From f8a5f5fb3769a0332f46eb6bdcb62abde3ed61e6 Mon Sep 17 00:00:00 2001
From: montezdesousa <79287829+montezdesousa@users.noreply.github.com>
Date: Tue, 20 Feb 2024 23:48:27 +0000
Subject: [PATCH 1/8] [Feature] - OBB.GET (#6097)
* create OBB.GET and data slicer page
* remove quotes
* format description
* wording
* functions.json
* sidebar position
* remove if stamente
---
website/content/excel/data-connectors.md | 2 +-
website/content/excel/data-slicer.md | 61 ++++++++++++++++++++++++
website/content/excel/functions.json | 33 ++++++++++++-
website/generate_excel_markdown.py | 18 ++++---
4 files changed, 105 insertions(+), 9 deletions(-)
create mode 100644 website/content/excel/data-slicer.md
diff --git a/website/content/excel/data-connectors.md b/website/content/excel/data-connectors.md
index 07656ec36e0..24bb98376b3 100644
--- a/website/content/excel/data-connectors.md
+++ b/website/content/excel/data-connectors.md
@@ -37,7 +37,7 @@ If your backend supports it is possible to pass a symbol, date or other optional
```
:::tip
-The easiest way to pass optional parameters is write them into cells and reference them in the function. For example, `=OBB.BYOD(...,A1:B2)` where A1 contains "param1", B1 "value1", A2 "param2", B2 "value2" and so on.
+The easiest way to pass optional parameters is to write them into cells and reference them in the function. For example, `=OBB.BYOD(...,A1:B2)` where A1 contains "param1", B1 "value1", A2 "param2", B2 "value2" and so on.
:::
#### Additional notes
diff --git a/website/content/excel/data-slicer.md b/website/content/excel/data-slicer.md
new file mode 100644
index 00000000000..a1cc27ba7fd
--- /dev/null
+++ b/website/content/excel/data-slicer.md
@@ -0,0 +1,61 @@
+---
+title: Data slicer
+sidebar_position: 5
+description: Slice Excel ranges by label or index
+keywords:
+- Microsoft Excel
+- Add-in
+- Advanced
+- Slice data
+- Data slicer
+- Get specific fields
+---
+
+
+import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
+
+
+
+To help you slice parts of data, we provide the [OBB.GET](https://docs.openbb.co/excel/reference/get) function. This function allows to slice rows, columns or range subsets. It is useful to extract specific fields from the `OBB.` custom functions. Data can be sliced by label or index.
+
+### Example
+
+- Suppose you called an `OBB.` function and it returned the following data at cells A1:D3:
+
+| period_ending | revenue | cost_of_revenue | gross_profit |
+|---|---|---|---|---|
+| 2023/09/30 | 383 285 000 000.00 | 214 137 000 000.00 | 169 148 000 000.00 |
+| 2022/09/24 | 394 328 000 000.00 | 223 546 000 000.00 | 170 782 000 000.00 |
+| 2021/09/25 | 365 817 000 000.00 | 212 981 000 000.00 | 152 836 000 000.00 |
+
+- Slicing a single row:
+
+```excel
+=OBB.GET(A1:D3,DATE(2023,9,30))
+```
+
+:::note
+When passing date labels make sure to use the format `YYYY/MM/DD` or refer to a cell range containing Excel date format - DATE(year,month,day).
+:::
+
+- Slicing a single column:
+
+```excel
+=OBB.GET(A1:D3,,"revenue")
+```
+
+- Slicing by index:
+
+```excel
+=OBB.GET(A1:D3,2,3)
+```
+
+- Slicing multiple rows and columns:
+
+```excel
+=OBB.GET(A1:D3,{"2023/09/30","2021/09/25"},{"cost_of_revenue","gross_profit"})
+```
+
+:::tip
+The easiest way to pass ranges is to write them into cells and reference them in the function. For example, `=OBB.GET(...,A1:A2)` where A1 contains "item1", A2 "item2".
+:::
diff --git a/website/content/excel/functions.json b/website/content/excel/functions.json
index 66f7a5fbdc1..70e99c565ea 100644
--- a/website/content/excel/functions.json
+++ b/website/content/excel/functions.json
@@ -1,5 +1,36 @@
{
"functions": [
+ {
+ "description": "Slice data from a range.",
+ "helpUrl": "https://docs.openbb.co/excel/data-slicer",
+ "id": "GET",
+ "name": "GET",
+ "parameters": [
+ {
+ "description": "Data to slice.",
+ "dimensionality": "matrix",
+ "name": "data",
+ "type": "any"
+ },
+ {
+ "description": "Range of row labels or indices.",
+ "dimensionality": "matrix",
+ "name": "row",
+ "optional": true,
+ "type": "any"
+ },
+ {
+ "description": "Range of column labels or indices.",
+ "dimensionality": "matrix",
+ "name": "column",
+ "optional": true,
+ "type": "any"
+ }
+ ],
+ "result": {
+ "dimensionality": "matrix"
+ }
+ },
{
"description": "Bring Your Own Data features from OpenBB Terminal Pro.",
"helpUrl": "https://docs.openbb.co/excel/data-connectors",
@@ -4403,4 +4434,4 @@
}
}
]
-}
\ No newline at end of file
+}
diff --git a/website/generate_excel_markdown.py b/website/generate_excel_markdown.py
index b86e5bf230c..49d414802c6 100644
--- a/website/generate_excel_markdown.py
+++ b/website/generate_excel_markdown.py
@@ -33,8 +33,14 @@ class CommandLib:
# These examples will be generated in the core, but we keep them here meanwhile
EXAMPLE_PARAMS: Dict[str, Dict] = {
+ "/get": {
+ "data": '{"a","b","c";"d","e","f"}',
+ "row": '"d"',
+ "column": '"c"',
+ },
"/byod": {
"widget": '"widget_name"',
+ "backend": '"backend_name"',
},
"crypto": {
"symbol": '"BTCUSD"',
@@ -247,11 +253,9 @@ class CommandLib:
standard_eg += f"{p_value}{sep}"
standard_eg = standard_eg.strip(f"{sep} ") + ")"
- if required_eg == standard_eg:
- return {"A. Required": required_eg}
- # Uncomment to add standard examples
- # return {"A. Required": required_eg, "B. Standard": standard_eg}
- return {"A. Required": required_eg}
+ if cmd in ("/get", "/byod"):
+ return {"Required": required_eg, "Standard": standard_eg}
+ return {"Required": required_eg}
def get_info(self, cmd: str) -> Dict[str, Any]:
"""Get the info for a command."""
@@ -337,7 +341,7 @@ class Editor:
def get_syntax() -> str:
if sig := cmd_info["signature"]:
syntax = "## Syntax\n\n"
- syntax += f"```{self.interface} wordwrap\n"
+ syntax += f"```{self.interface}\n"
syntax += f"{sig}\n"
syntax += "```\n\n"
return syntax
@@ -381,7 +385,7 @@ class Editor:
examples = "### Example\n\n"
for _, v in cmd_examples.items():
# examples += f"### {k}\n\n"
- examples += f"```{self.interface} wordwrap\n"
+ examples += f"```{self.interface}\n"
examples += f"{v}\n"
examples += "```\n\n"
From ad2dae277d035e88bbccc1d36c8150e1dddba923 Mon Sep 17 00:00:00 2001
From: Danglewood <85772166+deeleeramone@users.noreply.github.com>
Date: Wed, 21 Feb 2024 01:05:38 -0800
Subject: [PATCH 2/8] [Docs] Remove Reference to `to_chart` in OBBject. (#6096)
* remove reference to to_chart in OBBject.
* put chart description back in OBBject properties
---
.../developer-guidelines/architectural_considerations.md | 5 ++---
1 file changed, 2 insertions(+), 3 deletions(-)
diff --git a/website/content/platform/development/developer-guidelines/architectural_considerations.md b/website/content/platform/development/developer-guidelines/architectural_considerations.md
index 0ca133d1b31..c22071c5702 100644
--- a/website/content/platform/development/developer-guidelines/architectural_considerations.md
+++ b/website/content/platform/development/developer-guidelines/architectural_considerations.md
@@ -91,6 +91,7 @@ The `Fetcher` class implementation is based on the [TET pattern](/platform/devel
- Transform the query: the output of this method should be `QueryParams` child.
- Extract the data: the output of this method can be `Any` but it's recommended to be a `Dict` (will facilitate the next step of the fetcher's action, the data transformation).
- Transform the data: the output of this method should be a `List[Data]` or `Data` (or a child of it).
+
:::
### The `OBBject` class
@@ -101,9 +102,7 @@ The class includes several fields such as results, provider, warnings, chart, an
The class provides several methods for converting the fetched data into different formats, including `to_df` (or `to_dataframe`) for converting to a pandas DataFrame, `to_polars` for converting to a Polars DataFrame, `to_numpy` for converting to a numpy array, and `to_dict` for converting to a dictionary.
-The `to_chart` method allows for creating or updating a chart based on the fetched data, and the show method is used to display the chart.
-
-The class also includes a `__repr__` method for a human-readable representation of the object, and a `model_parametrized_name method` for returning the model name with its parameters.
+The class also includes a `__repr__` method for a human-readable representation of the object, as well as the familiar Pydantic BaseModel methods like `model_dump()` and `model_json_schema()`.
### The `Router` class
From 5f9d958260aabe2e1f4fe0f6571ea2b53c1ff803 Mon Sep 17 00:00:00 2001
From: montezdesousa <79287829+montezdesousa@users.noreply.github.com>
Date: Wed, 21 Feb 2024 12:31:18 +0000
Subject: [PATCH 3/8] [Feature] - Create openbb assets folder (#6101)
* create openbb assets folder
* fix unittest
* rebuild
---------
Co-authored-by: Igor Radovanovic <74266147+IgorWounds@users.noreply.github.com>
---
.../openbb_core/app/static/package_builder.py | 42 ++++++++++--------
.../tests/app/static/test_package_builder.py | 44 ++++++++++---------
.../{package => assets}/extension_map.json | 0
.../{package => assets}/module_map.json | 1 -
openbb_platform/openbb/package/equity.py | 8 ++--
.../openbb/package/equity_discovery.py | 12 ++---
.../openbb/package/equity_fundamental.py | 28 ++++++++----
.../openbb/package/equity_ownership.py | 2 +-
.../openbb/package/equity_price.py | 10 +++--
openbb_platform/openbb/package/etf.py | 33 ++++++++++----
.../openbb/package/fixedincome_government.py | 26 ++++++-----
openbb_platform/openbb/package/index.py | 11 +++--
openbb_platform/openbb/package/news.py | 2 +-
.../openbb/package/regulators_sec.py | 8 ++--
openbb_platform/tests/test_extension_map.py | 2 +-
15 files changed, 135 insertions(+), 94 deletions(-)
rename openbb_platform/openbb/{package => assets}/extension_map.json (100%)
rename openbb_platform/openbb/{package => assets}/module_map.json (99%)
diff --git a/openbb_platform/core/openbb_core/app/static/package_builder.py b/openbb_platform/core/openbb_core/app/static/package_builder.py
index 52a16185467..3caa6d31974 100644
--- a/openbb_platform/core/openbb_core/app/static/package_builder.py
+++ b/openbb_platform/core/openbb_core/app/static/package_builder.py
@@ -79,7 +79,9 @@ class PackageBuilder:
def auto_build(self) -> None:
"""Trigger build if there are differences between built and installed extensions."""
if Env().AUTO_BUILD:
- add, remove = PackageBuilder._diff(self.directory / "package")
+ add, remove = PackageBuilder._diff(
+ self.directory / "assets" / "extension_map.json"
+ )
if add:
a = ", ".join(add)
print(f"Extensions to add: {a}") # noqa: T201
@@ -98,7 +100,7 @@ class PackageBuilder:
) -> None:
"""Build the extensions for the Platform."""
self.console.log("\nBuilding extensions package...\n")
- self._clean_package(modules)
+ self._clean(modules)
ext_map = self._get_extension_map()
self._save_extension_map(ext_map)
self._save_module_map()
@@ -107,8 +109,9 @@ class PackageBuilder:
if self.lint:
self._run_linters()
- def _clean_package(self, modules: Optional[Union[str, List[str]]] = None) -> None:
- """Delete the package folder or modules before building."""
+ def _clean(self, modules: Optional[Union[str, List[str]]] = None) -> None:
+ """Delete the assets and package folder or modules before building."""
+ shutil.rmtree(self.directory / "assets", ignore_errors=True)
if modules:
for module in modules:
module_path = self.directory / "package" / f"{module}.py"
@@ -141,7 +144,7 @@ class PackageBuilder:
"""Save the map of extensions available at build time."""
code = dumps(obj=dict(sorted(ext_map.items())), indent=4)
self.console.log("Writing extension map...")
- self._write(code=code, name="extension_map", extension="json")
+ self._write(code=code, name="extension_map", extension="json", folder="assets")
def _save_module_map(self):
"""Save the module map."""
@@ -152,7 +155,7 @@ class PackageBuilder:
}
code = dumps(obj=dict(sorted(module_map.items())), indent=4)
self.console.log("\nWriting module map...")
- self._write(code=code, name="module_map", extension="json")
+ self._write(code=code, name="module_map", extension="json", folder="assets")
def _save_modules(
self,
@@ -197,9 +200,11 @@ class PackageBuilder:
linters.ruff()
linters.black()
- def _write(self, code: str, name: str, extension="py") -> None:
+ def _write(
+ self, code: str, name: str, extension: str = "py", folder: str = "package"
+ ) -> None:
"""Write the module to the package."""
- package_folder = self.directory / "package"
+ package_folder = self.directory / folder
package_path = package_folder / f"{name}.{extension}"
package_folder.mkdir(exist_ok=True)
@@ -209,25 +214,24 @@ class PackageBuilder:
file.write(code.replace("typing.", ""))
@staticmethod
- def _read_extension_map(package: Path) -> dict:
- """Get extension map from package folder."""
- ext_map_file = Path(package, "extension_map.json")
+ def _read(path: Path) -> dict:
+ """Get content from folder."""
try:
- with open(ext_map_file) as fp:
- ext_map = load(fp)
+ with open(Path(path)) as fp:
+ content = load(fp)
except Exception:
- ext_map = {}
+ content = {}
- return ext_map
+ return content
@staticmethod
- def _diff(package: Path) -> Tuple[Set[str], Set[str]]:
+ def _diff(path: Path) -> Tuple[Set[str], Set[str]]:
"""Check differences between built and installed extensions.
Parameters
----------
- package: Path
- The path to the package
+ path: Path
+ The path to the folder where the extension map is stored.
Returns
-------
@@ -235,7 +239,7 @@ class PackageBuilder:
First element: set of installed extensions that are not in the package.
Second element: set of extensions in the package that are not installed.
"""
- ext_map = PackageBuilder._read_extension_map(package)
+ ext_map = PackageBuilder._read(path)
add: Set[str] = set()
remove: Set[str] = set()
diff --git a/openbb_platform/core/tests/app/static/test_package_builder.py b/openbb_platform/core/tests/app/static/test_package_builder.py
index 44e7ab698d5..9057c932bcc 100644
--- a/openbb_platform/core/tests/app/static/test_package_builder.py
+++ b/openbb_platform/core/tests/app/static/test_package_builder.py
@@ -26,14 +26,14 @@ from typing_extensions import Annotated
@pytest.fixture(scope="module")
-def tmp_package_dir(tmp_path_factory):
- return tmp_path_factory.mktemp("package")
+def tmp_openbb_dir(tmp_path_factory):
+ return tmp_path_factory.mktemp("openbb")
@pytest.fixture(scope="module")
-def package_builder(tmp_package_dir):
+def package_builder(tmp_openbb_dir):
"""Return package builder."""
- return PackageBuilder(tmp_package_dir)
+ return PackageBuilder(tmp_openbb_dir)
def test_package_builder_init(package_builder):
@@ -544,14 +544,16 @@ def test_generate(docstring_generator):
assert "Returns" in doc
-def test_read_extension_map(package_builder, tmp_package_dir):
+def test_read_extension_map(package_builder, tmp_openbb_dir):
"""Test read extension map."""
PATH = "openbb_core.app.static.package_builder."
open_mock = mock_open()
with patch(PATH + "open", open_mock), patch(PATH + "load") as mock_load:
- package_builder._read_extension_map(tmp_package_dir)
- open_mock.assert_called_once_with(Path(tmp_package_dir, "extension_map.json"))
+ package_builder._read(Path(tmp_openbb_dir / "assets" / "extension_map.json"))
+ open_mock.assert_called_once_with(
+ Path(tmp_openbb_dir / "assets" / "extension_map.json")
+ )
mock_load.assert_called_once()
@@ -606,7 +608,7 @@ def test_read_extension_map(package_builder, tmp_package_dir):
)
def test_package_diff(
package_builder,
- tmp_package_dir,
+ tmp_openbb_dir,
ext_built,
ext_installed,
ext_inst_version,
@@ -619,21 +621,19 @@ def test_package_diff(
return ext_installed.select(**{"group": group})
PATH = "openbb_core.app.static.package_builder."
