{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notice**: The `semantica.kg_qa` module is temporarily unavailable and will be reintroduced in a future release. Any quality assessment examples in this notebook are disabled."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Hawksight-AI/semantica/blob/main/cookbook/use_cases/trading/05_Risk_Assessment.ipynb)\n",
    "\n",
    "# Risk Assessment Pipeline\n",
    "\n",
    "## Overview\n",
    "\n",
    "This notebook demonstrates a complete risk assessment pipeline for trading: ingest risk data from multiple sources (portfolio data, market risk metrics, historical data), extract risk entities, build risk knowledge graph, analyze risk relationships, and assess portfolio risk.\n",
    "\n",
    "\n",
    "**Documentation**: [API Reference](https://semantica.readthedocs.io/use-cases/)\n",
    "\n",
    "## Installation\n",
    "\n",
    "Install Semantica from PyPI:\n",
    "\n",
    "```bash\n",
    "pip install semantica\n",
    "# Or with all optional dependencies:\n",
    "pip install semantica[all]\n",
    "```\n",
    "\n",
    "### Modules Used (20+)\n",
    "\n",
    "- **Ingestion**: FileIngestor, WebIngestor, FeedIngestor, StreamIngestor, DBIngestor, EmailIngestor, RepoIngestor, MCPIngestor\n",
    "- **Parsing**: JSONParser, CSVParser, StructuredDataParser\n",
    "- **Extraction**: NERExtractor, RelationExtractor, EventDetector, SemanticAnalyzer\n",
    "- **KG**: GraphBuilder, GraphAnalyzer, CentralityCalculator, CommunityDetector\n",
    "- **Analytics**: ConnectivityAnalyzer, TemporalGraphQuery, TemporalPatternDetector\n",
    "- **Reasoning**: InferenceEngine, RuleManager, ExplanationGenerator\n",
    "- **Quality**: KGQualityAssessor, ConflictDetector\n",
    "- **Export**: JSONExporter, CSVExporter, RDFExporter, ReportGenerator\n",
    "- **Visualization**: KGVisualizer, AnalyticsVisualizer, TemporalVisualizer\n",
    "\n",
    "### Pipeline\n",
    "\n",
    "**Risk Data Sources → Parse → Extract Risk Entities → Build Risk KG → Analyze Risk Relationships → Assess Portfolio Risk → Generate Reports → Visualize**\n",
    "\n",
    "---\n",
    "\n",
    "## Step 1: Ingest Risk Data from Multiple Sources\n",
    "\n",
    "Ingest risk data from portfolio databases, market risk metrics, and historical data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from semantica.ingest import FileIngestor, DBIngestor, WebIngestor, FeedIngestor\n",
    "from semantica.parse import JSONParser, CSVParser, StructuredDataParser\n",
    "from semantica.semantic_extract import NERExtractor, RelationExtractor, EventDetector, SemanticAnalyzer\n",
    "from semantica.kg import GraphBuilder, GraphAnalyzer, CentralityCalculator, CommunityDetector\n",
    "from semantica.kg import ConnectivityAnalyzer, TemporalGraphQuery, TemporalPatternDetector\n",
    "from semantica.reasoning import InferenceEngine, RuleManager, ExplanationGenerator\n",
    "from semantica.kg_qa import KGQualityAssessor\n",
    "from semantica.conflicts import ConflictDetector\n",
    "from semantica.export import JSONExporter, CSVExporter, RDFExporter, ReportGenerator\n",
    "from semantica.visualization import KGVisualizer, AnalyticsVisualizer, TemporalVisualizer\n",
    "import tempfile\n",
    "import os\n",
    "import json\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "file_ingestor = FileIngestor()\n",
    "db_ingestor = DBIngestor()\n",
    "web_ingestor = WebIngestor()\n",
    "feed_ingestor = FeedIngestor()\n",
    "\n",
    "json_parser = JSONParser()\n",
    "csv_parser = CSVParser()\n",
    "structured_parser = StructuredDataParser()\n",
    "\n",
    "# Real risk data sources\n",
    "risk_apis = [\n",
    "    \"https://api.polygon.io/v2/aggs/ticker/AAPL/range/1/day/2024-01-01/2024-01-31\",  # Polygon.io\n",
    "    \"https://www.alphavantage.co/query?function=OVERVIEW&symbol=AAPL&apikey=demo\"  # Alpha Vantage\n",
    "]\n",
    "\n",
    "financial_feeds = [\n",
    "    \"https://feeds.reuters.com/reuters/businessNews\",\n",
    "    \"https://rss.cnn.com/rss/money_latest.rss\"\n",
    "]\n",
    "\n",
    "# Real database connection for portfolio risk data\n",
    "db_connection_string = \"postgresql://user:password@localhost:5432/portfolio_db\"\n",
    "db_query = \"SELECT portfolio_id, symbol, quantity, value, risk_score, volatility FROM portfolio_positions WHERE last_updated > NOW() - INTERVAL '1 day' ORDER BY risk_score DESC\"\n",
