TA (Technical Analysis Library)

https://technical-analysis-library-in-python.readthedocs.io/en/latest/
Admin
TA is a Python library for technical analysis of financial time series datasets that provides...
Tokens:34,539
Snippets:264
Trust Score:9.6
Update:4 months ago
Show doc for...
Tokens:
Raw
### Install Technical Analysis Library

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/index.html

Installs the TA library using pip within a virtual environment. Ensure Python 3.6 or higher is used.

```bash
> virtualenv -p python3 virtualenvironment
> source virtualenvironment/bin/activate
> pip install ta

```

--------------------------------

### WMAIndicator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Initializes the Weighted Moving Average (WMA) indicator calculation.

```APIDOC
## WMAIndicator

### Description
Weighted Moving Average (WMA).

### Parameters
- **close** (pandas.Series) – dataset ‘Close’ column.
- **window** (int) – n period. Default is 9.
- **fillna** (bool) – if True, fill nan values. Default is False.
```

--------------------------------

### Percentage Price Oscillator (PPO) Signal Line Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the signal line component of the Percentage Price Oscillator. Requires a price series.

```python
from ta.momentum import PercentagePriceOscillator

ppo_indicator = PercentagePriceOscillator(close=close, window_slow=26, window_fast=12, window_sign=9)
ppo_indicator.ppo_signal()
```

--------------------------------

### Percentage Price Oscillator (PPO) Histogram Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the histogram component of the Percentage Price Oscillator. Requires a price series.

```python
from ta.momentum import PercentagePriceOscillator

ppo_indicator = PercentagePriceOscillator(close=close, window_slow=26, window_fast=12, window_sign=9)
ppo_indicator.ppo_hist()
```

--------------------------------

### Percentage Price Oscillator (PPO) Line Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Percentage Price Oscillator line, which measures the difference between two moving averages as a percentage of the larger moving average. Requires a price series.

```python
from ta.momentum import PercentagePriceOscillator

ppo_indicator = PercentagePriceOscillator(close=close, window_slow=26, window_fast=12, window_sign=9)
ppo_indicator.ppo()
```

--------------------------------

### Kaufman's Adaptive Moving Average (KAMA) Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates Kaufman's Adaptive Moving Average, which adjusts to market noise and volatility. Requires a closing price series.

```python
from ta.momentum import KAMAIndicator

kama_indicator = KAMAIndicator(close=close, window=10, pow1=2, pow2=30)
kama_indicator.kama()
```

--------------------------------

### bollinger_wband

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Bollinger Channel Band Width.

```APIDOC
## bollinger_wband

### Description
Bollinger Channel Band Width.

### Parameters
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period. Defaults to 20.
* **window_dev** (int) – n factor standard deviation. Defaults to 2.
* **fillna** (bool) – if True, fill nan values. Defaults to False.

### Returns
New feature generated. pandas.Series
```

--------------------------------

### bollinger_pband

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Bollinger Channel Percentage Band.

```APIDOC
## bollinger_pband

### Description
Bollinger Channel Percentage Band.

### Parameters
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period. Defaults to 20.
* **window_dev** (int) – n factor standard deviation. Defaults to 2.
* **fillna** (bool) – if True, fill nan values. Defaults to False.

### Returns
New feature generated. pandas.Series
```

--------------------------------

### ta.others.DailyLogReturnIndicator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Initializes the Daily Log Return (DLR) indicator.

```APIDOC
## ta.others.DailyLogReturnIndicator

### Description
Initializes the Daily Log Return (DLR) indicator.

### Parameters
#### Path Parameters
- **close** (pandas.Series) – dataset ‘Close’ column.
- **fillna** (bool) – if True, fill nan values.

### Method
`daily_log_return()`

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### PercentagePriceOscillator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Provides calculations for the Percentage Price Oscillator (PPO) and its components (histogram and signal line).

```APIDOC
## PercentagePriceOscillator

### Description
Calculates the Percentage Price Oscillator (PPO), which measures the difference between two moving averages as a percentage of the larger moving average.

### Parameters
* **close** (pandas.Series) – dataset ‘Price’ column.
* **window_slow** (int) – n period long-term. Defaults to 26.
* **window_fast** (int) – n period short-term. Defaults to 12.
* **window_sign** (int) – n period to signal. Defaults to 9.
* **fillna** (bool) – if True, fill nan values. Defaults to False.

### ppo()

Calculates the Percentage Price Oscillator Line.

Returns:
    pandas.Series: New feature generated.

### ppo_hist()

Calculates the Percentage Price Oscillator Histogram.

Returns:
    pandas.Series: New feature generated.

### ppo_signal()

Calculates the Percentage Price Oscillator Signal Line.

