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Get rid of funds menu....for now (#3292)

Browse Source 
* Get rid of funds menu....for now

* Website + sdk

* GEt rid of website/sdk files

* GEt rid of website/termina/funds files

* rerun generate py twice. committing all

Co-authored-by: Colin Delahunty <72827203+colin99d@users.noreply.github.com>
This commit is contained in:
James Maslek
2022-11-04 05:29:52 -04:00
committed by GitHub
parent 73a622058f
commit a0cc590e28
95 changed files with 416 additions and 770 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
openbb_terminal/sdk.py
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@@ -1144,18 +1144,6 @@ functions = {
"model": "openbb_terminal.forex.oanda.oanda_model.positionbook_plot_data_request",
"view": "openbb_terminal.forex.oanda.oanda_view.get_position_book",
},
"funds.info": {
"model": "openbb_terminal.mutual_funds.investpy_model.get_fund_info",
"view": "openbb_terminal.mutual_funds.investpy_view.display_fund_info",
},
"funds.overview": {
"model": "openbb_terminal.mutual_funds.investpy_model.get_overview",
"view": "openbb_terminal.mutual_funds.investpy_view.display_overview",
},
"funds.search": {
"model": "openbb_terminal.mutual_funds.investpy_model.search_funds",
"view": "openbb_terminal.mutual_funds.investpy_view.display_search",
},
"portfolio.holdv": {
"model": "openbb_terminal.portfolio.portfolio_model.get_holdings_value",
"view": "openbb_terminal.portfolio.portfolio_view.display_holdings_value",

10
openbb_terminal/terminal_controller.py
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@@ -87,7 +87,6 @@ class TerminalController(BaseController):
"etf",
"reports",
"dashboards",
"funds",
"alternative",
"econometrics",
"sources",
@@ -166,7 +165,6 @@ class TerminalController(BaseController):
mt.add_menu("etf")
mt.add_menu("economy")
mt.add_menu("forex")
mt.add_menu("funds")
mt.add_menu("futures")
mt.add_menu("alternative")
mt.add_raw("\n")
@@ -383,14 +381,6 @@ class TerminalController(BaseController):
self.queue = self.load_class(ETFController, self.queue)
def call_funds(self, _):
"""Process etf command."""
from openbb_terminal.mutual_funds.mutual_fund_controller import (
FundController,
)
self.queue = self.load_class(FundController, self.queue)
def call_forex(self, _):
"""Process forex command."""
from openbb_terminal.forex.forex_controller import ForexController

4
website/content/SDK/common/news/_index.md vendored
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@@ -1,5 +1,5 @@
## Get underlying data
### common.news(term: str = '', sources: str = 'bloomberg.com', sort: str = 'published') -> pandas.core.frame.DataFrame
### common.news(term: str = '', sources: str = '', sort: str = 'published') -> pandas.core.frame.DataFrame
Get news for a given term and source. [Source: Feedparser]
@@ -8,7 +8,7 @@ Get news for a given term and source. [Source: Feedparser]
term : str
term to search on the news articles
sources: str
sources to exclusively show news from
sources to exclusively show news from (separated by commas)
sort: str
the column to sort by

10
website/content/SDK/common/ta/adx/_index.md vendored
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@@ -1,18 +1,14 @@
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)
### common.ta.adx(data: pandas.core.frame.DataFrame, window: int = 14, scalar: int = 100, drift: int = 1) -> pandas.core.frame.DataFrame
ADX technical indicator
Parameters
----------
high_values: pd.Series
High prices
low_values: pd.Series
Low prices
close_values: pd.Series
close prices
data : pd.DataFrame
Dataframe with OHLC price data
window: int
Length of window
scalar: int

8
website/content/SDK/common/ta/aroon/_index.md vendored
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@@ -1,16 +1,14 @@
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
### common.ta.aroon(data: pandas.core.frame.DataFrame, 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
data : pd.DataFrame
Dataframe with OHLC price data
window : int
Length of window
scalar : int

10
website/content/SDK/common/ta/atr/_index.md vendored
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@@ -1,18 +1,14 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### common.ta.atr(high_prices: pandas.core.series.Series, low_prices: pandas.core.series.Series, close_prices: pandas.core.series.Series, window: int = 14, mamode: str = 'ema', offset: int = 0) -> pandas.core.frame.DataFrame
### common.ta.atr(data: pandas.core.frame.DataFrame, window: int = 14, mamode: str = 'ema', offset: int = 0) -> pandas.core.frame.DataFrame
Average True Range
Parameters
----------
high_prices : pd.DataFrame
High prices
low_prices : pd.DataFrame
Low prices
close_prices : pd.DataFrame
Close prices
data : pd.DataFrame
Dataframe of ohlc prices
window : int
Length of window
mamode: str

6
website/content/SDK/common/ta/bbands/_index.md vendored
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@@ -1,14 +1,14 @@
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
### common.ta.bbands(data: pandas.core.frame.DataFrame, 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
data : pd.DataFrame
Dataframe of ohlc prices
window : int
Length of window to calculate BB
n_std : float

8
website/content/SDK/common/ta/donchian/_index.md vendored
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@@ -1,16 +1,14 @@
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
### common.ta.donchian(data: pandas.core.frame.DataFrame, 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
data : pd.DataFrame
Dataframe of ohlc prices
upper_length : int
Length of window to calculate upper channel
lower_length : int

4
website/content/SDK/common/ta/ema/_index.md vendored
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@@ -1,11 +1,11 @@
## Get underlying data
### common.ta.ema(data: pandas.core.frame.DataFrame, length: int = 50, offset: int = 0) -> pandas.core.frame.DataFrame
### common.ta.ema(data: pandas.core.series.Series, length: int = 50, offset: int = 0) -> pandas.core.frame.DataFrame
Gets exponential moving average (EMA) for stock
Parameters
----------
data: pd.DataFrame
data: pd.Series
Dataframe of dates and prices
length: int
Length of EMA window

8
website/content/SDK/common/ta/fisher/_index.md vendored
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@@ -1,16 +1,14 @@
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
### common.ta.fisher(data: pandas.core.frame.DataFrame, window: int = 14) -> pandas.core.frame.DataFrame
Fisher Transform
Parameters
----------
high_vals: pd.Series
High values
low_vals: pd.Series
Low values
data : pd.DataFrame
Dataframe of OHLC prices
window: int
Length for indicator window
Returns

4
website/content/SDK/common/ta/hma/_index.md vendored
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@@ -1,11 +1,11 @@
## Get underlying data
### common.ta.hma(data: pandas.core.frame.DataFrame, length: int = 50, offset: int = 0) -> pandas.core.frame.DataFrame
### common.ta.hma(data: pandas.core.series.Series, length: int = 50, offset: int = 0) -> pandas.core.frame.DataFrame
Gets hull moving average (HMA) for stock
Parameters
----------
data: pd.DataFrame
data: pd.Series
Dataframe of dates and prices
length: int
Length of SMA window

10
website/content/SDK/common/ta/kc/_index.md vendored
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@@ -1,18 +1,14 @@
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
### common.ta.kc(data: pandas.core.frame.DataFrame, 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
data: pd.DataFrame
Dataframe of ohlc prices
window : int
Length of window
scalar: float

4
website/content/SDK/common/ta/macd/_index.md vendored
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@@ -1,13 +1,13 @@
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
### common.ta.macd(data: pandas.core.series.Series, n_fast: int = 12, n_slow: int = 26, n_signal: int = 9) -> pandas.core.frame.DataFrame
Moving average convergence divergence
Parameters
----------
values: pd.Series
data: pd.Series
Values for calculation
n_fast : int
Fast period

4
website/content/SDK/common/ta/rsi/_index.md vendored
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@@ -1,13 +1,13 @@
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
### common.ta.rsi(data: pandas.core.series.Series, window: int = 14, scalar: float = 100, drift: int = 1) -> pandas.core.frame.DataFrame
Relative strength index
Parameters
----------
values: pd.Series
data: pd.Series
Dataframe of prices
window: int
Length of window

4
website/content/SDK/common/ta/sma/_index.md vendored
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@@ -1,11 +1,11 @@
## Get underlying data
### common.ta.sma(data: pandas.core.frame.DataFrame, length: int = 50, offset: int = 0) -> pandas.core.frame.DataFrame
### common.ta.sma(data: pandas.core.series.Series, length: int = 50, offset: int = 0) -> pandas.core.frame.DataFrame
Gets simple moving average (EMA) for stock
Parameters
----------
data: pd.DataFrame
data: pd.Series
Dataframe of dates and prices
length: int
Length of SMA window

10
website/content/SDK/common/ta/stoch/_index.md vendored
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@@ -1,18 +1,14 @@
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)
### common.ta.stoch(data: pandas.core.frame.DataFrame, 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
data : pd.DataFrame
Dataframe of OHLC prices
fastkperiod : int
Fast k period
slowdperiod : int

2
website/content/SDK/common/ta/vwap/_index.md vendored
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@@ -1,7 +1,7 @@
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
### common.ta.vwap(data: pandas.core.series.Series, offset: int = 0) -> pandas.core.frame.DataFrame
Gets volume weighted average price (VWAP)

4
website/content/SDK/common/ta/wma/_index.md vendored
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@@ -1,11 +1,11 @@
## Get underlying data
### common.ta.wma(data: pandas.core.frame.DataFrame, length: int = 50, offset: int = 0) -> pandas.core.frame.DataFrame
### common.ta.wma(data: pandas.core.series.Series, length: int = 50, offset: int = 0) -> pandas.core.frame.DataFrame
Gets weighted moving average (WMA) for stock
Parameters
----------
data: pd.DataFrame
data: pd.Series
Dataframe of dates and prices
length: int
Length of SMA window

4
website/content/SDK/common/ta/zlma/_index.md vendored
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@@ -1,11 +1,11 @@
## Get underlying data
### common.ta.zlma(data: pandas.core.frame.DataFrame, length: int = 50, offset: int = 0) -> pandas.core.frame.DataFrame
### common.ta.zlma(data: pandas.core.series.Series, length: int = 50, offset: int = 0) -> pandas.core.frame.DataFrame
Gets zero-lagged exponential moving average (ZLEMA) for stock
Parameters
----------
data: pd.DataFrame
data: pd.Series
Dataframe of dates and prices
length: int
Length of EMA window

2
website/content/SDK/crypto/dd/active/_index.md vendored
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@@ -1,7 +1,7 @@
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 = 1262300400, end_date: int = 1666777200) -> pandas.core.frame.DataFrame
### crypto.dd.active(symbol: str, interval: str = '24h', start_date: int = 1262322000, end_date: int = 1667510580) -> pandas.core.frame.DataFrame
Returns active addresses of a certain symbol
[Source: https://glassnode.com]

4
website/content/SDK/crypto/dd/btcrb/_index.md vendored
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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### crypto.dd.btcrb(start_date: str = '2010-01-01', end_date: str = '2022-10-26')
### crypto.dd.btcrb(start_date: str = '2010-01-01', end_date: str = '2022-11-03')
Get bitcoin price data
[Price data from source: https://glassnode.com]
@@ -15,7 +15,7 @@ Get bitcoin price data
Final date, format YYYY-MM-DD
## Getting charts
### crypto.dd.btcrb(start_date: str = '2010-01-01', end_date: str = '2022-10-26', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
### crypto.dd.btcrb(start_date: str = '2010-01-01', end_date: str = '2022-11-03', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
Displays bitcoin rainbow chart
[Price data from source: https://glassnode.com]

