard deviation
+    df_var: pd.DataFrame
+        Dataframe of rolling standard deviation
+
+## Getting charts 
+###common.qa.spread(data: pandas.core.frame.DataFrame, target: str, symbol: str = '', window: int = 14, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+View rolling spread
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Dataframe
+    target: str
+        Column in data to look at
+    target: str
+        Column in data to look at
+    symbol : str
+        Stock ticker
+    window : int
+        Length of window
+    export: str
+        Format to export data
+    external_axes: Optional[List[plt.Axes]], optional
+        External axes (3 axes are expected in the list), by default None

website/content/api/common/qa/summary/_index.md

@@ -0,0 +1,30 @@
+# common.qa.summary
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.qa.summary(data: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame
+
+Print summary statistics
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Dataframe to get summary statistics for
+
+    Returns
+    -------
+    summary : pd.DataFrame
+        Summary statistics
+
+## Getting charts 
+###common.qa.summary(data: pandas.core.frame.DataFrame, export: str = '', chart=True) -> None
+
+Show summary statistics
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        DataFrame to get statistics of
+    export : str
+        Format to export data

website/content/api/common/qa/unitroot/_index.md

@@ -0,0 +1,40 @@
+# common.qa.unitroot
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.qa.unitroot(data: pandas.core.frame.DataFrame, fuller_reg: str = 'c', kpss_reg: str = 'c') -> pandas.core.frame.DataFrame
+
+Calculate test statistics for unit roots
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        DataFrame of target variable
+    fuller_reg : str
+        Type of regression of ADF test. Can be ‘c’,’ct’,’ctt’,’nc’ 'c' - Constant and t - trend order
+    kpss_reg : str
+        Type of regression for KPSS test.  Can be ‘c’,’ct'
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe with results of ADF test and KPSS test
+
+## Getting charts 
+###common.qa.unitroot(data: pandas.core.frame.DataFrame, target: str, fuller_reg: str = 'c', kpss_reg: str = 'c', export: str = '', chart=True)
+
+Show unit root test calculations
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        DataFrame
+    target : str
+        Column of data to look at
+    fuller_reg : str
+        Type of regression of ADF test. Can be ‘c’,’ct’,’ctt’,’nc’ 'c' - Constant and t - trend order
+    kpss_reg : str
+        Type of regression for KPSS test. Can be ‘c’,’ct'
+    export : str
+        Format for exporting data

website/content/api/common/qa/var/_index.md

@@ -0,0 +1,54 @@
+# common.qa.var
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.qa.var(data: pandas.core.frame.DataFrame, use_mean: bool = False, adjusted_var: bool = False, student_t: bool = False, percentile: Union[int, float] = 0.999, portfolio: bool = False) -> pandas.core.frame.DataFrame
+
+Gets value at risk for specified stock dataframe
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Data dataframe
+    use_mean: bool
+        If one should use the data mean for calculation
+    adjusted_var: bool
+        If one should return VaR adjusted for skew and kurtosis
+    student_t: bool
+        If one should use the student-t distribution
+    percentile: Union[int,float]
+        VaR percentile
+    portfolio: bool
+        If the data is a portfolio
+
+    Returns
+    -------
+    list
+        list of VaR
+    list
+        list of historical VaR
+
+## Getting charts 
+###common.qa.var(data: pandas.core.frame.DataFrame, symbol: str = '', use_mean: bool = False, adjusted_var: bool = False, student_t: bool = False, percentile: float = 0.999, data_range: int = 0, portfolio: bool = False, chart=True) -> None
+
+Displays VaR of dataframe
+
+    Parameters
+    ----------
+    data: pd.Dataframe
+        Data dataframe
+    use_mean: bool
+        if one should use the data mean return
+    symbol: str
+        name of the data
+    adjusted_var: bool
+        if one should have VaR adjusted for skew and kurtosis (Cornish-Fisher-Expansion)
+    student_t: bool
+        If one should use the student-t distribution
+    percentile: int
+        var percentile
+    data_range: int
+        Number of rows you want to use VaR over
+    portfolio: bool
+        If the data is a portfolio

website/content/api/common/ta/ad/_index.md

@@ -0,0 +1,38 @@
+# common.ta.ad
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.ad(data: pandas.core.frame.DataFrame, use_open: bool = False) -> pandas.core.frame.DataFrame
+
+Calculate AD technical indicator
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Dataframe of prices with OHLC and Volume
+    use_open : bool
+        Whether to use open prices
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe with technical indicator
+
+## Getting charts 
+###common.ta.ad(data: pandas.core.frame.DataFrame, use_open: bool = False, symbol: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Plot AD technical indicator
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Dataframe of ohlc prices
+    use_open : bool
+        Whether to use open prices in calculation
+    symbol : str
+        Ticker symbol
+    export: str
+        Format to export data as
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (3 axes are expected in the list), by default None

website/content/api/common/ta/adosc/_index.md

@@ -0,0 +1,46 @@
+# common.ta.adosc
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.adosc(data: pandas.core.frame.DataFrame, use_open: bool = False, fast: int = 3, slow: int = 10) -> pandas.core.frame.DataFrame
+
+Calculate AD oscillator technical indicator
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Dataframe of OHLC prices
+    use_open : bool
+        Whether to use open prices
+    fast: int
+        Fast value
+    slow: int
+        Slow value
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe with technical indicator
+
+## Getting charts 
+###common.ta.adosc(data: pandas.core.frame.DataFrame, fast: int = 3, slow: int = 10, use_open: bool = False, symbol: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Display AD Osc Indicator
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Dataframe of ohlc prices
+    use_open : bool
+        Whether to use open prices in calculation
+    fast: int
+         Length of fast window
+    slow : int
+        Length of slow window
+    symbol : str
+        Stock ticker
+    export : str
+        Format to export data
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (3 axes are expected in the list), by default None

website/content/api/common/ta/adx/_index.md

@@ -0,0 +1,50 @@
+# common.ta.adx
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.adx(high_values: pandas.core.series.Series, low_values: pandas.core.series.Series, close_values: pandas.core.series.Series, window: int = 14, scalar: int = 100, drift: int = 1)
+
+ADX technical indicator
+
+    Parameters
+    ----------
+    high_values: pd.Series
+        High prices
+    low_values: pd.Series
+        Low prices
+    close_values: pd.Series
+        close prices
+    window: int
+        Length of window
+    scalar: int
+        Scalar variable
+    drift: int
+        Drift variable
+
+    Returns
+    -------
+    pd.DataFrame
+        DataFrame with adx indicator
+
+## Getting charts 
+###common.ta.adx(data: pandas.core.frame.DataFrame, window: int = 14, scalar: int = 100, drift: int = 1, symbol: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Plot ADX indicator
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Dataframe with OHLC price data
+    window : int
+        Length of window
+    scalar : int
+        Scalar variable
+    drift : int
+        Drift variable
+    symbol : str
+        Ticker
+    export : str
+        Format to export data
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (2 axes are expected in the list), by default None

