Update app.py

app.py

@@ -7,7 +7,6 @@ import dateutil.relativedelta
 import os
 
 # ---- PAGE CONFIG ----
-# Makes the layout span the full width of the browser
 st.set_page_config(layout="wide")
 
 # ---- GLOBALS ----
@@ -17,24 +16,42 @@ API_KEY = os.getenv("FMP_API_KEY")
 st.sidebar.title("User Inputs")
 
 with st.sidebar.expander("Configuration", expanded=True):
-    # Provide a tooltip for clarity
     ticker = st.text_input("Ticker:", "ASML", help="Insert the stock ticker.")
-    # Let the user pick how many years of data to retrieve
-    years_back = st.number_input(
-        "Years of historical data:", 
-        min_value=1, 
-        max_value=50, 
-        value=15, 
-        help="Choose how many years of data to retrieve."
-    )
+    
+    # Radio selection for Annual vs Quarterly data
+    data_period = st.radio("Select Data Period", ("Annual", "Quarterly"))
+    
+    if data_period == "Annual":
+        period_api = "annual"
+        period_count = st.number_input(
+            "Years of historical data:", 
+            min_value=1, 
+            max_value=50, 
+            value=15, 
+            help="Choose how many years of historical data to retrieve."
+        )
+        cutoff_date = datetime.now() - dateutil.relativedelta.relativedelta(years=period_count)
+        xaxis_title = "Year"
+        tickformat = "%Y"
+        dtick = "M12"
+    else:
+        period_api = "quarter"
+        period_count = st.number_input(
+            "Quarters of historical data:", 
+            min_value=1, 
+            max_value=200, 
+            value=20, 
+            help="Choose how many quarters of historical data to retrieve."
+        )
+        cutoff_date = datetime.now() - dateutil.relativedelta.relativedelta(months=period_count * 3)
+        xaxis_title = "Quarter"
+        tickformat = "%Y-%m"
+        dtick = "M3"
 
-# A key button that triggers the data fetching and analysis
 run_button = st.sidebar.button("Run Analysis")
 
-
 # ---- HELPER FUNCTION: VALUE FORMATTING ----
 def format_value(x):
-    # Formats large numeric values for readability
     if abs(x) >= 1e9:
         return f"{x/1e9:.1f}B"
     elif abs(x) >= 1e6:
@@ -44,50 +61,43 @@ def format_value(x):
     else:
         return f"{x:.1f}"
 
-
 # ---- MAIN APP START ----
 def main():
     st.title("Analyst Forecasts & Estimates")
-    st.write("This tool fetches historical financial data and analyst forecasts. It helps you see past trends and future estimates.")
-    
+    st.write("This tool fetches historical financial data and analyst forecasts. "
+             "It helps you see past trends and future estimates over your selected period.")
+
     if not run_button:
-        st.info("Set your preferred inputs on the sidebar, then click **Run Analysis**.")
+        st.info("Set your inputs in the sidebar, then click **Run Analysis**.")
         return
 
-    # Validate if ticker is provided
     if not ticker.strip():
         st.error("Please enter a valid ticker.")
         return
 
     # ---- FETCH AND PREPARE DATA ----
-    # Build the URLs using the global API_KEY
     hist_url = (
         f"https://financialmodelingprep.com/api/v3/income-statement/{ticker}"
-        f"?period=annual&limit={years_back}&apikey={API_KEY}"
+        f"?period={period_api}&limit={period_count}&apikey={API_KEY}"
     )
     forecast_url = (
         f"https://financialmodelingprep.com/api/v3/analyst-estimates/{ticker}"
-        f"?apikey={API_KEY}"
+        f"?period={period_api}&apikey={API_KEY}"
     )
-
     try:
-        # Attempt to request the data
         hist_data = requests.get(hist_url, timeout=10).json()
         forecast_data = requests.get(forecast_url, timeout=10).json()
     except Exception:
         st.error("Could not retrieve data at this time.")
         return
 
-    # Convert raw JSON into DataFrames
     historical_df = pd.DataFrame(hist_data)
     forecast_df = pd.DataFrame(forecast_data)
 
-    # Basic check if data is not empty
     if historical_df.empty and forecast_df.empty:
         st.warning("No data found for the specified ticker.")
         return
 
-    # Parse dates
     if not historical_df.empty and "date" in historical_df.columns:
         historical_df["date"] = pd.to_datetime(historical_df["date"])
         historical_df.sort_values("date", inplace=True)
@@ -95,16 +105,12 @@ def main():
         forecast_df["date"] = pd.to_datetime(forecast_df["date"])
         forecast_df.sort_values("date", inplace=True)
 
