Volume Indicators

# On Balance Volume (OBV): does volume confirm the price trend?
import os
from datetime import datetime, timedelta

from openalgo import api, ta

client = api(
    api_key=os.getenv("OPENALGO_API_KEY", "your_api_key_here"),
    host=os.getenv("OPENALGO_HOST", "http://127.0.0.1:5000"),
)

end = datetime.now().strftime("%Y-%m-%d")
start = (datetime.now() - timedelta(days=90)).strftime("%Y-%m-%d")
df = client.history(symbol="RELIANCE", exchange="NSE", interval="D", start_date=start, end_date=end)

# OBV adds volume on up-close days and subtracts it on down-close days.
df["OBV"] = ta.obv(df["close"], df["volume"])
print("ta.obv returns a", type(df["OBV"]).__name__)

# Is OBV trending the same way as price over the last 10 sessions?
price_chg = df["close"].iloc[-1] - df["close"].iloc[-11]
obv_chg = df["OBV"].iloc[-1] - df["OBV"].iloc[-11]
print(df[["close", "volume", "OBV"]].tail(5).round(2))
print(f"\n10-day price change: {price_chg:+.2f}")
print(f"10-day OBV change  : {obv_chg:+,.0f}")
print("Verdict:", "volume confirms the move" if (price_chg > 0) == (obv_chg > 0)
      else "DIVERGENCE - volume disagrees with price")

# VWAP: the volume-weighted average price the day's traders actually paid.
import os
from datetime import datetime, timedelta

from openalgo import api, ta

client = api(
    api_key=os.getenv("OPENALGO_API_KEY", "your_api_key_here"),
    host=os.getenv("OPENALGO_HOST", "http://127.0.0.1:5000"),
)

# VWAP is an intraday tool, so we pull 5-minute candles for one symbol.
end = datetime.now().strftime("%Y-%m-%d")
start = (datetime.now() - timedelta(days=5)).strftime("%Y-%m-%d")
df = client.history(symbol="SBIN", exchange="NSE", interval="5m", start_date=start, end_date=end)

# VWAP needs high, low, close AND volume - price weighted by how much traded there.
df["VWAP"] = ta.vwap(df["high"], df["low"], df["close"], df["volume"])

last = df.iloc[-1]
print(df[["close", "volume", "VWAP"]].tail(5).round(2))
print(f"\nLast close: {last['close']:.2f}   VWAP: {last['VWAP']:.2f}")
print("Bias:", "above VWAP - buyers in control" if last["close"] > last["VWAP"]
      else "below VWAP - sellers in control")

# Money Flow Index (MFI): a volume-weighted RSI for overbought/oversold reads.
import os
from datetime import datetime, timedelta

from openalgo import api, ta

client = api(
    api_key=os.getenv("OPENALGO_API_KEY", "your_api_key_here"),
    host=os.getenv("OPENALGO_HOST", "http://127.0.0.1:5000"),
)

end = datetime.now().strftime("%Y-%m-%d")
start = (datetime.now() - timedelta(days=120)).strftime("%Y-%m-%d")
df = client.history(symbol="INFY", exchange="NSE", interval="D", start_date=start, end_date=end)

# MFI runs 0-100 like RSI, but weights each move by its volume.
df["MFI"] = ta.mfi(df["high"], df["low"], df["close"], df["volume"], period=14)

mfi = df["MFI"].iloc[-1]
zone = "OVERBOUGHT (>80)" if mfi > 80 else "OVERSOLD (<20)" if mfi < 20 else "neutral (20-80)"
print(df[["close", "volume", "MFI"]].tail(5).round(2))
print(f"\nLatest MFI: {mfi:.1f} -> {zone}")
print("Days overbought in window:", int((df["MFI"] > 80).sum()))
print("Days oversold in window  :", int((df["MFI"] < 20).sum()))

# Chaikin Money Flow (CMF): are buyers or sellers winning over the period?
import os
from datetime import datetime, timedelta

from openalgo import api, ta

client = api(
    api_key=os.getenv("OPENALGO_API_KEY", "your_api_key_here"),
    host=os.getenv("OPENALGO_HOST", "http://127.0.0.1:5000"),
)

end = datetime.now().strftime("%Y-%m-%d")
start = (datetime.now() - timedelta(days=120)).strftime("%Y-%m-%d")
df = client.history(symbol="GOLDM03JUL26FUT", exchange="MCX", interval="D", start_date=start, end_date=end)

# CMF sums money-flow volume over 20 days. Positive = accumulation, negative = distribution.
df["CMF"] = ta.cmf(df["high"], df["low"], df["close"], df["volume"], period=20)

cmf = df["CMF"].iloc[-1]
print(df[["close", "CMF"]].tail(5).round(4))
print(f"\nLatest CMF: {cmf:+.4f}")
print("Pressure:", "buying / accumulation" if cmf > 0 else "selling / distribution")
print("Strong reading:", "yes" if abs(cmf) > 0.2 else "no (|CMF| under 0.20 is mild)")

# Accumulation/Distribution Line (ADL): a running tally of money flow.
import os
from datetime import datetime, timedelta

from openalgo import api, ta

client = api(
    api_key=os.getenv("OPENALGO_API_KEY", "your_api_key_here"),
    host=os.getenv("OPENALGO_HOST", "http://127.0.0.1:5000"),
)

end = datetime.now().strftime("%Y-%m-%d")
start = (datetime.now() - timedelta(days=90)).strftime("%Y-%m-%d")
df = client.history(symbol="TCS", exchange="NSE", interval="D", start_date=start, end_date=end)

