Technical analysis terms, defined
Candles, levels, indicators and chart patterns. 20 terms with full definitions, and the honest limits of each.
Almost every term here is derived from price
One structural fact makes this vocabulary easier to hold. With a single exception, every indicator defined below is a transformation of the price series itself. A moving average is an average of past price. RSI, MACD and Bollinger Bands are arithmetic performed on past price. None of them can contain information that price does not already contain; they reorganise it so a pattern becomes visible.
The exception is volume, which is a genuinely independent input. That is why a move on heavy volume carries more information than the same move on thin volume, and why volume appears in most serious trading systems even when several indicators do not.
Each definition below states what the term measures rather than what it predicts. That distinction matters: these tools describe probability under specific market conditions, and every one of them fails in a particular condition — trend indicators whipsaw in a range, oscillators mislead in a strong trend.
20 technical analysis terms, A to Z
Definitions are unabridged. Worked examples for every term live in the interactive glossary.
Bid-Ask Spread
The bid-ask spread is the difference between the highest price a buyer is willing to pay (bid) and the lowest price a seller is willing to accept (ask) for a security at any given moment. It represents a fundamental transaction cost in financial markets — the “invisible” cost of trading that investors pay simply by buying at the ask and selling at the bid. If a stock’s bid is Rs 99.50 and the ask is Rs 100, the spread is Rs 0.50, or 0.5% of the asset’s value. Over time and across multiple trades, this seemingly small cost can compound into a significant drag on returns, especially for active traders who transact frequently. The bid-ask spread is primarily determined by liquidity, trading volume, volatility, and market maker competition. Highly liquid securities like Reliance Industries, TCS, or Nifty futures have extremely tight spreads — often 5–10 paisa — because thousands of market participants compete to provide the best prices. Illiquid securities, such as small-cap stocks or newly listed SME companies, can have spreads of Rs 5–20 or more, representing 2–10% of the stock’s value. In options markets, the bid-ask spread is particularly significant: far out-of-the-money options or low-open-interest contracts may have spreads equal to 20–50% of the option’s premium, making them very expensive to trade from an execution cost perspective. The spread also serves as a real-time indicator of market conditions. During volatile sessions — such as during the COVID crash of March 2020, or immediately following unexpected macroeconomic data releases — spreads widen dramatically as market makers increase their risk buffers, effectively increasing the cost of trading. Algorithmic traders monitor spread dynamics to time their entries and exits; entering a large position when spreads are wide means higher immediate transaction costs. For long-term investors who transact infrequently, spreads are less material. For day traders or HFT firms, whose profitability depends on executing at the best possible prices, minimising spread costs through smart order routing and limit orders is central to their strategy.
Bollinger Bands
Bollinger Bands, developed by John Bollinger in the 1980s, consist of three lines: a middle band (20-period SMA) and upper/lower bands set 2 standard deviations above/below the middle band. Because standard deviation measures volatility, the bands widen during high-volatility periods and contract during low volatility. This dynamic width distinguishes Bollinger Bands from static support/resistance tools. Key concepts: The Bollinger Squeeze — bands contracting to their narrowest in months signals an impending significant breakout (direction unknown). Walking the band — in strong uptrends price consistently touches or exceeds the upper band. The %B indicator shows where price sits relative to the bands (1.0 = upper band, 0.5 = middle, 0 = lower band). Bandwidth quantifies volatility and identifies squeeze conditions for systematic scanning. Trading strategies: The Bollinger Bounce (mean-reversion — buy lower band touch in uptrend, sell upper band touch in downtrend) and the Bollinger Breakout (trade in the direction of a squeeze resolution on high volume). The squeeze is particularly powerful — after an extended low-volatility period, the subsequent breakout often produces a trend that runs for weeks. Combining with volume analysis improves reliability significantly: a squeeze breakout on high volume is far more credible than a low-volume move.
