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// BIBLIOGRAFÍA · EVIDENCIA DEL CURSO

Referencias · 421 fuentes verificadas

Toda la evidencia del curso: papers, documentación oficial, blogs de ingeniería y fuentes primarias, verificadas a julio de 2026.

Lectura ~66 minNivel intermedio-avanzadoEdición jul 2026Read in English

  1. J.P. Morgan Markets — e-Trading Survey Report 2026 (GenAI 43%, ML/NLP 18%, API/EMS 15%)[EB/OL]. Encuesta 12–27 ene 2026 (consultado 2026-07-29). https://markets.jpmorgan.com/discover-more/e-trading-survey-report
  2. oneZero — Liquidity and AI are top priorities in JPMorgan's annual e-Trading survey (serie 25%→53%→65%)[EB/OL]. 2024-03-05. https://www.onezero.com/in-the-news/liquidity-and-ai-are-top-priorities-in-jpmorgans-annual-e-trading-survey/
  3. Generative AI and Asset Management (Rutgers Business School / CoreData Research; 70% hedge funds; 21%→33%→63%)[EB/OL]. ago-2025. https://www.business.rutgers.edu/sites/default/files/documents/generative-ai-and-asset-management.pdf
  4. EY — GenAI in Wealth & Asset Management Survey 2025 (95% escalado, 78% agéntico, ~25% impacto sustancial)[EB/OL]. 2025-09-19. https://www.ey.com/en_us/insights/wealth-asset-management/gen-ai-in-wealth-asset-management-survey
  5. Kensho — How Kensho built a multi-agent framework with LangGraph to solve trusted financial data retrieval (Grounding; router + DRAs; eval exact-match)[EB/OL]. 2026-03-26. https://kensho.com/news/how-kensho-built-a-multi-agent-framework-with-langgraph-to-solve-trusted-financial-data-retrieval
  6. The TRADE — Bloomberg embeds agentic AI into the Terminal (ASKB, beta 23-feb-2026)[EB/OL]. 2026-02-23. https://www.thetradenews.com/bloomberg-embeds-agentic-ai-into-the-terminal/
  7. Finance Director Europe (citando FT) — JPMorgan rolls out AI-based chatbot LLM Suite (~50.000 empleados)[EB/OL]. 2024-07-29. https://www.financedirectoreurope.com/news/jpmorgan-rolls-out-ai-based-chatbot/
  8. Microsoft Cloud Blog — BlackRock Aladdin Copilot (sin consejo de inversión; filtros anti-alucinación)[EB/OL]. 2024-09-30. https://www.microsoft.com/en-us/microsoft-cloud/blog/financial-services/2024/09/30/elevating-investment-management-tech-ai-powered-leadership-from-blackrock-and-microsoft/
  9. Man Group — What AI Can (and Can't Yet) Do for Alpha (AlphaGPT, 3 roles, umbrales idénticos al research humano)[EB/OL]. 2025-11-13. https://www.man.com/insights/what-ai-can-do-for-alpha (copia archivada)
  10. Longterm Wiki — Bridgewater AIA Labs (fondo macro 2.000 M USD jul-2024; 11,9% 2025; guardarraíles 8%→1,6%; kill switch)[EB/OL]. compilación 2026-02-01. https://www.longtermwiki.com/wiki/bridgewater-aia-labs
  11. Bloomberg vía fa-mag — AQR bets on machine learning (ML ~1/5 de señales de Apex; Asness)[EB/OL]. 2025-04-23. https://www.fa-mag.com/news/aqr-bets-on-machine-learning-as-cliff-asness-becomes-ai-believer-82224.html
  12. OpenAI — Balyasny Asset Management (95% equipos de inversión; Central Bank Speech Analyst 2 días → 30 min)[EB/OL]. 2026-03-06. https://openai.com/index/balyasny-asset-management/