2
website/content/SDK/crypto/dd/change/_index.md vendored
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@@ -1,7 +1,7 @@
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 = 1262300400, end_date: int = 1666777200) -> pandas.core.frame.DataFrame
### crypto.dd.change(symbol: str, exchange: str = 'binance', start_date: int = 1262322000, end_date: int = 1667510580) -> pandas.core.frame.DataFrame
Returns 30d change of the supply held in exchange wallets of a certain symbol.
[Source: https://glassnode.com]

2
website/content/SDK/crypto/dd/close/_index.md vendored
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@@ -1,5 +1,5 @@
## Get underlying data
### crypto.dd.close(symbol: str, start_date: str = '2010-01-01', end_date: str = '2022-10-26', print_errors: bool = True) -> pandas.core.frame.DataFrame
### crypto.dd.close(symbol: str, start_date: str = '2010-01-01', end_date: str = '2022-11-03', print_errors: bool = True) -> pandas.core.frame.DataFrame
Returns the price of a cryptocurrency
[Source: https://glassnode.com]

2
website/content/SDK/crypto/dd/eb/_index.md vendored
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@@ -1,7 +1,7 @@
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 = 1262300400, end_date: int = 1666777200) -> pandas.core.frame.DataFrame
### crypto.dd.eb(symbol: str, exchange: str = 'binance', start_date: int = 1262322000, end_date: int = 1667510580) -> pandas.core.frame.DataFrame
Returns the total amount of coins held on exchange addresses in units and percentage.
[Source: https://glassnode.com]

4
website/content/SDK/crypto/dd/gh/_index.md vendored
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@@ -1,7 +1,7 @@
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 = '2021-10-26T11:40:00Z', end_date: str = '2022-10-26T11:40:00Z') -> pandas.core.frame.DataFrame
### crypto.dd.gh(symbol: str, dev_activity: bool = False, interval: str = '1d', start_date: str = '2021-11-03T17:23:00Z', end_date: str = '2022-11-03T17:23:00Z') -> pandas.core.frame.DataFrame
Returns a list of developer activity for a given coin and time interval.
@@ -26,7 +26,7 @@ Returns a list of developer activity for a given coin and time interval.
developer activity over time
## Getting charts
### crypto.dd.gh(symbol: str, start_date: str = '2021-10-26T11:40:00Z', dev_activity: bool = False, end_date: str = '2022-10-26T11:40:00Z', interval: str = '1d', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
### crypto.dd.gh(symbol: str, start_date: str = '2021-11-03T17:23:00Z', dev_activity: bool = False, end_date: str = '2022-11-03T17:23:00Z', 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.

4
website/content/SDK/crypto/dd/mcapdom/_index.md vendored
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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### crypto.dd.mcapdom(symbol: str, interval: str = '1d', start_date: str = '2021-10-26', end_date: str = '2022-10-26') -> pandas.core.frame.DataFrame
### crypto.dd.mcapdom(symbol: str, interval: str = '1d', start_date: str = '2021-11-03', end_date: str = '2022-11-03') -> pandas.core.frame.DataFrame
Returns market dominance of a coin over time
[Source: https://messari.io/]
@@ -23,7 +23,7 @@ Returns market dominance of a coin over time
market dominance percentage over time
## Getting charts
### crypto.dd.mcapdom(symbol: str, start_date: str = '2021-10-26', end_date: str = '2022-10-26', interval: str = '1d', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
### crypto.dd.mcapdom(symbol: str, start_date: str = '2021-11-03', end_date: str = '2022-11-03', interval: str = '1d', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
Display market dominance of a coin over time
[Source: https://messari.io/]

4
website/content/SDK/crypto/dd/mt/_index.md vendored
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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### crypto.dd.mt(symbol: str, timeseries_id: str, interval: str = '1d', start_date: str = '2021-10-26', end_date: str = '2022-10-26') -> Tuple[pandas.core.frame.DataFrame, str]
### crypto.dd.mt(symbol: str, timeseries_id: str, interval: str = '1d', start_date: str = '2021-11-03', end_date: str = '2022-11-03') -> Tuple[pandas.core.frame.DataFrame, str]
Returns messari timeseries
[Source: https://messari.io/]
@@ -27,7 +27,7 @@ Returns messari timeseries
timeserie title
## Getting charts
### crypto.dd.mt(symbol: str, timeseries_id: str, start_date: str = '2021-10-26', end_date: str = '2022-10-26', interval: str = '1d', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
### crypto.dd.mt(symbol: str, timeseries_id: str, start_date: str = '2021-11-03', end_date: str = '2022-11-03', interval: str = '1d', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
Display messari timeseries
[Source: https://messari.io/]

2
website/content/SDK/crypto/dd/nonzero/_index.md vendored
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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### crypto.dd.nonzero(symbol: str, start_date: int = 1262300400, end_date: int = 1666777200) -> pandas.core.frame.DataFrame
### crypto.dd.nonzero(symbol: str, start_date: int = 1262322000, end_date: int = 1667510580) -> pandas.core.frame.DataFrame
Returns addresses with non-zero balance of a certain symbol
[Source: https://glassnode.com]

37
website/content/SDK/crypto/dd/ob/_index.md vendored Normal file
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@@ -0,0 +1,37 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### crypto.dd.ob(exchange_id: str, symbol: str, to_symbol: str) -> Dict
Returns orderbook for a coin in a given exchange
[Source: https://docs.ccxt.com/en/latest/manual.html]
Parameters
----------
exchange_id : str
exchange id
symbol : str
coin symbol
to_symbol : str
currency to compare coin against
Returns
-------
Dict with bids and asks
## Getting charts
### crypto.dd.ob(exchange: str, symbol: str, to_symbol: str, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
Displays order book for a coin in a given exchange
[Source: https://docs.ccxt.com/en/latest/manual.html]
Parameters
----------
exchange : str
exchange id
symbol : str
coin symbol
vs : str
currency to compare coin against
export : str
Export dataframe data to csv,json,xlsx file

39
website/content/SDK/crypto/dd/trades/_index.md vendored
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@@ -1,35 +1,40 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### crypto.dd.trades(symbol: str, limit: int = 1000, side: Optional[Any] = None) -> pandas.core.frame.DataFrame
### crypto.dd.trades(exchange_id: str, symbol: str, to_symbol: str) -> pandas.core.frame.DataFrame
Get last N trades for chosen trading pair. [Source: Coinbase]
Returns trades for a coin in a given exchange
[Source: https://docs.ccxt.com/en/latest/manual.html]
Parameters
----------
symbol: str
Trading pair of coins on Coinbase e.g ETH-USDT or UNI-ETH
limit: int
Last <limit> of trades. Maximum is 1000.
side: str
You can chose either sell or buy side. If side is not set then all trades will be displayed.
exchange_id : str
exchange id
symbol : str
coin symbol
to_symbol : str
currency to compare coin against
Returns
-------
pd.DataFrame
Last N trades for chosen trading pairs.
trades for a coin in a given exchange
## Getting charts
### crypto.dd.trades(symbol: str, limit: int = 20, side: Optional[str] = None, export: str = '', chart=True) -> None
### crypto.dd.trades(exchange: str, symbol: str, to_symbol: str, limit: int = 10, export: str = '', chart=True)
Display last N trades for chosen trading pair. [Source: Coinbase]
Displays trades for a coin in a given exchange
[Source: https://docs.ccxt.com/en/latest/manual.html]
Parameters
----------
symbol: str
Trading pair of coins on Coinbase e.g ETH-USDT or UNI-ETH
limit: int
Last <limit> of trades. Maximum is 1000.
side: Optional[str]
You can chose either sell or buy side. If side is not set then all trades will be displayed.
exchange : str
exchange id
symbol : str
coin symbol
to_symbol : str
currency to compare coin against
limit : int
number of trades to display
export : str
Export dataframe data to csv,json,xlsx file

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### crypto.defi.ldapps(limit: int = 100) -> pandas.core.frame.DataFrame
### crypto.defi.ldapps(limit: int = 100, sortby: str = '', ascend: bool = False, description: bool = False, drop_chain: bool = True) -> pandas.core.frame.DataFrame
Returns information about listed DeFi protocols, their current TVL and changes to it in the last hour/day/week.
[Source: https://docs.llama.fi/api]
@@ -10,6 +10,14 @@ Returns information about listed DeFi protocols, their current TVL and changes t
----------
limit: int
The number of dApps to display
sortby: str
Key by which to sort data
ascend: bool
Flag to sort data descending
description: bool
Flag to display description of protocol
drop_chain: bool
Whether to drop the chain column
Returns
-------
@@ -17,7 +25,7 @@ Returns information about listed DeFi protocols, their current TVL and changes t
Information about DeFi protocols
## Getting charts
### crypto.defi.ldapps(limit: int, sortby: str, ascend: bool = False, description: bool = False, export: str = '', chart=True) -> None
### crypto.defi.ldapps(sortby: str, limit: int = 20, ascend: bool = False, description: bool = False, export: str = '', chart=True) -> None
Display information about listed DeFi protocols, their current TVL and changes to it in
the last hour/day/week. [Source: https://docs.llama.fi/api]

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### crypto.defi.swaps(limit: int = 100) -> pandas.core.frame.DataFrame
### crypto.defi.swaps(limit: int = 100, sortby: str = 'timestamp', ascend: bool = False) -> pandas.core.frame.DataFrame
Get the last 100 swaps done on Uniswap [Source: https://thegraph.com/en/]
@@ -9,6 +9,11 @@ Get the last 100 swaps done on Uniswap [Source: https://thegraph.com/en/]
-------
limit: int
Number of swaps to return. Maximum possible number: 1000.
sortby: str
Key by which to sort data. The table can be sorted by every of its columns
(see https://api.thegraph.com/subgraphs/name/uniswap/uniswap-v2).
ascend: bool
Flag to sort data descending
Returns
-------
pd.DataFrame

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### crypto.disc.coins(limit: int = 250, category: str = '', sortby='Symbol') -> pandas.core.frame.DataFrame
### crypto.disc.coins(limit: int = 250, category: str = '', sortby: str = 'Symbol', ascend: bool = False) -> pandas.core.frame.DataFrame
Get N coins from CoinGecko [Source: CoinGecko]
@@ -9,8 +9,12 @@ Get N coins from CoinGecko [Source: CoinGecko]
----------
limit: int
Number of top coins to grab from CoinGecko
category: str
Category of the coins we want to retrieve
sortby: str
Key to sort data
ascend: bool
Sort data in ascending order
Returns
-------
@@ -18,7 +22,7 @@ Get N coins from CoinGecko [Source: CoinGecko]
N coins
## Getting charts
### crypto.disc.coins(category: str, limit: int = 250, sortby: str = 'Symbol', export: str = '', chart=True) -> None
### crypto.disc.coins(category: str, limit: int = 250, sortby: str = 'Symbol', export: str = '', ascend: bool = False, chart=True) -> None
Display top coins [Source: CoinGecko]
@@ -32,3 +36,5 @@ Display top coins [Source: CoinGecko]
Key to sort data
export : str
Export dataframe data to csv,json,xlsx file
ascend: bool
Sort data in ascending order

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@@ -6,7 +6,7 @@ Returns data about top gainers - coins which gain the most in given period and
Parameters
----------
top: int
limit: int
Num of coins to get
sortby: str
Key to sort data. The table can be sorted by every of its columns. Refer to

