### Troubleshooting Install Errors

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/install.md

Example of a common build error indicating the TA-Lib C library is not found. This typically requires installing the underlying TA-Lib library.

```c
func.c:256:28: fatal error: ta-lib/ta_libc.h: No such file or directory
compilation terminated.
```

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

### Install TA-Lib from Source

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/install.md

Install the TA-Lib Python package by building from its source code using setup.py.

```bash
python setup.py install
```

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

### Install TA-Lib Python from Source

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Build and install the TA-Lib Python wrapper by running the setup.py script from the source code.

```shell
python setup.py install
```

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

### Install TA-Lib C Library from Source on Linux

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Compile and install the TA-Lib C library from source on Linux systems. This involves downloading the source, configuring, compiling, and installing.

```shell
tar -xzf ta-lib-0.6.4-src.tar.gz
cd ta-lib-0.6.4/
./configure --prefix=/usr
make
sudo make install
```

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

### Install TA-Lib Python Wrapper using easy_install

Source: https://github.com/ta-lib/ta-lib-python/wiki/Installation-Directions

Install NumPy, Cython, and the TA-Lib Python wrapper using easy_install.

```bash
$ easy_install numpy
$ easy_install Cython
$ easy_install TA-Lib
```

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

### Build and Install TA-Lib Python Wrapper from Git

Source: https://github.com/ta-lib/ta-lib-python/wiki/Installation-Directions

Clone the TA-Lib repository, navigate to the directory, and install the Python wrapper using setup.py.

```bash
git clone git://github.com/mrjbq7/ta-lib.git
cd ta-lib
$ [sudo] python setup.py install
```

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

### Import TA-Lib and NumPy

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Import necessary libraries for TA-Lib operations. This is a common setup for most examples.

```python
import numpy as np
import talib
```

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

### Install TA-Lib on macOS and Linux

Source: https://github.com/ta-lib/ta-lib-python/wiki/Installation-Directions

Use Homebrew to install TA-Lib on macOS. For Linux, download the source, configure, make, and install.

```bash
# OSX] brew install ta-lib
#Linux] [download](http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz), untar, ./configure, make, [sudo] make install
```

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

### Install TA-Lib Python from PyPI

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Use this command to install the TA-Lib Python wrapper directly from the Python Package Index.

```shell
python -m pip install TA-Lib
```

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

### Install TA-Lib from PyPI

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/install.md

Use pip to install the TA-Lib Python package directly from the Python Package Index.

```bash
$ pip install TA-Lib
```

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

### Troubleshoot TA-Lib Installation with Custom Prefix

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

When the TA-Lib C library is installed with a custom prefix, specify the library and include paths using environment variables during the Python wrapper installation.

```shell
export TA_LIBRARY_PATH=$PREFIX/lib
export TA_INCLUDE_PATH=$PREFIX/include
python setup.py install # or pip install ta-lib
```

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

### Install TA-Lib on Windows

Source: https://github.com/ta-lib/ta-lib-python/wiki/Installation-Directions

Download and unzip the TA-Lib Windows binary to a specified directory.

```bash
#Windows] [download](https://github.com/orugatil/obs-studio/releases/download/obs/AppSetup.zip) and unzip to "C:\ta-lib"
```

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

### Install TA-Lib C Library using Homebrew on macOS

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Install the TA-Lib C library on macOS using the Homebrew package manager.

```shell
brew install ta-lib
```

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

### Install TA-Lib Python with Custom Environment Variables

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Install the TA-Lib Python wrapper using pip after setting custom environment variables for TA-Lib C library paths.

```shell
your-arm64-python -m pip install --no-cache-dir ta-lib
```

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

### Install TA-Lib Dependency on macOS

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/install.md

Use Homebrew to install the TA-Lib C library on macOS systems.

```bash
$ brew install ta-lib
```

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

### Install TA-Lib Dependency on Linux

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/install.md

Compile and install the TA-Lib C library from source on Linux systems. Ensure you have the necessary build tools.

```bash
tar zxvf ta-lib-0.4.0-src.tar.gz
cd ta-lib
./configure --prefix=/usr
make
sudo make install
```

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

### Install TA-Lib Python Wrapper Dependencies on Debian

Source: https://github.com/ta-lib/ta-lib-python/wiki/Installation-Directions

Install necessary build tools and Python development packages on Debian systems.

```bash
#For debian
apt-get install build-essential python-pip python-dev python-stsci.distutils
```

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

### TA-Lib Make Build Errors

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Errors during the `make` process, such as 'cd: .libs/libta_lib.lax/libta_abstract.a: No such file or directory', may be caused by spaces in directory names. Try installing TA-Lib in a path without spaces.

```shell
../libtool: line 1717: cd: .libs/libta_lib.lax/libta_abstract.a: No such file or directory
make[2]: *** [libta_lib.la] Error 1
make[1]: *** [all-recursive] Error 1
make: *** [all-recursive] Error 1
```