- with patch.object(
- PackageBuilder, "_read_extension_map"
- ) as mock_read_extension_map, patch(
+ with patch.object(PackageBuilder, "_read") as mock_read, patch(
PATH + "entry_points", mock_entry_points
- ), patch.object(
- EntryPoint, "dist", new_callable=PropertyMock
- ) as mock_obj:
+ ), patch.object(EntryPoint, "dist", new_callable=PropertyMock) as mock_obj:
class MockPathDistribution:
version = ext_inst_version
mock_obj.return_value = MockPathDistribution()
- mock_read_extension_map.return_value = ext_built
+ mock_read.return_value = ext_built
- add, remove = package_builder._diff(tmp_package_dir)
+ add, remove = package_builder._diff(
+ Path(tmp_openbb_dir, "assets", "extension_map.json")
+ )
# We add whatever is not built, but is installed
assert add == expected_add
@@ -651,20 +651,22 @@ def test_package_diff(
({"this"}, {"that"}, False),
],
)
-def test_auto_build(package_builder, tmp_package_dir, add, remove, openbb_auto_build):
+def test_auto_build(package_builder, tmp_openbb_dir, add, remove, openbb_auto_build):
"""Test auto build."""
- with patch.object(PackageBuilder, "_diff") as mock_package_diff, patch.object(
+ with patch.object(PackageBuilder, "_diff") as mock_assets_diff, patch.object(
PackageBuilder, "build"
) as mock_build, patch.object(Env, "AUTO_BUILD", openbb_auto_build):
- mock_package_diff.return_value = add, remove
+ mock_assets_diff.return_value = add, remove
package_builder.auto_build()
if openbb_auto_build:
- mock_package_diff.assert_called_once_with(Path(tmp_package_dir, "package"))
+ mock_assets_diff.assert_called_once_with(
+ Path(tmp_openbb_dir, "assets", "extension_map.json")
+ )
if add or remove:
mock_build.assert_called_once()
else:
- mock_package_diff.assert_not_called()
+ mock_assets_diff.assert_not_called()
mock_build.assert_not_called()
diff --git a/openbb_platform/openbb/package/extension_map.json b/openbb_platform/openbb/assets/extension_map.json
similarity index 100%
rename from openbb_platform/openbb/package/extension_map.json
rename to openbb_platform/openbb/assets/extension_map.json
diff --git a/openbb_platform/openbb/package/module_map.json b/openbb_platform/openbb/assets/module_map.json
similarity index 99%
rename from openbb_platform/openbb/package/module_map.json
rename to openbb_platform/openbb/assets/module_map.json
index 1188f16fbab..6b9c08e3199 100644
--- a/openbb_platform/openbb/package/module_map.json
+++ b/openbb_platform/openbb/assets/module_map.json
@@ -136,7 +136,6 @@
"index_market": "/index/market",
"index_price": "/index/price",
"index_price_historical": "/index/price/historical",
- "index_sectors": "/index/sectors",
"news": "/news",
"news_company": "/news/company",
"news_world": "/news/world",
diff --git a/openbb_platform/openbb/package/equity.py b/openbb_platform/openbb/package/equity.py
index b6bde8a8562..3b3e52a05e5 100644
--- a/openbb_platform/openbb/package/equity.py
+++ b/openbb_platform/openbb/package/equity.py
@@ -227,7 +227,7 @@ class ROUTER_equity(Container):
symbol: Annotated[
Union[str, List[str]],
OpenBBCustomParameter(
- description="Symbol to get data for. Multiple items allowed: intrinio, yfinance."
+ description="Symbol to get data for. Multiple items allowed: fmp, intrinio, yfinance."
),
],
provider: Optional[Literal["fmp", "intrinio", "yfinance"]] = None,
@@ -238,7 +238,7 @@ class ROUTER_equity(Container):
Parameters
----------
symbol : Union[str, List[str]]
- Symbol to get data for. Multiple items allowed: intrinio, yfinance.
+ Symbol to get data for. Multiple items allowed: fmp, intrinio, yfinance.
provider : Optional[Literal['fmp', 'intrinio', 'yfinance']]
The provider to use for the query, by default None.
If None, the provider specified in defaults is selected or 'fmp' if there is
@@ -403,7 +403,9 @@ class ROUTER_equity(Container):
},
extra_params=kwargs,
extra_info={
- "symbol": {"multiple_items_allowed": ["intrinio", "yfinance"]}
+ "symbol": {
+ "multiple_items_allowed": ["fmp", "intrinio", "yfinance"]
+ }
},
)
)
diff --git a/openbb_platform/openbb/package/equity_discovery.py b/openbb_platform/openbb/package/equity_discovery.py
index 6b272d10c0b..2e152439734 100644
--- a/openbb_platform/openbb/package/equity_discovery.py
+++ b/openbb_platform/openbb/package/equity_discovery.py
@@ -77,7 +77,7 @@ class ROUTER_equity_discovery(Container):
Percent change.
volume : float
The trading volume.
- market_cap : Optional[str]
+ market_cap : Optional[float]
Market Cap displayed in billions. (provider: yfinance)
avg_volume_3_months : Optional[float]
Average volume over the last 3 months in millions. (provider: yfinance)
@@ -158,7 +158,7 @@ class ROUTER_equity_discovery(Container):
Percent change.
volume : float
The trading volume.
- market_cap : Optional[str]
+ market_cap : Optional[float]
Market Cap. (provider: yfinance)
avg_volume_3_months : Optional[float]
Average volume over the last 3 months in millions. (provider: yfinance)
@@ -341,7 +341,7 @@ class ROUTER_equity_discovery(Container):
Percent change.
volume : float
The trading volume.
- market_cap : Optional[str]
+ market_cap : Optional[float]
Market Cap. (provider: yfinance)
avg_volume_3_months : Optional[float]
Average volume over the last 3 months in millions. (provider: yfinance)
@@ -422,7 +422,7 @@ class ROUTER_equity_discovery(Container):
Percent change.
volume : float
The trading volume.
- market_cap : Optional[str]
+ market_cap : Optional[float]
Market Cap. (provider: yfinance)
avg_volume_3_months : Optional[float]
Average volume over the last 3 months in millions. (provider: yfinance)
@@ -503,7 +503,7 @@ class ROUTER_equity_discovery(Container):
Percent change.
volume : float
The trading volume.
- market_cap : Optional[str]
+ market_cap : Optional[float]
Market Cap. (provider: yfinance)
avg_volume_3_months : Optional[float]
Average volume over the last 3 months in millions. (provider: yfinance)
@@ -584,7 +584,7 @@ class ROUTER_equity_discovery(Container):
Percent change.
volume : float
The trading volume.
- market_cap : Optional[str]
+ market_cap : Optional[float]
Market Cap. (provider: yfinance)
avg_volume_3_months : Optional[float]
Average volume over the last 3 months in millions. (provider: yfinance)
diff --git a/openbb_platform/openbb/package/equity_fundamental.py b/openbb_platform/openbb/package/equity_fundamental.py
index 008cda94948..8e3b669d4ea 100644
--- a/openbb_platform/openbb/package/equity_fundamental.py
+++ b/openbb_platform/openbb/package/equity_fundamental.py
@@ -1270,7 +1270,10 @@ class ROUTER_equity_fundamental(Container):
def historical_attributes(
self,
symbol: Annotated[
- str, OpenBBCustomParameter(description="Symbol to get data for.")
+ Union[str, List[str]],
+ OpenBBCustomParameter(
+ description="Symbol to get data for. Multiple items allowed: intrinio."
+ ),
],
tag: Annotated[
Union[str, List[str]],
@@ -1313,8 +1316,8 @@ class ROUTER_equity_fundamental(Container):
Parameters
----------
- symbol : str
- Symbol to get data for.
+ symbol : Union[str, List[str]]
+ Symbol to get data for. Multiple items allowed: intrinio.
tag : Union[str, List[str]]
Intrinio data tag ID or code. Multiple items allowed: intrinio.
start_date : Union[datetime.date, None, str]
@@ -1386,7 +1389,10 @@ class ROUTER_equity_fundamental(Container):
"sort": sort,
},
extra_params=kwargs,
- extra_info={"tag": {"multiple_items_allowed": ["intrinio"]}},
+ extra_info={
+ "symbol": {"multiple_items_allowed": ["intrinio"]},
+ "tag": {"multiple_items_allowed": ["intrinio"]},
+ },
)
)
@@ -2033,7 +2039,10 @@ class ROUTER_equity_fundamental(Container):
def latest_attributes(
self,
symbol: Annotated[
- str, OpenBBCustomParameter(description="Symbol to get data for.")
+ Union[str, List[str]],
+ OpenBBCustomParameter(
+ description="Symbol to get data for. Multiple items allowed: intrinio."
+ ),
],
tag: Annotated[
Union[str, List[str]],
@@ -2048,8 +2057,8 @@ class ROUTER_equity_fundamental(Container):
Parameters
----------
- symbol : str
- Symbol to get data for.
+ symbol : Union[str, List[str]]
+ Symbol to get data for. Multiple items allowed: intrinio.
tag : Union[str, List[str]]
Intrinio data tag ID or code. Multiple items allowed: intrinio.
provider : Optional[Literal['intrinio']]
@@ -2101,7 +2110,10 @@ class ROUTER_equity_fundamental(Container):
"tag": tag,
},
extra_params=kwargs,
- extra_info={"tag": {"multiple_items_allowed": ["intrinio"]}},
+ extra_info={
+ "symbol": {"multiple_items_allowed": ["intrinio"]},
+ "tag": {"multiple_items_allowed": ["intrinio"]},
+ },
)
)
diff --git a/openbb_platform/openbb/package/equity_ownership.py b/openbb_platform/openbb/package/equity_ownership.py
index ca38f7f2311..bec7986b08c 100644
--- a/openbb_platform/openbb/package/equity_ownership.py
+++ b/openbb_platform/openbb/package/equity_ownership.py
@@ -74,7 +74,7 @@ class ROUTER_equity_ownership(Container):
InsiderTrading
--------------
- symbol : str
+ symbol : Optional[str]
Symbol representing the entity requested in the data.
company_cik : Optional[Union[int, str]]
CIK number of the company.
diff --git a/openbb_platform/openbb/package/equity_price.py b/openbb_platform/openbb/package/equity_price.py
index bb1f681acd7..712204e4dbf 100644
--- a/openbb_platform/openbb/package/equity_price.py
+++ b/openbb_platform/openbb/package/equity_price.py
@@ -335,7 +335,10 @@ class ROUTER_equity_price(Container):
def performance(
self,
symbol: Annotated[
- str, OpenBBCustomParameter(description="Symbol to get data for.")
+ Union[str, List[str]],
+ OpenBBCustomParameter(
+ description="Symbol to get data for. Multiple items allowed: fmp."
+ ),
],
provider: Optional[Literal["fmp"]] = None,
**kwargs
@@ -344,8 +347,8 @@ class ROUTER_equity_price(Container):
Parameters
----------
- symbol : str
- Symbol to get data for.
+ symbol : Union[str, List[str]]
+ Symbol to get data for. Multiple items allowed: fmp.
provider : Optional[Literal['fmp']]
The provider to use for the query, by default None.
If None, the provider specified in defaults is selected or 'fmp' if there is
@@ -418,6 +421,7 @@ class ROUTER_equity_price(Container):
"symbol": symbol,
},
extra_params=kwargs,
+ extra_info={"symbol": {"multiple_items_allowed": ["fmp"]}},
)
)
diff --git a/openbb_platform/openbb/package/etf.py b/openbb_platform/openbb/package/etf.py
index e076e815c9c..f50f4f2376b 100644
--- a/openbb_platform/openbb/package/etf.py
+++ b/openbb_platform/openbb/package/etf.py
@@ -32,7 +32,10 @@ class ROUTER_etf(Container):
def countries(
self,
symbol: Annotated[
- str, OpenBBCustomParameter(description="Symbol to get data for. (ETF)")
+ Union[str, List[str]],
+ OpenBBCustomParameter(
+ description="Symbol to get data for. (ETF) Multiple items allowed: fmp."
+ ),
],
provider: Optional[Literal["fmp"]] = None,
**kwargs
@@ -41,8 +44,8 @@ class ROUTER_etf(Container):
Parameters
----------
- symbol : str
- Symbol to get data for. (ETF)
+ symbol : Union[str, List[str]]
+ Symbol to get data for. (ETF) Multiple items allowed: fmp.
provider : Optional[Literal['fmp']]
The provider to use for the query, by default None.
If None, the provider specified in defaults is selected or 'fmp' if there is
@@ -87,6 +90,7 @@ class ROUTER_etf(Container):
"symbol": symbol,
},
extra_params=kwargs,
+ extra_info={"symbol": {"multiple_items_allowed": ["fmp"]}},
)
)
@@ -237,6 +241,9 @@ class ROUTER_etf(Container):
-------
>>> from openbb import obb
>>> obb.etf.historical(symbol="SPY")
+ >>> obb.etf.historical("SPY", provider="yfinance")
+ >>> #### This function accepts multiple tickers. ####
+ >>> obb.etf.historical("SPY,IWM,QQQ,DJIA", provider="yfinance")
""" # noqa: E501
return self._run(
@@ -558,7 +565,10 @@ class ROUTER_etf(Container):
def holdings_performance(
self,
symbol: Annotated[
- str, OpenBBCustomParameter(description="Symbol to get data for.")
+ Union[str, List[str]],
+ OpenBBCustomParameter(
+ description="Symbol to get data for. Multiple items allowed: fmp."
+ ),
],
provider: Optional[Literal["fmp"]] = None,
**kwargs
@@ -567,8 +577,8 @@ class ROUTER_etf(Container):
Parameters
----------
- symbol : str
- Symbol to get data for.
+ symbol : Union[str, List[str]]
+ Symbol to get data for. Multiple items allowed: fmp.
provider : Optional[Literal['fmp']]
The provider to use for the query, by default None.
If None, the provider specified in defaults is selected or 'fmp' if there is
@@ -641,6 +651,7 @@ class ROUTER_etf(Container):
"symbol": symbol,
},
extra_params=kwargs,
+ extra_info={"symbol": {"multiple_items_allowed": ["fmp"]}},
)
)
@@ -808,7 +819,10 @@ class ROUTER_etf(Container):
def price_performance(
self,
symbol: Annotated[
- str, OpenBBCustomParameter(description="Symbol to get data for.")
+ Union[str, List[str]],
+ OpenBBCustomParameter(
+ description="Symbol to get data for. Multiple items allowed: fmp."
+ ),
],
provider: Optional[Literal["fmp"]] = None,
**kwargs
@@ -817,8 +831,8 @@ class ROUTER_etf(Container):
Parameters
----------
- symbol : str
- Symbol to get data for.
+ symbol : Union[str, List[str]]
+ Symbol to get data for. Multiple items allowed: fmp.
provider : Optional[Literal['fmp']]
The provider to use for the query, by default None.
If None, the provider specified in defaults is selected or 'fmp' if there is
@@ -891,6 +905,7 @@ class ROUTER_etf(Container):
"symbol": symbol,
},
extra_params=kwargs,
+ extra_info={"symbol": {"multiple_items_allowed": ["fmp"]}},
)
)
diff --git a/openbb_platform/openbb/package/fixedincome_government.py b/openbb_platform/openbb/package/fixedincome_government.py
index ed4cc770d9c..205cb3e4308 100644
--- a/openbb_platform/openbb/package/fixedincome_government.py
+++ b/openbb_platform/openbb/package/fixedincome_government.py
@@ -69,30 +69,32 @@ class ROUTER_fixedincome_government(Container):
-------------
date : date
The date of the data.
+ week_4 : Optional[float]
+ 4 week Treasury bills rate (secondary market).
month_1 : Optional[float]
- 1 month treasury rate.
+ 1 month Treasury rate.
month_2 : Optional[float]
- 2 month treasury rate.
+ 2 month Treasury rate.
month_3 : Optional[float]
- 3 month treasury rate.
+ 3 month Treasury rate.
month_6 : Optional[float]
- 6 month treasury rate.
+ 6 month Treasury rate.
year_1 : Optional[float]
- 1 year treasury rate.
+ 1 year Treasury rate.
year_2 : Optional[float]
- 2 year treasury rate.
+ 2 year Treasury rate.
year_3 : Optional[float]
- 3 year treasury rate.
+ 3 year Treasury rate.
year_5 : Optional[float]
- 5 year treasury rate.
+ 5 year Treasury rate.
year_7 : Optional[float]
- 7 year treasury rate.
+ 7 year Treasury rate.
year_10 : Optional[float]
- 10 year treasury rate.
+ 10 year Treasury rate.
year_20 : Optional[float]
- 20 year treasury rate.
+ 20 year Treasury rate.
year_30 : Optional[float]
- 30 year treasury rate.
+ 30 year Treasury rate.