website/content/api/common/ta/aroon/_index.md

@@ -0,0 +1,44 @@
+# common.ta.aroon
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.aroon(high_values: pandas.core.series.Series, low_values: pandas.core.series.Series, window: int = 25, scalar: int = 100) -> pandas.core.frame.DataFrame
+
+Aroon technical indicator
+
+    Parameters
+    ----------
+    high_values: pd.Series
+        High prices
+    low_values: pd.Series
+        Low prices
+    window : int
+        Length of window
+    scalar : int
+        Scalar variable
+
+    Returns
+    -------
+    pd.DataFrame
+        DataFrame with aroon indicator
+
+## Getting charts 
+###common.ta.aroon(data: pandas.core.frame.DataFrame, window: int = 25, scalar: int = 100, symbol: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Plot Aroon indicator
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Dataframe with OHLC price data
+    window: int
+        Length of window
+    symbol: str
+        Ticker
+    scalar: int
+        Scalar variable
+    export: str
+        Format to export data
+    external_axes: Optional[List[plt.Axes]], optional
+        External axes (3 axes are expected in the list), by default None

website/content/api/common/ta/bbands/_index.md

@@ -0,0 +1,46 @@
+# common.ta.bbands
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.bbands(close_values: pandas.core.series.Series, window: int = 15, n_std: float = 2, mamode: str = 'ema') -> pandas.core.frame.DataFrame
+
+Calculate Bollinger Bands
+
+    Parameters
+    ----------
+    close_values : pd.DataFrame
+        DataFrame of sclose prices
+    window : int
+        Length of window to calculate BB
+    n_std : float
+        Number of standard deviations to show
+    mamode : str
+        Method of calculating average
+
+    Returns
+    -------
+    df_ta: pd.DataFrame
+        Dataframe of bollinger band data
+
+## Getting charts 
+###common.ta.bbands(data: pandas.core.frame.DataFrame, symbol: str = '', window: int = 15, n_std: float = 2, mamode: str = 'sma', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Show bollinger bands
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Dataframe of ohlc prices
+    symbol : str
+        Ticker symbol
+    window : int
+        Length of window to calculate BB
+    n_std : float
+        Number of standard deviations to show
+    mamode : str
+        Method of calculating average
+    export : str
+        Format of export file
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/common/ta/cci/_index.md

@@ -0,0 +1,47 @@
+# common.ta.cci
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.cci(data: pandas.core.frame.DataFrame, window: int = 14, scalar: float = 0.0015) -> pandas.core.frame.DataFrame
+
+Commodity channel index
+
+    Parameters
+    ----------
+    high_vals: pd.Series
+        High values
+    low_values: pd.Series
+        Low values
+    close-values: pd.Series
+        Close values
+    window: int
+        Length of window
+    scalar: float
+        Scalar variable
+
+    Returns
+    ----------
+    pd.DataFrame
+        Dataframe of technical indicator
+
+## Getting charts 
+###common.ta.cci(data: pandas.core.frame.DataFrame, window: int = 14, scalar: float = 0.0015, symbol: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Display CCI Indicator
+
+    Parameters
+    ----------
+
+    data : pd.DataFrame
+        Dataframe of OHLC
+    window : int
+        Length of window
+    scalar : float
+        Scalar variable
+    symbol : str
+        Stock ticker
+    export : str
+        Format to export data
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (2 axes are expected in the list), by default None

website/content/api/common/ta/cg/_index.md

@@ -0,0 +1,37 @@
+# common.ta.cg
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.cg(values: pandas.core.series.Series, window: int) -> pandas.core.frame.DataFrame
+
+Center of gravity
+
+    Parameters
+    ----------
+    values: pd.DataFrame
+        Data to use with close being titled values
+    window: int
+        Length for indicator window
+    Returns
+    ----------
+    d.DataFrame
+        Dataframe of technical indicator
+
+## Getting charts 
+###common.ta.cg(data: pandas.core.series.Series, window: int = 14, symbol: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Display center of gravity Indicator
+
+    Parameters
+    ----------
+    data : pd.Series
+        Series of values
+    window : int
+        Length of window
+    symbol : str
+        Stock ticker
+    export : str
+        Format to export data
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (2 axes are expected in the list), by default None

website/content/api/common/ta/donchian/_index.md

@@ -0,0 +1,44 @@
+# common.ta.donchian
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.donchian(high_prices: pandas.core.series.Series, low_prices: pandas.core.series.Series, upper_length: int = 20, lower_length: int = 20) -> pandas.core.frame.DataFrame
+
+Calculate Donchian Channels
+
+    Parameters
+    ----------
+    high_prices : pd.DataFrame
+        High prices
+    low_prices : pd.DataFrame
+        Low prices
+    upper_length : int
+        Length of window to calculate upper channel
+    lower_length : int
+        Length of window to calculate lower channel
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe of upper and lower channels
+
+## Getting charts 
+###common.ta.donchian(data: pandas.core.frame.DataFrame, symbol: str = '', upper_length: int = 20, lower_length: int = 20, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Show donchian channels
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Dataframe of ohlc prices
+    symbol : str
+        Ticker symbol
+    upper_length : int
+        Length of window to calculate upper channel
+    lower_length : int
+        Length of window to calculate lower channel
+    export : str
+        Format of export file
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/common/ta/ema/_index.md

@@ -0,0 +1,20 @@
+# common.ta.ema
+
+## Get underlying data 
+###common.ta.ema(data: pandas.core.frame.DataFrame, length: int = 50, offset: int = 0) -> pandas.core.frame.DataFrame
+
+Gets exponential moving average (EMA) for stock
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Dataframe of dates and prices
+    length: int
+        Length of EMA window
+    offset: int
+        Length of offset
+
+    Returns
+    ----------
+    pd.DataFrame
+        Dataframe containing prices and EMA

website/content/api/common/ta/fib/_index.md

@@ -0,0 +1,54 @@
+# common.ta.fib
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.fib(data: pandas.core.frame.DataFrame, limit: int = 120, start_date: Any = None, end_date: Any = None) -> Tuple[pandas.core.frame.DataFrame, pandas._libs.tslibs.timestamps.Timestamp, pandas._libs.tslibs.timestamps.Timestamp, float, float]
+
+Calculate Fibonacci levels
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Dataframe of prices
+    limit : int
+        Days to look back for retracement
+    start_date : Any
+        Custom start date for retracement
+    end_date : Any
+        Custom end date for retracement
+
+    Returns
+    -------
+    df : pd.DataFrame
+        Dataframe of fib levels
+    min_date: pd.Timestamp
+        Date of min point
+    max_date: pd.Timestamp:
+        Date of max point
+    min_pr: float
+        Price at min point
+    max_pr: float
+        Price at max point
+
+## Getting charts 
+###common.ta.fib(data: pandas.core.frame.DataFrame, limit: int = 120, start_date: Optional[str] = None, end_date: Optional[str] = None, symbol: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Calculate fibonacci retracement levels
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        OHLC data
+    limit: int
+        Days to lookback
+    start_date: Optional[str, None]
+        User picked date for starting retracement
+    end_date: Optional[str, None]
+        User picked date for ending retracement
+    symbol: str
+        Ticker symbol
+    export: str
+        Format to export data
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (2 axes are expected in the list), by default None

