Add Fed data

Author: lsbardel

notebooks/api/data/fed.rst

@@ -0,0 +1,11 @@
+================
+Federal Reserve
+================
+
+.. currentmodule:: quantflow.data.fed
+
+.. autoclass:: FederalReserve
+   :members:
+   :member-order: groupwise
+   :autosummary:
+   :autosummary-nosignatures:

notebooks/api/data/index.rst

@@ -14,5 +14,6 @@ pip install quantflow[data]
     :maxdepth: 1
 
     fmp
+    fed
     fred
     deribit

notebooks/data/fed.md

@@ -0,0 +1,40 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.16.6
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# Federal Reserve Data
+
+```{code-cell} ipython3
+from quantflow.data.fed import FederalReserve
+```
+
+```{code-cell} ipython3
+async with FederalReserve() as fed:
+    rates = await fed.ref_rates()
+```
+
+```{code-cell} ipython3
+rates
+```
+
+```{code-cell} ipython3
+async with FederalReserve() as fed:
+    curves = await fed.yield_curves()
+```
+
+```{code-cell} ipython3
+curves
+```
+
+```{code-cell} ipython3
+
+```

notebooks/data/fmp.md

@@ -16,45 +16,45 @@ kernelspec:
 
 The library provides a python client for the [Financial Modelling Prep API](https://site.financialmodelingprep.com/developer/docs). To use the client one needs to provide the API key aither directly to the client or via the `FMP_API_KEY` environment variable. The API offers 1 minute, 5 minutes, 15 minutes, 30 minutes, 1 hour and daily historical prices.
 
-```{code-cell}
+```{code-cell} ipython3
 from quantflow.data.fmp import FMP
 cli = FMP()
 cli.url
 ```
 
-```{code-cell}
+```{code-cell} ipython3
 stock = "KNOS.L"
 ```
 
 ## Company Profile
 
-```{code-cell}
+```{code-cell} ipython3
 d = await cli.profile(stock)
 d
 ```
 
-```{code-cell}
+```{code-cell} ipython3
 c = await cli.peers(stock)
 c
 ```
 
 ## Executive trading
 
-```{code-cell}
+```{code-cell} ipython3
 stock = "KNOS.L"
 ```
 
-```{code-cell}
+```{code-cell} ipython3
 await cli.executives(stock)
 ```
 
-```{code-cell}
+```{code-cell} ipython3
 await cli.insider_trading(stock)
 ```
 
 ## News
 
-```{code-cell}
+```{code-cell} ipython3
 c = await cli.news(stock)
 c
 ```

notebooks/data/timeseries.md

@@ -13,27 +13,27 @@ kernelspec:
 
 ## Timeseries
 
-```{code-cell}
+```{code-cell} ipython3
 from quantflow.data.fmp import FMP
 from quantflow.utils.plot import candlestick_plot
 cli = FMP()
 ```
 
-```{code-cell}
+```{code-cell} ipython3
 prices = await cli.prices("ethusd", frequency="")
 ```
 
-```{code-cell}
+```{code-cell} ipython3
 candlestick_plot(prices).update_layout(height=500)
 ```
 
-```{code-cell}
+```{code-cell} ipython3
 from quantflow.utils.df import DFutils
 
 df = DFutils(prices).with_rogers_satchel().with_parkinson()
 df
 ```
 