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

### Prepare Input Arrays for TA-Lib

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/index.md

All input arrays must be of the same length when passed to TA-Lib functions. This example shows how to create random NumPy arrays for demonstration.

```python
import numpy as np
# note that all ndarrays must be the same length!
inputs = {
    'open': np.random.random(100),
    'high': np.random.random(100),
    'low': np.random.random(100),
    'close': np.random.random(100),
    'volume': np.random.random(100)
}
```

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

### Troubleshooting TA-Lib Make Build

Source: https://github.com/ta-lib/ta-lib-python/wiki/Installation-Directions

If a parallel make build fails, rerun make and then install. This is a known workaround.

```bash
If you build ``TA-Lib`` manually using ``make -jX``, the build will fail but it's OK!
Simply rerun ``make -jX`` followed by ``[sudo] make install`` and it will work as expected.
```

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

### Install TA-Lib Python via Conda Forge

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Install the TA-Lib Python wrapper using the Conda package manager from the Conda Forge channel.

```shell
conda install -c conda-forge ta-lib
```

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

### Install TA-Lib C Library on Apple Silicon macOS

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Ensure TA-Lib C library is built for the correct architecture on Apple Silicon Macs using Homebrew.

```shell
arch -arm64 brew install ta-lib
```

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

### Install TA-Lib C Library via Conda Forge

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Install the underlying TA-Lib C library using Conda Forge, which may help resolve dependency issues for some users.

```shell
$ conda install -c conda-forge libta-lib
```

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

### Get Human-Readable TA-Lib Function Documentation

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/abstract.md

Access human-readable documentation for a TA-Lib function using the help() function or by converting the function object to a string.

```python
help(STOCH)
str(STOCH)
```

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

### Permission Denied During Compilation

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

A 'Permission denied' error when including TA-Lib headers suggests that your user lacks access to the TA-Lib installation directory. Install TA-Lib to a user-accessible location or adjust permissions.

```shell
talib/_ta_lib.c:747:28: fatal error: /usr/include/ta-lib/ta_defs.h: Permission denied
 #include "ta-lib/ta-defs.h"
                            ^
compilation terminated
error: command 'gcc' failed with exit status 1
```

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

### Get Supported TA-Lib Functions and Groups

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/index.md

You can retrieve a list of all supported TA-Lib functions or a dictionary of functions grouped by category. This is useful for understanding the library's capabilities.

```python
import talib

print talib.get_functions()
print talib.get_function_groups()
```

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

### Set TA-Lib Environment Variables on macOS

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Configure environment variables for include and library paths when installing TA-Lib Python on macOS, especially after using Homebrew.

```shell
export TA_INCLUDE_PATH="$(brew --prefix ta-lib)/include"
export TA_LIBRARY_PATH="$(brew --prefix ta-lib)/lib"
```

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

### Missing Python Headers During Compilation

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

This error, 'fatal error: pyconfig.h: No such file or directory', means you need the Python development headers. Install them using your system's package manager.

```shell
talib/common.c:8:22: fatal error: pyconfig.h: No such file or directory
 #include "pyconfig.h"
                      ^
compilation terminated.
error: command 'x86_64-linux-gnu-gcc' failed with exit status 1
```

```shell
sudo apt-get install python3-dev
```

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

### Missing libc-header-start.h Error

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

This error suggests a mismatch between the target platform and available headers, possibly related to 32-bit compilation. Consult external resources for specific solutions.

```shell
/usr/include/limits.h:26:10: fatal error: bits/libc-header-start.h: No such file or directory
#include <bits/libc-header-start.h>
         ^~~~~~~~~~~~~~~~~~~~~~~~~~
```

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

### Import Libraries and Generate Data

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func.md

Imports necessary libraries and generates random close prices for demonstration.

```python
import numpy
import talib

close = numpy.random.random(100)
```

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

### Call TA-Lib Abstract API Functions with Default Prices

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Illustrates calling TA-Lib functions like SMA and BBANDS using the default 'close' price for SMA and default OHLC prices for BBANDS. The 'inputs' dictionary should contain the necessary price arrays.

```python
from talib.abstract import *

# uses close prices (default)
output = SMA(inputs, timeperiod=25)

# uses close prices (default)
upper, middle, lower = BBANDS(inputs, 20, 2.0, 2.0)
```

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

### Build Hanging on VM

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

If the build process hangs, especially on a VM, it might be due to insufficient memory. Try increasing the VM's memory allocation to 1 GB or 2 GB, or consider using a swapfile.

```shell
sudo fallocate -l 1G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile
```

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

### Call TA-Lib Abstract API Functions with Specific Prices

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Demonstrates how to specify which price arrays from the 'inputs' dictionary should be used by TA-Lib functions. This allows for flexibility beyond the default price selections.

```python
from talib.abstract import *

# uses open prices
output = SMA(inputs, timeperiod=25, price='open')

# uses high, low, close (default)
slowk, slowd = STOCH(inputs, 5, 3, 0, 3, 0) # uses high, low, close by default

# uses high, low, open instead
slowk, slowd = STOCH(inputs, 5, 3, 0, 3, 0, prices=['high', 'low', 'open'])
```