  13. HyperAI — Point72 Turion (estrategia IA de Eric Sanchez, supera al insignia en 2025)[EB/OL]. 2026-07-28. https://hyper.ai/en/stories/7986a83e5e06a56baada10b139e6ad6a
  14. S&P Global — press release: app para ChatGPT vía MCP connector de Kensho (Capital IQ, transcripts, sin entrenamiento con datos licenciados)[EB/OL]. 2026-02-09. https://www.spglobal.com/en/press/press-release/sp-global-delivers-trusted-financial-data-and-insights-to-customers-through-app-for-chatgpt
  15. Alpha FMC — AI transformation across middle and back office operations (91% usa/planea; 7% escalado; reconciliación medible)[EB/OL]. 2026-05-28. https://alphafmc.com/blog/2026/05/28/ai-transformation-across-middle-and-back-office-operations/
  16. WatersTechnology / MD Market Insights — reconciliación con IA >99% de matches, ~10 excepciones de alto riesgo[EB/OL]. 2025-02-26. https://www.waterstechnology.com/topics/artificial-intelligence
  17. Análisis de FinBen (beancount.io): forecasting ~0,54 accuracy (apenas sobre azar); FinQA EM 0,63[EB/OL]. 2026-04-15. https://beancount.io/bean-labs/research-logs/2026/04/15/finben-financial-llm-benchmark
  18. Advisor Perspectives / fa-mag — Asness (AQR): de escéptico ML (FT 2017) a admitir retraso de 1–2 años[EB/OL]. 2024-12-06. https://www.advisorperspectives.com/articles/2024/12/06/asness-ai-end-human-fund-managers
  19. Fortune — MIT report: 95 percent of generative AI pilots at companies failing (NANDA, The GenAI Divide)[EB/OL]. 2025-08-18. https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/
  20. Timspark — Why AI projects fail (S&P Global 42% iniciativas abandonadas en 2025 vs 17%; RAND 80%)[EB/OL]. 2025-09-25. https://timspark.com/pl/blog/why-ai-projects-fail-artificial-intelligence-failures/
  21. Resonanz Capital — How Hedge Funds Are Really Using Generative AI (pilotos abandonados anonimizados)[EB/OL]. 2025-04-20. https://resonanzcapital.com/insights/how-hedge-funds-are-really-using-generative-ai-and-why-it-matters-for-manager-selection
  22. SEC — Press Release 2024-36: AI washing, Delphia 225.000 USD y Global Predictions 175.000 USD[EB/OL]. 2024-03-18. https://www.sec.gov/news/press-release/2024-36
  23. Yu et al. — FinCon (arXiv:2407.06567; NeurIPS 2024): CR 113,84%, SR 3,269; gating CVaR; jerarquía manager-analyst[EB/OL]. 2024-07. https://arxiv.org/html/2407.06567v3
  24. ThesisAgent (GitHub fafawlf/thesis-agent): separación razonamiento LLM / decisión matemática determinista (Decision Hub)[EB/OL]. 2026-04-04. https://github.com/fafawlf/thesis-agent
  25. Islam et al. — FinanceBench (arXiv:2311.11944): GPT-4-Turbo+RAG falla/rechaza el 81%[EB/OL]. 2023-11-20. https://arxiv.org/abs/2311.11944
  26. Macfarlanes — ESMA supervisory briefing (26-feb-2026): IA en trading algorítmico integrada en autoevaluación RTS 6; responsabilidad no transferible[EB/OL]. feb-2026. https://www.macfarlanes.com/insights/102mpep/algorithmic-trading-and-artificial-intelligence-esma-supervisory-briefing
  27. Xiao et al. — TradingAgents (arXiv:2412.20138), Tabla 1: CR 26,62% / Sharpe 8,21 en AAPL[EB/OL]. 2024-12-28 (v1). https://arxiv.org/pdf/2412.20138