Example
-------
diff --git a/openbb_platform/openbb/package/index.py b/openbb_platform/openbb/package/index.py
index c88ed601abf..bb8554eed04 100644
--- a/openbb_platform/openbb/package/index.py
+++ b/openbb_platform/openbb/package/index.py
@@ -90,9 +90,8 @@ class ROUTER_index(Container):
@validate
def constituents(
self,
- index: Annotated[
- str,
- OpenBBCustomParameter(description="Index to fetch the constituents of."),
+ symbol: Annotated[
+ str, OpenBBCustomParameter(description="Symbol to get data for.")
],
provider: Optional[Literal["fmp"]] = None,
**kwargs
@@ -101,8 +100,8 @@ class ROUTER_index(Container):
Parameters
----------
- index : str
- Index to fetch the constituents of.
+ symbol : str
+ Symbol to get data for.
provider : Optional[Literal['fmp']]
The provider to use for the query, by default None.
If None, the provider specified in defaults is selected or 'fmp' if there is
@@ -160,7 +159,7 @@ class ROUTER_index(Container):
)
},
standard_params={
- "index": index,
+ "symbol": symbol,
},
extra_params=kwargs,
)
diff --git a/openbb_platform/openbb/package/news.py b/openbb_platform/openbb/package/news.py
index bcbfb6f1885..e3d19482cea 100644
--- a/openbb_platform/openbb/package/news.py
+++ b/openbb_platform/openbb/package/news.py
@@ -133,7 +133,7 @@ class ROUTER_news(Container):
Author of the article. (provider: benzinga, polygon)
teaser : Optional[str]
Teaser of the news. (provider: benzinga)
- images : Optional[Union[List[Dict[str, str]], List[str], str]]
+ images : Optional[Union[List[Dict[str, str]], str, List[str]]]
URL to the images of the news. (provider: benzinga, fmp)
channels : Optional[str]
Channels associated with the news. (provider: benzinga)
diff --git a/openbb_platform/openbb/package/regulators_sec.py b/openbb_platform/openbb/package/regulators_sec.py
index 9238d28bd7d..012bb4803b7 100644
--- a/openbb_platform/openbb/package/regulators_sec.py
+++ b/openbb_platform/openbb/package/regulators_sec.py
@@ -29,7 +29,7 @@ class ROUTER_regulators_sec(Container):
symbol: Annotated[
Union[str, List[str]],
OpenBBCustomParameter(
- description="Symbol to get data for. Multiple items allowed: intrinio, yfinance."
+ description="Symbol to get data for. Multiple items allowed: fmp, intrinio, yfinance."
),
],
provider: Optional[Literal["sec"]] = None,
@@ -40,7 +40,7 @@ class ROUTER_regulators_sec(Container):
Parameters
----------
symbol : Union[str, List[str]]
- Symbol to get data for. Multiple items allowed: intrinio, yfinance.
+ Symbol to get data for. Multiple items allowed: fmp, intrinio, yfinance.
provider : Optional[Literal['sec']]
The provider to use for the query, by default None.
If None, the provider specified in defaults is selected or 'sec' if there is
@@ -87,7 +87,9 @@ class ROUTER_regulators_sec(Container):
},
extra_params=kwargs,
extra_info={
- "symbol": {"multiple_items_allowed": ["intrinio", "yfinance"]}
+ "symbol": {
+ "multiple_items_allowed": ["fmp", "intrinio", "yfinance"]
+ }
},
)
)
diff --git a/openbb_platform/tests/test_extension_map.py b/openbb_platform/tests/test_extension_map.py
index 0c881bfd3f1..2bf082e891d 100644
--- a/openbb_platform/tests/test_extension_map.py
+++ b/openbb_platform/tests/test_extension_map.py
@@ -37,7 +37,7 @@ def test_extension_map():
"""Ensure only required extensions are built and versions respect pyproject.toml"""
this_dir = Path(__file__).parent
ext_map = load_ext_map(
- Path(this_dir, "..", "openbb", "package", "extension_map.json")
+ Path(this_dir, "..", "openbb", "assets", "extension_map.json")
)
req_ext = load_req_ext(Path(this_dir, "..", "pyproject.toml"))
From 754e14ca71a8d08ac002036b0fc9edfa1459e44a Mon Sep 17 00:00:00 2001
From: Danglewood <85772166+deeleeramone@users.noreply.github.com>
Date: Wed, 21 Feb 2024 04:37:09 -0800
Subject: [PATCH 4/8] [BugFix] Fixes to_df() where the date series contains
multiple TZ-offsets. (#6099)
* allow mixed utc offset in a tz-aware date column
* enforce ascending from all providers for price.historical
* fix tests
* add comments and apply treatment to basemodel_from_df before JSON output
* yfinance consistency
* add unit test
* add unit test
* better test
* ruff
---------
Co-authored-by: Igor Radovanovic <74266147+IgorWounds@users.noreply.github.com>
---
.../core/openbb_core/app/model/obbject.py | 16 +++---
openbb_platform/core/openbb_core/app/utils.py | 25 +++++++--
.../standard_models/crypto_historical.py | 8 ++-
.../standard_models/currency_historical.py | 8 ++-
.../standard_models/equity_historical.py | 7 ++-
.../standard_models/etf_historical.py | 15 ++++--
.../standard_models/index_historical.py | 7 ++-
.../core/tests/app/model/test_obbject.py | 54 +++++++++++++++----
.../models/equity_historical.py | 20 +++++--
.../openbb_fmp/models/crypto_historical.py | 5 +-
.../openbb_fmp/models/currency_historical.py | 5 +-
.../openbb_fmp/models/equity_historical.py | 5 +-
.../fmp/openbb_fmp/models/index_historical.py | 8 +--
.../models/equity_historical.py | 5 +-
.../models/crypto_historical.py | 7 ++-
.../models/currency_historical.py | 7 ++-
.../models/equity_historical.py | 9 ++--
.../openbb_polygon/models/index_historical.py | 9 ++--
.../models/crypto_historical.py | 13 ++---
.../models/currency_historical.py | 6 ++-
.../models/equity_historical.py | 4 +-
.../models/index_historical.py | 5 +-
22 files changed, 173 insertions(+), 75 deletions(-)
diff --git a/openbb_platform/core/openbb_core/app/model/obbject.py b/openbb_platform/core/openbb_core/app/model/obbject.py
index 7251dc9969b..9f2a2da8746 100644
--- a/openbb_platform/core/openbb_core/app/model/obbject.py
+++ b/openbb_platform/core/openbb_core/app/model/obbject.py
@@ -113,13 +113,13 @@ class OBBject(Tagged, Generic[T]):
return f"OBBject[{cls.results_type_repr(params)}]"
def to_df(
- self, index: Optional[str] = None, sort_by: Optional[str] = None
+ self, index: Optional[Union[str, None]] = "date", sort_by: Optional[str] = None
) -> pd.DataFrame:
"""Alias for `to_dataframe`."""
return self.to_dataframe(index=index, sort_by=sort_by)
def to_dataframe(
- self, index: Optional[str] = None, sort_by: Optional[str] = None
+ self, index: Optional[Union[str, None]] = "date", sort_by: Optional[str] = None
) -> pd.DataFrame:
"""Convert results field to pandas dataframe.
@@ -173,7 +173,7 @@ class OBBject(Tagged, Generic[T]):
for k, v in r.items():
# Dict[str, List[BaseModel]]
if is_list_of_basemodel(v):
- dict_of_df[k] = basemodel_to_df(v, index or "date")
+ dict_of_df[k] = basemodel_to_df(v, index)
sort_columns = False
# Dict[str, Any]
else:
@@ -184,14 +184,14 @@ class OBBject(Tagged, Generic[T]):
# List[BaseModel]
elif is_list_of_basemodel(res):
dt: Union[List[Data], Data] = res # type: ignore
- df = basemodel_to_df(dt, index or "date")
+ df = basemodel_to_df(dt, index)
sort_columns = False
# List[List | str | int | float] | Dict[str, Dict | List | BaseModel]
else:
try:
df = pd.DataFrame(res)
# Set index, if any
- if index and index in df.columns:
+ if index is not None and index in df.columns:
df.set_index(index, inplace=True)
except ValueError:
@@ -234,11 +234,11 @@ class OBBject(Tagged, Generic[T]):
"Please install polars: `pip install polars pyarrow` to use this method."
) from exc
- return from_pandas(self.to_dataframe().reset_index())
+ return from_pandas(self.to_dataframe(index=None))
def to_numpy(self) -> ndarray:
"""Convert results field to numpy array."""
- return self.to_dataframe().reset_index().to_numpy()
+ return self.to_dataframe(index=None).to_numpy()
def to_dict(
self,
@@ -259,7 +259,7 @@ class OBBject(Tagged, Generic[T]):
Dict[str, List]
Dictionary of lists.
"""
- df = self.to_dataframe() # type: ignore
+ df = self.to_dataframe(index=None) # type: ignore
transpose = False
if orient == "list":
transpose = True
diff --git a/openbb_platform/core/openbb_core/app/utils.py b/openbb_platform/core/openbb_core/app/utils.py
index b1a563131ac..4004e3060a9 100644
--- a/openbb_platform/core/openbb_core/app/utils.py
+++ b/openbb_platform/core/openbb_core/app/utils.py
@@ -2,6 +2,7 @@
import ast
import json
+from datetime import time
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
@@ -16,7 +17,7 @@ from openbb_core.provider.abstract.data import Data
def basemodel_to_df(
data: Union[List[Data], Data],
- index: Optional[Union[str, Iterable]] = None,
+ index: Optional[Union[None, str, Iterable]] = None,
) -> pd.DataFrame:
"""Convert list of BaseModel to a Pandas DataFrame."""
if isinstance(data, list):
@@ -32,12 +33,20 @@ def basemodel_to_df(
df = df.set_index(col_names)
df = df.drop(["is_multiindex", "multiindex_names"], axis=1)
+ # If the date column contains dates only, convert them to a date to avoid encoding time data.
+ if "date" in df.columns:
+ df["date"] = df["date"].apply(pd.to_datetime)
+ if all(t.time() == time(0, 0) for t in df["date"]):
+ df["date"] = df["date"].apply(lambda x: x.date())
+
if index and index in df.columns:
- df = df.set_index(index)
- # TODO: This should probably check if the index can be converted to a datetime instead of just assuming
- if df.index.name == "date":
- df.index = pd.to_datetime(df.index)
+ if index == "date":
+ df.set_index("date", inplace=True)
df.sort_index(axis=0, inplace=True)
+ else:
+ df = (
+ df.set_index(index) if index is not None and index in df.columns else df
+ )
return df
@@ -59,6 +68,12 @@ def df_to_basemodel(
df["multiindex_names"] = str(df.index.names)
df = df.reset_index()
+ # Converting to JSON will add T00:00:00.000 to all dates with no time element unless we format it as a string first.
+ if "date" in df.columns:
+ df["date"] = df["date"].apply(pd.to_datetime)
+ if all(t.time() == time(0, 0) for t in df["date"]):
+ df["date"] = df["date"].apply(lambda x: x.date().strftime("%Y-%m-%d"))
+
return [
Data(**d) for d in json.loads(df.to_json(orient="records", date_format="iso"))
]
diff --git a/openbb_platform/core/openbb_core/provider/standard_models/crypto_historical.py b/openbb_platform/core/openbb_core/provider/standard_models/crypto_historical.py
index eeeda10a212..f3385d4932d 100644
--- a/openbb_platform/core/openbb_core/provider/standard_models/crypto_historical.py
+++ b/openbb_platform/core/openbb_core/provider/standard_models/crypto_historical.py
@@ -45,7 +45,9 @@ class CryptoHistoricalQueryParams(QueryParams):
class CryptoHistoricalData(Data):
"""Crypto Historical Price Data."""
- date: datetime = Field(description=DATA_DESCRIPTIONS.get("date", ""))
+ date: Union[dateType, datetime] = Field(
+ description=DATA_DESCRIPTIONS.get("date", "")
+ )
open: PositiveFloat = Field(description=DATA_DESCRIPTIONS.get("open", ""))
high: PositiveFloat = Field(description=DATA_DESCRIPTIONS.get("high", ""))
low: PositiveFloat = Field(description=DATA_DESCRIPTIONS.get("low", ""))
@@ -59,4 +61,6 @@ class CryptoHistoricalData(Data):
@classmethod
def date_validate(cls, v): # pylint: disable=E0213
"""Return formatted datetime."""
- return parser.isoparse(str(v))
+ if ":" in str(v):
+ return parser.isoparse(str(v))
+ return parser.parse(str(v)).date()
diff --git a/openbb_platform/core/openbb_core/provider/standard_models/currency_historical.py b/openbb_platform/core/openbb_core/provider/standard_models/currency_historical.py
index b7cedc20860..196ce3e1c37 100644
--- a/openbb_platform/core/openbb_core/provider/standard_models/currency_historical.py
+++ b/openbb_platform/core/openbb_core/provider/standard_models/currency_historical.py
@@ -46,7 +46,9 @@ class CurrencyHistoricalQueryParams(QueryParams):
class CurrencyHistoricalData(Data):
"""Currency Historical Price Data."""
- date: datetime = Field(description=DATA_DESCRIPTIONS.get("date", ""))
+ date: Union[dateType, datetime] = Field(
+ description=DATA_DESCRIPTIONS.get("date", "")
+ )
open: PositiveFloat = Field(description=DATA_DESCRIPTIONS.get("open", ""))
high: PositiveFloat = Field(description=DATA_DESCRIPTIONS.get("high", ""))
low: PositiveFloat = Field(description=DATA_DESCRIPTIONS.get("low", ""))
@@ -61,4 +63,6 @@ class CurrencyHistoricalData(Data):
@field_validator("date", mode="before", check_fields=False)
def date_validate(cls, v): # pylint: disable=E0213
"""Return formatted datetime."""
- return parser.isoparse(str(v))
+ if ":" in str(v):
+ return parser.isoparse(str(v))
+ return parser.parse(str(v)).date()
diff --git a/openbb_platform/core/openbb_core/provider/standard_models/equity_historical.py b/openbb_platform/core/openbb_core/provider/standard_models/equity_historical.py
index 2789e57ba35..e888928e683 100644
--- a/openbb_platform/core/openbb_core/provider/standard_models/equity_historical.py
+++ b/openbb_platform/core/openbb_core/provider/standard_models/equity_historical.py
@@ -61,7 +61,6 @@ class EquityHistoricalData(Data):
@field_validator("date", mode="before", check_fields=False)
def date_validate(cls, v): # pylint: disable=E0213
"""Return formatted datetime."""
- v = parser.isoparse(str(v))
- if v.hour == 0 and v.minute == 0:
- return v.date()
- return v
+ if ":" in str(v):
+ return parser.isoparse(str(v))
+ return parser.parse(str(v)).date()
diff --git a/openbb_platform/core/openbb_core/provider/standard_models/etf_historical.py b/openbb_platform/core/openbb_core/provider/standard_models/etf_historical.py
index 6a2eb9d5dee..f9a4b5448ed 100644
--- a/openbb_platform/core/openbb_core/provider/standard_models/etf_historical.py
+++ b/openbb_platform/core/openbb_core/provider/standard_models/etf_historical.py
@@ -1,7 +1,10 @@
"""ETF Historical Price Standard Model."""
-from datetime import date as dateType
-from typing import Optional
+from datetime import (
+ date as dateType,
+ datetime,
+)
+from typing import Optional, Union
from dateutil import parser
from pydantic import Field, NonNegativeInt, PositiveFloat, field_validator
@@ -37,7 +40,9 @@ class EtfHistoricalQueryParams(QueryParams):
class EtfHistoricalData(Data):
"""ETF Historical Price Data."""
- date: dateType = Field(description=DATA_DESCRIPTIONS.get("date", ""))
+ date: Union[dateType, datetime] = Field(
+ description=DATA_DESCRIPTIONS.get("date", "")
+ )
open: PositiveFloat = Field(description=DATA_DESCRIPTIONS.get("open", ""))
high: PositiveFloat = Field(description=DATA_DESCRIPTIONS.get("high", ""))
low: PositiveFloat = Field(description=DATA_DESCRIPTIONS.get("low", ""))
@@ -49,4 +54,6 @@ class EtfHistoricalData(Data):
@field_validator("date", mode="before", check_fields=False)
def date_validate(cls, v): # pylint: disable=E0213
"""Return formatted datetime."""
- return parser.isoparse(str(v))
+ if ":" in str(v):
+ return parser.isoparse(str(v))
+ return parser.parse(str(v)).date()
diff --git a/openbb_platform/core/openbb_core/provider/standard_models/index_historical.py b/openbb_platform/core/openbb_core/provider/standard_models/index_historical.py
index e6183bf9921..f9a381698cb 100644
--- a/openbb_platform/core/openbb_core/provider/standard_models/index_historical.py
+++ b/openbb_platform/core/openbb_core/provider/standard_models/index_historical.py
@@ -73,7 +73,6 @@ class IndexHistoricalData(Data):
@classmethod
def date_validate(cls, v):
"""Return formatted datetime."""
