InFinder structures market intelligence into an interconnected graph of directional patterns, investment theses, and ticker positions — with typed relationships, cascade detection, and full audit trails.
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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 perceptron, scenario, and position — instantly surfacing which theses need revisiting.
The graph explicitly flags when perceptrons contradict each other. If "Consumer Spending Weakening" and "Retail Revenue Growing" both exist, InFinder forces intellectual honesty.
If 80% of your scenarios depend on one perceptron 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.
43 active sources 165921 impulses ingestedAI decomposes each impulse into 3–7 perceptrons — atomic, directional statements about market dynamics. Each carries an importance score, a probability estimate, and a 1536-dimensional vector embedding.
Perceptrons automatically discover related signals via cosine similarity. They form typed relationships — reinforcing, contradicting, validating, invalidating — building the knowledge graph without manual tagging.
457482 active perceptrons 0 inter-perceptron edgesPerceptrons 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, linked perceptrons update, and the scenario state automatically recalculates via graph propagation. Every position traces back to specific content — full audit trail.
43700 active scenarios 44603 long 4338 shortEvery ticker aggregates its complete intelligence picture — all connected scenarios, contributing perceptrons, 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.
1096 tracked tickers 51548 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 perceptrons and scenarios — each scored, linked, and auditable.
A perceptron is a structured, directional statement about a market force — not a keyword or sentiment, but a persistent, observable pattern extracted by AI from content.
Each carries a dual score: Importance (significance for markets) and Probability (likelihood of persisting). The system discovers typed relationships — one may reinforce, contradict, validate, or invalidate another.
A scenario is an investment thesis built from multiple supporting perceptrons — 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 perceptrons — 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 perceptrons 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 perceptrons 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 perceptrons into a compound state ranging −1 to +1. As new impulses arrive, perceptrons update and 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 extracts 3-7 directional market patterns per content item. Dual Importance/Probability scoring, 4-context vector embeddings, automatic linking.
Typed relationships (reinforcing, contradicting, validating, invalidating) across all entities. Multi-hop reasoning and cascade detection.
AI-generated or manual investment theses. Clusters related perceptrons, suggests L/S positions with rationale, and tracks confidence over time.
Every scenario links to source perceptrons, every perceptron 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 perceptron 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 perceptrons. Surface counter-evidence to your thesis automatically. Track which patterns persist vs decay over time.
Every investment decision traces back through scenarios → perceptrons → source content. Graph-based concentration analysis. Revertible agent actions with full state logging.
Search across 668199 nodes and 1117549 edges — or begin adding your own content sources.