FIN is designed to work with default configuration, but every stock is different. This page walks you through the settings you can adjust from the dashboard to get the best results for your specific situation.

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Quick Overview — The 6 Tuning Areas

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RSS Feed Frequency

How often FIN checks for news — more frequent means more accurate

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Prediction Gating

When FIN skips a prediction to protect you from bad forecasts

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Signal Decay

How fast old news and psychology signals lose their influence

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ML Training

How many predictions before the system learns from its mistakes

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Surprise Detection

Sensitivity to unexpected events in the news

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Prompt Content

What data the AI sees when it makes predictions

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Understanding the Flow

Before diving into settings, here's how FIN's pipeline works and which tuning areas affect each stage:

Collect

→

Analyze

→

Predict

→

Validate

→

Learn

Stage	What Happens	Tuning Areas That Affect It
Collect	RSS feeds are checked, stock prices fetched, calendar events gathered	Feed frequency, cache settings, competitors
Analyze	News is scored for sentiment, surprises, and psychology	Surprise thresholds, decay rates
Predict	AI combines all signals into a thesis and price prediction	Prompt content, temperature, gating, ML tier
Validate	After market close, predictions are compared to actual results	MAPE thresholds, accuracy decay
Learn	Resolved snapshots train new GBM models	Training thresholds, drift detection

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1. RSS Feed Frequency

RSS feeds if one of the channels that FIN uses to stays informed about your stock. More frequent checking means FIN reacts to news sooner, which leads to more accurate predictions.

How to Adjust

Go to Scheduler → Feed Schedules in the dashboard. Each feed has its own schedule and settings.

Recommended Patterns

Feed Type	Recommended Frequency	Why
Ticker-specific news (Google News, Yahoo Finance, etc)	Every 1–2 hours	Your stock's direct news matters most
Central bank / policy feeds (ECB, Fed)	Every 6 hours	These publish infrequently — checking more often wastes resources
Macro / market-wide news	Every 6 hours	Broad economic context, not time-critical
Reddit discussions (with buzz)	Every 6 hours	Social sentiment changes slowly; buzz scraping is heavier

Feed Settings

Setting	What It Does	Recommendation
Schedule	Cron expression for how often to check	Every 2–3h for ticker feeds, 6h for macro
Buzz	Also collect comments and engagement data	Enable for Reddit-style feeds
Buzz Comments	Number of top comments to scrape per post	3 is a good default
Max Days	Ignore articles older than this (in days)	Default 3 — lower for faster processing
Extract Text	Full article text extraction	Enable if you want deeper analysis
Active	Toggle feed on/off	Disable feeds that consistently error

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Tip: The biggest accuracy gains come from checking your ticker-specific feeds more often. If you only change one thing, increase the frequency of those feeds.

What to Tune Based on Your Stock

Stock Type	Strategy
Active stock with lots of news	Check ticker feeds every 1–2 hours
Quiet stock with infrequent news	Every 4–6 hours is fine
Want faster processing	Lower Max Days (e.g., 2 instead of 3)
Want more context	Add more feeds, increase Buzz Comments
Resource-constrained	Reduce parallelism, increase cache TTLs

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2. Prediction Gating

When FIN's predictions have been consistently off, the system automatically skips predictions until accuracy improves. This protects you from bad forecasts.

How It Works

1. FIN tracks prediction accuracy using MAPE (Mean Absolute Percentage Error)
2. When MAPE exceeds your threshold, predictions are "gated" (skipped)
3. After a cooldown period, FIN forces a real prediction to check if accuracy has recovered
4. If still inaccurate, gating continues

Settings (Config → Prediction Config)

Setting	Default	What It Does
Prediction Gating Enabled	On	Master switch — turn off to bypass all MAPE gating
MAPE Threshold (per horizon)	8.0%	Predictions are skipped when error exceeds this
Skip Cooldown (per horizon)	5 days	After gating, force a real prediction after this many skips
Accuracy Decay Days	60 days	How far back to look when calculating MAPE

How to Tune

MAPE Threshold controls how strict gating is:

Setting	Label	When to Use
≤ 5%	Strict	Conservative — skip early, only show high-confidence predictions
≤ 10%	Moderate	Default — good balance for most stocks
≤ 15%	Relaxed	Tolerate more error — useful for volatile stocks
> 15%	Lenient	Rarely gate — useful during earnings season

