TA-Lib Python

https://github.com/ta-lib/ta-lib-python
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A fast Python wrapper for the TA-Lib technical analysis library, built with Cython and NumPy,...
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### Build and install TA-Lib from Git

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

Build and install TA-Lib from the Git repository. Clone the repository, change into the directory, and run the setup script with sudo if necessary.

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

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

### Install TA-Lib from source with setup.py

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

Install TA-Lib by checking out the sources and running setup.py.

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

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

### Install TA-Lib from PyPI

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

Install TA-Lib from PyPI on Debian-based systems. First install build dependencies, then use easy_install to install numpy, Cython, and TA-Lib.

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


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

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

### Install TA-Lib from PyPI

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

Install the TA-Lib Python package from PyPI using pip.

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

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

### Build and install TA-Lib C library from source on Linux

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

Download and build the TA-Lib C library from source on Linux, including configure, make, and install.

```shell
tar -xzf ta-lib-0.8.1-src.tar.gz
cd ta-lib-0.8.1/
./configure --prefix=/usr
make
sudo make install
```

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

### TA-Lib Build Error Example

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

Example build error indicating that the underlying TA-Lib library cannot be found.

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

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

### Import TA-Lib and create sample data

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

Sets up a NumPy array of random close prices for use with the TA-Lib Function API examples.

```python
import numpy as np
import talib

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

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

### Install TA-Lib C library with Homebrew

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

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

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

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

### Install TA-Lib with pip for Apple Silicon

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

Install TA-Lib with pip using a Python built for arm64, with --no-cache-dir to avoid cached issues.

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

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

### Import numpy and talib and generate random close prices

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

Sets up the environment by importing numpy and talib, then generates a random array of 100 close prices. This is the prerequisite for all subsequent examples.

```python
import numpy
import talib

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

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

### Warning when TA-Lib library not found

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

Example warning message shown when setup.py cannot find the TA-Lib library.

```shell
setup.py:79: UserWarning: Cannot find ta-lib library, installation may fail.
warnings.warn('Cannot find ta-lib library, installation may fail.')
```

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

### Set custom TA_LIBRARY_PATH and TA_INCLUDE_PATH

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

Set environment variables to specify custom search paths for the TA-Lib library and headers, then install.

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

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

### TA-Lib manual build note

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

Notes that building TA-Lib manually with make -jX may fail, but rerunning the command and then running make install will succeed.

```text
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.
```

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

### Missing pyconfig.h error

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

This error means Python headers are missing. Install them with the provided apt command.

```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
```

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

### Install TA-Lib via Conda Forge

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

Install TA-Lib using Conda from the conda-forge channel.

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

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

### Permission denied error for ta_defs.h

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

This error indicates the user lacks access to the TA-Lib C library installation. Grant access or install to a user-accessible location.

```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
```

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

### Install TA-Lib C library for Apple Silicon

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

Install the TA-Lib C library on Apple Silicon (M1) using arch -arm64 to ensure the correct architecture.

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

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

### Install libta-lib C library via Conda

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

Install the underlying TA-Lib C library using the libta-lib package from conda-forge.

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

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

### Human-readable function info

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

Use help() or str() to get a human-readable representation of a Function.

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

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

### TA-Lib NaN propagation example

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

Shows how TA-Lib propagates NaN values to the end of the output, unlike 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)
array([nan, nan,  2., nan, nan, nan, nan])
```

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

### Missing ta_defs.h header error

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

This error indicates the underlying TA-Lib C library is not installed or not found. On Windows, it may be due to a 32-bit/64-bit mismatch.

```shell
talib/_ta_lib.c:601:10: fatal error: ta-lib/ta_defs.h: No such file or directory
  601 | #include "ta-lib/ta_defs.h"
      |          ^~~~~~~~~~~~~~~~~~
compilation terminated.
```

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

### Create swapfile to fix build hang

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

If the build hangs, possibly due to low memory in a VM, create a swapfile with these commands.

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

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

### Call abstract functions with price overrides

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

Demonstrates calling abstract functions with the same signature as the function API. Shows default price handling and how to override with the 'price' or 'prices' argument.

