wiring deployment

Author: lsbardel

.dockerignore

@@ -0,0 +1,16 @@
+dist/
+build/
+
+__marimo__
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.data
+.env
+.venv
+.mypy_cache
+.ruff_cache
+.pytest_cache
+.codebuild
+.gihub

.github/workflows/build.yml

@@ -51,3 +51,17 @@ jobs:
       - name: publish
         if: ${{ matrix.python-version == '3.13' && github.event.head_commit.message == 'release' }}
         run: make publish
+
+  deploy:
+    runs-on: ubuntu-latest
+    needs:
+      - build
+    env:
+      PYTHON_ENV: ci
+      FMP_API_KEY: ${{ secrets.FMP_API_KEY }}
+
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v4
+      - name: install rops
+        run: curl -L https://raw.githubusercontent.com/quantmind/rops/main/dev/install-rops | bash

.gitignore

@@ -34,3 +34,6 @@ dist
 *.ipynb
 .ipynb_checkpoints
 _build
+
+# builds
+app/docs

app/__main__.py

@@ -1,21 +1,26 @@
-from typing import Annotated, Callable, Coroutine
-from fastapi.responses import HTMLResponse, RedirectResponse
 import marimo
-from fastapi import FastAPI, Form, Request, Response
+from fastapi.staticfiles import StaticFiles
+from fastapi import FastAPI
+from pathlib import Path
+
+APP_PATH = Path(__file__).parent
 
 
 def crate_app() -> FastAPI:
     # Create a marimo asgi app
-    server = (
-        marimo.create_asgi_app()
-        .with_app(path="/supersmoother", root="./app/supersmoother.py")
-    )
+    server = marimo.create_asgi_app()
+    for path in APP_PATH.glob("*.py"):
+        if path.name.startswith("_"):
+            continue
+        dashed = path.stem.replace("_", "-")
+        server = server.with_app(path=f"/{dashed}", root=f"./app/{path.name}")
     # Create a FastAPI app
     app = FastAPI()
-    app.mount("/", server.build())
+    app.mount("/examples", server.build())
+    app.mount("/", StaticFiles(directory=APP_PATH / "docs", html=True), name="static")
     return app
 
 # Run the server
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(crate_app(), host="localhost", port=8001)
+    uvicorn.run(crate_app(), host="0.0.0.0", port=8001)

app/gaussian_sampling.py

@@ -0,0 +1,92 @@
+import marimo
+
+__generated_with = "0.19.7"
+app = marimo.App(width="medium")
+
+
+@app.cell
+def _():
+    import marimo as mo
+    from app.utils import nav_menu
+
+    nav_menu()
+    return (mo,)
+
+
+@app.cell
+def _(mo):
+    mo.md(r"""
+    # Gaussian Sampling
+
+    Here we sample the gaussian OU process for different mean reversion speed and number of paths.
+    """)
+    return
+
+
+@app.cell
+def _(mo):
+    import inspect
+    from quantflow.sp.ou import Vasicek
+    import pandas as pd
+
+    def simulate_vasicek(kappa: float, samples: int) -> pd.DataFrame:
+        pr = Vasicek(rate=0.5, kappa=kappa)
+        paths = pr.sample(samples, 1, 1000)
+        pdf = paths.pdf(num_bins=50)
+        pdf["simulation"] = pdf["pdf"]
+        pdf["analytical"] = pr.marginal(1).pdf(pdf.index)
+        return pdf
+
+    # 1. Get the source code of your function
+    # Note: The function must be defined in a previous cell or imported
+    try:
+        source_code = inspect.getsource(simulate_vasicek)
+    except OSError:
+        source_code = "# Code not available (source file not found)"
+
+    # 2. Display it inside an accordion so it doesn't clutter the view
+    mo.accordion({
+        "Show Simulation Code": mo.md(f"```python\n{source_code}\n```")
+    })
+    return (simulate_vasicek,)
+
+
+@app.cell
+def _(mo):
+    samples = mo.ui.slider(start=100, stop=10000, step=100, value=1000, debounce=True, full_width=True)
+    kappa = mo.ui.slider(start=0.1, stop=5, step=0.1, debounce=True, full_width=True)
+
+    def input_label(text):
+        return mo.Html(f"<span style='width: 250px; display: inline-block; font-weight: 500;'>{text}</span>")
+
+    controls = mo.vstack([
+        mo.hstack([input_label("Samples:"), samples], align="center"),
+        mo.hstack([input_label("Kappa (Mean reversion):"), kappa], align="center")
+    ])
+    controls
+    return kappa, samples
+
+
+@app.cell
+def _(kappa, samples, simulate_vasicek):
+    df = simulate_vasicek(kappa=kappa.value, samples=samples.value)
+    return (df,)
+
+
+@app.cell
+def _(df):
+    import plotly.graph_objects as go
+    simulation = go.Bar(x=df.index, y=df["simulation"], name="simulation")
+    analytical = go.Scatter(x=df.index, y=df["analytical"], name="analytical")
+    fig = go.Figure(data=[simulation, analytical])
+    fig
+    return
+
+
+@app.cell
+def _():
+    return
+
+
+if __name__ == "__main__":
+    app.run()

app/supersmoother.py

@@ -15,10 +15,6 @@ def _(mo):
 @app.cell
 def _():
     import marimo as mo
-
-    app = mo.App(
-        requirements=["quantflow"] 
-    )
     return (mo,)
 
