### Install project dependencies

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/CONTRIBUTING.md

Installs the project in editable mode along with development tools.

```shell
pip install -e '.[dev]'
```

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

### Install TTI Library

Source: https://context7.com/vsaveris/trading-technical-indicators/llms.txt

Commands to install the stable release via pip or clone the development version from GitHub.

```bash
# Stable release
pip install tti

# Development version
git clone https://github.com/vsaveris/trading-technical-indicators.git
pip install .
```

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

### Install TTI Unreleased Development Version

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/README.md

Clone the repository and install the development version for the latest features and fixes.

```bash
git clone https://github.com/vsaveris/trading-technical-indicators.git
pip install .
```

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

### Install the latest version of tti

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/installation.md

Use this command to install the most recent release of the package.

```bash
$ pip install -U tti
```

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

### Install a specific version of tti

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/installation.md

Use this command to install a specific previous release by version number.

```bash
$ pip install tti==0.1.b0
```

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

### Install pre-commit hooks

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/CONTRIBUTING.md

Configures pre-commit hooks to run automatically before commits.

```shell
pre-commit install
```

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

### Accumulation Distribution Line Indicator Example

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/README.md

This example demonstrates how to use the Accumulation Distribution Line indicator from the TTI library. It includes reading data, calculating the indicator, retrieving values, generating signals, and plotting the graph. Ensure you have the 'SCMN.SW.csv' data file in the './data/' directory.

```python
import pandas as pd
from tti.indicators import AccumulationDistributionLine

# Read data from csv file. Set the index to the correct column
# (dates column)
df = pd.read_csv('./data/SCMN.SW.csv', parse_dates=True, index_col=0)

# Create indicator
adl_indicator = AccumulationDistributionLine(input_data=df)

# Get indicator's calculated data
print('\nTechnical Indicator data:\n', adl_indicator.getTiData())

# Get indicator's value for a specific date
print('\nTechnical Indicator value at 2012-09-06:', adl_indicator.getTiValue('2012-09-06'))

# Get the most recent indicator's value
print('\nMost recent Technical Indicator value:', adl_indicator.getTiValue())

# Get signal from indicator
print('\nTechnical Indicator signal:', adl_indicator.getTiSignal())

# Show the Graph for the calculated Technical Indicator
adl_indicator.getTiGraph().show()
```

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

### TypicalPrice Calculation Example

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/examples/indicators/README.txt

Demonstrates the calculation of the TypicalPrice technical indicator. The indicator is calculated quickly (0.0 seconds) and a visualization is saved.

```python
Example code execution for technical indicator: TypicalPrice

- Indicator calculated in: 0.0 seconds.

- Graph ./figures/example_TypicalPrice.png saved.

- Technical Indicator data:
                   tp
date
2012-01-03  130.9900
2012-01-04  128.0600
2012-01-05  128.9167
2012-01-06  128.9433
2012-01-09  127.5367
...              ...
2012-09-06  143.4100
2012-09-07  144.3633
2012-09-10  142.1833
2012-09-11  141.3500
2012-09-12  140.6100

[176 rows x 1 columns]

- Technical Indicator value at 2012-03-20 00:00:00 : [138.78]

- Technical Indicator value at 2012-09-12 00:00:00 : [140.61]

- Technical Indicator signal: ('hold', 0)
```

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

### UltimateOscillator Calculation Example

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/examples/indicators/README.txt

Shows the calculation of the UltimateOscillator technical indicator. The calculation is efficient (0.0 seconds) and a graph is generated.

```python
Example code execution for technical indicator: UltimateOscillator

- Indicator calculated in: 0.0 seconds.

- Graph ./figures/example_UltimateOscillator.png saved.

- Technical Indicator data:
                uosc
date
2012-01-03      NaN
2012-01-04      NaN
2012-01-05      NaN
2012-01-06      NaN
2012-01-09      NaN
...             ...
2012-09-06  59.4288
2012-09-07  59.1439
2012-09-10  50.4252
2012-09-11  49.2713
2012-09-12  45.7378

[176 rows x 1 columns]

- Technical Indicator value at 2012-02-01 00:00:00 : [nan]

- Technical Indicator value at 2012-09-12 00:00:00 : [45.7378]

- Technical Indicator signal: ('buy', -1)
```