-    # Define a cutoff based on the number of years
-    cutoff_date = datetime.now() - dateutil.relativedelta.relativedelta(years=years_back)
-
-    # Filter the data within that range
+    # Filter data based on cutoff_date
     if "date" in historical_df.columns:
         historical_df = historical_df[historical_df["date"] >= cutoff_date]
     if "date" in forecast_df.columns:
         forecast_df = forecast_df[forecast_df["date"] >= cutoff_date]
 
-    # Dictionary that maps metric names to the corresponding columns
     metrics = {
         "Revenue": {
             "historical": "revenue",
@@ -182,22 +188,19 @@ def main():
                         name=f"Forecast {label}"
                     ))
 
-        # Analyst count differs if metric is EPS
+        # Determine which analyst count field to display
         if metric_name == "EPS":
             analyst_field = "numberAnalystsEstimatedEps"
         else:
             analyst_field = "numberAnalystEstimatedRevenue"
 
-        # Average number of analysts, if data present
         if analyst_field in forecast_df.columns and not forecast_df.empty:
             analysts_count = int(round(forecast_df[analyst_field].mean()))
         else:
             analysts_count = "N/A"
 
-        # Title
         title_text = f"{ticker} - {metric_name} | Analysts: {analysts_count}"
 
-        # Layout updates
         fig.update_layout(
             title=title_text,
             barmode="stack",
@@ -205,10 +208,10 @@ def main():
             paper_bgcolor="#0e1117",
             plot_bgcolor="#0e1117",
             xaxis=dict(
-                title="Year",
+                title=xaxis_title,
                 tickangle=45,
-                tickformat="%Y",
-                dtick="M12",
+                tickformat=tickformat,
+                dtick=dtick,
                 showgrid=True,
                 gridcolor="rgba(255, 255, 255, 0.1)"
             ),
@@ -227,37 +230,31 @@ def main():
     for metric, mapping in metrics.items():
         st.subheader(metric)
         st.write(
-            f"This chart shows {metric} through the years. "
-            f"Bars represent historical numbers. Lines represent various forecast ranges. "
-            "Hover over the markers for details."
+            f"This chart shows {metric} over the selected time periods. "
+            f"Bars represent historical data and lines represent forecast ranges. "
+            "Hover over markers for details."
         )
-
         fig = create_plot(metric, mapping["historical"], mapping["forecast"])
         st.plotly_chart(fig, use_container_width=True)
 
-        # Data expander at the end of each section
         with st.expander(f"View {metric} Data", expanded=False):
-            # Show historical portion if available
             relevant_cols = []
             hc = mapping["historical"]
             if hc in historical_df.columns:
                 relevant_cols.append(hc)
-            # Include forecast columns if present
             for fc in mapping["forecast"].values():
                 if fc in forecast_df.columns:
                     relevant_cols.append(fc)
 
-            # Merge data for display
-            # We'll add a prefix to historical vs forecast columns to keep them separate
             hist_disp = historical_df[["date", hc]].copy() if hc in historical_df.columns else pd.DataFrame()
-            hist_disp.rename(columns={hc: f"{metric}_Historical"}, inplace=True)
+            if not hist_disp.empty:
+                hist_disp.rename(columns={hc: f"{metric}_Historical"}, inplace=True)
 
             forecast_disp = forecast_df[["date"] + list(mapping["forecast"].values())].copy() if not forecast_df.empty else pd.DataFrame()
             for fc_key, fc_val in mapping["forecast"].items():
                 if fc_val in forecast_disp.columns:
                     forecast_disp.rename(columns={fc_val: f"{metric}_Forecast_{fc_key}"}, inplace=True)
 
-            # Merge on date if both are non-empty
             if not hist_disp.empty and not forecast_disp.empty:
                 merged_df = pd.merge(hist_disp, forecast_disp, on="date", how="outer")
                 merged_df.sort_values("date", inplace=True)
@@ -271,15 +268,12 @@ def main():
             if merged_df.empty:
                 st.write("No data found for this metric.")
             else:
-                # Show the data
                 st.dataframe(merged_df.reset_index(drop=True))
 
 # ---- RUN ----
 if __name__ == "__main__":
     main()
 
-
-# Hide default Streamlit style
 st.markdown(
     """
     <style>