Volume

Volume indicators analyze trading volume to assess the strength of price movements and identify potential trend changes. These indicators help determine whether price movements are supported by volume activity.

Import Statement

from openalgo import ta, api

Getting Sample Data

# Initialize API client
client = api(api_key='your_api_key_here', host='http://127.0.0.1:5000')

# Fetch historical data
df = client.history(symbol="SBIN", 
                   exchange="NSE", 
                   interval="5m", 
                   start_date="2025-04-01", 
                   end_date="2025-04-08")

# Extract OHLCV data
high = df['high']
low = df['low'] 
close = df['close']
open_price = df['open']
volume = df['volume']

---

On Balance Volume (OBV)

OBV is a momentum indicator that uses volume flow to predict changes in stock price by adding volume on up days and subtracting volume on down days.

Usage

obv = ta.obv(close, volume)

Parameters

• close (pandas.Series): Closing prices

• volume (pandas.Series): Volume data

Returns

• pandas.Series: OBV values with same index as input

Example

# Calculate OBV
obv_values = ta.obv(df['close'], df['volume'])

# Add to DataFrame
df['OBV'] = obv_values

print(df[['close', 'volume', 'OBV']].tail())

---

On Balance Volume with Smoothing (OBV Smoothed)

Enhanced OBV with various smoothing options including moving averages and Bollinger Bands support.

Usage

# Basic smoothed OBV
obv_smoothed = ta.obv_smoothed(close, volume, ma_type="SMA", ma_length=20)

# With Bollinger Bands
obv_bb_middle, obv_bb_upper, obv_bb_lower = ta.obv_smoothed(
    close, volume, ma_type="SMA + Bollinger Bands", bb_length=20, bb_mult=2.0
)

Parameters

• close (pandas.Series): Closing prices

• volume (pandas.Series): Volume data

• ma_type (str, default="None"): Smoothing type - "None", "SMA", "SMA + Bollinger Bands", "EMA", "SMMA (RMA)", "WMA", "VWMA"

• ma_length (int, default=20): Moving average length

• bb_length (int, default=20): Bollinger Bands length

• bb_mult (float, default=2.0): Bollinger Bands multiplier

Returns

• pandas.Series: Smoothed OBV values (for most ma_types)

• tuple: (middle, upper, lower) for "SMA + Bollinger Bands"

Example

# Calculate various OBV smoothing options
obv_sma = ta.obv_smoothed(df['close'], df['volume'], ma_type="SMA", ma_length=20)
obv_ema = ta.obv_smoothed(df['close'], df['volume'], ma_type="EMA", ma_length=20)

# OBV with Bollinger Bands
obv_bb_mid, obv_bb_up, obv_bb_low = ta.obv_smoothed(
    df['close'], df['volume'], ma_type="SMA + Bollinger Bands"
)

df['OBV_SMA'] = obv_sma
df['OBV_EMA'] = obv_ema
df['OBV_BB_Mid'] = obv_bb_mid
df['OBV_BB_Upper'] = obv_bb_up
df['OBV_BB_Lower'] = obv_bb_low

---

Volume Weighted Average Price (VWAP)

VWAP is the average price a security has traded at throughout the day, based on both volume and price, giving more weight to prices with higher volume.

Usage

vwap = ta.vwap(high, low, close, volume, source="hlc3", anchor="Session")

Parameters

• high (pandas.Series): High prices

• low (pandas.Series): Low prices

• close (pandas.Series): Closing prices

• volume (pandas.Series): Volume data

• source (str, default="hlc3"): Price source - "hlc3", "hl2", "ohlc4", "close"

• anchor (str, default="Session"): Anchor period - "Session", "Week", "Month", etc.

Returns

• pandas.Series: VWAP values

Example

# Calculate VWAP
vwap_values = ta.vwap(df['high'], df['low'], df['close'], df['volume'])

# VWAP with different source
vwap_close = ta.vwap(df['high'], df['low'], df['close'], df['volume'], source="close")

df['VWAP'] = vwap_values
df['VWAP_Close'] = vwap_close

print(df[['close', 'VWAP', 'VWAP_Close']].tail())

---

Money Flow Index (MFI)

MFI is a momentum indicator that uses both price and volume to measure buying and selling pressure. Also known as Volume-Weighted RSI.

Usage

mfi = ta.mfi(high, low, close, volume, period=14)

Parameters

• high (pandas.Series): High prices

• low (pandas.Series): Low prices

• close (pandas.Series): Closing prices

• volume (pandas.Series): Volume data

• period (int, default=14): Number of periods for MFI calculation

Returns

• pandas.Series: MFI values (range: 0 to 100)

Example

# Calculate MFI with default period
mfi_14 = ta.mfi(df['high'], df['low'], df['close'], df['volume'])

# Calculate MFI with different period
mfi_21 = ta.mfi(df['high'], df['low'], df['close'], df['volume'], period=21)

df['MFI_14'] = mfi_14
df['MFI_21'] = mfi_21

print(df[['close', 'volume', 'MFI_14']].tail())

---

Accumulation/Distribution Line (ADL)

ADL is a volume-based indicator designed to measure the cumulative flow of money into and out of a security.

Usage

adl = ta.adl(high, low, close, volume)

Parameters

• high (pandas.Series): High prices

• low (pandas.Series): Low prices

• close (pandas.Series): Closing prices

• volume (pandas.Series): Volume data

Returns

• pandas.Series: ADL values

Example

# Calculate Accumulation/Distribution Line
adl_values = ta.adl(df['high'], df['low'], df['close'], df['volume'])

df['ADL'] = adl_values

print(df[['close', 'volume', 'ADL']].tail())

---

Chaikin Money Flow (CMF)

CMF is the sum of Money Flow Volume over a period divided by the sum of volume over the same period.

Usage

cmf = ta.cmf(high, low, close, volume, period=20)

Parameters

• high (pandas.Series): High prices

• low (pandas.Series): Low prices

• close (pandas.Series): Closing prices

• volume (pandas.Series): Volume data

• period (int, default=20): Number of periods for CMF calculation

Returns

• pandas.Series: CMF values

Example

# Calculate Chaikin Money Flow
cmf_20 = ta.cmf(df['high'], df['low'], df['close'], df['volume'])

# CMF with different period
cmf_10 = ta.cmf(df['high'], df['low'], df['close'], df['volume'], period=10)

df['CMF_20'] = cmf_20
df['CMF_10'] = cmf_10

print(df[['close', 'CMF_20']].tail())

---

Ease of Movement (EMV)

EMV relates price change to volume and is particularly useful for assessing the strength of a trend.

Usage

emv = ta.emv(high, low, volume, length=14, divisor=10000)

Parameters

• high (pandas.Series): High prices

• low (pandas.Series): Low prices

• volume (pandas.Series): Volume data

• length (int, default=14): Period for SMA smoothing

• divisor (int, default=10000): Divisor for scaling EMV values

Returns

• pandas.Series: EMV values

Example

# Calculate Ease of Movement
emv_14 = ta.emv(df['high'], df['low'], df['volume'])

# EMV with custom parameters
emv_custom = ta.emv(df['high'], df['low'], df['volume'], length=21, divisor=50000)

df['EMV_14'] = emv_14
df['EMV_Custom'] = emv_custom

print(df[['close', 'volume', 'EMV_14']].tail())

---

Elder Force Index (FI)

Force Index combines price and volume to assess the power used to move the price of an asset.

Usage

fi = ta.force_index(close, volume, length=13)

Parameters

• close (pandas.Series): Closing prices

• volume (pandas.Series): Volume data

• length (int, default=13): Period for EMA smoothing

Returns

• pandas.Series: Elder Force Index values

Example

# Calculate Elder Force Index
fi_13 = ta.force_index(df['close'], df['volume'])

# Force Index with different period
fi_21 = ta.force_index(df['close'], df['volume'], length=21)

df['Force_Index_13'] = fi_13
df['Force_Index_21'] = fi_21

print(df[['close', 'volume', 'Force_Index_13']].tail())

---

Negative Volume Index (NVI)

NVI focuses on days when volume decreases from the previous day, using cumulative rate of change.

