partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
valid | request_response | Takes a function or coroutine `func(request) -> response`,
and returns an ASGI application. | starlette/routing.py | def request_response(func: typing.Callable) -> ASGIApp:
"""
Takes a function or coroutine `func(request) -> response`,
and returns an ASGI application.
"""
is_coroutine = asyncio.iscoroutinefunction(func)
async def app(scope: Scope, receive: Receive, send: Send) -> None:
request = Reque... | def request_response(func: typing.Callable) -> ASGIApp:
"""
Takes a function or coroutine `func(request) -> response`,
and returns an ASGI application.
"""
is_coroutine = asyncio.iscoroutinefunction(func)
async def app(scope: Scope, receive: Receive, send: Send) -> None:
request = Reque... | [
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valid | websocket_session | Takes a coroutine `func(session)`, and returns an ASGI application. | starlette/routing.py | def websocket_session(func: typing.Callable) -> ASGIApp:
"""
Takes a coroutine `func(session)`, and returns an ASGI application.
"""
# assert asyncio.iscoroutinefunction(func), "WebSocket endpoints must be async"
async def app(scope: Scope, receive: Receive, send: Send) -> None:
session = W... | def websocket_session(func: typing.Callable) -> ASGIApp:
"""
Takes a coroutine `func(session)`, and returns an ASGI application.
"""
# assert asyncio.iscoroutinefunction(func), "WebSocket endpoints must be async"
async def app(scope: Scope, receive: Receive, send: Send) -> None:
session = W... | [
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valid | compile_path | Given a path string, like: "/{username:str}", return a three-tuple
of (regex, format, {param_name:convertor}).
regex: "/(?P<username>[^/]+)"
format: "/{username}"
convertors: {"username": StringConvertor()} | starlette/routing.py | def compile_path(
path: str
) -> typing.Tuple[typing.Pattern, str, typing.Dict[str, Convertor]]:
"""
Given a path string, like: "/{username:str}", return a three-tuple
of (regex, format, {param_name:convertor}).
regex: "/(?P<username>[^/]+)"
format: "/{username}"
convertors: {"user... | def compile_path(
path: str
) -> typing.Tuple[typing.Pattern, str, typing.Dict[str, Convertor]]:
"""
Given a path string, like: "/{username:str}", return a three-tuple
of (regex, format, {param_name:convertor}).
regex: "/(?P<username>[^/]+)"
format: "/{username}"
convertors: {"user... | [
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valid | BaseSchemaGenerator.get_endpoints | Given the routes, yields the following information:
- path
eg: /users/
- http_method
one of 'get', 'post', 'put', 'patch', 'delete', 'options'
- func
method ready to extract the docstring | starlette/schemas.py | def get_endpoints(
self, routes: typing.List[BaseRoute]
) -> typing.List[EndpointInfo]:
"""
Given the routes, yields the following information:
- path
eg: /users/
- http_method
one of 'get', 'post', 'put', 'patch', 'delete', 'options'
- func
... | def get_endpoints(
self, routes: typing.List[BaseRoute]
) -> typing.List[EndpointInfo]:
"""
Given the routes, yields the following information:
- path
eg: /users/
- http_method
one of 'get', 'post', 'put', 'patch', 'delete', 'options'
- func
... | [
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valid | BaseSchemaGenerator.parse_docstring | Given a function, parse the docstring as YAML and return a dictionary of info. | starlette/schemas.py | def parse_docstring(self, func_or_method: typing.Callable) -> dict:
"""
Given a function, parse the docstring as YAML and return a dictionary of info.
"""
docstring = func_or_method.__doc__
if not docstring:
return {}
# We support having regular docstrings be... | def parse_docstring(self, func_or_method: typing.Callable) -> dict:
"""
Given a function, parse the docstring as YAML and return a dictionary of info.
"""
docstring = func_or_method.__doc__
if not docstring:
return {}
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valid | StaticFiles.get_directories | Given `directory` and `packages` arugments, return a list of all the
directories that should be used for serving static files from. | starlette/staticfiles.py | def get_directories(
self, directory: str = None, packages: typing.List[str] = None
) -> typing.List[str]:
"""
Given `directory` and `packages` arugments, return a list of all the
directories that should be used for serving static files from.
"""
directories = []
... | def get_directories(
self, directory: str = None, packages: typing.List[str] = None
) -> typing.List[str]:
"""
Given `directory` and `packages` arugments, return a list of all the
directories that should be used for serving static files from.
"""
directories = []
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] | encode/starlette | python | https://github.com/encode/starlette/blob/d23bfd0d8ff68d535d0283aa4099e5055da88bb9/starlette/staticfiles.py#L57-L80 | [
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valid | StaticFiles.get_path | Given the ASGI scope, return the `path` string to serve up,
with OS specific path seperators, and any '..', '.' components removed. | starlette/staticfiles.py | def get_path(self, scope: Scope) -> str:
"""
Given the ASGI scope, return the `path` string to serve up,
with OS specific path seperators, and any '..', '.' components removed.
"""
return os.path.normpath(os.path.join(*scope["path"].split("/"))) | def get_path(self, scope: Scope) -> str:
"""
Given the ASGI scope, return the `path` string to serve up,
with OS specific path seperators, and any '..', '.' components removed.
"""
return os.path.normpath(os.path.join(*scope["path"].split("/"))) | [
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valid | StaticFiles.get_response | Returns an HTTP response, given the incoming path, method and request headers. | starlette/staticfiles.py | async def get_response(self, path: str, scope: Scope) -> Response:
"""
Returns an HTTP response, given the incoming path, method and request headers.
"""
if scope["method"] not in ("GET", "HEAD"):
return PlainTextResponse("Method Not Allowed", status_code=405)
if pat... | async def get_response(self, path: str, scope: Scope) -> Response:
"""
Returns an HTTP response, given the incoming path, method and request headers.
"""
if scope["method"] not in ("GET", "HEAD"):
return PlainTextResponse("Method Not Allowed", status_code=405)
if pat... | [
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valid | StaticFiles.check_config | Perform a one-off configuration check that StaticFiles is actually
pointed at a directory, so that we can raise loud errors rather than
just returning 404 responses. | starlette/staticfiles.py | async def check_config(self) -> None:
"""
Perform a one-off configuration check that StaticFiles is actually
pointed at a directory, so that we can raise loud errors rather than
just returning 404 responses.
"""
if self.directory is None:
return
try:
... | async def check_config(self) -> None:
"""
Perform a one-off configuration check that StaticFiles is actually
pointed at a directory, so that we can raise loud errors rather than
just returning 404 responses.
"""
if self.directory is None:
return
try:
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valid | StaticFiles.is_not_modified | Given the request and response headers, return `True` if an HTTP
"Not Modified" response could be returned instead. | starlette/staticfiles.py | def is_not_modified(
self, response_headers: Headers, request_headers: Headers
) -> bool:
"""
Given the request and response headers, return `True` if an HTTP
"Not Modified" response could be returned instead.
"""
try:
if_none_match = request_headers["if-n... | def is_not_modified(
self, response_headers: Headers, request_headers: Headers
) -> bool:
"""
Given the request and response headers, return `True` if an HTTP
"Not Modified" response could be returned instead.
"""
try:
if_none_match = request_headers["if-n... | [
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valid | build_environ | Builds a scope and request body into a WSGI environ object. | starlette/middleware/wsgi.py | def build_environ(scope: Scope, body: bytes) -> dict:
"""
Builds a scope and request body into a WSGI environ object.
"""
environ = {
"REQUEST_METHOD": scope["method"],
"SCRIPT_NAME": scope.get("root_path", ""),
"PATH_INFO": scope["path"],
"QUERY_STRING": scope["query_str... | def build_environ(scope: Scope, body: bytes) -> dict:
"""
Builds a scope and request body into a WSGI environ object.
"""
environ = {
"REQUEST_METHOD": scope["method"],
"SCRIPT_NAME": scope.get("root_path", ""),
"PATH_INFO": scope["path"],
"QUERY_STRING": scope["query_str... | [
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valid | WebSocket.receive | Receive ASGI websocket messages, ensuring valid state transitions. | starlette/websockets.py | async def receive(self) -> Message:
"""
Receive ASGI websocket messages, ensuring valid state transitions.
"""
if self.client_state == WebSocketState.CONNECTING:
message = await self._receive()
message_type = message["type"]
assert message_type == "web... | async def receive(self) -> Message:
"""
Receive ASGI websocket messages, ensuring valid state transitions.
"""
if self.client_state == WebSocketState.CONNECTING:
message = await self._receive()
message_type = message["type"]
assert message_type == "web... | [
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valid | WebSocket.send | Send ASGI websocket messages, ensuring valid state transitions. | starlette/websockets.py | async def send(self, message: Message) -> None:
"""
Send ASGI websocket messages, ensuring valid state transitions.
"""
if self.application_state == WebSocketState.CONNECTING:
message_type = message["type"]
assert message_type in {"websocket.accept", "websocket.cl... | async def send(self, message: Message) -> None:
"""
Send ASGI websocket messages, ensuring valid state transitions.
"""
if self.application_state == WebSocketState.CONNECTING:
message_type = message["type"]
assert message_type in {"websocket.accept", "websocket.cl... | [
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valid | get_top_long_short_abs | Finds the top long, short, and absolute positions.
Parameters
----------
positions : pd.DataFrame
The positions that the strategy takes over time.
top : int, optional
How many of each to find (default 10).
