InFinder structures market intelligence into an interconnected graph of content signals, investment theses, and ticker positions — with typed relationships, cascade detection, and full audit trails.
Investment theses moved in the last weeks by breaking or high-impact market signals — and the impulses that excited them.
View by impulse → tickerSouth Korea's KOSPI plunges 7.2% to 5,923.37 — its lowest since July — as the semiconductor sector bears the brunt of Chinese …
Middle East risks keep traders on edge, supporting higher Brent and WTI prices. This systemic oil price uplift directly benefits NOG as …
High financing costs and stretched valuations fuel investor doubts over AI infrastructure spending returns after the S&P 500's 120% rally, with Broadcom …
Technology stocks across Asia-Pacific fall sharply as concerns over Chinese semiconductor competition and AI investment returns trigger a broad selloff in chipmakers …
Samsung and SK Hynix face intensifying competition from Chinese rival ChangXin Memory Technologies (CXMT), which achieved breakthrough on 4F² DRAM technology and …
Foreign investors sold $1.6 billion of Korean shares in a single session, with the KOSPI plunging nearly 7%, reflecting broad risk-off sentiment …
CXMT's 5th-gen DRAM platform achieving mass production at ~12nm with 50% more dies per wafer positions it as a credible cost-competitive threat …
Higher energy-driven inflation could force policymakers to raise rates, worsening debt burdens for nations with high sovereign debt and short-term refinancing needs, …
If AI infrastructure spending slows as hyperscalers fail to see ROI, semiconductor and cloud infrastructure stocks will selloff together, dragging down index-heavy …
Market rotation away from leading-edge AI enablers toward legacy and commodity semiconductor supply chain segments following rapid agentic AI adoption, creating near-term …
QCOM's HBC architecture enables hyperscalers to bypass HBM, reducing dependence on memory suppliers and pressuring HBM pricing power and demand growth that …
Global tech stocks fall as investors question AI investment returns, with Broadcom CEO likening AI buying frenzy to gambling and warning the …
Asian semiconductor stocks (SK hynix, Samsung, Kioxia, Tokyo Electron, Advantest, TSMC) fall as China's CXMT advances memory technology, threatening market share and …
State Street acknowledges missing the AI trade's tail — capex transformation means rate cuts are delayed, supporting higher-for-longer rate thesis
With corporate credit spreads at historical tightness and the risk/reward unfavorable, the next phase involves spread normalization. As spreads widen from current …
As US export controls tighten, China responds with aggressive domestic semiconductor investment and retaliatory measures, accelerating the full technology decoupling. This creates …
GPU shipments to China fell 26% while non-GPU AI accelerators rose ~80%, as US export bans on cutting-edge chips drive adoption of …
AMD's $4.9B acquisition of ZT Systems creates integrated AI infrastructure capabilities, enabling rack-scale server deployments for hyperscale cloud providers and closing the …
Nvidia's launch of the BioNeMo Agent Toolkit deepens its software ecosystem in the biomedical space, increasing customer reliance on its accelerated computing …
Coca-Cola Consolidated (COKE) faces downside as margin pressures from higher aluminum and fuel costs make its cash flow yield unattractive despite strong …
Geopolitical supply risks from the Middle East could spike oil prices, but if accompanied by risk-off flight to USD or growth concerns, …
AMD maintains data center leadership through 2027 as projected, then navigates the transition from agentic AI infrastructure buildout to mainstream AI deployment, …
AMD's next-generation AI accelerators (with 432GB memory) will drive significant market share gains against NVIDIA, as the 50% memory increase enables larger …
With AMD confirmed to be eating Intel's lunch and potentially gaining GPU edge over NVIDIA in 2027, the next phase targets AMD's …
Data center operators face mounting local opposition and potential moratoriums in key states (TX, OH, GA), threatening project timelines and economics as …
Failed ceasefire efforts and continued attacks on energy infrastructure in Ukraine/Russia will keep global oil prices elevated, contributing to inflation pressure
Trump administration may cut intelligence sharing and Patriot missile procurement (PURL) if Ukraine continues deep strikes on Russian energy, threatening Ukraine's air …
Ukrainian strikes on Russian energy infrastructure confirmed with casualties, validating Trump's 'escalation to end' framing and increasing probability of sustained attacks on …
The heavy selling in crowded AI/long positions (down 4-9%) while power/cooling plays (VRT, GEV, ETN) barely moved indicates this is a positioning …
The selloff in Micron and AI infrastructure stocks triggered by China's CXMT DRAM progress is a positioning-driven crowding unwind, not fundamentals — …
Every market pattern, investment thesis, and ticker position lives in a typed knowledge graph. This isn't a dashboard — it's a reasoning engine that exposes hidden dependencies across your entire investment universe.
Not just a visualisation — a computational structure that enables capabilities impossible with spreadsheets or flat databases.
A knowledge graph isn't just a visualisation — it's a computational structure that enables capabilities impossible with spreadsheets or flat databases.
When "Fed Pivots Dovish" strengthens, the graph traces impact across every linked impulse, scenario, and position — instantly surfacing which theses need revisiting.
The graph explicitly flags when scenarios contradict each other. If "Consumer Spending Weakening" and "Retail Revenue Growing" both exist, InFinder forces intellectual honesty.
If 80% of your positions depend on one scenario and it weakens, the graph makes hidden danger visible — showing exactly how many theses collapse at once.
Signal strength decays through the graph with ML-learned rates. Recent evidence weighs more — the system optimises decay from actual market outcomes.
From raw content to optimised strategy — six building blocks, each feeding the next.