- v = parser.isoparse(str(v))
- if v.hour == 0 and v.minute == 0:
- return v.date()
- return v
+ if ":" in str(v):
+ return parser.isoparse(str(v))
+ return parser.parse(str(v)).date()
diff --git a/openbb_platform/core/tests/app/model/test_obbject.py b/openbb_platform/core/tests/app/model/test_obbject.py
index 0e9692b8f70..ffe5b8368b2 100644
--- a/openbb_platform/core/tests/app/model/test_obbject.py
+++ b/openbb_platform/core/tests/app/model/test_obbject.py
@@ -5,6 +5,7 @@ from unittest.mock import MagicMock
import pandas as pd
import pytest
from openbb_core.app.model.obbject import Chart, OBBject, OpenBBError
+from openbb_core.app.utils import basemodel_to_df
from openbb_core.provider.abstract.data import Data
from pandas.testing import assert_frame_equal
@@ -48,6 +49,13 @@ class MockMultiData(Data):
value: float
+class MockDataFrame(Data):
+ """Test helper."""
+
+ date: str
+ value: float
+
+
@pytest.mark.parametrize(
"results, expected_df",
[
@@ -171,27 +179,27 @@ class MockMultiData(Data):
"df1": pd.DataFrame(
{
"date": [
- pd.to_datetime("1956-01-01"),
- pd.to_datetime("1956-02-01"),
- pd.to_datetime("1956-03-01"),
+ pd.to_datetime("1956-01-01").date(),
+ pd.to_datetime("1956-02-01").date(),
+ pd.to_datetime("1956-03-01").date(),
],
"another_date": ["2023-09-01", "2023-09-01", "2023-09-01"],
"value": [0.0, 0.0, 0.0],
},
columns=["date", "another_date", "value"],
- ).set_index("date"),
+ ),
"df2": pd.DataFrame(
{
"date": [
- pd.to_datetime("1955-03-01"),
- pd.to_datetime("1955-04-01"),
- pd.to_datetime("1955-05-01"),
+ pd.to_datetime("1955-03-01").date(),
+ pd.to_datetime("1955-04-01").date(),
+ pd.to_datetime("1955-05-01").date(),
],
"another_date": ["2023-09-01", "2023-09-01", "2023-09-01"],
"value": [0.0, 0.0, 0.0],
},
columns=["date", "another_date", "value"],
- ).set_index("date"),
+ ),
},
axis=1,
sort=True,
@@ -210,11 +218,11 @@ def test_to_dataframe(results, expected_df):
# Act and Assert
if isinstance(expected_df, pd.DataFrame):
- result = co.to_dataframe()
+ result = co.to_dataframe(index=None)
assert_frame_equal(result, expected_df)
else:
with pytest.raises(expected_df.__class__) as exc_info:
- co.to_dataframe()
+ co.to_dataframe(index=None)
assert str(exc_info.value) == str(expected_df)
@@ -256,6 +264,32 @@ def test_to_dataframe_w_args(results, index, sort_by):
assert result[sort_by].is_monotonic_increasing
+@pytest.mark.parametrize(
+ "results",
+ # Test case 1: List of models with daylight savings crossover.
+ (
+ [
+ MockDataFrame(date="2023-11-03 00:00:00-04:00", value=10),
+ MockDataFrame(date="2023-11-03 08:00:00-04:00", value=9),
+ MockDataFrame(date="2023-11-03 16:00:00-04:00", value=8),
+ MockDataFrame(date="2023-11-06 00:00:00-05:00", value=11),
+ MockDataFrame(date="2023-11-06 08:00:00-05:00", value=7),
+ MockDataFrame(date="2023-11-06 16:00:00-05:00", value=12),
+ ],
+ ),
+)
+def test_to_df_daylight_savings(results):
+ """Test helper."""
+ # Arrange
+ co = OBBject(results=results)
+
+ # Act and Assert
+ expected_df = basemodel_to_df(results, index="date")
+ result = co.to_dataframe(index="date")
+ assert isinstance(result, pd.DataFrame)
+ assert_frame_equal(expected_df, result)
+
+
@pytest.mark.parametrize(
"results, expected_dict",
[ # Case 1: Normal results with "date" column
diff --git a/openbb_platform/providers/alpha_vantage/openbb_alpha_vantage/models/equity_historical.py b/openbb_platform/providers/alpha_vantage/openbb_alpha_vantage/models/equity_historical.py
index 972d563c7aa..98a5194b955 100644
--- a/openbb_platform/providers/alpha_vantage/openbb_alpha_vantage/models/equity_historical.py
+++ b/openbb_platform/providers/alpha_vantage/openbb_alpha_vantage/models/equity_historical.py
@@ -1,12 +1,11 @@
"""Alpha Vantage Equity Historical Price Model."""
-from datetime import datetime
+from datetime import datetime, timedelta
from typing import Any, Dict, List, Literal, Optional
from dateutil.relativedelta import relativedelta
from openbb_alpha_vantage.utils.helpers import (
extract_key_name,
- filter_by_dates,
get_interval,
)
from openbb_core.provider.abstract.fetcher import Fetcher
@@ -19,6 +18,7 @@ from openbb_core.provider.utils.descriptions import (
QUERY_DESCRIPTIONS,
)
from openbb_core.provider.utils.helpers import amake_request, get_querystring
+from pandas import to_datetime
from pydantic import (
Field,
NonNegativeFloat,
@@ -201,7 +201,17 @@ class AVEquityHistoricalFetcher(
}
for date, values in content.items()
]
- filter_by_dates(d, query.start_date, query.end_date)
- transformed_data += d
+ if query.start_date and query.end_date:
+ transformed_data = list(
+ filter(
+ lambda x: to_datetime(query.start_date)
+ <= to_datetime(x["date"])
+ <= to_datetime(query.end_date) + timedelta(days=1),
+ d,
+ )
+ )
- return [AVEquityHistoricalData.model_validate(d) for d in transformed_data]
+ return [
+ AVEquityHistoricalData.model_validate(d)
+ for d in sorted(transformed_data, key=lambda x: x["date"], reverse=False)
+ ]
diff --git a/openbb_platform/providers/fmp/openbb_fmp/models/crypto_historical.py b/openbb_platform/providers/fmp/openbb_fmp/models/crypto_historical.py
index a9a0c1fceaa..69018d01003 100644
--- a/openbb_platform/providers/fmp/openbb_fmp/models/crypto_historical.py
+++ b/openbb_platform/providers/fmp/openbb_fmp/models/crypto_historical.py
@@ -107,4 +107,7 @@ class FMPCryptoHistoricalFetcher(
query: FMPCryptoHistoricalQueryParams, data: List[Dict], **kwargs: Any
) -> List[FMPCryptoHistoricalData]:
"""Return the transformed data."""
- return [FMPCryptoHistoricalData.model_validate(d) for d in data]
+ return [
+ FMPCryptoHistoricalData.model_validate(d)
+ for d in sorted(data, key=lambda x: x["date"], reverse=False)
+ ]
diff --git a/openbb_platform/providers/fmp/openbb_fmp/models/currency_historical.py b/openbb_platform/providers/fmp/openbb_fmp/models/currency_historical.py
index e54de3b4fa0..11b4aa780ed 100644
--- a/openbb_platform/providers/fmp/openbb_fmp/models/currency_historical.py
+++ b/openbb_platform/providers/fmp/openbb_fmp/models/currency_historical.py
@@ -107,4 +107,7 @@ class FMPCurrencyHistoricalFetcher(
and d.get("change") != 0
and d.get("changeOverTime") != 0
]
- return [FMPCurrencyHistoricalData.model_validate(d) for d in data]
+ return [
+ FMPCurrencyHistoricalData.model_validate(d)
+ for d in sorted(data, key=lambda x: x["date"], reverse=False)
+ ]
diff --git a/openbb_platform/providers/fmp/openbb_fmp/models/equity_historical.py b/openbb_platform/providers/fmp/openbb_fmp/models/equity_historical.py
index 105ce3c72e1..86cb7635e83 100644
--- a/openbb_platform/providers/fmp/openbb_fmp/models/equity_historical.py
+++ b/openbb_platform/providers/fmp/openbb_fmp/models/equity_historical.py
@@ -132,4 +132,7 @@ class FMPEquityHistoricalFetcher(
query: FMPEquityHistoricalQueryParams, data: List[Dict], **kwargs: Any
) -> List[FMPEquityHistoricalData]:
"""Return the transformed data."""
- return [FMPEquityHistoricalData.model_validate(d) for d in data]
+ return [
+ FMPEquityHistoricalData.model_validate(d)
+ for d in sorted(data, key=lambda x: x["date"], reverse=False)
+ ]
diff --git a/openbb_platform/providers/fmp/openbb_fmp/models/index_historical.py b/openbb_platform/providers/fmp/openbb_fmp/models/index_historical.py
index 01de38af1a8..8ebf3db2f38 100644
--- a/openbb_platform/providers/fmp/openbb_fmp/models/index_historical.py
+++ b/openbb_platform/providers/fmp/openbb_fmp/models/index_historical.py
@@ -109,7 +109,7 @@ class FMPIndexHistoricalFetcher(
query: FMPIndexHistoricalQueryParams, data: List[Dict], **kwargs: Any
) -> List[FMPIndexHistoricalData]:
"""Return the transformed data."""
- if query.sort == "asc":
- data = sorted(data, key=lambda x: x["date"], reverse=True)
-
- return [FMPIndexHistoricalData.model_validate(d) for d in data]
+ return [
+ FMPIndexHistoricalData.model_validate(d)
+ for d in sorted(data, key=lambda x: x["date"], reverse=False)
+ ]
diff --git a/openbb_platform/providers/intrinio/openbb_intrinio/models/equity_historical.py b/openbb_platform/providers/intrinio/openbb_intrinio/models/equity_historical.py
index c4db7c181e5..6b98eb5a53a 100644
--- a/openbb_platform/providers/intrinio/openbb_intrinio/models/equity_historical.py
+++ b/openbb_platform/providers/intrinio/openbb_intrinio/models/equity_historical.py
@@ -229,4 +229,7 @@ class IntrinioEquityHistoricalFetcher(
**kwargs: Any,
) -> List[IntrinioEquityHistoricalData]:
"""Return the transformed data."""
- return [IntrinioEquityHistoricalData.model_validate(d) for d in data]
+ return [
+ IntrinioEquityHistoricalData.model_validate(d)
+ for d in sorted(data, key=lambda x: x["date"], reverse=False)
+ ]
diff --git a/openbb_platform/providers/polygon/openbb_polygon/models/crypto_historical.py b/openbb_platform/providers/polygon/openbb_polygon/models/crypto_historical.py
index 9c19a47579a..4239e7cbab9 100644
--- a/openbb_platform/providers/polygon/openbb_polygon/models/crypto_historical.py
+++ b/openbb_platform/providers/polygon/openbb_polygon/models/crypto_historical.py
@@ -152,7 +152,7 @@ class PolygonCryptoHistoricalFetcher(
for r in results:
r["t"] = datetime.fromtimestamp(r["t"] / 1000, tz=timezone("UTC"))
if query._timespan not in ["second", "minute", "hour"]:
- r["t"] = r["t"].date()
+ r["t"] = r["t"].date().strftime("%Y-%m-%d")
else:
r["t"] = r["t"].strftime("%Y-%m-%dT%H:%M:%S%z")
if "," in query.symbol:
@@ -172,4 +172,7 @@ class PolygonCryptoHistoricalFetcher(
"""Transform the data."""
if not data:
raise EmptyDataError()
- return [PolygonCryptoHistoricalData.model_validate(d) for d in data]
+ return [
+ PolygonCryptoHistoricalData.model_validate(d)
+ for d in sorted(data, key=lambda x: x["t"], reverse=False)
+ ]
diff --git a/openbb_platform/providers/polygon/openbb_polygon/models/currency_historical.py b/openbb_platform/providers/polygon/openbb_polygon/models/currency_historical.py
index 166fa4262c6..44df98e9f8e 100644
--- a/openbb_platform/providers/polygon/openbb_polygon/models/currency_historical.py
+++ b/openbb_platform/providers/polygon/openbb_polygon/models/currency_historical.py
@@ -149,7 +149,7 @@ class PolygonCurrencyHistoricalFetcher(
for r in results:
r["t"] = datetime.fromtimestamp(r["t"] / 1000, tz=timezone("UTC"))
if query._timespan not in ["second", "minute", "hour"]:
- r["t"] = r["t"].date()
+ r["t"] = r["t"].date().strftime("%Y-%m-%d")
else:
r["t"] = r["t"].strftime("%Y-%m-%dT%H:%M:%S%z")
if "," in query.symbol:
@@ -169,4 +169,7 @@ class PolygonCurrencyHistoricalFetcher(
"""Return the transformed data."""
if not data:
raise EmptyDataError()
- return [PolygonCurrencyHistoricalData.model_validate(d) for d in data]
+ return [
+ PolygonCurrencyHistoricalData.model_validate(d)
+ for d in sorted(data, key=lambda x: x["t"], reverse=False)
+ ]
diff --git a/openbb_platform/providers/polygon/openbb_polygon/models/equity_historical.py b/openbb_platform/providers/polygon/openbb_polygon/models/equity_historical.py
index 039c9fd0fdd..93935bcfd8d 100644
--- a/openbb_platform/providers/polygon/openbb_polygon/models/equity_historical.py
+++ b/openbb_platform/providers/polygon/openbb_polygon/models/equity_historical.py
@@ -156,9 +156,9 @@ class PolygonEquityHistoricalFetcher(
r["t"] / 1000, tz=timezone("America/New_York")
)
if query._timespan not in ["second", "minute", "hour"]:
- r["t"] = r["t"].date()
+ r["t"] = r["t"].date().strftime("%Y-%m-%d")
else:
- r["t"] = r["t"].strftime("%Y-%m-%dT%H:%M:%S")
+ r["t"] = r["t"].strftime("%Y-%m-%dT%H:%M:%S%z")
if "," in query.symbol:
r["symbol"] = symbol
@@ -178,4 +178,7 @@ class PolygonEquityHistoricalFetcher(
"""Transform the data from the Polygon endpoint."""
if not data:
raise EmptyDataError()
- return [PolygonEquityHistoricalData.model_validate(d) for d in data]
+ return [
+ PolygonEquityHistoricalData.model_validate(d)
+ for d in sorted(data, key=lambda x: x["t"], reverse=False)
+ ]
diff --git a/openbb_platform/providers/polygon/openbb_polygon/models/index_historical.py b/openbb_platform/providers/polygon/openbb_polygon/models/index_historical.py
index 41169af79dc..0b34c48d8c5 100644
--- a/openbb_platform/providers/polygon/openbb_polygon/models/index_historical.py
+++ b/openbb_platform/providers/polygon/openbb_polygon/models/index_historical.py
@@ -151,9 +151,9 @@ class PolygonIndexHistoricalFetcher(
r["t"] / 1000, tz=timezone("America/New_York")
)
if query._timespan not in ["second", "minute", "hour"]:
- r["t"] = r["t"].date()
+ r["t"] = r["t"].date().strftime("%Y-%m-%d")
else:
- r["t"] = r["t"].strftime("%Y-%m-%dT%H:%M:%S")
+ r["t"] = r["t"].strftime("%Y-%m-%dT%H:%M:%S%z")
if "," in query.symbol:
r["symbol"] = symbol
@@ -173,4 +173,7 @@ class PolygonIndexHistoricalFetcher(
"""Transform the data."""
if not data:
raise EmptyDataError()
- return [PolygonIndexHistoricalData.model_validate(d) for d in data]
+ return [
+ PolygonIndexHistoricalData.model_validate(d)
+ for d in sorted(data, key=lambda x: x["t"], reverse=False)
+ ]
diff --git a/openbb_platform/providers/yfinance/openbb_yfinance/models/crypto_historical.py b/openbb_platform/providers/yfinance/openbb_yfinance/models/crypto_historical.py
index 0cdb4375c23..845b5215147 100644
--- a/openbb_platform/providers/yfinance/openbb_yfinance/models/crypto_historical.py
+++ b/openbb_platform/providers/yfinance/openbb_yfinance/models/crypto_historical.py
@@ -17,7 +17,7 @@ from openbb_core.provider.utils.errors import EmptyDataError
from openbb_yfinance.utils.helpers import yf_download
from openbb_yfinance.utils.references import INTERVALS, PERIODS
from pandas import to_datetime
-from pydantic import Field, field_validator
+from pydantic import Field
class YFinanceCryptoHistoricalQueryParams(CryptoHistoricalQueryParams):
@@ -37,14 +37,6 @@ class YFinanceCryptoHistoricalQueryParams(CryptoHistoricalQueryParams):
class YFinanceCryptoHistoricalData(CryptoHistoricalData):
"""Yahoo Finance Crypto Historical Price Data."""
- @field_validator("date", mode="before", check_fields=False)
- @classmethod
- def date_validate(cls, v):
- """Return datetime object from string."""