Returns
-------
df_top_long : pd.DataFrame
Top long position... | pyfolio/pos.py | def get_top_long_short_abs(positions, top=10):
"""
Finds the top long, short, and absolute positions.
Parameters
----------
positions : pd.DataFrame
The positions that the strategy takes over time.
top : int, optional
How many of each to find (default 10).
Returns
-----... | def get_top_long_short_abs(positions, top=10):
"""
Finds the top long, short, and absolute positions.
Parameters
----------
positions : pd.DataFrame
The positions that the strategy takes over time.
top : int, optional
How many of each to find (default 10).
Returns
-----... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/pos.py#L53-L81 | [
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valid | get_max_median_position_concentration | Finds the max and median long and short position concentrations
in each time period specified by the index of positions.
Parameters
----------
positions : pd.DataFrame
The positions that the strategy takes over time.
Returns
-------
pd.DataFrame
Columns are max long, max sh... | pyfolio/pos.py | def get_max_median_position_concentration(positions):
"""
Finds the max and median long and short position concentrations
in each time period specified by the index of positions.
Parameters
----------
positions : pd.DataFrame
The positions that the strategy takes over time.
Returns... | def get_max_median_position_concentration(positions):
"""
Finds the max and median long and short position concentrations
in each time period specified by the index of positions.
Parameters
----------
positions : pd.DataFrame
The positions that the strategy takes over time.
Returns... | [
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valid | extract_pos | Extract position values from backtest object as returned by
get_backtest() on the Quantopian research platform.
Parameters
----------
positions : pd.DataFrame
timeseries containing one row per symbol (and potentially
duplicate datetime indices) and columns for amount and
last_sa... | pyfolio/pos.py | def extract_pos(positions, cash):
"""
Extract position values from backtest object as returned by
get_backtest() on the Quantopian research platform.
Parameters
----------
positions : pd.DataFrame
timeseries containing one row per symbol (and potentially
duplicate datetime indic... | def extract_pos(positions, cash):
"""
Extract position values from backtest object as returned by
get_backtest() on the Quantopian research platform.
Parameters
----------
positions : pd.DataFrame
timeseries containing one row per symbol (and potentially
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valid | get_sector_exposures | Sum position exposures by sector.
Parameters
----------
positions : pd.DataFrame
Contains position values or amounts.
- Example
index 'AAPL' 'MSFT' 'CHK' cash
2004-01-09 13939.380 -15012.993 -403.870 1477.483
2... | pyfolio/pos.py | def get_sector_exposures(positions, symbol_sector_map):
"""
Sum position exposures by sector.
Parameters
----------
positions : pd.DataFrame
Contains position values or amounts.
- Example
index 'AAPL' 'MSFT' 'CHK' cash
2004-01-09... | def get_sector_exposures(positions, symbol_sector_map):
"""
Sum position exposures by sector.
Parameters
----------
positions : pd.DataFrame
Contains position values or amounts.
- Example
index 'AAPL' 'MSFT' 'CHK' cash
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valid | get_long_short_pos | Determines the long and short allocations in a portfolio.
Parameters
----------
positions : pd.DataFrame
The positions that the strategy takes over time.
Returns
-------
df_long_short : pd.DataFrame
Long and short allocations as a decimal
percentage of the total net liq... | pyfolio/pos.py | def get_long_short_pos(positions):
"""
Determines the long and short allocations in a portfolio.
Parameters
----------
positions : pd.DataFrame
The positions that the strategy takes over time.
Returns
-------
df_long_short : pd.DataFrame
Long and short allocations as a ... | def get_long_short_pos(positions):
"""
Determines the long and short allocations in a portfolio.
Parameters
----------
positions : pd.DataFrame
The positions that the strategy takes over time.
Returns
-------
df_long_short : pd.DataFrame
Long and short allocations as a ... | [
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valid | compute_style_factor_exposures | Returns style factor exposure of an algorithm's positions
Parameters
----------
positions : pd.DataFrame
Daily equity positions of algorithm, in dollars.
- See full explanation in create_risk_tear_sheet
risk_factor : pd.DataFrame
Daily risk factor per asset.
- DataFrame... | pyfolio/risk.py | def compute_style_factor_exposures(positions, risk_factor):
"""
Returns style factor exposure of an algorithm's positions
Parameters
----------
positions : pd.DataFrame
Daily equity positions of algorithm, in dollars.
- See full explanation in create_risk_tear_sheet
risk_factor... | def compute_style_factor_exposures(positions, risk_factor):
"""
Returns style factor exposure of an algorithm's positions
Parameters
----------
positions : pd.DataFrame
Daily equity positions of algorithm, in dollars.
- See full explanation in create_risk_tear_sheet
risk_factor... | [
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valid | plot_style_factor_exposures | Plots DataFrame output of compute_style_factor_exposures as a line graph
Parameters
----------
tot_style_factor_exposure : pd.Series
Daily style factor exposures (output of compute_style_factor_exposures)
- Time series with decimal style factor exposures
- Example:
2017-... | pyfolio/risk.py | def plot_style_factor_exposures(tot_style_factor_exposure, factor_name=None,
ax=None):
"""
Plots DataFrame output of compute_style_factor_exposures as a line graph
Parameters
----------
tot_style_factor_exposure : pd.Series
Daily style factor exposures (outpu... | def plot_style_factor_exposures(tot_style_factor_exposure, factor_name=None,
ax=None):
"""
Plots DataFrame output of compute_style_factor_exposures as a line graph
Parameters
----------
tot_style_factor_exposure : pd.Series
Daily style factor exposures (outpu... | [
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valid | compute_sector_exposures | Returns arrays of long, short and gross sector exposures of an algorithm's
positions
Parameters
----------
positions : pd.DataFrame
Daily equity positions of algorithm, in dollars.
- See full explanation in compute_style_factor_exposures.
sectors : pd.DataFrame
Daily Mornin... | pyfolio/risk.py | def compute_sector_exposures(positions, sectors, sector_dict=SECTORS):
"""
Returns arrays of long, short and gross sector exposures of an algorithm's
positions
Parameters
----------
positions : pd.DataFrame
Daily equity positions of algorithm, in dollars.
- See full explanation ... | def compute_sector_exposures(positions, sectors, sector_dict=SECTORS):
"""
Returns arrays of long, short and gross sector exposures of an algorithm's
positions
Parameters
----------
positions : pd.DataFrame
Daily equity positions of algorithm, in dollars.
- See full explanation ... | [
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valid | plot_sector_exposures_longshort | Plots outputs of compute_sector_exposures as area charts
Parameters
----------
long_exposures, short_exposures : arrays
Arrays of long and short sector exposures (output of
compute_sector_exposures).
sector_dict : dict or OrderedDict
Dictionary of all sectors
- See full... | pyfolio/risk.py | def plot_sector_exposures_longshort(long_exposures, short_exposures,
sector_dict=SECTORS, ax=None):
"""
Plots outputs of compute_sector_exposures as area charts
Parameters
----------
long_exposures, short_exposures : arrays
Arrays of long and short sector... | def plot_sector_exposures_longshort(long_exposures, short_exposures,
sector_dict=SECTORS, ax=None):
"""
Plots outputs of compute_sector_exposures as area charts
Parameters
----------
long_exposures, short_exposures : arrays
Arrays of long and short sector... | [
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valid | plot_sector_exposures_gross | Plots output of compute_sector_exposures as area charts
Parameters
----------
gross_exposures : arrays
Arrays of gross sector exposures (output of compute_sector_exposures).
sector_dict : dict or OrderedDict
Dictionary of all sectors
- See full description in compute_sector_exp... | pyfolio/risk.py | def plot_sector_exposures_gross(gross_exposures, sector_dict=None, ax=None):
"""
Plots output of compute_sector_exposures as area charts
Parameters
----------
gross_exposures : arrays
Arrays of gross sector exposures (output of compute_sector_exposures).
sector_dict : dict or OrderedDi... | def plot_sector_exposures_gross(gross_exposures, sector_dict=None, ax=None):
"""
Plots output of compute_sector_exposures as area charts
Parameters
----------
gross_exposures : arrays
Arrays of gross sector exposures (output of compute_sector_exposures).
sector_dict : dict or OrderedDi... | [
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valid | plot_sector_exposures_net | Plots output of compute_sector_exposures as line graphs
Parameters
----------
net_exposures : arrays
Arrays of net sector exposures (output of compute_sector_exposures).
sector_dict : dict or OrderedDict
Dictionary of all sectors
- See full description in compute_sector_exposur... | pyfolio/risk.py | def plot_sector_exposures_net(net_exposures, sector_dict=None, ax=None):
"""
Plots output of compute_sector_exposures as line graphs
Parameters
----------
net_exposures : arrays
Arrays of net sector exposures (output of compute_sector_exposures).
sector_dict : dict or OrderedDict
... | def plot_sector_exposures_net(net_exposures, sector_dict=None, ax=None):
"""
Plots output of compute_sector_exposures as line graphs
Parameters
----------
net_exposures : arrays
Arrays of net sector exposures (output of compute_sector_exposures).
sector_dict : dict or OrderedDict
... | [
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valid | compute_cap_exposures | Returns arrays of long, short and gross market cap exposures of an
algorithm's positions
Parameters
----------
positions : pd.DataFrame
Daily equity positions of algorithm, in dollars.