Usage

# Basic NVI
nvi = ta.nvi(close, volume)

# NVI with EMA signal line
nvi, nvi_ema = ta.nvi_with_ema(close, volume, ema_length=255)

Parameters

• close (pandas.Series): Closing prices

• volume (pandas.Series): Volume data

• ema_length (int, default=255): EMA period for signal line

Returns

• pandas.Series: NVI values

• tuple: (nvi, nvi_ema) for nvi_with_ema method

Example

# Calculate NVI
nvi_values = ta.nvi(df['close'], df['volume'])

# Calculate NVI with EMA signal
nvi_line, nvi_signal = ta.nvi_with_ema(df['close'], df['volume'])

df['NVI'] = nvi_values
df['NVI_Line'] = nvi_line
df['NVI_Signal'] = nvi_signal

print(df[['close', 'volume', 'NVI']].tail())

---

Positive Volume Index (PVI)

PVI focuses on days when volume increases from the previous day.

Usage

# Basic PVI
pvi = ta.pvi(close, volume, initial_value=100.0)

# PVI with signal line
pvi, pvi_signal = ta.pvi_with_signal(close, volume, signal_type="EMA", signal_length=255)

Parameters

• close (pandas.Series): Closing prices

• volume (pandas.Series): Volume data

• initial_value (float, default=100.0): Initial PVI value

• signal_type (str, default="EMA"): Signal smoothing type ("EMA" or "SMA")

• signal_length (int, default=255): Signal line period

Returns

• pandas.Series: PVI values

• tuple: (pvi, signal) for pvi_with_signal method

Example

# Calculate PVI
pvi_values = ta.pvi(df['close'], df['volume'])

# Calculate PVI with signal line
pvi_line, pvi_signal = ta.pvi_with_signal(df['close'], df['volume'])

df['PVI'] = pvi_values
df['PVI_Line'] = pvi_line
df['PVI_Signal'] = pvi_signal

print(df[['close', 'volume', 'PVI']].tail())

---

Volume Oscillator (VOLOSC)

Volume Oscillator shows the relationship between two exponential moving averages of volume.

Usage

vo = ta.volosc(volume, short_length=5, long_length=10)

Parameters

• volume (pandas.Series): Volume data

• short_length (int, default=5): Short EMA length

• long_length (int, default=10): Long EMA length

• check_volume_validity (bool, default=True): Check for valid volume data

Returns

• pandas.Series: Volume Oscillator values

Example

# Calculate Volume Oscillator
vo_5_10 = ta.volosc(df['volume'])

# Volume Oscillator with custom periods
vo_3_15 = ta.volosc(df['volume'], short_length=3, long_length=15)

df['VO_5_10'] = vo_5_10
df['VO_3_15'] = vo_3_15

print(df[['volume', 'VO_5_10']].tail())

---

Volume Rate of Change (VROC)

VROC measures the rate of change in volume over a specified period.

Usage

vroc = ta.vroc(volume, period=25)

Parameters

• volume (pandas.Series): Volume data

• period (int, default=25): Number of periods to look back

Returns

• pandas.Series: VROC values

Example

# Calculate Volume Rate of Change
vroc_25 = ta.vroc(df['volume'])

# VROC with different period
vroc_12 = ta.vroc(df['volume'], period=12)

df['VROC_25'] = vroc_25
df['VROC_12'] = vroc_12

print(df[['volume', 'VROC_25']].tail())

---

Klinger Volume Oscillator (KVO)

KVO is designed to predict price reversals by comparing volume to price movement.

Usage

kvo, kvo_trigger = ta.kvo(high, low, close, volume, trig_len=13, fast_x=34, slow_x=55)

Parameters

• high (pandas.Series): High prices

• low (pandas.Series): Low prices

• close (pandas.Series): Closing prices

• volume (pandas.Series): Volume data

• trig_len (int, default=13): Trigger line EMA period

• fast_x (int, default=34): Fast EMA period

• slow_x (int, default=55): Slow EMA period

Returns

• tuple: (kvo, trigger) pandas.Series

Example

# Calculate Klinger Volume Oscillator
kvo_line, kvo_trigger = ta.kvo(df['high'], df['low'], df['close'], df['volume'])

# KVO with custom parameters
kvo_custom, kvo_trig_custom = ta.kvo(df['high'], df['low'], df['close'], df['volume'], 
                                    trig_len=9, fast_x=21, slow_x=34)

df['KVO'] = kvo_line
df['KVO_Trigger'] = kvo_trigger

print(df[['close', 'volume', 'KVO', 'KVO_Trigger']].tail())

---

Price Volume Trend (PVT)

PVT combines price and volume to show cumulative volume based on price changes.

