egutierrez
63a9cb5273
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>
1.2 KiB
1.2 KiB
name, kind, lang, domain, version, purity, signature, description, tags, uses_functions, uses_types, returns, returns_optional, error_type, imports, tested, tests, test_file_path, file_path
| name | kind | lang | domain | version | purity | signature | description | tags | uses_functions | uses_types | returns | returns_optional | error_type | imports | tested | tests | test_file_path | file_path | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| generate_gbm_prices | function | py | finance | 1.0.0 | pure | generate_gbm_prices(initial_price: float, n_ticks: int, sigma: float, mu: float, jump_intensity: float, jump_size_std: float, seed: int) -> list[float] | Genera serie de precios fundamentales con Geometric Brownian Motion + jump-diffusion. S(t+1) = S(t) * exp((mu - sigma^2/2)*dt + sigma*sqrt(dt)*Z + J*N). |
|
false |
|
false | python/functions/finance/finance.py |
Ejemplo
prices = generate_gbm_prices(
initial_price=100.0,
n_ticks=1000,
sigma=0.02,
mu=0.0,
jump_intensity=0.01,
jump_size_std=0.05,
seed=42,
)
# prices[0] == 100.0
# len(prices) == 1000
Notas
Funcion pura — el seed fija el resultado deterministicamente.
jump_intensity=0.0 desactiva los saltos (GBM puro).
dt=1.0 por tick (tiempo discreto). Para tiempo continuo, ajustar sigma y mu en consecuencia.
Requiere numpy para la generacion de numeros aleatorios y el calculo de exp.