website/content/api/common/ta/fisher/_index.md

@@ -0,0 +1,39 @@
+# common.ta.fisher
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.fisher(high_vals: pandas.core.series.Series, low_vals: pandas.core.series.Series, window: int = 14) -> pandas.core.frame.DataFrame
+
+Fisher Transform
+
+    Parameters
+    ----------
+    high_vals: pd.Series
+        High values
+    low_vals: pd.Series
+        Low values
+    window: int
+        Length for indicator window
+    Returns
+    ----------
+    df_ta: pd.DataFrame
+        Dataframe of technical indicator
+
+## Getting charts 
+###common.ta.fisher(data: pandas.core.frame.DataFrame, window: int = 14, symbol: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Display Fisher Indicator
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Dataframe of OHLC prices
+    window : int
+        Length of window
+    symbol : str
+        Ticker string
+    export : str
+        Format to export data
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (3 axes are expected in the list), by default None

website/content/api/common/ta/hma/_index.md

@@ -0,0 +1,20 @@
+# common.ta.hma
+
+## Get underlying data 
+###common.ta.hma(data: pandas.core.frame.DataFrame, length: int = 50, offset: int = 0) -> pandas.core.frame.DataFrame
+
+Gets hull moving average (HMA) for stock
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Dataframe of dates and prices
+    length: int
+        Length of SMA window
+    offset: int
+        Length of offset
+
+    Returns
+    ----------
+    df_ta: pd.DataFrame
+        Dataframe containing prices and HMA

website/content/api/common/ta/kc/_index.md

@@ -0,0 +1,55 @@
+# common.ta.kc
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.kc(high_prices: pandas.core.series.Series, low_prices: pandas.core.series.Series, close_prices: pandas.core.series.Series, window: int = 20, scalar: float = 2, mamode: str = 'ema', offset: int = 0) -> pandas.core.frame.DataFrame
+
+Keltner Channels
+
+    Parameters
+    ----------
+    high_prices : pd.DataFrame
+        High prices
+    low_prices : pd.DataFrame
+        Low prices
+    close_prices : pd.DataFrame
+        Close prices
+    window : int
+        Length of window
+    scalar: float
+        Scalar value
+    mamode: str
+        Type of filter
+    offset : int
+        Offset value
+
+    Returns
+    -------
+    df_en : pd.DataFrame
+        Dataframe of rolling kc
+
+## Getting charts 
+###common.ta.kc(data: pandas.core.frame.DataFrame, window: int = 20, scalar: float = 2, mamode: str = 'ema', offset: int = 0, symbol: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+View Keltner Channels Indicator
+
+    Parameters
+    ----------
+
+    data: pd.DataFrame
+        Dataframe of ohlc prices
+    window: int
+        Length of window
+    scalar: float
+        Scalar value
+    mamode: str
+        Type of filter
+    offset: int
+        Offset value
+    symbol: str
+        Ticker symbol
+    export: str
+        Format to export data
+    external_axes: Optional[List[plt.Axes]], optional
+        External axes (2 axes are expected in the list), by default None

website/content/api/common/ta/ma/_index.md

@@ -0,0 +1,47 @@
+# common.ta.ma
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.ma(data: pandas.core.series.Series, window: List[int] = None, offset: int = 0, ma_type: str = 'EMA', symbol: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None) -> None
+
+Plots MA technical indicator
+
+    Parameters
+    ----------
+    data: pd.Series
+        Series of prices
+    window: List[int]
+        Length of EMA window
+    offset: int
+        Offset variable
+    ma_type: str
+        Type of moving average.  Either "EMA" "ZLMA" or "SMA"
+    symbol: str
+        Ticker
+    export: str
+        Format to export data
+    external_axes: Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None
+
+## Getting charts 
+###common.ta.ma(data: pandas.core.series.Series, window: List[int] = None, offset: int = 0, ma_type: str = 'EMA', symbol: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Plots MA technical indicator
+
+    Parameters
+    ----------
+    data: pd.Series
+        Series of prices
+    window: List[int]
+        Length of EMA window
+    offset: int
+        Offset variable
+    ma_type: str
+        Type of moving average.  Either "EMA" "ZLMA" or "SMA"
+    symbol: str
+        Ticker
+    export: str
+        Format to export data
+    external_axes: Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/common/ta/macd/_index.md

@@ -0,0 +1,45 @@
+# common.ta.macd
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.macd(values: pandas.core.frame.DataFrame, n_fast: int = 12, n_slow: int = 26, n_signal: int = 9) -> pandas.core.frame.DataFrame
+
+Moving average convergence divergence
+
+    Parameters
+    ----------
+    values: pd.Series
+        Values for calculation
+    n_fast : int
+        Fast period
+    n_slow : int
+        Slow period
+    n_signal : int
+        Signal period
+    Returns
+    ----------
+    pd.DataFrame
+        Dataframe of technical indicator
+
+## Getting charts 
+###common.ta.macd(data: pandas.core.series.Series, n_fast: int = 12, n_slow: int = 26, n_signal: int = 9, symbol: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Plot MACD signal
+
+    Parameters
+    ----------
+    data : pd.Series
+        Values to input
+    n_fast : int
+        Fast period
+    n_slow : int
+        Slow period
+    n_signal : int
+        Signal period
+    symbol : str
+        Stock ticker
+    export : str
+        Format to export data
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (2 axes are expected in the list), by default None

website/content/api/common/ta/obv/_index.md

@@ -0,0 +1,34 @@
+# common.ta.obv
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.obv(data: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame
+
+On Balance Volume
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Dataframe of OHLC prices
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe with technical indicator
+
+## Getting charts 
+###common.ta.obv(data: pandas.core.frame.DataFrame, symbol: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Plot OBV technical indicator
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Dataframe of ohlc prices
+    symbol : str
+        Ticker
+    export: str
+        Format to export data as
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/common/ta/rsi/_index.md