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

### Python Example for Accumulation Distribution Line Indicator

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti_indicators_examples.md

This script demonstrates how to use the AccumulationDistributionLine indicator from the tti.indicators package. It includes reading data, calculating the indicator, retrieving values and signals, and generating/saving graphs for both the indicator and a trading simulation. Ensure you have the necessary data file ('./data/SCMN.SW.csv') and directories ('./figures/') set up.

```python
import pandas as pd
from tti.indicators import AccumulationDistributionLine

# Read data from csv file. Set the index to the correct column
# (dates column)
df = pd.read_csv('./data/SCMN.SW.csv', parse_dates=True, index_col=0)

# Create indicator
adl_indicator = AccumulationDistributionLine(input_data=df)

# Get indicator's calculated data
print('\nTechnical Indicator data:\n', adl_indicator.getTiData())

# Get indicator's value for a specific date
print('\nTechnical Indicator value at 2012-09-06:', adl_indicator.getTiValue('2012-09-06'))

# Get the most recent indicator's value
print('\nMost recent Technical Indicator value:', adl_indicator.getTiValue())

# Get signal from indicator
print('\nTechnical Indicator signal:', adl_indicator.getTiSignal())

# Show the Graph for the calculated Technical Indicator
adl_indicator.getTiGraph().show()

# Save the Graph for the calculated Technical Indicator
adl_indicator.getTiGraph().savefig('./figures/example_AccumulationDistributionLine.png')
print('\nGraph for the calculated ADL indicator data, saved.')

# Execute simulation based on trading signals
simulation_data, simulation_statistics, simulation_graph = \
    adl_indicator.getTiSimulation(
        close_values=df[['close']], max_exposure=None,
        short_exposure_factor=1.5)
print('\nSimulation Data:\n', simulation_data)
print('\nSimulation Statistics:\n', simulation_statistics)

# Save the Graph for the executed trading signal simulation
simulation_graph.savefig('./figures/simulation_AccumulationDistributionLine.png')
print('\nGraph for the executed trading signal simulation, saved.')
```

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

### SwingIndex Calculation Example

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/examples/indicators/README.txt

Demonstrates the calculation of the SwingIndex technical indicator. The indicator is calculated in 0.0 seconds and a graph is saved.

```python
Example code execution for technical indicator: SwingIndex

- Indicator calculated in: 0.0 seconds.

- Graph ./figures/example_SwingIndex.png saved.

- Technical Indicator data:
                 swi
date
2012-01-03      NaN
2012-01-04 -50.8371
2012-01-05  40.6143
2012-01-06 -18.4259
2012-01-09 -21.8606
...             ...
2012-09-06  13.3789
2012-09-07  17.0256
2012-09-10 -65.2951
2012-09-11 -13.9607
2012-09-12 -10.4854

[176 rows x 1 columns]

- Technical Indicator value at 2012-06-26 00:00:00 : [5.2975]

- Technical Indicator value at 2012-09-12 00:00:00 : [-10.4854]

- Technical Indicator signal: ('hold', 0)
```

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

### VolatilityChaikins Calculation Example

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/examples/indicators/README.txt

Shows the calculation of the VolatilityChaikins technical indicator. The calculation is fast (0.0 seconds) and a graph is generated.

```python
Example code execution for technical indicator: VolatilityChaikins

- Indicator calculated in: 0.0 seconds.

- Graph ./figures/example_VolatilityChaikins.png saved.

- Technical Indicator data:
                 vch
date
2012-01-03      NaN
2012-01-04      NaN
2012-01-05      NaN
2012-01-06      NaN
2012-01-09      NaN
...             ...
2012-09-06 -13.7945
2012-09-07 -24.8259
2012-09-10 -13.4037
2012-09-11  -7.1835
2012-09-12 -11.3849

[176 rows x 1 columns]

- Technical Indicator value at 2012-05-21 00:00:00 : [21.2588]

- Technical Indicator value at 2012-09-12 00:00:00 : [-11.3849]

- Technical Indicator signal: ('hold', 0)
```

