Create app.py

app.py

@@ -0,0 +1,257 @@
+import streamlit as st
+import pandas as pd
+import requests
+import plotly.express as px
+from datetime import datetime, timedelta
+import textwrap
+import os
+
+API_KEY = os.getenv("FMP_API_KEY")
+
+# Set wide page layout
+st.set_page_config(page_title="Analyst Recommendations", layout="wide")
+
+# Sidebar: Global ticker and page navigation
+st.sidebar.header("Inputs")
+with st.sidebar.expander("Ticker and Page Settings", expanded=True):
+    ticker = st.text_input(
+        "Enter Ticker Symbol",
+        value="AAPL",
+        help="Input the stock ticker symbol (e.g., AAPL, MSFT)."
+    )
+    page = st.radio(
+        "Select Page",
+        ["Historical Ratings", "Recommendations"],
+        help="Choose which analysis page to view."
+    )
+
+# Reset stored results if ticker changes
+if "ticker" not in st.session_state or st.session_state.ticker != ticker:
+    st.session_state.ticker = ticker
+    st.session_state.historical_data = None
+    st.session_state.analyst_data = None
+    st.session_state.run_pressed_hist = False
+    st.session_state.run_pressed_analyst = False
+
+# Cached function for historical data
+@st.cache_data(show_spinner=False)
+def load_historical_data(ticker):
+    try:
+        API_KEY = API_KEY
+        url = f"https://financialmodelingprep.com/api/v3/historical-rating/{ticker}?apikey={API_KEY}"
+        response = requests.get(url)
+        if response.status_code != 200:
+            st.error("Error retrieving historical data.")
+            return None
+        data = response.json()
+        df = pd.DataFrame(data)
+        # Define required columns.
+        req_cols = [
+            'date', 'rating', 'ratingScore', 'ratingRecommendation', 'ratingDetailsDCFScore',
+            'ratingDetailsROEScore', 'ratingDetailsROERecommendation',
+            'ratingDetailsROAScore', 'ratingDetailsROARecommendation',
+            'ratingDetailsDEScore', 'ratingDetailsDERecommendation',
+            'ratingDetailsPEScore', 'ratingDetailsPERecommendation',
+            'ratingDetailsPBScore', 'ratingDetailsPBRecommendation'
+        ]
+        # Use alternative column if available.
+        if 'ratingDetailsROCFRecommendation' in df.columns:
+            req_cols.insert(4, 'ratingDetailsROCFRecommendation')
+        else:
+            req_cols.insert(4, 'ratingDetailsDCFRecommendation')
+        df = df[req_cols]
+        df['date'] = pd.to_datetime(df['date'])
+        df = df.sort_values('date')
+        return df
+    except Exception:
+        st.error("An error occurred while loading historical data.")
+        return None
+
+# Cached function for analyst recommendations data
+@st.cache_data(show_spinner=False)
+def load_analyst_data(ticker):
+    try:
+        API_KEY = API_KEY
+        url = f"https://financialmodelingprep.com/api/v3/analyst-stock-recommendations/{ticker}?apikey={API_KEY}"
+        response = requests.get(url)
+        if response.status_code != 200:
+            st.error("Error retrieving analyst data.")
+            return None
+        data = response.json()
+        if isinstance(data, dict):
+            data = [data]
+        df = pd.DataFrame(data)
+        df['date'] = pd.to_datetime(df['date'])
+        df = df.sort_values('date')
+        return df
+    except Exception:
+        st.error("An error occurred while loading analyst data.")
+        return None
+
+# Main area explanation
+#st.title("Analysts Recommendations")
+#st.write("This app displays historical rating scores and analyst recommendations for the specified ticker.")
+
+
+# PAGE: Historical Ratings
+if page == "Historical Ratings":
+    st.header("Historical Ratings")
+    # Sidebar inputs for this page
+    st.write("Below are a series of bar charts that show historical trends in key financial metrics.")
+    with st.expander("Category Description", expanded=False):
+        description = textwrap.dedent("""
+            - **Overall Rating Score**: Reflects the general analyst rating, summarizing overall performance.
+            - **DCF Score**: Represents the discounted cash flow valuation, which estimates a stock's intrinsic value.
+            - **ROE Score**: Measures return on equity to assess how efficiently a company uses shareholder funds.
+            - **ROA Score**: Indicates return on assets to gauge the effectiveness of asset use in generating profits.
+            - **DE Score**: Shows the debt-to-equity ratio, highlighting the financial leverage of the company.
+            - **PE Score**: Provides the price-to-earnings ratio, indicating market valuation relative to earnings.
+            - **PB Score**: Measures the price-to-book ratio to assess if the stock is undervalued compared to its book value.
+            Each chart displays the numerical score with a text recommendation. Colors denote recommendations like "Strong Buy", "Buy", "Neutral", "Sell", and "Strong Sell".
+        """)
+        st.markdown(description)
+    default_date = datetime.today() - timedelta(days=365)
+    with st.sidebar.expander("Date Settings", expanded=True):
+        start_date = st.date_input(
+            "Start Date",
+            value=default_date,
+            help="Select a start date for filtering historical data."
+        )
+    # Place run button below the start date input
+    if st.sidebar.button("Run Analysis", key="hist_run_button"):
+        st.session_state.run_pressed_hist = True
+
+    if st.session_state.run_pressed_hist:
+        # Load data if not already loaded
+        if st.session_state.historical_data is None:
+            st.session_state.historical_data = load_historical_data(ticker)
+        df_hist = st.session_state.historical_data
+        if df_hist is not None:
+            # Filter data based on start date.
