Research & Studies

# Ontology-driven rule engine framework class ExpertSystem: def __init__(self, ontology_path, rules_path): self.ontology = load_owl_ontology(ontology_path) self.rules = parse_rule_set(rules_path) self.inference_engine = build_inference_engine() def query(self, question, context): # Convert natural language to structured query structured_query = self.nl_to_logic(question, context) # Apply rules with full traceability result = self.inference_engine.solve(structured_query) # Return answer with explanation chain return { 'answer': result.conclusion, 'confidence': result.certainty, 'reasoning': result.trace_path, 'sources': result.applied_rules }

# Sovereign debt sustainability analysis def analyze_debt_sustainability(country_data): """ Multi-factor analysis of sovereign debt sustainability Based on IMF frameworks and historical crisis patterns """ metrics = { 'debt_to_gdp': calculate_debt_ratio(country_data), 'debt_service_ratio': calculate_service_burden(country_data), 'fiscal_space': assess_fiscal_capacity(country_data), 'external_vulnerability': analyze_external_debt(country_data) } # Apply stress testing scenarios stress_results = stress_test_scenarios(metrics, country_data) # Risk assessment using historical patterns risk_score = calculate_composite_risk(metrics, stress_results) return { 'sustainability_score': risk_score, 'key_vulnerabilities': identify_risks(metrics), 'policy_recommendations': generate_recommendations(risk_score), 'scenario_outcomes': stress_results }

# High-frequency market data analysis pipeline class MarketDataAnalyzer: def __init__(self, data_sources, timeframes): self.data_sources = data_sources # 12+ exchanges self.timeframes = timeframes # microsecond to daily self.pattern_detector = AdvancedPatternEngine() def analyze_market_structure(self, symbol, date_range): """ Comprehensive market microstructure analysis Processes tick-by-tick data for pattern recognition """ # Load and clean massive datasets raw_data = self.load_market_data(symbol, date_range) # Multi-timeframe analysis patterns = {} for timeframe in self.timeframes: aggregated_data = resample_data(raw_data, timeframe) patterns[timeframe] = self.pattern_detector.find_patterns( aggregated_data, min_confidence=0.85 ) # Cross-timeframe correlation analysis correlations = analyze_timeframe_correlations(patterns) # Statistical arbitrage opportunities opportunities = identify_arbitrage_signals( patterns, correlations, risk_threshold=0.02 ) return { 'patterns': patterns, 'correlations': correlations, 'opportunities': opportunities, 'confidence_metrics': calculate_confidence(patterns) }