AI Conviction Scoring Explained (Paused Feature)

Status (September 2026): Tradewink's ai_conviction signal type is still paused (paused April 23, 2026; not published on any plan). This guide explains the historical signal methodology and how to think about multi-factor confidence scores in general. It is educational only — not trade instructions.

Separately, day-trade pipeline conviction scoring is live: default reject below 60, additive capped boost (not a 1.15× multiplier), optional 3-agent team on a few top candidates per scan. Do not confuse the paused signal product with that pipeline feature.

How Multi-Factor Confidence Scores Work

Traditional trading signals are binary: buy or don't buy. This misses critical nuance. A buy signal on a stock with perfect technical setup, strong volume, bullish options flow, and favorable market regime is very different from a buy signal on a stock that barely triggered one indicator.

Conviction-style scoring turns a binary buy/skip into a continuous 0–100 quality label so a trader can compare setups. Higher scores mean stronger multi-factor alignment in the model's view — not a promise of better outcomes, and not an instruction to size up or auto-enter.

The AI trading context: Grand View Research puts the AI trading platform market at $13.45 billion in 2025 ($33.45 billion by 2030, 20.0% CAGR). ABI Research puts broader AI software at about $174 billion in 2025. Conviction scoring is one way to turn model output into a 0–100 quality label — not a promise of better fills.

The Components of Conviction

A reliable conviction score synthesizes multiple evidence sources:

1. Technical Score (30–40% weight)

The baseline technical quality of the setup: How clean is the breakout? Is volume confirming? How many indicators align?

Inputs typically include:

• RSI positioning (not overbought at entry)
• MACD momentum direction
• VWAP relationship (above = bullish)
• Volume vs. average (relative volume > 1.5x preferred)
• Support/resistance clarity (defined levels vs. ambiguous)
• Pattern quality (clean flag vs. messy consolidation)

Each factor contributes 0–10 points to a technical sub-score. The sub-score normalizes to 0–100 and gets weighted into the composite.

2. Market Regime Score (15–20% weight)

Does the current market environment favor this type of trade?

• Trending regime + breakout setup: high conviction boost
• Choppy regime + breakout setup: major conviction penalty
• High volatility + any setup: size reduction flag

Regime alignment is multiplicative — a technically perfect setup in a choppy regime gets penalized significantly. A mediocre setup in a strongly trending regime gets a boost.

3. Options Flow Score (10–15% weight)

What are the options players doing? "Smart money" options activity often precedes price moves.

• Large unusual call sweeps above ask: bullish signal
• Heavy put buying at key strike: bearish signal
• High IV rank with directional flow: strong conviction
• Options activity confirms price direction: score boost

Not all tickers have meaningful options flow. For low-options-volume stocks, this component gets zero-weighted.

4. Sentiment Score (10–15% weight)

News sentiment, social momentum, and analyst activity:

• Breaking positive news + positive price action: high conviction
• Analyst upgrade: moderate boost
• Negative sector news contradicting the setup: conviction penalty
• Dark pool prints (large non-public block trades): can be highly significant

5. AI-Specific Factors (10–20% weight)

This is where machine learning adds unique value:

Signal Quality Classification: An ML classifier trained on thousands of historical setups assigns a quality label (Very High, High, Medium, Low) based on how similar past setups performed. A "Very High" quality label boosts conviction; "Low" is a disqualifying factor.

Historical Win Rate at This Setup Type: If momentum breakout on stocks with this RSI + volume profile has a 68% historical win rate in current regime, that base rate factors into conviction.

Trade Lessons: Post-trade AI reflection generates natural language lessons. Similar patterns are retrieved via embedding search and factored into confidence.

Multi-Agent Debate: High-Stakes Conviction Verification

For the highest-stakes decisions (large positions, volatile markets, borderline conviction scores), a single AI model's assessment isn't enough. In internal testing, structured bull/bear review was used to surface counterarguments; it does not guarantee fewer losers.

Bull Agent: Analyzes the setup from an optimistic perspective. Identifies supporting factors: technical setup quality, tailwinds, options flow confirmation, favorable regime.

Bear Agent: Argues the opposing case. Identifies risks: overhead resistance, high short interest, recent false breakouts in this stock, regime uncertainty.