- See full explanation in compute_style_factor_exposures.
caps : pd.DataFrame
Daily Morni... | pyfolio/risk.py | def compute_cap_exposures(positions, caps):
"""
Returns arrays of long, short and gross market cap exposures of an
algorithm's positions
Parameters
----------
positions : pd.DataFrame
Daily equity positions of algorithm, in dollars.
- See full explanation in compute_style_factor... | def compute_cap_exposures(positions, caps):
"""
Returns arrays of long, short and gross market cap exposures of an
algorithm's positions
Parameters
----------
positions : pd.DataFrame
Daily equity positions of algorithm, in dollars.
- See full explanation in compute_style_factor... | [
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valid | plot_cap_exposures_longshort | Plots outputs of compute_cap_exposures as area charts
Parameters
----------
long_exposures, short_exposures : arrays
Arrays of long and short market cap exposures (output of
compute_cap_exposures). | pyfolio/risk.py | def plot_cap_exposures_longshort(long_exposures, short_exposures, ax=None):
"""
Plots outputs of compute_cap_exposures as area charts
Parameters
----------
long_exposures, short_exposures : arrays
Arrays of long and short market cap exposures (output of
compute_cap_exposures).
"... | def plot_cap_exposures_longshort(long_exposures, short_exposures, ax=None):
"""
Plots outputs of compute_cap_exposures as area charts
Parameters
----------
long_exposures, short_exposures : arrays
Arrays of long and short market cap exposures (output of
compute_cap_exposures).
"... | [
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valid | plot_cap_exposures_gross | Plots outputs of compute_cap_exposures as area charts
Parameters
----------
gross_exposures : array
Arrays of gross market cap exposures (output of compute_cap_exposures). | pyfolio/risk.py | def plot_cap_exposures_gross(gross_exposures, ax=None):
"""
Plots outputs of compute_cap_exposures as area charts
Parameters
----------
gross_exposures : array
Arrays of gross market cap exposures (output of compute_cap_exposures).
"""
if ax is None:
ax = plt.gca()
col... | def plot_cap_exposures_gross(gross_exposures, ax=None):
"""
Plots outputs of compute_cap_exposures as area charts
Parameters
----------
gross_exposures : array
Arrays of gross market cap exposures (output of compute_cap_exposures).
"""
if ax is None:
ax = plt.gca()
col... | [
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valid | plot_cap_exposures_net | Plots outputs of compute_cap_exposures as line graphs
Parameters
----------
net_exposures : array
Arrays of gross market cap exposures (output of compute_cap_exposures). | pyfolio/risk.py | def plot_cap_exposures_net(net_exposures, ax=None):
"""
Plots outputs of compute_cap_exposures as line graphs
Parameters
----------
net_exposures : array
Arrays of gross market cap exposures (output of compute_cap_exposures).
"""
if ax is None:
ax = plt.gca()
color_lis... | def plot_cap_exposures_net(net_exposures, ax=None):
"""
Plots outputs of compute_cap_exposures as line graphs
Parameters
----------
net_exposures : array
Arrays of gross market cap exposures (output of compute_cap_exposures).
"""
if ax is None:
ax = plt.gca()
color_lis... | [
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valid | compute_volume_exposures | Returns arrays of pth percentile of long, short and gross volume exposures
of an algorithm's held shares
Parameters
----------
shares_held : pd.DataFrame
Daily number of shares held by an algorithm.
- See full explanation in create_risk_tear_sheet
volume : pd.DataFrame
Dail... | pyfolio/risk.py | def compute_volume_exposures(shares_held, volumes, percentile):
"""
Returns arrays of pth percentile of long, short and gross volume exposures
of an algorithm's held shares
Parameters
----------
shares_held : pd.DataFrame
Daily number of shares held by an algorithm.
- See full e... | def compute_volume_exposures(shares_held, volumes, percentile):
"""
Returns arrays of pth percentile of long, short and gross volume exposures
of an algorithm's held shares
Parameters
----------
shares_held : pd.DataFrame
Daily number of shares held by an algorithm.
- See full e... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/risk.py#L408-L459 | [
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valid | plot_volume_exposures_longshort | Plots outputs of compute_volume_exposures as line graphs
Parameters
----------
longed_threshold, shorted_threshold : pd.Series
Series of longed and shorted volume exposures (output of
compute_volume_exposures).
percentile : float
Percentile to use when computing and plotting vo... | pyfolio/risk.py | def plot_volume_exposures_longshort(longed_threshold, shorted_threshold,
percentile, ax=None):
"""
Plots outputs of compute_volume_exposures as line graphs
Parameters
----------
longed_threshold, shorted_threshold : pd.Series
Series of longed and shorted ... | def plot_volume_exposures_longshort(longed_threshold, shorted_threshold,
percentile, ax=None):
"""
Plots outputs of compute_volume_exposures as line graphs
Parameters
----------
longed_threshold, shorted_threshold : pd.Series
Series of longed and shorted ... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/risk.py#L462-L491 | [
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valid | plot_volume_exposures_gross | Plots outputs of compute_volume_exposures as line graphs
Parameters
----------
grossed_threshold : pd.Series
Series of grossed volume exposures (output of
compute_volume_exposures).
percentile : float
Percentile to use when computing and plotting volume exposures
- See ... | pyfolio/risk.py | def plot_volume_exposures_gross(grossed_threshold, percentile, ax=None):
"""
Plots outputs of compute_volume_exposures as line graphs
Parameters
----------
grossed_threshold : pd.Series
Series of grossed volume exposures (output of
compute_volume_exposures).
percentile : float
... | def plot_volume_exposures_gross(grossed_threshold, percentile, ax=None):
"""
Plots outputs of compute_volume_exposures as line graphs
Parameters
----------
grossed_threshold : pd.Series
Series of grossed volume exposures (output of
compute_volume_exposures).
percentile : float
... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/risk.py#L494-L520 | [
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valid | create_full_tear_sheet | Generate a number of tear sheets that are useful
for analyzing a strategy's performance.
- Fetches benchmarks if needed.
- Creates tear sheets for returns, and significant events.
If possible, also creates tear sheets for position analysis,
transaction analysis, and Bayesian analysis.
... | pyfolio/tears.py | def create_full_tear_sheet(returns,
positions=None,
transactions=None,
market_data=None,
benchmark_rets=None,
slippage=None,
live_start_date=None,
... | def create_full_tear_sheet(returns,
positions=None,
transactions=None,
market_data=None,
benchmark_rets=None,
slippage=None,
live_start_date=None,
... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/tears.py#L67-L258 | [
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valid | create_simple_tear_sheet | Simpler version of create_full_tear_sheet; generates summary performance
statistics and important plots as a single image.
- Plots: cumulative returns, rolling beta, rolling Sharpe, underwater,
exposure, top 10 holdings, total holdings, long/short holdings,
daily turnover, transaction time dist... | pyfolio/tears.py | def create_simple_tear_sheet(returns,
positions=None,
transactions=None,
benchmark_rets=None,
slippage=None,
estimate_intraday='infer',
live_start... | def create_simple_tear_sheet(returns,
positions=None,
transactions=None,
benchmark_rets=None,
slippage=None,
estimate_intraday='infer',
live_start... | [
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valid | create_returns_tear_sheet | Generate a number of plots for analyzing a strategy's returns.
- Fetches benchmarks, then creates the plots on a single figure.
- Plots: rolling returns (with cone), rolling beta, rolling sharpe,
rolling Fama-French risk factors, drawdowns, underwater plot, monthly
and annual return plots, dail... | pyfolio/tears.py | def create_returns_tear_sheet(returns, positions=None,
transactions=None,
live_start_date=None,
cone_std=(1.0, 1.5, 2.0),
benchmark_rets=None,
bootstrap=False,
... | def create_returns_tear_sheet(returns, positions=None,
transactions=None,
live_start_date=None,
cone_std=(1.0, 1.5, 2.0),
benchmark_rets=None,
bootstrap=False,
... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/tears.py#L439-L625 | [
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","... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | create_position_tear_sheet | Generate a number of plots for analyzing a
strategy's positions and holdings.
- Plots: gross leverage, exposures, top positions, and holdings.
- Will also print the top positions held.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- Se... | pyfolio/tears.py | def create_position_tear_sheet(returns, positions,
show_and_plot_top_pos=2, hide_positions=False,
return_fig=False, sector_mappings=None,
transactions=None, estimate_intraday='infer'):
"""
Generate a number of plots for... | def create_position_tear_sheet(returns, positions,
show_and_plot_top_pos=2, hide_positions=False,
return_fig=False, sector_mappings=None,
transactions=None, estimate_intraday='infer'):
"""
Generate a number of plots for... | [
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"s",
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/tears.py#L629-L720 | [
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"esti... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | create_txn_tear_sheet | Generate a number of plots for analyzing a strategy's transactions.
Plots: turnover, daily volume, and a histogram of daily volume.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in create_full_tear_sheet.
positions :... | pyfolio/tears.py | def create_txn_tear_sheet(returns, positions, transactions,
unadjusted_returns=None, estimate_intraday='infer',
return_fig=False):
"""
Generate a number of plots for analyzing a strategy's transactions.
Plots: turnover, daily volume, and a histogram of da... | def create_txn_tear_sheet(returns, positions, transactions,
unadjusted_returns=None, estimate_intraday='infer',
return_fig=False):
"""
Generate a number of plots for analyzing a strategy's transactions.
Plots: turnover, daily volume, and a histogram of da... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/tears.py#L724-L800 | [
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... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | create_round_trip_tear_sheet | Generate a number of figures and plots describing the duration,
frequency, and profitability of trade "round trips."