Returns:
    pandas.Series: New feature generated.
```

--------------------------------

### Awesome Oscillator Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Awesome Oscillator using the difference between two Simple Moving Averages of the median price. Requires high and low price series.

```python
from ta.momentum import AwesomeOscillatorIndicator

aoi = AwesomeOscillatorIndicator(high=high, low=low, window1=5, window2=34)
aoi.awesome_oscillator()
```

--------------------------------

### bollinger_mavg

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the N-period simple moving average for Bollinger Bands.

```APIDOC
## bollinger_mavg

### Description
N-period simple moving average (MA).

### Parameters
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period. Defaults to 20.
* **fillna** (bool) – if True, fill nan values. Defaults to False.

### Returns
New feature generated. pandas.Series
```

--------------------------------

### KST Oscillator Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the KST Oscillator and its components. Requires 'Close' price series and various lookback periods for ROC and smoothing.

```python
from ta.trend import KSTIndicator

close = pandas.Series([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])

kst_indicator = KSTIndicator(close=close, roc1=10, roc2=15, roc3=20, roc4=30, window1=10, window2=10, window3=10, window4=15, nsig=9)

kst_indicator.kst()
kst_indicator.kst_diff()
kst_indicator.kst_sig()
```

--------------------------------

### Calculate Percentage Price Oscillator (PPO)

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Percentage Price Oscillator, a momentum indicator measuring the difference between two moving averages as a percentage of the larger average. Use this to analyze momentum and potential trend changes.

```python
import pandas as pd
from ta.momentum import PPO

# Assuming 'close' is a pandas Series
close = pd.Series([...])

ppo = PPO(close=close, window_slow=26, window_fast=12, window_sign=9, fillna=False)

ppo_values = ppo.ppo()
```

--------------------------------

### ppo

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Percentage Price Oscillator (PPO), a momentum oscillator that measures the difference between two moving averages as a percentage of the larger moving average.

```APIDOC
## ppo

### Description
The Percentage Price Oscillator (PPO) is a momentum oscillator that measures the difference between two moving averages as a percentage of the larger moving average.

### Parameters
* **close** (pandas.Series) – dataset ‘Price’ column.
* **window_slow** (int) – n period long-term.
* **window_fast** (int) – n period short-term.
* **window_sign** (int) – n period to signal.
* **fillna** (bool) – if True, fill nan values.

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### PercentageVolumeOscillator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Offers calculations for the Percentage Volume Oscillator (PVO) and its histogram.

```APIDOC
## PercentageVolumeOscillator

### Description
Calculates the Percentage Volume Oscillator (PVO), a momentum oscillator for volume that measures the difference between two volume-based moving averages as a percentage of the larger moving average.

### Parameters
* **volume** (pandas.Series) – dataset ‘Volume’ column.
* **window_slow** (int) – n period long-term. Defaults to 26.
* **window_fast** (int) – n period short-term. Defaults to 12.
* **window_sign** (int) – n period to signal. Defaults to 9.
* **fillna** (bool) – if True, fill nan values. Defaults to False.

### pvo()

Calculates the PVO Line.

Returns:
    pandas.Series: New feature generated.

### pvo_hist()

Calculates the PVO Histogram.

Returns:
    pandas.Series: New feature generated.
```

--------------------------------

### KAMAIndicator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Implements Kaufman's Adaptive Moving Average (KAMA), a moving average that adjusts to market volatility.

```APIDOC
## KAMAIndicator

### Description
Calculates Kaufman's Adaptive Moving Average (KAMA), a trend-following indicator that adjusts its responsiveness based on market noise and volatility.

### Parameters
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period. Defaults to 10.
* **pow1** (int) – number of periods for the fastest EMA constant. Defaults to 2.
* **pow2** (int) – number of periods for the slowest EMA constant. Defaults to 30.
* **fillna** (bool) – if True, fill nan values. Defaults to False.

### kama()

Calculates Kaufman’s Adaptive Moving Average (KAMA).

Returns:
    pandas.Series: New feature generated.
```

--------------------------------

### MACD Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Moving Average Convergence Divergence (MACD) line, signal line, and histogram. Requires 'Close' price series.

```python
from ta.trend import MACD

close = pandas.Series([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])

macd_indicator = MACD(close=close, window_slow=26, window_fast=12, window_sign=9)

macd_indicator.macd()
macd_indicator.macd_diff()
macd_indicator.macd_signal()
```

--------------------------------

### ta.others.DailyReturnIndicator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Initializes the Daily Return (DR) indicator.

```APIDOC
## ta.others.DailyReturnIndicator

### Description
Initializes the Daily Return (DR) indicator.

### Parameters
#### Path Parameters
- **close** (pandas.Series) – dataset ‘Close’ column.
- **fillna** (bool) – if True, fill nan values.

### Method
`daily_return()`

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### True Strength Index (TSI) Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the True Strength Index (TSI) using closing prices over specified slow and fast windows. Fill NaN values if required.