Candlestick Patterns
Candlestick charts, developed by Japanese rice trader Munehisa Homma in the 18th century and popularised in the West by Steve Nison, display OHLC data as visual “candlesticks.” Green/white candles indicate bullish periods (close > open); red/black indicate bearish (close < open). The body shows the open-to-close range; wicks extend to the period’s high and low. Individual candle shapes and multi-candle patterns are used to gauge sentiment and anticipate short-term direction. Key single-candle patterns: Doji (tiny body — open ≈ close — indecision at a crossroads); Hammer (small body at top, long lower wick — bullish reversal after downtrend; buyers absorbed all selling); Shooting Star (small body at bottom, long upper wick — bearish reversal after uptrend; sellers overwhelmed late buyers); Marubozu (no wicks — pure conviction candle, no retracement from open to close). Two-candle patterns: Bullish Engulfing (second green candle completely engulfs the red first candle — strong reversal signal); Bearish Engulfing (opposite). Three-candle patterns: Morning Star (bearish, Doji, bullish — reversal from downtrend); Evening Star (bullish, Doji, bearish — reversal from uptrend); Three White Soldiers (three consecutive strong green candles — strong bullish momentum). Candlestick patterns are significantly more reliable when: (1) They appear at a meaningful support/resistance level. (2) They’re confirmed by above-average volume. (3) They align with the broader timeframe trend direction.
Cup and Handle
The Cup and Handle pattern, described by William O’Neil in his influential 1988 book “How to Make Money in Stocks,” is a bullish continuation pattern that typically forms over weeks to months in established uptrends. The “cup” is a U-shaped rounded bottom (not a sharp V — the rounded shape indicates gradual selling exhaustion and gradual recovery without panic or euphoria). After the cup forms and price returns to the prior high, a brief consolidation called the “handle” forms — a minor pullback of 10-30% within the cup’s right side, representing the final shakeout of weak holders. The breakout occurs when price moves above the handle’s upper boundary (the “pivot point”) — ideally on volume at least 50% above the 10-day average. This volume confirms institutional buying. The measured price target = the depth of the cup added to the breakout point. O’Neil’s research through thousands of historical chart patterns found this pattern preceded some of the largest stock market winners in history. The pattern works across individual stocks, indices, and cryptocurrency assets. Ideal cup characteristics: smooth, round base (not jagged), a decline of 20-35% from left side to the bottom, and a right side that retests the old high without exceeding it. Handle characteristics: volume should decline during handle formation (consolidation — no selling pressure) and expand dramatically on the breakout.
Divergence Trading
Divergence occurs when price action and a momentum indicator move in opposite directions, revealing a disconnect between price trend and underlying momentum. Regular Bullish Divergence: price makes lower lows while the indicator (RSI, MACD, Stochastic) makes higher lows — sellers are losing strength; a reversal upward may be near. Regular Bearish Divergence: price makes higher highs while the indicator makes lower highs — buyers are losing conviction despite rising prices; a reversal downward may be approaching. Hidden Divergence is used for trend continuation signals. Hidden Bullish Divergence: price makes a higher low (healthy pullback in an uptrend) while the indicator makes a lower low — signals the uptrend is likely to continue. Hidden Bearish Divergence: price makes a lower high (pullback in a downtrend) while the indicator makes a higher high — signals the downtrend is likely to continue. Hidden divergences are counterintuitive but highly reliable in trending environments. Key principles for reliable divergence trading: (1) Divergence must span at least 2 clear swing points. (2) It should appear at a significant support/resistance level. (3) A reversal candlestick at the divergence point provides entry confirmation. (4) Time compression: the same divergence on a higher timeframe (weekly) carries more weight than on a lower timeframe (15-minute). Divergence alone is insufficient for entry — it is a high-probability warning that requires price confirmation before acting.
Double Top / Double Bottom
The Double Top is a bearish reversal pattern that forms when price reaches a high twice within a relatively close range, separated by a moderate trough (the “neckline”), before reversing downward. The two peaks at approximately the same price level suggest that the market has twice tested and failed to overcome that resistance — indicating that selling pressure at that price is overwhelming. The pattern is confirmed when price breaks below the neckline (the trough between the two peaks) on increased volume. The measured target = the height of the pattern (from peaks to neckline) subtracted from the neckline breakout. The Double Bottom is the mirror image — two troughs at approximately equal lows separated by a peak, with confirmation on a break above the neckline. It signals that buyers have twice absorbed all available selling at that price level, indicating a reversal from downtrend to uptrend. Volume ideally should be higher on the second bottom’s subsequent rally than during the initial recovery between the two troughs, confirming accumulation. Variants: The Triple Top/Bottom (three tests of the level before breaking) is rarer but more reliable. The distinction between a double top and a continuation pattern (consolidation at a prior high before breaking higher) is determined by the character of the two peaks — if volume declines on the second peak (buyers weakening) and price forms a distinct peak rather than consolidating, it leans toward double top.
