diff --git a/model.py b/model.py
index eb9cfe8..91a6f88 100644
--- a/model.py
+++ b/model.py
@@ -646,14 +646,20 @@ def create_plots(df, start=None, end=None, project_days=365):
     # Set up the style
     plt.style.use("seaborn-v0_8")
 
-    # Create figure with adjusted size for additional subplot
-    _ = plt.figure(figsize=(15, 15))  # Increased height to accommodate new subplot
+    # Create figure with adjusted size and spacing
+    fig = plt.figure(figsize=(15, 15))
+
+    # Use GridSpec for better control over subplot spacing
+    gs = plt.GridSpec(5, 1, height_ratios=[3, 1.5, 1.5, 1.5, 2], hspace=0.4)
 
     # Date range for titles
     hist_date_range = f" ({plot_df['Date'].min().strftime('%Y-%m-%d')} to {plot_df['Date'].max().strftime('%Y-%m-%d')})"
 
+    # Calculate full date range including projections
+    full_date_range = pd.date_range(plot_df["Date"].min(), projections.index.max())
+
     # 1. Price history and projections (log scale)
-    ax1 = plt.subplot(4, 1, 1)
+    ax1 = fig.add_subplot(gs[0])
 
     # Plot historical prices
     ax1.semilogy(plot_df["Date"], plot_df["Close"], "b-", label="Historical Price")
@@ -703,22 +709,43 @@ def create_plots(df, start=None, end=None, project_days=365):
     ax1.set_title("Bitcoin Price History and Projections (Log Scale)" + hist_date_range)
     ax1.legend(fontsize=8)
 
+    # Set x-axis limits to full range
+    ax1.set_xlim(full_date_range[0], full_date_range[-1])
+    ax1.tick_params(axis="x", rotation=45)
+
     # 3. Rolling volatility
-    ax3 = plt.subplot(5, 1, 2)
+    ax3 = fig.add_subplot(gs[1])
     ax3.plot(
         plot_df["Date"],
         plot_df["Rolling_Volatility_30d"],
         "r-",
         label="30-Day Rolling Volatility",
     )
+
+    # Add empty space to match price plot x-axis
+    ax3.set_xlim(full_date_range[0], full_date_range[-1])
+
+    # Add vertical line to mark start of projections
+    ax3.axvline(plot_df["Date"].max(), color="gray", linestyle="--", alpha=0.5)
+    ax3.text(
+        plot_df["Date"].max(),
+        ax3.get_ylim()[1],
+        "Projection Start",
+        rotation=90,
+        va="top",
+        ha="right",
+        alpha=0.7,
+    )
+
     ax3.set_title("30-Day Rolling Volatility (Annualized)" + hist_date_range)
     ax3.set_ylabel("Volatility")
     ax3.grid(True)
     ax3.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: "{:.0%}".format(y)))
     ax3.legend()
+    ax3.tick_params(axis="x", rotation=45)
 
     # 4. Returns distribution
-    ax4 = plt.subplot(5, 1, 3)
+    ax4 = fig.add_subplot(gs[2])
     returns_mean = plot_df["Daily_Return"].mean()
     returns_std = plot_df["Daily_Return"].std()
     filtered_returns = plot_df["Daily_Return"][
@@ -746,7 +773,7 @@ def create_plots(df, start=None, end=None, project_days=365):
     )
 
     # 5. Projection ranges
-    ax5 = plt.subplot(5, 1, 4)
+    ax5 = fig.add_subplot(gs[3:])  # Use last two grid spaces for larger plot
     timepoints = np.array(range(30, project_days, 30))
     timepoints = timepoints[timepoints <= project_days]
 
@@ -786,13 +813,13 @@ def create_plots(df, start=None, end=None, project_days=365):
     ax5.grid(True, alpha=0.3)
     ax5.set_xticks(timepoints)
 
-    # Adjust layout
-    plt.tight_layout(h_pad=1.0)  # Increased spacing between subplots
-
     # Save the plot
     start_str = start if start else plot_df["Date"].min().strftime("%Y-%m-%d")
     end_str = end if end else plot_df["Date"].max().strftime("%Y-%m-%d")
     filename = f"bitcoin_analysis_{start_str}_to_{end_str}_with_projections.png"
+
+    # Use tight_layout with adjusted parameters
+    plt.tight_layout(pad=2.0)
     plt.savefig(filename, dpi=300, bbox_inches="tight")
     plt.close()