```python
from ta.momentum import TSIIndicator

tsi_indicator = TSIIndicator(close=df['Close'], window_slow=25, window_fast=13, fillna=False)
tsi_indicator.tsi()
```

--------------------------------

### DPO Indicator Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Detrended Price Oscillator (DPO). Requires a close price series.

```python
from ta.trend import DPOIndicator

close = [9, 11, 10, 12, 13, 14, 15, 16, 17, 18]

dpo_indicator = DPOIndicator(close=close)

dpo_indicator.dpo()
```

--------------------------------

### ppo_signal

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the signal line component of the Percentage Price Oscillator (PPO).

```APIDOC
## ppo_signal

### Description
The Percentage Price Oscillator (PPO) signal line is a momentum oscillator that measures the difference between two moving averages as a percentage of the larger moving average.

### Parameters
* **close** (pandas.Series) – dataset ‘Price’ column.
* **window_slow** (int) – n period long-term.
* **window_fast** (int) – n period short-term.
* **window_sign** (int) – n period to signal.
* **fillna** (bool) – if True, fill nan values.

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### OnBalanceVolumeIndicator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the On-Balance Volume (OBV). OBV relates price and volume, using a cumulative total volume.

```APIDOC
## OnBalanceVolumeIndicator

### Description
Calculates the On-Balance Volume (OBV). OBV relates price and volume, using a cumulative total volume.

### Parameters
* **close** (pandas.Series) - Required - dataset ‘Close’ column.
* **volume** (pandas.Series) - Required - dataset ‘Volume’ column.
* **fillna** (bool) - Optional - if True, fill nan values.

### Method
`on_balance_volume()`

### Returns
pandas.Series - New feature generated.
```

--------------------------------

### Percentage Volume Oscillator (PVO) Histogram Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the histogram component of the Percentage Volume Oscillator. Requires a volume series.

```python
from ta.momentum import PercentageVolumeOscillator

pvo_indicator = PercentageVolumeOscillator(volume=volume, window_slow=26, window_fast=12, window_sign=9)
pvo_indicator.pvo_hist()
```

--------------------------------

### Ultimate Oscillator Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Ultimate Oscillator using high, low, and closing prices across three different timeframes with customizable weights. NaN values can be filled.

```python
from ta.momentum import UltimateOscillator

uo_indicator = UltimateOscillator(high=df['High'], low=df['Low'], close=df['Close'], window1=7, window2=14, window3=28, weight1=4.0, weight2=2.0, weight3=1.0, fillna=False)
uo_indicator.ultimate_oscillator()
```

--------------------------------

### ta.others.daily_log_return

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Daily Log Return (DLR).

```APIDOC
## ta.others.daily_log_return

### Description
Calculates the Daily Log Return (DLR).

### Parameters
#### Path Parameters
- **close** (pandas.Series) – dataset ‘Close’ column.
- **fillna** (bool) – if True, fill nan values.

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### ta.others.daily_return

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Daily Return (DR).

```APIDOC
## ta.others.daily_return

### Description
Calculates the Daily Return (DR).

### Parameters
#### Path Parameters
- **close** (pandas.Series) – dataset ‘Close’ column.
- **fillna** (bool) – if True, fill nan values.

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### Calculate Kaufman's Adaptive Moving Average (KAMA)

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates Kaufman's Adaptive Moving Average, which adjusts to market noise and volatility. Use it to identify trends, time turning points, and filter price movements.

```python
import pandas as pd
from ta.momentum import Kama

# Assuming 'close' is a pandas Series
close = pd.Series([...])

kama = Kama(close=close, window=10, pow1=2, pow2=30, fillna=False)

kama_values = kama.kama()
```

--------------------------------

### Percentage Volume Oscillator (PVO) Line Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Percentage Volume Oscillator line, a momentum oscillator for volume. Requires a volume series.

```python
from ta.momentum import PercentageVolumeOscillator

pvo_indicator = PercentageVolumeOscillator(volume=volume, window_slow=26, window_fast=12, window_sign=9)
pvo_indicator.pvo()
```