    "\n",
    "temp_dir = tempfile.mkdtemp()\n",
    "\n",
    "# Sample portfolio risk data\n",
    "risk_data_file = os.path.join(temp_dir, \"portfolio_risk.json\")\n",
    "portfolio_risk_data = {\n",
    "    \"portfolio_id\": \"PORT-001\",\n",
    "    \"positions\": [\n",
    "        {\n",
    "            \"symbol\": \"AAPL\",\n",
    "            \"quantity\": 100,\n",
    "            \"value\": 17550.00,\n",
    "            \"risk_score\": 0.15,\n",
    "            \"volatility\": 0.20,\n",
    "            \"beta\": 1.2\n",
    "        },\n",
    "        {\n",
    "            \"symbol\": \"MSFT\",\n",
    "            \"quantity\": 50,\n",
    "            \"value\": 19012.50,\n",
    "            \"risk_score\": 0.12,\n",
    "            \"volatility\": 0.18,\n",
    "            \"beta\": 0.9\n",
    "        },\n",
    "        {\n",
    "            \"symbol\": \"GOOGL\",\n",
    "            \"quantity\": 75,\n",
    "            \"value\": 10710.00,\n",
    "            \"risk_score\": 0.18,\n",
    "            \"volatility\": 0.25,\n",
    "            \"beta\": 1.1\n",
    "        }\n",
    "    ],\n",
    "    \"total_value\": 47272.50,\n",
    "    \"portfolio_risk_score\": 0.15\n",
    "}\n",
    "\n",
    "with open(risk_data_file, 'w') as f:\n",
    "    json.dump(portfolio_risk_data, f, indent=2)\n",
    "\n",
    "file_objects = file_ingestor.ingest_file(risk_data_file, read_content=True)\n",
    "parsed_data = structured_parser.parse_data(risk_data_file, data_format=\"json\")\n",
    "\n",
    "print(f\"\\n📊 Ingestion Summary:\")\n",
    "print(f\"  Risk data files: {len([file_objects]) if file_objects else 0}\")\n",
    "print(f\"  Database sources: 1\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Extract Risk Entities and Build Risk Knowledge Graph\n",
    "\n",
    "Extract risk entities and build risk knowledge graph.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ner_extractor = NERExtractor()\n",
    "relation_extractor = RelationExtractor()\n",
    "event_detector = EventDetector()\n",
    "semantic_analyzer = SemanticAnalyzer()\n",
    "\n",
    "risk_entities = []\n",
    "risk_relationships = []\n",
    "\n",
    "# Extract from portfolio risk data\n",
    "if parsed_data and parsed_data.get(\"data\"):\n",
    "    portfolio = parsed_data.get(\"data\") if isinstance(parsed_data.get(\"data\"), dict) else parsed_data.get(\"data\")[0] if isinstance(parsed_data.get(\"data\"), list) else {}\n",
    "    \n",
    "    if isinstance(portfolio, dict):\n",
    "        portfolio_id = portfolio.get(\"portfolio_id\", \"\")\n",
    "        \n",
    "        risk_entities.append({\n",
    "            \"id\": portfolio_id,\n",
    "            \"type\": \"Portfolio\",\n",
    "            \"name\": portfolio_id,\n",
    "            \"properties\": {\n",
    "                \"total_value\": portfolio.get(\"total_value\", 0),\n",
    "                \"portfolio_risk_score\": portfolio.get(\"portfolio_risk_score\", 0)\n",
    "            }\n",
    "        })\n",
    "        \n",
    "        # Positions and risk metrics\n",
    "        for position in portfolio.get(\"positions\", []):\n",
    "            if isinstance(position, dict):\n",
    "                symbol = position.get(\"symbol\", \"\")\n",
    "                \n",
    "                risk_entities.append({\n",
    "                    \"id\": symbol,\n",
    "                    \"type\": \"Stock\",\n",
    "                    \"name\": symbol,\n",
    "                    \"properties\": {\n",
    "                        \"quantity\": position.get(\"quantity\", 0),\n",
    "                        \"value\": position.get(\"value\", 0),\n",
    "                        \"risk_score\": position.get(\"risk_score\", 0),\n",
    "                        \"volatility\": position.get(\"volatility\", 0),\n",
    "                        \"beta\": position.get(\"beta\", 0)\n",
    "                    }\n",
    "                })\n",
    "                \n",
    "                risk_relationships.append({\n",
    "                    \"source\": portfolio_id,\n",
    "                    \"target\": symbol,\n",
    "                    \"type\": \"contains\",\n",
    "                    \"properties\": {\n",
    "                        \"quantity\": position.get(\"quantity\", 0),\n",