```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'])
```

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

### Handle insufficient history with talib.InsufficientHistory

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

Shows how to handle insufficient history when opening a streaming handle: compute the required lookback plus one and catch talib.InsufficientHistory.

```python
from talib import abstract

need = abstract.Function('RSI', timeperiod=14).lookback + 1   # 15, usually enough

try:
    s = stream.RSI(history, timeperiod=14)
except talib.InsufficientHistory:
    ...                                                       # collect more bars
```

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

### PPO - Percentage Price Oscillator

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

Computes the Percentage Price Oscillator.

```python
real = PPO(close, fastperiod=12, slowperiod=26, matype=1)
```

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

### List all TA-Lib functions and groups in Python

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

Use talib.get_functions() to print every function name, and talib.get_function_groups() to print each group with its member functions. The output is a list of names and a dict of group names to lists of function names.

```python
import talib

# list of functions
for name in talib.get_functions():
    print(name)

# dict of functions by group
for group, names in talib.get_function_groups().items():
    print(group)
    for name in names:
        print(f"  {name}")
```

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

### ARM64 configure and config.guess fix

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

For ARM64 builds, configure with an explicit build type or update config.guess, then re-run configure.

```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
```

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

### Set parameters and run with new input data

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

Functions maintain state and remember parameters/input_arrays. Use run() to recalculate with new input data, or set input_arrays and change parameters.

```python
SMA.parameters = {'timeperiod': 15}
result1 = SMA.run(input_arrays1)
result2 = SMA.run(input_arrays2)

# Or set input_arrays and change the parameters:
SMA.input_arrays = input_arrays1
ma10 = SMA(timeperiod=10)
ma20 = SMA(20)
```

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

### 32-bit compile error with bits/libc-header-start.h

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

This error suggests a 32-bit compilation without the appropriate headers. See the linked StackOverflow question for help.

```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>
         ^~~~~~~~~~~~~~~~~~~~~~~~~~
```

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

### PPO - Percentage Price Oscillator

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

Computes the Percentage Price Oscillator.

```APIDOC
## PPO - Percentage Price Oscillator

### Description
Computes the Percentage Price Oscillator.

### Function Signature
```python
real = PPO(close, fastperiod=12, slowperiod=26, matype=1)
```

### Parameters
- **close** (array) - Required - Close prices.
- **fastperiod** (int) - Optional - Fast period (default: 12).
- **slowperiod** (int) - Optional - Slow period (default: 26).
- **matype** (int) - Optional - Moving average type (default: 1).

### Returns
- **real** (array) - The PPO values.
```

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

### Set TA_INCLUDE_PATH and TA_LIBRARY_PATH for Homebrew

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

Set environment variables to help pip find the TA-Lib library and headers when using Homebrew.

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

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

### Inspect out_range and advance a streaming handle

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

Shows how to inspect the output range of a handle and how to advance past a skipped bar without changing the value.

```python
s.out_range                       # (begidx, nbelement), as the Function API's output
s.advance()                       # count a skipped bar: the range moves, the value holds
```

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

### Import or instantiate a Function by name

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

Functions can be imported directly or instantiated by name using abstract.Function.

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

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

### Open and fill history with stream.RSI.open_and_fill

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

Opens a streaming handle and fills it with the history in one pass, returning both the handle and the series over the history.

```python
s, rsi = stream.RSI.open_and_fill(history, timeperiod=14)     # rsi == talib.RSI(history)
```

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

### Import or instantiate TA-Lib function

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

Import a TA-Lib function directly or instantiate it by name using abstract.Function.

