Add command line client

notebooks/data/fmp.md

@@ -5,7 +5,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.14.7
+    jupytext_version: 1.16.1
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python

notebooks/data/timeseries.md

@@ -0,0 +1,32 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.16.1
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+## Timeseries
+
+```{code-cell} ipython3
+from quantflow.data.fmp import FMP
+from quantflow.utils.plot import candlestick_plot
+cli = FMP()
+```
+
+```{code-cell} ipython3
+prices = await cli.prices("aapl")
+```
+
+```{code-cell} ipython3
+candlestick_plot(prices).update_layout(height=500)
+```
+
+```{code-cell} ipython3
+
+```

pyproject.toml

@@ -20,6 +20,9 @@ aiohttp = {version = "^3.8.1", optional = true}
 pydantic = "^2.0.2"
 pyarrow = "^15.0.0"
 ccy = {version="^1.4.0", extras=["cli"]}
+asciichart = "^0.1"
+python-dotenv = "^1.0.1"
+asciichartpy = "^1.5.25"
 
 [tool.poetry.group.dev.dependencies]
 black = "^24.1.1"
@@ -40,11 +43,14 @@ optional = true
 jupyter-book = "^1.0.0"
 nbconvert = "^7.16.3"
 jupytext = "^1.13.8"
-plotly = "^5.7.0"
+plotly = "^5.20.0"
 jupyterlab = "^4.0.2"
 sympy = "^1.12"
 ipywidgets = "^8.0.7"
 
+[tool.poetry.scripts]
+qf = "quantflow.cli:qf"
+
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"

quantflow/cli/__init__.py

@@ -0,0 +1,58 @@
+import asyncio
+
+import click
+import dotenv
+import pandas as pd
+from asciichartpy import plot
+
+from quantflow.data.fmp import FMP
+
+dotenv.load_dotenv()
+
+
+@click.group()
+def qf():
+    pass
+
+
+@qf.command()
+@click.argument("symbol")
+def profile(symbol: str):
+    """Company profile"""
+    data = asyncio.run(get_profile(symbol))[0]
+    print(data.pop("description"))
+
+
+@qf.command()
+@click.argument("symbol")
+@click.option(
+    "-h",
+    "--height",
+    type=int,
+    default=20,
+    show_default=True,
+    help="Chart height",
+)
+@click.option(
+    "-l",
+    "--length",
+    type=int,
+    default=100,
+    show_default=True,
+    help="Number of data points",
+)
+def chart(symbol: str, height: int, length: int):
+    """Symbol chart"""
+    df = asyncio.run(get_prices(symbol))
+    data = df["close"].tolist()
+    print(plot(data[:length], {"height": height}))
+
+
+async def get_prices(symbol: str) -> pd.DataFrame:
+    async with FMP() as cli:
+        return await cli.prices(symbol)
+
+
+async def get_profile(symbol: str) -> dict:
+    async with FMP() as cli:
+        return await cli.profile(symbol)

quantflow/data/client.py

@@ -1,7 +1,7 @@
 import json
 import os
 from dataclasses import dataclass
-from typing import Any
+from typing import Any, Self
 
 from aiohttp import ClientResponse, ClientSession
 from aiohttp.client_exceptions import ContentTypeError
@@ -52,7 +52,7 @@ async def close(self) -> None:
             await self.session.close()
             self.session = None
 
-    async def __aenter__(self) -> "HttpClient":
+    async def __aenter__(self) -> Self:
         return self
 
     async def __aexit__(self, *args: Any) -> None:

quantflow/data/fmp.py

@@ -1,5 +1,5 @@
 import os
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from datetime import date, timedelta
 from typing import Any, cast
 
@@ -12,7 +12,7 @@
 @dataclass
 class FMP(HttpClient):
     url: str = "https://financialmodelingprep.com/api"
-    key: str = os.environ.get("FMP_API_KEY", "")
+    key: str = field(default_factory=lambda: os.environ.get("FMP_API_KEY", ""))
 
     async def stocks(self, **kw: Any) -> list[dict]:
         return await self.get_path("v3/stock/list", **kw)
@@ -111,7 +111,9 @@ async def search(
             **self.params(compact(query=query, exchange=exchange, limit=limit), **kw),
         )
 
-    async def prices(self, ticker: str, frequency: str = "", **kw: Any) -> pd.DataFrame:
+    async def prices(
+        self, ticker: str, frequency: str = "", to_date: bool = False, **kw: Any
+    ) -> pd.DataFrame:
         base = (
             "historical-price-full/"
             if not frequency
@@ -121,10 +123,14 @@ async def prices(self, ticker: str, frequency: str = "", **kw: Any) -> pd.DataFr
         if isinstance(data, dict):
             data = data.get("historical", [])
         df = pd.DataFrame(data)
-        if "date" in df.columns:
+        if to_date and "date" in df.columns:
             df["date"] = pd.to_datetime(df["date"])
         return df
 
+    # forex
+    def forex_list(self) -> list[str]:
+        return self.get_path("v3/symbol/available-forex-currency-pairs")
+
     def historical_frequencies(self) -> dict:
         return {
             "1min": 1,

quantflow/utils/plot.py

@@ -183,3 +183,18 @@ def plot3d(
     if kwargs:
         fig.update_layout(**kwargs)
     return fig
+
+
+def candlestick_plot(df, slider: bool = True) -> Any:
+    fig = go.Figure(
+        data=go.Candlestick(
+            x=df["date"],
+            open=df["open"],
+            high=df["high"],
+            low=df["low"],
+            close=df["close"],
+        )
+    )
+    if slider is False:
+        fig.update_layout(xaxis_rangeslider_visible=False)
+    return fig

readme.md

@@ -24,3 +24,11 @@ pip install quantflow
 * [quantflow.data](https://github.com/quantmind/quantflow/tree/main/quantflow/data) data APIs (requires `quantflow[data]`)
 * [quantflow.options](https://github.com/quantmind/quantflow/tree/main/quantflow/options) option pricing and calibration
 * [quantflow.sp](https://github.com/quantmind/quantflow/tree/main/quantflow/sp) stochastic process primitives
+
+
+## Command line tools
+
+When installing with the extra `data` dependencies, it is possible to use the command line tool `qf`
+```
+qf --help
+```