---
title: Decoding Conviction Scores
description: Learn how Strat Ai's consensus engine combines trend overlays, news sentiment, and volume profiles into a single action-oriented percentage.
canonical: https://www.stratai.live/blog/decoding-conviction-scores
author: Trading & Research Wing
category: Trading Insights
published: 2026-05-28
last-updated: 2026-07-26
tags: [conviction score, indicators, consensus, analysis]---

# Decoding Conviction Scores

> Learn how Strat Ai's consensus engine combines trend overlays, news sentiment, and volume profiles into a single action-oriented percentage.


## Understanding the Evaluation Pipeline

A conviction score is the culmination of evaluating multiple technical dimensions simultaneously. Instead of focusing on a single trendline, our consensus model checks several independent signals at once and combines them into a unified 1–100 score that represents the strength and direction of the fused evidence.

The score is not a black box. Every score the terminal displays comes with a breakdown — which inputs contributed, what each one was bullish or bearish about, and how much weight each carried in the final fusion. You can audit the score before you act on it. That auditability is the point: a score you can't decompose is just a hunch with a number on it.

## The four input classes

The conviction engine fuses four classes of input, each with its own confidence weighting:

1. **Technical indicators** — EMA crossovers, MACD convergence/divergence, RSI divergence vs. price, Bollinger band width as a volatility regime classifier. These are computed on the raw tick stream with sub-50ms latency via the ingestion path described in [our real-time ingestion post](/blog/importance-of-real-time-ingestion).
2. **Options derivatives** — open interest build-up rates, put-call ratio velocity (not just the level — the *rate of change*), strike-level max pain coordinates, and Greek sensitivity matrices (delta/gamma/theta/vega) for single and multi-leg structures.
3. **Sentiment classification** — news headlines from NSE-listed equities, classified by a structured LLM evaluator into `{ direction, impact_magnitude, confidence, time_horizon }` tuples. Cached in Redis to prevent re-evaluation of unchanged headlines.
4. **Historical context** — how the same configuration of the above three inputs has resolved over the prior 50–200 trading sessions on the same underlying. This is the regime-awareness layer: a PCR of 0.6 means something different in a trending regime than it does in a ranging one.

## The fusion math

The aggregator applies configurable technical/sentiment weighting (default 70/30) and produces an `AggregatedDecision` — `BUY`, `SELL`, or `HOLD` — paired with a conviction score from 1 to 100. The decision boundaries are:

| Score range | Decision | Interpretation |
|---|---|---|
| 65–100 | BUY | Strong fused bullish evidence |
| 35–65 | HOLD | Conflicting evidence — capital preservation |
| 1–35 | SELL | Strong fused bearish evidence |

The HOLD zone is deliberately wide. When technicals and sentiment disagree, the system shifts toward HOLD rather than picking a side. This is the most important property of the score: **it tells you when not to trade**. For a fuller treatment of the math, see [the unified consensus model docs](/docs/unified-consensus-model).

## Why confidence weighting matters

Not every input is equally reliable in every regime. A PCR shift is a strong signal in expiry week on index options but mostly noise on individual stock options mid-month. RSI divergence is high-confidence in a ranging market and low-confidence in a trending breakout. The weighting layer is what makes the score regime-aware — the same PCR velocity produces a different conviction contribution depending on whether the historical-context layer has classified the current regime as trending, ranging, volatile, or quiet.

This is also why you should never look at a single indicator in isolation. An RSI of 30 means a completely different thing in a trending Nifty session than it does in a choppy mid-cap stock. The conviction score is the way you carry that context with you, rather than trying to remember it.

## Reading the breakdown

Every score on the terminal comes with a per-input breakdown. A score of 72 on a Nifty call options setup might decompose as:

- Technical indicators: bullish, weight 0.70 → contribution +0.42
- Options derivatives: bullish, weight 0.85 → contribution +0.51
- Sentiment classification: neutral, weight 0.40 → contribution 0
- Historical context: regime = trending, weight 0.80 → contribution +0.40
- **Fused conviction: 0.72 → BUY (72/100)**

If the same setup had sentiment shift to bearish with weight 0.85, the score would drop to ~0.55 — HOLD zone. The breakdown tells you *why* the score moved, which lets you decide whether you trust the new information or want to override it.

For the operational side of using these scores in your workflow, see [how consensus eliminates confirmation bias](/blog/how-consensus-eliminates-bias) and [our conviction score docs](/docs/understanding-conviction-scores).

## External context

The idea of fusing multiple weak signals into one strong one is not new — it's the basis of ensemble methods in machine learning and composite scoring in traditional quant finance. The CBOE's VIX is itself a fused indicator (weighted implied volatility across S&P 500 options). What's different about Strat Ai's conviction score is that the fusion is *auditable* — you see the inputs, you see the weights, and you can change them. That's the part that matters for a trader who's trying to improve, not just follow a black-box recommendation.


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Canonical HTML version: https://www.stratai.live/blog/decoding-conviction-scores
Published by Trading & Research Wing — https://www.stratai.live