@@ -0,0 +1,46 @@
+# common.ta.rsi
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.rsi(values: pandas.core.series.Series, window: int = 14, scalar: float = 100, drift: int = 1) -> pandas.core.frame.DataFrame
+
+Relative strength index
+
+    Parameters
+    ----------
+    values: pd.Series
+        Dataframe of prices
+    window: int
+        Length of window
+    scalar: float
+        Scalar variable
+    drift: int
+        Drift variable
+
+    Returns
+    ----------
+    pd.DataFrame
+        Dataframe of technical indicator
+
+## Getting charts 
+###common.ta.rsi(data: pandas.core.series.Series, window: int = 14, scalar: float = 100.0, drift: int = 1, symbol: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Display RSI Indicator
+
+    Parameters
+    ----------
+    data : pd.Series
+        Values to input
+    window : int
+        Length of window
+    scalar : float
+        Scalar variable
+    drift : int
+        Drift variable
+    symbol : str
+        Stock ticker
+    export : str
+        Format to export data
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (2 axes are expected in the list), by default None

website/content/api/common/ta/sma/_index.md

@@ -0,0 +1,20 @@
+# common.ta.sma
+
+## Get underlying data 
+###common.ta.sma(data: pandas.core.frame.DataFrame, length: int = 50, offset: int = 0) -> pandas.core.frame.DataFrame
+
+Gets simple moving average (EMA) for stock
+
+     Parameters
+     ----------
+     data: pd.DataFrame
+         Dataframe of dates and prices
+     length: int
+         Length of SMA window
+     offset: int
+         Length of offset
+
+     Returns
+     ----------
+    pd.DataFrame
+         Dataframe containing prices and SMA

website/content/api/common/ta/stoch/_index.md

@@ -0,0 +1,49 @@
+# common.ta.stoch
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.stoch(high_vals: pandas.core.series.Series, low_vals: pandas.core.series.Series, close_vals: pandas.core.series.Series, fastkperiod: int = 14, slowdperiod: int = 3, slowkperiod: int = 3)
+
+Stochastic oscillator
+
+    Parameters
+    ----------
+    high_vals: pd.Series
+        High values
+    low_vals: pd.Series
+        Low values
+    close-vals: pd.Series
+        Close values
+    fastkperiod : int
+        Fast k period
+    slowdperiod : int
+        Slow d period
+    slowkperiod : int
+        Slow k period
+    Returns
+    ----------
+    pd.DataFrame
+        Dataframe of technical indicator
+
+## Getting charts 
+###common.ta.stoch(data: pandas.core.frame.DataFrame, fastkperiod: int = 14, slowdperiod: int = 3, slowkperiod: int = 3, symbol: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Plot stochastic oscillator signal
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Dataframe of OHLC prices
+    fastkperiod : int
+        Fast k period
+    slowdperiod : int
+        Slow d period
+    slowkperiod : int
+        Slow k period
+    symbol : str
+        Stock ticker symbol
+    export : str
+        Format to export data
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (3 axes are expected in the list), by default None

website/content/api/common/ta/vwap/_index.md

@@ -0,0 +1,43 @@
+# common.ta.vwap
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###common.ta.vwap(data: pandas.core.frame.DataFrame, offset: int = 0) -> pandas.core.frame.DataFrame
+
+Gets volume weighted average price (VWAP)
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Dataframe of dates and prices
+    offset: int
+        Length of offset
+    Returns
+    ----------
+    df_vwap: pd.DataFrame
+        Dataframe with VWAP data
+
+## Getting charts 
+###common.ta.vwap(data: pandas.core.frame.DataFrame, symbol: str = '', start_date: str = None, end_date: str = None, offset: int = 0, interval: str = '', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Plots VWMA technical indicator
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Dataframe of OHLC prices
+    symbol : str
+        Ticker
+    offset : int
+        Offset variable
+    start_date: datetime
+        Start date to get data from with
+    end_date: datetime
+        End date to get data from with
+    interval : str
+        Interval of data
+    export : str
+        Format to export data
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (3 axes are expected in the list), by default None

website/content/api/common/ta/wma/_index.md

@@ -0,0 +1,20 @@
+# common.ta.wma
+
+## Get underlying data 
+###common.ta.wma(data: pandas.core.frame.DataFrame, length: int = 50, offset: int = 0) -> pandas.core.frame.DataFrame
+
+Gets weighted moving average (WMA) for stock
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Dataframe of dates and prices
+    length: int
+        Length of SMA window
+    offset: int
+        Length of offset
+
+    Returns
+    ----------
+    df_ta: pd.DataFrame
+        Dataframe containing prices and WMA

website/content/api/common/ta/zlma/_index.md

@@ -0,0 +1,20 @@
+# common.ta.zlma
+
+## Get underlying data 
+###common.ta.zlma(data: pandas.core.frame.DataFrame, length: int = 50, offset: int = 0) -> pandas.core.frame.DataFrame
+
+Gets zero-lagged exponential moving average (ZLEMA) for stock
+
+    Parameters
+    ----------
+    data: pd.DataFrame
+        Dataframe of dates and prices
+    length: int
+        Length of EMA window
+    offset: int
+        Length of offset
+
+    Returns
+    ----------
+    df_ta: pd.DataFrame
+        Dataframe containing prices and EMA

website/content/api/crypto/candles/_index.md

@@ -0,0 +1,19 @@
+# crypto.candles
+
+## Get underlying data 
+###crypto.candles(candles_df: 'pd.DataFrame', volume: 'bool' = True, ylabel: 'str' = '', title: 'str' = '', external_axes: 'list[plt.Axes] | None' = None) -> 'None'
+
+Plot candle chart from dataframe. [Source: Binance]
+
+    Parameters
+    ----------
+    candles_df: pd.DataFrame
+        Dataframe containing time and OHLCV
+    volume: bool
+        If volume data shall be plotted, by default True
+    ylabel: str
+        Y-label of the graph, by default ""
+    title: str
+        Title of graph, by default ""
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/crypto/chart/_index.md

@@ -0,0 +1,31 @@
+# crypto.chart
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.chart(prices_df: 'pd.DataFrame', symbol: 'str' = '', currency: 'str' = '', source: 'str' = '', exchange: 'str' = '', interval: 'str' = '', external_axes: 'list[plt.Axes] | None' = None) -> 'None'
+
+Load data for Technical Analysis
+
+    Parameters
+    ----------
+    prices_df: pd.DataFrame
+        Cryptocurrency
+    symbol: str
+        Coin (only used for chart title), by default ""
+    currency: str
+        Currency (only used for chart title), by default ""
+
+## Getting charts 
+###crypto.chart(prices_df: 'pd.DataFrame', symbol: 'str' = '', currency: 'str' = '', source: 'str' = '', exchange: 'str' = '', interval: 'str' = '', external_axes: 'list[plt.Axes] | None' = None, chart=True) -> 'None'
+
+Load data for Technical Analysis
+
+    Parameters
+    ----------
+    prices_df: pd.DataFrame
+        Cryptocurrency
+    symbol: str
+        Coin (only used for chart title), by default ""
+    currency: str
+        Currency (only used for chart title), by default ""