-```{code-cell}
+```{code-cell} ipython3
 
 ```

notebooks/examples/gaussian_sampling.md

@@ -6,7 +6,7 @@ jupytext:
     format_version: 0.13
     jupytext_version: 1.16.6
 kernelspec:
-  display_name: .venv
+  display_name: Python 3 (ipykernel)
   language: python
   name: python3
 ---

poetry.lock

@@ -0,0 +1,91 @@
+import io
+from dataclasses import dataclass, field
+from typing import Any
+
+import numpy as np
+import pandas as pd
+from fluid.utils.http_client import AioHttpClient
+
+URL = (
+    "https://www.federalreserve.gov/datadownload/Output.aspx?"
+    "rel=H15&series=bf17364827e38702b42a58cf8eaa3f78&lastobs=&"
+)
+
+maturities = [
+    "month_1",
+    "month_3",
+    "month_6",
+    "year_1",
+    "year_2",
+    "year_3",
+    "year_5",
+    "year_7",
+    "year_10",
+    "year_20",
+    "year_30",
+]
+
+
+@dataclass
+class FederalReserve(AioHttpClient):
+    """Federal Reserve API client.
+
+    This class is used to fetch yield curves from the Federal Reserve at
+    https://www.federalreserve.gov/datadownload/
+    """
+
+    url: str = "https://www.federalreserve.gov/datadownload/Output.aspx"
+    default_params: dict[str, Any] = field(
+        default_factory=lambda: {
+            "from": "",
+            "to": "",
+            "lastobs": "",
+            "filetype": "csv",
+            "label": "include",
+            "layout": "seriescolumn",
+            "type": "package",
+        }
+    )
+
+    async def yield_curves(self, **params: Any) -> pd.DataFrame:
+        """Get treasury constant maturities rates"""
+        params.update(series="bf17364827e38702b42a58cf8eaa3f78", rel="H15")
+        data = await self._get_text(params)
+        df = pd.read_csv(data, header=5, index_col=None, parse_dates=True)
+        df.columns = ["date"] + maturities  # type: ignore
+        df = df.set_index("date").replace("ND", np.nan)
+        return df.dropna(axis=0, how="all").reset_index()
+
+    async def ref_rates(self, **params: Any) -> pd.DataFrame:
+        """Get policy rates
+
+        Prior to 2021-07-08 it is the rate on excess reserves (IOER rate)
+        After 2021-07-08 it is the rate on reserve balances (IORB rate)
+
+        The IOER rate was the primary tool used by the Federal Reserve to set
+        a floor on the federal funds rate.
+        While the Interest rate on required reserves (IORR rate) existed,
+        the IOER rate had a more direct impact on market rates,
+        as banks typically held far more excess reserves than required reserves.
+        Therefore, the IOER rate was more influential
+        in the Fed's monetary policy implementation.
+        """
+        params.update(series="c27939ee810cb2e929a920a6bd77d9f6", rel="PRATES")
+        data = await self._get_text(params)
+        df = pd.read_csv(data, header=5, index_col=None, parse_dates=True)
+        ioer = df["RESBME_N.D"]
+        iorb = df["RESBM_N.D"]
+        rate = iorb.combine_first(ioer)
+        return pd.DataFrame(
+            {
+                "date": df["Time Period"],
+                "rate": rate,
+            }
+        )
+
+    async def _get_text(self, params: dict[str, Any]) -> io.StringIO:
+        """Get parameters for the request."""
+        params = {**self.default_params, **params}
+        response = await self.get(self.url, params=params, callback=True)
+        data = await response.text()
+        return io.StringIO(data)

quantflow/data/fed.py

@@ -1,7 +1,9 @@
 from typing import AsyncIterator
 
 import pytest
+from aiohttp.client_exceptions import ClientError
 
+from quantflow.data.fed import FederalReserve
 from quantflow.data.fmp import FMP
 
 pytestmark = pytest.mark.skipif(not FMP().key, reason="No FMP API key found")
@@ -26,3 +28,25 @@ async def test_historical(fmp: FMP) -> None:
 async def test_dividends(fmp: FMP) -> None:
     data = await fmp.dividends()
     assert data is not None
+
+
+async def test_fed_yc() -> None:
+    try:
+        async with FederalReserve() as fed:
+            df = await fed.yield_curves()
+            assert df is not None
+            assert df.shape[0] > 0
+            assert df.shape[1] == 12
+    except (ConnectionError, ClientError) as e:
+        pytest.skip(f"Skipping test_fed due to network issue: {e}")
+
+
+async def test_fed_rates() -> None:
+    try:
+        async with FederalReserve() as fed:
+            df = await fed.ref_rates()
+            assert df is not None
+            assert df.shape[0] > 0
+            assert df.shape[1] == 2
+    except (ConnectionError, ClientError) as e:
+        pytest.skip(f"Skipping test_fed due to network issue: {e}")