  28. TradingAgents, nota al pie 1: autocrítica del Sharpe 8,21 fuera de rango empírico (ventana 3 meses)[EB/OL]. 2025. https://arxiv.org/html/2412.20138v5
  29. Li et al. — HedgeAgents (arXiv:2502.13165; WWW 2025 Companion): 70% anualizado, 400% en 3 años[EB/OL]. 2025-02. https://arxiv.org/html/2502.13165
  30. Li et al. — Profit Mirage / FinLake-Bench (arXiv:2510.07920): los retornos se evaporan tras la ventana de conocimiento[EB/OL]. 2025-10-09. https://arxiv.org/abs/2510.07920
  31. Lopez-Lira & Tang — Can ChatGPT Forecast Stock Price Movements? (arXiv:2304.07619): Sharpe 3,8 (v4) → 3,28 (v5); >500% sin costes[EB/OL]. 2023-04-15 (v1); v5 2024-09-11. https://arxiv.org/pdf/2304.07619
  32. Lopez-Lira, Tang & Zhu — The Memorization Problem (arXiv:2504.14765)[EB/OL]. 2025-04. https://arxiv.org/abs/2504.14765
  33. Li, Kim, Cucuringu & Ma — FINSABER (arXiv:2505.07078): 20 años × 100+ símbolos, deterioro de ventajas LLM[EB/OL]. 2025-05-11 (v1). https://arxiv.org/abs/2505.07078
  34. Li et al. — DeepFund, Time Travel is Cheating (arXiv:2505.11065; NeurIPS 2025 D&B): pérdidas netas en vivo post-cutoff[EB/OL]. 2025-05-16 (v1). https://arxiv.org/abs/2505.11065
  35. A Verifiable Correctness Property for Backtesting and Agentic Trading Pipelines (arXiv:2607.04958): look-ahead paramétrico indetectable por inspección de código[EB/OL]. 2026-07-06. https://arxiv.org/html/2607.04958
  36. McLean & Pontiff (JoF 2016) y Bailey & López de Prado (DSR 2014), síntesis: −26% OOS, −58% post-publicación, ~3 trials[EB/OL]. primarias 2014–2016; síntesis 2026. https://www.turbinefi.com/blog/why-backtests-lie-prediction-market-overfitting-2026
  37. The Alpha Illusion (arXiv:2605.16895): backtest corto positivo ≠ alfa desplegable[EB/OL]. 2026-05-16. https://arxiv.org/html/2605.16895v1
  38. EY-Parthenon — Generative AI in wealth and asset management, survey highlights (52% hedge funds desplegada)[EB/OL]. mar-2024. https://www.ey.com/content/dam/ey-unified-site/ey-com/en-gl/industries/wealth-asset-management/documents/ey-gl-genai-wam-survey-highlights-03-2024.pdf
  39. LangChain Blog — "LangChain and LangGraph Agent Frameworks Reach v1.0 Milestones"[EB/OL]. 2025-10-22. https://www.langchain.com/blog/langchain-langgraph-1dot0
  40. PyPI JSON API — langchain (timestamps de release 1.0.0)[EB/OL]. consulta 2026-07-29. https://pypi.org/pypi/langchain/json
  41. LangChain Docs — Release policy (LTS, cadencia minors/patches)[EB/OL]. 2026-07-28. https://docs.langchain.com/oss/python/release-policy
  42. LangChain Docs — LangChain v1 migration guide (breaking changes, imports espejo)[EB/OL]. 2026-07-28. https://docs.langchain.com/oss/python/migrate/langchain-v1
  43. LangChain Docs — "What's new in LangChain v1" (namespace reducido, contenido de langchain-classic)[EB/OL]. 2026-07-28. https://docs.langchain.com/oss/python/releases/langchain-v1
  44. LangChain Docs — Models (init_chat_model, reintentos 6x backoff, InMemoryRateLimiter, configurable_fields, bind_tools sobre configurable)[EB/OL]. 2026-07-28. https://docs.langchain.com/oss/python/langchain/models
  45. LangChain API Reference — init_chat_model (firma, configurable_fields, config_prefix)[EB/OL]. 2026-07-14. https://reference.langchain.com/python/langchain-classic/chat_models/base/init_chat_model