# ADL is cumulative, like OBV, but weights volume by WHERE the close lands in the bar.
df["ADL"] = ta.adl(df["high"], df["low"], df["close"], df["volume"])

# Rising ADL into a flat/up price = accumulation supporting the move.
adl_slope = df["ADL"].iloc[-1] - df["ADL"].iloc[-6]
print(df[["close", "ADL"]].tail(5).round(0))
print(f"\n5-day ADL change: {adl_slope:+,.0f}")
print("Money flow is", "building (accumulation)" if adl_slope > 0 else "leaking out (distribution)")

# Ease of Movement (EMV): how little volume it took to move price.
import os
from datetime import datetime, timedelta

from openalgo import api, ta

client = api(
    api_key=os.getenv("OPENALGO_API_KEY", "your_api_key_here"),
    host=os.getenv("OPENALGO_HOST", "http://127.0.0.1:5000"),
)

end = datetime.now().strftime("%Y-%m-%d")
start = (datetime.now() - timedelta(days=120)).strftime("%Y-%m-%d")
df = client.history(symbol="CRUDEOIL20JUL26FUT", exchange="MCX", interval="D", start_date=start, end_date=end)

# EMV uses high, low and volume (not close). High positive EMV = price rose easily
# on light volume; near zero = it took heavy volume to budge.
df["EMV"] = ta.emv(df["high"], df["low"], df["volume"], length=14)

emv = df["EMV"].iloc[-1]
print(df[["close", "volume", "EMV"]].tail(5).round(2))
print(f"\nLatest EMV: {emv:+.2f}")
print("Reading:", "rising easily (low effort up-move)" if emv > 0
      else "falling easily (low effort down-move)")

# Elder Force Index: price change times volume = the "force" behind a move.
import os
from datetime import datetime, timedelta

from openalgo import api, ta

client = api(
    api_key=os.getenv("OPENALGO_API_KEY", "your_api_key_here"),
    host=os.getenv("OPENALGO_HOST", "http://127.0.0.1:5000"),
)

end = datetime.now().strftime("%Y-%m-%d")
start = (datetime.now() - timedelta(days=120)).strftime("%Y-%m-%d")
df = client.history(symbol="HDFCBANK", exchange="NSE", interval="D", start_date=start, end_date=end)

# Force Index multiplies the day's price change by its volume, then EMA-smooths it.
df["FI"] = ta.force_index(df["close"], df["volume"], length=13)

fi = df["FI"].iloc[-1]
print(df[["close", "volume", "FI"]].tail(5).round(0))
print(f"\nLatest Force Index: {fi:+,.0f}")
print("Sign:", "positive - bulls have the force" if fi > 0 else "negative - bears have the force")
# A fresh cross from negative to positive often flags a momentum shift.
crossed_up = df["FI"].iloc[-2] < 0 < df["FI"].iloc[-1]
print("Just crossed up through zero:", crossed_up)

# Relative Volume (RVOL): is today busier than a normal day?
import os
from datetime import datetime, timedelta

from openalgo import api, ta

client = api(
    api_key=os.getenv("OPENALGO_API_KEY", "your_api_key_here"),
    host=os.getenv("OPENALGO_HOST", "http://127.0.0.1:5000"),
)

end = datetime.now().strftime("%Y-%m-%d")
start = (datetime.now() - timedelta(days=90)).strftime("%Y-%m-%d")
df = client.history(symbol="NIFTY30JUN26FUT", exchange="NFO", interval="D", start_date=start, end_date=end)

# RVOL = today's volume / average volume over the period. 1.0 = average, 2.0 = double.
df["RVOL"] = ta.rvol(df["volume"], period=20)

rvol = df["RVOL"].iloc[-1]
print(df[["close", "volume", "RVOL"]].tail(5).round(2))
print(f"\nLatest RVOL: {rvol:.2f}x the 20-day average")
print("Activity:", "unusually high - breakouts are more trustworthy" if rvol > 1.5
      else "quiet - moves may not stick" if rvol < 0.7 else "roughly normal")

# Volume Rate of Change (VROC): how fast volume itself is changing.
import os
from datetime import datetime, timedelta

from openalgo import api, ta

client = api(
    api_key=os.getenv("OPENALGO_API_KEY", "your_api_key_here"),
    host=os.getenv("OPENALGO_HOST", "http://127.0.0.1:5000"),
)

end = datetime.now().strftime("%Y-%m-%d")
start = (datetime.now() - timedelta(days=120)).strftime("%Y-%m-%d")
df = client.history(symbol="ICICIBANK", exchange="NSE", interval="D", start_date=start, end_date=end)

# VROC is a percentage: how much volume changed vs N bars ago.
df["VROC"] = ta.vroc(df["volume"], period=12)

vroc = df["VROC"].iloc[-1]
print(df[["close", "volume", "VROC"]].tail(5).round(2))
print(f"\nLatest VROC(12): {vroc:+.1f}%")
print("Participation is", "surging" if vroc > 50 else "drying up" if vroc < -50 else "steady")
print("A volume surge alongside a breakout adds conviction; a drop warns of a fake move.")