{"repo": "beepboop2025/economic-intelligence-agent", "files": {"src/demo.py": {"b39a0b4668b2cfb5f1e9279a01dbebfb660ba35a": {"t": 1785596454.3268187, "n": 14742, "h": "f6dba038177d56ba", "c": "#!/usr/bin/env python3\n\"\"\"\nDemo runner for Economic Intelligence Agent v2.0\nRuns a full 9-step pipeline using mock data (no API keys needed)\n\"\"\"\n\nimport os\nimport sys\nimport json\nfrom datetime import datetime\n\nfrom demo_data import generate_all_demo_data\nfrom analysis_engine import AnalysisEngine, ReportGenerator\nfrom report_generator import EnhancedReportGenerator\nfrom quant_engine import QuantEngine\nfrom risk_engine import RiskEngine\nfrom sentiment_engine import SentimentEngine\nfrom alert_engine import AlertEngine\nfrom storage import DataStore\n\n\ndef run_demo(report_format: str = \"markdown\"):\n    \"\"\"Run a full demo analysis with the v2.0 pipeline\"\"\"\n\n    print(\"\\n\" + \"=\" * 60)\n    print(\" ECONOMIC INTELLIGENCE AGENT v2.0 - DEMO MODE\")\n    print(\"=\" * 60 + \"\\n\")\n    print(\"Using simulated market data for demonstration\\n\")\n\n    # Step 1: Generate Demo Data\n    print(\"[1/9] Generating demo market data...\")\n    print(\"-\" * 40)\n\n    data = generate_all_demo_data()\n\n    crypto_count = len(data[\"crypto\"][\"top_coins\"])\n    news_count = len(data[\"news\"])\n    equity_count = len(data[\"equities\"][\"indices\"])\n    bond_count = len(data[\"bonds\"][\"yields\"])\n    commodity_count = len(data[\"commodities\"][\"prices\"])\n    reddit_count = sum(len(v) for v in data.get(\"reddit\", {}).values())\n\n    print(f\"   Crypto: {crypto_count} coins\")\n    print(f\"   Equities: {equity_count} indices\")\n    print(f\"   Bonds: {bond_count} yields\")\n    print(f\"   Commodities: {commodity_count} items\")\n    print(f\"   Forex: USD + EUR rates\")\n    print(f\"   News: {news_count} articles\")\n    print(f\"   Reddit: {reddit_count} posts\")\n    print(f\"   GDELT: {len(data.get('gdelt', []))} articles\")\n    print(f\"   Events: {len(data['economic_events'])} upcoming\")\n\n    # Save raw data\n    os.makedirs(\"data\", exist_ok=True)\n    raw_data_path = f\"data/demo_data_{datetime.now().strftime('%Y%m%d_%H%M')}.json\"\n    with open(raw_data_path, \"w\") as f:\n        json.dump(data, f, indent=2, default=str)\n    print(f\"   Raw data saved: {raw_data_path}\")\n\n    # Step 2: Store\n    print(\"\\n[2/9] Persisting to database...\")\n    store = DataStore(\"data/economic_intelligence.db\")\n    stored = 0\n    for key in (\"top_coins\",):\n        items = data.get(\"crypto\", {}).get(key, [])\n        if items:\n            stored += store.store_market_data_batch(items)\n    for key in (\"indices\",):\n        items = data.get(\"equities\", {}).get(key, [])\n        if items:\n            stored += store.store_market_data_batch(items)\n    stored += store.store_market_data_batch(data.get(\"bonds\", {}).get(\"yields\", []))\n    stored += store.store_market_data_batch(data.get(\"commodities\", {}).get(\"prices\", []))\n    print(f\"   Stored {stored} records\")\n\n    # Step 3: Quant\n    print(\"\\n[3/9] Running quantitative analysis...\")\n    quant_engine = QuantEngine()\n    quant_summary = quant_engine.generate_quant_summary(data)\n    yc = quant_summary.get(\"yield_curve\", {})\n    print(f\"   Yield curve 2Y-10Y spread: {yc.get('spread_2y10y', 'N/A')}%\")\n    print(f\"   Inverted: {'Yes' if yc.get('inverted') else 'No'}\")\n\n    # Step 4: Sentiment\n    print(\"\\n[4/9] Analyzing sentiment...\")\n    sent_engine = SentimentEngine()\n    sent_summary = sent_engine.generate_sentiment_summary(data)\n    fg = sent_summary.get(\"fear_greed\", {})\n    print(f\"   Fear/Greed: {fg.get('value', 50):.0f}/100 ({fg.get('label', 'neutral')})\")\n    ns = sent_summary.get(\"news_sentiment\", {})\n    print(f\"   News sentiment: {ns.get('label', 'N/A')} (score: {ns.get('compound', 0):.3f})\")\n\n    # Step 5: Risk\n    print(\"\\n[5/9] Computing risk metrics...