Skip Cooldown controls how quickly FIN tries again after gating:

• Lower (1–3): Resume predictions sooner — good if you want to see updates even with lower accuracy
• Higher (7–14): Stay in safety mode longer — good if you only want high-confidence predictions

Accuracy Decay Days controls how fast old errors age out:

• Lower (30 days): Recover from bad spells quicker — old high-error predictions stop affecting gating sooner
• Higher (120+ days): Keep a longer memory — more stable but slower to recover from a bad period

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Tip: Start with defaults (8% / 5 / 60). If predictions seem too conservative, raise the MAPE threshold. If the system stays in gating too long, lower the accuracy decay days.

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3. Signal Decay — Sentiment & Psychology

News and psychology signals lose relevance over time. Decay rates control how fast older signals lose their influence on predictions.

Why It Matters

A news article from yesterday is more relevant than one from two weeks ago. But for long-term predictions (60+ days out), even older articles carry some useful information. Decay rates let you control this balance.

Settings (Config → Decay Config)

Each horizon has two independent decay sliders:

Setting	Short (1–10 day)	Medium (100 day)	Long (300–600 day)
Sentiment Decay	0.3	0.1	0.05
Psychology Decay	0.15	0.08	0.04

What the Numbers Mean

Sentiment Decay controls how fast news articles lose weight:

• Higher (0.3–0.5): Older news fades fast — recent headlines dominate
• Lower (0.01–0.1): News retains influence longer — more memory of past events

Psychology Decay controls how fast bias scores age:

• Higher (0.15–0.3): Recent bias readings dominate (FOMO, anchoring, herding)
• Lower (0.01–0.08): Historical bias patterns stay relevant longer

When to Adjust

Situation	Adjustment
Predictions overreact to old news	Increase sentiment decay
Predictions miss recent shifts	Decrease sentiment decay
Volatile, fast-moving market	Higher decay (focus on fresh signals)
Stable, trending market	Lower decay (longer memory helps)
Short-term predictions too noisy	Increase psychology decay for short horizon
Long-term predictions missing bias patterns	Decrease psychology decay for long horizon

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4. ML Training — Snapshots & Tiers

This is the heart of how FIN learns. Every prediction creates a feature snapshot — a complete record of everything that went into it. When the target date passes, the snapshot is resolved with the actual outcome, turning it into a training example.

What Are Feature Snapshots?

Each snapshot captures 86+ features:

Category	Examples
Technical	RSI, ATR, trend direction, volatility, SMA crossover
Sentiment	Ticker sentiment, economy sentiment, momentum, buzz
Surprise	Count, magnitude, direction of recent surprises
Psychology	12 cognitive bias scores (FOMO, anchoring, herding, etc.)
Fundamentals	P/E ratio, revenue growth, EPS growth, debt/equity
Calendar	Days to next earnings, days to next dividend
Market Context	Beta, correlation with SPY, relative strength
Options	Put/call ratio, implied volatility
Rates & FX	USD/EUR sovereign yields (2Y/5Y/10Y), EUR/USD exchange rate
Thesis	18 features: conviction, regime, bull/bear probabilities, etc.

After the target date, the snapshot is resolved with the actual return — the real percentage move from prediction date to target date. This becomes the training label.

Training Thresholds (Config → ML Training Config)

Setting	Default	What It Does
Min Training Samples	100	Minimum resolved snapshots before any model trains
Min New Samples for Retrain	20	New snapshots needed to trigger automatic retraining
Horizon Min Samples — Short	50	Minimum for short-horizon Tier 1 activation
Horizon Min Samples — Medium	40	Minimum for medium-horizon Tier 1 activation
Horizon Min Samples — Long	30	Minimum for long-horizon Tier 1 activation
Drift MAE Threshold	30%	MAE degradation that triggers a drift alert
Deactivate Underperform Days	30 days	How long a bad model survives before auto-deactivation

How Many Snapshots for Tier 1?

The system needs enough resolved snapshots to train a reliable model. Here's what to expect:

Horizon	Snapshots per Day	Days to 50 Snapshots	With Backfill
Short (1, 2, 10 day)	3	~17 days	Same day
Medium (100 day)	1	~50 days	Same day
Long (300, 600 day)	2	~25 days	Same day

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Tip: With backfill (which runs automatically on first retrain), the system typically generates hundreds of resolved snapshots from your existing stock price history. This means Tier 1 training usually succeeds on the very first retrain — no waiting weeks for data.

