Create app.py

app.py

@@ -0,0 +1,440 @@
+import streamlit as st
+import requests
+import pandas as pd
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import os
+
+API_KEY = os.getenv("FMP_API_KEY")
+
+# Global constants (not exposed to the frontend)
+MAX_PAGES = 5
+DEFAULT_TOP_N = 10
+
+# Initialize session state variables if not already set
+if "historical_run" not in st.session_state:
+    st.session_state.historical_run = False
+if "trending_run" not in st.session_state:
+    st.session_state.trending_run = False
+if "change_run" not in st.session_state:
+    st.session_state.change_run = False
+if "historical_ticker" not in st.session_state:
+    st.session_state.historical_ticker = "AAPL"
+if "trending_top_n" not in st.session_state:
+    st.session_state.trending_top_n = DEFAULT_TOP_N
+if "change_top_n" not in st.session_state:
+    st.session_state.change_top_n = DEFAULT_TOP_N
+
+#############################
+# PAGE 1: Historical Sentiment
+#############################
+
+@st.cache_data(show_spinner=False)
+def get_historical_sentiment(ticker, pages=MAX_PAGES):
+    frames = []
+    for page in range(pages):
+        url = (
+            f"https://financialmodelingprep.com/api/v4/historical/social-sentiment?"
+            f"symbol={ticker}&page={page}&apikey={API_KEY}"
+        )
+        r = requests.get(url)
+        r.raise_for_status()
+        data = r.json()
+        if not data:
+            break
+        df = pd.DataFrame(data)
+        frames.append(df)
+    if frames:
+        full_df = pd.concat(frames, ignore_index=True)
+        full_df["date"] = pd.to_datetime(full_df["date"])
+        full_df.sort_values("date", inplace=True)
+        return full_df
+    return pd.DataFrame()
+
+def plot_dual_axes(
+    df, x_col, y_left, y_right,
+    title, y_left_label, y_right_label,
+    left_color="red", right_color="blue",
+    date_range=None
+):
+    fig = make_subplots(specs=[[{"secondary_y": True}]])
+    fig.add_trace(
+        go.Scatter(
+            x=df[x_col],
+            y=df[y_left],
+            mode="lines+markers",
+            name=y_left_label,
+            line=dict(color=left_color)
+        ),
+        secondary_y=False
+    )
+    fig.add_trace(
+        go.Scatter(
+            x=df[x_col],
+            y=df[y_right],
+            mode="lines+markers",
+            name=y_right_label,
+            line=dict(color=right_color)
+        ),
+        secondary_y=True
+    )
+    fig.update_layout(
+        title=title,
+        xaxis=dict(range=date_range),
+        xaxis_title="Date",
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="center",
+            x=0.5
+        ),
+        margin=dict(l=60, r=60, t=80, b=60),
+        width=1600,
+        height=800
+    )
+    fig.update_yaxes(
+        title_text=y_left_label,
+        secondary_y=False,
+        tickfont_color=left_color,
+        titlefont_color=left_color
+    )
+    fig.update_yaxes(
+        title_text=y_right_label,
+        secondary_y=True,
+        tickfont_color=right_color,
+        titlefont_color=right_color
+    )
+    return fig
+
+def run_historical_sentiment(ticker):
+    st.write(
+        "This page displays historical social sentiment data for the ticker. "
+        "Five separate charts are generated: sentiment scores, posts, comments, likes, and impressions."
+    )
+    df = get_historical_sentiment(ticker)
+    if df.empty:
+        st.error("No data found for ticker " + ticker)
+        return
+    common_range = [df["date"].min(), df["date"].max()]
+    
+    st.subheader(f"{ticker} Sentiment Scores")
+    fig1 = plot_dual_axes(
+        df, "date", "stocktwitsSentiment", "twitterSentiment",
+        f"{ticker} Sentiment Scores",
+        "Stocktwits Sentiment", "Twitter Sentiment",
+        date_range=common_range
+    )
+    st.plotly_chart(fig1, use_container_width=True)
+
+    st.subheader(f"{ticker} Posts")
+    fig2 = plot_dual_axes(
+        df, "date", "stocktwitsPosts", "twitterPosts",
+        f"{ticker} Posts",
+        "Stocktwits Posts", "Twitter Posts",
+        date_range=common_range
+    )
+    st.plotly_chart(fig2, use_container_width=True)
+
+    st.subheader(f"{ticker} Comments")
+    fig3 = plot_dual_axes(
+        df, "date", "stocktwitsComments", "twitterComments",
+        f"{ticker} Comments",
+        "Stocktwits Comments", "Twitter Comments",
+        date_range=common_range
+    )
+    st.plotly_chart(fig3, use_container_width=True)
+
+    st.subheader(f"{ticker} Likes")
+    fig4 = plot_dual_axes(
+        df, "date", "stocktwitsLikes", "twitterLikes",
+        f"{ticker} Likes",
+        "Stocktwits Likes", "Twitter Likes",
+        date_range=common_range
+    )
+    st.plotly_chart(fig4, use_container_width=True)
+
+    st.subheader(f"{ticker} Impressions")
+    fig5 = plot_dual_axes(
+        df, "date", "stocktwitsImpressions", "twitterImpressions",
+        f"{ticker} Impressions",
+        "Stocktwits Impressions", "Twitter Impressions",
+        date_range=common_range
+    )
+    st.plotly_chart(fig5, use_container_width=True)
+
+#############################
+# PAGE 2: Trending Sentiment
+#############################
+
+@st.cache_data(show_spinner=False)
+def fetch_trending_sentiment(sentiment_type="bullish", source="stocktwits"):
+    url = (
+        f"https://financialmodelingprep.com/api/v4/social-sentiments/trending?"