Elliott Wave Theory
Elliott Wave Theory, developed by Ralph Nelson Elliott in the 1930s, proposes that financial markets move in repetitive, fractal wave patterns reflecting collective investor psychology. The basic cycle: a 5-wave motive sequence in the direction of the primary trend (waves 1-2-3-4-5) followed by a 3-wave corrective sequence (waves A-B-C). Each wave subdivides into smaller waves of the same structure — a fractal repeating across all timeframes. Three inviolable rules: (1) Wave 2 never retraces 100%+ of Wave 1. (2) Wave 3 is never the shortest of Waves 1, 3, and 5. (3) Wave 4 never overlaps Wave 1 (in non-leveraged markets). Fibonacci ratios govern wave relationships: Wave 3 is often 1.618× Wave 1; Wave 2 retraces 61.8% of Wave 1; Wave 4 retraces 38.2% of Wave 3; Wave 5 often equals Wave 1. Corrective patterns include Zigzag (sharp A-B-C), Flat (A-B-C with full B retracement), Triangle (converging A-B-C-D-E, typically preceding the final wave of the motive sequence), and the complex Double/Triple Three. Critics note that EWT counts are highly subjective — two analysts rarely agree on the exact count, and the abundance of “alternate” counts makes the theory difficult to falsify. Proponents use EWT as a probabilistic framework for understanding the market’s position within larger cycles, not as a mechanical trading system. Best used with Fibonacci tools to project wave targets and potential reversal zones.
Fibonacci Retracement
Fibonacci retracement uses horizontal levels at key Fibonacci ratios — 23.6%, 38.2%, 50%, 61.8%, 78.6% — to identify potential support/resistance during pullbacks within a trend. These ratios derive from the Fibonacci sequence, where each number is the sum of the two preceding (0, 1, 1, 2, 3, 5, 8, 13…). The 61.8% level (the golden ratio) appears throughout nature and mathematics and shows particularly robust support/resistance behaviour across all financial markets. To draw: identify a significant swing high and swing low, apply the Fibonacci tool, and the levels are automatically placed. In an uptrend, the key retracement levels (38.2%, 61.8%) represent likely areas where the pullback will find support before the trend resumes. The “golden pocket” (61.8% to 65%) is a frequently traded zone — representing deep retracement that tests conviction but stops short of invalidating the trend. Fibonacci extension levels (127.2%, 161.8%, 261.8%) project targets beyond the original swing’s extreme. Fibonacci works best when levels coincide with other technical factors: a historical support/resistance level, a moving average, or a candlestick reversal pattern at the Fibonacci zone creates a high-confluence setup. Used in isolation, Fibonacci levels are inconsistent; combined with structure, they become powerful.
Flag and Pennant
Flag and Pennant patterns are short-term continuation patterns that appear within strongly trending markets, representing a brief consolidation (the flag or pennant) before the trend resumes. A Flag forms after a sharp directional move (the “flagpole”): price consolidates in a narrow, parallel channel that slopes slightly against the prior trend — like a flag attached to a pole. A Pennant is similar but the consolidation forms in a symmetrical triangle (converging trendlines) rather than a parallel channel. Both patterns are typically resolved with a breakout in the direction of the original trend. Key characteristics of valid flags/pennants: The prior trend (flagpole) should be sharp and strong — a clear impulse move with good volume. The consolidation phase should exhibit declining volume — indicating lack of selling/buying pressure, just digestion. The breakout should occur on volume expansion. The price target is measured from the flagpole’s length added to the breakout point: if the flagpole was a ₹100 move and the flag breaks out at ₹500, target = ₹600. These patterns are among the most reliable continuation signals. They appear on all timeframes — a 5-minute chart flag is as valid as a weekly chart flag. On shorter timeframes, they are frequently used by intraday traders for momentum entries. The declining volume during consolidation is the critical qualifier — rising volume during the flag formation warns that the move is being redistributed (sold), not consolidated.
Head and Shoulders
The Head and Shoulders pattern, described in Edwards and Magee’s foundational 1948 text, is widely considered the most reliable reversal chart pattern. It consists of three peaks: a left shoulder, a higher head, and a right shoulder roughly equal in height to the left shoulder. A “neckline” connects the troughs between the peaks. The pattern illustrates exhaustion: buyers pushed to a new high (the head) but failed to hold it, then failed to reach the head on the next attempt (right shoulder) — progressively weakening bullish conviction. Completion signal: a decisive break below the neckline on high volume — the final capitulation of bulls. Price target = head-to-neckline distance subtracted from the breakout point. E.g., head at 18,900, neckline at 17,800, breakout below 17,800 → target 16,700. Bulkowski’s research shows H&S patterns reach their measured target approximately 74% of the time. The Inverse Head and Shoulders is the mirror image — three troughs signalling reversal from downtrend to uptrend, equally reliable. False breakouts are a known risk — a neckline break followed by immediate price reversal is called a “failure” and often signals a sharp move in the opposite direction, as traders who shorted the breakdown are rapidly forced to cover. Waiting for a neckline retest after the initial break (old neckline now acting as resistance) provides a safer entry with more information.
