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E-Mini NASDAQ 100 Futures Market Report(2025-08-22)

August 22, 2025 Prices for 2025-08-22 On August 22, 2025, the Mini NASDAQ 100 futures (NQU25) experienced a notable upward trend. Detailed Price Metrics Metric Price High 23,650.00 Low 23,076.75 Open 23,225.25 Close 23,569.75 Explanation of Price Movements Mini NASDAQ 100 futures saw a significant rally on August 22, 2025, with the Nasdaq 100 E-mini […]

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Mini NASDAQ 100 Futures Market Analysis Report

Report Date: 2025-08-19 Estimated Price Ranges for 2025-08-19 Metric Price Open 23,797.75 High 23,942.00 Low 23,403.00 Close 23,376.25 Explanation of Price Movements The Mini NASDAQ 100 futures experienced a notable decline on August 19, 2025, with contracts slipping 0.2% after a 1.4% drop, marking the second-worst decline since April’s tariff shock. This downturn was primarily

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Mini NASDAQ 100 Futures Market Analysis Report(2025-08-15)

Report Date: 2025-08-15 Key Price Metrics (2025-08-15) Open Price Close Price Highest Price Lowest Price 23930.50 23796.75 23950.00 23700.00 Explanation of Price Movements On August 15, 2025, Mini NASDAQ 100 futures experienced downward pressure. This decline, evidenced by the E-mini Nasdaq-100 (NQU5) being down 133.75 points (-0.56%) to 23796.75, was primarily driven by several economic

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Mini NASDAQ 100 Futures Market Daily Report(Aug. 1, 2025)

August 1, 2025 Estimated Mini NASDAQ 100 Futures Prices Estimated High: 23,347.50 Estimated Low: 22,788.25 Estimated Open: 23,021.66 (based on pre-market indicator) Estimated Close: 22,883.75 Explanation of Potential Price Movements Mini NASDAQ 100 futures experienced significant downward pressure on August 1, 2025, driven by a confluence of negative economic news and market sentiment. The primary factors contributing to this decline

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경제성장이론의 재평가 : AI 시대(새로운 성장 엔진)

AI 시대 경제 이론 재평가 브리핑 문서 1. 서론: AI의 변혁적 본질과 경제 이론에 대한 도전 인공지능(AI)은 단순한 기술적 진보를 넘어, 지난 반세기 동안 경제 정책과 이해를 이끌어온 기존 경제 성장 모델의 근본 원칙에 도전하고 재구성하는 “분수령적 순간”을 의미합니다. 이 보고서는 AI가 어떻게 전통적인 경제 성장 이론, 특히 솔로우-스완 신고전학파 모델과 내생적 성장 이론의 핵심

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Retrieval-Augmented Generation (RAG) with Python Code

The Imperative for Knowledge Grounding in Large Language Models The advent of Large Language Models (LLMs) has marked a revolutionary milestone in artificial intelligence, demonstrating remarkable capabilities in natural language understanding and generation.1 However, the very architecture that grants them their fluency also imposes fundamental limitations. These inherent constraints, namely the static nature of their

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