--------------------------------

### BollingerBands

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Implements Bollinger Bands, a technical analysis tool to measure market volatility. It provides upper, middle, and lower bands, along with indicators for band crossings and percentage band width.

```APIDOC
## Class: BollingerBands

### Description
Calculates Bollinger Bands, a technical analysis tool to measure market volatility.

### Parameters
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period. Default is 20.
* **window_dev** (int) – n factor standard deviation. Default is 2.
* **fillna** (bool) – if True, fill nan values. Default is False.

## Method: bollinger_hband

### Description
Calculates the Bollinger Channel High Band.

### Returns
New feature generated.

### Return type
pandas.Series

## Method: bollinger_hband_indicator

### Description
Bollinger Channel Indicator Crossing High Band (binary). It returns 1, if close is higher than bollinger_hband. Else, it returns 0.

### Returns
New feature generated.

### Return type
pandas.Series

## Method: bollinger_lband

### Description
Calculates the Bollinger Channel Low Band.

### Returns
New feature generated.

### Return type
pandas.Series

## Method: bollinger_lband_indicator

### Description
Bollinger Channel Indicator Crossing Low Band (binary). It returns 1, if close is lower than bollinger_lband. Else, it returns 0.

### Returns
New feature generated.

### Return type
pandas.Series

## Method: bollinger_mavg

### Description
Calculates the Bollinger Channel Middle Band.

### Returns
New feature generated.

### Return type
pandas.Series

## Method: bollinger_pband

### Description
Calculates the Bollinger Channel Percentage Band.

### Returns
New feature generated.

### Return type
pandas.Series

## Method: bollinger_wband

### Description
Calculates the Bollinger Channel Band Width.

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### Calculate Awesome Oscillator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Awesome Oscillator, which measures market momentum by comparing two simple moving averages of midpoints. Use this to affirm trends or anticipate reversals.

```python
import pandas as pd
from ta.momentum import AwesomeOscillator

# Assuming 'high' and 'low' are pandas Series
high = pd.Series([...])
low = pd.Series([...])

ao = AwesomeOscillator(high=high, low=low, window1=5, window2=34, fillna=False)

ao_values = ao.awesome_oscillator()
```

--------------------------------

### AwesomeOscillatorIndicator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Awesome Oscillator, a momentum indicator measuring the difference between two simple moving averages of the median price.

```APIDOC
## AwesomeOscillatorIndicator

### Description
Calculates the Awesome Oscillator, which measures market momentum by finding the difference between a 5-period and a 34-period Simple Moving Average of the median price.

### Parameters
* **high** (pandas.Series) – dataset ‘High’ column.
* **low** (pandas.Series) – dataset ‘Low’ column.
* **window1** (int) – short period. Defaults to 5.
* **window2** (int) – long period. Defaults to 34.
* **fillna** (bool) – if True, fill nan values with -50. Defaults to False.

### awesome_oscillator()

Calculates the Awesome Oscillator.

Returns:
    pandas.Series: New feature generated.
```

--------------------------------

### ForceIndexIndicator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Force Index (FI), illustrating the strength of buying or selling pressure. It uses close prices, volume, and a lookback window.

```APIDOC
## ForceIndexIndicator

### Description
Calculates the Force Index (FI), illustrating the strength of buying or selling pressure.

### Parameters
* **close** (pandas.Series) - Required - dataset ‘Close’ column.
* **volume** (pandas.Series) - Required - dataset ‘Volume’ column.
* **window** (int) - Optional - n period. Defaults to 13.
* **fillna** (bool) - Optional - if True, fill nan values.

### Method
`force_index()`

### Returns
New feature generated (pandas.Series).
```

--------------------------------

### Calculate PPO Signal Line

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the signal line for the Percentage Price Oscillator. This is typically a moving average of the PPO line, used to generate buy/sell signals.

```python
import pandas as pd
from ta.momentum import PPO

# Assuming 'close' is a pandas Series
close = pd.Series([...])

ppo = PPO(close=close, window_slow=26, window_fast=12, window_sign=9, fillna=False)

ppo_signal_values = ppo.ppo_signal()
```

--------------------------------

### volume_price_trend

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Volume-Price Trend (VPT) indicator. VPT is based on a cumulative volume that adjusts based on the percentage change in share price trend and current volume.

```APIDOC
## volume_price_trend

### Description
Calculates the Volume-Price Trend (VPT) indicator. Is based on a running cumulative volume that adds or substracts a multiple of the percentage change in share price trend and current volume, depending upon the investment’s upward or downward movements.

### Parameters
* **close** (pandas.Series) – dataset ‘Close’ column.
* **volume** (pandas.Series) – dataset ‘Volume’ column.
* **fillna** (bool) – if True, fill nan values.