- if isinstance(v, str):
- return datetime.strptime(v, "%Y-%m-%dT%H:%M:%S")
- return v
-
class YFinanceCryptoHistoricalFetcher(
Fetcher[
@@ -118,6 +110,9 @@ class YFinanceCryptoHistoricalFetcher(
data.reset_index(inplace=True)
data.rename(columns={"index": "date"}, inplace=True)
+ if query.interval in ["1d", "1W", "1M", "3M"]:
+ data["date"] = data["date"].dt.strftime("%Y-%m-%d")
+
return data.to_dict("records")
@staticmethod
diff --git a/openbb_platform/providers/yfinance/openbb_yfinance/models/currency_historical.py b/openbb_platform/providers/yfinance/openbb_yfinance/models/currency_historical.py
index 549e4e8bee0..749a6d1ebfd 100644
--- a/openbb_platform/providers/yfinance/openbb_yfinance/models/currency_historical.py
+++ b/openbb_platform/providers/yfinance/openbb_yfinance/models/currency_historical.py
@@ -1,7 +1,7 @@
"""Yahoo Finance Currency Price Model."""
# ruff: noqa: SIM105
-
+# pylint: disable=unused-argument
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional
@@ -71,7 +71,7 @@ class YFinanceCurrencyHistoricalFetcher(
@staticmethod
def extract_data(
- query: YFinanceCurrencyHistoricalQueryParams, # pylint: disable=unused-argument
+ query: YFinanceCurrencyHistoricalQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> List[Dict]:
@@ -106,6 +106,8 @@ class YFinanceCurrencyHistoricalFetcher(
data.reset_index(inplace=True)
data.rename(columns={"index": "date"}, inplace=True)
+ if query.interval in ["1d", "1W", "1M", "3M"]:
+ data["date"] = data["date"].dt.strftime("%Y-%m-%d")
return data.to_dict("records")
diff --git a/openbb_platform/providers/yfinance/openbb_yfinance/models/equity_historical.py b/openbb_platform/providers/yfinance/openbb_yfinance/models/equity_historical.py
index df55675d525..5fa2e971fe0 100644
--- a/openbb_platform/providers/yfinance/openbb_yfinance/models/equity_historical.py
+++ b/openbb_platform/providers/yfinance/openbb_yfinance/models/equity_historical.py
@@ -105,7 +105,7 @@ class YFinanceEquityHistoricalFetcher(
@staticmethod
def extract_data(
- query: YFinanceEquityHistoricalQueryParams, # pylint: disable=unused-argument
+ query: YFinanceEquityHistoricalQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> List[Dict]:
@@ -161,6 +161,8 @@ class YFinanceEquityHistoricalFetcher(
data.reset_index(inplace=True)
data.rename(columns={"index": "date"}, inplace=True)
+ if query.interval in ["1d", "1W", "1M", "3M"]:
+ data["date"] = data["date"].dt.strftime("%Y-%m-%d")
return data.to_dict("records")
diff --git a/openbb_platform/providers/yfinance/openbb_yfinance/models/index_historical.py b/openbb_platform/providers/yfinance/openbb_yfinance/models/index_historical.py
index 009ff33991c..e78fd3f301f 100644
--- a/openbb_platform/providers/yfinance/openbb_yfinance/models/index_historical.py
+++ b/openbb_platform/providers/yfinance/openbb_yfinance/models/index_historical.py
@@ -1,6 +1,7 @@
"""Yahoo Finance Index Historical Model."""
# ruff: noqa: SIM105
+# pylint: disable=unused-argument
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional
@@ -89,7 +90,7 @@ class YFinanceIndexHistoricalFetcher(
@staticmethod
def extract_data(
- query: YFinanceIndexHistoricalQueryParams, # pylint: disable=unused-argument
+ query: YFinanceIndexHistoricalQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> List[dict]:
@@ -125,6 +126,8 @@ class YFinanceIndexHistoricalFetcher(
data.reset_index(inplace=True)
data.rename(columns={"index": "date"}, inplace=True)
+ if query.interval in ["1d", "1W", "1M", "3M"]:
+ data["date"] = data["date"].dt.strftime("%Y-%m-%d")
return data.to_dict("records")
From 3cc6025ab4c7cf4195a04e8ad6b2fa2d03abe97d Mon Sep 17 00:00:00 2001
From: James Maslek
Date: Wed, 21 Feb 2024 12:09:57 -0500
Subject: [PATCH 5/8] Update the quantitative extension to make more sense
(#6087)
* Split out a rolling submenu for the rolling functions
* Make a performance and a stats submenu.
* Test the statistics functions
* lint
* lint
* dupe test
* pylint
* ruff
* Try tests quick
* black magic signature funcs
* fix my custom tests
* Fix the existing imports/urls
* push the api update
* okay I figured out whats going on
* this should be all of them
* Correct docstringing examples
---------
Co-authored-by: Igor Radovanovic <74266147+IgorWounds@users.noreply.github.com>
Co-authored-by: Danglewood <85772166+deeleeramone@users.noreply.github.com>
---
.../integration/test_quantitative_api.py | 212 +++++++++-
.../integration/test_quantitative_python.py | 249 +++++++++++-
.../performance/performance_router.py | 203 ++++++++++
.../quantitative_router.py | 363 +-----------------
.../rolling/rolling_router.py | 319 +++++++++++++++
.../openbb_quantitative/statistics.py | 41 ++
.../openbb_quantitative/stats/stats_router.py | 258 +++++++++++++
.../quantitative/tests/test_statistics.py | 25 ++
.../tests/test_integration_tests_python.py | 4 +-
.../extensions/tests/utils/router_testers.py | 2 +-
10 files changed, 1298 insertions(+), 378 deletions(-)
create mode 100644 openbb_platform/extensions/quantitative/openbb_quantitative/performance/performance_router.py
create mode 100644 openbb_platform/extensions/quantitative/openbb_quantitative/rolling/rolling_router.py
create mode 100644 openbb_platform/extensions/quantitative/openbb_quantitative/statistics.py
create mode 100644 openbb_platform/extensions/quantitative/openbb_quantitative/stats/stats_router.py
create mode 100644 openbb_platform/extensions/quantitative/tests/test_statistics.py
diff --git a/openbb_platform/extensions/quantitative/integration/test_quantitative_api.py b/openbb_platform/extensions/quantitative/integration/test_quantitative_api.py
index 6bcaa5da5e0..10e9432d1f8 100644
--- a/openbb_platform/extensions/quantitative/integration/test_quantitative_api.py
+++ b/openbb_platform/extensions/quantitative/integration/test_quantitative_api.py
@@ -127,12 +127,12 @@ def test_quantitative_capm(params, data_type):
],
)
@pytest.mark.integration
-def test_quantitative_omega_ratio(params, data_type):
+def test_quantitative_performance_omega_ratio(params, data_type):
params = {p: v for p, v in params.items() if v}
data = json.dumps(get_data(data_type))
query_str = get_querystring(params, [])
- url = f"http://0.0.0.0:8000/api/v1/quantitative/omega_ratio?{query_str}"
+ url = f"http://0.0.0.0:8000/api/v1/quantitative/performance/omega_ratio?{query_str}"
result = requests.post(url, headers=get_headers(), timeout=10, data=data)
assert isinstance(result, requests.Response)
assert result.status_code == 200
@@ -146,12 +146,12 @@ def test_quantitative_omega_ratio(params, data_type):
],
)
@pytest.mark.integration
-def test_quantitative_kurtosis(params, data_type):
+def test_quantitative_rolling_kurtosis(params, data_type):
params = {p: v for p, v in params.items() if v}
data = json.dumps(get_data(data_type))
query_str = get_querystring(params, [])
- url = f"http://0.0.0.0:8000/api/v1/quantitative/kurtosis?{query_str}"
+ url = f"http://0.0.0.0:8000/api/v1/quantitative/rolling/kurtosis?{query_str}"
result = requests.post(url, headers=get_headers(), timeout=10, data=data)
assert isinstance(result, requests.Response)
assert result.status_code == 200
@@ -212,12 +212,14 @@ def test_quantitative_unitroot_test(params, data_type):
],
)
@pytest.mark.integration
-def test_quantitative_sharpe_ratio(params, data_type):
+def test_quantitative_performance_sharpe_ratio(params, data_type):
params = {p: v for p, v in params.items() if v}
data = json.dumps(get_data(data_type))
query_str = get_querystring(params, [])
- url = f"http://0.0.0.0:8000/api/v1/quantitative/sharpe_ratio?{query_str}"
+ url = (
+ f"http://0.0.0.0:8000/api/v1/quantitative/performance/sharpe_ratio?{query_str}"
+ )
result = requests.post(url, headers=get_headers(), timeout=10, data=data)
assert isinstance(result, requests.Response)
assert result.status_code == 200
@@ -251,12 +253,14 @@ def test_quantitative_sharpe_ratio(params, data_type):
],
)
@pytest.mark.integration
-def test_quantitative_sortino_ratio(params, data_type):
+def test_quantitative_performance_sortino_ratio(params, data_type):
params = {p: v for p, v in params.items() if v}
data = json.dumps(get_data(data_type))
query_str = get_querystring(params, [])
- url = f"http://0.0.0.0:8000/api/v1/quantitative/sortino_ratio?{query_str}"
+ url = (
+ f"http://0.0.0.0:8000/api/v1/quantitative/performance/sortino_ratio?{query_str}"
+ )
result = requests.post(url, headers=get_headers(), timeout=10, data=data)
assert isinstance(result, requests.Response)
assert result.status_code == 200
@@ -269,12 +273,66 @@ def test_quantitative_sortino_ratio(params, data_type):
],
)
@pytest.mark.integration
-def test_quantitative_skewness(params, data_type):
+def test_quantitative_rolling_skew(params, data_type):
params = {p: v for p, v in params.items() if v}
data = json.dumps(get_data(data_type))
query_str = get_querystring(params, [])
- url = f"http://0.0.0.0:8000/api/v1/quantitative/skewness?{query_str}"
+ url = f"http://0.0.0.0:8000/api/v1/quantitative/rolling/skew?{query_str}"
+ result = requests.post(url, headers=get_headers(), timeout=60, data=data)
+ assert isinstance(result, requests.Response)
+ assert result.status_code == 200
+
+
+@parametrize(
+ "params, data_type",
+ [
+ ({"data": "", "target": "close", "window": "220", "index": "date"}, "equity"),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_rolling_variance(params, data_type):
+ params = {p: v for p, v in params.items() if v}
+ data = json.dumps(get_data(data_type))
+
+ query_str = get_querystring(params, [])
+ url = f"http://0.0.0.0:8000/api/v1/quantitative/rolling/variance?{query_str}"
+ result = requests.post(url, headers=get_headers(), timeout=60, data=data)
+ assert isinstance(result, requests.Response)
+ assert result.status_code == 200
+
+
+@parametrize(
+ "params, data_type",
+ [
+ ({"data": "", "target": "close", "window": "220", "index": "date"}, "equity"),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_rolling_stdev(params, data_type):
+ params = {p: v for p, v in params.items() if v}
+ data = json.dumps(get_data(data_type))
+
+ query_str = get_querystring(params, [])
+ url = f"http://0.0.0.0:8000/api/v1/quantitative/rolling/stdev?{query_str}"
+ result = requests.post(url, headers=get_headers(), timeout=60, data=data)
+ assert isinstance(result, requests.Response)
+ assert result.status_code == 200
+
+
+@parametrize(
+ "params, data_type",
+ [
+ ({"data": "", "target": "close", "window": "220", "index": "date"}, "equity"),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_rolling_mean(params, data_type):
+ params = {p: v for p, v in params.items() if v}
+ data = json.dumps(get_data(data_type))
+
+ query_str = get_querystring(params, [])
+ url = f"http://0.0.0.0:8000/api/v1/quantitative/rolling/mean?{query_str}"
result = requests.post(url, headers=get_headers(), timeout=60, data=data)
assert isinstance(result, requests.Response)
assert result.status_code == 200
@@ -306,12 +364,12 @@ def test_quantitative_skewness(params, data_type):
],
)
@pytest.mark.integration
-def test_quantitative_quantile(params, data_type):
+def test_quantitative_rolling_quantile(params, data_type):
params = {p: v for p, v in params.items() if v}
data = json.dumps(get_data(data_type))
query_str = get_querystring(params, [])
- url = f"http://0.0.0.0:8000/api/v1/quantitative/quantile?{query_str}"
+ url = f"http://0.0.0.0:8000/api/v1/quantitative/rolling/quantile?{query_str}"
result = requests.post(url, headers=get_headers(), timeout=10, data=data)
assert isinstance(result, requests.Response)
assert result.status_code == 200
@@ -334,3 +392,133 @@ def test_quantitative_summary(params, data_type):
result = requests.post(url, headers=get_headers(), timeout=10, data=data)
assert isinstance(result, requests.Response)
assert result.status_code == 200
+
+
+############
+# quantitative/stats
+############
+
+
+@parametrize(
+ "params, data_type",
+ [
+ ({"data": "", "target": "close", "index": "date"}, "equity"),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_stats_skew(params, data_type):
+ params = {p: v for p, v in params.items() if v}
+ data = json.dumps(get_data(data_type))
+
+ query_str = get_querystring(params, [])
+ url = f"http://0.0.0.0:8000/api/v1/quantitative/stats/skew?{query_str}"
+ result = requests.post(url, headers=get_headers(), timeout=60, data=data)
+ assert isinstance(result, requests.Response)
+ assert result.status_code == 200
+
+
+@parametrize(
+ "params, data_type",
+ [
+ ({"data": "", "target": "close", "index": "date"}, "equity"),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_stats_kurtosis(params, data_type):
+ params = {p: v for p, v in params.items() if v}
+ data = json.dumps(get_data(data_type))
+
+ query_str = get_querystring(params, [])
+ url = f"http://0.0.0.0:8000/api/v1/quantitative/stats/kurtosis?{query_str}"
+ result = requests.post(url, headers=get_headers(), timeout=60, data=data)
+ assert isinstance(result, requests.Response)
+ assert result.status_code == 200
+
+
+@parametrize(
+ "params, data_type",
+ [
+ ({"data": "", "target": "close", "index": "date"}, "equity"),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_stats_mean(params, data_type):
+ params = {p: v for p, v in params.items() if v}
+ data = json.dumps(get_data(data_type))
+
+ query_str = get_querystring(params, [])
+ url = f"http://0.0.0.0:8000/api/v1/quantitative/stats/mean?{query_str}"
+ result = requests.post(url, headers=get_headers(), timeout=60, data=data)
+ assert isinstance(result, requests.Response)
+ assert result.status_code == 200
+
+
+@parametrize(
+ "params, data_type",
+ [
+ ({"data": "", "target": "close", "index": "date"}, "equity"),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_stats_stdev(params, data_type):
+ params = {p: v for p, v in params.items() if v}
+ data = json.dumps(get_data(data_type))
+
+ query_str = get_querystring(params, [])
+ url = f"http://0.0.0.0:8000/api/v1/quantitative/stats/stdev?{query_str}"
+ result = requests.post(url, headers=get_headers(), timeout=60, data=data)
+ assert isinstance(result, requests.Response)
+ assert result.status_code == 200
+
+
+@parametrize(
+ "params, data_type",
+ [
+ ({"data": "", "target": "close", "index": "date"}, "equity"),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_stats_variance(params, data_type):
+ params = {p: v for p, v in params.items() if v}
+ data = json.dumps(get_data(data_type))
+
+ query_str = get_querystring(params, [])
+ url = f"http://0.0.0.0:8000/api/v1/quantitative/stats/variance?{query_str}"
+ result = requests.post(url, headers=get_headers(), timeout=60, data=data)
+ assert isinstance(result, requests.Response)
+ assert result.status_code == 200
+
+
+@parametrize(
+ "params, data_type",
+ [
+ (
+ {
+ "data": "",
+ "target": "close",
+ "quantile_pct": "",
+ "index": "date",
+ },
+ "equity",
+ ),
+ (
+ {
+ "data": "",
+ "target": "high",
+ "quantile_pct": "0.6",
+ "index": "date",
+ },
+ "crypto",
+ ),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_stats_quantile(params, data_type):
+ params = {p: v for p, v in params.items() if v}
+ data = json.dumps(get_data(data_type))
+
+ query_str = get_querystring(params, [])
+ url = f"http://0.0.0.0:8000/api/v1/quantitative/stats/quantile?{query_str}"
+ result = requests.post(url, headers=get_headers(), timeout=10, data=data)
+ assert isinstance(result, requests.Response)
+ assert result.status_code == 200
diff --git a/openbb_platform/extensions/quantitative/integration/test_quantitative_python.py b/openbb_platform/extensions/quantitative/integration/test_quantitative_python.py
index f41f26613b1..5c55621a8ae 100644
--- a/openbb_platform/extensions/quantitative/integration/test_quantitative_python.py
+++ b/openbb_platform/extensions/quantitative/integration/test_quantitative_python.py
@@ -117,11 +117,11 @@ def test_quantitative_capm(params, data_type, obb):
],
)
@pytest.mark.integration
-def test_quantitative_omega_ratio(params, data_type, obb):
+def test_quantitative_performance_omega_ratio(params, data_type, obb):
params = {p: v for p, v in params.items() if v}
params["data"] = get_data(data_type)