InFinder continuously monitors YouTube channels, Reddit threads, RSS feeds, and news wires. Each new piece of content becomes an impulse — a timestamped, scored record of raw market intelligence. No more scattered notes or lost insights.
Every impulse is automatically parsed, relevance-scored, and queued for AI extraction. The system handles transcript extraction, engagement scoring, and deduplication.
41 active sources 276085 impulses ingestedAI evaluates each impulse and links it directly to the scenarios (investment theses) it supports or challenges. Every link carries a relevance score and a 1536-dimensional vector embedding for semantic matching.
Impulses automatically discover the right theses via cosine similarity. Each edge is typed — an impulse may support or challenge a scenario — building the knowledge graph without manual tagging.
276085 impulses ingested 1197802 impulse→scenario edgesLinked impulses excite scenarios — structured investment theses with long/short ticker positions. Each scenario has a compound state from −1 (fully bearish) to +1 (fully bullish) that shifts in real time as evidence accumulates.
As new impulses arrive and attach to their theses, the scenario state automatically recalculates via graph propagation. Every position traces back to specific content — full audit trail.
38244 active scenarios 283000 long 98174 shortEvery ticker aggregates its complete intelligence picture — all connected scenarios, contributing impulses, and actual price performance. Entry prices are tracked for P&L attribution.
Real-time and end-of-day prices flow in from EODHD, along with fundamentals and news catalysts. The FINAN agent generates deep analysis covering technicals, fundamentals, and sentiment — typically in under 60 seconds.
87147 tracked tickers 424015 ticker-scenario linksDefine portfolio rules in natural language. Set signal thresholds, rebalance schedules, position limits, and sector constraints. The LLM reads the strategy prompt and the current scenario signals at every rebalance point to decide: buy, sell, or hold.
Different prompt → different trades → measurable P&L delta. This is what makes the prompt itself an optimisable parameter.
Run historical backtests against real price data. The engine detects forks — decision points where the strategy made a suboptimal choice — then analyzes root cause, branches the prompt, and re-tests the new version from the fork point forward.
If the evolved prompt outperforms, it gets promoted. The cycle repeats: backtest → detect forks → evolve prompt → branch-backtest → promote. Like gradient descent on the strategy itself.
Anti-overfitting: modifications must be timeless general principles. The LLM self-checks for temporal specificity. Full version tree with unified diffs.
Sharpe 1.90 → 3.39 Alpha −0.9% → +5.7%InFinder structures the full pipeline — every step automated, every decision auditable, every link traceable back to source content.
InFinder doesn't deal in vague sentiment. It structures market knowledge into impulses and scenarios — each scored, linked, and auditable.
An impulse is a structured, directional signal extracted from a single piece of content — not a keyword or sentiment, but a scored, timestamped observation about a market force.
Each carries a relevance score and a vector embedding. The system links it directly to the scenarios it bears on — an impulse may support or challenge a thesis, building the graph automatically.
A scenario is an investment thesis built from multiple supporting impulses — a structured argument for why specific assets should be long or short.
Scenarios are AI-generated or manually curated, tracked with entry prices, and scored against actual outcomes. Every scenario links back to its source impulses — creating a full audit trail from content to trade.
Three specialised agents operate directly on the investment graph — augmenting human judgment, never replacing it.
Runs a 10-node agentic analysis workflow per ticker: technical analysis, fundamental screening, news assessment, graph context, and overall synthesis.
Autonomously manages portfolio construction: proposes position changes, links impulses to scenarios, and deprecates stale theses — with human-in-the-loop approval.
Detects unexplained ticker movements and autonomously searches for information that could explain them — surfacing new impulses the graph doesn't yet contain.
Scenarios hold the investment thesis. Strategies turn theses into executable portfolio rules. The backtesting engine iterates on strategy versions to find the best one — automatically.
Each scenario aggregates excitation from its linked impulses into a compound state ranging −1 to +1. As new impulses arrive and attach to the thesis, the scenario state shifts in real time — giving you a continuous, auditable signal.
Define portfolio rules using natural language prompts. Set signal thresholds, rebalance schedules, position constraints, and risk limits. The strategy consults the graph state and the LLM at every decision point.
Run historical backtests against real price data. The engine detects forks — moments where the strategy made a suboptimal decision — then branches the prompt, re-tests, and promotes the best-performing version.
Free drives adoption. Select unlocks strategy creation. Omni delivers full backtesting evolution.
A complete AI pipeline from raw information to investment action — every step automated, every decision auditable.
Continuously monitor YouTube channels, Reddit, RSS feeds, and manual inputs. Automated polling, transcript extraction, and engagement scoring.
AI turns each content item into a scored, directional impulse. Relevance scoring, vector embeddings, and automatic linking straight to the scenarios it bears on.
Typed relationships (reinforcing, contradicting, validating, invalidating) across all entities. Multi-hop reasoning and cascade detection.
AI-generated or manual investment theses. Clusters related impulses, suggests L/S positions with rationale, and tracks confidence over time.
Every scenario links to source impulses, every impulse to source content. Compliance-grade traceability.
Learns optimal score decay rates from market outcomes. Predicts scenario performance. Source importance scoring based on impulse outcome quality.
From hedge funds and RIAs to independent research analysts — InFinder augments the investment process at every level.
Reduce 200+ daily sources to scored directional patterns. See concentration risk, cascade exposure, and thesis contradictions at a glance. Full compliance audit trail.
Structure your research into linked impulses and scenarios. Surface counter-evidence to your thesis automatically. Track which signals persist vs decay over time.
Every investment decision traces back through scenarios → impulses → source content. Graph-based concentration analysis. Revertible agent actions with full state logging.
Search across 401476 nodes and 1621817 edges — or begin adding your own content sources.