```python
from talib import abstract

# directly
SMA = abstract.SMA

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

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

### HT_PHASOR(close)

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

Computes the Hilbert Transform - Phasor Components for the given price data.

```APIDOC
## HT_PHASOR(close)

### Description
Computes the Hilbert Transform - Phasor Components for the given price data.

### Function Signature
```python
inphase, quadrature = HT_PHASOR(close)
```

### Parameters
- **close** (array-like) - Input price data (typically close prices).

### Returns
- **inphase** (array) - In-phase component.
- **quadrature** (array) - Quadrature component.

### Notes
- This function has an unstable period.
```

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

### Multi-output streaming with stream.MACD

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

Demonstrates multi-output streaming with stream.MACD: update returns a tuple, and the handle's value is a tuple-like object indexed by output position.

```python
m = stream.MACD(close)
macd, macdsignal, macdhist = m.update(price)
m.value[2]                                    # the histogram, last bar

abstract.Function('MACD').output_names        # ['macd', 'macdsignal', 'macdhist']
```

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

### TYPPRICE - Typical Price

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

Computes the typical price from high, low, and close arrays. Returns a single output array.

```python
real = TYPPRICE(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

Computes MACD with fixed 12/26 periods.

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

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

### MFI - Money Flow Index

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

Computes the Money Flow Index.

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

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

### Streaming API basic usage with stream.SMA

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

Shows the basic usage of the Streaming API: create a handle with stream.SMA, compare its value to the Function API's last output, update it bar by bar, peek at a forming bar, and copy the handle.

```python
import talib
from talib import stream

# the Function API: the whole series, from the whole array
output = talib.SMA(close)

# the Streaming API: a handle, positioned at the end of the history
s = stream.SMA(close)
assert s.value == output[-1]

for price in feed:
    latest = s.update(price)      # one closed bar in, its value out

s.peek(forming)                   # what update would return, committing nothing
fork = s.copy()                   # an independent handle at the same bar
```

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

### QSTICK - Qstick

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

Computes the Qstick indicator.

```python
real = QSTICK(open, close, timeperiod=10)
```

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

### ROCP - Rate of change Percentage: (price-prevPrice)/prevPrice

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

Computes the Rate of Change Percentage.

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

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

### TYPPRICE

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

Calculates the typical price from high, low, and close prices.

```APIDOC
## TYPPRICE

### Description
Calculates the typical price from the high, low, and close prices.

### Function Signature
```python
real = TYPPRICE(high, low, close)
```

### Parameters
- **high** (array-like) - Required - High prices.
- **low** (array-like) - Required - Low prices.
- **close** (array-like) - Required - Close prices.

### Returns
- **real** (array-like) - The typical price values.
```

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

### STOCHRSI vs STOCH applied to RSI

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

Shows the difference between STOCHRSI and STOCH applied to RSI. STOCHRSI is STOCHF applied to RSI, not STOCH.

```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)
```

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

### AVGPRICE

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

Calculates the average price from the open, high, low, and close prices.

```APIDOC
## AVGPRICE

### Description
Calculates the average price from the open, high, low, and close prices.

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

### Parameters
- **open** (array-like) - Required - Open prices.
- **high** (array-like) - Required - High prices.
- **low** (array-like) - Required - Low prices.
- **close** (array-like) - Required - Close prices.

### Returns
- **real** (array-like) - The average price values.
```

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

### Create OHLCV input dictionary

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

Create a dictionary of numpy arrays for OHLCV data to pass to Abstract API functions. All arrays must be 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)
}
```

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

### Call Abstract API functions

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

Call Abstract API functions with the same parameters as the function API. Specify the price field with the 'price' argument or a list of prices with 'prices' for multi-input functions.

```python
from talib.abstract import *

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

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

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

# 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'])
```

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

### PyInstaller hidden import for talib.stream

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

Use this command to fix PyInstaller packaging issues by explicitly including the talib.stream module as a hidden import.

```shell
pyinstaller --hidden-import talib.stream "replaceToYourFileName.py"
```

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

### HT_SINE(close)

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

Computes the Hilbert Transform - SineWave for the given price data.

```APIDOC
## HT_SINE(close)

### Description
Computes the Hilbert Transform - SineWave for the given price data.

### Function Signature
```python
sine, leadsine = HT_SINE(close)
```

### Parameters
- **close** (array-like) - Input price data (typically close prices).

### Returns
- **sine** (array) - Sine wave values.
- **leadsine** (array) - Lead sine wave values.

### Notes
- This function has an unstable period.
```

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

### 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

Computes MACD with fixed 12/26 periods.