Moderator Agent: Receives both analyses, synthesizes, and produces a final conviction score with explicit reasoning. It can override the initial score in either direction based on compelling arguments.

This three-agent process takes longer and costs more compute, but was tested to reduce false-positive signals. In testing, 3-agent evaluation could improve signal quality versus single-agent evaluation — particularly in filtering setups that look technically clean but have hidden structural weaknesses.

When Does 3-Agent Mode Activate?

The multi-agent evaluation is triggered when:

• Initial composite score is in the "borderline" range (40–65): unclear whether to take the trade
• Position size would exceed 5% of portfolio
• Stock has had poor recent performance (last 3 trades lost)
• Regime is uncertain (probability distribution is flat, not peaked)
• User account tier qualifies for premium AI analysis

For fast-moving opportunities (breakout happening right now), the single-agent path is used to stay within execution time budgets.

Example: Conviction Score Interpretation

For reference, conviction score bands historically indicated research priority and multi-factor alignment strength:

Review priority only — not order-size instructions. In a historical pipeline (paused May 2026), these bands helped prioritize which candidates warranted deeper analysis. The score was a research quality label.

Signal Quality Classification

Separate from the conviction score, Tradewink runs a signal quality classifier — an ML model that assigns one of five labels:

• Strong Buy (>75 confidence): Rare. Significant position warranted.
• Buy (60–75): Standard positive signal.
• Hold/Neutral (40–60): No clear edge. Wait for better setup.
• Sell (25–40): Avoid long positions, consider short.
• Strong Sell (<25): Avoid or actively short if setup confirms.

The quality classifier is trained on historical signal outcomes using walk-forward validation. It uses discretized features to avoid overfitting: RSI bins (not raw values), volume relative to 20-day average (bins), pattern type categories, and regime label.

How to Build a Simple Conviction System

Even without AI, you can build a manual conviction scoring system:

1. List 5–8 factors that matter for your setup type
2. Assign a maximum point value to each (total to 100)
3. Score each trade objectively on each factor
4. Add a minimum threshold: only trade if score exceeds 50
5. Size proportionally: $1,000 × (score / 100)

Example for a momentum breakout:

• Volume > 2x average: 20 points
• Clean chart pattern: 15 points
• Strong sector momentum: 15 points
• RSI not overbought (<70): 10 points
• Market regime trending: 20 points
• News catalyst: 10 points
• Options flow confirmation: 10 points

A trade scoring 80/100 gets full size. A trade scoring 45/100 might still be interesting but with reduced size and tighter stops.

Common Conviction Mistakes

Inflating scores on high-conviction "feeling": Conviction scoring only works if it's objective. If you're pre-deciding you want to take the trade and working backward to justify it, the system fails.

Ignoring regime in the score: Market regime is often the most important factor. A technically excellent setup in a choppy regime has statistically worse outcomes than a mediocre setup in a strong trending regime.

Using the same threshold for all setup types: Momentum breakouts and mean-reversion setups have different base rates. Consider separate scoring systems or threshold calibration per setup type.

Frequently Asked Questions

How is conviction scoring different from a simple signal?

A binary signal says "buy." Conviction scoring says "buy at 72% confidence with these specific supporting and undermining factors." The score drives position sizing and gives you explicit reasoning to review after the trade.

Does higher conviction always mean better outcomes?

Over large samples, it should — if your scoring system is well-calibrated. Trades with conviction >70 should outperform trades with conviction <40. Individual trades can still be random — even a 90-conviction trade fails sometimes. No conviction score guarantees better outcomes.

How do I know if my conviction system is well-calibrated?

Track conviction score vs. actual outcome over 100+ trades. Group by score bucket (0-40, 40-60, 60-80, 80-100) and measure win rate in each bucket. A well-calibrated system shows win rate monotonically increasing with conviction. If the 80+ bucket has the same win rate as the 40-60 bucket, your high-conviction factors aren't actually predictive.

Is conviction scoring available on Tradewink plans?

No — as of September 2026, Tradewink's AI conviction signal type is still paused and is not a live plan feature. Day-trade pipeline conviction scoring is a different, live gate (default floor 60). This guide is educational only.