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

### Example Data for Technical Indicators

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti_indicators_examples.md

This is a sample of the input data format expected by the tti.indicators package, specifically for the Accumulation Distribution Line. It requires 'High', 'Low', 'close', and 'Volume' columns, indexed by 'Date'.

```bash
               High        Low        close      Volume
Date
1998-10-05  388.084991  342.890991  369.908997  2732524.0
1998-10-06  385.628998  371.382996  385.138000   477160.0
1998-10-07  403.313995  377.769012  398.402008   647460.0
1998-10-08  396.928009  376.295013  382.190002   292460.0
1998-10-09  394.963013  386.119995  388.084991   259155.0
```

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

### VerticalHorizontalFilter Calculation Example

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/examples/indicators/README.txt

Illustrates the calculation of the VerticalHorizontalFilter technical indicator. The indicator is calculated in 0.0 seconds and a graph is saved.

```python
Example code execution for technical indicator: VerticalHorizontalFilter

- Indicator calculated in: 0.0 seconds.

- Graph ./figures/example_VerticalHorizontalFilter.png saved.

- Technical Indicator data:
                vhf
date
2012-01-03     NaN
2012-01-04     NaN
2012-01-05     NaN
2012-01-06     NaN
2012-01-09     NaN
...            ...
2012-09-06  0.5833
2012-09-07  0.9161
2012-09-10  0.5227
2012-09-11  0.6520
2012-09-12  0.6894

[176 rows x 1 columns]

- Technical Indicator value at 2012-03-29 00:00:00 : [0.3822]

- Technical Indicator value at 2012-09-12 00:00:00 : [0.6894]

- Technical Indicator signal: ('buy', -1)
```

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

### TripleExponentialMovingAverage Calculation Example

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/examples/indicators/README.txt

Shows the calculation of the TripleExponentialMovingAverage technical indicator. The indicator calculation is fast (0.0 seconds) and a corresponding graph is saved.

```python
Example code execution for technical indicator: TripleExponentialMovingAverage

- Indicator calculated in: 0.0 seconds.

- Graph ./figures/example_TripleExponentialMovingAverage.png saved.

- Technical Indicator data:
                 tema
date
2012-01-03       NaN
2012-01-04       NaN
2012-01-05       NaN
2012-01-06       NaN
2012-01-09       NaN
...              ...
2012-09-06  143.1025
2012-09-07  144.0617
2012-09-10  142.2558
2012-09-11  141.2326
2012-09-12  140.4494

[176 rows x 1 columns]

- Technical Indicator value at 2012-03-07 00:00:00 : [134.6989]

- Technical Indicator value at 2012-09-12 00:00:00 : [140.4494]

- Technical Indicator signal: ('buy', -1)
```

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

### GET /getTiGraph

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Generates a visualization plot for the calculated indicator.

```APIDOC
## GET /getTiGraph

### Description
Generates a plot customized for each Technical Indicator.

### Method
GET

### Endpoint
/getTiGraph

### Response
#### Success Response (200)
- **plot** (matplotlib.pyplot) - The generated plot.
```

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

### PriceAndVolumeTrend Class Initialization

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Initializes the PriceAndVolumeTrend indicator with input data and configuration.

```APIDOC
## class tti.indicators.PriceAndVolumeTrend

### Description
Initializes the Price And Volume Trend Technical Indicator.

### Parameters
- **input_data** (pandas.DataFrame) - Required - The input data containing 'close' and 'volume' columns with a DatetimeIndex.
- **fill_missing_values** (bool) - Optional - If True, missing values in the input data are filled.
```

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

### GET /ti/signal

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Calculates and returns the trading signal for the Technical Indicator.

```APIDOC
## GET /ti/signal

### Description
Calculates and returns the trading signal for the calculated technical indicator.

### Method
GET

### Endpoint
/ti/signal

### Response
#### Success Response (200)
- **signal** (dict) - The calculated trading signal. Possible values are ('hold', 0), ('buy', -1), ('sell', 1).

#### Response Example
```json
{
  "signal": {
    "action": "buy",
    "code": -1
  }
}
```
```