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### ta.volume.on_balance_volume

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the On-balance volume (OBV), which relates price and volume in the stock market using a cumulative total volume.

```APIDOC
## ta.volume.on_balance_volume

### Description
Calculates the On-balance volume (OBV), which relates price and volume in the stock market using a cumulative total volume.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **close** (pandas.Series) – dataset ‘Close’ column.
* **volume** (pandas.Series) – dataset ‘Volume’ column.
* **fillna** (bool) – if True, fill nan values.

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### ppo_hist

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the histogram component of the Percentage Price Oscillator (PPO).

```APIDOC
## ppo_hist

### Description
The Percentage Price Oscillator (PPO) histogram is a momentum oscillator that measures the difference between two moving averages as a percentage of the larger moving average.

### Parameters
* **close** (pandas.Series) – dataset ‘Price’ column.
* **window_slow** (int) – n period long-term.
* **window_fast** (int) – n period short-term.
* **window_sign** (int) – n period to signal.
* **fillna** (bool) – if True, fill nan values.

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### Mass Index Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Mass Index (MI) indicator. Requires 'High' and 'Low' price series and specified lookback windows.

```python
from ta.trend import MassIndex

high = pandas.Series([10, 12, 11, 13, 14, 15, 16, 17, 18, 19])
low = pandas.Series([8, 9, 10, 11, 12, 13, 14, 15, 16, 17])

mass_indicator = MassIndex(high=high, low=low, window_fast=9, window_slow=25)

mass_indicator.mass_index()
```

--------------------------------

### volume_weighted_average_price

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Volume Weighted Average Price (VWAP) indicator. VWAP equals the dollar value of all trading periods divided by the total trading volume for the current day.

```APIDOC
## volume_weighted_average_price

### Description
Calculates the Volume Weighted Average Price (VWAP) indicator. VWAP equals the dollar value of all trading periods divided by the total trading volume for the current day. The calculation starts when trading opens and ends when it closes. Because it is good for the current trading day only, intraday periods and data are used in the calculation.

### Parameters
* **high** (pandas.Series) – dataset ‘High’ column.
* **low** (pandas.Series) – dataset ‘Low’ column.
* **close** (pandas.Series) – dataset ‘Close’ column.
* **volume** (pandas.Series) – dataset ‘Volume’ column.
* **window** (int) – n period. Defaults to 14.
* **fillna** (bool) – if True, fill nan values.

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### Aroon Indicator Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Aroon Indicator, which includes Aroon Up and Aroon Down. Requires a close price series.

```python
from ta.trend import AroonIndicator

close = [9, 11, 10, 12, 13, 14, 15, 16, 17, 18]

aroon_indicator = AroonIndicator(close=close)

aroon_indicator.aroon_indicator()
aroon_indicator.aroon_up()
aroon_indicator.aroon_down()
```

--------------------------------

### Calculate PPO Histogram

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the histogram component of the Percentage Price Oscillator. This represents the difference between the PPO line and its signal line, indicating momentum shifts.

```python
import pandas as pd
from ta.momentum import PPO

# Assuming 'close' is a pandas Series
close = pd.Series([...])

ppo = PPO(close=close, window_slow=26, window_fast=12, window_sign=9, fillna=False)

ppo_hist_values = ppo.ppo_hist()
```

--------------------------------

### Williams %R Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates Williams %R, an inverse momentum indicator, using high, low, and closing prices over a specified lookback period. NaN values can be filled.

```python
from ta.momentum import WilliamsRIndicator

wr_indicator = WilliamsRIndicator(high=df['High'], low=df['Low'], close=df['Close'], lbp=14, fillna=False)
wr_indicator.williams_r()
```

--------------------------------

### Ichimoku Indicator Calculation

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the components of the Ichimoku Kinko Hyo indicator. Requires 'High' and 'Low' price series.

```python
from ta.trend import IchimokuIndicator

high = pandas.Series([10, 12, 11, 13, 14, 15, 16, 17, 18, 19])
low = pandas.Series([8, 9, 10, 11, 12, 13, 14, 15, 16, 17])

ichi_indicator = IchimokuIndicator(high=high, low=low, window1=9, window2=26, window3=52)

ichi_indicator.ichimoku_a()
ichi_indicator.ichimoku_b()
ichi_indicator.ichimoku_base_line()
ichi_indicator.ichimoku_conversion_line()
```

--------------------------------

### KSTIndicator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the KST Oscillator (KST Signal) and its related components (KST, KST diff, KST signal). It's useful for identifying major stock market cycle junctures.

```APIDOC
## KSTIndicator

### Description
Calculates the KST Oscillator (KST Signal) and its related components (KST, KST diff, KST signal). It's useful for identifying major stock market cycle junctures.