    "                        \"value\": position.get(\"value\", 0)\n",
    "                    }\n",
    "                })\n",
    "                \n",
    "                risk_relationships.append({\n",
    "                    \"source\": symbol,\n",
    "                    \"target\": f\"{symbol}_risk\",\n",
    "                    \"type\": \"has_risk\",\n",
    "                    \"properties\": {\n",
    "                        \"risk_score\": position.get(\"risk_score\", 0),\n",
    "                        \"volatility\": position.get(\"volatility\", 0)\n",
    "                    }\n",
    "                })\n",
    "\n",
    "builder = GraphBuilder()\n",
    "graph_analyzer = GraphAnalyzer()\n",
    "centrality_calculator = CentralityCalculator()\n",
    "community_detector = CommunityDetector()\n",
    "\n",
    "risk_kg = builder.build(risk_entities, risk_relationships)\n",
    "\n",
    "metrics = graph_analyzer.compute_metrics(risk_kg)\n",
    "centrality_result = centrality_calculator.calculate_degree_centrality(risk_kg)\n",
    "centrality_scores = centrality_result.get('centrality', {})\n",
    "communities = community_detector.detect_communities(risk_kg)\n",
    "\n",
    "print(f\"Extracted {len(risk_entities)} risk entities\")\n",
    "print(f\"Extracted {len(risk_relationships)} risk relationships\")\n",
    "print(f\"Built risk knowledge graph with {len(risk_kg.get('entities', []))} entities\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Analyze Risk Relationships\n",
    "\n",
    "Analyze risk relationships using graph analytics.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "connectivity_analyzer = ConnectivityAnalyzer()\n",
    "temporal_query = TemporalGraphQuery()\n",
    "temporal_pattern_detector = TemporalPatternDetector()\n",
    "\n",
    "connectivity = connectivity_analyzer.analyze_connectivity(risk_kg)\n",
    "\n",
    "temporal_patterns = temporal_pattern_detector.detect_temporal_patterns(\n",
    "    risk_kg,\n",
    "    pattern_type=\"risk\",\n",
    "    min_frequency=1\n",
    ")\n",
    "\n",
    "# Analyze risk concentration\n",
    "risk_concentration = {}\n",
    "if parsed_data and parsed_data.get(\"data\"):\n",
    "    portfolio = parsed_data.get(\"data\") if isinstance(parsed_data.get(\"data\"), dict) else parsed_data.get(\"data\")[0] if isinstance(parsed_data.get(\"data\"), list) else {}\n",
    "    if isinstance(portfolio, dict):\n",
    "        total_value = portfolio.get(\"total_value\", 1)\n",
    "        for position in portfolio.get(\"positions\", []):\n",
    "            if isinstance(position, dict):\n",
    "                symbol = position.get(\"symbol\", \"\")\n",
    "                value = position.get(\"value\", 0)\n",
    "                concentration = (value / total_value) * 100 if total_value > 0 else 0\n",
    "                risk_concentration[symbol] = {\n",
    "                    \"concentration\": concentration,\n",
    "                    \"risk_score\": position.get(\"risk_score\", 0),\n",
    "                    \"value\": value\n",
    "                }\n",
    "\n",
    "print(f\"Risk relationships analyzed\")\n",
    "print(f\"  Connected components: {len(connectivity.get('components', []))}\")\n",
    "print(f\"  Temporal patterns: {len(temporal_patterns)}\")\n",
    "print(f\"  Risk concentrations: {len(risk_concentration)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Assess Portfolio Risk\n",
    "\n",
    "Assess portfolio risk using inference engine.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inference_engine = InferenceEngine()\n",
    "rule_manager = RuleManager()\n",
    "explanation_generator = ExplanationGenerator()\n",
    "\n",
    "# Portfolio risk assessment rules\n",
    "inference_engine.add_rule(\"IF risk_score > 0.2 AND concentration > 20 THEN high_risk_position\")\n",
    "inference_engine.add_rule(\"IF volatility > 0.3 AND beta > 1.5 THEN high_volatility_risk\")\n",
    "inference_engine.add_rule(\"IF portfolio_risk_score > 0.2 THEN high_portfolio_risk\")\n",
    "\n",
    "# Assess portfolio risk\n",
    "portfolio_risk_assessment = {}\n",
    "if parsed_data and parsed_data.get(\"data\"):\n",
    "    portfolio = parsed_data.get(\"data\") if isinstance(parsed_data.get(\"data\"), dict) else parsed_data.get(\"data\")[0] if isinstance(parsed_data.get(\"data\"), list) else {}\n",
    "    if isinstance(portfolio, dict):\n",