Cold Start — What Happens with a New Ticker

When you add a new ticker, here's the journey:

Day 0: Tier 4

→

First Retrain: Backfill

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Same Day: Tier 1

Day 0 — Tier 4 (Thesis Heuristic): No training data yet, so FIN uses the AI's qualitative thesis to make rough predictions. These are directionally useful but lack the precision of trained models.

First Retrain — Backfill: The system automatically reconstructs historical feature snapshots from your existing stock price data:

• Technical features — reconstructed from stored OHLCV price history using rolling calculations
• Fundamental features — computed on-the-fly from financial statements (P/E, revenue growth, etc.)
• Calendar features — derived from stored earnings and dividend dates
• Sentiment, psychology, surprise, thesis, options — left blank for these reconstructed records (the learning model handles missing information gracefully)
• Outcomes resolved immediately — the close price at each target date is looked up from stored data, so every backfilled snapshot becomes a resolved training example

Result: Typically hundreds of resolved snapshots in one run, enabling Tier 1 training immediately.

Drift Detection

Over time, market conditions change and old models may become stale. FIN monitors this automatically:

• Drift MAE Threshold (30%): When recent prediction error exceeds this percentage above the baseline, a drift alert fires
• Deactivate Underperform Days (30): A model that consistently underperforms for this many days is automatically deactivated
• Set to 0 to disable auto-deactivation

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5. Surprise Detection

FIN scans articles for unexpected events — earnings beats/misses, economic surprises, guidance changes, and more. You can control how sensitive this detection is.

Settings (Config → Surprise Signals)

Setting	Default	Range	What It Does
Min Ticker Relevance	15	0–100	Articles below this relevance score are skipped
Min Magnitude Threshold	20	0–100	Surprises below this magnitude are not stored

How to Tune

Setting	Lower (Permissive)	Higher (Strict)
Min Ticker Relevance	More articles analyzed — catches borderline cases	Only highly relevant articles — less noise
Min Magnitude Threshold	Minor deviations captured — more data points	Only big surprises — cleaner signal

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Tip: For most stocks, the defaults (15 relevance, 20 magnitude) work well. Lower the thresholds if you want more data points for ML training. Raise them if you're seeing too many false positives.

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6. Prompt Content — What the AI Sees

You can toggle on/off every data section in the AI's briefing. All on = most informed prediction. Turn off sections to experiment with how different data affects the forecast.

Prediction Prompt (Config → Prediction Prompt)

These control what data the AI sees when making its initial prediction:

Category	Sections	What They Provide
Sentiment	Ticker Sentiment, Economy Sentiment, Country Economy Sentiment	Positive/negative feeling from news
Reliability	Ticker Uncertainty, Economy Uncertainty, Country Uncertainty	How trustworthy each sentiment signal is
Momentum	Avg Buzz Score, Re-acceleration, Momentum Score, User Interest	How much attention and momentum the stock has
Fundamentals	Estimates Context, Financials Context	Analyst forecasts, revenue, margins, balance sheet
Events	Surprise Context, Surprise Outcomes Context	Recent unexpected events and past reactions
Psychology	Bias Context	12 cognitive bias scores weighted by horizon
Calendar	Calendar Context	Upcoming earnings, dividends, events
Market	Market Context, Competitor Context	Benchmark comparison, peer performance
Technical	OHLCV Context	Price action, RSI, ATR, trend, volume
Options	Options Context	Put/call ratios, implied volatility
Self-awareness	Accuracy Context, ML Knowledgebase	Past prediction accuracy, model data

Self-Critique Prompt (Config → Self-Critique Prompt)

After making its initial prediction, the AI reviews its own work. These control what it sees during that review:

Section	Purpose
Ticker Sentiment	Check if prediction aligns with stock-specific news
Economy Sentiment	Check if prediction aligns with economic conditions
Momentum Score	Check if prediction aligns with recent trends
Re-acceleration	Check if prediction accounts for accelerating/decelerating signals
Surprise Summary	Check if prediction accounts for recent surprises

LLM Temperature (Config → LLM Temperature)

Controls how deterministic the AI's outputs are:

Range	Label	Effect
0.0–0.2	Deterministic	Consistent, repeatable outputs — best for predictions
0.2–0.4	Precise	Slight variation — default (0.2) sits here
0.4–0.7	Balanced	More varied — useful for thesis generation
0.7–1.0	Creative	High variation — rarely needed
1.0–2.0	Experimental	Very random — not recommended for predictions

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Tip: Keep temperature at 0.2 for predictions. Only increase it if you want more diverse thesis reasoning. Lower it to 0.0 for maximum consistency.