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@@ -1,7 +1,7 @@
## Get underlying data
### crypto.load(symbol: 'str', start_date: 'datetime' = datetime.datetime(2019, 10, 22, 11, 40, 0, 303083), interval: 'str' = '1440', exchange: 'str' = 'binance', vs_currency: 'str' = 'usdt', end_date: 'datetime' = datetime.datetime(2022, 10, 26, 11, 40, 0, 303103), source: 'str' = 'CCXT') -> 'pd.DataFrame'
### crypto.load(symbol: 'str', start_date: 'datetime' = datetime.datetime(2019, 10, 30, 17, 23, 0, 264750), interval: 'str' = '1440', exchange: 'str' = 'binance', vs_currency: 'str' = 'usdt', end_date: 'datetime' = datetime.datetime(2022, 11, 3, 17, 23, 0, 264752), source: 'str' = 'CCXT') -> 'pd.DataFrame'
Load crypto currency to get data for.
Load crypto currency to get data for
Parameters
----------

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@@ -0,0 +1,28 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### crypto.nft.collections() -> pandas.core.frame.DataFrame
Get nft collections [Source: https://nftpricefloor.com/]
Parameters
-------
Returns
-------
pd.DataFrame
nft collections
## Getting charts
### crypto.nft.collections(show_fp: bool = False, show_sales: bool = False, limit: int = 5, export: str = '', chart=True)
Display NFT collections. [Source: https://nftpricefloor.com/]
Parameters
----------
show_fp : bool
Show NFT Price Floor for top collections
limit: int
Number of NFT collections to display
export : str
Export dataframe data to csv,json,xlsx file

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@@ -0,0 +1,34 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### crypto.nft.fp(slug) -> pandas.core.frame.DataFrame
Get nft collections [Source: https://nftpricefloor.com/]
Parameters
-------
slug: str
nft collection slug
Returns
-------
pd.DataFrame
nft collections
## Getting charts
### crypto.nft.fp(slug: str, limit: int = 10, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, raw: bool = False, chart=True)
Display NFT collection floor price over time. [Source: https://nftpricefloor.com/]
Parameters
----------
slug: str
NFT collection slug
raw: bool
Flag to display raw data
limit: int
Number of raw data to show
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

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@@ -11,7 +11,7 @@ Returns BTC circulating supply [Source: https://api.blockchain.info/]
BTC circulating supply
## Getting charts
### crypto.onchain.btc_supply(start_date: int = 1262300400, end_date: int = 1666777200, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
### crypto.onchain.btc_supply(start_date: int = 1262322000, end_date: int = 1667510580, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
Returns BTC circulating supply [Source: https://api.blockchain.info/]

2
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@@ -11,7 +11,7 @@ Returns BTC confirmed transactions [Source: https://api.blockchain.info/]
BTC confirmed transactions
## Getting charts
### crypto.onchain.btc_transac(start_date: int = 1262300400, end_date: int = 1666777200, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
### crypto.onchain.btc_transac(start_date: int = 1262322000, end_date: int = 1667510580, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
Returns BTC confirmed transactions [Source: https://api.blockchain.info/]

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### crypto.onchain.hr(symbol: str, interval: str = '24h', start_date: int = 1288345200, end_date: int = 1666777200) -> pandas.core.frame.DataFrame
### crypto.onchain.hr(symbol: str, interval: str = '24h', start_date: int = 1289078580, end_date: int = 1667510580) -> pandas.core.frame.DataFrame
Returns dataframe with mean hashrate of btc or eth blockchain and symbol price
[Source: https://glassnode.com]
@@ -23,7 +23,7 @@ Returns dataframe with mean hashrate of btc or eth blockchain and symbol price
mean hashrate and symbol price over time
## Getting charts
### crypto.onchain.hr(symbol: str, start_date: int = 1635241200, end_date: int = 1666777200, interval: str = '24h', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
### crypto.onchain.hr(symbol: str, start_date: int = 1635974580, end_date: int = 1667510580, 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]

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### crypto.ov.altindex(period: int = 30, start_date: int = 1262300400, end_date: int = 1666777200) -> pandas.core.frame.DataFrame
### crypto.ov.altindex(period: int = 30, start_date: int = 1262322000, end_date: int = 1667510580) -> pandas.core.frame.DataFrame
Get altcoin index overtime
[Source: https://blockchaincenter.net]
@@ -23,7 +23,7 @@ Get altcoin index overtime
Date, Value (Altcoin Index)
## Getting charts
### crypto.ov.altindex(period: int = 365, start_date: int = 1262300400, end_date: int = 1666777200, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
### crypto.ov.altindex(period: int = 365, start_date: int = 1262322000, end_date: int = 1667510580, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> None
Displays altcoin index overtime
[Source: https://blockchaincenter.net]

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### crypto.ov.cghm(limit: int = 250, category: str = '', sortby='Symbol') -> pandas.core.frame.DataFrame
### crypto.ov.cghm(limit: int = 250, category: str = '', sortby: str = 'Symbol', ascend: bool = False) -> pandas.core.frame.DataFrame
Get N coins from CoinGecko [Source: CoinGecko]
@@ -9,8 +9,12 @@ Get N coins from CoinGecko [Source: CoinGecko]
----------
limit: int
Number of top coins to grab from CoinGecko
category: str
Category of the coins we want to retrieve
sortby: str
Key to sort data
ascend: bool
Sort data in ascending order
Returns
-------

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### crypto.ov.exchanges(sortby: str = 'name', ascend: bool = False) -> pandas.core.frame.DataFrame
### crypto.ov.exchanges(sortby: str = 'Rank', ascend: bool = True) -> pandas.core.frame.DataFrame
Get list of top exchanges from CoinGecko API [Source: CoinGecko]
@@ -18,7 +18,7 @@ Get list of top exchanges from CoinGecko API [Source: CoinGecko]
Trust_Score, Id, Name, Country, Year_Established, Trade_Volume_24h_BTC, Url
## Getting charts
### crypto.ov.exchanges(sortby: str = 'name', ascend: bool = False, limit: int = 15, links: bool = False, export: str = '', chart=True) -> None
### crypto.ov.exchanges(sortby: str = 'Rank', ascend: bool = False, limit: int = 15, links: bool = False, export: str = '', chart=True) -> None
Shows list of top exchanges from CoinGecko. [Source: CoinGecko]

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## Get underlying data
### crypto.price(symbol: str)
Returns price and confidence interval from pyth live feed. [Source: Pyth]
Parameters
----------
symbol : str
Symbol of the asset to get price and confidence interval from
Returns
-------
float
Price of the asset
float
Confidence level
float
Previous price of the asset

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@@ -1,22 +1,18 @@
## Get underlying data
### economy.events(country: str = 'all', importance: str = '', category: str = '', start_date: str = '', end_date: str = '', limit=100) -> Tuple[pandas.core.frame.DataFrame, str]
### economy.events(countries: Union[List[str], str] = '', start_date: str = '2022-11-03', end_date: str = '2022-11-03') -> pandas.core.frame.DataFrame
Get economic calendar [Source: Investing.com]
Get economic calendar for countries between specified dates
Parameters
----------
country: str
Country selected. List of available countries is accessible through get_events_countries().
importance: str
Importance selected from high, medium, low or all
category: str
Event category. List of available categories is accessible through get_events_categories().
start_date: datetime.date
First date to get events.
end_date: datetime.date
Last date to get events.
countries : [List[str],str]
List of countries to include in calendar. Empty returns all
start_date : str
Start date for calendar
end_date : str
End date for calendar
Returns
-------
Tuple[pd.DataFrame, str]
Economic calendar Dataframe and detail string about country/time zone.
pd.DataFrame
Economic calendar

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### economy.macro(parameters: list = None, countries: list = None, transform: str = '', start_date: str = '1900-01-01', end_date=datetime.date(2022, 10, 26), symbol: str = '') -> Tuple[Any, Dict[Any, Dict[Any, Any]], str]
### economy.macro(parameters: list = None, countries: list = None, transform: str = '', start_date: str = '1900-01-01', end_date=datetime.date(2022, 11, 3), symbol: str = '') -> Tuple[Any, Dict[Any, Dict[Any, Any]], str]
This functions groups the data queried from the EconDB database [Source: EconDB]
@@ -30,7 +30,7 @@ This functions groups the data queried from the EconDB database [Source: EconDB]
Denomination which can be Trillions, Billions, Millions, Thousands
## Getting charts
### economy.macro(parameters: list = None, countries: list = None, transform: str = '', start_date: str = '1900-01-01', end_date: str = '2022-10-26', symbol: str = '', raw: bool = False, external_axes: Optional[List[axes]] = None, export: str = '', chart=True)
### economy.macro(parameters: list = None, countries: list = None, transform: str = '', start_date: str = '1900-01-01', end_date: str = '2022-11-03', symbol: str = '', raw: bool = False, external_axes: Optional[List[axes]] = None, export: str = '', chart=True)
Show the received macro data about a company [Source: EconDB]

4
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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### economy.treasury(instruments: list = None, maturities: list = None, frequency: str = 'monthly', start_date: str = '1900-01-01', end_date: str = '2022-10-26') -> pandas.core.frame.DataFrame
### economy.treasury(instruments: list = None, maturities: list = None, frequency: str = 'monthly', start_date: str = '1900-01-01', end_date: str = '2022-11-03') -> pandas.core.frame.DataFrame
Get U.S. Treasury rates [Source: EconDB]
@@ -25,7 +25,7 @@ Get U.S. Treasury rates [Source: EconDB]
Holds data of the selected types and maturities
## Getting charts
### economy.treasury(instruments: list = None, maturities: list = None, frequency: str = 'monthly', start_date: str = '1900-01-01', end_date: str = '2022-10-26', raw: bool = False, external_axes: Optional[List[axes]] = None, export: str = '', chart=True)
### economy.treasury(instruments: list = None, maturities: list = None, frequency: str = 'monthly', start_date: str = '1900-01-01', end_date: str = '2022-11-03', raw: bool = False, external_axes: Optional[List[axes]] = None, export: str = '', chart=True)
Display U.S. Treasury rates [Source: EconDB]

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@@ -1,7 +1,9 @@
## Get underlying data
### etf.candle(symbol: str, data: pandas.core.frame.DataFrame = None, use_matplotlib: bool = True, intraday: bool = False, add_trend: bool = False, ma: Optional[Iterable[int]] = None, asset_type: str = '', start_date: datetime.datetime = datetime.datetime(2019, 10, 22, 11, 39, 59, 858379), interval: int = 1440, end_date: datetime.datetime = datetime.datetime(2022, 10, 26, 11, 39, 59, 858399), prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False, external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, raw: bool = False, yscale: str = 'linear')
### etf.candle(symbol: str, data: pandas.core.frame.DataFrame = None, use_matplotlib: bool = True, intraday: bool = False, add_trend: bool = False, ma: Optional[Iterable[int]] = None, asset_type: str = '', start_date: datetime.datetime = datetime.datetime(2019, 10, 30, 17, 22, 59, 707808), interval: int = 1440, end_date: datetime.datetime = datetime.datetime(2022, 11, 3, 17, 22, 59, 707818), prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False, external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, raw: bool = False, yscale: str = 'linear')
Shows candle plot of loaded ticker. [Source: Yahoo Finance, IEX Cloud or Alpha Vantage]
Show candle plot of loaded ticker.
[Source: Yahoo Finance, IEX Cloud or Alpha Vantage]
Parameters
----------