 
@@ -55,11 +51,15 @@ def _(mo):
 def _(data, period):
     from quantflow.ta.supersmoother import SuperSmoother
     from quantflow.ta.ewma import EWMA
+    # create the filters
     smoother = SuperSmoother(period=period.value)
     ewma = EWMA(period=period.value)
-    sm = data[["date", "close"]].copy()
-    sm["supersmoother"] = data["close"].apply(smoother.update)
-    sm["ewma"] = data["close"].apply(ewma.update)
+    ewma_min = EWMA(period=period.value, tau=0.5)
+    # sort dates ascending
+    sm = data[["date", "close"]].copy().sort_values("date", ascending=True).reset_index(drop=True)
+    sm["supersmoother"] = sm["close"].apply(smoother.update)
+    sm["ewma"] = sm["close"].apply(ewma.update)
+    sm["ewma_min"] = sm["close"].apply(ewma_min.update)
     return (sm,)
 
 
@@ -68,7 +68,7 @@ def _(alt, sm):
     # Melt the dataframe to a long format suitable for Altair
     sm_long = sm.melt(
         id_vars=['date'],
-        value_vars=['close', 'supersmoother', "ewma"],
+        value_vars=['close', 'supersmoother', "ewma", "ewma_min"],
         var_name='Signal',
         value_name='Price'
     )
@@ -79,8 +79,8 @@ def _(alt, sm):
         y=alt.Y('Price:Q', title='Price (USD)', scale=alt.Scale(zero=False)),
         color=alt.Color('Signal:N', title='Signal',
                         scale=alt.Scale(
-                            domain=['close', 'supersmoother', 'ewma'],
-                            range=['#4c78a8', '#f58518', '#e45756'])  # Vega-Lite default palette
+                            domain=['close', 'supersmoother', 'ewma', "ewma_min"],
+                            range=['#4c78a8', '#f58518', '#e45756', '#e45756'])  # Vega-Lite default palette
                        ),
         tooltip=[
             alt.Tooltip('date:T', title='Date'),

app/utils/__init__.py

@@ -0,0 +1,9 @@
+import marimo as mo
+
+def nav_menu():
+    return mo.nav_menu(
+        {
+            "/": "Quantflow",
+            "/api": "API Reference",
+        }
+    )

docs/api/sp/cir.md

@@ -1,14 +1,5 @@
-================
-CIR
-================
+# CIR
 
 The Cox–Ingersoll–Ross (CIR) model
 
-.. currentmodule:: quantflow.sp.cir
-
-.. autoclass:: CIR
-   :members:
-   :member-order: groupwise
-   :autosummary:
-   :autosummary-nosignatures:
-
+::: quantflow.sp.cir.CIR

docs/api/sp/compound_poisson.md

@@ -1,12 +1,3 @@
-===================
-Compound Poisson
-===================
-
-.. currentmodule:: quantflow.sp.poisson
-
-.. autoclass:: CompoundPoissonProcess
-   :members:
-   :member-order: groupwise
-   :autosummary:
-   :autosummary-nosignatures:
+# Compound Poisson
 
+::: quantflow.sp.poisson.CompoundPoissonProcess

docs/api/sp/ou.md

@@ -1,22 +1,13 @@
-================
-OU Processes
-================
+# OU Processes
 
 These are the classes that implement gaussian and non-gaussian
-`Ornstein-Uhlenbeck <https://en.wikipedia.org/wiki/Ornstein%E2%80%93Uhlenbeck_process>`_ process.
+[Ornstein-Uhlenbeck](https://en.wikipedia.org/wiki/Ornstein%E2%80%93Uhlenbeck_process) process.
 
 
-.. currentmodule:: quantflow.sp.ou
+## Gaussian OU process
 
-.. autoclass:: Vasicek
-   :members:
-   :member-order: groupwise
-   :autosummary:
-   :autosummary-nosignatures:
+::: quantflow.sp.ou.Vasicek
 
+## Non-Gaussian OU process
 
-.. autoclass:: GammaOU
-   :members:
-   :member-order: groupwise
-   :autosummary:
-   :autosummary-nosignatures:
+::: quantflow.sp.ou.GammaOU