Moving Averages

The Truth About Moving Averages. Many trading guides recommend specific moving averages, like a 21-period exponential or a 50-period simple moving average. However, when someone suggests using a particular length, the logical question is, “Why that one and not another?” The reality is that no moving average holds any inherent magic—they are all similar in that they sometimes work and sometimes don’t. If you consider moving averages as key support or resistance levels, or view price crossing a moving average as a significant signal, it may be time to rethink your approach to these tools.

MA as a Trend Indicator. One practical application of moving averages is as a trend-confirmation tool. When scanning multiple charts, a moving average can provide a quick summary of market structure by smoothing out price fluctuations and highlighting the overall trend. This makes it a useful visual aid for identifying the broader market context at a glance.

Avoid Markets in Equilibrium. Markets are often in equilibrium, where price behavior resembles a random walk. In such conditions, no consistent trading edge exists. The core of technical analysis is to identify markets with temporary imbalances in buying and selling pressure and limit trading to those situations. Markets in equilibrium tend to stay near an average price, which moving averages can approximate. This serves as a rough marker of market consensus and can help identify when conditions are less favorable for trading.

Using Moving Averages for Trading Pullbacks. While expecting moving averages to act as precise support or resistance is misguided, they can still serve as useful reference points for trading pullbacks. Markets typically alternate between momentum moves and consolidations, and it’s generally unwise to trade in the direction of an overextended move. Instead, it’s better to wait for the market to return to a short-term state of balance during a pullback. A simple trading rule could be to avoid buying or selling pullbacks that are too far from a moving average—assuming “too far” is well-defined. This approach prevents hasty decisions, such as entering trades in overextended markets.

Slope of a Moving Average as a Trend Cue. The slope of a moving average is another application, albeit a subjective one, that doesn’t perform well in quantitative tests. However, it can still serve as a scanning tool or a guide for developing discretionary traders. The slope indicates trend direction, with the moving average’s time frame roughly aligning with the trend duration. For instance:

• Longer-term moving averages (50-100-200 period or more) reflect long-term trends.
• Shorter-term moving averages (5-10-20-period) provide insights into shorter-term price movements.

An important note: attentive traders can often spot inflection points in price structure well before the moving average itself changes slope.

Comparing EMA and SMA Behavior. While formulas for constructing moving averages are widely available, understanding their behavioral differences is crucial:

• Simple Moving Average (SMA): This averages prices over a look-back window, ignoring data outside that period. One limitation is that the SMA reacts twice to a single large price event: first when the event occurs, and again when it drops out of the evaluation window.
• Exponential Moving Average (EMA): This gives more weight to recent prices and technically incorporates all past data, which decays exponentially. While distant data has negligible influence, the EMA does not react twice as the SMA does, making it smoother.

Key differences between the two include:

• The EMA responds more quickly to large price changes due to its front-weighting of data.
• The EMA is slower to stabilize after sharp price changes because its long effective look-back period retains the impact of past data longer.

No single moving average length or type is universally better than another; each has strengths and weaknesses depending on the market and context. The real value of moving averages lies in their role as tools to provide context.

Volume weighed average price (VWAP): VWAP is a widely used technical indicator that calculates the average price of a security based on both price and volume over a specific time period. It is computed by dividing the total dollar value of transactions (price multiplied by the number of shares traded) by the total volume of shares traded during that period. VWAP is particularly popular among institutional traders and intraday traders because it combines price and volume into a single metric.

Its utility goes beyond being just a moving average—it offers key insights into market sentiment and trader behavior:

• When the price is above the VWAP, it indicates that the average buyer is in profit, which often reflects a bullish sentiment.
• Conversely, when the price is below the VWAP, the average seller is in profit, signaling a bearish sentiment.

This concept is useful for traders gauging the strength of a trend and determining whether the market is overbought or oversold relative to its average traded price. Large institutional investors and mutual funds use VWAP as a benchmark for executing large trades. Their goal is to trade near the VWAP to minimize market impact and avoid distorting the stock price.

Channels

A moving average offers a snapshot of market consensus, perceived value, and relative rest. Channels, when properly constructed, extend this concept by defining excursions away from the area of consensus, providing insights into where emotional extremes in the market might occur. The primary purpose of channels is to identify these meaningful extensions. To do this effectively, channels must adapt to the ever-changing volatility conditions of the underlying market. It’s important to note that channels do not create actual barriers to price movement. The idea of consistently buying or selling at channel boundaries is flawed, except in highly mean-reverting instruments. A more effective use of channels is to treat them as alert levels—monitoring price action carefully when prices touch or extend beyond the channel boundaries. This approach provides a discretionary trader with an edge by highlighting potential areas of interest rather than acting as rigid entry or exit points.

Commonly Used Channels

• Bollinger Bands. Bollinger bands are among the most widely used channel tools. They are calculated as a multiple of the standard deviation of price around a simple moving average (commonly a 20-period SMA with bands set at two standard deviations). As market volatility increases, the bands widen to reflect larger price swings. Approximately 88% of closes, across various markets, fall within the bounds of two Bollinger standard deviations around the 20-period SMA. However, standard deviation, as used in Bollinger bands, does not always align with other accepted measures of volatility. This can result in discrepancies where the bands may not accurately reflect true market conditions.
• Keltner Channels. Chester Keltner developed Keltner channels, which use a more stable and consistent measure of volatility: the true range of each bar. Unlike Bollinger bands, Keltner channels can be applied to close-only data, such as economic reports, funds, or calculated indexes. These channels are robust and reliable, with larger bars signaling increased volatility and resulting in wider bands. When set to 2.25 ATR (Average True Range) around a 20-period exponential moving average, Keltner channels typically encompass 85–90% of all trading activity (not just closes but the full range of bars) across various markets.

Channels can be a powerful tool for scanning markets. For example, markets with Bollinger bands that contract inside Keltner channels indicate consolidated price action and compressed volatility. This technique is simple yet effective for identifying setups where breakout opportunities may emerge.

A lesser-known but effective approach involves building channels around the VWAP (Volume-Weighted Average Price).Many software platforms offer channels based on the average price deviation from the VWAP, traders can gauge how far a stock is trading from its average price relative to volume. This helps in assessing how extended the market might be, providing a dynamic reference for overbought or oversold conditions.

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