+        f"type={sentiment_type}&source={source}&apikey={API_KEY}"
+    )
+    response = requests.get(url)
+    response.raise_for_status()
+    data = response.json()
+    if not data:
+        return pd.DataFrame()
+    df = pd.DataFrame(data)
+    df = df.sort_values("rank").reset_index(drop=True)
+    return df
+
+def plot_trending(df, title, top_n):
+    if df.empty:
+        st.error("No data available to plot.")
+        return
+    df = df.head(top_n)
+    fig = go.Figure()
+    fig.add_trace(
+        go.Bar(
+            x=df["symbol"],
+            y=df["sentiment"],
+            name="Sentiment",
+            text=df["sentiment"],
+            textposition="auto",
+            marker_color="red",
+            offsetgroup="group1"
+        )
+    )
+    fig.add_trace(
+        go.Bar(
+            x=df["symbol"],
+            y=df["lastSentiment"],
+            name="Last Sentiment",
+            text=df["lastSentiment"],
+            textposition="auto",
+            marker_color="blue",
+            offsetgroup="group2",
+            yaxis="y2"
+        )
+    )
+    fig.update_layout(
+        title=title,
+        barmode="group",
+        bargap=0.15,
+        bargroupgap=0.1,
+        xaxis=dict(
+            title="Ticker",
+            type="category",
+            categoryorder="array",
+            categoryarray=df["symbol"].tolist()
+        ),
+        yaxis=dict(
+            title="Sentiment",
+            titlefont=dict(color="red"),
+            tickfont=dict(color="red")
+        ),
+        yaxis2=dict(
+            title="Last Sentiment",
+            titlefont=dict(color="blue"),
+            tickfont=dict(color="blue"),
+            anchor="x",
+            overlaying="y",
+            side="right"
+        ),
+        width=1000 + 100 * len(df),
+        height=600,
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="center",
+            x=0.5
+        )
+    )
+    st.plotly_chart(fig, use_container_width=True)
+
+def run_trending_sentiment(top_n):
+    st.write(
+        "This page shows trending sentiment data. Two grouped bar charts are created for bullish and bearish sentiments. "
+        "Each chart displays two bars per ticker: one for Sentiment and one for Last Sentiment."
+    )
+    st.write("## Bullish Trending Sentiment")
+    bullish_df = fetch_trending_sentiment(sentiment_type="bullish", source="stocktwits")
+    plot_trending(bullish_df, title="Bullish Trending Sentiment", top_n=top_n)
+    st.write("## Bearish Trending Sentiment")
+    bearish_df = fetch_trending_sentiment(sentiment_type="bearish", source="stocktwits")
+    plot_trending(bearish_df, title="Bearish Trending Sentiment", top_n=top_n)
+
+#############################
+# PAGE 3: Sentiment Change
+#############################
+
+@st.cache_data(show_spinner=False)
+def fetch_change_sentiment(sentiment_type="bullish", source="stocktwits"):
+    url = (
+        f"https://financialmodelingprep.com/api/v4/social-sentiments/change?"
+        f"type={sentiment_type}&source={source}&apikey={API_KEY}"
+    )
+    response = requests.get(url)
+    response.raise_for_status()
+    data = response.json()
+    if not data:
+        return pd.DataFrame()
+    df = pd.DataFrame(data)
+    df = df.sort_values("rank").reset_index(drop=True)
+    return df
+
+def plot_change_sentiment(df, title, top_n):
+    if df.empty:
+        st.error("No data available to plot.")