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@@ -1,8 +1,7 @@
## Get underlying data
### etf.load(symbol: str, start_date: datetime.datetime = None, interval: int = 1440, end_date: datetime.datetime = None, prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False)
### etf.load(symbol: str, start_date: datetime.datetime = None, interval: int = 1440, end_date: datetime.datetime = None, prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False, verbose: bool = True)
Load a symbol to perform analysis using the string above as a template.
Load a symbol to perform analysis using the string above as a template.
Optional arguments and their descriptions are listed above.
@@ -15,7 +14,7 @@
Please note that certain analytical features are exclusive to the specific source.
To load a symbol from an exchange outside of the NYSE/NASDAQ default, use yFinance as the source and
add the corresponding exchange to the end of the symbol. i.e. ‘BNS.TO’. Note this may be possible with
add the corresponding exchange to the end of the symbol. i.e. `BNS.TO`. Note this may be possible with
other paid sources check their docs.
BNS is a dual-listed stock, there are separate options chains and order books for each listing.
@@ -26,8 +25,8 @@
https://help.yahoo.com/kb/exchanges-data-providers-yahoo-finance-sln2310.html
Certain analytical features, such as VWAP, require the ticker to be loaded as intraday
using the ‘-i x’ argument. When encountering this error, simply reload the symbol using
the interval argument. i.e. ‘load -t BNS -s YYYY-MM-DD -i 1 -p’ loads one-minute intervals,
using the `-i x` argument. When encountering this error, simply reload the symbol using
the interval argument. i.e. `load -t BNS -s YYYY-MM-DD -i 1 -p` loads one-minute intervals,
including Pre/After Market data, using the default source, yFinance.
Certain features, such as the Prediction menu, require the symbol to be loaded as daily and not intraday.
@@ -52,6 +51,8 @@
Flag to get weekly data
monthly: bool
Flag to get monthly data
verbose: bool
Display verbose information on what was the symbol that was loaded
Returns
-------

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### etf.news(query: str, limit: int = 10, start_date: str = '2022-10-19', show_newest: bool = True, sources: str = '') -> List[Tuple[Any, Any]]
### etf.news(query: str, limit: int = 10, start_date: str = '2022-10-27', show_newest: bool = True, sources: str = '') -> List[Tuple[Any, Any]]
Get news for a given term. [Source: NewsAPI]
@@ -14,7 +14,7 @@ Get news for a given term. [Source: NewsAPI]
show_newest: bool
flag to show newest articles first
sources: str
sources to exclusively show news from
sources to exclusively show news from (comma separated)
Returns
----------
@@ -22,7 +22,7 @@ Get news for a given term. [Source: NewsAPI]
List of tuples containing news df in first index and dict containing title of news df
## Getting charts
### etf.news(query: str, limit: int = 3, start_date: str = '2022-10-19', show_newest: bool = True, sources: str = '', export: str = '', chart=True) -> None
### etf.news(query: str, limit: int = 3, start_date: str = '2022-10-27', show_newest: bool = True, sources: str = '', export: str = '', chart=True) -> None
Display news for a given term. [Source: NewsAPI]

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@@ -1,5 +1,5 @@
## Get underlying data
### forecast.atr(dataset: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame
### forecast.atr(dataset: pandas.core.frame.DataFrame, close_column: str = 'close', high_column: str = 'high', low_column: str = 'low') -> pandas.core.frame.DataFrame
Calculate the Average True Range of a variable based on a a specific stock ticker.

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@@ -0,0 +1,78 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### forecast.autoets(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', seasonal_periods: int = 7, n_predict: int = 5, start_window: float = 0.85, forecast_horizon: int = 5) -> Tuple[list[numpy.ndarray], List[numpy.ndarray], List[numpy.ndarray], Optional[float], Any]
Performs Automatic ETS forecasting
This is a wrapper around StatsForecast ETS;
we refer to this link for the original and more complete documentation of the parameters.
https://nixtla.github.io/statsforecast/models.html#ets
Parameters
----------
data : Union[pd.Series, np.ndarray]
Input data.
target_column (str, optional):
Target column to forecast. Defaults to "close".
seasonal_periods: int
Number of seasonal periods in a year (7 for daily data)
If not set, inferred from frequency of the series.
n_predict: int
Number of days to forecast
start_window: float
Size of sliding window from start of timeseries and onwards
forecast_horizon: int
Number of days to forecast when backtesting and retraining historical
Returns
-------
list[float]
Adjusted Data series
list[float]
List of historical fcast values
list[float]
List of predicted fcast values
Optional[float]
precision
Any
Fit ETS model object.
## Getting charts
### forecast.autoets(data: Union[pandas.core.frame.DataFrame, pandas.core.series.Series], target_column: str = 'close', dataset_name: str = '', seasonal_periods: int = 7, n_predict: int = 5, start_window: float = 0.85, forecast_horizon: int = 5, export: str = '', residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, export_pred_raw: bool = False, external_axes: Optional[List[axes]] = None, chart=True)
Display Automatic ETS (Error, Trend, Sesonality) Model
Parameters
----------
data : Union[pd.Series, np.array]
Data to forecast
dataset_name str
The name of the ticker to be predicted
target_column (str, optional):
Target column to forecast. Defaults to "close".
seasonal_periods: int
Number of seasonal periods in a year
If not set, inferred from frequency of the series.
n_predict: int
Number of days to forecast
start_window: float
Size of sliding window from start of timeseries and onwards
forecast_horizon: int
Number of days to forecast when backtesting and retraining historical
export: str
Format to export data
residuals: bool
Whether to show residuals for the model. Defaults to False.
forecast_only: bool
Whether to only show dates in the forecasting range. Defaults to False.
start_date: Optional[datetime]
The starting date to perform analysis, data before this is trimmed. Defaults to None.
end_date: Optional[datetime]
The ending date to perform analysis, data after this is trimmed. Defaults to None.
naive: bool
Whether to show the naive baseline. This just assumes the closing price will be the same
as the previous day's closing price. Defaults to False.
external_axes:Optional[List[plt.axes]]
External axes to plot on

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### forecast.brnn(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', n_predict: int = 5, train_split: float = 0.85, past_covariates: str = None, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, model_type: str = 'LSTM', n_rnn_layers: int = 1, dropout: float = 0.0, batch_size: int = 32, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = 'brnn_model', force_reset: bool = True, save_checkpoints: bool = True) -> Tuple[list[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], Optional[float], Any]
### forecast.brnn(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', n_predict: int = 5, train_split: float = 0.85, past_covariates: str = None, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, model_type: str = 'LSTM', n_rnn_layers: int = 1, dropout: float = 0.0, batch_size: int = 32, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = 'brnn_model', force_reset: bool = True, save_checkpoints: bool = True) -> Tuple[List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], Optional[float], Any]
Performs Block RNN forecasting

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### forecast.expo(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', trend: str = 'A', seasonal: str = 'A', seasonal_periods: int = 7, dampen: str = 'F', n_predict: int = 5, start_window: float = 0.85, forecast_horizon: int = 5) -> Tuple[list[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], Optional[float], Any]
## Get underlying data
### forecast.expo(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', trend: str = 'A', seasonal: str = 'A', seasonal_periods: int = 7, dampen: str = 'F', n_predict: int = 5, start_window: float = 0.85, forecast_horizon: int = 5) -> Tuple[List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], Optional[float], Any]
Performs Probabilistic Exponential Smoothing forecasting
This is a wrapper around statsmodels Holt-Winters' Exponential Smoothing;
@@ -46,7 +46,7 @@ Performs Probabilistic Exponential Smoothing forecasting
Any
Fit Prob. Expo model object.
## Getting charts
## Getting charts
### forecast.expo(data: Union[pandas.core.frame.DataFrame, pandas.core.series.Series], target_column: str = 'close', dataset_name: str = '', trend: str = 'A', seasonal: str = 'A', seasonal_periods: int = 7, dampen: str = 'F', n_predict: int = 5, start_window: float = 0.85, forecast_horizon: int = 5, export: str = '', residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, export_pred_raw: bool = False, external_axes: Optional[List[axes]] = None, chart=True)
Display Probabilistic Exponential Smoothing forecast

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### forecast.nhits(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', n_predict: int = 5, train_split: float = 0.85, past_covariates: str = None, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, num_stacks: int = 3, num_blocks: int = 1, num_layers: int = 2, layer_widths: int = 512, pooling_kernel_sizes: Optional[Tuple[Tuple[int]]] = None, n_freq_downsample: Optional[Tuple[Tuple[int]]] = None, dropout: float = 0.1, activation: str = 'ReLU', max_pool_1d: bool = True, batch_size: int = 32, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = 'brnn_model', force_reset: bool = True, save_checkpoints: bool = True) -> Tuple[list[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], Optional[float], Any]
### forecast.nhits(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', n_predict: int = 5, train_split: float = 0.85, past_covariates: str = None, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, num_stacks: int = 3, num_blocks: int = 1, num_layers: int = 2, layer_widths: int = 512, pooling_kernel_sizes: Optional[Tuple[Tuple[int]]] = None, n_freq_downsample: Optional[Tuple[Tuple[int]]] = None, dropout: float = 0.1, activation: str = 'ReLU', max_pool_1d: bool = True, batch_size: int = 32, n_epochs: int = 100, learning_rate: float = 0.001, model_save_name: str = 'brnn_model', force_reset: bool = True, save_checkpoints: bool = True) -> Tuple[List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], Optional[float], Any]
Performs Nhits forecasting

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## Get underlying data
### forecast.signal(dataset: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame
### forecast.signal(dataset: pandas.core.frame.DataFrame, target_column: str = 'close') -> pandas.core.frame.DataFrame
A price signal based on short/long term price.

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## Get underlying data
### forecast.sto(dataset: pandas.core.frame.DataFrame, period: int = 10) -> pandas.core.frame.DataFrame
### forecast.sto(dataset: pandas.core.frame.DataFrame, close_column: str = 'close', high_column: str = 'high', low_column: str = 'low', period: int = 10) -> pandas.core.frame.DataFrame
Stochastic Oscillator %K and %D : A stochastic oscillator is a momentum indicator comparing a particular closing
price of a security to a range of its prices over a certain period of time. %K and %D are slow and fast indicators.

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To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
## Get underlying data
### forecast.tft(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, hidden_size: int = 16, lstm_layers: int = 1, num_attention_heads: int = 4, full_attention: bool = False, dropout: float = 0.1, hidden_continuous_size: int = 8, n_epochs: int = 200, batch_size: int = 32, model_save_name: str = 'tft_model', force_reset: bool = True, save_checkpoints: bool = True) -> Tuple[List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], Optional[float], Any]
Performs Temporal Fusion Transformer forecasting
@@ -70,7 +70,7 @@ Performs Temporal Fusion Transformer forecasting
Any
Fit Prob. TFT model object.
## Getting charts
## Getting charts
### forecast.tft(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', dataset_name: str = '', n_predict: int = 5, past_covariates: str = None, train_split: float = 0.85, forecast_horizon: int = 5, input_chunk_length: int = 14, output_chunk_length: int = 5, hidden_size: int = 16, lstm_layers: int = 1, num_attention_heads: int = 4, full_attention: bool = False, dropout: float = 0.1, hidden_continuous_size: int = 8, n_epochs: int = 200, batch_size: int = 32, model_save_name: str = 'tft_model', force_reset: bool = True, save_checkpoints: bool = True, export: str = '', residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, export_pred_raw: bool = False, external_axes: Optional[List[axes]] = None, chart=True)
Display Temporal Fusion Transformer forecast

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To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
## Get underlying data
### forecast.theta(data: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], target_column: str = 'close', seasonal: str = 'M', seasonal_periods: int = 7, n_predict: int = 5, start_window: float = 0.85, forecast_horizon: int = 5) -> Tuple[List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], List[darts.timeseries.TimeSeries], float, float, Any]
Performs Theta forecasting
@@ -42,7 +42,7 @@ Performs Theta forecasting
Any
Theta Model
## Getting charts
## Getting charts
### forecast.theta(data: Union[pandas.core.frame.DataFrame, pandas.core.series.Series], target_column: str = 'close', dataset_name: str = '', seasonal: str = 'M', seasonal_periods: int = 7, n_predict: int = 5, start_window: float = 0.85, forecast_horizon: int = 5, export: str = '', residuals: bool = False, forecast_only: bool = False, start_date: Optional[datetime.datetime] = None, end_date: Optional[datetime.datetime] = None, naive: bool = False, export_pred_raw: bool = False, external_axes: Optional[List[axes]] = None, chart=True)
Display Theta forecast