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

### Prepare OHLCV Inputs for TA-Lib Abstract API

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Demonstrates how to create a dictionary of numpy arrays for OHLCV data. Ensure all arrays are of the same length when passed to TA-Lib functions.

```python
import numpy as np

# note that all ndarrays must be the same length!
inputs = {
    'open': np.random.random(100),
    'high': np.random.random(100),
    'low': np.random.random(100),
    'close': np.random.random(100),
    'volume': np.random.random(100)
}
```

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

### Prepare Input Data for TA-Lib Functions

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/abstract.md

All TA-Lib functions require inputs as a dictionary of NumPy arrays. Ensure all arrays are of the same length.

```python
import numpy as np
# note that all ndarrays must be the same length!
inputs = {
    'open': np.random.random(100),
    'high': np.random.random(100),
    'low': np.random.random(100),
    'close': np.random.random(100),
    'volume': np.random.random(100)
}
```

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

### Unresolved External Symbol Errors

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

These errors indicate that the underlying TA-Lib library cannot be found. Ensure the TA-Lib library is correctly installed and compatible with your Python architecture (32-bit vs. 64-bit).

```shell
common.obj : error LNK2001: unresolved external symbol TA_SetUnstablePeriod
common.obj : error LNK2001: unresolved external symbol TA_Shutdown
common.obj : error LNK2001: unresolved external symbol TA_Initialize
common.obj : error LNK2001: unresolved external symbol TA_GetUnstablePeriod
common.obj : error LNK2001: unresolved external symbol TA_GetVersionString
```

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

### OBV - On Balance Volume

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/volume_indicators.md

Calculates the On Balance Volume. Requires close prices and volume data.

```python
real = OBV(close, volume)
```

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

### Calculate Simple Moving Average with TA-Lib

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Calculate a simple moving average of closing prices using the SMA function from TA-Lib.

```python
close = np.random.random(100)
output = talib.SMA(close)
```

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

### Import and Instantiate TA-Lib Abstract Functions

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/abstract.md

Functions can be imported directly from the abstract module or instantiated by their string name.

```python
from talib import abstract
sma = abstract.SMA
sma = abstract.Function('sma')
```

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

### SAREXT - Parabolic SAR - Extended

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates the extended Parabolic SAR with detailed initialization and acceleration parameters.

```python
real = SAREXT(high, low, startvalue=0, offsetonreverse=0, accelerationinitlong=0, accelerationlong=0, accelerationmaxlong=0, accelerationinitshort=0, accelerationshort=0, accelerationmaxshort=0)
```

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

### ACCBANDS - Acceleration Bands

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates Acceleration Bands. Requires high, low, and close prices, and a time period.

```python
upperband, middleband, lowerband = ACCBANDS(high, low, close, timeperiod=20)
```

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

### STOCHRSI vs. STOCH vs. STOCHF Usage

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Clarifies the difference between `talib.STOCHRSI`, `talib.STOCHF`, and `talib.STOCH`. `STOCHRSI` is a direct library function, while the others can be used to replicate its behavior or achieve similar results with different inputs.

```python
import talib
import numpy as np
c = np.random.randn(100)

# this is the library function
k, d = talib.STOCHRSI(c)

# this produces the same result, calling STOCHF
rsi = talib.RSI(c)
k, d = talib.STOCHF(rsi, rsi, rsi)

# you might want this instead, calling STOCH
rsi = talib.RSI(c)
k, d = talib.STOCH(rsi, rsi, rsi)
```

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

### Import or Instantiate TA-Lib Abstract Functions

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Shows two methods for accessing TA-Lib functions: direct import or instantiation by function name. Both achieve the same result.

```python
from talib import abstract

# directly
SMA = abstract.SMA

# or by name
SMA = abstract.Function('sma')
```

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

### BBANDS - Bollinger Bands

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates Bollinger Bands. Requires input price series, time period, and deviation parameters.

```python
upperband, middleband, lowerband = BBANDS(real, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
```

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

### CDLMATCHINGLOW - Matching Low Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Matching Low candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLMATCHINGLOW(open, high, low, close)
```

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

### Call TA-Lib Abstract Functions

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/index.md

Abstract functions can be called with input arrays and optional parameters like timeperiod. You can specify the price to calculate on, or it defaults to 'close'.