\")\n    risk_engine = RiskEngine()\n    risk_summary = risk_engine.generate_risk_summary(data)\n    print(f\"   Overall risk: {risk_summary.get('overall_risk_level', 'N/A').upper()}\")\n    stress = risk_summary.get(\"stress_tests\", [])\n    if stress:\n        worst = stress[0]\n        print(f\"   Worst scenario: {worst['description']} ({worst['pnl_pct']:+.1f}%)\")\n\n    # Step 6: LLM Analysis\n    print(\"\\n[6/9] AI analysis...\")\n    api_key = os.getenv(\"OPENROUTER_KEY\") or os.getenv(\"OPENAI_API_KEY\") or os.getenv(\"ANTHROPIC_API_KEY\")\n\n    if not api_key:\n        print(\"   No LLM key — using mock analysis\")\n        analysis = generate_mock_analysis(data)\n    else:\n        llm_config = {\n            \"provider\": \"openrouter\" if os.getenv(\"OPENROUTER_KEY\") else (\"openai\" if os.getenv(\"OPENAI_API_KEY\") else \"anthropic\"),\n            \"model\": \"anthropic/claude-3.5-sonnet\" if os.getenv(\"OPENROUTER_KEY\") else \"gpt-4\",\n            \"api_key\": api_key,\n            \"temperature\": 0.3,\n            \"max_tokens\": 4000,\n        }\n        try:\n            analyzer = AnalysisEngine(llm_config)\n            analysis = analyzer.analyze_market_data(data)\n            if \"error\" in analysis:\n                print(f\"   LLM failed: {analysis['error']}\")\n                analysis = generate_mock_analysis(data)\n            else:\n                print(f\"   Tone: {analysis.get('executive_summary', {}).get('market_tone', 'N/A').upper()}\")\n        except Exception as e:\n            print(f\"   Analysis error: {e}\")\n            analysis = generate_mock_analysis(data)\n\n    # Step 7: Alerts\n    print(\"\\n[7/9] Evaluating alerts...\")\n    alert_engine = AlertEngine({\"channels\": [\"console\"]}, store=store)\n    new_alerts = alert_engine.evaluate_and_notify(data, quant_summary, sent_summary, risk_summary)\n    print(f\"   {len(new_alerts)} alerts triggered\")\n\n    # Step 8: Report\n    print(f\"\\n[8/9] Generating {report_format} report...\")\n    reporter = EnhancedReportGenerator(\"reports\")\n    previous = store.get_last_analysis()\n\n    if report_format == \"html\":\n        report = reporter.generate_html(analysis, data, quant=quant_summary, risk=risk_summary, sentiment=sent_summary, alerts=new_alerts, previous=previous)\n    elif report_format == \"json\":\n        report = reporter.generate_json(analysis, data, quant=quant_summary, risk=risk_summary, sentiment=sent_summary, alerts=new_alerts)\n    else:\n        report = reporter.generate_markdown(analysis, data, quant=quant_summary, risk=risk_summary, sentiment=sent_summary, alerts=new_alerts, previous=previous)\n\n    ts = datetime.now().strftime(\"%Y%m%d_%H%M\")\n    ext = {\"markdown\": \"md\", \"html\": \"html\", \"json\": \"json\"}.get(report_format, \"md\")\n    report_path = reporter.save_report(report, f\"demo_report_{ts}.{ext}\", fmt=report_format)\n    print(f\"   Report saved: {report_path}\")\n\n    # Step 9: Archive\n    print(\"\\n[9/9] Archiving analysis...\")\n    es = analysis.get(\"executive_summary\", {})\n    store.store_analysis(\n        market_tone=es.get(\"market_tone\", \"unknown\"),\n        key_theme=es.get(\"key_theme\", \"\"),\n        summary=analysis,\n        report_path=report_path,\n        report_format=report_format,\n    )\n    print(\"   Archived to database\")\n\n    # Summary\n    print(\"\\n\" + \"=\" * 60)\n    print(\" DEMO ANALYSIS COMPLETE\")\n    print(\"=\" * 60)\n\n    print(f\"\\nMarket Tone: {es.get('market_tone', 'N/A').upper()}\")\n    print(f\"Key Theme: {es.get('key_theme', 'N/A')}\")\n    print(f\"Risk Level: {risk_summary.get('overall_risk_level', 'N/A').upper()}\")\n    print(f\"Fear/Greed: {fg.get('value', 50):.0f}/100 ({fg.get('label', 'neutral')})\")\n    print(f\"Alerts: {len(new_alerts)}\")\n\n    # DB stats\n    stats = store.get_stats()\n    print(f\"\\nDatabase: {stats}\")\n\n    # Report preview\n    print(\"\\n\" + \"=\" * 60)\n    print(\" REPORT