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

### Description
Computes MACD with fixed 12/26 periods.

### Function Signature
```python
macd, macdsignal, macdhist = MACDFIX(close, signalperiod=9)
```

### Parameters
- **close** (array) - Required - Close prices.
- **signalperiod** (int) - Optional - Signal period (default: 9).

### Returns
- **macd** (array) - MACD line values.
- **macdsignal** (array) - Signal line values.
- **macdhist** (array) - Histogram values.
```

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

### STOCHF - Stochastic Fast

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

Computes the Fast Stochastic oscillator, returning fastk and fastd.

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

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

### BOP - Balance Of Power

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

Computes the Balance of Power indicator.

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

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

### List supported indicators and function groups

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

Lists all supported TA functions and groups them by category.

```python
import talib

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

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

### STOCHF - Stochastic Fast

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

Computes the Fast Stochastic oscillator.

```APIDOC
## STOCHF - Stochastic Fast

### Description
Computes the Fast Stochastic oscillator.

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

### Parameters
- **high** (array) - Required - High prices.
- **low** (array) - Required - Low prices.
- **close** (array) - Required - Close prices.
- **fastk_period** (int) - Optional - Fast K period (default: 5).
- **fastd_period** (int) - Optional - Fast D period (default: 3).
- **fastd_matype** (int) - Optional - Fast D MA type (default: 0).

### Returns
- **fastk** (array) - Fast K values.
- **fastd** (array) - Fast D values.
```

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

### BETA

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

Calculates the Beta of the high and low price series over a specified time period.

```APIDOC
## BETA

### Description
Calculates the Beta of the high and low price series over a specified time period.

### Method
Function Call

### Endpoint
BETA(high, low, timeperiod=5)

### Parameters
- **high** (array) - Required - High price series.
- **low** (array) - Required - Low price series.
- **timeperiod** (integer) - Optional - Time period for calculation. Default is 5.

### Returns
- **real** (array) - The Beta values.
```

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

### BETA - Beta

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

Computes the Beta of two price series (high and low) over a period of 5. Returns a single real value.

```python
real = BETA(high, low, timeperiod=5)
```

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

### make error with libtool and spaces in path

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

This error may occur when the path to the TA-Lib library contains spaces. Move it to 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
```

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

### MFI - Money Flow Index

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

Computes the Money Flow Index.

```APIDOC
## MFI - Money Flow Index

### Description
Computes the Money Flow Index.

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

### Parameters
- **high** (array) - Required - High prices.
- **low** (array) - Required - Low prices.
- **close** (array) - Required - Close prices.
- **volume** (array) - Required - Volume.
- **timeperiod** (int) - Optional - Time period (default: 14).

### Returns
- **real** (array) - The MFI values.
```

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

### AVGPRICE - Average Price

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

Computes the average price from open, high, low, and close arrays. Returns a single output array.

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

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

### QSTICK - Qstick

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

Computes the Qstick indicator.

```APIDOC
## QSTICK - Qstick

### Description
Computes the Qstick indicator.

### Function Signature
```python
real = QSTICK(open, close, timeperiod=10)
```

### Parameters
- **open** (array) - Required - Open prices.
- **close** (array) - Required - Close prices.
- **timeperiod** (int) - Optional - Time period (default: 10).

### Returns
- **real** (array) - The QSTICK values.
```

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

### WCLPRICE - Weighted Close Price

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

Computes the weighted close price from high, low, and close arrays. Returns a single output array.

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

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

### ZLEMA - Zero-Lag Exponential Moving Average

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

Computes Zero-Lag Exponential Moving Average.

```python
real = ZLEMA(close, timeperiod=30)
```

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

### IMI - Intraday Momentum Index

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

Computes the Intraday Momentum Index.

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

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

### ROCP - Rate of change Percentage: (price-prevPrice)/prevPrice

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

Computes the Rate of Change Percentage.