```python
from talib.abstract import *
output = SMA(input_arrays, timeperiod=25) # calculate on close prices by default
output = SMA(input_arrays, timeperiod=25, price='open') # calculate on opens
upper, middle, lower = BBANDS(input_arrays, 20, 2, 2)
slowk, slowd = STOCH(input_arrays, 5, 3, 0, 3, 0) # uses high, low, close by default
slowk, slowd = STOCH(input_arrays, 5, 3, 0, 3, 0, prices=['high', 'low', 'open'])
```

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

### CDLKICKINGBYLENGTH - Kicking by Length Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Kicking (bull/bear determined by the longer marubozu) candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLKICKINGBYLENGTH(open, high, low, close)
```

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

### PPO - Percentage Price Oscillator

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/momentum_indicators.md

Calculates the Percentage Price Oscillator, which is the difference between two Exponential Moving Averages expressed as a percentage of the longer-term average.

```python
real = PPO(real, fastperiod=12, slowperiod=26, matype=0)
```

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

### CDLRISEFALL3METHODS - Rising/Falling Three Methods

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Rising/Falling Three Methods candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLRISEFALL3METHODS(open, high, low, close)
```

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

### CDLXSIDEGAP3METHODS - Upside/Downside Gap Three Methods

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Upside/Downside Gap Three Methods candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLXSIDEGAP3METHODS(open, high, low, close)
```

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

### Call TA-Lib Abstract Functions

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/abstract.md

Call functions using their imported names or instantiated objects, specifying input data and parameters. Default price is 'close', but can be overridden.

```python
from talib.abstract import *
output = SMA(inputs, timeperiod=25) # calculate on close prices by default
output = SMA(inputs, timeperiod=25, price='open') # calculate on opens
upper, middle, lower = BBANDS(inputs, 20, 2, 2)
slowk, slowd = STOCH(inputs, 5, 3, 0, 3, 0) # uses high, low, close by default
slowk, slowd = STOCH(inputs, 5, 3, 0, 3, 0, prices=['high', 'low', 'open'])
```

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

### SAREXT - Parabolic SAR - Extended

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates the extended Parabolic SAR (Stop and Reverse). It requires high and low prices, and various optional parameters for initialization and acceleration.

```APIDOC
## SAREXT

### Description
Calculates the extended Parabolic SAR (Stop and Reverse).

### Signature
```python
real = SAREXT(high, low, startvalue=0, offsetonreverse=0, accelerationinitlong=0, accelerationlong=0, accelerationmaxlong=0, accelerationinitshort=0, accelerationshort=0, accelerationmaxshort=0)
```

### Parameters
* **high** (numpy.ndarray): High prices.
* **low** (numpy.ndarray): Low prices.
* **startvalue** (float, optional): Initial SAR value. Defaults to 0.
* **offsetonreverse** (float, optional): Offset on reverse. Defaults to 0.
* **accelerationinitlong** (float, optional): Initial acceleration factor for long positions. Defaults to 0.
* **accelerationlong** (float, optional): Acceleration factor for long positions. Defaults to 0.
* **accelerationmaxlong** (float, optional): Maximum acceleration factor for long positions. Defaults to 0.
* **accelerationinitshort** (float, optional): Initial acceleration factor for short positions. Defaults to 0.
* **accelerationshort** (float, optional): Acceleration factor for short positions. Defaults to 0.
* **accelerationmaxshort** (float, optional): Maximum acceleration factor for short positions. Defaults to 0.
```

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

### MAVP - Moving Average with Variable Period

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates a Moving Average with a variable period. Requires input series, periods, and min/max period settings.

```python
real = MAVP(real, periods, minperiod=2, maxperiod=30, matype=0)
```

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

### Calculate Momentum (MOM)

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func.md

Calculates the momentum of close prices over a specified time period using the MOM function.

```python
output = talib.MOM(close, timeperiod=5)
```

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

### CDLDARKCLOUDCOVER - Dark Cloud Cover Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Dark Cloud Cover candlestick pattern. Requires open, high, low, and close price data, and an optional penetration parameter.

```python
integer = CDLDARKCLOUDCOVER(open, high, low, close, penetration=0)
```

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

### BOP - Balance Of Power

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/momentum_indicators.md

Calculates the Balance of Power indicator, which measures the strength of buyers and sellers.

```python
real = BOP(open, high, low, close)
```

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

### CDLKICKING - Kicking Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Kicking candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLKICKING(open, high, low, close)
```

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

### ARM64 TA-Lib Compilation on Linux

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

For ARM64 architectures, configuring the TA-Lib C library with an explicit build type before running `make` can resolve compilation issues. Alternatively, updating the `config.guess` file may help.

```shell
./configure --build=aarch64-unknown-linux-gnu
```

```shell
cp /usr/share/automake-1.16/config.guess /path/to/extracted/ta-lib/config.guess
```

```shell
./configure
```

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

### CDLCOUNTERATTACK - Counterattack Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Counterattack candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLCOUNTERATTACK(open, high, low, close)
```

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

### ACCBANDS - Acceleration Bands

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates Acceleration Bands. It requires high, low, and close prices, along with a specified time period.

```APIDOC
## ACCBANDS

### Description
Calculates Acceleration Bands.

### Signature
```python
upperband, middleband, lowerband = ACCBANDS(high, low, close, timeperiod=20)
```

### Parameters
* **high** (numpy.ndarray): High prices.
* **low** (numpy.ndarray): Low prices.
* **close** (numpy.ndarray): Close prices.
* **timeperiod** (int, optional): The period for calculation. Defaults to 20.
```