  46. LangChain Blog — "Standard message content" (content blocks estándar)[EB/OL]. 2025-09-03. https://www.langchain.com/blog/standard-message-content
  47. LangChain Docs — Messages (content block reference, atributos AIMessage)[EB/OL]. 2026-07-27. https://docs.langchain.com/oss/python/langchain/messages
  48. LangChain API Reference — Runnable (interfaz, métodos, composición, with_retry, with_fallbacks, config, callbacks)[EB/OL]. consulta 2026-07-29. https://reference.langchain.com/python/langchain_core/runnables/base/Runnable/
  49. LangChain API Reference — RunnablePassthrough (assign)[EB/OL]. consulta 2026-07-29. https://reference.langchain.com/python/langchain_core/runnables/passthrough/RunnablePassthrough/
  50. GitHub — langchain-playground Runnables.md (RunnableLambda como wrapper de callables)[EB/OL]. 2026-01-31. https://github.com/himanshu231204/langchain-playground--for-llms-/blob/main/Runnables/Runnables.md
  51. qiankunli.github.io — LangGraph (RunnableParallel 3 formas equivalentes, RunnableBranch routing)[EB/OL]. 2026-03-05. http://qiankunli.github.io/2024/05/16/langchain_graph.html
  52. LangChain Docs — Streaming (stream modes, event streaming v1.3, tool_call_chunk, tool_choice="any")[EB/OL]. consulta 2026-07-29. https://docs.langchain.com/oss/python/langchain/streaming
  53. 51CTO blog — ejemplo de config con tags/metadata (trazabilidad)[EB/OL]. 2026-07-17. https://blog.51cto.com/u_16213580/14771671
  54. LangChain API Reference — DynamicRunnable (métodos heredados: bind, pick, assign, as_tool)[EB/OL]. 2026-06-20. https://reference.langchain.com/python/langchain-core/runnables/configurable/DynamicRunnable
  55. LangChain Docs — LangSmith Model fallbacks (LLM Gateway, triggers HTTP 429/5xx)[EB/OL]. 2026-07-24. https://docs.langchain.com/langsmith/llm-gateway-fallbacks
  56. LangChain API Reference — with_structured_output (schema Pydantic/TypedDict/dict, include_raw raw/parsed/parsing_error)[EB/OL]. 2026-07-24. https://reference.langchain.com/python/langchain-core/language_models/chat_models/BaseChatModel/with_structured_output
  57. LangChain Forum — cómo se usan las descripciones Pydantic en with_structured_output (schema como prompt; estrategia tool vs prompt+parser)[EB/OL]. 2025-09-23. https://forum.langchain.com/t/clarification-on-how-pydantic-schema-descriptions-are-used-in-with-structured-output/1612
  58. LangChain Forum — with_structured_output con method="json_schema", strict=True[EB/OL]. 2026-02-22. https://forum.langchain.com/t/parsing-error-with-structured-output-with-model-output/2985
  59. GitHub — langchain issue #38223 (Responses API + streaming + Pydantic anidado)[EB/OL]. 2026-06-17. https://github.com/langchain-ai/langchain/issues/38223
  60. OpenAI structured outputs JSON schema: a practical guide (CodeWords) — 100% vs ~86% schema compliance[EB/OL]. 2026-06-09. https://www.codewords.ai/blog/openai-structured-outputs-json-schema
  61. Structured outputs engineering (datarekha) — fallo "confidently wrong", null como parte del schema[EB/OL]. 2026-04-12. https://datarekha.com/blog/structured-outputs-engineering/
