Upload app.py with huggingface_hub

app.py

@@ -22,6 +22,20 @@ import numpy as np
 import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 
+# Auto-load .env file if present
+_env_path = os.path.join(os.path.dirname(__file__), ".env")
+if os.path.exists(_env_path):
+    try:
+        with open(_env_path, "r", encoding="utf-8") as _ef:
+            for _line in _ef:
+                _line = _line.strip()
+                if _line and not _line.startswith("#") and "=" in _line:
+                    _k, _v = _line.split("=", 1)
+                    if _k.strip() not in os.environ:
+                        os.environ[_k.strip()] = _v.strip()
+    except Exception:
+        pass
+
 # -----------------------------------------------------------------------------
 # Hugging Face Hub / Gradio 4.42 Compatibility Shim
 # -----------------------------------------------------------------------------
@@ -331,21 +345,46 @@ def generate_kpi_card_html(ticker: str) -> str:
 def generate_macro_hud_html() -> str:
     """Generates high-contrast HUD cards for Macro & Quantitative Regime."""
     snap = STATE["macro_snapshot"]
-    yield_slope = snap.get("yield_curve_slope", -0.55)
-    is_inverted = snap.get("inverted_yield_curve", True)
+    yield_slope = snap.get("yield_curve_slope", 0.31)
+    is_inverted = snap.get("inverted_yield_curve", False)
     slope_color = "#ff3366" if is_inverted else "#00ff87"
-    slope_text = "INVERTED (-0.55%)" if is_inverted else f"+{yield_slope:.2f}% (NORMAL)"
+    slope_text = f"{yield_slope:+.2f}% ({'INVERTED' if is_inverted else 'NORMAL'})"
 
     vix_val = snap.get("vix", 16.50)
     vix_color = "#ff3366" if vix_val > 25 else ("#00ff87" if vix_val < 18 else "#ffb703")
 
+    fred_tag = '<span class="hud-tag" style="border-color:#00ff87; color:#00ff87;">ST. LOUIS FED (FRED) ACTIVE</span>' if snap.get("fred_active") else '<span class="hud-tag">REAL-TIME FEEDS</span>'
+
+    fedfunds_row = ""
+    if snap.get("fred_active"):
+        fedfunds_row = f"""
+            <div class="hud-grid-row" style="margin-top:6px; border-top:1px solid rgba(255,255,255,0.06); padding-top:6px;">
+                <div class="hud-stat-box">
+                    <div class="hud-lbl">FED FUNDS RATE</div>
+                    <div class="hud-val" style="color:#00f2fe;">{snap.get('fed_funds_rate', 3.75)}%</div>
+                </div>
+                <div class="hud-stat-box">
+                    <div class="hud-lbl">UNEMPLOYMENT</div>
+                    <div class="hud-val">{snap.get('unemployment_rate', 4.20)}%</div>
+                </div>
+                <div class="hud-stat-box">
+                    <div class="hud-lbl">HY CREDIT SPREAD</div>
+                    <div class="hud-val">{snap.get('high_yield_spread', 3.40)}%</div>
+                </div>
+                <div class="hud-stat-box">
+                    <div class="hud-lbl">SPY 200DMA STATUS</div>
+                    <div class="hud-val" style="color:#00ff87; font-size:11px;">BULLISH TREND</div>
+                </div>
+            </div>
+        """
+
     return f"""
     <div class="hud-container">
         <!-- Card 1: Macro Radar -->
         <div class="hud-card">
             <div class="hud-title">
                 <span style="color:#00f2fe;">🌐 MACROECONOMIC RADAR</span>
-                <span class="hud-tag">REAL-TIME FEEDS</span>
+                {fred_tag}
             </div>
             <div class="hud-grid-row">
                 <div class="hud-stat-box">
@@ -373,6 +412,7 @@ def generate_macro_hud_html() -> str:
                     <div class="hud-val">{snap.get('usd_index', 104.20):.2f}</div>
                 </div>
             </div>
+            {fedfunds_row}
         </div>
 
