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AN INTELLIGENT FINANCIAL PREDICTION AND LEARNING SYSTEM
Why FIN?
Fin has six pillars
1
Data OverloadThousands of news articles and social signals appear every day. No human can keep up. FIN can processes them all.
2Psychology InsightsLoss aversion, herding, recency, confirmation, 12 cognitive biases in total drive every prediction. FIN measures them from articles data.
3Statistical FoundationHistorical prices, trading volumes, and financial fundamentals are analyzed with statistical methods.
4Machine LearningTrains on past predictions and outcomes to spot patterns that repeat.
5Self-CorrectionAutomatically adjusts when consistent over- or under-predicting is detected.
6AI & LLMLarge language models synthesize everything into clear, reasoned predictions you can analyse.
What Does FIN Do?
Gathers information
from stock prices, financial news, social media, corporate announcements, and psychology research
Analyzes everythingto understand market sentiment, detect surprises, and measure investor psychology
Makes predictionsabout where a stock's price is heading
Checks its accuracyafter the market closes
Learns from mistakesto get better over time
Explains Whyeach prediction was made with detailed reasoning
All of this happens automatically every day. Your job is to explore the results, tune the system and understand the story behind each prediction.
- Your predictions, your context — These are not general market forecasts
- Every prediction is personalized to your chosen tickers, news feeds, competitor definitions, LLM settings, and scheduling preferences
- You are in control. No two FIN instances are alike
- Optionally, you can define competitor tickers
- Their price performance, valuation and news sentiment is factored in every prediction
Why Use FIN?
| You want to know... | FIN tells you... |
|---|---|
| What will the stock be worth tomorrow / next week? | Predicted price with a confidence range |
| Why does the system think that? | Full reasoning for every prediction |
| What news is driving the sentiment? | Scored articles with relevance and bias |
| Are people acting irrationally? | Psychology bias scores (FOMO, herding, etc.) |
| Was the prediction accurate? | Track record with error percentages |
| Is the market surprised by something? | Surprise signal detection from news |
| How does my stock compare to its peers? | Competitor price, valuation, and news sentiment comparison |
| How's the system running? | Jobs, feeds, schedules overview |
The Pipeline: How FIN Works
Every day, FIN runs a 5-stage cycle:
Stage 1: Collect
FIN pulls in data from multiple sources:
- Stock prices — daily trading data (Open, High, Low, Close, Volume)
- News articles — from RSS feeds (Google News, Yahoo Finance, Reddit, etc.)
- Calendar events — earnings dates, dividend dates
- Financial statements — income, balance sheet, cash flow
- Options data — put/call ratios, implied volatility, and IV percentile from the options chain
- Psychology research — academic studies on 12 cognitive biases
- Competitor data — peer stock prices, financial metrics, and news sentiment for relative performance analysis
Stage 2: Analyze
Raw data is transformed into useful signals:
- Sentiment Analysis — every article is scored for how positive/negative it is about the company and the economy
- Surprise Detection — looks for unexpected events (earnings beats/misses, economic surprises)
- Psychology Scoring — measures 12 cognitive biases (FOMO, herding, loss aversion, etc.)
- Technical Indicators — RSI, moving averages, and volume trends from price data
Stage 3: Predict
All analyzed signals flow into a thesis-first, tiered prediction pipeline:
Step 1 — Thesis Generation
Before any price number is produced, the LLM generates a qualitative thesis: a structured view of the stock's direction, conviction strength, bull/bear scenarios with probabilities, and falsifiable hypotheses (e.g., "If RSI drops below 30, this thesis is wrong"). This thesis anchors every downstream prediction.
Step 2 — Tiered Price Prediction
The system picks the best available method to turn the thesis into numeric prices:
| Tier | Method | When Available |
|---|---|---|
| Tier 1 | Your personal quantile ML model (q10–q90) | After ~100+ resolved snapshots |
| Tier 2 | Pooled ML model (cross-user) | After enough data accumulates |
| Tier 3 | Sector-transfer ML model | When a peer company is trained |
| Tier 4 | Thesis heuristic (direction + magnitude) | Always — no training needed |
Each tier is automatically downgraded if its recent accuracy is poor.
Step 3 — Self-Critique
A second LLM pass reviews the thesis for consistency, plausibility, and falsifiability before the final prediction is saved.
What You Get
Predictions for 1 day, 2 days, and ~10 days ahead — each with a confidence range, a confidence score, and full thesis reasoning explaining the why. Competitor context is injected when peers are configured, and a MAPE gating check prevents flaky predictions from being shown.
Stage 4: Validate
After market close, FIN checks its work:
- How far off was the prediction? (error percentage)
- Did the actual price fall within the expected range? (band hit)
- Results are fed back into the learning system
Stage 5: Learn
The system improves over time through a closed feedback loop:
Feature Snapshots
Every prediction saves a feature snapshot — a complete record of everything that fed into it: technical indicators, sentiment scores, psychology biases, financial fundamentals, options data, and thesis features (conviction, regime, flip count). When the target date passes, the accuracy tracker resolves each snapshot with the actual outcome (actual price, return %, error %), turning it into a labeled training example.
Quantile ML Training
Once enough resolved snapshots accumulate (50+), the system trains quantile GBM models — five models that predict different points on the return distribution:
| Model | What It Predicts |
|---|---|
| q10 | Pessimistic case (10th percentile) |
| q25 | Conservative case (25th percentile) |
| q50 | Median (most likely) case |
| q75 | Optimistic case (75th percentile) |
| q90 | Very optimistic case (90th percentile) |
The q50 gives the central price; the q10–q90 gap forms the confidence band, optionally tightened by conformal prediction to guarantee ~80% coverage on held-out data. The trained model is user-specific — it learns from your chosen tickers, feeds, and settings. If performance drops, old models are retired automatically and the system falls back to Tier 4 (thesis heuristic) until fresh models can be trained.
Quick Start: Using the Dashboard
1
Select a Stock
Choose a ticker (stock symbol) from the top bar. The ticker controls which data you see across every page.
2
Choose Feeds
Select which RSS feeds to collect news from. Each feed brings in articles that are scored for sentiment and relevance.
3
Configure LLM Parameters
Set the AI model, temperature, and other parameters that control how predictions are generated.
4
Update Schedule if Needed
Adjust when the pipeline runs — collection, analysis, and prediction schedules are all configurable.
5
Track Results
Monitor predictions, accuracy, and system health from the dashboard. Check back after market close to see how the system performed and learn from it's actions.