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## Get underlying data
### forex.load(to_symbol: str, from_symbol: str, resolution: str = 'd', interval: str = '1day', start_date: str = '2021-10-26', source: str = 'YahooFinance') -> pandas.core.frame.DataFrame
### forex.load(to_symbol: str, from_symbol: str, resolution: str = 'd', interval: str = '1day', start_date: str = '2021-11-03', source: str = 'YahooFinance', verbose: bool = True) -> pandas.core.frame.DataFrame
Loads forex for two given symbols
Load forex for two given symbols.
Parameters
----------
to_symbol : str
The from currency symbol. Ex: USD, EUR, GBP, YEN
from_symbol: str
from_symbol : str
The from currency symbol. Ex: USD, EUR, GBP, YEN
resolution: str
The resolution for the data
interval: str
What interval to get data for
start_date: str
When to begin loading in data
source: str
Where to get data from
resolution : str, optional
The resolution for the data, by default "d"
interval : str, optional
What interval to get data for, by default "1day"
start_date : str, optional
When to begin loading in data, by default last_year.strftime("%Y-%m-%d")
source : str, optional
Where to get data from, by default "YahooFinance"
verbose : bool, optional
Display verbose information on what was the pair that was loaded, by default True
Returns
-------

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### forex.oanda.calendar(days: int, instrument: Optional[str] = None) -> Union[pandas.core.frame.DataFrame, bool]
### forex.oanda.calendar(days: int = 14, instrument: Optional[str] = None) -> Union[pandas.core.frame.DataFrame, bool]
Request data of significant events calendar.

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@@ -1,6 +0,0 @@
---
title: funds
keywords: ""
excerpt: ""
geekdocCollapseSection: true
---

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@@ -1,30 +0,0 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### funds.info(name: str, country: str = 'united states') -> pandas.core.frame.DataFrame
Parameters
----------
name: str
Name of fund (not symbol) to get information
country: str
Country of fund
Returns
-------
pd.DataFrame
Dataframe of fund information
## Getting charts
### funds.info(name: str, country: str = 'united states', chart=True)
Display fund information. Finds name from symbol first if name is false
Parameters
----------
name: str
Fund name to get info for
country : str
Country of fund

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To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### funds.overview(country: str = 'united states', limit: int = 20) -> pandas.core.frame.DataFrame
Parameters
----------
country: str
Country to get overview for
limit: int
Number of results to get
Returns
-------
pd.DataFrame
Dataframe containing overview
## Getting charts
### funds.overview(country: str = 'united states', limit: int = 10, export: str = '', chart=True)
Displays an overview of the main funds from a country.
Parameters
----------
country: str
Country to get overview for
limit: int
Number to show
export : str
Format to export data

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@@ -1,38 +0,0 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### funds.search(by: str = 'name', value: str = '') -> pandas.core.frame.DataFrame
Search investpy for matching funds
Parameters
----------
by : str
Field to match on. Can be name, issuer, isin or symbol
value : str
String that will be searched for
Returns
-------
pd.DataFrame
Dataframe containing matches
## Getting charts
### funds.search(by: str = 'name', value: str = '', country: str = 'united states', limit: int = 10, sortby: str = '', ascend: bool = False, chart=True)
Display results of searching for Mutual Funds
Parameters
----------
by : str
Field to match on. Can be name, issuer, isin or symbol
value : str
String that will be searched for
country: str
Country to filter on
limit: int
Number to show
sortby: str
Column to sort by
ascend: bool
Flag to sort in ascending order

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website/content/SDK/futures/historical/_index.md vendored
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@@ -18,7 +18,7 @@ Get historical futures [Source: Yahoo Finance]
Dictionary with sector weightings allocation
## Getting charts
### futures.historical(tickers: List[str], expiry: str = '', start_date: str = '2019-10-27', raw: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
### futures.historical(tickers: List[str], expiry: str = '', start_date: str = '2019-11-04', raw: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
Display historical futures [Source: Yahoo Finance]

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website/content/SDK/stocks/ba/cnews/_index.md vendored
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@@ -1,5 +1,5 @@
## Get underlying data
### stocks.ba.cnews(symbol: str, start_date: str = '2022-09-26', end_date: str = '2022-10-26') -> List[Dict]
### stocks.ba.cnews(symbol: str, start_date: str = '2022-10-04', end_date: str = '2022-11-03') -> List[Dict]
Get news from a company. [Source: Finnhub]

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### stocks.ba.hist(symbol: str, start_date: str = '2022-10-19', end_date: str = '2022-10-26', number: int = 100) -> pandas.core.frame.DataFrame
### stocks.ba.hist(symbol: str, start_date: str = '2022-10-27', end_date: str = '2022-11-03', number: int = 100) -> pandas.core.frame.DataFrame
Get hour-level sentiment data for the chosen symbol
@@ -25,7 +25,7 @@ Get hour-level sentiment data for the chosen symbol
Dataframe of historical sentiment
## Getting charts
### stocks.ba.hist(symbol: str, start_date: str = '2022-10-19', end_date: str = '2022-10-26', number: int = 100, raw: bool = False, limit: int = 10, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
### stocks.ba.hist(symbol: str, start_date: str = '2022-10-27', end_date: str = '2022-11-03', number: int = 100, raw: bool = False, limit: int = 10, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
Display historical sentiment data of a ticker,
and plot a chart with RHI and AHI.

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### stocks.ba.trend(start_date: datetime.datetime = datetime.datetime(2022, 10, 26, 11, 40, 0, 860201), hour: int = 0, number: int = 10) -> pandas.core.frame.DataFrame
### stocks.ba.trend(start_date: datetime.datetime = datetime.datetime(2022, 11, 3, 17, 23, 0, 782290), hour: int = 0, number: int = 10) -> pandas.core.frame.DataFrame
Get sentiment data on the most talked about tickers
within the last hour
@@ -24,7 +24,7 @@ Get sentiment data on the most talked about tickers
Dataframe of trending data
## Getting charts
### stocks.ba.trend(start_date: datetime.datetime = datetime.datetime(2022, 10, 26, 11, 40, 0, 860431, chart=True), hour: int = 0, number: int = 10, limit: int = 10, export: str = '')
### stocks.ba.trend(start_date: datetime.datetime = datetime.datetime(2022, 11, 3, 17, 23, 0, 782624, chart=True), hour: int = 0, number: int = 10, limit: int = 10, export: str = '')
Display most talked about tickers within
the last hour together with their sentiment data.

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### stocks.ca.hcorr(similar: List[str], start_date: str = '2021-10-25', candle_type: str = 'a')
### stocks.ca.hcorr(similar: List[str], start_date: str = '2021-11-02', candle_type: str = 'a')
Get historical price correlation. [Source: Yahoo Finance]
@@ -18,7 +18,7 @@ To obtain charts, make sure to add `chart=True` as the last parameter
OHLCA column to use for candles or R for returns, by default "a" for Adjusted Close
## Getting charts
### stocks.ca.hcorr(similar: List[str], start_date: str = '2021-10-25', candle_type: str = 'a', display_full_matrix: bool = False, raw: bool = False, external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, export: str = '', chart=True)
### stocks.ca.hcorr(similar: List[str], start_date: str = '2021-11-02', candle_type: str = 'a', display_full_matrix: bool = False, raw: bool = False, external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, export: str = '', chart=True)
Correlation heatmap based on historical price comparison

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### stocks.ca.hist(similar: List[str], start_date: str = '2021-10-25', candle_type: str = 'a') -> pandas.core.frame.DataFrame
### stocks.ca.hist(similar: List[str], start_date: str = '2021-11-02', candle_type: str = 'a') -> pandas.core.frame.DataFrame
Get historical prices for all comparison stocks
@@ -22,7 +22,7 @@ Get historical prices for all comparison stocks
Dataframe containing candle type variable for each ticker
## Getting charts
### stocks.ca.hist(similar: List[str], start_date: str = '2021-10-25', candle_type: str = 'a', normalize: bool = True, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
### stocks.ca.hist(similar: List[str], start_date: str = '2021-11-02', candle_type: str = 'a', normalize: bool = True, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
Display historical stock prices. [Source: Yahoo Finance]

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website/content/SDK/stocks/ca/volume/_index.md vendored
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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### stocks.ca.volume(similar: List[str], start_date: str = '2021-10-25') -> pandas.core.frame.DataFrame
### stocks.ca.volume(similar: List[str], start_date: str = '2021-11-02') -> pandas.core.frame.DataFrame
Get stock volume. [Source: Yahoo Finance]
@@ -15,7 +15,7 @@ Get stock volume. [Source: Yahoo Finance]
Start date of comparison, by default 1 year ago
## Getting charts
### stocks.ca.volume(similar: List[str], start_date: str = '2021-10-25', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
### stocks.ca.volume(similar: List[str], start_date: str = '2021-11-02', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
Display stock volume. [Source: Yahoo Finance]

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@@ -1,7 +1,9 @@
## Get underlying data
### stocks.candle(symbol: str, data: pandas.core.frame.DataFrame = None, use_matplotlib: bool = True, intraday: bool = False, add_trend: bool = False, ma: Optional[Iterable[int]] = None, asset_type: str = '', start_date: datetime.datetime = datetime.datetime(2019, 10, 22, 11, 39, 59, 858379), interval: int = 1440, end_date: datetime.datetime = datetime.datetime(2022, 10, 26, 11, 39, 59, 858399), prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False, external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, raw: bool = False, yscale: str = 'linear')
### stocks.candle(symbol: str, data: pandas.core.frame.DataFrame = None, use_matplotlib: bool = True, intraday: bool = False, add_trend: bool = False, ma: Optional[Iterable[int]] = None, asset_type: str = '', start_date: datetime.datetime = datetime.datetime(2019, 10, 30, 17, 22, 59, 707808), interval: int = 1440, end_date: datetime.datetime = datetime.datetime(2022, 11, 3, 17, 22, 59, 707818), prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False, external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, raw: bool = False, yscale: str = 'linear')
Shows candle plot of loaded ticker. [Source: Yahoo Finance, IEX Cloud or Alpha Vantage]
Show candle plot of loaded ticker.
[Source: Yahoo Finance, IEX Cloud or Alpha Vantage]
Parameters
----------

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@@ -16,7 +16,7 @@ Get analysts' price targets for a given stock. [Source: Business Insider]
Analysts data
## Getting charts
### stocks.dd.pt(symbol: str, data: pandas.core.frame.DataFrame, start_date: str = '2022-10-26', limit: int = 10, raw: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
### stocks.dd.pt(symbol: str, data: pandas.core.frame.DataFrame, start_date: str = '2022-11-03', limit: int = 10, raw: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
Display analysts' price targets for a given stock. [Source: Business Insider]

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website/content/SDK/stocks/disc/dividends/_index.md vendored
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@@ -1,5 +1,5 @@
## Get underlying data
### stocks.disc.dividends(date: str = '2022-10-26') -> pandas.core.frame.DataFrame
### stocks.disc.dividends(date: str = '2022-11-03') -> pandas.core.frame.DataFrame
Gets dividend calendar for given date. Date represents Ex-Dividend Date

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@@ -1,5 +1,5 @@
## Get underlying data
### stocks.disc.ipo(start_date: str = '2022-10-21', end_date: str = '2022-10-26') -> pandas.core.frame.DataFrame
### stocks.disc.ipo(start_date: str = '2022-10-29', end_date: str = '2022-11-03') -> pandas.core.frame.DataFrame
Get IPO calendar