### Parameters
* **close** (pandas.Series) – dataset ‘Close’ column.
* **roc1** (int) – roc1 period.
* **roc2** (int) – roc2 period.
* **roc3** (int) – roc3 period.
* **roc4** (int) – roc4 period.
* **window1** (int) – n1 smoothed period.
* **window2** (int) – n2 smoothed period.
* **window3** (int) – n3 smoothed period.
* **window4** (int) – n4 smoothed period.
* **nsig** (int) – n period to signal.
* **fillna** (bool) – if True, fill nan values.

### Methods
#### kst()
Know Sure Thing (KST)

#### kst_diff()
Diff Know Sure Thing (KST)

#### kst_sig()
Signal Line Know Sure Thing (KST)
```

--------------------------------

### awesome_oscillator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Awesome Oscillator, an indicator used to measure market momentum by comparing the difference between two simple moving averages of the median price.

```APIDOC
## awesome_oscillator

### Description
Calculates the Awesome Oscillator, an indicator used to measure market momentum. It is generally used to affirm trends or to anticipate possible reversals.

### Parameters
* **high** (pandas.Series) – dataset ‘High’ column.
* **low** (pandas.Series) – dataset ‘Low’ column.
* **window1** (int) – short period.
* **window2** (int) – long period.
* **fillna** (bool) – if True, fill nan values with -50.

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### ADXIndicator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Average Directional Movement Index (ADX), Plus Directional Indicator (+DI), and Minus Directional Indicator (-DI). These indicators help measure trend direction and strength.

```APIDOC
## ADXIndicator

### Description
Calculates the Average Directional Movement Index (ADX), Plus Directional Indicator (+DI), and Minus Directional Indicator (-DI). These indicators help measure trend direction and strength.

### Parameters
* **high** (pandas.Series) – dataset ‘High’ column.
* **low** (pandas.Series) – dataset ‘Low’ column.
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period.
* **fillna** (bool) – if True, fill nan values.

### Methods
#### adx()

Returns the Average Directional Movement Index (ADX).

**Return type**: pandas.Series

#### adx_neg()

Returns the Minus Directional Indicator (-DI).

**Return type**: pandas.Series

#### adx_pos()

Returns the Plus Directional Indicator (+DI).

**Return type**: pandas.Series
```

--------------------------------

### ulcer_index

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Ulcer Index (UI), a volatility indicator that measures risk or drawdown.

```APIDOC
## ulcer_index

### Description
Ulcer Index (UI) calculation.

### Method
Not specified (assumed to be a function call within a library).

### Parameters
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period.
* **fillna** (bool) – if True, fill nan values.

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### pvo_signal

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Signal Line for the Price Oscillator. Returns a pandas Series.

```APIDOC
## pvo_signal()

### Description
Calculates the Signal Line for the Price Oscillator.

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### keltner_channel_lband_indicator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Generates a binary indicator for Keltner Channel Low Band crossing. Returns 1 if the closing price is lower than the low band, otherwise 0.

```APIDOC
## keltner_channel_lband_indicator

### Description
Keltner Channel Indicator Crossing Low Band (binary). It returns 1, if close is lower than keltner_channel_lband. Else, it returns 0.

### Method
Not specified (assumed to be a function call within a library).

### Parameters
* **high** (pandas.Series) – dataset ‘High’ column.
* **low** (pandas.Series) – dataset ‘Low’ column.
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period.
* **window_atr** (int) – n atr period. Only valid if original_version param is False.
* **fillna** (bool) – if True, fill nan values.
* **original_version** (bool) – if True, use original version as the centerline (SMA of typical price) if False, use EMA of close as the centerline. More info: https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels
* **multiplier** (int) – The multiplier has the most effect on the channel width. default is 2

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### donchian_channel_wband

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Donchian Channel Band Width (DC).

```APIDOC
## donchian_channel_wband

### Description
Donchian Channel Band Width (DC)

### Parameters
* **high** (pandas.Series) – dataset ‘High’ column.
* **low** (pandas.Series) – dataset ‘Low’ column.
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period. Defaults to 10.
* **offset** (int) – Defaults to 0.
* **fillna** (bool) – if True, fill nan values. Defaults to False.

### Returns
New feature generated. pandas.Series
```

--------------------------------

### keltner_channel_pband

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Keltner Channel Percentage Band, a volatility indicator used to identify price channels.

```APIDOC
## keltner_channel_pband

### Description
Calculates the Keltner Channel Percentage Band, a volatility indicator used to identify price channels.

### Method
`ta.volatility.keltner_channel_pband`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **high** (pandas.Series) – dataset ‘High’ column.
* **low** (pandas.Series) – dataset ‘Low’ column.
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period. Defaults to 20.
* **window_atr** (int) – n atr period. Only valid if original_version param is False. Defaults to 10.
* **fillna** (bool) – if True, fill nan values. Defaults to False.
* **original_version** (bool) – if True, use original version as the centerline (SMA of typical price) if False, use EMA of close as the centerline. More info: https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels. Defaults to True.

