cointegration

Author: lsbardel

app/cointegration.py

@@ -25,8 +25,8 @@ def _(mo):
     from quantflow.data.fmp import FMP
 
     frequency = mo.ui.dropdown(
-        options={x.value: x.value for x in FMP.freq},
-        value="1min",
+        options={x.name.replace("_", " "): x.value for x in FMP.freq},
+        value="daily",
         label="Frequency",
     )
     frequency
@@ -128,17 +128,15 @@ def _(mo):
     mo.md(r"""
     ### Why Pick the Largest Eigenvalue?
 
-        In the Johansen cointegration test, the eigenvalues ($\lambda$) are sorted in descending order, and each one corresponds to a different potential cointegrating vector.
+    In the Johansen cointegration test, the eigenvalues ($\lambda$) are sorted in descending order, and each one corresponds to a different potential cointegrating vector.
 
-        **The magnitude of the eigenvalue represents the strength and stability of the corresponding cointegrating relationship.**
+    **The magnitude of the eigenvalue represents the strength and stability of the corresponding cointegrating relationship.**
 
-        1.  **Strongest Relationship:** The largest eigenvalue corresponds to the linear combination of the time series that is "most stationary." This means the resulting spread (the residuals) has the strongest tendency to revert to its mean over time.
+    1.  **Strongest Relationship:** The largest eigenvalue corresponds to the linear combination of the time series that is "most stationary." This means the resulting spread (the residuals) has the strongest tendency to revert to its mean over time.
+    2.  **Statistical Significance:** The test statistics used in the Johansen test (the Trace test and the Maximum Eigenvalue test) are functions of these eigenvalues. These tests help us determine how many statistically significant cointegrating relationships exist, starting from the one associated with the largest eigenvalue.
+    3.  **Practical Application:** For applications like pairs trading, we want to find the most reliable and predictable long-run equilibrium relationship. By choosing the cointegrating vector associated with the largest eigenvalue, we are selecting the portfolio of assets whose value is most likely to be mean-reverting, making it the best candidate for a statistical arbitrage strategy.
 
-        2.  **Statistical Significance:** The test statistics used in the Johansen test (the Trace test and the Maximum Eigenvalue test) are functions of these eigenvalues. These tests help us determine how many statistically significant cointegrating relationships exist, starting from the one associated with the largest eigenvalue.
-
-        3.  **Practical Application:** For applications like pairs trading, we want to find the most reliable and predictable long-run equilibrium relationship. By choosing the cointegrating vector associated with the largest eigenvalue, we are selecting the portfolio of assets whose value is most likely to be mean-reverting, making it the best candidate for a statistical arbitrage strategy.
-
-        In short, picking the largest eigenvalue is equivalent to picking the **most significant and stable cointegrating vector** found by the test.
+    In short, picking the largest eigenvalue is equivalent to picking the **most significant and stable cointegrating vector** found by the test.
     """)
     return
 
@@ -157,5 +155,10 @@ def _(mo):
     return
 
 
+@app.cell
+def _():
+    return
+
+
 if __name__ == "__main__":
     app.run()