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### stocks.dps.ftd(symbol: str, start_date: str = '2022-08-27', end_date: str = '2022-10-26', limit: int = 0) -> pandas.core.frame.DataFrame
### stocks.dps.ftd(symbol: str, start_date: str = '2022-09-04', end_date: str = '2022-11-03', limit: int = 0) -> pandas.core.frame.DataFrame
Display fails-to-deliver data for a given ticker. [Source: SEC]
@@ -22,7 +22,7 @@ Display fails-to-deliver data for a given ticker. [Source: SEC]
Fail to deliver data
## Getting charts
### stocks.dps.ftd(symbol: str, data: pandas.core.frame.DataFrame, start_date: str = '2022-08-27', end_date: str = '2022-10-26', limit: int = 0, raw: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
### stocks.dps.ftd(symbol: str, data: pandas.core.frame.DataFrame, start_date: str = '2022-09-04', end_date: str = '2022-11-03', limit: int = 0, raw: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
Display fails-to-deliver data for a given ticker. [Source: SEC]

4
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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### stocks.fa.mktcap(symbol: str, start_date: str = '2019-10-24') -> Tuple[pandas.core.frame.DataFrame, str]
### stocks.fa.mktcap(symbol: str, start_date: str = '2019-11-01') -> Tuple[pandas.core.frame.DataFrame, str]
Get market cap over time for ticker. [Source: Yahoo Finance]
@@ -20,7 +20,7 @@ Get market cap over time for ticker. [Source: Yahoo Finance]
Currency of ticker
## Getting charts
### stocks.fa.mktcap(symbol: str, start_date: str = '2019-10-24', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
### stocks.fa.mktcap(symbol: str, start_date: str = '2019-11-01', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
Display market cap over time. [Source: Yahoo Finance]

2
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@@ -16,7 +16,7 @@ Get insider activity. [Source: Business Insider]
Get insider activity data
## Getting charts
### stocks.ins.act(data: pandas.core.frame.DataFrame, symbol: str, start_date: str = '2019-10-22', interval: str = '1440min', limit: int = 10, raw: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
### stocks.ins.act(data: pandas.core.frame.DataFrame, symbol: str, start_date: str = '2019-10-30', interval: str = '1440min', limit: int = 10, raw: bool = False, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
Display insider activity. [Source: Business Insider]

13
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@@ -1,8 +1,7 @@
## Get underlying data
### stocks.load(symbol: str, start_date: datetime.datetime = None, interval: int = 1440, end_date: datetime.datetime = None, prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False)
### stocks.load(symbol: str, start_date: datetime.datetime = None, interval: int = 1440, end_date: datetime.datetime = None, prepost: bool = False, source: str = 'YahooFinance', iexrange: str = 'ytd', weekly: bool = False, monthly: bool = False, verbose: bool = True)
Load a symbol to perform analysis using the string above as a template.
Load a symbol to perform analysis using the string above as a template.
Optional arguments and their descriptions are listed above.
@@ -15,7 +14,7 @@
Please note that certain analytical features are exclusive to the specific source.
To load a symbol from an exchange outside of the NYSE/NASDAQ default, use yFinance as the source and
add the corresponding exchange to the end of the symbol. i.e. ‘BNS.TO’. Note this may be possible with
add the corresponding exchange to the end of the symbol. i.e. `BNS.TO`. Note this may be possible with
other paid sources check their docs.
BNS is a dual-listed stock, there are separate options chains and order books for each listing.
@@ -26,8 +25,8 @@
https://help.yahoo.com/kb/exchanges-data-providers-yahoo-finance-sln2310.html
Certain analytical features, such as VWAP, require the ticker to be loaded as intraday
using the ‘-i x’ argument. When encountering this error, simply reload the symbol using
the interval argument. i.e. ‘load -t BNS -s YYYY-MM-DD -i 1 -p’ loads one-minute intervals,
using the `-i x` argument. When encountering this error, simply reload the symbol using
the interval argument. i.e. `load -t BNS -s YYYY-MM-DD -i 1 -p` loads one-minute intervals,
including Pre/After Market data, using the default source, yFinance.
Certain features, such as the Prediction menu, require the symbol to be loaded as daily and not intraday.
@@ -52,6 +51,8 @@
Flag to get weekly data
monthly: bool
Flag to get monthly data
verbose: bool
Display verbose information on what was the symbol that was loaded
Returns
-------

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### stocks.options.grhist(symbol: str, expiry: str, strike: float, chain_id: str = '', put: bool = False) -> pandas.core.frame.DataFrame
### stocks.options.grhist(symbol: str, expiry: str, strike: Union[str, float], chain_id: str = '', put: bool = False) -> pandas.core.frame.DataFrame
Get histoical option greeks
@@ -11,7 +11,7 @@ Get histoical option greeks
Stock ticker symbol
expiry: str
Option expiration date
strike: float
strike: Union[str, float]
Strike price to look for
chain_id: str
OCC option symbol. Overwrites other inputs
@@ -24,7 +24,7 @@ Get histoical option greeks
Dataframe containing historical greeks
## Getting charts
### stocks.options.grhist(symbol: str, expiry: str, strike: float, greek: str = 'Delta', chain_id: str = '', put: bool = False, raw: bool = False, limit: int = 20, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
### stocks.options.grhist(symbol: str, expiry: str, strike: Union[float, str], greek: str = 'Delta', chain_id: str = '', put: bool = False, raw: bool = False, limit: Union[int, str] = 20, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
Plots historical greeks for a given option. [Source: Syncretism]
@@ -34,7 +34,7 @@ Plots historical greeks for a given option. [Source: Syncretism]
Stock ticker
expiry: str
Expiration date
strike: float
strike: Union[str, float]
Strike price to consider
greek: str
Greek variable to plot

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### stocks.options.pcr(symbol: str, window: int = 30, start_date: str = '2021-10-25') -> pandas.core.frame.DataFrame
### stocks.options.pcr(symbol: str, window: int = 30, start_date: str = '2021-11-02') -> pandas.core.frame.DataFrame
Gets put call ratio over last time window [Source: AlphaQuery.com]
@@ -15,7 +15,7 @@ Gets put call ratio over last time window [Source: AlphaQuery.com]
Start date to plot, by default last 366 days
## Getting charts
### stocks.options.pcr(symbol: str, window: int = 30, start_date: str = '2021-10-25', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
### stocks.options.pcr(symbol: str, window: int = 30, start_date: str = '2021-11-02', export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True)
Display put call ratio [Source: AlphaQuery.com]

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@@ -1,7 +1,7 @@
## Get underlying data
### stocks.process_candle(data: pandas.core.frame.DataFrame) -> pandas.core.frame.DataFrame
Process DataFrame into candle style plot
Process DataFrame into candle style plot.
Parameters
----------

2
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@@ -1,7 +1,7 @@
## Get underlying data
### stocks.quote(symbol: str) -> pandas.core.frame.DataFrame
Ticker quote
Ticker quote.
Parameters
----------

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@@ -1,7 +1,7 @@
To obtain charts, make sure to add `chart=True` as the last parameter
## Get underlying data
### stocks.screener.historical(preset_loaded: str = 'top_gainers', limit: int = 10, start_date: str = '2022-04-29', type_candle: str = 'a', normalize: bool = True)
### stocks.screener.historical(preset_loaded: str = 'top_gainers', limit: int = 10, start_date: str = '2022-05-07', type_candle: str = 'a', normalize: bool = True)
View historical price of stocks that meet preset
@@ -28,7 +28,7 @@ View historical price of stocks that meet preset
Whether some random stock selection due to limitations
## Getting charts
### stocks.screener.historical(preset_loaded: str = 'top_gainers', limit: int = 10, start_date: str = '2022-04-29', type_candle: str = 'a', normalize: bool = True, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> List[str]
### stocks.screener.historical(preset_loaded: str = 'top_gainers', limit: int = 10, start_date: str = '2022-05-07', type_candle: str = 'a', normalize: bool = True, export: str = '', external_axes: Optional[List[matplotlib.axes._axes.Axes]] = None, chart=True) -> List[str]
View historical price of stocks that meet preset