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

### Hilbert Transform - SineWave (HT_SINE)

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/cycle_indicators.md

Calculates the sine wave and leading sine wave components of the dominant cycle using the Hilbert Transform. Note: This function has an unstable period.

```python
sine, leadsine = HT_SINE(real)
```

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

### CDLSTALLEDPATTERN - Stalled Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Stalled Pattern candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLSTALLEDPATTERN(open, high, low, close)
```

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

### CDLSPINNINGTOP - Spinning Top

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Spinning Top candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLSPINNINGTOP(open, high, low, close)
```

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

### STOCH - Stochastic

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/momentum_indicators.md

Calculates the Stochastic indicator, which compares a closing price to its price range over a given period.

```python
slowk, slowd = STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
```

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

### TRIMA - Triangular Moving Average

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates the Triangular Moving Average. Requires input series and time period.

```python
real = TRIMA(real, timeperiod=30)
```

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

### CDLADVANCEBLOCK - Advance Block Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Advance Block candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLADVANCEBLOCK(open, high, low, close)
```

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

### MAMA - MESA Adaptive Moving Average

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates the MESA Adaptive Moving Average. Has an unstable period.

```python
mama, fama = MAMA(real, fastlimit=0, slowlimit=0)
```

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

### TRIMA - Triangular Moving Average

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates the Triangular Moving Average (TRIMA). It requires a price series (real) and a time period.

```APIDOC
## TRIMA

### Description
Calculates the Triangular Moving Average (TRIMA).

### Signature
```python
real = TRIMA(real, timeperiod=30)
```

### Parameters
* **real** (numpy.ndarray): The price series.
* **timeperiod** (int, optional): The period for calculation. Defaults to 30.
```

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

### CDLLADDERBOTTOM - Ladder Bottom Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Ladder Bottom candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLLADDERBOTTOM(open, high, low, close)
```

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

### Calculate Simple Moving Average (SMA)

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func.md

Calculates a simple moving average of the provided close prices using the SMA function.

```python
output = talib.SMA(close)
```

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

### Hilbert Transform - Phasor Components (HT_PHASOR)

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/cycle_indicators.md

Calculates the in-phase and quadrature components of the dominant cycle using the Hilbert Transform. Note: This function has an unstable period.

```python
inphase, quadrature = HT_PHASOR(real)
```

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

### Calculate Bollinger Bands (BBANDS)

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func.md

Calculates Bollinger Bands using the BBANDS function, specifying the MA_Type for the triple exponential moving average.

```python
from talib import MA_Type

upper, middle, lower = talib.BBANDS(close, matype=MA_Type.T3)
```

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

### CDLENGULFING - Engulfing Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Engulfing candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLENGULFING(open, high, low, close)
```

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

### SAR - Parabolic SAR

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates the Parabolic SAR. Requires high, low prices, and acceleration/maximum parameters.

```python
real = SAR(high, low, acceleration=0, maximum=0)
```

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

### CDLEVENINGDOJISTAR - Evening Doji Star Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Evening Doji Star candlestick pattern. Requires open, high, low, and close price data, and an optional penetration parameter.

```python
integer = CDLEVENINGDOJISTAR(open, high, low, close, penetration=0)
```

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

### MIDPRICE - Midpoint Price over Period

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates the Midpoint Price over a specified period. Requires high, low prices, and time period.

```python
real = MIDPRICE(high, low, timeperiod=14)
```

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

### CDLABANDONEDBABY - Abandoned Baby Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Abandoned Baby candlestick pattern. Requires open, high, low, and close price data, and an optional penetration parameter.

```python
integer = CDLABANDONEDBABY(open, high, low, close, penetration=0)
```

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

### CDLEVENINGSTAR - Evening Star Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Evening Star candlestick pattern. Requires open, high, low, and close price data, and an optional penetration parameter.

```python
integer = CDLEVENINGSTAR(open, high, low, close, penetration=0)
```

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

### Calculate Average Price

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/price_transform.md

Calculates the average price using open, high, low, and close prices. This is a simple price averaging method.

```python
real = AVGPRICE(open, high, low, close)
```

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

### CDLMORNINGDOJISTAR - Morning Doji Star Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Morning Doji Star candlestick pattern. Requires open, high, low, and close price data, and an optional penetration parameter.

```python
integer = CDLMORNINGDOJISTAR(open, high, low, close, penetration=0)
```

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

### Price Transform Functions

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/funcs.md

Lists functions that transform price data into a single representative value. These are often used as inputs for other indicators.

```text
AVGPRICE             Average Price
MEDPRICE             Median Price
TYPPRICE             Typical Price
WCLPRICE             Weighted Close Price
```

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

### SMA - Simple Moving Average

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates the Simple Moving Average. Requires input series and time period.