```APIDOC
## ROCP - Rate of change Percentage: (price-prevPrice)/prevPrice

### Description
Computes the Rate of Change Percentage.

### Function Signature
```python
real = ROCP(close, timeperiod=10)
```

### Parameters
- **close** (array) - Required - Close prices.
- **timeperiod** (int) - Optional - Time period (default: 10).

### Returns
- **real** (array) - The ROCP values.
```

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

### CMOU - Chande Momentum Oscillator (Unsmoothed)

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

Computes the unsmoothed Chande Momentum Oscillator.

```python
real = CMOU(close, timeperiod=14)
```

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

### FOSC - Forecast Oscillator

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

Computes the Forecast Oscillator.

```python
real = FOSC(close, timeperiod=5)
```

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

### WCLPRICE

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

Calculates the weighted close price from high, low, and close prices.

```APIDOC
## WCLPRICE

### Description
Calculates the weighted close price from the high, low, and close prices.

### Function Signature
```python
real = WCLPRICE(high, low, close)
```

### Parameters
- **high** (array-like) - Required - High prices.
- **low** (array-like) - Required - Low prices.
- **close** (array-like) - Required - Close prices.

### Returns
- **real** (array-like) - The weighted close price values.
```

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

### CORREL - Pearson's Correlation Coefficient

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

Computes Pearson's Correlation Coefficient (r) between high and low prices over a period of 30. Returns a single real value.

```python
real = CORREL(high, low, timeperiod=30)
```

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

### STOCH - Stochastic

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

Computes the Stochastic oscillator.

```APIDOC
## STOCH - Stochastic

### Description
Computes the Stochastic oscillator.

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

### Parameters
- **high** (array) - Required - High prices.
- **low** (array) - Required - Low prices.
- **close** (array) - Required - Close prices.
- **fastk_period** (int) - Optional - Fast K period (default: 5).
- **slowk_period** (int) - Optional - Slow K period (default: 3).
- **slowk_matype** (int) - Optional - Slow K MA type (default: 0).
- **slowd_period** (int) - Optional - Slow D period (default: 3).
- **slowd_matype** (int) - Optional - Slow D MA type (default: 0).

### Returns
- **slowk** (array) - Slow K values.
- **slowd** (array) - Slow D values.
```

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

### SMA - Simple Moving Average

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

Computes Simple Moving Average.

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

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

### DPO - Detrended Price Oscillator

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

Computes the Detrended Price Oscillator.

```python
real = DPO(close, timeperiod=20)
```

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

### WAD - Williams' Accumulation/Distribution

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

Computes Williams' Accumulation/Distribution (WAD) using high, low, and close prices.

```APIDOC
## WAD - Williams' Accumulation/Distribution

### Description
Computes Williams' Accumulation/Distribution (WAD) using high, low, and close prices.

### Function Signature
```python
real = WAD(high, low, close)
```

### Parameters
- **high** (array-like) - High price series.
- **low** (array-like) - Low price series.
- **close** (array-like) - Close price series.

### Returns
- **real** (array-like) - The Williams' Accumulation/Distribution values.
```

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

### SMI - Stochastic Momentum Index

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

Computes the Stochastic Momentum Index, returning smi and smisignal.

```python
smi, smisignal = SMI(high, low, close, timeperiod=13, fastperiod=2, slowperiod=25, signalperiod=9)
```

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

### ROCR - Rate of change ratio: (price/prevPrice)

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

Computes the Rate of Change Ratio.

```python
real = ROCR(close, timeperiod=10)
```

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

### SAREXT - Parabolic SAR - Extended

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

Computes Parabolic SAR - Extended.

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

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

### ERI - Elder Ray Index (Bull Power / Bear Power)

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

Computes the Elder Ray Index, returning bullpower and bearpower.

