NinethirtyAI streamlines US stock analysis with a single chat interface
Switching between screens for technicals, fundamentals, and news data—then copying values into an LLM—adds unnecessary friction. NinethirtyAI eliminates this by overlaying a chat interface on top of a specialized data pipeline, consolidating the entire workflow into one tool.
The core innovation lies in its unified screener. While other platforms split criteria into separate modules for fundamentals, technical analysis, and news filters, NinethirtyAI combines them into a single query. Behind the scenes, it includes historical backtesting to show which stocks would have met the screen’s conditions and their subsequent performance.
Beyond basic filters, the platform offers heatmaps, sector trends, and seasonal patterns. But its AI assistant, “Dr. Market,” stands apart. Unlike generic LLMs that may misstate a P/E ratio or invent a price target, Dr. Market operates on verified data feeds. When asked to validate a technical setup, it pulls live indicators rather than generating hypothetical figures. Errors still occur—but they stem from interpreting real data incorrectly, not fabricating numbers out of thin air.
The system’s architecture integrates three key layers: the Unified Screener (merging technical, fundamental, and news filters), Instant Backtesting (validating screen performance against past records), and Data-Driven AI (grounding responses in real-time equity data to reduce hallucinations). Additional features include Market Intelligence, which embeds sector-wide insights and event tracking directly in the dashboard.
Current limitations include US equities coverage and a focus on analysis rather than actionable advice. Users won’t find automated “Buy this stock” prompts, but the platform explains why a stock aligns with their search criteria.
For practitioners testing prompt-engineering in finance, NinethirtyAI demonstrates how a RAG (Retrieval-Augmented Generation) framework can turn an LLM into a practical tool for equity research. Instead of relying on the model’s internal parameters for financial metrics, it fetches and presents the actual figures first.
If manual stock vetting across multiple platforms feels tedious—whether for quantitative research or casual due diligence—it may be worth testing. The remaining question is whether the screener can sustain complex, multi-variable queries without performance degradation.
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I'd love to see it pull from actual 10-K filings instead of just summarized API metrics. Juggling a dozen browser tabs for charts, financial statements, and screeners, then pasting figures into an LLM for analysis, destroys productivity. I've been relying on NinethirtyAI to unify this entire workflow, and it genuinely resolves the fragmentation by placing a chat layer atop a curated data engine. The screener is the core strength. Most platforms require separate modules for fundamental, technical, and news-driven criteria. NinethirtyAI merges them into one query. Even more valuable is the integrated backtesting, which reveals which stocks would have satisfied that screen historically and how they subsequently performed. A hunch becomes a data-backed strategy in short order. The platform supplies the expected toolkit—heatmaps, sector overviews, and seasonality—yet the standout is "Dr. Market," the AI assistant. A generic LLM might hallucinate a P/E ratio or fabricate a price target, but Dr. Market runs on a processed data layer. Ask it to confirm a technical setup and it reads live indicators. Hallucinations aren't eliminated—no LLM is flawless—but mistakes stem from misreading actual data rather than inventing figures from nothing. Practical feature breakdown Unified Screener: merges technicals, fundamentals, and news events in a single pass Instant Backtesting: checks screen performance against historical records Data-Driven AI: an LLM agent that taps real-time equity data to curb plausible-sounding fabrications Market Intelligence: sector-wide perspectives and event tracking built into the dashboard.
Huge relief to ditch those 5 tabs. Is the P/E ratio comparison instant or laggy? Juggling a dozen browser tabs for charts, financial statements, and screeners, then pasting figures into an LLM for analysis, destroys productivity. I've been relying on NinethirtyAI to unify this entire workflow, and it genuinely resolves the fragmentation by placing a chat layer atop a curated data engine. ## What makes a good screener? The screener is the core strength. Most platforms require separate modules for fundamental, technical, and news-driven criteria. NinethirtyAI merges them into one query. Even more valuable is the integrated backtesting, which reveals which stocks would have satisfied that screen historically and how they subsequently performed. A hunch becomes a data-backed strategy in short order. ## How does Dr. Market stand out? The platform supplies the expected toolkit—heatmaps, sector overviews, and seasonality—yet the standout is "Dr. Market," the AI assistant. A generic LLM might hallucinate a P/E ratio or fabricate a price target, but Dr. Market runs on a processed data layer. Ask it to confirm a technical setup and it reads live indicators. Hallucinations aren't eliminated—no LLM is flawless—but mistakes stem from misreading actual data rather than inventing figures from nothing. Practical feature breakdown Unified Screener: merges technicals, fundamentals, and news events in a single pass Instant Backtesting: checks screen performance against historical records Data-Driven AI: an LLM agent that taps real-time equity data to curb plausible-sounding fabrications Market Intelligence: sector-wide perspectives and event tracking built into the dashboard.
Does this track real-time dividends, or mainly price data? A concrete step is asking Dr. Market to confirm a technical setup, since it reads live indicators.
I'm skeptical about the dividend tracking. Does it actually pull real-time data or just estimates? Juggling a dozen browser tabs for charts, financial statements, and screeners, then pasting figures into an LLM for analysis, destroys productivity. I've been relying on NinethirtyAI to unify this entire workflow, and it genuinely resolves the fragmentation by placing a chat layer atop a curated data engine. The screener is the core strength. Most platforms require separate modules for fundamental, technical, and news-driven criteria. NinethirtyAI merges them into one query. Even more valuable is the integrated backtesting, which reveals which stocks would have satisfied that screen historically and how they subsequently performed. A hunch becomes a data-backed strategy in short order. The platform supplies the expected toolkit—heatmaps, sector overviews, and seasonality—yet the standout is "Dr. Market," the AI assistant. A generic LLM might hallucinate a P/E ratio or fabricate a price target, but Dr. Market runs on a processed data layer. Ask it to confirm a technical setup and it reads live indicators. Hallucinations aren't eliminated—no LLM is flawless—but mistakes stem from misreading actual data rather than inventing figures from nothing. Practical feature breakdown Unified Screener: merges technicals, fundamentals, and news events in a single pass Instant Backtesting: checks screen performance against historical records Data-Driven AI: an LLM agent that taps real-time equity data to curb plausible-sounding fabrications Market Intelligence: sector-wide perspectives and event tracking built into the dashboard.