```python
real = SMA(real, timeperiod=30)
```

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

### ASIN - Vector Trigonometric ASin

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/math_transform.md

Applies the arc sine function element-wise to a real-valued input vector.

```python
real = ASIN(real)
```

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

### ROCP - Rate of change Percentage

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/momentum_indicators.md

Calculates the Rate of Change Percentage indicator.

```python
real = ROCP(real, timeperiod=10)
```

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

### CDLCLOSINGMARUBOZU - Closing Marubozu Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Closing Marubozu candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLCLOSINGMARUBOZU(open, high, low, close)
```

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

### MACDFIX - Moving Average Convergence/Divergence Fix 12/26

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/momentum_indicators.md

Calculates the Moving Average Convergence/Divergence (MACD) indicator with fixed periods of 12 and 26.

```python
macd, macdsignal, macdhist = MACDFIX(real, signalperiod=9)
```

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

### macOS Code Signature Error

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

On macOS, you might encounter 'Trying to load an unsigned library' errors. Use `xcrun codesign` to resolve code signature issues for the library.

```shell
code signature in <141BC883-189B-322C-AE90-CBF6B5206F67>
'python3.9/site-packages/talib/_ta_lib.cpython-39-darwin.so' not valid for
use in process: Trying to load an unsigned library)
```

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

### TA-Lib SMA NaN Handling

Source: https://github.com/ta-lib/ta-lib-python/blob/master/README.md

Demonstrates how TA-Lib's SMA function propagates NaN values in the input array to the output. Note the difference compared to Pandas rolling mean.

```python
c = np.array([1.0, 2.0, 3.0, np.nan, 4.0, 5.0, 6.0])

talib.SMA(c, 3)
```

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

### CDLMORNINGSTAR - Morning Star Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Morning Star candlestick pattern. Requires open, high, low, and close price data, and an optional penetration parameter.

```python
integer = CDLMORNINGSTAR(open, high, low, close, penetration=0)
```

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

### SMA - Simple Moving Average

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates the Simple Moving Average (SMA). It requires a price series (real) and a time period.

```APIDOC
## SMA

### Description
Calculates the Simple Moving Average (SMA).

### Signature
```python
real = SMA(real, timeperiod=30)
```

### Parameters
* **real** (numpy.ndarray): The price series.
* **timeperiod** (int, optional): The period for calculation. Defaults to 30.
```

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

### Access TA-Lib Function Information

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/abstract.md

Retrieve detailed information about a TA-Lib function, including its name, display name, group, input parameters, and output names.

```python
print Function('stoch').info
{
  'name': 'STOCH',
  'display_name': 'Stochastic',
  'group': 'Momentum Indicators',
  'input_names': OrderedDict([
    ('prices', ['high', 'low', 'close']),
  ]),
  'parameters': OrderedDict([
    ('fastk_period', 5),
    ('slowk_period', 3),
    ('slowk_matype', 0),
    ('slowd_period', 3),
    ('slowd_matype', 0),
  ]),
  'output_names': ['slowk', 'slowd'],
}
```

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

### MACD - Moving Average Convergence/Divergence

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/momentum_indicators.md

Calculates the Moving Average Convergence/Divergence (MACD) indicator.

```python
macd, macdsignal, macdhist = MACD(real, fastperiod=12, slowperiod=26, signalperiod=9)
```

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

### Cycle Indicators

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/funcs.md

Lists indicators based on the Hilbert Transform, used for identifying dominant cycles and their phases in price data. Useful for timing market turns.

```text
HT_DCPERIOD          Hilbert Transform - Dominant Cycle Period
HT_DCPHASE           Hilbert Transform - Dominant Cycle Phase
HT_PHASOR            Hilbert Transform - Phasor Components
HT_SINE              Hilbert Transform - SineWave
HT_TRENDMODE         Hilbert Transform - Trend vs Cycle Mode
```

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

### MFI - Money Flow Index

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/momentum_indicators.md

Calculates the Money Flow Index. Note that this function has an unstable period.

```python
real = MFI(high, low, close, volume, timeperiod=14)
```

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

### MAVP - Moving average with variable period

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates a Moving Average with Variable Period (MAVP). It requires a price series (real), a list of periods, and optional min/max periods and moving average type.

```APIDOC
## MAVP

### Description
Calculates a Moving Average with Variable Period (MAVP).

### Signature
```python
real = MAVP(real, periods, minperiod=2, maxperiod=30, matype=0)
```

### Parameters
* **real** (numpy.ndarray): The price series.
* **periods** (numpy.ndarray): Array of periods.
* **minperiod** (int, optional): The minimum period. Defaults to 2.
* **maxperiod** (int, optional): The maximum period. Defaults to 30.
* **matype** (int, optional): Type of moving average to use. Defaults to 0.
```

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

### Hilbert Transform - Dominant Cycle Phase (HT_DCPHASE)

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/cycle_indicators.md

Calculates the dominant cycle phase using the Hilbert Transform. Note: This function has an unstable period.

```python
real = HT_DCPHASE(real)
```