How to Experiment

1. Start with everything on — the most informed predictions
2. If predictions seem erratic, try disabling the uncertainty sections
3. If you want to test a theory, compare accuracy with and without a specific section
4. Changes take effect on the next prediction run — no restart needed
5. Disabling sections reduces token usage if you're monitoring costs

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The Tiered Model Architecture

FIN uses a tiered system to produce the best predictions available. Each tier is a different method, and the system automatically picks the best one.

TIER 1Your Personal Model

5 quantile GBM models (q10/q25/q50/q75/q90) trained specifically on your ticker. Full prediction distribution with confidence bands. Best accuracy.

TIER 2Pooled Model

Trained across all users on the same instance. Produces median (q50) predictions only. Activates when your personal data is too sparse.

TIER 3Sector Transfer

Uses a model trained on a similar company in the same sector. q50 only, with 30% confidence penalty. Fallback when no same-ticker model exists.

TIER 4Thesis Heuristic

Always available — no training needed. Uses the AI's qualitative thesis (direction + conviction) to produce rough price estimates.

How Tiers Are Promoted

After each retrain, the system evaluates which tiers are available:

1. Tier 1: Your personal GBM models are trained for the horizon — activates when enough resolved snapshots exist
2. Tier 2: A pooled model exists — activates when user-specific training produces fewer than 3 models
3. Tier 3: A same-sector model exists — activates automatically if another stock in your sector has a trained model
4. Tier 4: Always available — the fallback when no trained model exists

Performance Gating

Even when a tier is trained, it must prove it's still accurate. The system checks the last 20 validation results:

Condition	Action
Coverage < 40% (actual price falls outside bands too often)	Tier downgraded one level
Interval score > 1.5× the stock's own baseline — ranges too wide or too inaccurate vs that instrument's norm	Tier downgraded one level

For example, if your Tier 1 model's coverage drops below 40%, the system automatically falls back to Tier 2. This prevents stale or inaccurate models from producing bad predictions. The model's weight in the blend also scales with its win rate — a badly-losing model is near-excluded rather than dominating.

These four thresholds — the coverage threshold, the interval-score degradation factor, the interval-score default threshold, and the floor — are per-ticker and adjustable from the dashboard under Config → ML Accuracy Gate. If you leave them unset, the system defaults (coverage 0.40, degradation 1.5×, default 50%, floor 20%) apply.

What This Means for You

• You don't need to manage tiers — the system handles promotion and demotion automatically
• Tier 1 is the goal — it gives the best predictions with full confidence bands
• Backfill accelerates Tier 1 — typically activates on the first retrain, not weeks later
• Performance gating protects you — bad models are automatically demoted

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Other Settings Worth Knowing

Market Context (Config → Prediction Prompt)

When enabled, FIN compares your stock to market benchmarks (SPY, QQQ) and your sector ETF. It computes:

• Beta — how much your stock moves relative to the market
• Correlation — how closely your stock tracks the market
• Relative Strength — whether your stock is outperforming or underperforming

Toggle off via Config → Prediction Prompt → Market Context if you want faster processing for niche stocks that don't correlate with benchmarks.

Competitors (Competitors Page)

Add peer tickers (e.g., ORCL for SAP) for relative performance analysis:

• System auto-generates RSS feeds for competitors
• Competitor news goes through the same sentiment/surprise pipeline
• Enables the AI to distinguish sector-wide moves from company-specific signals
• Adds competitor-aware ML features (cross-impact, relative strength)

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Tips for Getting Started

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Start with defaults. They work well for most stocks. The system is designed to learn and improve on its own.

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Check the Jobs page to monitor what stage the pipeline is in. You can see feeds being processed, predictions running, and models training.

