Add nbconvert command

Author: lsbardel

Makefile

@@ -35,6 +35,10 @@ book:				## Build static jupyter {book}
 nbconvert:			## Convert notebooks to myst markdown
 	poetry run ./dev/nbconvert
 
+.PHONY: nbsync
+nbsync:				## Sync python myst notebooks to .ipynb files - needed for vs notebook development
+	poetry run ./dev/nbsync
+
 .PHONY: sphinx-config
 sphinx-config:			## Build sphinx config
 	poetry run jupyter-book config sphinx notebooks

dev/nbsync

@@ -0,0 +1,7 @@
+#!/usr/bin/env bash
+set -e
+
+for file in notebooks/**/*.md
+do
+  jupytext "$file" -s
+done

notebooks/applications/calibration.md

@@ -5,7 +5,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.14.7
+    jupytext_version: 1.16.6
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -26,7 +26,7 @@ Early pointers
 For calibration we use {cite:p}`ukf`.
 Lets consider the Heston model as a test case
 
-```{code-cell} ipython3
+```{code-cell}
 from quantflow.sp.heston import Heston
 
 pr = Heston.create(vol=0.6, kappa=1.3, sigma=0.8, rho=-0.6)
@@ -82,11 +82,11 @@ the state equation is given by
 X_{t+1} &= \left[\begin{matrix}\kappa\left(\theta\right) dt \\ 0\end{matrix}\right] + 
 \end{align}
 
-```{code-cell} ipython3
+```{code-cell}
 [p for p in pr.variance_process.parameters]
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 
 ```
 
@@ -106,7 +106,7 @@ x_t &= \left[\begin{matrix}\nu_t && w_t && z_t\end{matrix}\right]^T \\
 \bar{x}_t = {\mathbb E}\left[x_t\right] &= \left[\begin{matrix}\nu_t && 0 && 0\end{matrix}\right]^T
 \end{align}
 
-```{code-cell} ipython3
+```{code-cell}
 from quantflow.data.fmp import FMP
 frequency = "1min"
 async with FMP() as cli:
@@ -115,13 +115,13 @@ df = df.sort_values("date").reset_index(drop=True)
 df
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 import plotly.express as px
 fig = px.line(df, x="date", y="close", markers=True)
 fig.show()
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 import numpy as np
 from quantflow.utils.volatility import parkinson_estimator, GarchEstimator
 df["returns"] = np.log(df["close"]) - np.log(df["open"])
@@ -132,43 +132,43 @@ fig = px.line(ds["returns"], markers=True)
 fig.show()
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 import plotly.express as px
 from quantflow.utils.bins import pdf
 df = pdf(ds["returns"], num=20)
 fig = px.bar(df, x="x", y="f")
 fig.show()
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 g1 = GarchEstimator.returns(ds["returns"], dt)
 g2 = GarchEstimator.pk(ds["returns"], ds["pk"], dt)
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 import pandas as pd
 yf = pd.DataFrame(dict(returns=g2.y2, pk=g2.p))
 fig = px.line(yf, markers=True)
 fig.show()
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 r1 = g1.fit()
 r1
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 r2 = g2.fit()
 r2
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 sig2 = pd.DataFrame(dict(returns=np.sqrt(g2.filter(r1["params"])), pk=np.sqrt(g2.filter(r2["params"]))))
 fig = px.line(sig2, markers=False, title="Stochastic volatility")
 fig.show()
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 class HestonCalibration:
     
     def __init__(self, dt: float, initial_std = 0.5):
@@ -186,19 +186,19 @@ class HestonCalibration:
         return np.array(((1-self.kappa*self.dt, 0),(-0.5*self.dt, 0)))
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 c = HestonCalibration(dt)
 c.x0
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 c.prediction(c.x0)
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 c.state_jacobian()
 ```

notebooks/applications/overview.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.14.7
+    jupytext_version: 1.16.6
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -18,6 +18,6 @@ Real-world applications of the library
 ```{tableofcontents}
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 
 ```

notebooks/applications/sampling.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.14.7
+    jupytext_version: 1.16.6
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -15,21 +15,21 @@ kernelspec:
 
 The library use the `Paths` class for managing monte carlo paths.
 
-```{code-cell} ipython3
+```{code-cell}
 from quantflow.utils.paths import Paths
 
 nv = Paths.normal_draws(paths=1000, time_horizon=1, time_steps=1000)
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 nv.var().mean()
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 nv = Paths.normal_draws(paths=1000, time_horizon=1, time_steps=1000, antithetic_variates=False)
 nv.var().mean()
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 
 ```

notebooks/data/fmp.md

@@ -5,7 +5,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.1
+    jupytext_version: 1.16.6
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -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} ipython3
+```{code-cell}
 from quantflow.data.fmp import FMP
 cli = FMP()
 cli.url
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 stock = "KNOS.L"
 ```
 
 ## Company Profile
 
-```{code-cell} ipython3
+```{code-cell}
 d = await cli.profile(stock)
 d
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 c = await cli.peers(stock)
 c
 ```
 
 ## Executive trading
 
-```{code-cell} ipython3
+```{code-cell}
 stock = "KNOS.L"
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 await cli.executives(stock)
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 await cli.insider_trading(stock)
 ```
 
 ## News
 
-```{code-cell} ipython3
+```{code-cell}
 c = await cli.news(stock)
 c
 ```

notebooks/data/timeseries.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.1
+    jupytext_version: 1.16.6
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -13,27 +13,27 @@ kernelspec:
 
 ## Timeseries
 
-```{code-cell} ipython3
+```{code-cell}
 from quantflow.data.fmp import FMP
 from quantflow.utils.plot import candlestick_plot
 cli = FMP()
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 prices = await cli.prices("ethusd", frequency="")
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 candlestick_plot(prices).update_layout(height=500)
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 from quantflow.utils.df import DFutils
 
 df = DFutils(prices).with_rogers_satchel().with_parkinson()
 df
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 
 ```

notebooks/examples/gaussian_sampling.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.14.7
+    jupytext_version: 1.16.6
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -15,7 +15,7 @@ kernelspec:
 
 Here we sample the gaussian OU process for different mean reversion speed and number of paths.
 
-```{code-cell} ipython3
+```{code-cell}
 from quantflow.sp.ou import Vasicek
 from quantflow.utils import plot
 import ipywidgets as widgets
@@ -51,6 +51,6 @@ fig = go.FigureWidget(data=[simulation, analytical])
 widgets.VBox([kappa, samples, fig])
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 
 ```

notebooks/examples/heston_vol_surface.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.14.7
+    jupytext_version: 1.16.6
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -13,7 +13,7 @@ kernelspec:
 
 # Heston Volatility Surface
 
-```{code-cell} ipython3
+```{code-cell}
 from quantflow.sp.heston import HestonJ
 from quantflow.options.pricer import OptionPricer
 
@@ -29,13 +29,13 @@ pricer = OptionPricer(model=HestonJ.create(
 pricer
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 pricer.plot3d(max_moneyness_ttm=1.5, support=31).update_layout(
     height=800,
     title="Heston volatility surface",
 )
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 
 ```

notebooks/examples/overview.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.14.7
+    jupytext_version: 1.16.6
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python