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

### GET /ti/graph

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Generates and returns a plot customized for the Technical Indicator.

```APIDOC
## GET /ti/graph

### Description
Generates a plot customized for each Technical Indicator.

### Method
GET

### Endpoint
/ti/graph

### Response
#### Success Response (200)
- **plot** (matplotlib.pyplot) - The generated plot.

#### Response Example
(Binary data representing a plot image, e.g., PNG, JPEG)
```

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

### Build project distributions

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/CONTRIBUTING.md

Builds the package using the build module.

```shell
python -m pip install --upgrade build twine
python -m build
```

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

### GET /getTiSignal

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Calculates and returns the trading signal based on the indicator.

```APIDOC
## GET /getTiSignal

### Description
Calculates and returns the trading signal for the calculated technical indicator.

### Method
GET

### Endpoint
/getTiSignal

### Response
#### Success Response (200)
- **signal** (dict) - The calculated trading signal, mapping to ('hold', 0), ('buy', -1), or ('sell', 1).
```

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

### Run tests and linting

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/CONTRIBUTING.md

Executes the test suite and runs code quality tools locally.

```shell
pytest -q
```

```shell
ruff check .
ruff format .
```

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

### GET /ti/data

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Retrieves the Technical Indicator values for the entire calculated period.

```APIDOC
## GET /ti/data

### Description
Returns the Technical Indicator values for the whole period.

### Method
GET

### Endpoint
/ti/data

### Response
#### Success Response (200)
- **data** (pandas.DataFrame) - The Technical Indicator values. The index is of type `pandas.DatetimeIndex` and contains one column, the `pvi`.

#### Response Example
```json
{
  "data": {
    "2023-01-01": 100.5,
    "2023-01-02": 101.2,
    "2023-01-03": 102.0
  }
}
```
```

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

### EaseOfMovement Class Initialization

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Initializes the EaseOfMovement indicator with input data and configuration parameters.

```APIDOC
## EaseOfMovement Initialization

### Description
Initializes the EaseOfMovement technical indicator class.

### Parameters
- **input_data** (pandas.DataFrame) - Required - Input data containing 'high', 'low', and 'volume' columns with a DatetimeIndex.
- **period** (int) - Optional - The past periods for moving average calculation (default=40).
- **fill_missing_values** (bool) - Optional - Whether to fill missing values in input data (default=True).
```

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

### GET /getTiData

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Retrieves the full set of calculated Technical Indicator values.

```APIDOC
## GET /getTiData

### Description
Returns the Technical Indicator values for the whole period.

### Method
GET

### Endpoint
/getTiData

### Response
#### Success Response (200)
- **data** (pandas.DataFrame) - The Technical Indicator values.
```

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

### GET /ti/value

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Retrieves the Technical Indicator value for a specific date or the most recent entry.

```APIDOC
## GET /ti/value

### Description
Returns the Technical Indicator value for a given date. If the date is None, it returns the most recent entry.

### Method
GET

### Endpoint
/ti/value

### Parameters
#### Query Parameters
- **date** (str) - Optional - A date string, in the same format as the format of the `input_data` index.

### Response
#### Success Response (200)
- **value** (float or None) - The value of the Technical Indicator for the given date. If no value is found for the given date, returns None.

#### Response Example
```json
{
  "value": 123.45
}
```
```

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

### Get Technical Indicator Signal

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Calculates and returns the trading signal for the Technical Indicator.

```APIDOC
## GET /api/indicators/ti_signal

### Description
Calculates and returns the trading signal for the calculated technical indicator.

### Method
GET

### Endpoint
/api/indicators/ti_signal

### Response
#### Success Response (200)
- **signal** (dict) - The calculated trading signal. Possible values are ('hold', 0), ('buy', -1), ('sell', 1).

#### Response Example
{
  "signal": {"type": "buy", "value": -1}
}
```

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

### NegativeVolumeIndex Class Initialization

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Initializes the NegativeVolumeIndex indicator with input data containing close and volume columns.

```APIDOC
## Class: tti.indicators.NegativeVolumeIndex

### Description
Initializes the Negative Volume Index Technical Indicator.

### Parameters
- **input_data** (pandas.DataFrame) - Required - The input data. Required input columns are `close`, `volume`. The index is of type `pandas.DatetimeIndex`.
- **fill_missing_values** (bool) - Optional - If set to True, missing values in the input data are being filled. Default is True.

### Raises
- **WrongTypeForInputParameter**
- **WrongValueForInputParameter**
- **NotEnoughInputData**
- **TypeError**
- **ValueError**
```