```python
bullpower, bearpower = ERI(high, low, close, timeperiod=13)
```

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

### Calculate momentum with time period of 5

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

Calculates momentum of the close prices with a time period of 5. The output is an array with the initial lookback period set to NaN.

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

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

### Access function info via info property

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

The info property returns a dictionary with details about the function, including input names, 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'],
}
```

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

### ACCBANDS - Acceleration Bands

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

Computes Acceleration Bands. Returns upper, middle, and lower bands.

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

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

### CCI - Commodity Channel Index

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

Computes the Commodity Channel Index.

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

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

### HT_DCPHASE(close)

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

Computes the Hilbert Transform - Dominant Cycle Phase for the given price data.

```APIDOC
## HT_DCPHASE(close)

### Description
Computes the Hilbert Transform - Dominant Cycle Phase for the given price data.

### Function Signature
```python
real = HT_DCPHASE(close)
```

### Parameters
- **close** (array-like) - Input price data (typically close prices).

### Returns
- **real** (array) - Dominant Cycle Phase values.

### Notes
- This function has an unstable period.
```

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

### TRIMA - Triangular Moving Average

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

Computes Triangular Moving Average.

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

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

### PERCENTRANK - Percent Rank

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

Computes the Percent Rank of the close price over a period of 100. Returns a single real value.

```python
real = PERCENTRANK(close, timeperiod=100)
```

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

### MIDPRICE - Midpoint Price over period

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

Computes Midpoint Price over period.

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

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

### BBANDS

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

Computes Bollinger Bands over a given period.

```APIDOC
## BBANDS - Bollinger Bands

### Description
Computes Bollinger Bands over a given period.

### Function Signature
```python
upperband, middleband, lowerband = BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
```

### Parameters
- **close** (array) - Required - Close price series.
- **timeperiod** (int) - Optional - Period for the bands (default: 20).
- **nbdevup** (float) - Optional - Number of standard deviations for the upper band (default: 2).
- **nbdevdn** (float) - Optional - Number of standard deviations for the lower band (default: 2).
- **matype** (int) - Optional - Moving average type (default: 0).

### Returns
- **upperband** (array) - Upper band values.
- **middleband** (array) - Middle band values.
- **lowerband** (array) - Lower band values.
```

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

### MACD - Moving Average Convergence/Divergence

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

Computes the MACD indicator, returning macd, macdsignal, and macdhist.

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

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

### ZLEMA

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

Computes the Zero-Lag Exponential Moving Average.

```APIDOC
## ZLEMA - Zero-Lag Exponential Moving Average

### Description
Computes the Zero-Lag Exponential Moving Average.

### Function Signature
```python
real = ZLEMA(close, timeperiod=30)
```

### Parameters
- **close** (array) - Required - Close price series.
- **timeperiod** (int) - Optional - Period for the moving average (default: 30).

### Returns
- **real** (array) - ZLEMA values.
```

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

### Calculate Bollinger Bands with triple exponential moving average

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

Calculates Bollinger Bands using the triple exponential moving average (T3) as the moving average type. The function returns upper, middle, and lower bands. Requires importing MA_Type from talib.

```python
from talib import MA_Type

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

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

### WILLR - Williams' %R

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

Computes Williams' %R (WILLR) using high, low, and close prices.

```APIDOC
## WILLR - Williams' %R

### Description
Computes Williams' %R (WILLR) using high, low, and close prices.

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

### Parameters
- **high** (array-like) - High price series.
- **low** (array-like) - Low price series.
- **close** (array-like) - Close price series.
- **timeperiod** (int, optional) - Time period. Default is 14.

### Returns
- **real** (array-like) - The Williams' %R values.
```

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

### PERCENTILE - Percentile (nearest rank)

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

Computes the Percentile (nearest rank) of the close price over a period of 30, using a percentile of 50. Returns a single real value.

```python
real = PERCENTILE(close, timeperiod=30, percentile=50)
```

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

### APO - Absolute Price Oscillator

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

Computes the Absolute Price Oscillator.

```python
real = APO(close, fastperiod=12, slowperiod=26, matype=1)
```

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

### VAR

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

Calculates the Variance of the close price series over a specified time period.