- result = obb.quantitative.omega_ratio(**params)
+ result = obb.quantitative.performance.omega_ratio(**params)
assert result
assert isinstance(result, OBBject)
@@ -134,11 +134,11 @@ def test_quantitative_omega_ratio(params, data_type, obb):
],
)
@pytest.mark.integration
-def test_quantitative_kurtosis(params, data_type, obb):
+def test_quantitative_rolling_kurtosis(params, data_type, obb):
params = {p: v for p, v in params.items() if v}
params["data"] = get_data(data_type)
- result = obb.quantitative.kurtosis(**params)
+ result = obb.quantitative.rolling.kurtosis(**params)
assert result
assert isinstance(result, OBBject)
assert len(result.results) > 0
@@ -203,11 +203,11 @@ def test_quantitative_unitroot_test(params, data_type, obb):
],
)
@pytest.mark.integration
-def test_quantitative_sharpe_ratio(params, data_type, obb):
+def test_quantitative_performance_sharpe_ratio(params, data_type, obb):
params = {p: v for p, v in params.items() if v}
params["data"] = get_data(data_type)
- result = obb.quantitative.sharpe_ratio(**params)
+ result = obb.quantitative.performance.sharpe_ratio(**params)
assert result
assert isinstance(result, OBBject)
@@ -240,11 +240,11 @@ def test_quantitative_sharpe_ratio(params, data_type, obb):
],
)
@pytest.mark.integration
-def test_quantitative_sortino_ratio(params, data_type, obb):
+def test_quantitative_performance_sortino_ratio(params, data_type, obb):
params = {p: v for p, v in params.items() if v}
params["data"] = get_data(data_type)
- result = obb.quantitative.sortino_ratio(**params)
+ result = obb.quantitative.performance.sortino_ratio(**params)
assert result
assert isinstance(result, OBBject)
@@ -256,11 +256,11 @@ def test_quantitative_sortino_ratio(params, data_type, obb):
],
)
@pytest.mark.integration
-def test_quantitative_skewness(params, data_type, obb):
+def test_quantitative_rolling_skew(params, data_type, obb):
params = {p: v for p, v in params.items() if v}
params["data"] = get_data(data_type)
- result = obb.quantitative.skewness(**params)
+ result = obb.quantitative.rolling.skew(**params)
assert result
assert isinstance(result, OBBject)
assert len(result.results) > 0
@@ -292,11 +292,11 @@ def test_quantitative_skewness(params, data_type, obb):
],
)
@pytest.mark.integration
-def test_quantitative_quantile(params, data_type, obb):
+def test_quantitative_rolling_quantile(params, data_type, obb):
params = {p: v for p, v in params.items() if v}
params["data"] = get_data(data_type)
- result = obb.quantitative.quantile(**params)
+ result = obb.quantitative.rolling.quantile(**params)
assert result
assert isinstance(result, OBBject)
assert len(result.results) > 0
@@ -317,3 +317,228 @@ def test_quantitative_summary(params, data_type, obb):
result = obb.quantitative.summary(**params)
assert result
assert isinstance(result, OBBject)
+
+
+@parametrize(
+ "params, data_type",
+ [
+ (
+ {
+ "data": "",
+ "target": "close",
+ "window": "10",
+ "quantile_pct": "",
+ "index": "date",
+ },
+ "equity",
+ ),
+ (
+ {
+ "data": "",
+ "target": "high",
+ "window": "50",
+ "quantile_pct": "0.6",
+ "index": "date",
+ },
+ "crypto",
+ ),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_rolling_stdev(params, data_type, obb):
+ params = {p: v for p, v in params.items() if v}
+ params["data"] = get_data(data_type)
+
+ result = obb.quantitative.rolling.stdev(**params)
+ assert result
+ assert isinstance(result, OBBject)
+ assert len(result.results) > 0
+
+
+@parametrize(
+ "params, data_type",
+ [
+ (
+ {
+ "data": "",
+ "target": "close",
+ "window": "10",
+ "quantile_pct": "",
+ "index": "date",
+ },
+ "equity",
+ ),
+ (
+ {
+ "data": "",
+ "target": "high",
+ "window": "50",
+ "quantile_pct": "0.6",
+ "index": "date",
+ },
+ "crypto",
+ ),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_rolling_mean(params, data_type, obb):
+ params = {p: v for p, v in params.items() if v}
+ params["data"] = get_data(data_type)
+
+ result = obb.quantitative.rolling.mean(**params)
+ assert result
+ assert isinstance(result, OBBject)
+ assert len(result.results) > 0
+
+
+@parametrize(
+ "params, data_type",
+ [
+ (
+ {
+ "data": "",
+ "target": "close",
+ "window": "10",
+ "quantile_pct": "",
+ "index": "date",
+ },
+ "equity",
+ ),
+ (
+ {
+ "data": "",
+ "target": "high",
+ "window": "50",
+ "quantile_pct": "0.6",
+ "index": "date",
+ },
+ "crypto",
+ ),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_rolling_variance(params, data_type, obb):
+ params = {p: v for p, v in params.items() if v}
+ params["data"] = get_data(data_type)
+
+ result = obb.quantitative.rolling.variance(**params)
+ assert result
+ assert isinstance(result, OBBject)
+ assert len(result.results) > 0
+
+
+@parametrize(
+ "params, data_type",
+ [
+ ({"data": "", "target": "close"}, "equity"),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_stats_skew(params, data_type, obb):
+ params = {p: v for p, v in params.items() if v}
+ params["data"] = get_data(data_type)
+
+ result = obb.quantitative.stats.skew(**params)
+ assert result
+ assert isinstance(result, OBBject)
+ assert len(result.results) > 0
+
+
+@parametrize(
+ "params, data_type",
+ [
+ ({"data": "", "target": "close"}, "equity"),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_stats_kurtosis(params, data_type, obb):
+ params = {p: v for p, v in params.items() if v}
+ params["data"] = get_data(data_type)
+
+ result = obb.quantitative.stats.kurtosis(**params)
+ assert result
+ assert isinstance(result, OBBject)
+ assert len(result.results) > 0
+
+
+@parametrize(
+ "params, data_type",
+ [
+ ({"data": "", "target": "close"}, "equity"),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_stats_variance(params, data_type, obb):
+ params = {p: v for p, v in params.items() if v}
+ params["data"] = get_data(data_type)
+
+ result = obb.quantitative.stats.variance(**params)
+ assert result
+ assert isinstance(result, OBBject)
+ assert len(result.results) > 0
+
+
+@parametrize(
+ "params, data_type",
+ [
+ ({"data": "", "target": "close"}, "equity"),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_stats_stdev(params, data_type, obb):
+ params = {p: v for p, v in params.items() if v}
+ params["data"] = get_data(data_type)
+
+ result = obb.quantitative.stats.stdev(**params)
+ assert result
+ assert isinstance(result, OBBject)
+ assert len(result.results) > 0
+
+
+@parametrize(
+ "params, data_type",
+ [
+ ({"data": "", "target": "close"}, "equity"),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_stats_mean(params, data_type, obb):
+ params = {p: v for p, v in params.items() if v}
+ params["data"] = get_data(data_type)
+
+ result = obb.quantitative.stats.mean(**params)
+ assert result
+ assert isinstance(result, OBBject)
+ assert len(result.results) > 0
+
+
+@parametrize(
+ "params, data_type",
+ [
+ (
+ {
+ "data": "",
+ "target": "close",
+ "quantile_pct": "",
+ },
+ "equity",
+ ),
+ (
+ {
+ "data": "",
+ "target": "close",
+ "quantile_pct": "0.6",
+ },
+ "crypto",
+ ),
+ ],
+)
+@pytest.mark.integration
+def test_quantitative_stats_quantile(params, data_type, obb):
+ params = {p: v for p, v in params.items() if v}
+ params["data"] = get_data(data_type)
+
+ result = obb.quantitative.stats.quantile(**params)
+ assert result
+ assert isinstance(result, OBBject)
+ assert len(result.results) > 0
diff --git a/openbb_platform/extensions/quantitative/openbb_quantitative/performance/performance_router.py b/openbb_platform/extensions/quantitative/openbb_quantitative/performance/performance_router.py
new file mode 100644
index 00000000000..92bd611a380
--- /dev/null
+++ b/openbb_platform/extensions/quantitative/openbb_quantitative/performance/performance_router.py
@@ -0,0 +1,203 @@
+from typing import List
+
+import numpy as np
+import pandas as pd
+from openbb_core.app.model.obbject import OBBject
+from openbb_core.app.router import Router
+from openbb_core.app.utils import (
+ basemodel_to_df,
+ df_to_basemodel,
+ get_target_column,
+)
+from openbb_core.provider.abstract.data import Data
+from openbb_quantitative.helpers import validate_window
+from openbb_quantitative.models import (
+ OmegaModel,
+)
+from pydantic import PositiveInt
+
+router = Router(prefix="/performance")
+
+
+@router.command(
+ methods=["POST"],
+ examples=[
+ 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()',
+ 'returns = stock_data["close"].pct_change().dropna()',
+ 'obb.quantitative.omega_ratio(data=returns, target="close")',
+ ],
+)
+def omega_ratio(
+ data: List[Data],
+ target: str,
+ threshold_start: float = 0.0,
+ threshold_end: float = 1.5,
+) -> OBBject[List[OmegaModel]]:
+ """Calculate the Omega Ratio.
+
+ The Omega Ratio is a sophisticated metric that goes beyond traditional performance measures by considering the
+ probability of achieving returns above a given threshold. It offers a more nuanced view of risk and reward,
+ focusing on the likelihood of success rather than just average outcomes.
+
+ Parameters
+ ----------
+ data : List[Data]
+ Time series data.
+ target : str
+ Target column name.
+ threshold_start : float, optional
+ Start threshold, by default 0.0
+ threshold_end : float, optional
+ End threshold, by default 1.5
+
+ Returns
+ -------
+ OBBject[List[OmegaModel]]
+ Omega ratios.
+ """
+ df = basemodel_to_df(data)
+ series_target = get_target_column(df, target)
+
+ epsilon = 1e-6 # to avoid division by zero
+
+ def get_omega_ratio(df_target: pd.Series, threshold: float) -> float:
+ """Get omega ratio."""
+ daily_threshold = (threshold + 1) ** np.sqrt(1 / 252) - 1
+ excess = df_target - daily_threshold
+ numerator = excess[excess > 0].sum()
+ denominator = -excess[excess < 0].sum() + epsilon
+
+ return numerator / denominator
+
+ threshold = np.linspace(threshold_start, threshold_end, 50)
+ results = []
+ for i in threshold:
+ omega_ = get_omega_ratio(series_target, i)
+ results.append(OmegaModel(threshold=i, omega=omega_))
+
+ return OBBject(results=results)
+
+
+@router.command(
+ methods=["POST"],
+ examples=[
+ 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()',
+ 'returns = stock_data["close"].pct_change().dropna()',
+ 'obb.quantitative.sharpe_ratio(data=returns, target="close")',
+ ],
+)
+def sharpe_ratio(
+ data: List[Data],
+ target: str,
+ rfr: float = 0.0,
+ window: PositiveInt = 252,
+ index: str = "date",
+) -> OBBject[List[Data]]:
+ """Get Rolling Sharpe Ratio.
+
+ This function calculates the Sharpe Ratio, a metric used to assess the return of an investment compared to its risk.
+ By factoring in the risk-free rate, it helps you understand how much extra return you're getting for the extra
+ volatility that you endure by holding a riskier asset. The Sharpe Ratio is essential for investors looking to
+ compare the efficiency of different investments, providing a clear picture of potential rewards in relation to their
+ risks over a specified period. Ideal for gauging the effectiveness of investment strategies, it offers insights into
+ optimizing your portfolio for maximum return on risk.
+
+ Parameters
+ ----------
+ data : List[Data]
+ Time series data.
+ target : str
+ Target column name.
+ rfr : float, optional
+ Risk-free rate, by default 0.0
+ window : PositiveInt, optional
+ Window size, by default 252
+ index : str, optional
+
+ Returns
+ -------
+ OBBject[List[Data]]
+ Sharpe ratio.
+ """
+ df = basemodel_to_df(data, index=index)
+ series_target = get_target_column(df, target)
+ validate_window(series_target, window)
+ series_target.name = f"sharpe_{window}"
+ returns = series_target.pct_change().dropna().rolling(window).sum()
+ std = series_target.rolling(window).std() / np.sqrt(window)
+ results = ((returns - rfr) / std).dropna().reset_index(drop=False)
+
+ results = df_to_basemodel(results)
+
+ return OBBject(results=results)
+
+
+@router.command(
+ methods=["POST"],
+ examples=[
+ 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()',
+ 'returns = stock_data["close"].pct_change().dropna()',
+ 'obb.quantitative.sortino_ratio(data=stock_data, target="close")',
+ 'obb.quantitative.sortino_ratio(data=stock_data, target="close", target_return=0.01, window=126, adjusted=True)',
+ ],
+)
+def sortino_ratio(
+ data: List[Data],
+ target: str,
+ target_return: float = 0.0,
+ window: PositiveInt = 252,
+ adjusted: bool = False,
+ index: str = "date",
+) -> OBBject[List[Data]]:
+ """Get rolling Sortino Ratio.
+
+ The Sortino Ratio enhances the evaluation of investment returns by distinguishing harmful volatility
+ from total volatility. Unlike other metrics that treat all volatility as risk, this command specifically assesses
+ the volatility of negative returns relative to a target or desired return.
+ It's particularly useful for investors who are more concerned with downside risk than with overall volatility.
+ By calculating the Sortino Ratio, investors can better understand the risk-adjusted return of their investments,
+ focusing on the likelihood and impact of negative returns.
+ This approach offers a more nuanced tool for portfolio optimization, especially in strategies aiming
+ to minimize the downside.
+
+ For method & terminology see:
+ http://www.redrockcapital.com/Sortino__A__Sharper__Ratio_Red_Rock_Capital.pdf
+
+ Parameters
+ ----------
+ data : List[Data]
+ Time series data.
+ target : str
+ Target column name.
+ target_return : float, optional
+ Target return, by default 0.0
+ window : PositiveInt, optional
+ Window size, by default 252
+ adjusted : bool, optional
+ Adjust sortino ratio to compare it to sharpe ratio, by default False
+ index:str
+ Index column for input data
+ Returns
+ -------
+ OBBject[List[Data]]
+ Sortino ratio.
+ """
+ df = basemodel_to_df(data, index=index)
+ series_target = get_target_column(df, target)
+ validate_window(series_target, window)
+ returns = series_target.pct_change().dropna().rolling(window).sum()
+ downside_deviation = returns.rolling(window).apply(
+ lambda x: (x.values[x.values < 0]).std() / np.sqrt(252) * 100
+ )
+ results = (
+ ((returns - target_return) / downside_deviation)
+ .dropna()
+ .reset_index(drop=False)
+ )
+
+ if adjusted:
+ results = results / np.sqrt(2)
+
+ results_ = df_to_basemodel(results)
+
+ return OBBject(results=results_)
diff --git a/openbb_platform/extensions/quantitative/openbb_quantitative/quantitative_router.py b/openbb_platform/extensions/quantitative/openbb_quantitative/quantitative_router.py
index a381da937cb..bede1e9ac84 100644
--- a/openbb_platform/extensions/quantitative/openbb_quantitative/quantitative_router.py
+++ b/openbb_platform/extensions/quantitative/openbb_quantitative/quantitative_router.py
@@ -2,32 +2,37 @@
from typing import List, Literal
-import numpy as np
import pandas as pd
from openbb_core.app.model.obbject import OBBject
from openbb_core.app.router import Router
from openbb_core.app.utils import (
basemodel_to_df,
- df_to_basemodel,
get_target_column,
get_target_columns,
)
from openbb_core.provider.abstract.data import Data
-from pydantic import NonNegativeFloat, PositiveInt
-from .helpers import get_fama_raw, validate_window
+from openbb_quantitative.performance.performance_router import (
+ router as performance_router,
+)
+from openbb_quantitative.rolling.rolling_router import router as rolling_router
+from openbb_quantitative.stats.stats_router import router as stats_router
+
+from .helpers import get_fama_raw
from .models import (
ADFTestModel,
CAPMModel,
KPSSTestModel,
NormalityModel,
- OmegaModel,
SummaryModel,
TestModel,
UnitRootModel,
)
router = Router(prefix="")
+router.include_router(rolling_router)
+router.include_router(stats_router)
+router.include_router(performance_router)
@router.command(
@@ -137,120 +142,7 @@ def capm(data: List[Data], target: str) -> OBBject[CAPMModel]:
return OBBject(results=results)
-@router.command(
- methods=["POST"],
- examples=[
- "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()",
- "obb.quantitative.omega_ratio(data=stock_data, target='close')",
- ],
-)
-def omega_ratio(
- data: List[Data],
- target: str,
- threshold_start: float = 0.0,
- threshold_end: float = 1.5,
-) -> OBBject[List[OmegaModel]]:
- """Calculate the Omega Ratio.