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Watch accuracy improve over time on the Predictions page. The system needs 1–2 weeks to build enough data for Tier 1 models.

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Tune RSS frequency based on your stock's news volume. Active stocks benefit from every 1–2 hour checks. Quiet stocks are fine at 4–6 hours.

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Use Config → Prediction Prompt to experiment. Toggle sections on/off and compare accuracy over the next few days to see what data helps most for your stock.

⏱️

After 1–2 weeks, the system should have enough data for Tier 1 training. Check the Accuracy tab on the Predictions page to see how it's progressing.

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How It Works Under the Hood

Click to expand — a plain-language look at the engine

From Prediction to Training Data

1. Snapshot: Every prediction stores a complete record of everything that went into it — the stock's technical indicators, financial fundamentals, psychology scores, calendar events, market context, and options data.

2. Resolution: After the target date passes, the accuracy tracker checks the prediction against what actually happened and calculates how far off the forecast was, as a percentage.

3. Training: Once enough resolved records accumulate, the system uses them to train fresh models. The more real outcomes a model has learned from, the more it is trusted.

Backfill: Training From Day One

A new stock doesn't have to wait weeks to build up a track record. When the system first retrains, it rebuilds the historical records from the stock's existing price history:

• Technical data (RSI, volatility, trend indicators) — recalculated from stored prices, no re-fetching needed
• Fundamentals (P/E, debt levels, revenue growth) — recomputed on the fly from the financial statements
• Calendar data (days to earnings, days to dividend) — derived from stored upcoming events
• Historical outcomes — for each past trading day, the system looks up what the price did afterwards, so every rebuilt record immediately becomes a fully resolved training example

Signals that can't be reconstructed from history alone (news sentiment, psychology, surprises, options) are simply left blank for those historical records. The learning model is designed to handle missing information gracefully, so the gap doesn't hurt quality.

Backfill kicks in automatically whenever the number of real training records for a horizon falls below half of the minimum needed — so short, medium, and long horizons each top up independently.

Keeping Bad Models Out

Every trained model is re-evaluated against its recent performance. If the actual price lands outside the predicted range too often, or the range quality degrades well beyond that stock's own normal, the model is downgraded one level (Tier 1 → 2 → 3 → 4). A poor model's output is never shown to you.

How the Confidence Range Is Calibrated

The system goes out of its way to make the confidence range honest. On past data it adjusts the range so the actual price lands inside it about 80% of the time. If recent results show the range was already accurate enough, no adjustment is applied. This is why an "80% band" is reliable, no matter how confident the model feels.

How Old News Loses Influence

News and psychology signals fade over time. The system applies a "decay rate" so older articles carry progressively less weight:

• Short-horizon predictions — decay is fastest (news from yesterday matters, news from last week fades quickly)
• Medium and long horizons — decay is slower, because even older signals can be relevant further out

The actual decay rates you can tune are described in the Signal Decay section above.

How Surprises Are Detected

Surprise detection works in two stages:

1. First pass: Articles are screened for language that typically announces an unexpected event — earnings beats or misses, economic surprises, guidance changes.
2. Second pass: The AI reads the shortlisted articles and scores each one for how genuine the surprise is, its magnitude (0–100), its direction (good or bad), how relevant it is to the tracked stock and to the broader market, and how confident the AI is in its assessment.

Only surprises that clear your minimum relevance and magnitude settings (the Surprise Detection settings above) are kept.

How Psychology Weighting Works

Each of the 12 biases matters more or less depending on how far out the prediction looks. For example:

• FOMO and herding weigh most on short-term forecasts — they're near-term noise
• Confirmation and overconfidence peak at the medium horizon — that's where they form
• Anchoring and narrative matter most for long-term forecasts — reference prices and stories compound over time

So the same news can move a 1-day prediction differently than a 30-day one.

When Confidence Is Automatically Lowered

The confidence score is also an honesty check on the data feeding the model. FIN trims confidence when:

• The price data is stale
• There are no upcoming calendar events
• There's no engagement or news data about the stock
• Recent predictions have been notably off, or the range has missed too often

It gets a small boost when the AI and the model agree, and is reduced whenever the forecast implies a very large move or when the two methods point in opposite directions. In other words, the confidence number reflects both how sure the model is and how trustworthy its inputs were.