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

### StandardDeviation Class Initialization

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Initializes the Standard Deviation indicator with input data and configuration parameters.

```APIDOC
## Class: tti.indicators.StandardDeviation

### Description
Standard Deviation Technical Indicator class implementation.

### Parameters
- **input_data** (pandas.DataFrame) - Required - The input data. Required input column is `close`. The index is of type `pandas.DatetimeIndex`.
- **period** (int) - Optional (default=20) - The past periods to be used for the calculation of the simple moving average.
- **fill_missing_values** (bool) - Optional (default=True) - If set to True, missing values in the input data are being filled.
```

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

### Get Technical Indicator Graph

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Generates and returns a plot customized for the Technical Indicator.

```APIDOC
## GET /api/indicators/ti_graph

### Description
Generates a plot customized for each Technical Indicator.

### Method
GET

### Endpoint
/api/indicators/ti_graph

### Response
#### Success Response (200)
- **plot** (matplotlib.pyplot) - The generated plot.

#### Response Example
(Image data or plot object representation)
```

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

### Upload to PyPI

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/CONTRIBUTING.md

Uploads the built distributions to PyPI using twine.

```shell
twine upload dist/*
```

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

### GET /getTiValue

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Retrieves the Technical Indicator value for a specific date or the most recent entry.

```APIDOC
## GET /getTiValue

### Description
Returns the Technical Indicator value for a given date. If the date is None, it returns the most recent entry.

### Method
GET

### Endpoint
/getTiValue

### Parameters
#### Query Parameters
- **date** (str) - Optional - A date string, in the same format as the input_data index.

### Response
#### Success Response (200)
- **value** (float or None) - The value of the Technical Indicator for the given date.
```

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

### Get Technical Indicator Data

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Retrieves all calculated Technical Indicator values for the entire period.

```APIDOC
## GET /api/indicators/ti_data

### Description
Returns the Technical Indicator values for the whole period.

### Method
GET

### Endpoint
/api/indicators/ti_data

### Response
#### Success Response (200)
- **data** (pandas.DataFrame) - The Technical Indicator values.

#### Response Example
{
  "data": {
    "2023-01-01": {"obv": 1000.0},
    "2023-01-02": {"obv": 1200.0}
  }
}
```

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

### Create and Use Stochastic Oscillator (%K and %D)

Source: https://context7.com/vsaveris/trading-technical-indicators/llms.txt

Instantiate Stochastic Oscillator to generate %K and %D lines. Supports Fast (k_slowing_periods=1) and Slow (k_slowing_periods=3) variants with configurable smoothing and methods. Identifies overbought (>80) and oversold (<20) conditions.

```python
import pandas as pd
from tti.indicators import StochasticOscillator

df = pd.read_csv('./data/stock_data.csv', parse_dates=True, index_col=0)

# Fast Stochastic (default)
fast_stoch = StochasticOscillator(
    input_data=df,
    k_periods=14,
    k_slowing_periods=1,  # Fast
    d_periods=3,
    d_method='simple'
)

# Slow Stochastic (smoother, fewer false signals)
slow_stoch = StochasticOscillator(
    input_data=df,
    k_periods=14,
    k_slowing_periods=3,  # Slow
    d_periods=3,
    d_method='exponential'
)

# Get %K and %D values
ti_data = slow_stoch.getTiData()
print("Stochastic values:\n", ti_data.tail())
# Output columns: %K, %D

# Get trading signal
signal = slow_stoch.getTiSignal()
print(f"Signal: {signal}")
# Sell signals:
#   - %K or %D falls below 80 from above
#   - %K crosses below %D in overbought region (>80)
# Buy signals:

