준 퀀트급 투자판단프로그램 개발

The description states that a solo developer built an AI-based investment decision support system for stock trading in South Korea. The system analyzes market data, financials, supply/demand, news, technical indicators, and risk factors through modular layers. It returns buy/hold/sell signals or "additional verification" when data is insufficient or conflicting. The author reports using various Python libraries and APIs including KIS OpenAPI, yfinance, pykrx, and LLMs for analysis but emphasizes that real trading orders are blocked in a paper-trading environment.

Key commercial due-diligence question: Is there any evidence of actual revenue generation, customer adoption or performance metrics beyond the author's self-reported claims?

This is a solo project with no external validation, funding, or traction data. The system appears to be conceptual and experimental, not yet deployed in production for real users or clients.

The description states that the product is an AI-based investment decision support system designed to assist stock trading decisions by analyzing multiple types of data including market trends, sector performance, individual stocks, supply/demand dynamics, financial statements, valuation metrics, news events, chart patterns, and risk management parameters.

It operates through a modular structure where each analytical function (market, sector, stock, supply/demand, fundamentals, valuation, news, charts, risk) is handled by separate layers. These layers produce standardized scores and justifications which are then synthesized by a final decision layer.

The system is described as not making forced decisions when data is lacking or conflicting, instead returning "HOLD" or "additional verification" states.

It uses multiple data sources including KIS OpenAPI, OpenDART, DartLab, pykrx, yfinance and integrates LLMs for certain tasks like news interpretation and red team validation.

The description states the product is positioned as an AI-based investment decision system that supports buy/hold/sell judgments by combining various data types including market data, financial information, supply/demand, news, technical indicators, and risk factors.

The author claims this is more than a simple buy/sell signal generator, but rather a multi-layered analysis structure that mimics how human investors review stocks. The system is described as being designed to avoid overconfidence in uncertain situations.

The claim evolution shows a progression from initial inspiration ("I thought AI could help with investing") to a detailed technical architecture and then to a focus on safety mechanisms (paper trading, no real orders, hold states).

The description does not state specific target customers or ideal customer profiles. The author mentions using the system for personal investment decisions but does not identify any defined market segment or user persona.

No evidence of identified customer types, use cases, or target industries is provided in the self-reported description.

The description states that the system is built as a personal investment tool and has no explicit business model or pricing structure described. The author mentions using paper trading and blocking real orders, suggesting it's not yet monetized.

There is no evidence of any revenue streams, pricing tiers, subscription models, or commercial partnerships in the self-reported account.

The description states that the system was built with a modular architecture where each analytical function is handled by separate layers. It uses multiple data sources including KIS OpenAPI, OpenDART, pykrx, yfinance and integrates LLMs for specific tasks like red team validation.

Key technical elements mentioned include:

• Modular layer structure (market, sector, stock, supply/demand, fundamentals, valuation, news, charts, risk)
• Standardized scoring and justification outputs from each layer
• Data source flexibility with fallback paths for missing data
• Internal testing mechanisms including unit tests, schema validation, logic consistency checks, red team validation, golden evaluation, paper trading verification
• Safety features like blocking real orders, hold states for uncertain situations

The system is described as having been tested through multiple versions and regression testing.

The description states that the system has been tested in a paper-trading environment using KIS OpenAPI but does not provide evidence of actual performance metrics or results from these tests. The author mentions conducting backtests and forward tests but no specific outcomes are shared.

There is no evidence of revenue, customers, user adoption, or measurable performance improvements beyond the author's own claims.

The description does not mention any competitors or competitive landscape. No information about existing solutions in the Korean investment decision support space is provided.

• Solo development with no external validation or team
• No evidence of revenue, customers, or performance metrics beyond self-reporting
• The system is described as being in an experimental phase with no production deployment
• Heavy reliance on LLMs for analysis without clear evidence of their effectiveness in investment decisions
• No mention of regulatory compliance or risk management frameworks
• The author states the system is not yet ready for real trading, suggesting it's not yet mature for commercial use

1. What specific performance metrics have been achieved in paper trading tests?
2. How does the system handle conflicting signals from different analytical modules?
3. What are the actual data quality issues encountered with the various APIs and data sources?
4. Has there been any external validation or peer review of the investment logic?
5. What is the timeline for moving from paper trading to live trading?
6. How will the system be updated or improved over time?
7. What specific risks have been identified in the current implementation?