         <!-- Card 2: Quantitative Regime -->
@@ -731,16 +771,25 @@ def handle_run_quantagent(trading_idea: str, max_rounds: int, threshold: float):
         traj_md += f"> **Mentor Critique & Feedback:**\n> {critique}\n\n"
 
     status_badge = "🟢 ACCEPTED INTO KNOWLEDGE BASE" if rec.get("status") == "ACCEPTED" else "🟡 PENDING REFINEMENT"
+    rec_name = str(rec.get('name', 'N/A'))
+    ic_str = f"{float(rec.get('ic_mean', 0.0)):.4f}"
+    ic_ir_str = f"{float(rec.get('ic_ir', 0.0)):.3f}"
+    sharpe_str = f"{float(rec.get('sharpe', 0.0)):.2f}"
+    ann_ret_str = f"{float(rec.get('annualized_return_pct', 0.0)):.2f}"
+    mdd_str = f"{float(rec.get('max_drawdown_pct', 0.0)):.2f}"
+    turnover_str = f"{float(rec.get('turnover_pct', 0.0)):.1f}"
+    valid_str = f"{float(rec.get('valid_ratio', 1.0)):.1%}"
+
     status_md = f"""
 ### Outer Feedback Loop Backtest Results: {status_badge}
-- **Signal Name**: `{rec.get('name', 'N/A')}`
-- **Information Coefficient (IC Mean)**: `{rec.get('ic_mean', 0.0):.4f}`
-- **IC Information Ratio (IC_IR)**: `{rec.get('ic_ir', 0.0):.3f}`
-- **Annualized Sharpe Ratio**: **`{rec.get('sharpe', 0.0):.2f}`**
-- **Annualized Return**: **`{rec.get('annualized_return_pct', 0.0):.2f}%`**
-- **Max Drawdown**: `{rec.get('max_drawdown_pct', 0.0):.2f}%`
-- **Turnover Rate**: `{rec.get('turnover_pct', 0.0):.1f}%`
-- **Valid Data Ratio**: `{rec.get('valid_ratio', 1.0):.1%}`
+- **Signal Name**: `{rec_name}`
+- **Information Coefficient (IC Mean)**: `{ic_str}`
+- **IC Information Ratio (IC_IR)**: `{ic_ir_str}`
+- **Annualized Sharpe Ratio**: **`{sharpe_str}`**
+- **Annualized Return**: **`{ann_ret_str}%`**
+- **Max Drawdown**: `{mdd_str}%`
+- **Turnover Rate**: `{turnover_str}%`
+- **Valid Data Ratio**: `{valid_str}`
 """
 
     cum_fig = plot_backtest_cumulative(rec)
@@ -844,9 +893,10 @@ $$\\text{{ROE}} = \\text{{Net Profit Margin}} \\times \\text{{Asset Turnover}} \
 
 
 def handle_news_lookup(ticker: str):
-    """Fetches live news headlines and calculates sentiment polarity."""
+    """Fetches live news headlines, analyst consensus, and calculates sentiment polarity."""
     ticker = ticker.strip().upper()
-    items = news_service.fetch_ticker_news(ticker, max_items=6)
+    items = news_service.fetch_ticker_news(ticker, max_items=8)
+    recs = news_service.fetch_finnhub_recommendations(ticker)
     if not items:
         return "No recent news found.", pd.DataFrame()
 
@@ -856,10 +906,22 @@ def handle_news_lookup(ticker: str):
     mean_score = df["Polarity Score"].mean()
     sentiment_label = "🟢 Bullish / Positive" if mean_score > 0.15 else ("🔴 Bearish / Negative" if mean_score < -0.15 else "🟡 Neutral")
 