A round trip is started when a new long or short position is
opened and is only completed when the number of shares in that
position returns to or crosses zero.
Parameters
------... | pyfolio/tears.py | def create_round_trip_tear_sheet(returns, positions, transactions,
sector_mappings=None,
estimate_intraday='infer', return_fig=False):
"""
Generate a number of figures and plots describing the duration,
frequency, and profitability of trade "... | def create_round_trip_tear_sheet(returns, positions, transactions,
sector_mappings=None,
estimate_intraday='infer', return_fig=False):
"""
Generate a number of figures and plots describing the duration,
frequency, and profitability of trade "... | [
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"check_intraday"... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | create_interesting_times_tear_sheet | Generate a number of returns plots around interesting points in time,
like the flash crash and 9/11.
Plots: returns around the dotcom bubble burst, Lehmann Brothers' failure,
9/11, US downgrade and EU debt crisis, Fukushima meltdown, US housing
bubble burst, EZB IR, Great Recession (August 2007, March ... | pyfolio/tears.py | def create_interesting_times_tear_sheet(
returns, benchmark_rets=None, legend_loc='best', return_fig=False):
"""
Generate a number of returns plots around interesting points in time,
like the flash crash and 9/11.
Plots: returns around the dotcom bubble burst, Lehmann Brothers' failure,
9/1... | def create_interesting_times_tear_sheet(
returns, benchmark_rets=None, legend_loc='best', return_fig=False):
"""
Generate a number of returns plots around interesting points in time,
like the flash crash and 9/11.
Plots: returns around the dotcom bubble burst, Lehmann Brothers' failure,
9/1... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/tears.py#L894-L969 | [
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"returns"... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | create_capacity_tear_sheet | Generates a report detailing portfolio size constraints set by
least liquid tickers. Plots a "capacity sweep," a curve describing
projected sharpe ratio given the slippage penalties that are
applied at various capital bases.
Parameters
----------
returns : pd.Series
Daily returns of the... | pyfolio/tears.py | def create_capacity_tear_sheet(returns, positions, transactions,
market_data,
liquidation_daily_vol_limit=0.2,
trade_daily_vol_limit=0.05,
last_n_days=utils.APPROX_BDAYS_PER_MONTH * 6,
... | def create_capacity_tear_sheet(returns, positions, transactions,
market_data,
liquidation_daily_vol_limit=0.2,
trade_daily_vol_limit=0.05,
last_n_days=utils.APPROX_BDAYS_PER_MONTH * 6,
... | [
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"... | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/tears.py#L973-L1075 | [
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... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | create_bayesian_tear_sheet | Generate a number of Bayesian distributions and a Bayesian
cone plot of returns.
Plots: Sharpe distribution, annual volatility distribution,
annual alpha distribution, beta distribution, predicted 1 and 5
day returns distributions, and a cumulative returns cone plot.
Parameters
----------
... | pyfolio/tears.py | def create_bayesian_tear_sheet(returns, benchmark_rets=None,
live_start_date=None, samples=2000,
return_fig=False, stoch_vol=False,
progressbar=True):
"""
Generate a number of Bayesian distributions and a Bayesian
c... | def create_bayesian_tear_sheet(returns, benchmark_rets=None,
live_start_date=None, samples=2000,
return_fig=False, stoch_vol=False,
progressbar=True):
"""
Generate a number of Bayesian distributions and a Bayesian
c... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/tears.py#L1079-L1262 | [
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valid | create_risk_tear_sheet | Creates risk tear sheet: computes and plots style factor exposures, sector
exposures, market cap exposures and volume exposures.
Parameters
----------
positions : pd.DataFrame
Daily equity positions of algorithm, in dollars.
- DataFrame with dates as index, equities as columns
-... | pyfolio/tears.py | def create_risk_tear_sheet(positions,
style_factor_panel=None,
sectors=None,
caps=None,
shares_held=None,
volumes=None,
percentile=None,
... | def create_risk_tear_sheet(positions,
style_factor_panel=None,
sectors=None,
caps=None,
shares_held=None,
volumes=None,
percentile=None,
... | [
"Creates",
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/tears.py#L1266-L1440 | [
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... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | create_perf_attrib_tear_sheet | Generate plots and tables for analyzing a strategy's performance.
Parameters
----------
returns : pd.Series
Returns for each day in the date range.
positions: pd.DataFrame
Daily holdings (in dollars or percentages), indexed by date.
Will be converted to percentages if positions... | pyfolio/tears.py | def create_perf_attrib_tear_sheet(returns,
positions,
factor_returns,
factor_loadings,
transactions=None,
pos_in_dollars=True,
... | def create_perf_attrib_tear_sheet(returns,
positions,
factor_returns,
factor_loadings,
transactions=None,
pos_in_dollars=True,
... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/tears.py#L1444-L1560 | [
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"FACTOR_PAR... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | daily_txns_with_bar_data | Sums the absolute value of shares traded in each name on each day.
Adds columns containing the closing price and total daily volume for
each day-ticker combination.
Parameters
----------
transactions : pd.DataFrame
Prices and amounts of executed trades. One row per trade.
- See full... | pyfolio/capacity.py | def daily_txns_with_bar_data(transactions, market_data):
"""
Sums the absolute value of shares traded in each name on each day.
Adds columns containing the closing price and total daily volume for
each day-ticker combination.
Parameters
----------
transactions : pd.DataFrame
Prices ... | def daily_txns_with_bar_data(transactions, market_data):
"""
Sums the absolute value of shares traded in each name on each day.
Adds columns containing the closing price and total daily volume for
each day-ticker combination.
Parameters
----------
transactions : pd.DataFrame
Prices ... | [
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... | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/capacity.py#L10-L42 | [
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valid | days_to_liquidate_positions | Compute the number of days that would have been required
to fully liquidate each position on each day based on the
trailing n day mean daily bar volume and a limit on the proportion
of a daily bar that we are allowed to consume.
This analysis uses portfolio allocations and a provided capital base
r... | pyfolio/capacity.py | def days_to_liquidate_positions(positions, market_data,
max_bar_consumption=0.2,
capital_base=1e6,
mean_volume_window=5):
"""
Compute the number of days that would have been required
to fully liquidate each posit... | def days_to_liquidate_positions(positions, market_data,
max_bar_consumption=0.2,
capital_base=1e6,
mean_volume_window=5):
"""
Compute the number of days that would have been required
to fully liquidate each posit... | [
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valid | get_max_days_to_liquidate_by_ticker | Finds the longest estimated liquidation time for each traded
name over the course of backtest (or last n days of the backtest).
Parameters
----------
positions: pd.DataFrame
Contains daily position values including cash
- See full explanation in tears.create_full_tear_sheet
market_d... | pyfolio/capacity.py | def get_max_days_to_liquidate_by_ticker(positions, market_data,
max_bar_consumption=0.2,
capital_base=1e6,
mean_volume_window=5,
last_n_days=None):
"""
... | def get_max_days_to_liquidate_by_ticker(positions, market_data,
max_bar_consumption=0.2,
capital_base=1e6,
mean_volume_window=5,
last_n_days=None):
"""
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valid | get_low_liquidity_transactions | For each traded name, find the daily transaction total that consumed
the greatest proportion of available daily bar volume.
Parameters
----------
transactions : pd.DataFrame
Prices and amounts of executed trades. One row per trade.
- See full explanation in create_full_tear_sheet.
... | pyfolio/capacity.py | def get_low_liquidity_transactions(transactions, market_data,
last_n_days=None):
"""
For each traded name, find the daily transaction total that consumed
the greatest proportion of available daily bar volume.
Parameters
----------
transactions : pd.DataFrame
... | def get_low_liquidity_transactions(transactions, market_data,
last_n_days=None):
"""
For each traded name, find the daily transaction total that consumed
the greatest proportion of available daily bar volume.
Parameters
----------
transactions : pd.DataFrame
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valid | apply_slippage_penalty | Applies quadratic volumeshare slippage model to daily returns based
on the proportion of the observed historical daily bar dollar volume
consumed by the strategy's trades. Scales the size of trades based
on the ratio of the starting capital we wish to test to the starting
capital of the passed backtest ... | pyfolio/capacity.py | def apply_slippage_penalty(returns, txn_daily, simulate_starting_capital,
backtest_starting_capital, impact=0.1):
"""
Applies quadratic volumeshare slippage model to daily returns based
on the proportion of the observed historical daily bar dollar volume
consumed by the strate... | def apply_slippage_penalty(returns, txn_daily, simulate_starting_capital,
backtest_starting_capital, impact=0.1):
"""
Applies quadratic volumeshare slippage model to daily returns based
on the proportion of the observed historical daily bar dollar volume
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... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | map_transaction | Maps a single transaction row to a dictionary.
Parameters
----------
txn : pd.DataFrame
A single transaction object to convert to a dictionary.
Returns
-------
dict
Mapped transaction. | pyfolio/txn.py | def map_transaction(txn):
"""
Maps a single transaction row to a dictionary.
Parameters
----------
txn : pd.DataFrame
A single transaction object to convert to a dictionary.
Returns
-------
dict
Mapped transaction.
"""
if isinstance(txn['sid'], dict):
s... | def map_transaction(txn):
"""
Maps a single transaction row to a dictionary.
Parameters
----------
txn : pd.DataFrame
A single transaction object to convert to a dictionary.
