Add hurst

Author: lsbardel

.github/workflows/build.yml

@@ -44,19 +44,13 @@ jobs:
       - name: run tests
         run: make tests
       - name: upload coverage reports to codecov
-        if: matrix.python-version == '3.13'
+        if: matrix.python-version == '3.14'
         uses: codecov/codecov-action@v3
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
           files: ./build/coverage.xml
-      - name: build book
-        if: ${{ matrix.python-version == '3.13' }}
-        run: make book
-      - name: publish book
-        if: ${{ matrix.python-version == '3.13' }}
-        run: make publish-book
       - name: publish
-        if: ${{ matrix.python-version == '3.13' && github.event.head_commit.message == 'release' }}
+        if: ${{ matrix.python-version == '3.14' && github.event.head_commit.message == 'release' }}
         run: make publish
 
   deploy:

.github/workflows/update.yml

@@ -35,9 +35,48 @@ def _(mo):
 
     * [Hurst Exponent for Algorithmic Trading](https://robotwealth.com/demystifying-the-hurst-exponent-part-1/)
     * [Basics of Statistical Mean Reversion Testing](https://www.quantstart.com/articles/Basics-of-Statistical-Mean-Reversion-Testing/)
+
+    ## Estimate from OHLC data
+
+    We want to construct a mechanism to estimate the Hurst exponent via OHLC data because it is widely available from data providers and easily constructed as an online signal during trading.
+
+    In order to evaluate results against known solutions, we consider the Weiner process as generator of timeseries.
+
+    We use the **WeinerProcess** from the stochastic process library and sample one path over a time horizon of 1 (day) with a time step every second.
+    """)
+    return
+
+
+@app.cell
+def _():
+    from quantflow.sp.weiner import WeinerProcess
+    p = WeinerProcess(sigma=2.0)
+    paths = p.sample(n=1, time_horizon=1, time_steps=24*60*60)
+    paths.plot(title="A path of Weiner process with sigma=2.0")
+    return (paths,)
+
+
+@app.cell
+def _(mo):
+    mo.md(r"""
+    In order to down-sample the timeseries, we need to convert it into a dataframe with dates as indices.
     """)
     return
 
 
+@app.cell
+def _(paths):
+
+    from quantflow.utils.dates import start_of_day
+    df = paths.as_datetime_df(start=start_of_day(), unit="d").reset_index()
+
+    return
+
+
+@app.cell
+def _():
+    return
+
+
 if __name__ == "__main__":
     app.run()