Add CDPR plot.

model.py

@@ -688,10 +688,79 @@ def analyze_bitcoin_prices(csv_path):
 # Main plotting functions
 
 
+def add_cdpr_plot(df, output: Output):
+    """
+    Add a plot showing the Compounding Daily Periodic Rate (CDPR) over different time periods.
+    """
+    plt.style.use("seaborn-v0_8")
+    fig, ax = plt.subplots(figsize=(15, 6))
+
+    # Calculate CDPR for different time periods
+    periods = [180, 360, 720]
+    cdpr = {}
+
+    # Find the longest CDPR series length
+    max_period = max(periods)
+
+    for period in periods:
+        daily_returns = df["Close"].pct_change().fillna(0)
+        cdpr[f"{period}d CDPR"] = (
+            daily_returns.rolling(period).apply(
+                lambda x: (1 + x).prod() ** (1 / period) - 1, raw=True
+            )
+        ) * 100
+
+        # Clip all CDPR series to the length of the longest one
+        cdpr[f"{period}d CDPR"] = cdpr[f"{period}d CDPR"][max_period:]
+
+    # Find the non-NaN min and max CDPR values
+    cdpr_values = [values for values in cdpr.values()]
+    min_cdpr = np.nanmin([np.nanmin(values) for values in cdpr_values])
+    max_cdpr = np.nanmax([np.nanmax(values) for values in cdpr_values])
+
+    # Ensure x-axis (dates) and y-axis (CDPR) have the same length
+    start_date = df["Date"].iloc[max_period:].min()
+    end_date = df["Date"].max()
+    plot_dates = pd.date_range(start=start_date, end=end_date, freq="D")
+
+    # Plot CDPR lines
+    for label, values in cdpr.items():
+        ax.plot(plot_dates, values, label=label)
+
+    # Customize the plot
+    ax.set_title("Compounding Daily Periodic Rate (CDPR)")
+    ax.set_xlabel("Date")
+    ax.set_ylabel("CDPR (%)")
+    ax.grid(True, alpha=0.3)
+
+    # Adjust y-axis tick marks and add shaded lines between ticks
+    yticks = list(np.arange(int(min_cdpr), int(max_cdpr) + 1, 0.5))
+    ax.set_yticks(yticks)
+    ax.tick_params(axis="y", which="major", labelsize=8)
+    ax.set_yticklabels(["{:.1f}%".format(y) for y in yticks])
+
+    # Add shaded lines between tick marks
+    for i in range(1, len(yticks)):
+        ax.axhline(
+            y=yticks[i], color="lightgray", linestyle="--", linewidth=1, alpha=0.5
+        )
+
+    ax.legend()
+
+    # Save the plot
+    filename = output.named("bitcoin_cdpr_plot.png")
+    plt.tight_layout()
+    plt.savefig(filename, dpi=300, bbox_inches="tight")
+    plt.close()
+
+
 def create_plots(df, output: Output, start=None, end=None, project_days=365):
     """
     Create enhanced plots including market maturity visualization.
     """
+    # Add the new CDPR plot
+    add_cdpr_plot(df, output)
+
     # Filter data based on date range
     mask = pd.Series(True, index=df.index)
     if start: