Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,247 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import requests
+import os
+
+# ── Page config ──────────────────────────────────────────────
+st.set_page_config(
+    page_title="Portfolio Monitoring Dashboard",
+    page_icon="📈",
+    layout="wide"
+)
+
+# ── Hugging Face AI (FinBERT sentiment) ──────────────────────
+HF_API_KEY = os.environ.get("HF_API_KEY", "")
+
+def analyze_sentiment(text: str) -> str:
+    """Calls FinBERT on Hugging Face to get financial sentiment."""
+    if not HF_API_KEY:
+        return "⚠️ No API key"
+    url = "https://api-inference.huggingface.co/models/ProsusAI/finbert"
+    headers = {"Authorization": f"Bearer {HF_API_KEY}"}
+    try:
+        r = requests.post(url, headers=headers,
+                          json={"inputs": text}, timeout=10)
+        result = r.json()
+        if isinstance(result, list) and result:
+            top = max(result[0], key=lambda x: x["score"])
+            emoji = {"positive": "🟢", "negative": "🔴",
+                     "neutral": "🟡"}.get(top["label"].lower(), "⚪")
+            return f"{emoji} {top['label'].capitalize()} ({top['score']:.0%})"
+    except Exception:
+        return "❌ Error"
+    return "❓ Unknown"
+
+# ── Load data ─────────────────────────────────────────────────
+@st.cache_data
+def load_data():
+    portfolio     = pd.read_csv("portfolio_output.csv")
+    risk_metrics  = pd.read_csv("risk_metrics_output.csv")
+    daily_returns = pd.read_csv("portfolio_daily_returns_output.csv",
+                                parse_dates=["date"])
+    return portfolio, risk_metrics, daily_returns
+
+try:
+    portfolio, risk_metrics, daily_returns = load_data()
+    data_loaded = True
+except FileNotFoundError:
+    data_loaded = False
+
+# ── Sidebar ───────────────────────────────────────────────────
+st.sidebar.image(
+    "https://upload.wikimedia.org/wikipedia/commons/"
+    "3/3b/Chart_icon_NOUN_project.svg",
+    width=80
+)
+st.sidebar.title("Portfolio Monitor")
+st.sidebar.caption("ESCP — Applied Data Science Workshop")
+
+# ── Main title ────────────────────────────────────────────────
+st.title("📈 Portfolio Monitoring Dashboard")
+st.caption("Real-time portfolio performance, risk alerts & AI-powered news sentiment")
+st.divider()
+
+# ── Demo mode if no CSV ───────────────────────────────────────
+if not data_loaded:
+    st.warning(
+        "⚠️ No data files found. Showing demo data. "
+        "Upload your CSV files to see real results."
+    )
+    np.random.seed(42)
+    portfolio = pd.DataFrame({
+        "Ticker":                 ["AAPL", "MSFT", "NVDA", "GOOGL", "AMZN"],
+        "Friendly name":          ["Apple", "Microsoft", "Nvidia",
+                                   "Alphabet", "Amazon"],
+        "market_value":           [12000, 9500, 8200, 6100, 5400],
+        "invested_amount":        [10000, 8000, 5000, 5500, 6000],
+        "unrealized_pnl":         [2000, 1500, 3200, 600, -600],
+        "cumulative_realized_pnl":[500, 300, 200, 100, 50],
+        "total_pnl":              [2500, 1800, 3400, 700, -550],
+        "weight":                 [0.29, 0.23, 0.20, 0.15, 0.13],
+        "asset_concentration_flag": [False, False, False, False, False],
+        "stressed_value":         [10200, 8075, 6970, 5185, 4590],
+        "stress_test_loss":       [-1800,-1425,-1230, -915, -810],
+        "alert_level":            ["Normal","Normal","Normal",
+                                   "Normal","Warning loss"],
+    })
+    portfolio["unrealized_return_pct"] = (
+        portfolio["unrealized_pnl"] / portfolio["invested_amount"]
+    )
+
+    dates = pd.date_range(end=pd.Timestamp.today(), periods=120, freq="B")
+    daily_returns = pd.DataFrame({
+        "date": dates,
+        "portfolio_daily_returns": np.random.normal(0.0005, 0.012, 120)
+    })
+    risk_metrics = pd.DataFrame({
+        "Metric": ["Mean daily return", "Daily volatility",
+                   "Annualized volatility", "Worst daily return",
+                   "Best daily return", "Sharpe ratio"],
+        "Value":  [0.0005, 0.012, 0.190, -0.032, 0.028, 0.66]
+    })
+
+# ── Ticker filter ─────────────────────────────────────────────
+ticker_list = ["All"] + sorted(portfolio["Ticker"].dropna().unique().tolist())
+selected = st.sidebar.selectbox("Filter by asset", ticker_list)
+pv = portfolio if selected == "All" else portfolio[portfolio["Ticker"] == selected]
+
+# ── KPI Cards ─────────────────────────────────────────────────
+st.subheader("Portfolio Summary")
+c1, c2, c3, c4 = st.columns(4)
+c1.metric("💰 Invested",       f"{pv['invested_amount'].sum():,.0f} €")
+c2.metric("📊 Market Value",   f"{pv['market_value'].sum():,.0f} €")
+c3.metric("📈 Total P&L",      f"{pv['total_pnl'].sum():,.0f} €")
+c4.metric("🏦 Positions",      int(pv["Ticker"].nunique()))
+st.divider()
+
+# ── Charts row 1 ──────────────────────────────────────────────
+col_l, col_r = st.columns(2)
+
+with col_l:
+    st.subheader("🥧 Portfolio Allocation")