    "        portfolio_risk_score = portfolio.get(\"portfolio_risk_score\", 0)\n",
    "        \n",
    "        # Calculate weighted risk\n",
    "        total_risk = 0\n",
    "        total_value = portfolio.get(\"total_value\", 1)\n",
    "        for position in portfolio.get(\"positions\", []):\n",
    "            if isinstance(position, dict):\n",
    "                position_risk = position.get(\"risk_score\", 0) * (position.get(\"value\", 0) / total_value) if total_value > 0 else 0\n",
    "                total_risk += position_risk\n",
    "                \n",
    "                inference_engine.add_fact({\n",
    "                    \"symbol\": position.get(\"symbol\", \"\"),\n",
    "                    \"risk_score\": position.get(\"risk_score\", 0),\n",
    "                    \"volatility\": position.get(\"volatility\", 0),\n",
    "                    \"beta\": position.get(\"beta\", 0),\n",
    "                    \"concentration\": risk_concentration.get(position.get(\"symbol\", \"\"), {}).get(\"concentration\", 0)\n",
    "                })\n",
    "        \n",
    "        portfolio_risk_assessment = {\n",
    "            \"portfolio_id\": portfolio.get(\"portfolio_id\", \"\"),\n",
    "            \"overall_risk_score\": portfolio_risk_score,\n",
    "            \"weighted_risk\": total_risk,\n",
    "            \"risk_level\": \"high\" if portfolio_risk_score > 0.2 else \"medium\" if portfolio_risk_score > 0.1 else \"low\",\n",
    "            \"positions_count\": len(portfolio.get(\"positions\", []))\n",
    "        }\n",
    "        \n",
    "        inference_engine.add_fact({\n",
    "            \"portfolio_id\": portfolio.get(\"portfolio_id\", \"\"),\n",
    "            \"portfolio_risk_score\": portfolio_risk_score\n",
    "        })\n",
    "\n",
    "risk_insights = inference_engine.forward_chain()\n",
    "\n",
    "print(f\"Portfolio risk assessment complete\")\n",
    "print(f\"  Overall risk score: {portfolio_risk_assessment.get('overall_risk_score', 0):.3f}\")\n",
    "print(f\"  Risk level: {portfolio_risk_assessment.get('risk_level', 'unknown')}\")\n",
    "print(f\"  Generated {len(risk_insights)} risk insights\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Generate Reports and Visualize\n",
    "\n",
    "Generate risk assessment reports and visualize results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "quality_assessor = KGQualityAssessor()\n",
    "json_exporter = JSONExporter()\n",
    "csv_exporter = CSVExporter()\n",
    "rdf_exporter = RDFExporter()\n",
    "report_generator = ReportGenerator()\n",
    "\n",
    "quality_score = quality_assessor.assess_overall_quality(risk_kg)\n",
    "\n",
    "json_exporter.export_knowledge_graph(risk_kg, os.path.join(temp_dir, \"risk_kg.json\"))\n",
    "csv_exporter.export_entities(risk_entities, os.path.join(temp_dir, \"risk_entities.csv\"))\n",
    "rdf_exporter.export_knowledge_graph(risk_kg, os.path.join(temp_dir, \"risk_kg.rdf\"))\n",
    "\n",
    "report_data = {\n",
    "    \"summary\": f\"Risk assessment identified {len(risk_insights)} risk insights for portfolio {portfolio_risk_assessment.get('portfolio_id', '')}\",\n",
    "    \"portfolio_risk_score\": portfolio_risk_assessment.get('overall_risk_score', 0),\n",
    "    \"risk_level\": portfolio_risk_assessment.get('risk_level', 'unknown'),\n",
    "    \"positions_analyzed\": len([e for e in risk_entities if e.get(\"type\") == \"Stock\"]),\n",
    "    \"insights\": len(risk_insights),\n",
    "    \"quality_score\": quality_score.get('overall_score', 0)\n",
    "}\n",
    "\n",
    "report = report_generator.generate_report(report_data, format=\"markdown\")\n",
    "\n",
    "kg_visualizer = KGVisualizer()\n",
    "analytics_visualizer = AnalyticsVisualizer()\n",
    "temporal_visualizer = TemporalVisualizer()\n",
    "\n",
    "kg_viz = kg_visualizer.visualize_network(risk_kg, output=\"interactive\")\n",
    "analytics_viz = analytics_visualizer.visualize_analytics(risk_kg, output=\"interactive\")\n",
    "temporal_viz = temporal_visualizer.visualize_timeline(risk_kg, output=\"interactive\")\n",
    "\n",
    "print(f\"Total modules used: 20+\")\n",
    "print(f\"Pipeline complete: Risk Data → Parse → Extract → Build Risk KG → Analyze Relationships → Assess Portfolio Risk → Reports → Visualize\")\n"
   ]
  }
 ],
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