Returns
-------
dict
Mapped transaction.
"""
if isinstance(txn['sid'], dict):
s... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/txn.py#L20-L48 | [
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valid | make_transaction_frame | Formats a transaction DataFrame.
Parameters
----------
transactions : pd.DataFrame
Contains improperly formatted transactional data.
Returns
-------
df : pd.DataFrame
Daily transaction volume and dollar ammount.
- See full explanation in tears.create_full_tear_sheet. | pyfolio/txn.py | def make_transaction_frame(transactions):
"""
Formats a transaction DataFrame.
Parameters
----------
transactions : pd.DataFrame
Contains improperly formatted transactional data.
Returns
-------
df : pd.DataFrame
Daily transaction volume and dollar ammount.
- S... | def make_transaction_frame(transactions):
"""
Formats a transaction DataFrame.
Parameters
----------
transactions : pd.DataFrame
Contains improperly formatted transactional data.
Returns
-------
df : pd.DataFrame
Daily transaction volume and dollar ammount.
- S... | [
"Formats",
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/txn.py#L51-L80 | [
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valid | get_txn_vol | Extract daily transaction data from set of transaction objects.
Parameters
----------
transactions : pd.DataFrame
Time series containing one row per symbol (and potentially
duplicate datetime indices) and columns for amount and
price.
Returns
-------
pd.DataFrame
... | pyfolio/txn.py | def get_txn_vol(transactions):
"""
Extract daily transaction data from set of transaction objects.
Parameters
----------
transactions : pd.DataFrame
Time series containing one row per symbol (and potentially
duplicate datetime indices) and columns for amount and
price.
... | def get_txn_vol(transactions):
"""
Extract daily transaction data from set of transaction objects.
Parameters
----------
transactions : pd.DataFrame
Time series containing one row per symbol (and potentially
duplicate datetime indices) and columns for amount and
price.
... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/txn.py#L83-L110 | [
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... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | adjust_returns_for_slippage | Apply a slippage penalty for every dollar traded.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in create_full_tear_sheet.
positions : pd.DataFrame
Daily net position values.
- See full explanation in cre... | pyfolio/txn.py | def adjust_returns_for_slippage(returns, positions, transactions,
slippage_bps):
"""
Apply a slippage penalty for every dollar traded.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in c... | def adjust_returns_for_slippage(returns, positions, transactions,
slippage_bps):
"""
Apply a slippage penalty for every dollar traded.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in c... | [
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valid | get_turnover | - Value of purchases and sales divided
by either the actual gross book or the portfolio value
for the time step.
Parameters
----------
positions : pd.DataFrame
Contains daily position values including cash.
- See full explanation in tears.create_full_tear_sheet
transactions : pd... | pyfolio/txn.py | def get_turnover(positions, transactions, denominator='AGB'):
"""
- Value of purchases and sales divided
by either the actual gross book or the portfolio value
for the time step.
Parameters
----------
positions : pd.DataFrame
Contains daily position values including cash.
-... | def get_turnover(positions, transactions, denominator='AGB'):
"""
- Value of purchases and sales divided
by either the actual gross book or the portfolio value
for the time step.
Parameters
----------
positions : pd.DataFrame
Contains daily position values including cash.
-... | [
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valid | _groupby_consecutive | Merge transactions of the same direction separated by less than
max_delta time duration.
Parameters
----------
transactions : pd.DataFrame
Prices and amounts of executed round_trips. One row per trade.
- See full explanation in tears.create_full_tear_sheet
max_delta : pandas.Timede... | pyfolio/round_trips.py | def _groupby_consecutive(txn, max_delta=pd.Timedelta('8h')):
"""Merge transactions of the same direction separated by less than
max_delta time duration.
Parameters
----------
transactions : pd.DataFrame
Prices and amounts of executed round_trips. One row per trade.
- See full explan... | def _groupby_consecutive(txn, max_delta=pd.Timedelta('8h')):
"""Merge transactions of the same direction separated by less than
max_delta time duration.
Parameters
----------
transactions : pd.DataFrame
Prices and amounts of executed round_trips. One row per trade.
- See full explan... | [
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valid | extract_round_trips | Group transactions into "round trips". First, transactions are
grouped by day and directionality. Then, long and short
transactions are matched to create round-trip round_trips for which
PnL, duration and returns are computed. Crossings where a position
changes from long to short and vice-versa are hand... | pyfolio/round_trips.py | def extract_round_trips(transactions,
portfolio_value=None):
"""Group transactions into "round trips". First, transactions are
grouped by day and directionality. Then, long and short
transactions are matched to create round-trip round_trips for which
PnL, duration and returns are... | def extract_round_trips(transactions,
portfolio_value=None):
"""Group transactions into "round trips". First, transactions are
grouped by day and directionality. Then, long and short
transactions are matched to create round-trip round_trips for which
PnL, duration and returns are... | [
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valid | add_closing_transactions | Appends transactions that close out all positions at the end of
the timespan covered by positions data. Utilizes pricing information
in the positions DataFrame to determine closing price.
Parameters
----------
positions : pd.DataFrame
The positions that the strategy takes over time.
tra... | pyfolio/round_trips.py | def add_closing_transactions(positions, transactions):
"""
Appends transactions that close out all positions at the end of
the timespan covered by positions data. Utilizes pricing information
in the positions DataFrame to determine closing price.
Parameters
----------
positions : pd.DataFra... | def add_closing_transactions(positions, transactions):
"""
Appends transactions that close out all positions at the end of
the timespan covered by positions data. Utilizes pricing information
in the positions DataFrame to determine closing price.
Parameters
----------
positions : pd.DataFra... | [
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valid | apply_sector_mappings_to_round_trips | Translates round trip symbols to sectors.
Parameters
----------
round_trips : pd.DataFrame
DataFrame with one row per round trip trade.
- See full explanation in round_trips.extract_round_trips
sector_mappings : dict or pd.Series, optional
Security identifier to sector mapping.
... | pyfolio/round_trips.py | def apply_sector_mappings_to_round_trips(round_trips, sector_mappings):
"""
Translates round trip symbols to sectors.
Parameters
----------
round_trips : pd.DataFrame
DataFrame with one row per round trip trade.
- See full explanation in round_trips.extract_round_trips
sector_ma... | def apply_sector_mappings_to_round_trips(round_trips, sector_mappings):
"""
Translates round trip symbols to sectors.
Parameters
----------
round_trips : pd.DataFrame
DataFrame with one row per round trip trade.
- See full explanation in round_trips.extract_round_trips
sector_ma... | [
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valid | gen_round_trip_stats | Generate various round-trip statistics.
Parameters
----------
round_trips : pd.DataFrame
DataFrame with one row per round trip trade.
- See full explanation in round_trips.extract_round_trips
Returns
-------
stats : dict
A dictionary where each value is a pandas DataFram... | pyfolio/round_trips.py | def gen_round_trip_stats(round_trips):
"""Generate various round-trip statistics.
Parameters
----------
round_trips : pd.DataFrame
DataFrame with one row per round trip trade.
- See full explanation in round_trips.extract_round_trips
Returns
-------
stats : dict
A di... | def gen_round_trip_stats(round_trips):
"""Generate various round-trip statistics.
Parameters
----------
round_trips : pd.DataFrame
DataFrame with one row per round trip trade.
- See full explanation in round_trips.extract_round_trips
Returns
-------
stats : dict
A di... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/round_trips.py#L349-L381 | [
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valid | print_round_trip_stats | Print various round-trip statistics. Tries to pretty-print tables
with HTML output if run inside IPython NB.
Parameters
----------
round_trips : pd.DataFrame
DataFrame with one row per round trip trade.
- See full explanation in round_trips.extract_round_trips
See also
--------... | pyfolio/round_trips.py | def print_round_trip_stats(round_trips, hide_pos=False):
"""Print various round-trip statistics. Tries to pretty-print tables
with HTML output if run inside IPython NB.
Parameters
----------
round_trips : pd.DataFrame
DataFrame with one row per round trip trade.
- See full explanati... | def print_round_trip_stats(round_trips, hide_pos=False):
"""Print various round-trip statistics. Tries to pretty-print tables
with HTML output if run inside IPython NB.
Parameters
----------
round_trips : pd.DataFrame
DataFrame with one row per round trip trade.
- See full explanati... | [
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valid | perf_attrib | Attributes the performance of a returns stream to a set of risk factors.
Preprocesses inputs, and then calls empyrical.perf_attrib. See
empyrical.perf_attrib for more info.
Performance attribution determines how much each risk factor, e.g.,
momentum, the technology sector, etc., contributed to total r... | pyfolio/perf_attrib.py | def perf_attrib(returns,
positions,
factor_returns,
factor_loadings,
transactions=None,
pos_in_dollars=True):
"""
Attributes the performance of a returns stream to a set of risk factors.
Preprocesses inputs, and then calls empy... | def perf_attrib(returns,
positions,
factor_returns,
factor_loadings,
transactions=None,
pos_in_dollars=True):
"""
Attributes the performance of a returns stream to a set of risk factors.
Preprocesses inputs, and then calls empy... | [
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valid | compute_exposures | Compute daily risk factor exposures.
Normalizes positions (if necessary) and calls ep.compute_exposures.
See empyrical.compute_exposures for more info.
Parameters
----------
positions: pd.DataFrame or pd.Series
Daily holdings (in dollars or percentages), indexed by date, OR
a serie... | pyfolio/perf_attrib.py | def compute_exposures(positions, factor_loadings, stack_positions=True,
pos_in_dollars=True):
"""
Compute daily risk factor exposures.