PREVIEW:\")\n    print(\"=\" * 60)\n    lines = report.split(\"\\n\")\n    preview = \"\\n\".join(lines[:50])\n    print(preview + \"\\n... [Report continues — see full file]\\n\")\n\n    store.close()\n    return report_path\n\n\ndef generate_mock_analysis(data: dict) -> dict:\n    \"\"\"Generate a mock analysis when LLM is unavailable\"\"\"\n\n    btc_data = next((c for c in data[\"crypto\"][\"top_coins\"] if c.symbol == \"BTC\"), None)\n    btc_price = btc_data.price if btc_data else 85000\n\n    return {\n        \"executive_summary\": {\n            \"market_tone\": \"mixed\",\n            \"key_theme\": \"Central bank divergence and crypto institutional adoption driving cross-asset volatility, with inflation concerns moderating but growth worries emerging.\",\n            \"primary_risk_factors\": [\n                \"Commercial real estate refinancing cliff with $1.5T debt maturing 2024-2025\",\n                \"Persistent inflation forcing central banks to maintain restrictive policy longer\",\n                \"Geopolitical tensions in Middle East threatening energy supply stability\",\n                \"China property sector stress with potential global contagion\",\n            ],\n            \"primary_opportunities\": [\n                \"Bitcoin ETF inflows creating structural demand floor\",\n                \"Gold benefiting from fiscal dominance and de-dollarization trends\",\n                \"Energy sector on supply constraints and infrastructure investment\",\n                \"AI productivity boom driving technology sector earnings\",\n            ],\n        },\n        \"market_overview\": {\n            \"equities\": {\n                \"trend\": \"Consolidation with rotation from mega-cap tech to value and small-caps\",\n                \"key_levels\": \"S&P 500 support at 5750/5650, resistance at 5900/6000\",\n                \"sentiment\": \"neutral\",\n            },\n            \"bonds\": {\n                \"yield_trend\": \"Range-bound with 10Y Treasury 4.1-4.4%, curve steepening beginning\",\n                \"curve_shape\": \"inverted\",\n                \"implications\": \"Historical recession indicator, but soft landing still possible\",\n            },\n            \"crypto\": {\n                \"market_phase\": \"accumulation\",\n                \"dominance_trends\": \"BTC dominance elevated at 58% on institutional flows\",\n                \"risk_appetite\": \"moderate\",\n            },\n            \"forex\": {\n                \"dollar_strength\": \"strong\",\n                \"major_moves\": \"USD/JPY near 150 intervention watch, EUR/USD testing 0.92 support\",\n                \"carry_trade_activity\": \"Reduced as BoJ normalizes, unwind risk elevated\",\n            },\n        },\n        \"key_events\": [\n            {\"event\": \"Fed Chair Powell Congressional testimony\", \"impact\": \"immediate\", \"affected_assets\": [\"USD\", \"Treasuries\", \"Gold\", \"Bitcoin\"], \"market_implication\": \"Dovish tone bullish for risk assets\"},\n            {\"event\": \"US Non-Farm Payrolls release\", \"impact\": \"immediate\", \"affected_assets\": [\"USD\", \"Treasuries\", \"Equities\"], \"market_implication\": \"Strong data delays rate cut expectations\"},\n            {\"event\": \"ECB Rate Decision with updated projections\", \"impact\": \"delayed\", \"affected_assets\": [\"EUR\", \"European equities\"], \"market_implication\": \"Easing signals bearish for EUR\"},\n        ],\n        \"cross_asset_analysis\": [\n            {\"observation\": \"Bitcoin and Gold decoupling from traditional risk assets\", \"assets_involved\": [\"Bitcoin\", \"Gold\", \"USD\"], \"historical_context\": \"Similar to 2020-2021 inflation hedge phase\", \"implications\": \"Treat both as distinct inflation/uncertainty hedges\"},\n            {\"observation\": \"Equity-bond correlation normalizing to negative\", \"assets_involved\": [\"S&P 500\", \"10Y Treasury\"], \"historical_context\": \"Positive