What Are Signals in Trading

What are price signals?

Price signals come from the market itself: the movement of prices and volume over time. These are the most common type of trading signal.

Examples of price signals:

• Moving averages. When a short-term average (like the 50-day) crosses above a long-term average (like the 200-day), it signals upward momentum.
• RSI (Relative Strength Index). Measures whether an asset is overbought (above 70) or oversold (below 30). An RSI below 30 often signals a buying opportunity.
• Volume spikes. A sudden increase in trading volume can signal that a major move is coming.
• Support and resistance. Price levels where an asset repeatedly bounces off (support) or gets rejected (resistance).

Price signals are backward-looking. They tell you what has happened. The assumption is that patterns tend to repeat.

What are sentiment signals?

Sentiment signals measure the mood of the market: what people think and feel about an asset or the market as a whole.

Examples of sentiment signals:

• News sentiment. AI can analyze thousands of news articles and classify them as positive, negative, or neutral. A shift from negative to neutral sentiment after a selloff often precedes a price recovery.
• Social media activity. Unusual spikes in mentions of a stock on platforms like X or Reddit can signal upcoming volatility.
• Fear & Greed Index. Composite measures of market anxiety. Extreme fear often signals buying opportunities. Extreme greed signals caution.
• Options flow. Large options trades by institutional investors can signal expected moves.

Sentiment signals are forward-looking. They reflect expectations about what will happen next. They're especially powerful when they diverge from price signals.

The alternative data market (which includes sentiment feeds, satellite imagery, and web scraping) is growing rapidly, projected to exceed $100 billion by 2030 according to industry research. Traders who incorporate sentiment analysis alongside traditional indicators gain a meaningful edge.

What are macro signals?

Macro signals come from the broader economy: interest rates, employment data, GDP growth, inflation reports, and central bank decisions.

Examples of macro signals:

• Interest rate decisions. When the Federal Reserve raises rates, it typically pressures stock prices down. Rate cuts tend to push them up.
• Jobs reports. Strong employment data signals economic strength, which generally supports stock prices.
• Inflation data. Rising inflation erodes purchasing power and can trigger rate hikes, which affect asset prices across the board.
• Earnings season. When many companies report earnings simultaneously, the aggregate results signal the health of specific sectors.

Macro signals move slowly and affect entire markets, not individual stocks. They set the backdrop that all other signals operate within.

Research by the Federal Reserve, including Bernanke and Kuttner's study on monetary policy and stock prices, shows that macroeconomic announcements significantly drive market movements. This is why AI trading agents monitor economic calendars alongside price data.

How do signals combine to form a trading decision?

No single signal is reliable enough to trade on alone. Trading agents combine signals from multiple categories to increase confidence.

A strong buy signal might look like this:

• Price signal: RSI dropped below 30 (oversold)
• Sentiment signal: News coverage shifted from negative to neutral
• Macro signal: Interest rates are stable, no economic headwinds

When multiple independent signals point in the same direction, the probability of a successful trade increases. This is called signal convergence.

AI trading agents can weigh dozens of signals simultaneously and adjust their confidence levels in real time. A human trader might track 3-5 signals. An AI agent can process hundreds.

As Ray Dalio, founder of Bridgewater Associates, emphasizes: "The key is to have uncorrelated bets. Diversifying your signal sources is just as important as diversifying your portfolio."