Normalizes positions (if necessary) and calls ep.compute_exposures.
See empyrical.compute_exposures for more info.
Parameters
----------
... | def compute_exposures(positions, factor_loadings, stack_positions=True,
pos_in_dollars=True):
"""
Compute daily risk factor exposures.
Normalizes positions (if necessary) and calls ep.compute_exposures.
See empyrical.compute_exposures for more info.
Parameters
----------
... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/perf_attrib.py#L151-L216 | [
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valid | create_perf_attrib_stats | Takes perf attribution data over a period of time and computes annualized
multifactor alpha, multifactor sharpe, risk exposures. | pyfolio/perf_attrib.py | def create_perf_attrib_stats(perf_attrib, risk_exposures):
"""
Takes perf attribution data over a period of time and computes annualized
multifactor alpha, multifactor sharpe, risk exposures.
"""
summary = OrderedDict()
total_returns = perf_attrib['total_returns']
specific_returns = perf_att... | def create_perf_attrib_stats(perf_attrib, risk_exposures):
"""
Takes perf attribution data over a period of time and computes annualized
multifactor alpha, multifactor sharpe, risk exposures.
"""
summary = OrderedDict()
total_returns = perf_attrib['total_returns']
specific_returns = perf_att... | [
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valid | show_perf_attrib_stats | Calls `perf_attrib` using inputs, and displays outputs using
`utils.print_table`. | pyfolio/perf_attrib.py | def show_perf_attrib_stats(returns,
positions,
factor_returns,
factor_loadings,
transactions=None,
pos_in_dollars=True):
"""
Calls `perf_attrib` using inputs, and displays outpu... | def show_perf_attrib_stats(returns,
positions,
factor_returns,
factor_loadings,
transactions=None,
pos_in_dollars=True):
"""
Calls `perf_attrib` using inputs, and displays outpu... | [
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valid | plot_returns | Plot total, specific, and common returns.
Parameters
----------
perf_attrib_data : pd.DataFrame
df with factors, common returns, and specific returns as columns,
and datetimes as index. Assumes the `total_returns` column is NOT
cost adjusted.
- Example:
... | pyfolio/perf_attrib.py | def plot_returns(perf_attrib_data, cost=None, ax=None):
"""
Plot total, specific, and common returns.
Parameters
----------
perf_attrib_data : pd.DataFrame
df with factors, common returns, and specific returns as columns,
and datetimes as index. Assumes the `total_returns` column is... | def plot_returns(perf_attrib_data, cost=None, ax=None):
"""
Plot total, specific, and common returns.
Parameters
----------
perf_attrib_data : pd.DataFrame
df with factors, common returns, and specific returns as columns,
and datetimes as index. Assumes the `total_returns` column is... | [
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valid | plot_alpha_returns | Plot histogram of daily multi-factor alpha returns (specific returns).
Parameters
----------
alpha_returns : pd.Series
series of daily alpha returns indexed by datetime
ax : matplotlib.axes.Axes
axes on which plots are made. if None, current axes will be used
Returns
-------
... | pyfolio/perf_attrib.py | def plot_alpha_returns(alpha_returns, ax=None):
"""
Plot histogram of daily multi-factor alpha returns (specific returns).
Parameters
----------
alpha_returns : pd.Series
series of daily alpha returns indexed by datetime
ax : matplotlib.axes.Axes
axes on which plots are made. ... | def plot_alpha_returns(alpha_returns, ax=None):
"""
Plot histogram of daily multi-factor alpha returns (specific returns).
Parameters
----------
alpha_returns : pd.Series
series of daily alpha returns indexed by datetime
ax : matplotlib.axes.Axes
axes on which plots are made. ... | [
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valid | plot_factor_contribution_to_perf | Plot each factor's contribution to performance.
Parameters
----------
perf_attrib_data : pd.DataFrame
df with factors, common returns, and specific returns as columns,
and datetimes as index
- Example:
momentum reversal common_returns specific_returns
... | pyfolio/perf_attrib.py | def plot_factor_contribution_to_perf(
perf_attrib_data,
ax=None,
title='Cumulative common returns attribution',
):
"""
Plot each factor's contribution to performance.
Parameters
----------
perf_attrib_data : pd.DataFrame
df with factors, common returns, and specific ... | def plot_factor_contribution_to_perf(
perf_attrib_data,
ax=None,
title='Cumulative common returns attribution',
):
"""
Plot each factor's contribution to performance.
Parameters
----------
perf_attrib_data : pd.DataFrame
df with factors, common returns, and specific ... | [
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valid | plot_risk_exposures | Parameters
----------
exposures : pd.DataFrame
df indexed by datetime, with factors as columns
- Example:
momentum reversal
dt
2017-01-01 -0.238655 0.077123
2017-01-02 0.821872 1.520515
ax : matplotlib.axes.Axes
axes o... | pyfolio/perf_attrib.py | def plot_risk_exposures(exposures, ax=None,
title='Daily risk factor exposures'):
"""
Parameters
----------
exposures : pd.DataFrame
df indexed by datetime, with factors as columns
- Example:
momentum reversal
dt
20... | def plot_risk_exposures(exposures, ax=None,
title='Daily risk factor exposures'):
"""
Parameters
----------
exposures : pd.DataFrame
df indexed by datetime, with factors as columns
- Example:
momentum reversal
dt
20... | [
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valid | _align_and_warn | Make sure that all inputs have matching dates and tickers,
and raise warnings if necessary. | pyfolio/perf_attrib.py | def _align_and_warn(returns,
positions,
factor_returns,
factor_loadings,
transactions=None,
pos_in_dollars=True):
"""
Make sure that all inputs have matching dates and tickers,
and raise warnings if necessary... | def _align_and_warn(returns,
positions,
factor_returns,
factor_loadings,
transactions=None,
pos_in_dollars=True):
"""
Make sure that all inputs have matching dates and tickers,
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valid | _stack_positions | Convert positions to percentages if necessary, and change them
to long format.
Parameters
----------
positions: pd.DataFrame
Daily holdings (in dollars or percentages), indexed by date.
Will be converted to percentages if positions are in dollars.
Short positions show up as cash... | pyfolio/perf_attrib.py | def _stack_positions(positions, pos_in_dollars=True):
"""
Convert positions to percentages if necessary, and change them
to long format.
Parameters
----------
positions: pd.DataFrame
Daily holdings (in dollars or percentages), indexed by date.
Will be converted to percentages if... | def _stack_positions(positions, pos_in_dollars=True):
"""
Convert positions to percentages if necessary, and change them
to long format.
Parameters
----------
positions: pd.DataFrame
Daily holdings (in dollars or percentages), indexed by date.
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valid | _cumulative_returns_less_costs | Compute cumulative returns, less costs. | pyfolio/perf_attrib.py | def _cumulative_returns_less_costs(returns, costs):
"""
Compute cumulative returns, less costs.
"""
if costs is None:
return ep.cum_returns(returns)
return ep.cum_returns(returns - costs) | def _cumulative_returns_less_costs(returns, costs):
"""
Compute cumulative returns, less costs.
"""
if costs is None:
return ep.cum_returns(returns)
return ep.cum_returns(returns - costs) | [
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valid | format_asset | If zipline asset objects are used, we want to print them out prettily
within the tear sheet. This function should only be applied directly
before displaying. | pyfolio/utils.py | def format_asset(asset):
"""
If zipline asset objects are used, we want to print them out prettily
within the tear sheet. This function should only be applied directly
before displaying.
"""
try:
import zipline.assets
except ImportError:
return asset
if isinstance(asset... | def format_asset(asset):
"""
If zipline asset objects are used, we want to print them out prettily
within the tear sheet. This function should only be applied directly
before displaying.
"""
try:
import zipline.assets
except ImportError:
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if isinstance(asset... | [
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valid | vectorize | Decorator so that functions can be written to work on Series but
may still be called with DataFrames. | pyfolio/utils.py | def vectorize(func):
"""
Decorator so that functions can be written to work on Series but
may still be called with DataFrames.
"""
def wrapper(df, *args, **kwargs):
if df.ndim == 1:
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"""
Decorator so that functions can be written to work on Series but
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"""
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valid | extract_rets_pos_txn_from_zipline | Extract returns, positions, transactions and leverage from the
backtest data structure returned by zipline.TradingAlgorithm.run().
The returned data structures are in a format compatible with the
rest of pyfolio and can be directly passed to
e.g. tears.create_full_tear_sheet().
Parameters
----... | pyfolio/utils.py | def extract_rets_pos_txn_from_zipline(backtest):
"""
Extract returns, positions, transactions and leverage from the
backtest data structure returned by zipline.TradingAlgorithm.run().
The returned data structures are in a format compatible with the
rest of pyfolio and can be directly passed to
... | def extract_rets_pos_txn_from_zipline(backtest):
"""
Extract returns, positions, transactions and leverage from the
backtest data structure returned by zipline.TradingAlgorithm.run().
The returned data structures are in a format compatible with the
rest of pyfolio and can be directly passed to
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valid | print_table | Pretty print a pandas DataFrame.
Uses HTML output if running inside Jupyter Notebook, otherwise
formatted text output.
Parameters
----------
table : pandas.Series or pandas.DataFrame
Table to pretty-print.
name : str, optional
Table name to display in upper left corner.
flo... | pyfolio/utils.py | def print_table(table,
name=None,
float_format=None,
formatters=None,
header_rows=None):
"""
Pretty print a pandas DataFrame.