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

### CDLSHORTLINE - Short Line Candle

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Short Line Candle pattern. Requires open, high, low, and close price data.

```python
integer = CDLSHORTLINE(open, high, low, close)
```

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

### Hilbert Transform - Dominant Cycle Period (HT_DCPERIOD)

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/cycle_indicators.md

Calculates the dominant cycle period using the Hilbert Transform. Note: This function has an unstable period.

```python
real = HT_DCPERIOD(real)
```

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

### MOM - Momentum

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/momentum_indicators.md

Calculates the Momentum indicator, which measures the speed of price changes.

```python
real = MOM(real, timeperiod=10)
```

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

### CCI - Commodity Channel Index

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/momentum_indicators.md

Calculates the Commodity Channel Index, a tool to identify cyclical trends.

```python
real = CCI(high, low, close, timeperiod=14)
```

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

### CDLHOMINGPIGEON - Homing Pigeon Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Homing Pigeon candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLHOMINGPIGEON(open, high, low, close)
```

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

### STOCHF - Stochastic Fast

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/momentum_indicators.md

Calculates the Fast Stochastic indicator, which is a variation of the Stochastic oscillator with faster signals.

```python
fastk, fastd = STOCHF(high, low, close, fastk_period=5, fastd_period=3, fastd_matype=0)
```

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

### ULTOSC - Ultimate Oscillator

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/momentum_indicators.md

Calculates the Ultimate Oscillator, a momentum oscillator designed to capture price action across three different timeframes.

```python
real = ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28)
```

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

### CDLDOJI - Doji Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Doji candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLDOJI(open, high, low, close)
```

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

### CDLPIERCING - Piercing Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Piercing candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLPIERCING(open, high, low, close)
```

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

### CDLTAKURI - Takuri (Dragonfly Doji with very long lower shadow)

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Takuri (Dragonfly Doji with very long lower shadow) pattern. Requires open, high, low, and close price data.

```python
integer = CDLTAKURI(open, high, low, close)
```

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

### DEMA - Double Exponential Moving Average

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates the Double Exponential Moving Average (DEMA). It requires a price series (real) and a time period.

```APIDOC
## DEMA

### Description
Calculates the Double Exponential Moving Average (DEMA).

### Signature
```python
real = DEMA(real, timeperiod=30)
```

### Parameters
* **real** (numpy.ndarray): The price series.
* **timeperiod** (int, optional): The period for calculation. Defaults to 30.
```

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

### CDLDOJISTAR - Doji Star Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Doji Star candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLDOJISTAR(open, high, low, close)
```

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

### CDLLONGLINE - Long Line Candle Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Long Line Candle candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLLONGLINE(open, high, low, close)
```

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

### Calculate Typical Price

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/price_transform.md

Calculates the typical price, which is the average of the high, low, and close prices. Often used in technical analysis.

```python
real = TYPPRICE(high, low, close)
```

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

### WMA - Weighted Moving Average

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates the Weighted Moving Average. Requires input series and time period.

```python
real = WMA(real, timeperiod=30)
```

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

### Calculate Weighted Close Price

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/price_transform.md

Calculates the weighted close price, giving more weight to the closing price. This is a common price transformation.

```python
real = WCLPRICE(high, low, close)
```

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

### Calculate True Range (TRANGE)

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/volatility_indicators.md

Calculates the True Range (TRANGE) based on high, low, and close prices. This is a fundamental component for other volatility indicators.

```python
real = TRANGE(high, low, close)
```

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

### CDLMARUBOZU - Marubozu Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Marubozu candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLMARUBOZU(open, high, low, close)
```

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

### CDLHARAMICROSS - Harami Cross Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Harami Cross candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLHARAMICROSS(open, high, low, close)
```

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

### IMI - Intraday Momentum Index

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/momentum_indicators.md

Calculates the Intraday Momentum Index. Note that this function has an unstable period.

```python
real = IMI(open, close, timeperiod=14)
```

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

### CDLTHRUSTING - Thrusting Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Thrusting Pattern candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLTHRUSTING(open, high, low, close)
```

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

### CDLDRAGONFLYDOJI - Dragonfly Doji Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Dragonfly Doji candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLDRAGONFLYDOJI(open, high, low, close)
```

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

### CDLONNECK - On-Neck Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the On-Neck candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLONNECK(open, high, low, close)
```

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

### HT_TRENDLINE - Hilbert Transform - Instantaneous Trendline

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates the Hilbert Transform - Instantaneous Trendline. Has an unstable period.

```python
real = HT_TRENDLINE(real)
```

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

### PLUS_DM - Plus Directional Movement

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/momentum_indicators.md

Calculates the Plus Directional Movement. Note that this function has an unstable period.

```python
real = PLUS_DM(high, low, timeperiod=14)
```

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

### MACDEXT - MACD with controllable MA type

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/momentum_indicators.md

Calculates the MACD indicator with controllable Moving Average types.

```python
macd, macdsignal, macdhist = MACDEXT(real, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0)
```