MACD
MACD (Moving Average Convergence Divergence) is a trend-following momentum indicator developed by Gerald Appel in the 1970s. It tracks the relationship between two EMAs of price. The MACD line = 12-period EMA minus 26-period EMA. A 9-period EMA of the MACD line (the signal line) is plotted alongside. The histogram = MACD line minus signal line, visually representing momentum expansion or contraction. Three primary MACD signals: (1) Signal line crossover — MACD crossing above the signal line is bullish; crossing below is bearish. (2) Zero line crossover — MACD above zero means 12-EMA > 26-EMA (bullish trend structure); below zero is bearish. (3) MACD divergence — when price makes new highs/lows but MACD doesn’t confirm, suggesting trend exhaustion. The histogram’s peak or trough often precedes the actual crossover, providing an early warning of momentum shift. MACD works best on daily and weekly charts for trending assets; on intraday charts it generates too many false signals. Combined with RSI — MACD for trend direction and RSI for overbought/oversold context — creates powerful confluent trade filters. A bullish MACD crossover when RSI is near 50 (not overbought) in an established uptrend provides a high-probability setup. MACD is a lagging indicator by definition, confirming trend shifts rather than predicting them.
Moving Average (SMA & EMA)
A Moving Average smooths price data by computing an average over a rolling period, filtering noise to reveal trend direction. The Simple Moving Average (SMA) weights all periods equally. The Exponential Moving Average (EMA) weights recent prices more heavily, reacting faster to price changes. Key periods: 9 and 21 EMA for short-term intraday/swing signals; 50 SMA/EMA for intermediate trend (widely watched by institutions); 200 SMA as the gold standard long-term trend divider — price above the 200 SMA = bull market structure; below = bear market. Moving average crossovers are the most widely traded signals: 9/21 EMA cross for intraday momentum; 50/200 SMA for the Golden Cross (50 crosses above 200 — bullish) and Death Cross (50 crosses below 200 — bearish). The 200 SMA acts as a mean-reversion magnet — markets far above it tend to pull back toward it; markets far below it tend to rally. This phenomenon underpins many reversion strategies. Key limitations: MAs are lagging indicators — they confirm trends that have already begun. In sideways markets they produce whipsaw false signals. As a filter rather than primary signal, the 200 SMA is most powerful: only buy setups when price is above the 200 SMA; only short when below. This single filter eliminates a significant proportion of losing trades across most trend-following strategies.
OBV (On-Balance Volume)
On-Balance Volume (OBV), developed by Joseph Granville in 1963, is a cumulative momentum indicator based on the principle that volume precedes price. The calculation is simple: on up days (close > prior close), the day’s volume is added to a running OBV total; on down days, it is subtracted. The absolute OBV value is irrelevant — what matters is OBV’s trend and its relationship to price. OBV visually represents the cumulative flow of money into or out of a security. OBV divergence is its most powerful signal. Bullish divergence: price makes a new low but OBV makes a higher low — indicating that volume on down days is shrinking, meaning sellers are losing conviction even as price continues lower. This “hidden accumulation” often precedes significant rallies. Bearish divergence: price makes a new high but OBV makes a lower high — indicating distribution (selling into strength) by smart money even as price looks strong. This often precedes significant declines. OBV trend alignment with price trend confirms the trend’s health. OBV breaking to new highs before price is a leading bullish indicator; OBV diverging and rolling over while price is still rising is one of the earliest warnings of a topping process. Granville’s famous remark: “The market is a device for transferring money from the impatient to the patient” — and OBV reveals where the patient money is flowing.