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@@ -1,187 +0,0 @@
---
title: Introduction to Funds
keywords: "funds, mutual funds, fund, basket, retail, institutional"
excerpt: "The Introduction to Funds explains how to use the
menu and provides a brief description of its sub-menus"
geekdocCollapseSection: true
---
The Funds menu enables you to lookup any fund based on the selected <a href="https://openbb-finance.github.io/OpenBBTerminal/terminal/funds/country/" target="_blank">country</a> by using
<a href="https://openbb-finance.github.io/OpenBBTerminal/terminal/funds/search/" target="_blank">search</a>. Then, there is the ability to load in the symbol with <a href="https://openbb-finance.github.io/OpenBBTerminal/terminal/funds/load/" target="_blank">load</a>.
This opens up options to look into <a href="https://openbb-finance.github.io/OpenBBTerminal/terminal/funds/info/" target="_blank">fund information</a>, <a href="https://openbb-finance.github.io/OpenBBTerminal/terminal/funds/sector/" target="_blank">sector holdings</a> and <a href="https://openbb-finance.github.io/OpenBBTerminal/terminal/funds/equity/" target="_blank">equity holdings</a>
as well as <a href="https://openbb-finance.github.io/OpenBBTerminal/terminal/funds/plot/" target="_blank">plot</a> the historical data.
## How to use
The Funds menu is called upon by typing `funds` which opens the following menu:
![Funds Menu](https://user-images.githubusercontent.com/46355364/174990788-3b432068-f303-4548-b9b5-0cb158e62628.png)
You have the ability to look up any fund (<a href="https://openbb-finance.github.io/OpenBBTerminal/terminal/funds/search/" target="_blank">search</a>). With the example below, the `search` command is used that searches the name and description of the fund. Then the search query is entered which is `total market`. Lastly, the `-l` argument is included which refers to `limit` and is maxed to `10` funds.
````
2022 Jun 22, 04:20 (🦋) /funds/ $ search total market -l 10
Mutual Funds with name matching total market
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━┓
┃ name ┃ symbol ┃ issuer ┃ isin ┃ asset_class ┃ currency ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━┩
│ State Street U.s. Total Market Index Non-lending Series Fund Class A │ 0P0000RT44 │ State Street Global Advisors │ US85744U4022 │ equity │ USD │
├───────────────────────────────────────────────────────────────────────┼────────────┼──────────────────────────────┼──────────────┼─────────────┼──────────┤
│ Nh Unique College Investing Plan Total Market Index Portfolio │ 0P0000O6ZV │ Fidelity Investments │ US37N9946965 │ equity │ USD │
├───────────────────────────────────────────────────────────────────────┼────────────┼──────────────────────────────┼──────────────┼─────────────┼──────────┤
│ Ma U.fund College Investing Plan Spartan Total Market Index Portfolio │ 0P0000O6ML │ Fidelity Investments │ US10W99R2702 │ equity │ USD │
├───────────────────────────────────────────────────────────────────────┼────────────┼──────────────────────────────┼──────────────┼─────────────┼──────────┤
│ Fidelity Zero Total Market Index Fund │ FZROX │ Fidelity Investments │ US31635T7081 │ equity │ USD │
├───────────────────────────────────────────────────────────────────────┼────────────┼──────────────────────────────┼──────────────┼─────────────┼──────────┤
│ Fidelity Total Market Index Fund Class F │ FFSMX │ Fidelity Investments │ US3159117767 │ equity │ USD │
├───────────────────────────────────────────────────────────────────────┼────────────┼──────────────────────────────┼──────────────┼─────────────┼──────────┤
│ Fidelity Total Market Index Fund │ FSKAX │ Fidelity Investments │ US3159116934 │ equity │ USD │
├───────────────────────────────────────────────────────────────────────┼────────────┼──────────────────────────────┼──────────────┼─────────────┼──────────┤
│ De College Investment Plan Spartan Total Market Index Portfolio │ 0P0000O6T1 │ Fidelity Investments │ US37Q99B3259 │ equity │ USD │
├───────────────────────────────────────────────────────────────────────┼────────────┼──────────────────────────────┼──────────────┼─────────────┼──────────┤
│ Blackrock Advantage U.s. Total Market Fund, Inc.investor C Shares │ MCSPX │ BlackRock │ US09252L3078 │ equity │ USD │
├───────────────────────────────────────────────────────────────────────┼────────────┼──────────────────────────────┼──────────────┼─────────────┼──────────┤
│ Blackrock Advantage U.s. Total Market Fund, Inc.investor A Shares │ MDSPX │ BlackRock │ US09252L1098 │ equity │ USD │
├───────────────────────────────────────────────────────────────────────┼────────────┼──────────────────────────────┼──────────────┼─────────────┼──────────┤
│ Blackrock Advantage U.s. Total Market Fund, Inc.institutional Shares │ MASPX │ BlackRock │ US09252L5057 │ equity │ USD │
└───────────────────────────────────────────────────────────────────────┴────────────┴──────────────────────────────┴──────────────┴─────────────┴──────────┘
````
This results in a selection of funds with their name, symbol, issuer, isin, asset class and currency. With this information, you can load in one of the symbols into the menu.
This can be done with <a href="https://openbb-finance.github.io/OpenBBTerminal/terminal/funds/load" target="_blank">load</a>.
See the following example:
````
2022 Jun 22, 04:30 (🦋) /funds/ $ load MDSPX
Name: Blackrock Advantage U.S. Total Market Fund, Inc.Investor A Shares found for MDSPX in country: United States.
````
To then view the stock chart, you can call <a href="https://openbb-finance.github.io/OpenBBTerminal/terminal/funds/plot/" target="_blank">plot</a>
which shows a chart for the defined period (by default set to a year by `load`):
![Plot](https://user-images.githubusercontent.com/46355364/174990848-8f0be3b3-945b-4e4d-801a-5a6a036b62c0.png)
By calling `?` or `help` the Funds menu re-appears. Here you can see that multiple commands have turned blue. Because of loading
a symbol, these commands can now be used in combination with the chosen symbol as seen above.
![Funds Menu with Fund Loaded](https://user-images.githubusercontent.com/46355364/174990913-0a4e69bd-30d6-4a7f-adda-b921cfb21076.png)
For example, the Fund's general statistics can now be depicted with the following:
````
2022 Jun 22, 04:34 (🦋) /funds/ $ info
Blackrock Advantage U.S. Total Market Fund, Inc.Investor A Shares Information
┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Info ┃ Value ┃
┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Fund Name │ blackrock advantage u.s. total market fund, inc.investor a shares │
├────────────────┼───────────────────────────────────────────────────────────────────┤
│ 1-Year Change │ - 25.21% │
├────────────────┼───────────────────────────────────────────────────────────────────┤
│ TTM Yield │ 0.98% │
├────────────────┼───────────────────────────────────────────────────────────────────┤
│ ROE │ 14.91% │
├────────────────┼───────────────────────────────────────────────────────────────────┤
│ Turnover │ 71% │
├────────────────┼───────────────────────────────────────────────────────────────────┤
│ ROA │ 4.94% │
├────────────────┼───────────────────────────────────────────────────────────────────┤
│ Inception Date │ 34628.00 │
├────────────────┼───────────────────────────────────────────────────────────────────┤
│ Total Assets │ 181230000.00 │
├────────────────┼───────────────────────────────────────────────────────────────────┤
│ Expenses │ 0.73% │
├────────────────┼───────────────────────────────────────────────────────────────────┤
│ Market Cap │ 6380000000.00 │
└────────────────┴───────────────────────────────────────────────────────────────────┘
````
## Examples
If we want to learn more about a bond fund, we can do the following, starting from the `funds` menu and using
the `search` command where we specify with `-l` we wish to see `15` funds max.
```
2022 Jun 22, 04:40 (🦋) /funds/ $ search bond fund -l 15
Mutual Funds with name matching bond fund
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━┓
┃ name ┃ symbol ┃ issuer ┃ isin ┃ asset_class ┃ currency ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━┩
│ Wilmington Short-term Bond Fund Institutional Class │ MVSTX │ Wilmington Funds │ US97181C5976 │ bond │ USD │
├────────────────────────────────────────────────────────────┼────────────┼──────────────────┼──────────────┼─────────────┼──────────┤
│ Wilmington Municipal Bond Fund Class Institutional │ WTAIX │ Wilmington Funds │ US97181C4565 │ equity │ USD │
├────────────────────────────────────────────────────────────┼────────────┼──────────────────┼──────────────┼─────────────┼──────────┤
│ Wilmington Intermediate-term Bond Fund Institutional Class │ ARIFX │ Wilmington Funds │ US97181C8459 │ bond │ USD │
├────────────────────────────────────────────────────────────┼────────────┼──────────────────┼──────────────┼─────────────┼──────────┤
│ Wilmington Broad Market Bond Fund Institutional Class │ ARKIX │ Wilmington Funds │ US97181C8111 │ bond │ USD │
├────────────────────────────────────────────────────────────┼────────────┼──────────────────┼──────────────┼─────────────┼──────────┤
│ Western Asset Short-term Bond Fund Class Is │ LWSTX │ Legg Mason │ US52468A7312 │ bond │ USD │
├────────────────────────────────────────────────────────────┼────────────┼──────────────────┼──────────────┼─────────────┼──────────┤
│ Western Asset Intermediate Bond Fund Class Is │ WABSX │ Legg Mason │ US9576636107 │ bond │ USD │
├────────────────────────────────────────────────────────────┼────────────┼──────────────────┼──────────────┼─────────────┼──────────┤
│ Western Asset Intermediate Bond Fund Class I │ WATIX │ Legg Mason │ US9576637014 │ bond │ USD │
├────────────────────────────────────────────────────────────┼────────────┼──────────────────┼──────────────┼─────────────┼──────────┤
│ Western Asset Inflation Indexed Plus Bond Fund Class Is │ WAFSX │ Legg Mason │ US9576635455 │ bond │ USD │
├────────────────────────────────────────────────────────────┼────────────┼──────────────────┼──────────────┼─────────────┼──────────┤
│ Western Asset Global High Yield Bond Fund Class Is │ LWGSX │ Legg Mason │ US52468A7239 │ bond │ USD │
├────────────────────────────────────────────────────────────┼────────────┼──────────────────┼──────────────┼─────────────┼──────────┤
│ Western Asset Global High Yield Bond Fund Class A │ SAHYX │ Legg Mason │ US52469F4651 │ bond │ USD │
├────────────────────────────────────────────────────────────┼────────────┼──────────────────┼──────────────┼─────────────┼──────────┤
│ Western Asset Corporate Bond Fund Class P │ 0P0000JCKF │ Legg Mason │ US52469L6609 │ bond │ USD │
├────────────────────────────────────────────────────────────┼────────────┼──────────────────┼──────────────┼─────────────┼──────────┤
│ Western Asset Corporate Bond Fund Class I │ 0P00002OJN │ Legg Mason │ US52469F2754 │ bond │ USD │
├────────────────────────────────────────────────────────────┼────────────┼──────────────────┼──────────────┼─────────────┼──────────┤
│ Western Asset Corporate Bond Fund Class C1 │ 0P00002Q4W │ Legg Mason │ US52469F2838 │ bond │ USD │
├────────────────────────────────────────────────────────────┼────────────┼──────────────────┼──────────────┼─────────────┼──────────┤
│ Western Asset Corporate Bond Fund Class A │ SIGAX │ Legg Mason │ US52469F3174 │ bond │ USD │
├────────────────────────────────────────────────────────────┼────────────┼──────────────────┼──────────────┼─────────────┼──────────┤
│ Western Asset Core Bond Fund Class R │ 0P0000VORL │ Legg Mason │ US9576634797 │ bond │ USD │
└────────────────────────────────────────────────────────────┴────────────┴──────────────────┴──────────────┴─────────────┴──────────┘
```
Here, we decide to grab a corporate bond fund, Western Asset Corporate Bond Fund Class A (SIGAX) by loading in the data with <a href="https://openbb-finance.github.io/OpenBBTerminal/terminal/funds/load" target="_blank">load</a>.
Also, a larger period is chosen by using `-s` as shown in the documentation:
````
2022 Jun 22, 04:58 (🦋) /funds/ $ load SIGAX -s 2015-01-01
Name: Western Asset Corporate Bond Fund Class A found for SIGAX in country: United States.
````
We can now plot the corresponding funds chart with <a href="https://openbb-finance.github.io/OpenBBTerminal/terminal/funds/plot/" target="_blank">plot</a> which shows
the company's historical data from `2015-01-01` until the current date.
```
2022 Jun 22, 04:58 (🦋) /funds/ $ plot
```
![Plot #2](https://user-images.githubusercontent.com/46355364/174990983-d052370c-825a-480e-adaf-239e75b26675.png)
Now we can go ahead and explore more about the fund by running <a href="https://openbb-finance.github.io/OpenBBTerminal/terminal/funds/info/" target="_blank">info</a>.
This provides some general statistics about the fund.
```
2022 Jun 22, 05:00 (🦋) /funds/ $ info
Western Asset Corporate Bond Fund Class A Information
┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Info ┃ Value ┃
┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Fund Name │ western asset corporate bond fund class a │
├────────────────┼───────────────────────────────────────────┤
│ 1-Year Change │ - 18.06% │
├────────────────┼───────────────────────────────────────────┤
│ TTM Yield │ 2.76% │
├────────────────┼───────────────────────────────────────────┤
│ ROE │ 8.87% │
├────────────────┼───────────────────────────────────────────┤
│ Turnover │ 63% │
├────────────────┼───────────────────────────────────────────┤
│ ROA │ 6.18% │
├────────────────┼───────────────────────────────────────────┤
│ Inception Date │ 33914.00 │
├────────────────┼───────────────────────────────────────────┤
│ Total Assets │ 268590000.00 │
├────────────────┼───────────────────────────────────────────┤
│ Expenses │ 0.87% │
├────────────────┼───────────────────────────────────────────┤
│ Market Cap │ 6070000000.00 │
└────────────────┴───────────────────────────────────────────┘
```

12
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@@ -1,12 +0,0 @@
```
usage: country [-n NAME [NAME ...]] [-h]
```
Set a country for funds
```
optional arguments:
-n NAME [NAME ...], --name NAME [NAME ...]
country to select (default: None)
-h, --help show this help message (default: False)
```

42
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@@ -1,42 +0,0 @@
```
usage: equity [-h]
```
Show fund equity holdings.
```
optional arguments:
-h, --help show this help message (default: False)
```
```
2022 Feb 15, 03:48 (✨) /funds/ $ equity
VFIAX Equity Holdings
┌─────────────────────────┬─────────────┐
│ Equity │ Holding │
├─────────────────────────┼─────────────┤
│ Price To Cash Flow │ 1757.00 │
├─────────────────────────┼─────────────┤
│ Price To Sales │ 296.00 │
├─────────────────────────┼─────────────┤
│ Price To Book Cat │ 498.00 │
├─────────────────────────┼─────────────┤
│ Price To Earnings Cat │ 2632.00 │
├─────────────────────────┼─────────────┤
│ Median Market Cap Cat │ 26587520.00 │
├─────────────────────────┼─────────────┤
│ 3Yr Earnings Growth Cat │ 1953.00 │
├─────────────────────────┼─────────────┤
│ 3Y Earnings Growth │ 1872.00 │
├─────────────────────────┼─────────────┤
│ Median Market Cap │ 19745245.00 │
├─────────────────────────┼─────────────┤
│ Price To Earnings │ 2646.00 │
├─────────────────────────┼─────────────┤
│ Price To Book │ 442.00 │
├─────────────────────────┼─────────────┤
│ Price To Sales Cat │ 279.00 │
├─────────────────────────┼─────────────┤
│ Price To Cashflow Cat │ 1761.00 │
└─────────────────────────┴─────────────┘
```