  62. Mind Your Step (by Step): CoT can Reduce Performance (arXiv:2410.21333) — +331% iteraciones en clasificación con excepciones[EB/OL]. 2025-06-13. https://arxiv.org/html/2410.21333v4
  63. Gao et al., PAL: Program-aided Language Models (ICML 2023) — GSM-Hard 61.2% vs ~20% CoT; fallo dominante = aritmética[EB/OL]. 2023. https://www.cs.cmu.edu/~callan/Papers/icml23-Luyu-Gao.pdf
  64. LangChain API Reference — bind_tools (firma, tool_choice)[EB/OL]. consulta 2026-07-29. https://reference.langchain.com/python/langchain-core/language_models/chat_models/BaseChatModel/bind_tools/
  65. zenvanriel — "Why Senior Engineers Are Ditching LangChain for Plain Python" (el LLM propone, el código ejecuta)[EB/OL]. 2026-07-15. https://zenvanriel.com/ai-engineer-blog/ditching-langchain-for-plain-python/
  66. Ry Walker Research — Octomind (post de jun-2024, 480 puntos HN, respuesta del CEO)[EB/OL]. 2026-06-11. https://rywalker.com/research/octomind
  67. Octomind Blog — "Why we no longer use LangChain for building our AI agents"[EB/OL]. 2024-06. https://octomind.dev/blog/why-we-no-longer-use-langchain-for-building-our-ai-agents (copia archivada)
  68. Skywork — "Octomind and the Great Migration" (comparativa 1 vs 4 abstracciones)[EB/OL]. 2025-10-14. https://skywork.ai/skypage/en/octomind-great-migration-teams-langchain/1976832104900653056
  69. Anthropic — "Building effective agents" (simple, composable patterns; reducir capas en producción)[EB/OL]. 2024-12-19. https://www.anthropic.com/engineering/building-effective-agents
  70. Theodo — "Don't use langchain anymore" (costes ocultos, restricción como framework)[EB/OL]. 2025-01-31. https://www.theodo.com/blog/dont-use-langchain-anymore-atomic-agents-is-the-new-paradigm
  71. GitHub community Discussion #182015 (RAG simple → vanilla Python + Pydantic; stack traces 5+ capas)[EB/OL]. 2025-12-16. https://github.com/orgs/community/discussions/182015
  72. GroovyWeb — "LangChain vs LlamaIndex in 2026" (patrón híbrido retrieval + orquestación)[EB/OL]. 2026-06-23. https://www.groovyweb.co/blog/langchain-vs-llamaindex-comparison
  73. Priorise — "RAG Framework Comparison 2026" (índices LlamaIndex como tools de LangChain)[EB/OL]. 2026-07-15. https://priorise.co/blog/rag-framework-comparison-langchain-vs-llamaindex-in-2026/
  74. arXiv — "ADK Arena: Evaluating Agent Development Kits" (top-5 >93% descargas; LangChain ~233M/mes)[EB/OL]. 2026-06-04. https://arxiv.org/html/2606.05548v1
  75. the-agent-report — "State of Agent Engineering 2026" (57.3% agentes en producción; 67% grandes empresas; telemetría Datadog 9%→18%)[EB/OL]. 2026-05-23. https://the-agent-report.com/2026/05/state-of-agent-engineering-2026-langchain-datadog/
  76. alatirok — "State of AI Agent Adoption 2026" (1,340 encuestados, campo 18-nov a 2-dic-2025; 57.3%)[EB/OL]. 2026-05-31. https://alatirok.com/ai-agent-adoption-2026/
  77. Stackademic — LangChain in Chains #54: Create Agents (firma create_agent)[EB/OL]. 2026-03-02. https://blog.stackademic.com/langchain-in-chains-54-create-agents-32362eaca10f