Parabolic SAR
Parabolic SAR (Stop and Reverse) is a trend-following indicator developed by J. Welles Wilder that places dots above or below the price to indicate potential stop-loss levels and the direction of the trend. SAR stands for “Stop and Reverse” — when the dots flip from below price (bullish) to above price (bearish), the system signals both a stop-out of the long position and a potential entry short (and vice versa). The dots accelerate as the trend progresses, pulling closer to price over time, which means a strong trend eventually triggers a SAR reversal when the trend decelerates. The Acceleration Factor (AF) starts at 0.02 and increases by 0.02 each time price makes a new high (in an uptrend), up to a maximum of 0.20. This acceleration means early in a trend the SAR is far from price (allowing room), but as the trend matures and new highs are made, the SAR tightens — reducing risk on a reversal. The SAR is useful for trailing stop-loss management in trending positions: it provides an objective, algorithm-defined stop that moves in the direction of the trade without requiring manual adjustment. Limitations: In sideways markets, Parabolic SAR whipsaws repeatedly, generating frequent false reversals. It is most effective in strongly trending environments (high ADX readings). Traders typically disable the “reverse” function and only use SAR for stop-loss trailing rather than the full stop-and-reverse system.
Pivot Points
Pivot Points are calculated support/resistance levels derived from the prior period’s high, low, and close. The central Pivot Point (PP) = (High + Low + Close) / 3. Additional resistance (R1, R2, R3) and support (S1, S2, S3) levels are calculated from the central pivot. Because these calculations are the same for all traders who use them, they create self-fulfilling levels where significant price reactions occur — institutional algorithms, intraday traders, and market makers all watch pivots, increasing their significance as self-reinforcing price levels. Variants: Standard (classic) pivots, Fibonacci Pivots (using Fibonacci ratios for S/R placement), Camarilla Pivots (8 tighter levels for scalping), Woodie’s Pivots (heavier weighting on close), DeMark Pivots (formula depends on relationship between open and close). Daily pivots for intraday trading; weekly pivots for swing trading; monthly pivots for positional strategies. The central Pivot Point represents “fair value” of the prior day’s trading. Markets trading above PP have bullish intraday bias; below PP is bearish. Common strategies: Buy PP bounce with target R1, stop below S1; sell R1 rejection with target PP, stop above R2; anticipate range-expansion days when price opens above R1 or below S1 and trade in the opening direction. Pivots are most useful during normal trending days; on high-impact news days, price can blow through all pivot levels without respecting them.
RSI (Relative Strength Index)
The Relative Strength Index (RSI) is a momentum oscillator developed by J. Welles Wilder Jr. in 1978. RSI measures the speed and magnitude of recent price changes on a scale of 0 to 100. The formula: RSI = 100 − [100 / (1 + RS)], where RS = Average Gain / Average Loss over the lookback period (default 14). Values above 70 are considered overbought; below 30 is oversold. RSI is best used in ranging markets where overbought/oversold signals are reliable. In strong trending markets, RSI can remain overbought for extended periods — blindly shorting an RSI reading of 75 in a strong bull trend is a common beginner mistake. Advanced RSI applications include: RSI divergence (price makes new high but RSI makes a lower high — bearish divergence; price makes new low but RSI makes a higher low — bullish divergence); RSI trendlines; and the RSI 50 midline crossover (above 50 = bullish momentum; below 50 = bearish). RSI settings can be adjusted by trading style: shorter periods (7, 9) for intraday sensitivity; longer periods (21, 25) for swing trading to reduce noise. RSI is best combined with other indicators — volume, moving averages, support/resistance — rather than in isolation. Andrew Cardwell’s positive and negative reversals added further depth to Wilder’s original framework, helping traders distinguish between exhausted trends and genuine reversals.
Support and Resistance
Support and resistance are price levels where buying or selling pressure has historically been strong enough to halt or reverse price movements. Support is a price floor where buyers emerge in force; resistance is a ceiling where sellers consistently overwhelm buyers. These are zones, not exact prices — the market is an auction of human decisions, not a precision machine. Key factors that determine a level’s significance: the number of times price has tested it, the volume traded there, the timeframe (weekly levels outweigh daily which outweigh hourly), and whether it’s a round number (psychological importance). Role reversal is a foundational principle: when a support level is decisively broken, it becomes resistance on future rallies; when resistance is broken, it becomes support on pullbacks. This occurs because traders who bought at support and are now sitting at a loss use rallies back to that level as a chance to exit at breakeven — creating selling pressure at the old support. Understanding role reversal allows traders to anticipate where price will find resistance/support after a structural break. Advanced support/resistance tools: Volume Profile (high-volume nodes = strong S/R; low-volume nodes = areas price moves through quickly); Fibonacci retracement levels (38.2%, 61.8%); Order Blocks (last directional candle before a significant move, where institutional orders were concentrated). The best trades occur when multiple S/R sources align — a Fibonacci level coinciding with a volume node and a historical price pivot creates a high-confluence zone.
