Volume (finance)

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⎧​OBVt−1​+Vt​OBVt−1​−Vt​OBVt−1​​if Ct​>Ct−1​if Ct​<Ct−1​if Ct​=Ct−1​​ where $V_t$Vt​ is the volume at time $t$t, and $C_t$Ct​ is the closing price at time $t$t. This calculation adds volume on up days (indicating buying pressure) and subtracts it on down days (indicating selling pressure), resulting in a running total that rises with net buying and falls with net selling. Traders use OBV to spot divergences, such as when price reaches a new high but OBV fails to confirm it (bearish signal) or declines while price rises (potential reversal), as Granville emphasized for identifying trend weaknesses.^[41][43][42] The Volume-Price Trend (VPT), also known as the Price-Volume Trend, extends this concept by weighting volume changes with the percentage price movement, offering a more nuanced measure of volume's role in price trends. Developed in the mid-20th century as part of evolving technical analysis methods, VPT quantifies the balance between supply and demand by incorporating relative price changes. Its formula is: $$\text{VPT}_t = \text{VPT}_{t-1} + V_t \times \frac{C_t - C_{t-1}}{C_{t-1}}$$VPTt​=VPTt−1​+Vt​×Ct−1​Ct​−Ct−1​​ This cumulative indicator adds volume multiplied by the percentage price increase on up days and subtracts it on down days, amplifying the impact of significant price swings. VPT helps traders gauge trend strength; for instance, rising VPT alongside rising prices signals sustained buying interest, while divergences may indicate weakening momentum.^[44][45] Other prominent indicators include the Accumulation/Distribution Line (A/D) and the Chaikin Money Flow (CMF), both created by Marc Chaikin to refine money flow analysis. The A/D line, developed in the 1970s, uses a "close location value" to position the close within the day's range, estimating whether volume reflects accumulation (buying) or distribution (selling). The multiplier is calculated as: $$\text{Multiplier} = \frac{(C_t - L_t) - (H_t - C_t)}{H_t - L_t}$$Multiplier=Ht​−Lt​(Ct​−Lt​)−(Ht​−Ct​)​ where $H_t$Ht​ and $L_t$Lt​ are the high and low prices at time $t$t. The A/D line then accumulates this as $\text{A/D}_t = \text{A/D}_{t-1} + (\text{Multiplier} \times V_t)$A/Dt​=A/Dt−1​+(Multiplier×Vt​), producing a line that rises with buying pressure near the high and falls with selling near the low. CMF, introduced by Chaikin in the early 1980s, extends this over a period (typically 20-21 days) to create an oscillator bounded between -1 and +1, measuring average money flow. Its formula is: $$\text{CMF}_n = \frac{\sum_{i=1}^n (\text{Multiplier}_i \times V_i)}{\sum_{i=1}^n V_i}$$CMFn​=∑i=1n​Vi​∑i=1n​(Multiplieri​×Vi​)​ Positive CMF values indicate net accumulation, while negative values suggest distribution, aiding in confirming trends or spotting reversals when crossing zero.^{[46][47][48][49]} In modern applications, these indicators are adjusted for corporate actions like stock splits to ensure historical consistency in backtesting; for example, volume data is scaled proportionally to split ratios to prevent distortions in cumulative calculations. They are widely integrated into algorithmic trading systems for generating signals, such as entry/exit rules based on OBV divergences or CMF thresholds, enabling quantitative strategies that backtest volume-price relationships across large datasets.^[50][51][52]

Market Dynamics

Impact on Liquidity and Volatility