Skip to content

Latest commit

 

History

History
240 lines (173 loc) · 8.66 KB

volume.md

File metadata and controls

240 lines (173 loc) · 8.66 KB
Raw

volume - Volume-Based Technical Indicators

Accumulation/Distribution Index (ADI)

Description

The Accumulation/Distribution Index (ADI) is a volume-based indicator that measures the cumulative flow of money into or out of a security. It uses the relationship between the price’s range (high and low) and its closing price to calculate the Close Location Value (CLV).

Usage Example

df['adi'] = bta.accumulation_distribution_index(df, fillna=True)['adi']

Parameters

  • df (pandas.DataFrame): Input DataFrame containing 'high', 'low', 'close', and 'volume' columns.
  • fillna (bool): If True, fill NaN values. Default is False.

Returns

  • DataFrame: A DataFrame with a single 'adi' column.

Chaikin Money Flow (CMF)

Description

The Chaikin Money Flow (CMF) indicator measures the amount of Money Flow Volume over a specific period. It uses the relationship between the close price and the range (high and low) to evaluate buying or selling pressure.

Usage Example

df['cmf'] = bta.chaikin_money_flow(df, window=20, fillna=True)['cmf']

Parameters

  • df (pandas.DataFrame): Input DataFrame containing 'high', 'low', 'close', and 'volume' columns.
  • window (int): Look-back period for the calculation. Default is 20.
  • fillna (bool): If True, fill NaN values. Default is False.

Returns

  • DataFrame: A DataFrame with a single 'cmf' column.

Ease of Movement (EoM)

Description

The Ease of Movement (EoM) indicator relates the price movement of an asset to its volume, helping assess trend strength. The Signal Ease of Movement (SEoM) is a smoothed version of EoM, calculated using a moving average.

Usage Example

eom_df = bta.ease_of_movement(df, eom_length=14, seom_length=14, fillna=True)
df['eom'] = eom_df['eom']
df['seom'] = eom_df['seom']

Parameters

  • df (pandas.DataFrame): Input DataFrame containing 'high', 'low', and 'volume' columns.
  • eom_length (int): Period for the EoM calculation. Default is 14.
  • seom_length (int): Period for the SEoM calculation. Default is 14.
  • fillna (bool): If True, fill NaN values. Default is False.

Returns

  • DataFrame: A DataFrame with 'eom' and 'seom' columns.

Force Index (FI)

Description

The Force Index (FI) measures the strength of price movements by combining price changes and volume. It helps illustrate the buying or selling pressure in the market.

Usage Example

df['fi'] = bta.force_index(df, window=13, fillna=True)['fi']

Parameters

  • df (pandas.DataFrame): Input DataFrame containing 'close' and 'volume' columns.
  • window (int): Period for the exponential moving average of the Force Index. Default is 13.
  • fillna (bool): If True, fill NaN values. Default is False.

Returns

  • DataFrame: A DataFrame with a single 'fi' column.

Money Flow Index (MFI)

Description

The Money Flow Index (MFI) is a momentum indicator that uses both price and volume data to measure buying and selling pressure. It is often referred to as a volume-weighted RSI.

Usage Example

df['mfi'] = bta.money_flow_index(df, window=14, fillna=True)['mfi']

Parameters

  • df (pandas.DataFrame): Input DataFrame containing 'high', 'low', 'close', and 'volume' columns.
  • window (int): Look-back period for the MFI calculation. Default is 14.
  • fillna (bool): If True, fill NaN values. Default is False.

Returns

  • DataFrame: A DataFrame with a single 'mfi' column.

Negative Volume Index (NVI)

Description

The Negative Volume Index (NVI) accumulates the percentage rate of change in price on days when trading volume decreases compared to the previous day. It helps identify trends driven by low-volume days.

Usage Example

nvi_df = bta.negative_volume_index(df, signal_type='EMA', signal_length=255, fillna=True)
df['nvi'] = nvi_df['nvi']
df['nvi_signal'] = nvi_df['nvi_signal']

Parameters

  • df (pandas.DataFrame): Input DataFrame containing 'close' and 'volume' columns.
  • signal_type (str): Type of signal smoothing ('EMA' or 'SMA'). Default is 'EMA'.
  • signal_length (int): Length for the signal smoothing calculation. Default is 255.
  • fillna (bool): If True, fill NaN values. Default is False.