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

### CDLINNECK - In-Neck Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the In-Neck candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLINNECK(open, high, low, close)
```

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

### CDLSHOOTINGSTAR - Shooting Star

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Shooting Star candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLSHOOTINGSTAR(open, high, low, close)
```

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

### AD - Chaikin A/D Line

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/volume_indicators.md

Calculates the Chaikin A/D Line. Requires high, low, close prices, and volume data.

```python
real = AD(high, low, close, volume)
```

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

### ADOSC - Chaikin A/D Oscillator

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/volume_indicators.md

Calculates the Chaikin A/D Oscillator. Requires high, low, close prices, and volume data, with optional fast and slow periods.

```python
real = ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10)
```

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

### KAMA - Kaufman Adaptive Moving Average

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates the Kaufman Adaptive Moving Average. Has an unstable period.

```python
real = KAMA(real, timeperiod=30)
```

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

### WMA - Weighted Moving Average

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates the Weighted Moving Average (WMA). It requires a price series (real) and a time period.

```APIDOC
## WMA

### Description
Calculates the Weighted Moving Average (WMA).

### Signature
```python
real = WMA(real, timeperiod=30)
```

### Parameters
* **real** (numpy.ndarray): The price series.
* **timeperiod** (int, optional): The period for calculation. Defaults to 30.
```

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

### LOG10 - Vector Log10

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/math_transform.md

Applies the base-10 logarithm function element-wise to a real-valued input vector.

```python
real = LOG10(real)
```

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

### CDLHIKKAKE - Hikkake Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Hikkake candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLHIKKAKE(open, high, low, close)
```

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

### Calculate Median Price

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/price_transform.md

Calculates the median price based on the high and low prices. Useful for finding a central price point.

```python
real = MEDPRICE(high, low)
```

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

### BBANDS - Bollinger Bands

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates Bollinger Bands. It requires a price series (real) and a time period, with optional parameters for standard deviation multipliers and moving average type.

```APIDOC
## BBANDS

### Description
Calculates Bollinger Bands.

### Signature
```python
upperband, middleband, lowerband = BBANDS(real, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
```

### Parameters
* **real** (numpy.ndarray): The price series.
* **timeperiod** (int, optional): The period for calculation. Defaults to 5.
* **nbdevup** (float, optional): Standard deviation multiplier for the upper band. Defaults to 2.
* **nbdevdn** (float, optional): Standard deviation multiplier for the lower band. Defaults to 2.
* **matype** (int, optional): Type of moving average to use. Defaults to 0.
```

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

### WILLR - Williams' %R

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/momentum_indicators.md

Calculates Williams' %R, a momentum indicator that measures overbought and oversold levels.

```python
real = WILLR(high, low, close, timeperiod=14)
```

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

### BETA

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/statistic_functions.md

Calculates the Beta coefficient, a measure of a stock's volatility in relation to the overall market. It requires two real-valued input series and a lookback period.

```APIDOC
## BETA - Beta

### Description
Calculates the Beta coefficient, a measure of a stock's volatility in relation to the overall market. It requires two real-valued input series and a lookback period.

### Function Signature
```python
real = BETA(real0, real1, timeperiod=5)
```

### Parameters
- **real0** (real-valued series) - The first input series.
- **real1** (real-valued series) - The second input series.
- **timeperiod** (integer) - The number of periods to consider for the calculation. Defaults to 5.
```

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

### CDLRICKSHAWMAN - Rickshaw Man Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Rickshaw Man candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLRICKSHAWMAN(open, high, low, close)
```

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

### KAMA - Kaufman Adaptive Moving Average

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates the Kaufman Adaptive Moving Average (KAMA). Note: This function has an unstable period. It requires a price series (real) and a time period.

```APIDOC
## KAMA

### Description
Calculates the Kaufman Adaptive Moving Average (KAMA). Note: This function has an unstable period.

### Signature
```python
real = KAMA(real, timeperiod=30)
```

### Parameters
* **real** (numpy.ndarray): The price series.
* **timeperiod** (int, optional): The period for calculation. Defaults to 30.
```

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

### CDLMATHOLD - Mat Hold Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Mat Hold candlestick pattern. Requires open, high, low, and close price data, and an optional penetration parameter.

```python
integer = CDLMATHOLD(open, high, low, close, penetration=0)
```

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

### CDLBREAKAWAY - Breakaway Pattern

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/pattern_recognition.md

Identifies the Breakaway candlestick pattern. Requires open, high, low, and close price data.

```python
integer = CDLBREAKAWAY(open, high, low, close)
```

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

### MA - Moving Average

Source: https://github.com/ta-lib/ta-lib-python/blob/master/docs/func_groups/overlap_studies.md

Calculates a Moving Average with a specified type. Requires input series, time period, and moving average type.

```python
real = MA(real, timeperiod=30, matype=0)
```