Usage

pvt = ta.pvt(close, volume)

Parameters

• close (pandas.Series): Closing prices

• volume (pandas.Series): Volume data

Returns

• pandas.Series: PVT values

Example

# Calculate Price Volume Trend
pvt_values = ta.pvt(df['close'], df['volume'])

df['PVT'] = pvt_values

print(df[['close', 'volume', 'PVT']].tail())

---

Relative Volume (RVOL)

RVOL compares current volume to average volume over a specified period.

Usage

rvol = ta.rvol(volume, period=20)

Parameters

• volume (pandas.Series): Volume data

• period (int, default=20): Period for average volume calculation

Returns

• pandas.Series: RVOL values

Example

# Calculate Relative Volume
rvol_20 = ta.rvol(df['volume'])

# RVOL with different period
rvol_10 = ta.rvol(df['volume'], period=10)

df['RVOL_20'] = rvol_20
df['RVOL_10'] = rvol_10

print(df[['volume', 'RVOL_20']].tail())

---

Complete Volume Analysis Example

from openalgo import ta, api
import pandas as pd

# Get data
client = api(api_key='your_api_key_here', host='http://127.0.0.1:5000')
df = client.history(symbol="SBIN", exchange="NSE", interval="5m", 
                   start_date="2025-04-01", end_date="2025-04-08")

# Calculate multiple volume indicators
df['OBV'] = ta.obv(df['close'], df['volume'])
df['VWAP'] = ta.vwap(df['high'], df['low'], df['close'], df['volume'])
df['MFI'] = ta.mfi(df['high'], df['low'], df['close'], df['volume'])
df['ADL'] = ta.adl(df['high'], df['low'], df['close'], df['volume'])
df['CMF'] = ta.cmf(df['high'], df['low'], df['close'], df['volume'])
df['EMV'] = ta.emv(df['high'], df['low'], df['volume'])
df['Force_Index'] = ta.force_index(df['close'], df['volume'])
df['Volume_Osc'] = ta.volosc(df['volume'])
df['PVT'] = ta.pvt(df['close'], df['volume'])
df['RVOL'] = ta.rvol(df['volume'])

# KVO requires multiple returns
df['KVO'], df['KVO_Trigger'] = ta.kvo(df['high'], df['low'], df['close'], df['volume'])

# Display results
volume_indicators = ['close', 'volume', 'OBV', 'VWAP', 'MFI', 'ADL', 'CMF', 
                    'EMV', 'Force_Index', 'Volume_Osc', 'PVT', 'RVOL', 'KVO']

print("Volume Indicators Analysis:")
print(df[volume_indicators].tail(10))

# Volume analysis summary
print("\nVolume Indicators Summary (Last Period):")
last_row = df.iloc[-1]
print(f"Close Price: {last_row['close']:.2f}")
print(f"Volume: {last_row['volume']:,}")
print(f"VWAP: {last_row['VWAP']:.2f}")
print(f"MFI: {last_row['MFI']:.2f}")
print(f"Relative Volume: {last_row['RVOL']:.2f}")
print(f"Volume Oscillator: {last_row['Volume_Osc']:.2f}")

Volume Analysis Interpretation

1. OBV: Rising OBV confirms uptrend, falling OBV confirms downtrend

2. VWAP: Price above VWAP suggests bullish momentum, below suggests bearish

3. MFI: Values above 80 indicate overbought, below 20 indicate oversold

4. ADL: Rising ADL confirms price uptrend with strong accumulation

5. CMF: Positive values indicate buying pressure, negative indicate selling

6. Volume Oscillator: Positive values show increasing volume momentum

7. Relative Volume: Values above 1.0 indicate higher than average volume