```APIDOC
## VAR

### Description
Calculates the Variance of the close price series over a specified time period.

### Method
Function Call

### Endpoint
VAR(close, timeperiod=5, nbdev=1)

### Parameters
- **close** (array) - Required - Close price series.
- **timeperiod** (integer) - Optional - Time period for calculation. Default is 5.
- **nbdev** (float) - Optional - Number of standard deviations. Default is 1.

### Returns
- **real** (array) - The variance values.
```

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

### ULTOSC - Ultimate Oscillator

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

Computes the Ultimate Oscillator (ULTOSC) using high, low, and close prices.

```APIDOC
## ULTOSC - Ultimate Oscillator

### Description
Computes the Ultimate Oscillator (ULTOSC) using high, low, and close prices.

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

### Parameters
- **high** (array-like) - High price series.
- **low** (array-like) - Low price series.
- **close** (array-like) - Close price series.
- **timeperiod1** (int, optional) - First time period. Default is 7.
- **timeperiod2** (int, optional) - Second time period. Default is 14.
- **timeperiod3** (int, optional) - Third time period. Default is 28.

### Returns
- **real** (array-like) - The Ultimate Oscillator values.
```

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

### BBANDS - Bollinger Bands

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

Computes Bollinger Bands. Returns upper, middle, and lower bands.

```python
upperband, middleband, lowerband = BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
```

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

### Price Transform list

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

Text table of the Price Transform group, listing each function name and its description.

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

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

### MAVP - Moving average with variable period

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

Computes Moving average with variable period.

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

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

### IMI - Intraday Momentum Index

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

Computes the Intraday Momentum Index.

```APIDOC
## IMI - Intraday Momentum Index

### Description
Computes the Intraday Momentum Index.

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

### Parameters
- **open** (array) - Required - Open prices.
- **close** (array) - Required - Close prices.
- **timeperiod** (int) - Optional - Time period (default: 14).

### Returns
- **real** (array) - The IMI values.
```

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

### Calculate simple moving average with talib.SMA

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

Calculates a simple moving average of the close prices using the function API. The output is an array with the same length as the input, with the initial lookback period set to NaN.

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

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

### STOCHRSI - Stochastic Relative Strength Index

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

Computes the Stochastic RSI, returning fastk and fastd.

```python
fastk, fastd = STOCHRSI(close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)
```

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

### FRACTAL - Williams Fractal

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

Computes Williams Fractal, returning swinghigh and swinglow.

```python
swinghigh, swinglow = FRACTAL(high, low, leftbars=2, rightbars=2)
```

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

### MAMA - MESA Adaptive Moving Average

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

Computes MESA Adaptive Moving Average. Note: The MAMA function has an unstable period.

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

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

### MACDEXT - MACD with controllable MA type

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

Computes MACD with controllable moving average types.

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

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

### WAD - Williams' Accumulation/Distribution

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

Computes Williams' Accumulation/Distribution (WAD) using high, low, and close prices. The function returns a single output array.

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

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

### OBV - On Balance Volume

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

Computes the On Balance Volume, a cumulative volume indicator that adds volume on up days and subtracts on down days.

```APIDOC
## OBV - On Balance Volume

### Description
Computes the On Balance Volume, a cumulative volume indicator that adds volume on up days and subtracts on down days.

### Function Signature
```python
real = OBV(close, volume)
```

### Parameters
- **close** (array) - Close prices.
- **volume** (array) - Volume values.

### Returns
- **real** (array) - The On Balance Volume values.
```

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

### MOM - Momentum

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

Computes the Momentum indicator.

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

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

### DX - Directional Movement Index

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

Computes the Directional Movement Index. Note: has an unstable period.

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

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

### BOP - Balance Of Power

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

Computes the Balance Of Power indicator.

```APIDOC
## BOP - Balance Of Power

### Description
Computes the Balance Of Power indicator.

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

### Parameters
- **open** (array) - Required - Open prices.
- **high** (array) - Required - High prices.
- **low** (array) - Required - Low prices.
- **close** (array) - Required - Close prices.

### Returns
- **real** (array) - The BOP values.
```
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