-
- The Omega Ratio is a sophisticated metric that goes beyond traditional performance measures by considering the
- probability of achieving returns above a given threshold. It offers a more nuanced view of risk and reward,
- focusing on the likelihood of success rather than just average outcomes.
-
- Parameters
- ----------
- data : List[Data]
- Time series data.
- target : str
- Target column name.
- threshold_start : float, optional
- Start threshold, by default 0.0
- threshold_end : float, optional
- End threshold, by default 1.5
-
- Returns
- -------
- OBBject[List[OmegaModel]]
- Omega ratios.
- """
- df = basemodel_to_df(data)
- series_target = get_target_column(df, target)
-
- epsilon = 1e-6 # to avoid division by zero
-
- def get_omega_ratio(df_target: pd.Series, threshold: float) -> float:
- """Get omega ratio."""
- daily_threshold = (threshold + 1) ** np.sqrt(1 / 252) - 1
- excess = df_target - daily_threshold
- numerator = excess[excess > 0].sum()
- denominator = -excess[excess < 0].sum() + epsilon
-
- return numerator / denominator
-
- threshold = np.linspace(threshold_start, threshold_end, 50)
- results = []
- for i in threshold:
- omega_ = get_omega_ratio(series_target, i)
- results.append(OmegaModel(threshold=i, omega=omega_))
-
- return OBBject(results=results)
-
-
-@router.command(
- methods=["POST"],
- examples=[
- "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()",
- "obb.quantitative.kurtosis(data=stock_data, target='close', window=252)",
- ],
-)
-def kurtosis(
- data: List[Data], target: str, window: PositiveInt = 21, index: str = "date"
-) -> OBBject[List[Data]]:
- """Get the rolling Kurtosis.
-
- Kurtosis provides insights into the shape of the data's distribution, particularly the heaviness of its tails.
- Kurtosis is a statistical measure that reveals whether the data points tend to cluster around the mean or if
- outliers are more common than a normal distribution would suggest. A higher kurtosis indicates more data points are
- found in the tails, suggesting potential volatility or risk.
- This analysis is crucial for understanding the underlying characteristics of your data, helping to anticipate
- extreme events or anomalies over a specified window of time.
-
- Parameters
- ----------
- data : List[Data]
- Time series data.
- target : str
- Target column name.
- window : PositiveInt
- Window size.
- index : str, optional
- Index column name, by default "date"
-
- Returns
- -------
- OBBject[List[Data]]
- Kurtosis.
- """
- import pandas_ta as ta # pylint: disable=import-outside-toplevel # type: ignore
-
- df = basemodel_to_df(data, index=index)
- series_target = get_target_column(df, target)
- validate_window(series_target, window)
- results = (
- ta.kurtosis(close=series_target, length=window).dropna().reset_index(drop=False)
- )
- results = df_to_basemodel(results)
-
- return OBBject(results=results)
-
-
-@router.command(
- methods=["POST"],
- examples=[
- "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()",
- "obb.quantitative.unitroot_test(data=stock_data, target='close')",
- 'obb.quantitative.unitroot_test(data=stock_data, target="close", fuller_reg="ct", kpss_reg="ct")',
- ],
-)
+@router.command(methods=["POST"])
def unitroot_test(
data: List[Data],
target: str,
@@ -309,238 +201,7 @@ def unitroot_test(
return OBBject(results=unitroot_summary)
-@router.command(
- methods=["POST"],
- examples=[
- "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()",
- "obb.quantitative.sharpe_ratio(data=stock_data, target='close')",
- "obb.quantitative.sharpe_ratio(data=stock_data, target='close', rfr=0.01, window=126)",
- ],
-)
-def sharpe_ratio(
- data: List[Data],
- target: str,
- rfr: float = 0.0,
- window: PositiveInt = 252,
- index: str = "date",
-) -> OBBject[List[Data]]:
- """Get Rolling Sharpe Ratio.
-
- This function calculates the Sharpe Ratio, a metric used to assess the return of an investment compared to its risk.
- By factoring in the risk-free rate, it helps you understand how much extra return you're getting for the extra
- volatility that you endure by holding a riskier asset. The Sharpe Ratio is essential for investors looking to
- compare the efficiency of different investments, providing a clear picture of potential rewards in relation to their
- risks over a specified period. Ideal for gauging the effectiveness of investment strategies, it offers insights into
- optimizing your portfolio for maximum return on risk.
-
- Parameters
- ----------
- data : List[Data]
- Time series data.
- target : str
- Target column name.
- rfr : float, optional
- Risk-free rate, by default 0.0
- window : PositiveInt, optional
- Window size, by default 252
- index : str, optional
-
- Returns
- -------
- OBBject[List[Data]]
- Sharpe ratio.
- """
- df = basemodel_to_df(data, index=index)
- series_target = get_target_column(df, target)
- validate_window(series_target, window)
- series_target.name = f"sharpe_{window}"
- returns = series_target.pct_change().dropna().rolling(window).sum()
- std = series_target.rolling(window).std() / np.sqrt(window)
- results = ((returns - rfr) / std).dropna().reset_index(drop=False)
-
- results = df_to_basemodel(results)
-
- return OBBject(results=results)
-
-
-@router.command(
- methods=["POST"],
- examples=[
- "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()",
- "obb.quantitative.sortino_ratio(data=stock_data, target='close')",
- "obb.quantitative.sortino_ratio(data=stock_data, target='close', target_return=0.01, window=126, adjusted=True)",
- ],
-)
-def sortino_ratio(
- data: List[Data],
- target: str,
- target_return: float = 0.0,
- window: PositiveInt = 252,
- adjusted: bool = False,
- index: str = "date",
-) -> OBBject[List[Data]]:
- """Get rolling Sortino Ratio.
-
- The Sortino Ratio enhances the evaluation of investment returns by distinguishing harmful volatility
- from total volatility. Unlike other metrics that treat all volatility as risk, this command specifically assesses
- the volatility of negative returns relative to a target or desired return.
- It's particularly useful for investors who are more concerned with downside risk than with overall volatility.
- By calculating the Sortino Ratio, investors can better understand the risk-adjusted return of their investments,
- focusing on the likelihood and impact of negative returns.
- This approach offers a more nuanced tool for portfolio optimization, especially in strategies aiming
- to minimize the downside.
-
- For method & terminology see:
- http://www.redrockcapital.com/Sortino__A__Sharper__Ratio_Red_Rock_Capital.pdf
-
- Parameters
- ----------
- data : List[Data]
- Time series data.
- target : str
- Target column name.
- target_return : float, optional
- Target return, by default 0.0
- window : PositiveInt, optional
- Window size, by default 252
- adjusted : bool, optional
- Adjust sortino ratio to compare it to sharpe ratio, by default False
- index:str
- Index column for input data
- Returns
- -------
- OBBject[List[Data]]
- Sortino ratio.
- """
- df = basemodel_to_df(data, index=index)
- series_target = get_target_column(df, target)
- validate_window(series_target, window)
- returns = series_target.pct_change().dropna().rolling(window).sum()
- downside_deviation = returns.rolling(window).apply(
- lambda x: (x.values[x.values < 0]).std() / np.sqrt(252) * 100
- )
- results = (
- ((returns - target_return) / downside_deviation)
- .dropna()
- .reset_index(drop=False)
- )
-
- if adjusted:
- results = results / np.sqrt(2)
-
- results_ = df_to_basemodel(results)
-
- return OBBject(results=results_)
-
-
-@router.command(
- methods=["POST"],
- examples=[
- "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()",
- "obb.quantitative.skewness(data=stock_data, target='close', window=252)",
- ],
-)
-def skewness(
- data: List[Data], target: str, window: PositiveInt = 21, index: str = "date"
-) -> OBBject[List[Data]]:
- """Get Rolling Skewness.
-
- Skewness is a statistical measure that reveals the degree of asymmetry of a distribution around its mean.
- Positive skewness indicates a distribution with an extended tail to the right, while negative skewness shows a tail
- that stretches left. Understanding skewness can provide insights into potential biases in data and help anticipate
- the nature of future data points. It's particularly useful for identifying the likelihood of extreme outcomes in
- financial returns, enabling more informed decision-making based on the distribution's shape over a specified period.
-
- Parameters
- ----------
- data : List[Data]
- Time series data.
- target : str
- Target column name.
- window : PositiveInt
- Window size.
- index : str, optional
- Index column name, by default "date"
- Returns
- -------
- OBBject[List[Data]]
- Skewness.
- """
- import pandas_ta as ta # pylint: disable=import-outside-toplevel # type: ignore
-
- df = basemodel_to_df(data, index=index)
- series_target = get_target_column(df, target)
- validate_window(series_target, window)
- results = (
- ta.skew(close=series_target, length=window).dropna().reset_index(drop=False)
- )
- results = df_to_basemodel(results)
-
- return OBBject(results=results)
-
-
-@router.command(
- methods=["POST"],
- examples=[
- "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()",
- 'obb.quantitative.quantile(data=stock_data, target="close", window=252, quantile_pct=0.25)',
- "obb.quantitative.quantile(data=stock_data, target='close', window=252, quantile_pct=0.75)",
- ],
-)
-def quantile(
- data: List[Data],
- target: str,
- window: PositiveInt = 21,
- quantile_pct: NonNegativeFloat = 0.5,
- index: str = "date",
-) -> OBBject[List[Data]]:
- """Get Rolling Quantile.
-
- Quantile is a statistical measure that identifies the value below which a given percentage of observations in a
- group of data falls. By setting the quantile percentage, you can determine any point in the distribution, not just
- the median. Whether you're interested in the median, quartiles, or any other position within your data's range,
- this tool offers a precise way to understand the distribution's characteristics.
- It's especially useful for identifying outliers, understanding dispersion, and setting thresholds for
- decision-making based on the distribution of data over a specified period.
-
- Parameters
- ----------
- data : List[Data]
- Time series data.
- target : str
- Target column name.
- window : PositiveInt
- Window size.
- quantile_pct : NonNegativeFloat, optional
- Quantile to get
- Returns
- -------
- OBBject[List[Data]]
- Quantile.
- """
- import pandas_ta as ta # pylint: disable=import-outside-toplevel # type: ignore
-
- df = basemodel_to_df(data, index=index)
- series_target = get_target_column(df, target)
- validate_window(series_target, window)
- df_median = ta.median(close=series_target, length=window).to_frame()
- df_quantile = ta.quantile(series_target, length=window, q=quantile_pct).to_frame()
- results = (
- pd.concat([df_median, df_quantile], axis=1).dropna().reset_index(drop=False)
- )
-
- results_ = df_to_basemodel(results)
-
- return OBBject(results=results_)
-
-
-@router.command(
- methods=["POST"],
- examples=[
- "stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()",
- "obb.quantitative.volatility(data=stock_data, target='close')",
- ],
-)
+@router.command(methods=["POST"])
def summary(data: List[Data], target: str) -> OBBject[SummaryModel]:
"""Get Summary Statistics.
diff --git a/openbb_platform/extensions/quantitative/openbb_quantitative/rolling/rolling_router.py b/openbb_platform/extensions/quantitative/openbb_quantitative/rolling/rolling_router.py
new file mode 100644
index 00000000000..6e464083aea
--- /dev/null
+++ b/openbb_platform/extensions/quantitative/openbb_quantitative/rolling/rolling_router.py
@@ -0,0 +1,319 @@
+"""Rolling submenu of quantitative models for rolling statistics."""
+
+from typing import List
+
+import pandas as pd
+from openbb_core.app.model.obbject import OBBject
+from openbb_core.app.router import Router
+from openbb_core.app.utils import (
+ basemodel_to_df,
+ df_to_basemodel,
+ get_target_column,
+)
+from openbb_core.provider.abstract.data import Data
+from openbb_quantitative.helpers import validate_window
+from openbb_quantitative.statistics import (
+ kurtosis_,
+ mean_,
+ skew_,
+ std_dev_,
+ var_,
+)
+from pydantic import NonNegativeFloat, PositiveInt
+
+router = Router(prefix="/rolling")
+
+
+@router.command(
+ methods=["POST"],
+ examples=[
+ 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()',
+ 'returns = stock_data["close"].pct_change().dropna()',
+ 'obb.quantitative.rolling.skew(data=returns, target="close")',
+ ],
+)
+def skew(
+ data: List[Data], target: str, window: PositiveInt = 21, index: str = "date"
+) -> OBBject[List[Data]]:
+ """Get Rolling Skew.
+
+ Skew is a statistical measure that reveals the degree of asymmetry of a distribution around its mean.
+ Positive skewness indicates a distribution with an extended tail to the right, while negative skewness shows a tail
+ that stretches left. Understanding skewness can provide insights into potential biases in data and help anticipate
+ the nature of future data points. It's particularly useful for identifying the likelihood of extreme outcomes in
+ financial returns, enabling more informed decision-making based on the distribution's shape over a specified period.
+
+ Parameters
+ ----------
+ data : List[Data]
+ Time series data.
+ target : str
+ Target column name.
+ window : PositiveInt
+ Window size.
+ index : str, optional
+ Index column name, by default "date"
+
+ Returns
+ -------
+ OBBject[List[Data]]
+ Rolling skew.
+
+ """
+
+ df = basemodel_to_df(data, index=index)
+ series_target = get_target_column(df, target)
+ series_target.name = f"rolling_skew_{window}"
+ validate_window(series_target, window)
+ results = (
+ series_target.rolling(window).apply(skew_).dropna().reset_index(drop=False)
+ )
+ results = df_to_basemodel(results)
+
+ return OBBject(results=results)
+
+
+@router.command(
+ methods=["POST"],
+ examples=[
+ 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()',
+ 'returns = stock_data["close"].pct_change().dropna()',
+ 'obb.quantitative.rolling.variance(data=returns, target="close", window=252)',
+ ],
+)
+def variance(
+ data: List[Data], target: str, window: PositiveInt = 21, index: str = "date"
+) -> OBBject[List[Data]]:
+ """
+ Calculate the rolling variance of a target column within a given window size.
+
+ Variance measures the dispersion of a set of data points around their mean. It is a key metric for
+ assessing the volatility and stability of financial returns or other time series data over a specified rolling window.
+
+ Parameters:
+ data: List[Data]
+ The time series data as a list of data points.
+ target: str
+ The name of the column for which to calculate variance.
+ window: PositiveInt
+ The number of observations used for calculating the rolling measure.
+ index: str, optional
+ The name of the index column, default is "date".
+
+ Returns:
+ -------
+ OBBject[List[Data]]
+ An object containing the rolling variance values.
+ """
+ df = basemodel_to_df(data, index=index)
+ series_target = get_target_column(df, target)
+ series_target.name = f"rolling_var_{window}"
+ validate_window(series_target, window)
+ results = series_target.rolling(window).apply(var_).dropna().reset_index(drop=False)
+ results = df_to_basemodel(results)
+
+ return OBBject(results=results)
+
+
+@router.command(
+ methods=["POST"],
+ examples=[
+ 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()',
+ 'returns = stock_data["close"].pct_change().dropna()',
+ 'obb.quantitative.rolling.stdev(data=returns, target="close", window=252)',
+ ],
+)
+def stdev(
+ data: List[Data], target: str, window: PositiveInt = 21, index: str = "date"
+) -> OBBject[List[Data]]:
+ """
+ Calculate the rolling standard deviation of a target column within a given window size.
+
+ Standard deviation is a measure of the amount of variation or dispersion of a set of values.
+ It is widely used to assess the risk and volatility of financial returns or other time series data
+ over a specified rolling window. It is the square root of the variance.
+
+ Parameters:
+ data: List[Data]
+ The time series data as a list of data points.
+ target: str
+ The name of the column for which to calculate standard deviation.
+ window: PositiveInt
+ The number of observations used for calculating the rolling measure.
+ index: str, optional
+ The name of the index column, default is "date".
+
+ Returns:
+ -------
+ OBBject[List[Data]]
+ An object containing the rolling standard deviation values.
+ """
+
+ df = basemodel_to_df(data, index=index)
+ series_target = get_target_column(df, target)
+ series_target.name = f"rolling_stdev_{window}"
+ validate_window(series_target, window)
+ results = (
+ series_target.rolling(window).apply(std_dev_).dropna().reset_index(drop=False)
+ )
+ results = df_to_basemodel(results)
+
+ return OBBject(results=results)
+
+
+@router.command(
+ methods=["POST"],
+ examples=[
+ 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()',
+ 'returns = stock_data["close"].pct_change().dropna()',
+ 'obb.quantitative.rolling.kurtosis(data=returns, target="close", window=252)',
+ ],
+)
+def kurtosis(
+ data: List[Data], target: str, window: PositiveInt = 21, index: str = "date"
+) -> OBBject[List[Data]]:
+ """
+ Calculate the rolling kurtosis of a target column within a given window size.
+
+ Kurtosis measures the "tailedness" of the probability distribution of a real-valued random variable.
+ High kurtosis indicates a distribution with heavy tails (outliers), suggesting a higher risk of extreme outcomes.
+ Low kurtosis indicates a distribution with lighter tails (less outliers), suggesting less risk of extreme outcomes.