Volume Analysis
Volume represents the total number of units traded in a given period — it is the fuel behind price movements. Price moves on high volume are more significant and durable than the same move on low volume. Volume analysis distinguishes between institutional activity (high volume, meaningful moves) and retail noise (low volume, unreliable signals). The foundational principle: in a healthy uptrend, up days should carry higher volume than down days; divergence from this pattern warns of trend weakness. Richard Wyckoff’s volume analysis, refined through decades of market study, identified Accumulation (smart money buying at low prices, characterised by declining volume on pullbacks and rising volume on rallies) and Distribution (smart money selling at high prices, characterised by rising volume on rallies that fail to make new highs and high volume selling on declines). Climactic volume — an enormous single-session spike — often marks trend exhaustion and potential reversal. On-Balance Volume (OBV) accumulates volume — adding on up days, subtracting on down days. OBV making new highs while price doesn’t = bullish divergence (hidden buying). OBV failing to confirm new price highs = bearish divergence (distribution). Volume Profile maps volume at each price level, creating a visual distribution: High Volume Nodes (HVN) act as strong support/resistance; Low Volume Nodes (LVN) are areas price moves through with low resistance.
VWAP (Volume Weighted Average Price)
Volume Weighted Average Price (VWAP) is a trading benchmark that represents the average price at which a security has traded throughout the day, weighted by volume. Unlike a simple average of prices, VWAP gives more weight to prices at which larger volumes were traded, making it a more accurate reflection of the “true” average price experienced by the market. The formula is: VWAP = Cumulative (Price x Volume) / Cumulative Volume, calculated from the market open and reset at the start of each new session. VWAP is plotted as a single continuous line on intraday charts and is one of the most widely used technical tools in Indian equity and derivatives markets. VWAP serves multiple purposes for different market participants. For institutional investors and mutual funds, executing large orders at or below VWAP is the standard benchmark of execution quality — buying below VWAP is considered “beating the market” and buying above VWAP is considered a suboptimal execution. VWAP-based algorithmic execution engines (VWAP algos) are standard tools at broking desks handling large institutional orders; they break up a large order into smaller slices timed to align with historical volume patterns throughout the day, aiming to achieve the VWAP benchmark price. For retail traders, VWAP acts as a dynamic support and resistance level — in an uptrend, price tends to hold above VWAP, while in a downtrend, it tends to remain below. The relationship between price and VWAP provides actionable signals for intraday traders. A stock opening above VWAP and bouncing off it during pullbacks signals bullish momentum — traders may use VWAP as a stop-loss reference for long positions. A stock trading below VWAP and rejecting it on rallies suggests bearish momentum. However, VWAP loses effectiveness toward the market close, as the cumulative nature of its calculation makes it increasingly slow to react to late-session price moves. Anchored VWAP (A-VWAP), a variant that starts the calculation from a user-defined price (such as a major swing high, an earnings date, or an IPO listing date), has gained popularity in India as a tool to assess whether prices are expensive or cheap relative to a meaningful historical reference point.
Wyckoff Method
The Wyckoff Method, developed by Richard Wyckoff in the early 1900s, proposes that markets move in four phases driven by the actions of large institutional operators (the “Composite Man” or Smart Money): Accumulation (institutions buy from retail sellers at low prices, creating a trading range), Markup (price rises as the public recognises the uptrend), Distribution (institutions sell to eager retail buyers at high prices), and Markdown (price falls as institutions are out and retail is left holding). Understanding which phase the market is in guides trading and investment decisions. Wyckoff identified specific events within accumulation and distribution phases: Preliminary Support/Supply (first signs of buying/selling entering), Selling/Buying Climax (climactic volume spike at an extreme — exhaustion), Automatic Rally/Reaction (reflexive bounce/decline after climax), Secondary Test (retest of climax on lower volume — confirming commitment of buyers/sellers), the Spring or Upthrust (false breakout to shake out weak hands), Last Point of Support/Supply (final shakeout before the major move), and Sign of Strength/Weakness (confirming breakout from the trading range). Modern applications integrate Wyckoff with on-chain crypto data (tracking whale accumulation/distribution in Bitcoin), order flow analysis, and large-print tape reading. Wyckoff bridges fundamental and technical analysis — it explains price patterns as the logical result of institutional order execution rather than mystical chart shapes.
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