  78. Docs LangChain — Middleware overview[EB/OL]. 2026-06-13. https://docs.langchain.com/oss/python/langchain/middleware/overview
  79. Docs LangChain — Context engineering in agents[EB/OL]. 2026-07-28. https://docs.langchain.com/oss/python/langchain/context-engineering
  80. Docs LangChain — Human-in-the-loop[EB/OL]. 2026-07-28. https://docs.langchain.com/oss/python/langchain/human-in-the-loop
  81. Docs LangChain — Use the graph API (reducers, add_messages, Overwrite)[EB/OL]. 2026-07-27. https://docs.langchain.com/oss/python/langgraph/use-graph-api
  82. Docs LangChain — Graph API overview (Command)[EB/OL]. 2026-07-28. https://docs.langchain.com/oss/python/langgraph/graph-api
  83. Docs LangChain — Use time-travel (interrupt, replay, fork)[EB/OL]. 2026-06-11. https://docs.langchain.com/oss/python/langgraph/use-time-travel
  84. Docs LangChain — Multi-agent (subagents/handoffs/skills/router; ahorro 40-50%)[EB/OL]. 2026-07-28. https://docs.langchain.com/oss/python/langchain/multi-agent
  85. Reference — langgraph-supervisor create_supervisor[EB/OL]. 2026-07-27. https://reference.langchain.com/python/langgraph-supervisor/supervisor/create_supervisor
  86. Reference — langgraph-swarm create_handoff_tool (+ internals active_agent, CSDN)[EB/OL]. 2026-07-12. https://reference.langchain.com/python/langgraph-swarm/handoff/create_handoff_tool
  87. Anthropic — Building effective agents (workflows vs agents; simplest solution)[EB/OL]. 2024-12-19 (archivado 2026-06-13). https://www.anthropic.com/research/building-effective-agents
  88. zylos.ai — Finite State Machines and Statecharts for AI Agent Orchestration[EB/OL]. 2026-04-02. https://zylos.ai/research/2026-04-02-finite-state-machines-statecharts-ai-agent-orchestration/
  89. Docs LangChain — What's new in LangGraph v1 ("create_agent runs on LangGraph")[EB/OL]. 2026-07-26. https://docs.langchain.com/oss/python/releases/langgraph-v1
  90. Docs LangChain — Streaming (v2, subgraphs=True)[EB/OL]. 2026-07-28. https://docs.langchain.com/oss/python/langgraph/streaming
  91. Docs LangChain — LangGraph v1 migration guide[EB/OL]. 2026-07-27. https://docs.langchain.com/oss/python/migrate/langgraph-v1
  92. Docs LangChain — Changelog (langchain v1.1.0: model retry / content moderation middleware)[EB/OL]. 2026-07-24. https://docs.langchain.com/oss/python/releases/changelog
  93. arXiv 2603.27299 — Apéndice B: LangGraph Implementation Notes (StateGraph, canales, Pregel, v1.1.3)[EB/OL]. 2026-03-28. https://arxiv.org/html/2603.27299v1
  94. ActiveWizards — LangGraph State Management: Checkpointing & Recovery (tabla de decisión de persistencia)[EB/OL]. 2026-05-06. https://activewizards.com/blog/langgraph-state-management-checkpointing-recovery-and-the-persistence-layer-decision/
  95. GitHub dbrowneup/Linus repo-notes/TradingAgents.md + juejin (StateGraph, InvestDebateState, SQLite checkpoints, deep/quick tiers; 30-50 llamadas vía fork A-share)[EB/OL]. 2026-04-22 / 2026-06-30. https://github.com/dbrowneup/Linus/blob/main/docs/repo-notes/TradingAgents.md; https://juejin.cn/post/7656801430955704358