website/content/api/crypto/dd/active/_index.md

@@ -0,0 +1,46 @@
+# crypto.dd.active
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.active(symbol: str, interval: str = '24h', start_date: int = 1262304000, end_date: int = 1663694553) -> pandas.core.frame.DataFrame
+
+Returns active addresses of a certain symbol
+    [Source: https://glassnode.com]
+
+    Parameters
+    ----------
+    symbol : str
+        Asset to search active addresses (e.g., BTC)
+    start_date : int
+        Initial date timestamp (e.g., 1_614_556_800)
+    end_date : int
+        End date timestamp (e.g., 1_614_556_800)
+    interval : str
+        Interval frequency (e.g., 24h)
+
+    Returns
+    -------
+    pd.DataFrame
+        active addresses over time
+
+## Getting charts 
+###crypto.dd.active(symbol: str, start_date: int = 1577836800, end_date: int = 1609459200, interval: str = '24h', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Display active addresses of a certain symbol over time
+    [Source: https://glassnode.org]
+
+    Parameters
+    ----------
+    symbol : str
+        Asset to search active addresses (e.g., BTC)
+    start_date : int
+        Initial date timestamp (e.g., 1_614_556_800)
+    end_date : int
+        End date timestamp (e.g., 1_614_556_800)
+    interval : str
+        Interval frequency (possible values are: 24h, 1w, 1month)
+    export : str
+        Export dataframe data to csv,json,xlsx file
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/crypto/dd/all_binance_trading_pairs/_index.md

@@ -0,0 +1,16 @@
+# crypto.dd.all_binance_trading_pairs
+
+## Get underlying data 
+###crypto.dd.all_binance_trading_pairs() -> pandas.core.frame.DataFrame
+
+Returns all available pairs on Binance in DataFrame format. DataFrame has 3 columns symbol, baseAsset, quoteAsset
+    example row: ETHBTC | ETH | BTC
+    [Source: Binance]
+
+
+    Returns
+    -------
+    pd.DataFrame
+        All available pairs on Binance
+        Columns: symbol, baseAsset, quoteAsset
+

website/content/api/crypto/dd/balance/_index.md

@@ -0,0 +1,34 @@
+# crypto.dd.balance
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.balance(from_symbol: str, to_symbol: str = 'USDT') -> pandas.core.frame.DataFrame
+
+Get account holdings for asset. [Source: Binance]
+
+    Parameters
+    ----------
+    from_symbol: str
+        Cryptocurrency
+    to_symbol: str
+        Cryptocurrency
+
+    Returns
+    -------
+    pd.DataFrame
+        Dataframe with account holdings for an asset
+
+## Getting charts 
+###crypto.dd.balance(from_symbol: str, to_symbol: str = 'USDT', export: str = '', chart=True) -> None
+
+Get account holdings for asset. [Source: Binance]
+
+    Parameters
+    ----------
+    from_symbol: str
+        Cryptocurrency
+    to_symbol: str
+        Cryptocurrency
+    export: str
+        Export dataframe data to csv,json,xlsx

website/content/api/crypto/dd/basic/_index.md

@@ -0,0 +1,32 @@
+# crypto.dd.basic
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.basic(symbol: str = 'btc-bitcoin') -> pandas.core.frame.DataFrame
+
+Basic coin information [Source: CoinPaprika]
+
+    Parameters
+    ----------
+    symbol: str
+        Coin id
+
+    Returns
+    -------
+    pd.DataFrame
+        Metric, Value
+
+## Getting charts 
+###crypto.dd.basic(symbol: str = 'BTC', export: str = '', chart=True) -> None
+
+Get basic information for coin. Like:
+        name, symbol, rank, type, description, platform, proof_type, contract, tags, parent.
+        [Source: CoinPaprika]
+
+    Parameters
+    ----------
+    symbol: str
+        Cryptocurrency symbol (e.g. BTC)
+    export: str
+        Export dataframe data to csv,json,xlsx

website/content/api/crypto/dd/binance_available_quotes_for_each_coin/_index.md

@@ -0,0 +1,13 @@
+# crypto.dd.binance_available_quotes_for_each_coin
+
+## Get underlying data 
+###crypto.dd.binance_available_quotes_for_each_coin() -> dict
+
+Helper methods that for every coin available on Binance add all quote assets. [Source: Binance]
+
+    Returns
+    -------
+    dict:
+        All quote assets for given coin
+        {'ETH' : ['BTC', 'USDT' ...], 'UNI' : ['ETH', 'BTC','BUSD', ...]
+

website/content/api/crypto/dd/book/_index.md

@@ -0,0 +1,43 @@
+# crypto.dd.book
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.book(from_symbol: str, limit: int = 100, to_symbol: str = 'USDT') -> pandas.core.frame.DataFrame
+
+Get order book for currency. [Source: Binance]
+
+    Parameters
+    ----------
+
+    from_symbol: str
+        Cryptocurrency symbol
+    limit: int
+        Limit parameter. Adjusts the weight
+    to_symbol: str
+        Quote currency (what to view coin vs)
+
+    Returns
+    -------
+
+    pd.DataFrame
+        Dataframe containing orderbook
+
+## Getting charts 
+###crypto.dd.book(from_symbol: str, limit: int = 100, to_symbol: str = 'USDT', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Get order book for currency. [Source: Binance]
+
+    Parameters
+    ----------
+
+    from_symbol: str
+        Cryptocurrency symbol
+    limit: int
+        Limit parameter. Adjusts the weight
+    to_symbol: str
+        Quote currency (what to view coin vs)
+    export: str
+        Export dataframe data to csv,json,xlsx
+    external_axes : Optional[List[plt.Axes]]
+        External axes (1 axis is expected in the list), by default None

website/content/api/crypto/dd/btcrb/_index.md

@@ -0,0 +1,43 @@
+# crypto.dd.btcrb
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.btcrb(symbol: str, start_date: int = 1262304000, end_date: int = 1663694553, print_errors: bool = True) -> pandas.core.frame.DataFrame
+
+Returns the price of a cryptocurrency
+    [Source: https://glassnode.com]
+
+    Parameters
+    ----------
+    symbol : str
+        Crypto to check close price (BTC or ETH)
+    start_date : int
+        Initial date timestamp (e.g., 1_614_556_800)
+    end_date : int
+        End date timestamp (e.g., 1_641_227_783_561)
+    print_errors: bool
+        Flag to print errors. Default: True
+
+    Returns
+    -------
+    pd.DataFrame
+        price over time
+
+## Getting charts 
+###crypto.dd.btcrb(start_date: int = 1262304000, end_date: int = 1663694553, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
+
+Displays bitcoin rainbow chart
+    [Price data from source: https://glassnode.com]
+    [Inspired by: https://blockchaincenter.net]
+
+    Parameters
+    ----------
+    start_date : int
+        Initial date timestamp. Default is initial BTC timestamp: 1_325_376_000
+    end_date : int
+        Final date timestamp. Default is current BTC timestamp
+    export : str
+        Export dataframe data to csv,json,xlsx file
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/crypto/dd/candles/_index.md