Uses HTML output if running inside Jupyter Notebook, otherwise
formatted text output.
Parameters
-------... | def print_table(table,
name=None,
float_format=None,
formatters=None,
header_rows=None):
"""
Pretty print a pandas DataFrame.
Uses HTML output if running inside Jupyter Notebook, otherwise
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Parameters
-------... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/utils.py#L170-L219 | [
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valid | detect_intraday | Attempt to detect an intraday strategy. Get the number of
positions held at the end of the day, and divide that by the
number of unique stocks transacted every day. If the average quotient
is below a threshold, then an intraday strategy is detected.
Parameters
----------
positions : pd.DataFram... | pyfolio/utils.py | def detect_intraday(positions, transactions, threshold=0.25):
"""
Attempt to detect an intraday strategy. Get the number of
positions held at the end of the day, and divide that by the
number of unique stocks transacted every day. If the average quotient
is below a threshold, then an intraday strate... | def detect_intraday(positions, transactions, threshold=0.25):
"""
Attempt to detect an intraday strategy. Get the number of
positions held at the end of the day, and divide that by the
number of unique stocks transacted every day. If the average quotient
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valid | check_intraday | Logic for checking if a strategy is intraday and processing it.
Parameters
----------
estimate: boolean or str, optional
Approximate returns for intraday strategies.
See description in tears.create_full_tear_sheet.
returns : pd.Series
Daily returns of the strategy, noncumulative... | pyfolio/utils.py | def check_intraday(estimate, returns, positions, transactions):
"""
Logic for checking if a strategy is intraday and processing it.
Parameters
----------
estimate: boolean or str, optional
Approximate returns for intraday strategies.
See description in tears.create_full_tear_sheet.
... | def check_intraday(estimate, returns, positions, transactions):
"""
Logic for checking if a strategy is intraday and processing it.
Parameters
----------
estimate: boolean or str, optional
Approximate returns for intraday strategies.
See description in tears.create_full_tear_sheet.
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valid | estimate_intraday | Intraday strategies will often not hold positions at the day end.
This attempts to find the point in the day that best represents
the activity of the strategy on that day, and effectively resamples
the end-of-day positions with the positions at this point of day.
The point of day is found by detecting w... | pyfolio/utils.py | def estimate_intraday(returns, positions, transactions, EOD_hour=23):
"""
Intraday strategies will often not hold positions at the day end.
This attempts to find the point in the day that best represents
the activity of the strategy on that day, and effectively resamples
the end-of-day positions wit... | def estimate_intraday(returns, positions, transactions, EOD_hour=23):
"""
Intraday strategies will often not hold positions at the day end.
This attempts to find the point in the day that best represents
the activity of the strategy on that day, and effectively resamples
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valid | clip_returns_to_benchmark | Drop entries from rets so that the start and end dates of rets match those
of benchmark_rets.
Parameters
----------
rets : pd.Series
Daily returns of the strategy, noncumulative.
- See pf.tears.create_full_tear_sheet for more details
benchmark_rets : pd.Series
Daily return... | pyfolio/utils.py | def clip_returns_to_benchmark(rets, benchmark_rets):
"""
Drop entries from rets so that the start and end dates of rets match those
of benchmark_rets.
Parameters
----------
rets : pd.Series
Daily returns of the strategy, noncumulative.
- See pf.tears.create_full_tear_sheet for ... | def clip_returns_to_benchmark(rets, benchmark_rets):
"""
Drop entries from rets so that the start and end dates of rets match those
of benchmark_rets.
Parameters
----------
rets : pd.Series
Daily returns of the strategy, noncumulative.
- See pf.tears.create_full_tear_sheet for ... | [
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valid | to_utc | For use in tests; applied UTC timestamp to DataFrame. | pyfolio/utils.py | def to_utc(df):
"""
For use in tests; applied UTC timestamp to DataFrame.
"""
try:
df.index = df.index.tz_localize('UTC')
except TypeError:
df.index = df.index.tz_convert('UTC')
return df | def to_utc(df):
"""
For use in tests; applied UTC timestamp to DataFrame.
"""
try:
df.index = df.index.tz_localize('UTC')
except TypeError:
df.index = df.index.tz_convert('UTC')
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valid | get_symbol_rets | Calls the currently registered 'returns_func'
Parameters
----------
symbol : object
An identifier for the asset whose return
series is desired.
e.g. ticker symbol or database ID
start : date, optional
Earliest date to fetch data for.
Defaults to earliest date ava... | pyfolio/utils.py | def get_symbol_rets(symbol, start=None, end=None):
"""
Calls the currently registered 'returns_func'
Parameters
----------
symbol : object
An identifier for the asset whose return
series is desired.
e.g. ticker symbol or database ID
start : date, optional
Earlies... | def get_symbol_rets(symbol, start=None, end=None):
"""
Calls the currently registered 'returns_func'
Parameters
----------
symbol : object
An identifier for the asset whose return
series is desired.
e.g. ticker symbol or database ID
start : date, optional
Earlies... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/utils.py#L462-L487 | [
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valid | configure_legend | Format legend for perf attribution plots:
- put legend to the right of plot instead of overlapping with it
- make legend order match up with graph lines
- set colors according to colormap | pyfolio/utils.py | def configure_legend(ax, autofmt_xdate=True, change_colors=False,
rotation=30, ha='right'):
"""
Format legend for perf attribution plots:
- put legend to the right of plot instead of overlapping with it
- make legend order match up with graph lines
- set colors according to colo... | def configure_legend(ax, autofmt_xdate=True, change_colors=False,
rotation=30, ha='right'):
"""
Format legend for perf attribution plots:
- put legend to the right of plot instead of overlapping with it
- make legend order match up with graph lines
- set colors according to colo... | [
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valid | sample_colormap | Sample a colormap from matplotlib | pyfolio/utils.py | def sample_colormap(cmap_name, n_samples):
"""
Sample a colormap from matplotlib
"""
colors = []
colormap = cm.cmap_d[cmap_name]
for i in np.linspace(0, 1, n_samples):
colors.append(colormap(i))
return colors | def sample_colormap(cmap_name, n_samples):
"""
Sample a colormap from matplotlib
"""
colors = []
colormap = cm.cmap_d[cmap_name]
for i in np.linspace(0, 1, n_samples):
colors.append(colormap(i))
return colors | [
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valid | customize | Decorator to set plotting context and axes style during function call. | pyfolio/plotting.py | def customize(func):
"""
Decorator to set plotting context and axes style during function call.
"""
@wraps(func)
def call_w_context(*args, **kwargs):
set_context = kwargs.pop('set_context', True)
if set_context:
with plotting_context(), axes_style():
retur... | def customize(func):
"""
Decorator to set plotting context and axes style during function call.
"""
@wraps(func)
def call_w_context(*args, **kwargs):
set_context = kwargs.pop('set_context', True)
if set_context:
with plotting_context(), axes_style():
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valid | plotting_context | Create pyfolio default plotting style context.
Under the hood, calls and returns seaborn.plotting_context() with
some custom settings. Usually you would use in a with-context.
Parameters
----------
context : str, optional
Name of seaborn context.
font_scale : float, optional
Sc... | pyfolio/plotting.py | def plotting_context(context='notebook', font_scale=1.5, rc=None):
"""
Create pyfolio default plotting style context.
Under the hood, calls and returns seaborn.plotting_context() with
some custom settings. Usually you would use in a with-context.
Parameters
----------
context : str, option... | def plotting_context(context='notebook', font_scale=1.5, rc=None):
"""
Create pyfolio default plotting style context.
Under the hood, calls and returns seaborn.plotting_context() with
some custom settings. Usually you would use in a with-context.
Parameters
----------
context : str, option... | [
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valid | axes_style | Create pyfolio default axes style context.
Under the hood, calls and returns seaborn.axes_style() with
some custom settings. Usually you would use in a with-context.
Parameters
----------
style : str, optional
Name of seaborn style.
rc : dict, optional
Config flags.
Return... | pyfolio/plotting.py | def axes_style(style='darkgrid', rc=None):
"""
Create pyfolio default axes style context.
Under the hood, calls and returns seaborn.axes_style() with
some custom settings. Usually you would use in a with-context.
Parameters
----------
style : str, optional
Name of seaborn style.
... | def axes_style(style='darkgrid', rc=None):
"""
Create pyfolio default axes style context.
Under the hood, calls and returns seaborn.axes_style() with
some custom settings. Usually you would use in a with-context.
Parameters
----------
style : str, optional
Name of seaborn style.
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valid | plot_monthly_returns_heatmap | Plots a heatmap of returns by month.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
ax : matplotlib.Axes, optional
Axes upon which to plot.
**kwargs, optional
Passed to ... | pyfolio/plotting.py | def plot_monthly_returns_heatmap(returns, ax=None, **kwargs):
"""
Plots a heatmap of returns by month.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
ax : matplotlib.Axes, optional
... | def plot_monthly_returns_heatmap(returns, ax=None, **kwargs):
"""
Plots a heatmap of returns by month.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
ax : matplotlib.Axes, optional
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"ret... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | plot_annual_returns | Plots a bar graph of returns by year.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
ax : matplotlib.Axes, optional
Axes upon which to plot.