  96. Vectorize/Hindsight — LangGraph Short-Term State vs Long-Term Memory (checkpointer vs Store)[EB/OL]. 2026-07-17. https://hindsight.vectorize.io/guides/2026/07/17/guide-langgraph-state-vs-long-term-memory
  97. Towards AI — LangGraph HITL: Pausing, Reviewing, Rewinding (time travel; "checkpoint entre decisión y consecuencia")[EB/OL]. 2026-06-15. https://pub.towardsai.net/langgraph-human-in-the-loop-pausing-reviewing-and-rewinding-your-agent-4028bd05b049
  98. ctaio.dev — Monolith, Handoff, or Swarm? (swarm 5 agentes ≈3× tokens) + skywork.ai (fiabilidad 90%^10 = 34,9%)[EB/OL]. 2026-04-23 / 2026-04-15. https://ctaio.dev/en/labs/agentic-orchestration/topology-patterns/; https://skywork.ai/skypage/en/ai-agent-skills-2025-2026/2064636941351976960
  99. mer.vin — When Not to Build AI Agents (playbook workflow-vs-agent)[EB/OL]. 2026-05-22. https://mer.vin/2026/05/when-not-to-build-ai-agents-anthropics-workflow-vs-agent-playbook/
  100. arXiv 2505.00753v5 — LLM-Based Human-Agent Collaboration Survey (accountability gap en finanzas)[EB/OL]. 2026-05-06. https://arxiv.org/html/2505.00753v5
  101. Docs LangChain — Deep Agents overview (FilesystemMiddleware allowlist)[EB/OL]. 2026-07-28. https://docs.langchain.com/oss/python/deepagents/overview
  102. Docs LangChain — Deep Agents human-in-the-loop (interrupt_on por subagente)[EB/OL]. 2026-07-03. https://docs.langchain.com/oss/python/deepagents/human-in-the-loop
  103. Docs LangChain — Model Context Protocol (MCP)[EB/OL]. 2026-07-28. https://docs.langchain.com/oss/python/langchain/mcp
  104. Reference — MultiServerMCPClient (stateless, handle_tool_errors)[EB/OL]. 2026-07-23. https://reference.langchain.com/python/langchain-mcp-adapters/client/MultiServerMCPClient
  105. GitHub TauricResearch/TradingAgents (fork operativo: watchlist 65 tickers, dashboard coste LLM vs P/L, MCP FSI)[EB/OL]. 2026-05-07. https://github.com/TauricResearch/TradingAgents
  106. arXiv — TradingAgents: Multi-Agents LLM Financial Trading Framework[EB/OL]. v1 2024-12-28; v7 2025-06-03. https://arxiv.org/abs/2412.20138
  107. arXiv 2606.08283 — crítica a tests controlados de razonamiento multi-agente financiero[EB/OL]. 2026-06-06. https://arxiv.org/html/2606.08283v1
  108. Investopedia — Bloomberg Terminal; GodelDiscount — Bloomberg Terminal Cost 2026[EB/OL]. 2025 / 2026-03-05. https://www.investopedia.com/terms/b/bloomberg_terminal.asp; https://godeldiscount.com/blog/bloomberg-terminal-cost-2026
  109. GlobalDatabase — Bloomberg vs Refinitiv vs S&P; UF Business Library FAQ[EB/OL]. 2026-02-16 / 2025-08-02. https://www.globaldatabase.com/bloomberg-vs-refinitiv-vs-sp-capital-iq-which-financial-terminal-is-worth-it; https://answers.businesslibrary.uflib.ufl.edu/genai/faq/401428
  110. WallStreetPrep — comparativa terminales; Vendr — FactSet (contratos reales ~$45K)[EB/OL]. 2025-04-07 / 2026-05-20. https://www.wallstreetprep.com/knowledge/bloomberg-vs-capital-iq-vs-factset-vs-thomson-reuters-eikon/; https://www.vendr.com/marketplace/factset
  111. GodelDiscount — Bloomberg Terminal Cost 2026 (B-PIPE, Data License, feeds enterprise)[EB/OL]. 2026-03-05. https://godeldiscount.com/blog/bloomberg-terminal-cost-2026
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