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### bollinger_lband_indicator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Bollinger Low Band Indicator. Returns 1 if the closing price is lower than the lower Bollinger Band, otherwise returns 0.

```APIDOC
## bollinger_lband_indicator

### Description
Returns 1, if close is lower than bollinger low band. Else, return 0.

### Parameters
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period. Defaults to 20.
* **window_dev** (int) – n factor standard deviation. Defaults to 2.
* **fillna** (bool) – if True, fill nan values. Defaults to False.

### Returns
New feature generated. pandas.Series
```

--------------------------------

### donchian_channel_pband

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Donchian Channel Percentage Band (DC).

```APIDOC
## donchian_channel_pband

### Description
Donchian Channel Percentage Band (DC)

### Parameters
* **high** (pandas.Series) – dataset ‘High’ column.
* **low** (pandas.Series) – dataset ‘Low’ column.
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period. Defaults to 10.
* **offset** (int) – Defaults to 0.
* **fillna** (bool) – if True, fill nan values. Defaults to False.

### Returns
New feature generated. pandas.Series
```

--------------------------------

### VolumeWeightedAveragePrice

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Volume Weighted Average Price (VWAP). VWAP is the average price a security has traded at throughout the day, based on both volume and price.

```APIDOC
## VolumeWeightedAveragePrice

### Description
Calculates the Volume Weighted Average Price (VWAP). VWAP is the average price a security has traded at throughout the day, based on both volume and price.

### Parameters
* **high** (pandas.Series) - Required - dataset ‘High’ column.
* **low** (pandas.Series) - Required - dataset ‘Low’ column.
* **close** (pandas.Series) - Required - dataset ‘Close’ column.
* **volume** (pandas.Series) - Required - dataset ‘Volume’ column.
* **window** (int) - Optional - n period. Defaults to 14.
* **fillna** (bool) - Optional - if True, fill nan values.

### Method
`volume_weighted_average_price()`

### Returns
pandas.Series - New feature generated.
```

--------------------------------

### stoch

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Stochastic Oscillator. Returns a pandas Series.

```APIDOC
## stoch()

### Description
Calculates the Stochastic Oscillator.

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### VolumePriceTrendIndicator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Volume-Price Trend (VPT). VPT is based on a running cumulative volume that adds or subtracts a multiple of the percentage change in share price trend.

```APIDOC
## VolumePriceTrendIndicator

### Description
Calculates the Volume-Price Trend (VPT). VPT is based on a running cumulative volume that adds or subtracts a multiple of the percentage change in share price trend.

### Parameters
* **close** (pandas.Series) - Required - dataset ‘Close’ column.
* **volume** (pandas.Series) - Required - dataset ‘Volume’ column.
* **fillna** (bool) - Optional - if True, fill nan values.

### Method
`volume_price_trend()`

### Returns
pandas.Series - New feature generated.
```

--------------------------------

### Add Bollinger Bands Indicator to DataFrame

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/index.html

Calculates and adds Bollinger Bands indicators (middle band, upper band, lower band, high indicator, low indicator) to a Pandas DataFrame. Requires 'ta' and 'pandas' libraries. NaN values are handled by dropping them initially.

```python
import pandas as pd
from ta.utils import dropna
from ta.volatility import BollingerBands


# Load datas
df = pd.read_csv('ta/tests/data/datas.csv', sep=',')

# Clean NaN values
df = dropna(df)

# Initialize Bollinger Bands Indicator
indicator_bb = BollingerBands(close=df["Close"], window=20, window_dev=2)

# Add Bollinger Bands features
df['bb_bbm'] = indicator_bb.bollinger_mavg()
df['bb_bbh'] = indicator_bb.bollinger_hband()
df['bb_bbl'] = indicator_bb.bollinger_lband()

# Add Bollinger Band high indicator
df['bb_bbhi'] = indicator_bb.bollinger_hband_indicator()

# Add Bollinger Band low indicator
df['bb_bbli'] = indicator_bb.bollinger_lband_indicator()

```

--------------------------------

### donchian_channel_lband

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Donchian Channel Low Band (DC). The lower band marks the lowest price for n periods.

```APIDOC
## donchian_channel_lband

### Description
Donchian Channel Low Band (DC)
The lower band marks the lowest price for n periods.

### Parameters
* **high** (pandas.Series) – dataset ‘High’ column.
* **low** (pandas.Series) – dataset ‘Low’ column.
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period. Defaults to 20.
* **offset** (int) – Defaults to 0.
* **fillna** (bool) – if True, fill nan values. Defaults to False.