+    fig_pie = px.pie(pv, names="Ticker", values="market_value",
+                     hole=0.35)
+    fig_pie.update_traces(textinfo="percent+label")
+    st.plotly_chart(fig_pie, use_container_width=True)
+
+with col_r:
+    st.subheader("📊 Market Value by Asset")
+    color_col = "alert_level" if "alert_level" in pv.columns else "Ticker"
+    fig_bar = px.bar(
+        pv.sort_values("market_value", ascending=False),
+        x="Ticker", y="market_value", color=color_col,
+        color_discrete_map={
+            "Normal": "#2ecc71",
+            "Warning loss": "#f39c12",
+            "Critical loss": "#e74c3c"
+        }
+    )
+    st.plotly_chart(fig_bar, use_container_width=True)
+
+# ── Cumulative return ─────────────────────────────────────────
+st.subheader("📉 Cumulative Portfolio Return")
+daily_returns["cumulative_return"] = (
+    (1 + daily_returns["portfolio_daily_returns"]).cumprod() - 1
+)
+fig_line = px.line(daily_returns, x="date", y="cumulative_return",
+                   labels={"cumulative_return": "Cumulative Return",
+                           "date": "Date"})
+fig_line.add_hline(y=0, line_dash="dash", line_color="black")
+fig_line.update_traces(line_color="#3498db")
+st.plotly_chart(fig_line, use_container_width=True)
+
+# ── Unrealized return scatter ──────────────────────────────────
+st.subheader("⚡ Unrealized Return by Asset")
+if "unrealized_return_pct" in pv.columns:
+    fig_ret = px.bar(
+        pv.sort_values("unrealized_return_pct"),
+        x="Ticker", y="unrealized_return_pct",
+        color=pv["unrealized_return_pct"].apply(
+            lambda x: "Gain" if x >= 0 else "Loss"
+        ),
+        color_discrete_map={"Gain": "#2ecc71", "Loss": "#e74c3c"},
+        labels={"unrealized_return_pct": "Unrealized Return %"}
+    )
+    fig_ret.add_hline(y=0, line_dash="dash", line_color="black")
+    st.plotly_chart(fig_ret, use_container_width=True)
+
+# ── Risk metrics ──────────────────────────────────────────────
+st.subheader("🔬 Risk Metrics")
+st.dataframe(risk_metrics, use_container_width=True, hide_index=True)
+
+# ── Stress test ───────────────────────────────────────────────
+if "stressed_value" in pv.columns:
+    st.subheader("💥 Stress Test (−15% market shock)")
+    sk1, sk2, sk3 = st.columns(3)
+    sk1.metric("Current Value",  f"{pv['market_value'].sum():,.0f} €")
+    sk2.metric("Stressed Value", f"{pv['stressed_value'].sum():,.0f} €",
+               delta=f"{pv['stress_test_loss'].sum():,.0f} €")
+    sk3.metric("Estimated Loss", f"{pv['stress_test_loss'].sum():,.0f} €")
+
+# ── Alert table ───────────────────────────────────────────────
+st.subheader("🚨 Risk Alert Table")
+alert_cols = [c for c in ["Ticker", "market_value", "weight",
+                           "unrealized_return_pct", "alert_level",
+                           "asset_concentration_flag"] if c in pv.columns]
+st.dataframe(pv[alert_cols].sort_values("market_value", ascending=False),
+             use_container_width=True, hide_index=True)
+
+st.divider()
+
+# ── AI Sentiment Analysis ─────────────────────────────────────
+st.subheader("🤖 AI Sentiment Analysis (FinBERT)")
+st.caption("Powered by Hugging Face — ProsusAI/finbert")
+
+news_examples = {
+    "AAPL": "Apple reports record quarterly earnings driven by iPhone sales",
+    "MSFT": "Microsoft faces antitrust investigation in European markets",
+    "NVDA": "Nvidia surges on strong AI chip demand forecast",
+    "GOOGL": "Alphabet announces major layoffs amid cost-cutting efforts",
+    "AMZN": "Amazon expands logistics network with new warehouse openings",
+}
+
+st.info(
+    "Enter a news headline below and click Analyze to get "
+    "an AI-powered sentiment score using FinBERT, "
+    "a model trained specifically on financial text."
+)
+
+col_input, col_btn = st.columns([4, 1])
+with col_input:
+    headline = st.text_input(
+        "News headline",
+        value="Apple reports record quarterly earnings driven by iPhone sales"
+    )
+with col_btn:
+    st.write("")
+    st.write("")
+    run_sentiment = st.button("🔍 Analyze")
+
+if run_sentiment and headline:
+    with st.spinner("Calling FinBERT model..."):
+        sentiment = analyze_sentiment(headline)
+    st.success(f"**Sentiment result:** {sentiment}")
+
+# Pre-loaded examples
+if st.checkbox("Show sentiment for example headlines"):
+    results = []
+    for ticker, text in news_examples.items():
+        with st.spinner(f"Analyzing {ticker}..."):
+            sent = analyze_sentiment(text)
+        results.append({"Ticker": ticker, "Headline": text, "Sentiment": sent})
+    st.dataframe(pd.DataFrame(results), use_container_width=True, hide_index=True)
+
+st.divider()
+
+# ── Download ──────────────────────────────────────────────────
+st.download_button(
+    label="⬇️ Download Portfolio Table (CSV)",
+    data=portfolio.to_csv(index=False).encode("utf-8"),
+    file_name="portfolio_monitoring_output.csv",
+    mime="text/csv"
+)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+st.caption("ESCP Business School — Applied Data Science Workshop | Group Project")
+```