correlation during 2022 was anomaly\", \"implications\": \"Traditional 60/40 diversification working again\"},\n        ],\n        \"risk_assessment\": {\n            \"systemic_risks\": [\n                {\"risk\": \"CRE refinancing crisis as $1.5T debt matures into higher rates\", \"severity\": \"high\", \"probability\": \"medium\", \"trigger_events\": [\"Regional bank failures\", \"Office vacancy >25%\"]},\n                {\"risk\": \"Treasury market liquidity stress amid QT\", \"severity\": \"medium\", \"probability\": \"medium\", \"trigger_events\": [\"Auction tail risks\", \"Foreign selling\"]},\n            ],\n            \"tail_risks\": [\n                \"Middle East conflict disrupting oil above $120/bbl\",\n                \"Major crypto exchange failure triggering DeFi contagion\",\n                \"AI bubble burst with concentrated tech valuations\",\n            ],\n            \"hedging_considerations\": \"Long volatility via VIX calls, increase gold to 10-15%, maintain USD cash buffer\",\n        },\n        \"sector_rotation\": {\n            \"current_flows\": \"Rotation from mega-cap tech to small-cap value and energy\",\n            \"leading_sectors\": [\"Energy\", \"Financials\", \"Materials\", \"Utilities\"],\n            \"lagging_sectors\": [\"Technology\", \"Communication Services\", \"Real Estate\"],\n            \"rotation_stage\": \"mid\",\n        },\n        \"outlook\": {\n            \"immediate_1_7_days\": f\"Range-bound. BTC consolidating ${btc_price - 3000:.0f}-${btc_price + 3000:.0f}. Dollar strong but overbought.\",\n            \"short_term_1_4_weeks\": \"Directional catalyst from NFP and CPI data. Soft landing = risk-on rally.\",\n            \"medium_term_1_6_months\": \"Baseline: soft landing, Fed cuts 2-3 times mid-year. Bear case: credit event.\",\n            \"key_levels_to_watch\": {\n                \"equities\": \"S&P 5650 support, 6000 breakout, VIX 20 fear threshold\",\n                \"bonds\": \"10Y 3.8% bullish, 4.5% bearish, 2s10s flattening\",\n                \"crypto\": f\"BTC ${btc_price - 5000:.0f} support, $100k psychological\",\n                \"forex\": \"DXY 108 resistance, USD/JPY 150 intervention\",\n            },\n            \"catalysts_to_monitor\": [\n                \"Fed policy pivot signals\",\n                \"Inflation trajectory (CPI/PCE)\",\n                \"Corporate earnings guidance\",\n                \"China stimulus effectiveness\",\n                \"Geopolitical escalations\",\n            ],\n        },\n        \"trade_setups\": [\n            {\"idea\": f\"Long BTC in ${btc_price - 5000:.0f}-${btc_price - 2000:.0f} accumulation zone\", \"rationale\": \"ETF inflows + halving catalyst\", \"risk_reward\": \"3:1\", \"timeframe\": \"medium\"},\n            {\"idea\": \"Yield curve steepener: Long 2Y, Short 10Y\", \"rationale\": \"Curve normalization as Fed cuts\", \"risk_reward\": \"2.5:1\", \"timeframe\": \"short\"},\n            {\"idea\": \"Gold long on dips to $2800-$2825\", \"rationale\": \"Fiscal dominance hedge + central bank buying\", \"risk_reward\": \"2:1\", \"timeframe\": \"short\"},\n        ],\n    }\n\n\nif __name__ == \"__main__\":\n    import argparse\n    parser = argparse.ArgumentParser(description=\"EconAgent Demo\")\n    parser.add_argument(\"--format\", choices=[\"markdown\", \"html\", \"json\"], default=\"markdown\")\n    args = parser.parse_args()\n\n    try:\n        report_path = run_demo(args.format)\n        print(f\"\\nFull report: {report_path}\")\n        print(\"\\nTo run with live data:\")\n        print(\"  1. Get API keys from OpenRouter (free): https://openrouter.ai/\")\n        print(\"  2. export OPENROUTER_KEY='your_key'\")\n        print(\"  3. python src/main.py\")\n    except KeyboardInterrupt:\n        print(\"\\n\\nDemo interrupted\")\n    except Exception as e:\n        print(f\"\\nError: {e}\")\n        import traceback\n        traceback.print_exc()\n"}}}}