Returns

  • DataFrame: A DataFrame with 'nvi' and 'nvi_signal' columns.

On Balance Volume (OBV)

Description

The On Balance Volume (OBV) indicator measures buying and selling pressure by adding volume when the close price is higher than the previous close and subtracting volume when the close price is lower.

Usage Example

obv_df = bta.on_balance_volume(df, signal_type='SMA', signal_length=21, show_signal=True, fillna=True)
df['obv'] = obv_df['obv']
df['signal'] = obv_df['signal']

Parameters

  • df (pandas.DataFrame): Input DataFrame containing 'close' and 'volume' columns.
  • signal_type (str): Type of signal smoothing ('SMA' or 'EMA'). Default is 'SMA'.
  • signal_length (int): Period for signal calculation. Default is 21.
  • show_signal (bool): If True, calculate and return the signal line. Default is True.
  • fillna (bool): If True, fill NaN values. Default is False.

Returns

  • DataFrame: A DataFrame with 'obv' and 'signal' columns.

On Balance Volume Oscillator (OBV Oscillator)

Description

The On Balance Volume Oscillator measures the difference between the OBV and its Exponential Moving Average (EMA). It helps identify trends and confirm price movements.

Usage Example

df['obv_oscillator'] = bta.on_balance_volume_oscillator(df, length=20, fillna=True)['obv_oscillator']

Parameters

  • df (pandas.DataFrame): Input DataFrame containing 'close' and 'volume' columns.
  • length (int): Length for the EMA calculation. Default is 20.
  • fillna (bool): If True, fill NaN values. Default is False.

Returns

  • DataFrame: A DataFrame with a single 'obv_oscillator' column.

Positive Volume Index (PVI)

Description

The Positive Volume Index (PVI) accumulates the percentage rate of change in price on days when trading volume increases compared to the previous day. It helps identify trends driven by high-volume days.

Usage Example

pvi_df = bta.positive_volume_index(df, signal_type='EMA', signal_length=255, fillna=True)
df['pvi'] = pvi_df['pvi']
df['pvi_signal'] = pvi_df['pvi_signal']

Parameters

  • df (pandas.DataFrame): Input DataFrame containing 'close' and 'volume' columns.
  • signal_type (str): Type of signal smoothing ('EMA' or 'SMA'). Default is 'EMA'.
  • signal_length (int): Length for signal calculation. Default is 255.
  • fillna (bool): If True, fill NaN values. Default is False.

Returns

  • DataFrame: A DataFrame with 'pvi' and 'pvi_signal' columns.

Price Volume Trend (PVT)

Description

The Price Volume Trend (PVT) indicator combines price and volume to measure the strength of trends. It accumulates the volume proportional to the price movement.

Usage Example

pvt_df = bta.price_volume_trend(df, fillna=True, signal_type='EMA', signal_length=21, dropnans=True)
df['pvt'] = pvt_df['price_volume_trend']
df['pvt_signal'] = pvt_df['signal']

Parameters

  • df (p

andas.DataFrame): Input DataFrame containing 'close' and 'volume' columns.

  • fillna (bool): If True, fill NaN values. Default is False.
  • smoothing_factor (int, optional): Apply SMA smoothing to the PVT values.
  • signal_type (str): Type of signal smoothing ('EMA' or 'SMA'). Default is 'SMA'.
  • signal_length (int): Length for signal calculation. Default is 21.
  • dropnans (bool): Drop NaN values after calculation. Default is False.

Returns

  • DataFrame: A DataFrame with 'price_volume_trend' and 'signal' columns.

Volume Weighted Average Price (VWAP)

Description

The Volume Weighted Average Price (VWAP) represents the average price a security has traded at throughout the day, based on both volume and price. It is often used to assess whether a security is trading above or below its average price.

Usage Example

df['vwap'] = bta.volume_weighted_average_price(df, window=14, fillna=True)['volume_weighted_average_price']

Parameters

  • df (pandas.DataFrame): Input DataFrame containing 'high', 'low', 'close', and 'volume' columns.
  • window (int): The number of periods for the rolling calculation. Default is 14.
  • fillna (bool): If True, fill NaN values. Default is False.

Returns

  • DataFrame: A DataFrame with a single 'volume_weighted_average_price' column.