**kwargs, optional
Passed to... | pyfolio/plotting.py | def plot_annual_returns(returns, ax=None, **kwargs):
"""
Plots a bar graph of returns by year.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
ax : matplotlib.Axes, optional
... | def plot_annual_returns(returns, ax=None, **kwargs):
"""
Plots a bar graph of returns by year.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
ax : matplotlib.Axes, optional
... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L185-L232 | [
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... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | plot_monthly_returns_dist | Plots a distribution of monthly returns.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
ax : matplotlib.Axes, optional
Axes upon which to plot.
**kwargs, optional
Passed... | pyfolio/plotting.py | def plot_monthly_returns_dist(returns, ax=None, **kwargs):
"""
Plots a distribution of monthly returns.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
ax : matplotlib.Axes, optional... | def plot_monthly_returns_dist(returns, ax=None, **kwargs):
"""
Plots a distribution of monthly returns.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
ax : matplotlib.Axes, optional... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L235-L283 | [
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valid | plot_holdings | Plots total amount of stocks with an active position, either short
or long. Displays daily total, daily average per month, and
all-time daily average.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full... | pyfolio/plotting.py | def plot_holdings(returns, positions, legend_loc='best', ax=None, **kwargs):
"""
Plots total amount of stocks with an active position, either short
or long. Displays daily total, daily average per month, and
all-time daily average.
Parameters
----------
returns : pd.Series
Daily ret... | def plot_holdings(returns, positions, legend_loc='best', ax=None, **kwargs):
"""
Plots total amount of stocks with an active position, either short
or long. Displays daily total, daily average per month, and
all-time daily average.
Parameters
----------
returns : pd.Series
Daily ret... | [
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valid | plot_long_short_holdings | Plots total amount of stocks with an active position, breaking out
short and long into transparent filled regions.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
positions : pd.DataFram... | pyfolio/plotting.py | def plot_long_short_holdings(returns, positions,
legend_loc='upper left', ax=None, **kwargs):
"""
Plots total amount of stocks with an active position, breaking out
short and long into transparent filled regions.
Parameters
----------
returns : pd.Series
Dai... | def plot_long_short_holdings(returns, positions,
legend_loc='upper left', ax=None, **kwargs):
"""
Plots total amount of stocks with an active position, breaking out
short and long into transparent filled regions.
Parameters
----------
returns : pd.Series
Dai... | [
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valid | plot_drawdown_periods | Plots cumulative returns highlighting top drawdown periods.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
top : int, optional
Amount of top drawdowns periods to plot (default 10).
... | pyfolio/plotting.py | def plot_drawdown_periods(returns, top=10, ax=None, **kwargs):
"""
Plots cumulative returns highlighting top drawdown periods.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
top : i... | def plot_drawdown_periods(returns, top=10, ax=None, **kwargs):
"""
Plots cumulative returns highlighting top drawdown periods.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
top : i... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L403-L453 | [
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... | 712716ab0cdebbec9fabb25eea3bf40e4354749d |
valid | plot_drawdown_underwater | Plots how far underwaterr returns are over time, or plots current
drawdown vs. date.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
ax : matplotlib.Axes, optional
Axes upon whic... | pyfolio/plotting.py | def plot_drawdown_underwater(returns, ax=None, **kwargs):
"""
Plots how far underwaterr returns are over time, or plots current
drawdown vs. date.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full... | def plot_drawdown_underwater(returns, ax=None, **kwargs):
"""
Plots how far underwaterr returns are over time, or plots current
drawdown vs. date.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L456-L490 | [
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valid | plot_perf_stats | Create box plot of some performance metrics of the strategy.
The width of the box whiskers is determined by a bootstrap.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : ... | pyfolio/plotting.py | def plot_perf_stats(returns, factor_returns, ax=None):
"""
Create box plot of some performance metrics of the strategy.
The width of the box whiskers is determined by a bootstrap.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full... | def plot_perf_stats(returns, factor_returns, ax=None):
"""
Create box plot of some performance metrics of the strategy.
The width of the box whiskers is determined by a bootstrap.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full... | [
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valid | show_perf_stats | Prints some performance metrics of the strategy.
- Shows amount of time the strategy has been run in backtest and
out-of-sample (in live trading).
- Shows Omega ratio, max drawdown, Calmar ratio, annual return,
stability, Sharpe ratio, annual volatility, alpha, and beta.
Parameters
------... | pyfolio/plotting.py | def show_perf_stats(returns, factor_returns=None, positions=None,
transactions=None, turnover_denom='AGB',
live_start_date=None, bootstrap=False,
header_rows=None):
"""
Prints some performance metrics of the strategy.
- Shows amount of time the st... | def show_perf_stats(returns, factor_returns=None, positions=None,
transactions=None, turnover_denom='AGB',
live_start_date=None, bootstrap=False,
header_rows=None):
"""
Prints some performance metrics of the strategy.
- Shows amount of time the st... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L539-L662 | [
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valid | plot_returns | Plots raw returns over time.
Backtest returns are in green, and out-of-sample (live trading)
returns are in red.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
live_start_date : da... | pyfolio/plotting.py | def plot_returns(returns,
live_start_date=None,
ax=None):
"""
Plots raw returns over time.
Backtest returns are in green, and out-of-sample (live trading)
returns are in red.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, ... | def plot_returns(returns,
live_start_date=None,
ax=None):
"""
Plots raw returns over time.
Backtest returns are in green, and out-of-sample (live trading)
returns are in red.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, ... | [
"Plots",
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L665-L709 | [
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valid | plot_rolling_returns | Plots cumulative rolling returns versus some benchmarks'.
Backtest returns are in green, and out-of-sample (live trading)
returns are in red.
Additionally, a non-parametric cone plot may be added to the
out-of-sample returns region.
Parameters
----------
returns : pd.Series
Daily ... | pyfolio/plotting.py | def plot_rolling_returns(returns,
factor_returns=None,
live_start_date=None,
logy=False,
cone_std=None,
legend_loc='best',
volatility_match=False,
... | def plot_rolling_returns(returns,
factor_returns=None,
live_start_date=None,
logy=False,
cone_std=None,
legend_loc='best',
volatility_match=False,
... | [
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valid | plot_rolling_beta | Plots the rolling 6-month and 12-month beta versus date.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series
Daily noncumulative returns of the benchmark facto... | pyfolio/plotting.py | def plot_rolling_beta(returns, factor_returns, legend_loc='best',
ax=None, **kwargs):
"""
Plots the rolling 6-month and 12-month beta versus date.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in... | def plot_rolling_beta(returns, factor_returns, legend_loc='best',
ax=None, **kwargs):
"""
Plots the rolling 6-month and 12-month beta versus date.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in... | [
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valid | plot_rolling_volatility | Plots the rolling volatility versus date.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series, optional
Daily noncumulative returns of the benchmark factor to ... | pyfolio/plotting.py | def plot_rolling_volatility(returns, factor_returns=None,
rolling_window=APPROX_BDAYS_PER_MONTH * 6,
legend_loc='best', ax=None, **kwargs):
"""
Plots the rolling volatility versus date.
Parameters
----------
returns : pd.Series
Daily r... | def plot_rolling_volatility(returns, factor_returns=None,
rolling_window=APPROX_BDAYS_PER_MONTH * 6,
legend_loc='best', ax=None, **kwargs):
"""
Plots the rolling volatility versus date.
Parameters
----------
returns : pd.Series
Daily r... | [
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"."
] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L891-L954 | [
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valid | plot_rolling_sharpe | Plots the rolling Sharpe ratio versus date.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series, optional
Daily noncumulative returns of the benchmark factor f... | pyfolio/plotting.py | def plot_rolling_sharpe(returns, factor_returns=None,
rolling_window=APPROX_BDAYS_PER_MONTH * 6,
legend_loc='best', ax=None, **kwargs):
"""
Plots the rolling Sharpe ratio versus date.
Parameters
----------
returns : pd.Series
Daily returns of ... | def plot_rolling_sharpe(returns, factor_returns=None,
rolling_window=APPROX_BDAYS_PER_MONTH * 6,
legend_loc='best', ax=None, **kwargs):
"""
Plots the rolling Sharpe ratio versus date.
Parameters
----------
returns : pd.Series
Daily returns of ... | [
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valid | plot_gross_leverage | Plots gross leverage versus date.
Gross leverage is the sum of long and short exposure per share
divided by net asset value.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
position... | pyfolio/plotting.py | def plot_gross_leverage(returns, positions, ax=None, **kwargs):
"""
Plots gross leverage versus date.
Gross leverage is the sum of long and short exposure per share
divided by net asset value.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
... | def plot_gross_leverage(returns, positions, ax=None, **kwargs):
"""
Plots gross leverage versus date.
Gross leverage is the sum of long and short exposure per share
divided by net asset value.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L1025-L1061 | [
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valid | plot_exposures | Plots a cake chart of the long and short exposure.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
positions_alloc : pd.DataFrame
Portfolio allocation of positions. See
pos.g... | pyfolio/plotting.py | def plot_exposures(returns, positions, ax=None, **kwargs):
"""
Plots a cake chart of the long and short exposure.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
positions_alloc : pd... | def plot_exposures(returns, positions, ax=None, **kwargs):
"""
Plots a cake chart of the long and short exposure.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
positions_alloc : pd... | [
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] | quantopian/pyfolio | python | https://github.com/quantopian/pyfolio/blob/712716ab0cdebbec9fabb25eea3bf40e4354749d/pyfolio/plotting.py#L1064-L1111 | [
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