@@ -0,0 +1,34 @@
+# crypto.dd.candles
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.candles(symbol: str, interval: str = '24h') -> pandas.core.frame.DataFrame
+
+Get candles for chosen trading pair and time interval. [Source: Coinbase]
+
+    Parameters
+    ----------
+    symbol: str
+        Trading pair of coins on Coinbase e.g ETH-USDT or UNI-ETH
+    interval: str
+        Time interval. One from 1min, 5min ,15min, 1hour, 6hour, 24hour
+
+    Returns
+    -------
+    pd.DataFrame
+        Candles for chosen trading pair.
+
+## Getting charts 
+###crypto.dd.candles(symbol: str, interval: str = '24h', export: str = '', chart=True) -> None
+
+Get candles for chosen trading pair and time interval. [Source: Coinbase]
+
+    Parameters
+    ----------
+    symbol: str
+        Trading pair of coins on Coinbase e.g ETH-USDT or UNI-ETH
+    interval: str
+        Time interval. One from 1m, 5m ,15m, 1h, 6h, 24h
+    export : str
+        Export dataframe data to csv,json,xlsx file

website/content/api/crypto/dd/cbbook/_index.md

@@ -0,0 +1,35 @@
+# crypto.dd.cbbook
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.cbbook(symbol: str) -> Tuple[numpy.ndarray, numpy.ndarray, str, dict]
+
+Get orders book for chosen trading pair. [Source: Coinbase]
+
+    Parameters
+    ----------
+    symbol: str
+        Trading pair of coins on Coinbase e.g ETH-USDT or UNI-ETH
+
+    Returns
+    -------
+    Tuple[np.array, np.array, str, dict]
+        array with bid prices, order sizes and cumulative order sizes
+        array with ask prices, order sizes and cumulative order sizes
+        trading pair
+        dict with raw data
+
+## Getting charts 
+###crypto.dd.cbbook(symbol: str, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Displays a list of available currency pairs for trading. [Source: Coinbase]
+
+    Parameters
+    ----------
+    symbol: str
+        Trading pair of coins on Coinbase e.g ETH-USDT or UNI-ETH
+    export : str
+        Export dataframe data to csv,json,xlsx file
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/crypto/dd/change/_index.md

@@ -0,0 +1,48 @@
+# crypto.dd.change
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.change(symbol: str, exchange: str = 'binance', start_date: int = 1262304000, end_date: int = 1663694553) -> pandas.core.frame.DataFrame
+
+Returns 30d change of the supply held in exchange wallets of a certain symbol.
+    [Source: https://glassnode.com]
+
+    Parameters
+    ----------
+    symbol : str
+        Asset symbol to search supply (e.g., BTC)
+    exchange : str
+        Exchange to check net position change (e.g., binance)
+    start_date : int
+        Initial date timestamp (e.g., 1_614_556_800)
+    end_date : int
+        End date timestamp (e.g., 1_614_556_800)
+
+    Returns
+    -------
+    pd.DataFrame
+        supply change in exchange wallets of a certain symbol over time
+
+## Getting charts 
+###crypto.dd.change(symbol: str, exchange: str = 'binance', start_date: int = 1577836800, end_date: int = 1609459200, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Display 30d change of the supply held in exchange wallets.
+    [Source: https://glassnode.org]
+
+    Parameters
+    ----------
+    symbol : str
+        Asset to search active addresses (e.g., BTC)
+    exchange : str
+        Exchange to check net position change (possible values are: aggregated, binance,
+        bittrex, coinex, gate.io, gemini, huobi, kucoin, poloniex, bibox, bigone, bitfinex,
+        hitbtc, kraken, okex, bithumb, zb.com, cobinhood, bitmex, bitstamp, coinbase, coincheck, luno)
+    start_date : int
+        Initial date timestamp (e.g., 1_614_556_800)
+    end_date : int
+        End date timestamp (e.g., 1_614_556_800)
+    export : str
+        Export dataframe data to csv,json,xlsx file
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/crypto/dd/check_valid_binance_str/_index.md

@@ -0,0 +1,6 @@
+# crypto.dd.check_valid_binance_str
+
+## Get underlying data 
+###crypto.dd.check_valid_binance_str(symbol: str) -> str
+
+Check if symbol is in defined binance. [Source: Bin

website/content/api/crypto/dd/coin/_index.md

@@ -0,0 +1,15 @@
+# crypto.dd.coin
+
+## Get underlying data 
+###crypto.dd.coin(symbol: str = 'eth-ethereum') -> dict
+
+Get coin by id [Source: CoinPaprika]
+
+    Parameters
+    ----------
+    symbol: str
+        id of coin from coinpaprika e.g. Ethereum - > 'eth-ethereum'
+    Returns
+    -------
+    dict
+        Coin response

website/content/api/crypto/dd/coin_market_chart/_index.md

@@ -0,0 +1,20 @@
+# crypto.dd.coin_market_chart
+
+## Get underlying data 
+###crypto.dd.coin_market_chart(symbol: str = '', vs_currency: str = 'usd', days: int = 30, **kwargs: Any) -> pandas.core.frame.DataFrame
+
+Get prices for given coin. [Source: CoinGecko]
+
+    Parameters
+    ----------
+    vs_currency: str
+        currency vs which display data
+    days: int
+        number of days to display the data
+    kwargs
+
+    Returns
+    -------
+    pandas.DataFrame
+        Prices for given coin
+        Columns: time, price, currency

website/content/api/crypto/dd/eb/_index.md

@@ -0,0 +1,50 @@
+# crypto.dd.eb
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.eb(symbol: str, exchange: str = 'binance', start_date: int = 1262304000, end_date: int = 1663694553) -> pandas.core.frame.DataFrame
+
+Returns the total amount of coins held on exchange addresses in units and percentage.
+    [Source: https://glassnode.com]
+
+    Parameters
+    ----------
+    symbol : str
+        Asset to search active addresses (e.g., BTC)
+    exchange : str
+        Exchange to check net position change (e.g., binance)
+    start_date : int
+        Initial date timestamp (e.g., 1_614_556_800)
+    end_date : int
+        End date timestamp (e.g., 1_614_556_800)
+
+    Returns
+    -------
+    pd.DataFrame
+        total amount of coins in units/percentage and symbol price over time
+
+## Getting charts 
+###crypto.dd.eb(symbol: str, exchange: str = 'binance', start_date: int = 1577836800, end_date: int = 1609459200, percentage: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Display total amount of coins held on exchange addresses in units and percentage.
+    [Source: https://glassnode.org]
+
+    Parameters
+    ----------
+    symbol : str
+        Asset to search active addresses (e.g., BTC)
+    exchange : str
+        Exchange to check net position change (possible values are: aggregated, binance, bittrex,
+        coinex, gate.io, gemini, huobi, kucoin, poloniex, bibox, bigone, bitfinex, hitbtc, kraken,
+        okex, bithumb, zb.com, cobinhood, bitmex, bitstamp, coinbase, coincheck, luno)
+    start_date : int
+        Initial date timestamp (e.g., 1_614_556_800)
+    end_date : int
+        End date timestamp (e.g., 1_614_556_800)
+    percentage : bool
+        Show percentage instead of stacked value.
+    export : str
+        Export dataframe data to csv,json,xlsx file
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (2 axes are expected in the list), by default None