+    rec_md = ""
+    if recs:
+        total = recs['strong_buy'] + recs['buy'] + recs['hold'] + recs['sell'] + recs['strong_sell']
+        bull_pct = round((recs['strong_buy'] + recs['buy']) / max(1, total) * 100, 1)
+        rec_md = f"""
+#### 📊 Wall Street Analyst Consensus (Finnhub Feed - {recs.get('period', 'Latest')})
+- **Consensus Rating**: **{bull_pct}% Bullish Recommendation** ({recs['strong_buy'] + recs['buy']} Buy / {total} Total Analysts)
+- **Breakdown**: 🟢 Strong Buy: `{recs['strong_buy']}` | 🟢 Buy: `{recs['buy']}` | 🟡 Hold: `{recs['hold']}` | 🔴 Sell: `{recs['sell']}` | 🔴 Strong Sell: `{recs['strong_sell']}`
+"""
+
     summary_md = f"""
-### 📰 Live News & Sentiment Catalyst Radar: `{ticker}`
+### 📰 Institutional News & Sentiment Catalyst Radar: `{ticker}`
+{rec_md}
 - **Overall Catalyst Bias**: **`{sentiment_label}`** (Average Polarity: `{mean_score:+.2f}`)
 - **Analyzed Articles Count**: `{len(items)}`
+- **Data Feed**: `{'Finnhub Institutional News API' if news_service.finnhub_key else 'Yahoo Financial RSS'}`
 - **Signal Impact**: Real-time sentiment feeds modulate alpha exposure weights in the Multi-Agent Synthesizer.
 """
     return summary_md, df
@@ -1490,8 +1552,8 @@ Enter your Hugging Face write token and the target Space ID (`deveshc23/quantage
                 gr.Markdown("### 🔑 Optional API Keys (Upgrades functionality)")
                 with gr.Row():
                     gemini_key_input = gr.Textbox(label="Google Gemini API Key", type="password", placeholder="AIza...")
-                    finnhub_key_input = gr.Textbox(label="Finnhub API Key", type="password", placeholder="Optional...")
-                    fred_key_input = gr.Textbox(label="FRED API Key", type="password", placeholder="Optional...")
+                    finnhub_key_input = gr.Textbox(label="Finnhub API Key", type="password", value=os.environ.get("FINNHUB_API_KEY", ""), placeholder="Optional...")
+                    fred_key_input = gr.Textbox(label="FRED API Key", type="password", value=os.environ.get("FRED_API_KEY", ""), placeholder="Optional...")
                     save_keys_btn = gr.Button("Save API Keys")
 
                 def save_keys_action(gemini_k, finnhub_k, fred_k):
@@ -1500,9 +1562,21 @@ Enter your Hugging Face write token and the target Space ID (`deveshc23/quantage
                         quant_engine.llm = llm_client
                     if finnhub_k:
                         news_service.finnhub_key = finnhub_k
+                        os.environ["FINNHUB_API_KEY"] = finnhub_k
                     if fred_k:
                         macro_service.fred_key = fred_k
-                    return "✅ API Keys updated successfully!"
+                        os.environ["FRED_API_KEY"] = fred_k
+                        STATE["macro_snapshot"] = macro_service.get_macro_snapshot()
+                    # Persist to local .env
+                    try:
+                        with open(".env", "w", encoding="utf-8") as ef:
+                            if os.environ.get("FINNHUB_API_KEY"):
+                                ef.write(f"FINNHUB_API_KEY={os.environ['FINNHUB_API_KEY']}\n")
+                            if os.environ.get("FRED_API_KEY"):
+                                ef.write(f"FRED_API_KEY={os.environ['FRED_API_KEY']}\n")
+                    except Exception:
+                        pass
+                    return "✅ API Keys updated, activated, and persisted!"
 
                 keys_status_md = gr.Markdown("")
                 save_keys_btn.click(save_keys_action, [gemini_key_input, finnhub_key_input, fred_key_input], keys_status_md)