41
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```
usage: info [--fund FUND] [--name] [-h]
```
Get fund information.
```
optional arguments:
--fund FUND Fund string to search for (default: None)
--name Flag to indicate name provided instead of symbol. (default: False)
-h, --help show this help message (default: False)
```
```
Vanguard 500 Index Fund Admiral Shares Information
┌────────────────┬────────────────────────────────────────┐
│ Info │ Value │
├────────────────┼────────────────────────────────────────┤
│ Fund Name │ vanguard 500 index fund admiral shares │
├────────────────┼────────────────────────────────────────┤
│ 1-Year Change │ 12.27% │
├────────────────┼────────────────────────────────────────┤
│ TTM Yield │ 1.30% │
├────────────────┼────────────────────────────────────────┤
│ ROE │ 32.76% │
├────────────────┼────────────────────────────────────────┤
│ Issuer │ Vanguard │
├────────────────┼────────────────────────────────────────┤
│ Turnover │ 1% │
├────────────────┼────────────────────────────────────────┤
│ ROA │ 12.24% │
├────────────────┼────────────────────────────────────────┤
│ Inception Date │ 13/11/2000 │
├────────────────┼────────────────────────────────────────┤
│ Total Assets │ 429580000000.00 │
├────────────────┼────────────────────────────────────────┤
│ Expenses │ 0.04% │
├────────────────┼────────────────────────────────────────┤
│ Market Cap │ 230900000000.00 │
└────────────────┴────────────────────────────────────────┘
```

16
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@@ -1,16 +0,0 @@
```txt
usage: load [--fund FUND [FUND ...]] [--name] [-s START] [-e END] [-h]
```
Get historical data.
```txt
optional arguments:
--fund FUND [FUND ...]
Fund string to search for (default: None)
--name Flag to indicate name provided instead of symbol. (default: False)
-s START, --start START
The starting date (format YYYY-MM-DD) of the fund (default: 2020-12-27)
-e END, --end END The ending date (format YYYY-MM-DD) of the fund (default: 2021-12-28)
-h, --help show this help message (default: False)
```

44
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@@ -1,44 +0,0 @@
```
usage: overview [-l LIMIT] [-h] [--export {csv,json,xlsx}]
```
Show overview of funds from selected country.
```
optional arguments:
-l LIMIT, --limit LIMIT
Number of search results to show (default: 20)
-h, --help show this help message (default: False)
--export {csv,json,xlsx}
Export raw data into csv, json, xlsx (default: )
```
Sample usage:
```
2022 Feb 15, 03:50 (✨) /funds/ $ overview
Fund overview for United States
┌──────────────────────────────────────────────────────────────────┬───────────────────────────────────────────────────┬────────┬────────┬────────┬──────────┬─────────────┐
│ name │ full_name │ symbol │ last │ change │ currency │ Assets (1B) │
├──────────────────────────────────────────────────────────────────┼───────────────────────────────────────────────────┼────────┼────────┼────────┼──────────┼─────────────┤
│ Vanguard 500 Index Fund Admiral Shares │ Vanguard 500 Index Admiral │ VFIAX │ 406.79 │ -0.37% │ USD │ 429.58 │
├──────────────────────────────────────────────────────────────────┼───────────────────────────────────────────────────┼────────┼────────┼────────┼──────────┼─────────────┤
│ Fidelity® 500 Index Fund │ Fidelity 500 Index Institutional Prem │ FXAIX │ 152.90 │ -0.38% │ USD │ 387.09 │
├──────────────────────────────────────────────────────────────────┼───────────────────────────────────────────────────┼────────┼────────┼────────┼──────────┼─────────────┤
│ Vanguard Total Stock Market Index Fund Admiral Shares │ Vanguard Total Stock Market Index Admiral │ VTSAX │ 108.10 │ -0.40% │ USD │ 321.58 │
├──────────────────────────────────────────────────────────────────┼───────────────────────────────────────────────────┼────────┼────────┼────────┼──────────┼─────────────┤
│ Vanguard Total Stock Market Index Fund Institutional Plus Shares │ Vanguard Total Stock Market Index Instl Plus │ VSMPX │ 202.76 │ -0.40% │ USD │ 289.35 │
├──────────────────────────────────────────────────────────────────┼───────────────────────────────────────────────────┼────────┼────────┼────────┼──────────┼─────────────┤
│ Fidelity® Government Money Market Fund │ Fidelity Government Money Market Fund │ SPAXX │ 1.00 │ 0% │ USD │ 230.36 │
├──────────────────────────────────────────────────────────────────┼───────────────────────────────────────────────────┼────────┼────────┼────────┼──────────┼─────────────┤
│ Vanguard Total Stock Market Index Fund Institutional Shares │ Vanguard Total Stock Market Index I │ VITSX │ 108.12 │ -0.40% │ USD │ 230.26 │
├──────────────────────────────────────────────────────────────────┼───────────────────────────────────────────────────┼────────┼────────┼────────┼──────────┼─────────────┤
│ Fidelity® Government Cash Reserves │ Fidelityֲ® Government Cash Reserves │ FDRXX │ 1.00 │ 0% │ USD │ 226.13 │
├──────────────────────────────────────────────────────────────────┼───────────────────────────────────────────────────┼────────┼────────┼────────┼──────────┼─────────────┤
│ Vanguard Total International Stock Index Fund Investor Shares │ Vanguard Total International Stock Index Inv │ VGTSX │ 19.75 │ -0.70% │ USD │ 188.28 │
├──────────────────────────────────────────────────────────────────┼───────────────────────────────────────────────────┼────────┼────────┼────────┼──────────┼─────────────┤
│ Vanguard Institutional Index Fund Institutional Plus Shares │ Vanguard Institutional Index Instl Pl │ VIIIX │ 375.32 │ -0.38% │ USD │ 167.72 │
├──────────────────────────────────────────────────────────────────┼───────────────────────────────────────────────────┼────────┼────────┼────────┼──────────┼─────────────┤
│ Vanguard Total Bond Market Ii Index Fund Investor Shares │ Vanguard Total Bond Market II Index Fund Investor │ VTBIX │ 10.59 │ -0.56% │ USD │ 134.58 │
└──────────────────────────────────────────────────────────────────┴───────────────────────────────────────────────────┴────────┴────────┴────────┴──────────┴─────────────┘
```

14
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@@ -1,14 +0,0 @@
```
usage: plot [-h] [--export {png,jpg,pdf,svg}]
```
Plot historical data.
```
optional arguments:
-h, --help show this help message (default: False)
--export {png,jpg,pdf,svg}
Export figure into png, jpg, pdf, svg (default: )
```
![plot](https://user-images.githubusercontent.com/46355364/154026130-08485b89-9ffd-41cb-b221-1f6bf9c74455.png)

18
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@@ -1,18 +0,0 @@
```txt
usage: search [-b {name,issuer,isin,symbol}] [--fund FUND] [-s {country,name,symbol,issuer,isin,asset_class,currency,underlying}] [-l LIMIT] [-a] [-h]
```
Search mutual funds in selected country based on selected field.
```txt
optional arguments:
-b {name,issuer,isin,symbol}, --by {name,issuer,isin,symbol}
Field to search by (default: name)
--fund FUND Fund string to search for (default: None)
-s {country,name,symbol,issuer,isin,asset_class,currency,underlying}, --sortby {country,name,symbol,issuer,isin,asset_class,currency,underlying}
Column to sort by (default: name)
-l LIMIT, --limit LIMIT
Number of search results to show (default: 10)
-a, --ascend Sort in ascending order (default: False)
-h, --help show this help message (default: False)
```

43
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@@ -1,43 +0,0 @@
```
usage: sector [-h]
```
Show fund sector weighting.
```
optional arguments:
-h, --help show this help message (default: False)
```
![sector](https://user-images.githubusercontent.com/46355364/154026261-1e0ec083-d311-4388-b8a5-25fd73b494e7.png)
```
2022 Feb 15, 03:48 (✨) /funds/ $ sector
VFIAX Sector Weightings
┌────────────────────────┬────────────┐
│ Sector │ Weight (%) │
├────────────────────────┼────────────┤
│ Technology │ 24.24 │
├────────────────────────┼────────────┤
│ Financial Services │ 14.20 │
├────────────────────────┼────────────┤
│ Healthcare │ 13.10 │
├────────────────────────┼────────────┤
│ Consumer Cyclical │ 12.01 │
├────────────────────────┼────────────┤
│ Communication Services │ 11.14 │
├────────────────────────┼────────────┤
│ Industrials │ 8.86 │
├────────────────────────┼────────────┤
│ Consumer Defensive │ 6.32 │
├────────────────────────┼────────────┤
│ Energy │ 2.84 │
├────────────────────────┼────────────┤
│ Real Estate │ 2.58 │
├────────────────────────┼────────────┤
│ Utilities │ 2.43 │
├────────────────────────┼────────────┤
│ Basic Materials │ 2.27 │
└────────────────────────┴────────────┘
```

34
website/data/menu/main.yml vendored
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@@ -835,23 +835,6 @@ main:
ref: /terminal/forex/oanda/positions
- name: quote
ref: /terminal/forex/quote
- name: funds
ref: /terminal/funds
sub:
- name: country
ref: /terminal/funds/country
- name: equity
ref: /terminal/funds/equity
- name: info
ref: /terminal/funds/info
- name: load
ref: /terminal/funds/load
- name: plot
ref: /terminal/funds/plot
- name: search
ref: /terminal/funds/search
- name: sector
ref: /terminal/funds/sector
- name: futures
ref: /terminal/futures
sub:
@@ -1888,6 +1871,8 @@ main:
ref: /SDK/crypto/dd/news
- name: nonzero
ref: /SDK/crypto/dd/nonzero
- name: ob
ref: /SDK/crypto/dd/ob
- name: ohlc_historical
ref: /SDK/crypto/dd/ohlc_historical
- name: oi
@@ -2003,6 +1988,10 @@ main:
- name: nft
ref: /SDK/crypto/nft
sub:
- name: collections
ref: /SDK/crypto/nft/collections
- name: fp
ref: /SDK/crypto/nft/fp
- name: stats
ref: /SDK/crypto/nft/stats
- name: onchain
@@ -2119,6 +2108,8 @@ main:
ref: /SDK/crypto/ov/wf
- name: wfpe
ref: /SDK/crypto/ov/wfpe
- name: price
ref: /SDK/crypto/price
- name: tools
ref: /SDK/crypto/tools
sub:
@@ -2286,6 +2277,8 @@ main:
sub:
- name: atr
ref: /SDK/forecast/atr
- name: autoets
ref: /SDK/forecast/autoets
- name: brnn
ref: /SDK/forecast/brnn
- name: clean
@@ -2388,13 +2381,6 @@ main:
ref: /SDK/forex/oanda/summary
- name: quote
ref: /SDK/forex/quote
- name: funds
ref: /SDK/funds
sub:
- name: info
ref: /SDK/funds/info
- name: search
ref: /SDK/funds/search
- name: futures
ref: /SDK/futures
sub: