Technical indicators, explained for how they're actually used.

RSI measures the speed and size of recent price moves on a scale of 0 to 100. It's calculated from the average size of up-moves versus down-moves over a lookback period, most commonly 14 candles: RSI = 100 − 100 / (1 + RS), where RS is the average gain divided by the average loss over that window.

Readings above 70 are conventionally read as overbought and below 30 as oversold, but in a strong trend RSI can sit in those zones for a long time without the price reversing — the more common use is spotting divergence, where price makes a new high or low that RSI doesn't confirm.

In an automated strategy, RSI is usually combined with a trend filter rather than used as a standalone signal, since raw overbought/oversold crossings produce a lot of false entries in trending markets.

MACD tracks the relationship between two exponential moving averages of price, typically the 12-period and 26-period EMA. The MACD line is the difference between them (12 EMA − 26 EMA), and a 9-period EMA of that line forms the signal line. The histogram is simply MACD line minus signal line.

A crossover of the MACD line above the signal line is read as bullish momentum building; below is bearish. The histogram's shape — expanding or contracting — shows whether that momentum is accelerating or fading before the crossover itself happens.

Because it's built from lagging moving averages, MACD confirms a trend more reliably than it predicts a reversal. Strategies that trade MACD crossovers directly tend to perform better with an added volatility or trend-strength filter to avoid whipsaws in sideways markets.

A simple moving average (SMA) is the arithmetic mean of price over N periods — it weights every candle in the window equally. An exponential moving average (EMA) weights recent candles more heavily, so it reacts faster to new price action than an SMA of the same length.

Moving averages are used two ways: as a trend filter (price above a rising average suggests an uptrend) and as dynamic support/resistance, where price tends to react around the average during a trend. Crossovers between a fast and slow average (e.g. 9 EMA crossing 21 EMA) are a common entry trigger.

The tradeoff between SMA and EMA is lag versus noise — EMA reacts faster but is more prone to reacting to short-lived spikes, while SMA is smoother but confirms moves later.

Bollinger Bands plot a moving average (typically a 20-period SMA) with an upper and lower band set a fixed number of standard deviations — usually 2 — away from it. Because the bands are based on standard deviation, they widen automatically when volatility rises and contract when it falls.

Price touching or exceeding a band isn't automatically a buy or sell signal on its own; in a strong trend, price can "walk the band" for an extended move. A tightening of the bands (a squeeze) is more often read as a signal that a volatility expansion — a breakout in either direction — is building.

Bollinger Bands are frequently paired with a momentum indicator like RSI so that a band touch combined with momentum confirmation carries more weight than either signal alone.

VWAP is the average price a security has traded at throughout the session, weighted by volume at each price level: the cumulative sum of price × volume, divided by cumulative volume. Unlike a moving average, it resets at the start of each trading day.

Intraday traders use VWAP as a benchmark for execution quality — a buy filled below VWAP or a sell filled above it is considered a good fill relative to the session's volume-weighted activity. It's also used as a directional bias: price holding above VWAP through the session is read as bullish control, below as bearish.

Because institutional execution algorithms are frequently benchmarked against VWAP, price does tend to gravitate toward it intraday, which is part of why it holds up as a reference level even for strategies that aren't explicitly execution-focused.

ATR measures volatility, not direction. True range for a single candle is the largest of: current high minus current low, current high minus previous close, or current low minus previous close. ATR is the moving average of true range over a lookback period, typically 14.

Because it's expressed in price units, ATR is used directly to size stop-losses and position sizes relative to how much a security actually moves — a 1×ATR stop on a high-volatility stock will be wider in absolute terms than the same multiple on a calmer one, which keeps risk consistent across different instruments.

It's also used to filter out low-volatility conditions entirely: many intraday strategies simply don't take trades when ATR falls below a threshold, since tight ranges tend to produce poor risk-to-reward setups.

The stochastic oscillator compares a security's closing price to its price range over a lookback period, on a 0–100 scale: %K = (Close − Lowest Low) / (Highest High − Lowest Low) × 100, over N periods (commonly 14). %D is a moving average of %K, usually over 3 periods.

The logic is that in an uptrend, price tends to close near the top of its recent range, and in a downtrend, near the bottom — so %K drifting toward 0 or 100 reflects that positioning rather than an explicit overbought/oversold reading in the RSI sense.

%K crossing %D is the standard trigger signal, and like RSI, it's most useful when combined with a broader trend read rather than traded on its own — a stochastic crossing in an uptrend's shallow pullback behaves very differently than the same crossing at a major top.