### Returns
New feature generated. pandas.Series
```

--------------------------------

### ease_of_movement

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Ease of Movement (EoM, EMV). EoM relates an asset’s price change to its volume and is useful for assessing trend strength.

```APIDOC
## ease_of_movement

### Description
Calculates the Ease of Movement (EoM, EMV). EoM relates an asset’s price change to its volume and is useful for assessing trend strength.

### Parameters
* **high** (pandas.Series) - Required - dataset ‘High’ column.
* **low** (pandas.Series) - Required - dataset ‘Low’ column.
* **volume** (pandas.Series) - Required - dataset ‘Volume’ column.
* **window** (int) - Optional - n period. Defaults to 14.
* **fillna** (bool) - Optional - if True, fill nan values.

### Returns
pandas.Series - New feature generated.
```

--------------------------------

### keltner_channel_wband

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Keltner Channel Band Width. This is part of the Keltner Channel indicator suite.

```APIDOC
## keltner_channel_wband

### Description
Keltner Channel Band Width calculation.

### Method
Not specified (assumed to be a function call within a library).

### Parameters
* **high** (pandas.Series) – dataset ‘High’ column.
* **low** (pandas.Series) – dataset ‘Low’ column.
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period.
* **window_atr** (int) – n atr period. Only valid if original_version param is False.
* **fillna** (bool) – if True, fill nan values.
* **original_version** (bool) – if True, use original version as the centerline (SMA of typical price) if False, use EMA of close as the centerline. More info: https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels
* **multiplier** (int) – The multiplier has the most effect on the channel width. default is 2

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### Stochastic RSI %k Line

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the %k line for the Stochastic RSI indicator, representing the primary Stochastic RSI value.

```python
from ta.momentum import StochRSIIndicator

# Example usage:
stochrsi_indicator = StochRSIIndicator(close=df['Close'], window=14, smooth1=3, smooth2=3)
stochrsi_indicator.stochrsi_k()
```

--------------------------------

### ta.volume.force_index

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Force Index (FI), which illustrates the strength of buying or selling pressure. High positive values indicate a strong rising trend, while low values signify a strong downward trend.

```APIDOC
## ta.volume.force_index

### Description
Calculates the Force Index (FI), which illustrates the strength of buying or selling pressure. High positive values indicate a strong rising trend, while low values signify a strong downward trend.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **close** (pandas.Series) – dataset ‘Close’ column.
* **volume** (pandas.Series) – dataset ‘Volume’ column.
* **window** (int) – n period.
* **fillna** (bool) – if True, fill nan values.

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### ta.others.CumulativeReturnIndicator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Initializes the Cumulative Return (CR) indicator.

```APIDOC
## ta.others.CumulativeReturnIndicator

### Description
Initializes the Cumulative Return (CR) indicator.

### Parameters
#### Path Parameters
- **close** (pandas.Series) – dataset ‘Close’ column.
- **fillna** (bool) – if True, fill nan values.

### Method
`cumulative_return()`

### Returns
New feature generated.

### Return type
pandas.Series
```

--------------------------------

### EMAIndicator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Calculates the Exponential Moving Average (EMA).

```APIDOC
## EMAIndicator

### Description
Calculates the Exponential Moving Average (EMA).

### Parameters
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period.
* **fillna** (bool) – if True, fill nan values.

### Methods
#### ema_indicator()

Returns the Exponential Moving Average (EMA).

**Return type**: pandas.Series
```

--------------------------------

### keltner_channel_lband_indicator

Source: https://technical-analysis-library-in-python.readthedocs.io/en/latest/ta.html

Keltner Channel Low Band Indicator (KC). Returns 1 if the closing price is lower than the Keltner low band channel, otherwise returns 0.

```APIDOC
## keltner_channel_lband_indicator

### Description
Keltner Channel Low Band Indicator (KC). Returns 1, if close is lower than keltner low band channel. Else, return 0.

### Method
`ta.volatility.keltner_channel_lband_indicator`

### Parameters
* **high** (pandas.Series) – dataset ‘High’ column.
* **low** (pandas.Series) – dataset ‘Low’ column.
* **close** (pandas.Series) – dataset ‘Close’ column.
* **window** (int) – n period. Defaults to 20.
* **window_atr** (int) – n atr period. Only valid if original_version param is False. Defaults to 10.
* **fillna** (bool) – if True, fill nan values. Defaults to False.
* **original_version** (bool) – if True, use original version as the centerline (SMA of typical price) if False, use EMA of close as the centerline. Defaults to True.

### Returns
New feature generated.

### Return type
pandas.Series
```
Tokens:
Raw