website/content/api/crypto/dd/events/_index.md

@@ -0,0 +1,62 @@
+# crypto.dd.events
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.events(symbol: str = 'eth-ethereum', sortby='date', ascend: bool = False) -> pandas.core.frame.DataFrame
+
+Get all events related to given coin like conferences, start date of futures trading etc.
+    [Source: CoinPaprika]
+
+    Example of response from API:
+
+    .. code-block:: json
+
+    {
+        "id": "17398-cme-april-first-trade",
+        "date": "2018-04-02T00:00:00Z",
+        "date_to": "string",
+        "name": "CME: April First Trade",
+        "description": "First trade of Bitcoin futures contract for April 2018.",
+        "is_conference": false,
+        "link": "http://www.cmegroup.com/trading/equity-index/us-index/bitcoin_product_calendar_futures.html",
+        "proof_image_link": "https://static.coinpaprika.com/storage/cdn/event_images/16635.jpg"
+    }
+
+    Parameters
+    ----------
+    symbol: str
+        id of coin from coinpaprika e.g. Ethereum - > 'eth-ethereum'
+    sortby: str
+        Key by which to sort data. Every column name is valid
+        (see for possible values:
+        https://api.coinpaprika.com/docs#tag/Coins/paths/~1coins~1%7Bcoin_id%7D~1events/get).
+    ascend: bool
+        Flag to sort data ascending
+    Returns
+    -------
+    pandas.DataFrame
+        Events found for given coin
+        Columns: id, date , date_to, name, description, is_conference, link, proof_image_link
+
+## Getting charts 
+###crypto.dd.events(symbol: str = 'BTC', limit: int = 10, sortby: str = 'date', ascend: bool = False, links: bool = False, export: str = '', chart=True) -> None
+
+Get all events for given coin id. [Source: CoinPaprika]
+
+    Parameters
+    ----------
+    symbol: str
+        Cryptocurrency symbol (e.g. BTC)
+    limit: int
+        Number of records to display
+    sortby: str
+        Key by which to sort data. Every column name is valid
+        (see for possible values:
+        https://api.coinpaprika.com/docs#tag/Coins/paths/~1coins~1%7Bcoin_id%7D~1events/get).
+    ascend: bool
+        Flag to sort data ascending
+    links: bool
+        Flag to display urls
+    export : str
+        Export dataframe data to csv,json,xlsx file

website/content/api/crypto/dd/ex/_index.md

@@ -0,0 +1,43 @@
+# crypto.dd.ex
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.ex(symbol: str = 'eth-ethereum', sortby: str = 'adjusted_volume_24h_share', ascend: bool = True) -> pandas.core.frame.DataFrame
+
+Get all exchanges for given coin id. [Source: CoinPaprika]
+
+    Parameters
+    ----------
+    symbol: str
+        Identifier of Coin from CoinPaprika
+    sortby: str
+        Key by which to sort data. Every column name is valid (see for possible values:
+        https://api.coinpaprika.com/v1).
+    ascend: bool
+        Flag to sort data ascending
+
+    Returns
+    -------
+    pandas.DataFrame
+        All exchanges for given coin
+        Columns: id, name, adjusted_volume_24h_share, fiats
+
+## Getting charts 
+###crypto.dd.ex(symbol: str = 'btc', limit: int = 10, sortby: str = 'adjusted_volume_24h_share', ascend: bool = True, export: str = '', chart=True) -> None
+
+Get all exchanges for given coin id. [Source: CoinPaprika]
+
+    Parameters
+    ----------
+    symbol: str
+        Cryptocurrency symbol (e.g. BTC)
+    limit: int
+        Number of records to display
+    sortby: str
+        Key by which to sort data. Every column name is valid (see for possible values:
+        https://api.coinpaprika.com/v1).
+    ascend: bool
+        Flag to sort data ascending
+    export : str
+        Export dataframe data to csv,json,xlsx file

website/content/api/crypto/dd/exchanges/_index.md

@@ -0,0 +1,15 @@
+# crypto.dd.exchanges
+
+## Get underlying data 
+###crypto.dd.exchanges()
+
+Helper method to get all the exchanges supported by ccxt
+    [Source: https://docs.ccxt.com/en/latest/manual.html]
+
+    Parameters
+    ----------
+
+    Returns
+    -------
+    List[str]
+        list of all the exchanges supported by ccxt

website/content/api/crypto/dd/fr/_index.md

@@ -0,0 +1,40 @@
+# crypto.dd.fr
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.fr(symbol: str) -> Tuple[str, pandas.core.frame.DataFrame, pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]
+
+Returns coin fundraising
+    [Source: https://messari.io/]
+
+    Parameters
+    ----------
+    symbol : str
+        Crypto symbol to check fundraising
+
+    Returns
+    -------
+    str
+        launch summary
+    pd.DataFrame
+        Sales rounds
+    pd.DataFrame
+        Treasury Accounts
+    pd.DataFrame
+        Metric Value launch details
+
+## Getting charts 
+###crypto.dd.fr(symbol: str, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Display coin fundraising
+    [Source: https://messari.io/]
+
+    Parameters
+    ----------
+    symbol : str
+        Crypto symbol to check coin fundraising
+    export : str
+        Export dataframe data to csv,json,xlsx file
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/crypto/dd/get_mt/_index.md

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+# crypto.dd.get_mt
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.get_mt(only_free: bool = True) -> pandas.core.frame.DataFrame
+
+Returns available messari timeseries
+    [Source: https://messari.io/]
+
+    Parameters
+    ----------
+    only_free : bool
+        Display only timeseries available for free
+
+    Returns
+    -------
+    pd.DataFrame
+        available timeseries
+
+## Getting charts 
+###crypto.dd.get_mt(limit: int = 10, query: str = '', only_free: bool = True, export: str = '', chart=True) -> None
+
+Display messari timeseries list
+    [Source: https://messari.io/]
+
+    Parameters
+    ----------
+    limit : int
+        number to show
+    query : str
+        Query to search across all messari timeseries
+    only_free : bool
+        Display only timeseries available for free
+    export : str
+        Export dataframe data to csv,json,xlsx file

website/content/api/crypto/dd/gh/_index.md

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+# crypto.dd.gh
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.gh(symbol: str, dev_activity: bool = False, interval: str = '1d', start_date: str = '2021-09-20T18:22:33Z', end_date: str = '2022-09-20T18:22:33Z') -> pandas.core.frame.DataFrame
+
+Returns  a list of developer activity for a given coin and time interval.
+
+    [Source: https://santiment.net/]
+
+    Parameters
+    ----------
+    symbol : str
+        Crypto symbol to check github activity
+    dev_activity: bool
+        Whether to filter only for development activity
+    start_date : int
+        Initial date like string (e.g., 2021-10-01)
+    end_date : int
+        End date like string (e.g., 2021-10-01)
+    interval : str
+        Interval frequency (e.g., 1d)
+
+    Returns
+    -------
+    pd.DataFrame
+        developer activity over time
+
+## Getting charts 
+###crypto.dd.gh(symbol: str, start_date: str = '2021-09-20T18:22:33Z', dev_activity: bool = False, end_date: str = '2022-09-20T18:22:33Z', interval: str = '1d', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Returns a list of github activity for a given coin and time interval.
+
+    [Source: https://santiment.net/]
+
+    Parameters
+    ----------
+    symbol : str
+        Crypto symbol to check github activity
+    dev_activity: bool
+        Whether to filter only for development activity
+    start_date : int
+        Initial date like string (e.g., 2021-10-01)
+    end_date : int
+        End date like string (e.g., 2021-10-01)
+    interval : str
+        Interval frequency (some possible values are: 1h, 1d, 1w)
+    export : str
+        Export dataframe data to csv,json,xlsx file
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/crypto/dd/gov/_index.md

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+# crypto.dd.gov
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.gov(symbol: str) -> Tuple[str, pandas.core.frame.DataFrame]
+
+Returns coin governance
+    [Source: https://messari.io/]
+
+    Parameters
+    ----------
+    symbol : str
+        Crypto symbol to check governance
+
+    Returns
+    -------
+    str
+        governance summary
+    pd.DataFrame
+        Metric Value with governance details
+
+## Getting charts 
+###crypto.dd.gov(symbol: str, export: str = '', chart=True) -> None
+
+Display coin governance
+    [Source: https://messari.io/]
+
+    Parameters
+    ----------
+    symbol : str
+        Crypto symbol to check coin governance
+    export : str
+        Export dataframe data to csv,json,xlsx file

website/content/api/crypto/dd/headlines/_index.md

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+# crypto.dd.headlines
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.headlines(symbol: str) -> pandas.core.frame.DataFrame
+
+Gets Sentiment analysis provided by FinBrain's API [Source: finbrain]
+
+    Parameters
+    ----------
+    symbol : str
+        Ticker symbol to get the sentiment analysis from
+
+    Returns
+    -------
+    DataFrame()
+        Empty if there was an issue with data retrieval
+
+## Getting charts 
+###crypto.dd.headlines(symbol: str, raw: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Sentiment analysis from FinBrain for Cryptocurrencies
+
+    FinBrain collects the news headlines from 15+ major financial news
+    sources on a daily basis and analyzes them to generate sentiment scores
+    for more than 4500 US stocks. FinBrain Technologies develops deep learning
+    algorithms for financial analysis and prediction, which currently serves
+    traders from more than 150 countries all around the world.
+    [Source:  https://finbrain.tech]
+
+    Parameters
+    ----------
+    symbol: str
+        Cryptocurrency
+    raw : False
+        Display raw table data
+    export : str
+        Export dataframe data to csv,json,xlsx file
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None

website/content/api/crypto/dd/hr/_index.md

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+# crypto.dd.hr
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.hr(symbol: str, interval: str = '24h', start_date: int = 1262304000, end_date: int = 1663694553) -> pandas.core.frame.DataFrame
+
+Returns dataframe with mean hashrate of btc or eth blockchain and symbol price
+    [Source: https://glassnode.com]
+
+    Parameters
+    ----------
+    symbol : str
+        Blockchain to check hashrate (BTC or ETH)
+    start_date : int
+        Initial date timestamp (e.g., 1_614_556_800)
+    end_date : int
+        End date timestamp (e.g., 1_614_556_800)
+    interval : str
+        Interval frequency (e.g., 24h)
+
+    Returns
+    -------
+    pd.DataFrame
+        mean hashrate and symbol price over time
+
+## Getting charts 
+###crypto.dd.hr(symbol: str, start_date: int = 1632158553, end_date: int = 1663694553, interval: str = '24h', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
+
+Display dataframe with mean hashrate of btc or eth blockchain and symbol price.
+    [Source: https://glassnode.org]
+
+    Parameters
+    ----------
+    symbol : str
+        Blockchain to check mean hashrate (BTC or ETH)
+    start_date : int
+        Initial date timestamp (e.g., 1_614_556_800)
+    end_date : int
+        End date timestamp (e.g., 1_614_556_800)
+    interval : str
+        Interval frequency (possible values are: 24, 1w, 1month)
+    export : str
+        Export dataframe data to csv,json,xlsx file
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (2 axes are expected in the list), by default None

website/content/api/crypto/dd/inv/_index.md

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+# crypto.dd.inv
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.inv(symbol: str) -> Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]
+
+Returns coin investors
+    [Source: https://messari.io/]
+
+    Parameters
+    ----------
+    symbol : str
+        Crypto symbol to check investors
+
+    Returns
+    -------
+    pd.DataFrame
+        individuals
+    pd.DataFrame
+        organizations
+
+## Getting charts 
+###crypto.dd.inv(symbol: str, export: str = '', chart=True) -> None
+
+Display coin investors
+    [Source: https://messari.io/]
+
+    Parameters
+    ----------
+    symbol : str
+        Crypto symbol to check coin investors
+    export : str
+        Export dataframe data to csv,json,xlsx file

website/content/api/crypto/dd/links/_index.md

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+# crypto.dd.links
+
+To obtain charts, make sure to add `chart=True` as the last parameter
+
+## Get underlying data 
+###crypto.dd.links(symbol: str) -> pandas.core.frame.DataFrame
+
+Returns asset's links
+    [Source: https://messari.io/]
+
+    Parameters
+    ----------
+    symbol : str
+        Crypto symbol to check links
+
+    Returns
+    -------
+    pd.DataFrame
+        asset links
+
+## Getting charts 
+###crypto.dd.links(symbol: str, export: str = '', chart=True) -> None
+
+Display coin links
+    [Source: https://messari.io/]
+
+    Parameters
+    ----------
+    symbol : str
+        Crypto symbol to check links
+    export : str
+        Export dataframe data to csv,json,xlsx file
+    external_axes : Optional[List[plt.Axes]], optional
+        External axes (1 axis is expected in the list), by default None