+            df_filtered = df_hist[df_hist['date'] >= pd.to_datetime(start_date)]
+            #st.subheader(f"Historical Ratings for {ticker}")
+            #st.write("Bar charts below show various score metrics over time.")
+            recommendation_colors = {
+                "Strong Buy": "green",
+                "Buy": "lightgreen",
+                "Neutral": "orange",
+                "Sell": "lightcoral",
+                "Strong Sell": "red"
+            }
+            categories = [
+                'ratingScore', 'ratingDetailsDCFScore', 'ratingDetailsROEScore',
+                'ratingDetailsROAScore', 'ratingDetailsDEScore', 'ratingDetailsPEScore',
+                'ratingDetailsPBScore'
+            ]
+            titles = [
+                'Overall Rating Score', 'DCF Score', 'ROE Score',
+                'ROA Score', 'DE Score', 'PE Score', 'PB Score'
+            ]
+            for category, title in zip(categories, titles):
+                recommendation_col = category.replace("Score", "Recommendation")
+                st.markdown(f"**{title} Chart**")
+                #st.write("This chart shows the score over time with its associated recommendation.")
+                try:
+                    fig = px.bar(
+                        df_filtered,
+                        x='date',
+                        y=category,
+                        text=category,
+                        labels={'date': 'Date', category: 'Score'},
+                        title=title,
+                        color=recommendation_col,
+                        color_discrete_map=recommendation_colors,
+                        custom_data=['rating', recommendation_col]
+                    )
+                    fig.update_traces(
+                        texttemplate="%{text}<br>%{customdata[0]} (%{customdata[1]})",
+                        textposition='outside',
+                        hovertemplate="<b>Date</b>: %{x}<br><b>Score</b>: %{y}<br><b>Rating</b>: %{customdata[0]}<br><b>Recommendation</b>: %{customdata[1]}<extra></extra>"
+                    )
+                    st.plotly_chart(fig, use_container_width=True)
+                except Exception:
+                    st.error("Error displaying the chart. Please check the data and inputs.")
+            with st.expander("Show Detailed Historical Data"):
+                st.dataframe(df_filtered)
+    else:
+        st.info("Press 'Run Analysis' in the sidebar to load historical ratings data.")
+
+# PAGE: Analyst Recommendations
+elif page == "Recommendations":
+    st.header("Analyst Recommendations")
+    st.write("This section presents the monthly analyst recommendations for the selected ticker. The stacked bar chart aggregates various recommendation types over time.")
+    with st.expander("Category Description", expanded=False):
+        description = textwrap.dedent("""
+            - **Strong Buy**: Indicates that analysts are very confident the stock will perform strongly.
+            - **Buy**: Reflects a positive outlook from analysts regarding future performance.
+            - **Hold**: Suggests that analysts expect the stock to maintain its current performance.
+            - **Sell**: Signals a negative outlook, indicating the stock may underperform.
+            - **Strong Sell**: Represents a very bearish sentiment, with analysts expecting significant underperformance.
+            The chart lets you observe shifts in market sentiment over time and compare the prevalence of each recommendation type.
+        """)
+        st.markdown(description)
+    # No additional page-specific inputs; thus, no expander is shown.
+    if st.sidebar.button("Run Analysis", key="analyst_run_button"):
+        st.session_state.run_pressed_analyst = True
+
+    if st.session_state.run_pressed_analyst:
+        if st.session_state.analyst_data is None:
+            st.session_state.analyst_data = load_analyst_data(ticker)
+        df_analyst = st.session_state.analyst_data
+        if df_analyst is not None:
+            st.subheader(f"Analyst Recommendations for {ticker}")
+            st.write("The stacked bar chart below shows monthly analyst recommendations.")
+            rating_cols = [
+                "analystRatingsStrongBuy",
+                "analystRatingsbuy",
+                "analystRatingsHold",
+                "analystRatingsSell",
+                "analystRatingsStrongSell"
+            ]
+            df_melted = pd.melt(
+                df_analyst,
+                id_vars=["date"],
+                value_vars=rating_cols,
+                var_name="RatingType",
+                value_name="Count"
+            )
+            color_map = {
+                "analystRatingsStrongBuy": "green",
+                "analystRatingsbuy": "lightgreen",
+                "analystRatingsHold": "orange",
+                "analystRatingsSell": "lightcoral",
+                "analystRatingsStrongSell": "red"
+            }
+            rating_order = [
+                "analystRatingsStrongBuy",
+                "analystRatingsbuy",
+                "analystRatingsHold",
+                "analystRatingsSell",
+                "analystRatingsStrongSell"
+            ]
+            try:
+                fig2 = px.bar(
+                    df_melted,
+                    x="date",
+                    y="Count",
+                    color="RatingType",
+                    text="Count",
+                    title=f"Monthly Analyst Recommendations for {ticker.upper()}",
+                    color_discrete_map=color_map,
+                    category_orders={"RatingType": rating_order},
+                    custom_data=["RatingType"],
+                    labels={"date": "Date", "Count": "Number of Recommendations", "RatingType": "Recommendation Type"}
+                )
+                fig2.update_layout(barmode="stack")
+                fig2.update_traces(
+                    texttemplate="%{text}",
+                    textposition="inside",
+                    hovertemplate="<b>Date</b>: %{x}<br><b>Count</b>: %{y}<br><b>Rating Type</b>: %{customdata[0]}<extra></extra>"
+                )
+                st.plotly_chart(fig2, use_container_width=True)
+            except Exception:
+                st.error("Error displaying the chart. Please check the data and inputs.")
+            with st.expander("Show Detailed Analyst Data"):
+                st.dataframe(df_analyst)
+    else:
+        st.info("Press 'Run Analysis' in the sidebar to load analyst recommendations data.")