egutierrez
837563c3ba
Datascience: aggregate_by_group, deduplicate_entities/relations, detect_drift, diff_entities/relations, extract_entities/relations_llm, hotness_score, melt, merge_graphs, pivot, build_entity/relation_schema_prompt. Finance: avellaneda_stoikov_quotes, generate_gbm_prices, generate_taker_order, hawkes_intensity + módulo finance.py. Cybersecurity: envelope_encrypt/decrypt + módulo cybersecurity.py. Pipelines: extraction_pipeline, monte_carlo_market, run_market_sim. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
44 lines
1.3 KiB
Markdown
44 lines
1.3 KiB
Markdown
---
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name: hawkes_intensity
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kind: function
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lang: py
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domain: finance
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version: "1.0.0"
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purity: pure
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signature: "hawkes_intensity(base_rate: float, hawkes_alpha: float, hawkes_beta: float, event_times: list[float], current_time: float) -> float"
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description: "Calcula la intensidad lambda(t) de un proceso de Hawkes en el tiempo actual. Modela la autocorrelacion temporal de eventos de mercado (rafagas de ordenes)."
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tags: [simulation, hawkes, stochastic-process, montecarlo, finance, point-process]
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uses_functions: []
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uses_types: []
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returns: []
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returns_optional: false
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error_type: ""
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imports: [numpy]
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tested: false
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tests: []
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test_file_path: ""
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file_path: "python/functions/finance/finance.py"
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---
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## Ejemplo
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```python
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intensity = hawkes_intensity(
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base_rate=1.0,
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hawkes_alpha=0.8,
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hawkes_beta=2.0,
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event_times=[0.5, 1.2, 1.8],
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current_time=2.5,
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)
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# Intensidad > base_rate por excitacion de eventos pasados
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```
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## Notas
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Funcion pura — determinista dado el mismo historial de eventos.
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`hawkes_alpha` controla la magnitud del salto de intensidad por evento.
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`hawkes_beta` controla la velocidad de decaimiento (mayor beta = decaimiento mas rapido).
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La condicion de estabilidad del proceso es hawkes_alpha < hawkes_beta.
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Eventos con ti >= current_time se ignoran automaticamente.
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Retorna max(0.0, ...) para garantizar intensidad no negativa.
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