+        return
+    df = df.head(top_n)
+    fig = go.Figure()
+    fig.add_trace(
+        go.Bar(
+            x=df["symbol"],
+            y=df["sentiment"],
+            name="Sentiment",
+            text=df["sentiment"],
+            textposition="auto",
+            marker_color="red",
+            offsetgroup="group1"
+        )
+    )
+    fig.add_trace(
+        go.Bar(
+            x=df["symbol"],
+            y=df["sentimentChange"],
+            name="Sentiment Change",
+            text=df["sentimentChange"],
+            textposition="auto",
+            marker_color="blue",
+            offsetgroup="group2",
+            yaxis="y2"
+        )
+    )
+    fig.update_layout(
+        title=title,
+        barmode="group",
+        bargap=0.15,
+        bargroupgap=0.1,
+        xaxis=dict(
+            title="Ticker",
+            type="category",
+            categoryorder="array",
+            categoryarray=df["symbol"].tolist()
+        ),
+        yaxis=dict(
+            title="Sentiment",
+            titlefont=dict(color="red"),
+            tickfont=dict(color="red")
+        ),
+        yaxis2=dict(
+            title="Sentiment Change",
+            titlefont=dict(color="blue"),
+            tickfont=dict(color="blue"),
+            anchor="x",
+            overlaying="y",
+            side="right"
+        ),
+        width=1000 + 100 * len(df),
+        height=600,
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="center",
+            x=0.5
+        )
+    )
+    st.plotly_chart(fig, use_container_width=True)
+
+def run_change_sentiment(top_n):
+    st.write(
+        "This page displays social sentiment change data. Two grouped bar charts are created for bullish and bearish sentiments. "
+        "Each chart shows two bars per ticker: one for Sentiment and one for Sentiment Change."
+    )
+    st.write("## Bullish Sentiment Change")
+    bullish_df = fetch_change_sentiment(sentiment_type="bullish", source="stocktwits")
+    plot_change_sentiment(bullish_df, title="Bullish Sentiment Change", top_n=top_n)
+    st.write("## Bearish Sentiment Change")
+    bearish_df = fetch_change_sentiment(sentiment_type="bearish", source="stocktwits")
+    plot_change_sentiment(bearish_df, title="Bearish Sentiment Change", top_n=top_n)
+
+#############################
+# MAIN APP
+#############################
+
+def main():
+    st.set_page_config(page_title="Social Sentiment Analysis", layout="wide")
+    st.title("Social Sentiment Analysis Dashboard")
+    st.write(
+        "This application provides three types of analysis on social sentiment data from FinancialModelingPrep. "
+        "Use the sidebar menu to select the type of analysis you want to run and adjust the options. "
+        "Click the 'Run' button to update the results."
+    )
+    
+    # Sidebar: Navigation and Options (inside an expander)
+    with st.sidebar.expander("Navigation and Options", expanded=True):
+        page = st.radio(
+            "Select Analysis Page",
+            ("Historical Sentiment", "Trending Sentiment", "Sentiment Change"),
+            help="Choose the analysis page you want to view."
+        )
+        
+        if page == "Historical Sentiment":
+            ticker = st.text_input(
+                "Ticker Symbol",
+                value=st.session_state.historical_ticker,
+                help="Enter the ticker symbol for which to display historical sentiment data."
+            )
+            if st.button("Run Historical Sentiment Analysis"):
+                st.session_state.historical_run = True
+                st.session_state.historical_ticker = ticker
+        elif page == "Trending Sentiment":
+            top_n = st.number_input(
+                "Select Top N Stocks",
+                min_value=1, max_value=100,
+                value=st.session_state.trending_top_n,
+                help="Number of top stocks to display based on rank."
+            )
+            if st.button("Run Trending Sentiment Analysis"):
+                st.session_state.trending_run = True
+                st.session_state.trending_top_n = top_n
+        elif page == "Sentiment Change":
+            top_n = st.number_input(
+                "Select Top N Stocks",
+                min_value=1, max_value=100,
+                value=st.session_state.change_top_n,
+                help="Number of top stocks to display based on rank."
+            )
+            if st.button("Run Sentiment Change Analysis"):
+                st.session_state.change_run = True
+                st.session_state.change_top_n = top_n
+
+    # Main body: Display results based on the selected page and if run button was clicked
+    if page == "Historical Sentiment":
+        st.header("Historical Social Sentiment")
+        if st.session_state.historical_run:
+            run_historical_sentiment(st.session_state.historical_ticker)
+        else:
+            st.info("Please select options in the sidebar and click 'Run Historical Sentiment Analysis'.")
+    elif page == "Trending Sentiment":
+        st.header("Trending Social Sentiment")
+        if st.session_state.trending_run:
+            run_trending_sentiment(st.session_state.trending_top_n)
+        else:
+            st.info("Please select options in the sidebar and click 'Run Trending Sentiment Analysis'.")
+    elif page == "Sentiment Change":
+        st.header("Social Sentiment Change")
+        if st.session_state.change_run:
+            run_change_sentiment(st.session_state.change_top_n)
+        else:
+            st.info("Please select options in the sidebar and click 'Run Sentiment Change Analysis'.")
+
+if __name__ == "__main__":
+    main()
+
+
+hide_streamlit_style = """
+<style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style>
+"""
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+