+ This function helps in assessing the risk of outliers in financial returns or other time series data over a specified
+ rolling window.
+
+ Parameters:
+ data: List[Data]
+ The time series data as a list of data points.
+ target: str
+ The name of the column for which to calculate kurtosis.
+ window: PositiveInt
+ The number of observations used for calculating the rolling measure.
+ index: str, optional
+ The name of the index column, default is "date".
+
+ Returns:
+ -------
+ OBBject[List[Data]]
+ An object containing the rolling kurtosis values.
+ """
+
+ df = basemodel_to_df(data, index=index)
+ series_target = get_target_column(df, target)
+ series_target.name = f"rolling_kurtosis_{window}"
+ validate_window(series_target, window)
+ results = (
+ series_target.rolling(window).apply(kurtosis_).dropna().reset_index(drop=False)
+ )
+ results = df_to_basemodel(results)
+
+ return OBBject(results=results)
+
+
+@router.command(
+ methods=["POST"],
+ examples=[
+ 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()',
+ 'returns = stock_data["close"].pct_change().dropna()',
+ 'obb.quantitative.rolling.quantile(data=returns, target="close", window=252, quantile_pct=0.25)',
+ 'obb.quantitative.rolling.quantile(data=returns, target="close", window=252, quantile_pct=0.75)',
+ ],
+)
+def quantile(
+ data: List[Data],
+ target: str,
+ window: PositiveInt = 21,
+ quantile_pct: NonNegativeFloat = 0.5,
+ index: str = "date",
+) -> OBBject[List[Data]]:
+ """
+ Calculate the rolling quantile of a target column within a given window size at a specified quantile percentage.
+
+ Quantiles are points dividing the range of a probability distribution into intervals with equal probabilities,
+ or dividing the sample in the same way. This function is useful for understanding the distribution of data
+ within a specified window, allowing for analysis of trends, identification of outliers, and assessment of risk..
+
+ Parameters:
+ data: List[Data]
+ The time series data as a list of data points.
+ target: str
+ The name of the column for which to calculate the quantile.
+ window: PositiveInt
+ The number of observations used for calculating the rolling measure.
+ quantile_pct: NonNegativeFloat, optional
+ The quantile percentage to calculate (e.g., 0.5 for median), default is 0.5.
+ index: str, optional
+ The name of the index column, default is "date".
+
+ Returns:
+ -------
+ OBBject[List[Data]]
+ An object containing the rolling quantile values with the median.
+ """
+
+ df = basemodel_to_df(data, index=index)
+ series_target = get_target_column(df, target)
+ validate_window(series_target, window)
+ roll = series_target.rolling(window)
+ df_median = roll.median()
+ df_quantile = roll.quantile(quantile_pct)
+ results = (
+ pd.concat(
+ [df_median, df_quantile],
+ axis=1,
+ keys=[
+ f"rolling_median_{window}",
+ f"rolling_quantile_{quantile_pct}_{window}",
+ ],
+ )
+ .dropna()
+ .reset_index(drop=False)
+ )
+
+ results = df_to_basemodel(results)
+
+ return OBBject(results=results)
+
+
+@router.command(
+ methods=["POST"],
+ examples=[
+ 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()',
+ 'returns = stock_data["close"].pct_change().dropna()',
+ 'obb.quantitative.rolling.mean(data=returns, target="close", window=252)',
+ ],
+)
+def mean(
+ data: List[Data], target: str, window: PositiveInt = 21, index: str = "date"
+) -> OBBject[List[Data]]:
+ """
+ Calculate the rolling mean (average) of a target column within a given window size.
+
+ The rolling mean is a simple moving average that calculates the average of a target variable over a specified window.
+ This function is widely used in financial analysis to smooth short-term fluctuations and highlight longer-term trends
+ or cycles in time series data.
+
+ Parameters:
+ data: List[Data]
+ The time series data as a list of data points.
+ target: str
+ The name of the column for which to calculate the mean.
+ window: PositiveInt
+ The number of observations used for calculating the rolling measure.
+ index: str, optional
+ The name of the index column, default is "date".
+
+ Returns:
+ OBBject[List[Data]]
+ An object containing the rolling mean values.
+ """
+
+ df = basemodel_to_df(data, index=index)
+ series_target = get_target_column(df, target)
+ series_target.name = f"rolling_mean_{window}"
+ validate_window(series_target, window)
+ results = (
+ series_target.rolling(window).apply(mean_).dropna().reset_index(drop=False)
+ )
+ results = df_to_basemodel(results)
+
+ return OBBject(results=results)
diff --git a/openbb_platform/extensions/quantitative/openbb_quantitative/statistics.py b/openbb_platform/extensions/quantitative/openbb_quantitative/statistics.py
new file mode 100644
index 00000000000..db137ac77cf
--- /dev/null
+++ b/openbb_platform/extensions/quantitative/openbb_quantitative/statistics.py
@@ -0,0 +1,41 @@
+"""Statistics Functions"""
+
+from typing import Union
+
+from numpy import (
+ mean as mean_np,
+ ndarray,
+ std,
+ var as var_np,
+)
+from pandas import DataFrame, Series
+from scipy import stats
+
+# Because python is weird and these being the same name as the fastapi router functions
+# which overwrites the function signature, we add the _ after the function name
+
+
+def kurtosis_(data: Union[DataFrame, Series, ndarray]) -> float:
+ """Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable."""
+ return stats.kurtosis(data)
+
+
+def skew_(data: Union[DataFrame, Series, ndarray]) -> float:
+ """Skewness is a measure of the asymmetry of the probability distribution of a
+ real-valued random variable about its mean."""
+ return stats.skew(data)
+
+
+def mean_(data: Union[DataFrame, Series, ndarray]) -> float:
+ """Mean is the average of the numbers."""
+ return mean_np(data)
+
+
+def std_dev_(data: Union[DataFrame, Series, ndarray]) -> float:
+ """Standard deviation is a measure of the amount of variation or dispersion of a set of values."""
+ return std(data)
+
+
+def var_(data: Union[DataFrame, Series, ndarray]) -> float:
+ """Variance is a measure of the amount of variation or dispersion of a set of values."""
+ return var_np(data)
diff --git a/openbb_platform/extensions/quantitative/openbb_quantitative/stats/stats_router.py b/openbb_platform/extensions/quantitative/openbb_quantitative/stats/stats_router.py
new file mode 100644
index 00000000000..deff80471b1
--- /dev/null
+++ b/openbb_platform/extensions/quantitative/openbb_quantitative/stats/stats_router.py
@@ -0,0 +1,258 @@
+"""Rolling submenu of quantitative models for rolling statistics."""
+
+from typing import List
+
+import pandas as pd
+from openbb_core.app.model.obbject import OBBject
+from openbb_core.app.router import Router
+from openbb_core.app.utils import (
+ basemodel_to_df,
+ df_to_basemodel,
+ get_target_column,
+)
+from openbb_core.provider.abstract.data import Data
+from openbb_quantitative.statistics import (
+ kurtosis_,
+ mean_,
+ skew_,
+ std_dev_,
+ var_,
+)
+from pydantic import NonNegativeFloat
+
+router = Router(prefix="/stats")
+
+
+@router.command(
+ methods=["POST"],
+ examples=[
+ 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()',
+ 'returns = stock_data["close"].pct_change().dropna()',
+ 'obb.quantitative.stats.skew(data=returns, target="close")',
+ ],
+)
+def skew(
+ data: List[Data],
+ target: str,
+) -> OBBject[List[Data]]:
+ """Get the skew. of the data set
+
+ Skew is a statistical measure that reveals the degree of asymmetry of a distribution around its mean.
+ Positive skewness indicates a distribution with an extended tail to the right, while negative skewness shows a tail
+ that stretches left. Understanding skewness can provide insights into potential biases in data and help anticipate
+ the nature of future data points. It's particularly useful for identifying the likelihood of extreme outcomes in
+ financial returns, enabling more informed decision-making based on the distribution's shape over a specified period.
+
+ Parameters
+ ----------
+ data : List[Data]
+ Time series data.
+ target : str
+ Target column name.
+
+ Returns
+ -------
+ OBBject[List[Data]]
+ Rolling skew.
+ """
+
+ df = basemodel_to_df(data)
+ series_target = get_target_column(df, target)
+ results = pd.DataFrame([skew_(series_target)], columns=["skew"])
+ results = df_to_basemodel(results)
+
+ return OBBject(results=results)
+
+
+@router.command(
+ methods=["POST"],
+ examples=[
+ 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()',
+ 'returns = stock_data["close"].pct_change().dropna()',
+ 'obb.quantitative.stats.variance(data=returns, target="close")',
+ ],
+)
+def variance(data: List[Data], target: str) -> OBBject[List[Data]]:
+ """
+ Calculate the variance of a target column.
+
+ Variance measures the dispersion of a set of data points around their mean. It is a key metric for
+ assessing the volatility and stability of financial returns or other time series data.
+
+ Parameters
+ ----------
+ data: List[Data]
+ The time series data as a list of data points.
+ target: str
+ The name of the column for which to calculate variance.
+
+ Returns
+ -------
+ OBBject[List[Data]]
+ An object containing the rolling variance values.
+ """
+ df = basemodel_to_df(data)
+ series_target = get_target_column(df, target)
+ results = pd.DataFrame([var_(series_target)], columns=["variance"])
+ results = df_to_basemodel(results)
+
+ return OBBject(results=results)
+
+
+@router.command(
+ methods=["POST"],
+ examples=[
+ 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()',
+ 'returns = stock_data["close"].pct_change().dropna()',
+ 'obb.quantitative.stats.stdev(data=returns, target="close")',
+ ],
+)
+def stdev(data: List[Data], target: str) -> OBBject[List[Data]]:
+ """
+ Calculate the rolling standard deviation of a target column.
+
+ Standard deviation is a measure of the amount of variation or dispersion of a set of values.
+ It is widely used to assess the risk and volatility of financial returns or other time series data
+ It is the square root of the variance.
+
+ Parameters
+ ----------
+ data: List[Data]
+ The time series data as a list of data points.
+ target: str
+ The name of the column for which to calculate standard deviation.
+
+ Returns
+ -------
+ OBBject[List[Data]]
+ An object containing the rolling standard deviation values.
+ """
+
+ df = basemodel_to_df(data)
+ series_target = get_target_column(df, target)
+ results = pd.DataFrame([std_dev_(series_target)], columns=["stdev"])
+ results = df_to_basemodel(results)
+
+ return OBBject(results=results)
+
+
+@router.command(
+ methods=["POST"],
+ examples=[
+ 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()',
+ 'returns = stock_data["close"].pct_change().dropna()',
+ 'obb.quantitative.stats.kurtosis(data=returns, target="close")',
+ ],
+)
+def kurtosis(data: List[Data], target) -> OBBject[List[Data]]:
+ """
+ Calculate the rolling kurtosis of a target column.
+
+ Kurtosis measures the "tailedness" of the probability distribution of a real-valued random variable.
+ High kurtosis indicates a distribution with heavy tails (outliers), suggesting a higher risk of extreme outcomes.
+ Low kurtosis indicates a distribution with lighter tails (less outliers), suggesting less risk of extreme outcomes.
+ This function helps in assessing the risk of outliers in financial returns or other time series data.
+
+ Parameters
+ data: List[Data]
+ The time series data as a list of data points.
+ target: str
+ The name of the column for which to calculate kurtosis.
+
+ Returns
+ ------
+ OBBject[List[Data]]
+ An object containing the kurtosis value
+ """
+
+ df = basemodel_to_df(data)
+ series_target = get_target_column(df, target)
+ results = pd.DataFrame([kurtosis_(series_target)], columns=["kurtosis"])
+ results = df_to_basemodel(results)
+
+ return OBBject(results=results)
+
+
+@router.command(
+ methods=["POST"],
+ examples=[
+ 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()',
+ 'returns = stock_data["close"].pct_change().dropna()',
+ 'obb.quantitative.stats.quantile(data=returns, target="close", quantile_pct=0.75)',
+ ],
+)
+def quantile(
+ data: List[Data],
+ target: str,
+ quantile_pct: NonNegativeFloat = 0.5,
+) -> OBBject[List[Data]]:
+ """
+ Calculate the quantile of a target column at a specified quantile percentage.
+
+ Quantiles are points dividing the range of a probability distribution into intervals with equal probabilities,
+ or dividing the sample in the same way.
+
+ Parameters
+ ----------
+ data: List[Data]
+ The time series data as a list of data points.
+ target: str
+ The name of the column for which to calculate the quantile.
+ quantile_pct: NonNegativeFloat, optional
+ The quantile percentage to calculate (e.g., 0.5 for median), default is 0.5.
+
+ Returns
+ -------
+ OBBject[List[Data]]
+ An object containing the rolling quantile values with the median.
+ """
+
+ df = basemodel_to_df(
+ data,
+ )
+ series_target = get_target_column(df, target)
+ results = pd.DataFrame(
+ [series_target.quantile(quantile_pct)], columns=[f"{quantile_pct}_quantile"]
+ )
+ results = df_to_basemodel(results)
+ return OBBject(results=results)
+
+
+@router.command(
+ methods=["POST"],
+ examples=[
+ 'stock_data = obb.equity.price.historical(symbol="TSLA", start_date="2023-01-01", provider="fmp").to_df()',
+ 'returns = stock_data["close"].pct_change().dropna()',
+ 'obb.quantitative.stats.mean(data=returns, target="close")',
+ ],
+)
+def mean(
+ data: List[Data],
+ target: str,
+) -> OBBject[List[Data]]:
+ """
+ Calculate the mean (average) of a target column.
+
+ The rolling mean is a simple moving average that calculates the average of a target variable.
+ This function is widely used in financial analysis to smooth short-term fluctuations and highlight longer-term trends
+ or cycles in time series data.
+
+ Parameters
+ ----------
+ data: List[Data]
+ The time series data as a list of data points.
+ target: str
+ The name of the column for which to calculate the mean.
+
+ Returns
+ -------
+ OBBject[List[Data]]
+ An object containing the mean value.
+ """
+
+ df = basemodel_to_df(data)
+ series_target = get_target_column(df, target)
+ results = pd.DataFrame([mean_(series_target)], columns=["mean"])
+ results = df_to_basemodel(results)
+
+ return OBBject(results=results)
diff --git a/openbb_platform/extensions/quantitative/tests/test_statistics.py b/openbb_platform/extensions/quantitative/tests/test_statistics.py
new file mode 100644
index 00000000000..11c1ce2ec43
--- /dev/null
+++ b/openbb_platform/extensions/quantitative/tests/test_statistics.py
@@ -0,0 +1,25 @@
+import pandas as pd
+import pytest
+from openbb_quantitative.statistics import kurtosis_, mean_, skew_, std_dev_, var_
+
+test_data = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+
+
+def test_kurtosis():
+ assert kurtosis_(test_data) == pytest.approx(-1.224, abs=1e-3)
+
+
+def test_skew():
+ assert skew_(test_data) == pytest.approx(0.0, abs=1e-3)
+
+
+def test_std_dev():
+ assert std_dev_(test_data) == pytest.approx(2.872, abs=1e-3)
+
+
+def test_mean():
+ assert mean_(test_data) == pytest.approx(5.5, abs=1e-3)
+
+
+def test_var():
+ assert var_(test_data) == pytest.approx(8.25, abs=1e-3)
diff --git a/openbb_platform/extensions/tests/test_integration_tests_python.py b/openbb_platform/extensions/tests/test_integration_tests_python.py
index 110034fa82f..8940db9821e 100644
--- a/openbb_platform/extensions/tests/test_integration_tests_python.py
+++ b/openbb_platform/extensions/tests/test_integration_tests_python.py
@@ -59,13 +59,13 @@ def test_charting_extension_function_coverage() -> None:
assert missing_items == [], "\n".join(missing_items)
-def test_missing_api_integration_tests() -> None:
+def test_missing_python_integration_tests() -> None:
"""Check if there are missing tests."""
missing = check_missing_integration_tests(test_type="python")
assert not missing, "\n".join(missing)
-def test_outdated_api_integration_tests() -> None:
+def test_outdated_python_integration_tests() -> None:
"""Check if there are outdated tests."""
outdated = check_outdated_integration_tests(test_type="python")
assert not outdated, "\n".join(outdated)
diff --git a/openbb_platform/extensions/tests/utils/router_testers.py b/openbb_platform/extensions/tests/utils/router_testers.py
index ed8127fe8d3..bbc9ff3b7ec 100644
--- a/openbb_platform/extensions/tests/utils/router_testers.py
+++ b/openbb_platform/extensions/tests/utils/router_testers.py
@@ -132,7 +132,7 @@ def check_router_model_functions_signature() -> List[str]:
)
if expected_return_type not in str(function.__annotations__["return"]):
missing_return_type.append(
- f"{function.__name__} in {router_name}"
+ f"{function.__name__} in {router_name} "
f"doesn't have the expected return type: {expected_return_type}"
)
From 11df8e1b054589091242cf10784d56863474c152 Mon Sep 17 00:00:00 2001
From: Danglewood <