```

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

### WilliamsR Indicator - Get Technical Indicator Data

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Retrieves all calculated Technical Indicator values for the entire period.

```APIDOC
## GET /api/indicators/williamsr/data

### Description
Returns the Technical Indicator values for the whole period.

### Method
GET

### Endpoint
/api/indicators/williamsr/data

### Response
#### Success Response (200)
- **data** (pandas.DataFrame) - The Technical Indicator values. Index is of type pandas.DatetimeIndex. It contains one column, the `wr`.

#### Response Example
```json
{
  "data": {
    "2023-01-01": -0.25,
    "2023-01-02": -0.50,
    "2023-01-03": -0.75
  }
}
```
```

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

### Forecast Oscillator Initialization

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Initializes the Forecast Oscillator indicator with input data and configuration parameters.

```APIDOC
## POST /indicators/forecast-oscillator

### Description
Initializes the Forecast Oscillator indicator class with the provided input data and calculation parameters.

### Parameters
#### Request Body
- **input_data** (pandas.DataFrame) - Required - The input data containing a 'close' column with a DatetimeIndex.
- **period** (int) - Optional - The number of past periods to use for calculation (default=14).
- **fill_missing_values** (bool) - Optional - Whether to fill missing values in the input data (default=True).
```

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

### Chande Momentum Oscillator (CMO) - Get Indicator Graph

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Generates and returns a plot of the Chande Momentum Oscillator.

```APIDOC
## GET /api/indicators/cmo/graph

### Description
Generates a plot customized for the Chande Momentum Oscillator.

### Method
GET

### Endpoint
/api/indicators/cmo/graph

### Response
#### Success Response (200)
- **graph** (matplotlib.pyplot) - The generated plot.

#### Response Example
(Image data or plot object representation)
```

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

### POST /getTiSimulation

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Executes a trading simulation based on technical indicator signals, managing positions and calculating performance metrics.

```APIDOC
## POST /getTiSimulation

### Description
Executes a trading simulation based on the trading signals produced by the technical indicator, by applying an Active trading strategy. It tracks long and short positions, exposure limits, and portfolio balance.

### Method
POST

### Endpoint
/getTiSimulation

### Parameters
#### Request Body
- **close_values** (pandas.DataFrame) - Required - The close prices of the stock for the simulation period. Index is DateTimeIndex, contains column 'close'.
- **max_exposure** (float) - Optional - Maximum allowed exposure for all opened positions. Defaults to None.
- **short_exposure_factor** (float) - Optional - The exposure factor when a new short position is opened. Defaults to 1.5.

### Response
#### Success Response (200)
- **Dataframe** (pandas.DataFrame) - Contains simulation details: signal, open_trading_action, stock_value, exposure, portfolio_value, earnings, balance.
- **Statistics** (dict) - Contains simulation stats: number_of_trading_days, number_of_buy_signals, number_of_ignored_buy_signals, number_of_sell_signals, number_of_ignored_sell_signals, last_stock_value, last_exposure, last_open_long_positions, last_open_short_positions, last_portfolio_value, last_earnings, final_balance.
- **Graph** (matplotlib.pyplot) - Visual representation of stock price, exposure, and balance.

### Errors
- **WrongTypeForInputParameter**: Input argument has wrong type.
- **WrongValueForInputParameter**: Unsupported value for input argument.
- **NotValidInputDataForSimulation**: Invalid close_values passed for the simulation.
```

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

### WilliamsR Indicator - Get Trading Signal

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Calculates and returns the trading signal based on the Williams %R Technical Indicator.

```APIDOC
## GET /api/indicators/williamsr/signal

### Description
Calculates and returns the trading signal for the calculated technical indicator.

### Method
GET

### Endpoint
/api/indicators/williamsr/signal

### Response
#### Success Response (200)
- **signal** (string) - The calculated trading signal. Possible values are 'hold', 'buy', or 'sell'.

#### Response Example
```json
{
  "signal": "buy"
}
```
```

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

### VolumeRateOfChange Initialization and Methods

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

This section details the initialization of the Volume Rate of Change (VRC) indicator and its associated methods for retrieving data, graphs, and trading signals.

```APIDOC
## Class: tti.indicators.VolumeRateOfChange

### Description
Volume Rate of Change Technical Indicator class implementation.

### Parameters
#### Path Parameters
- **input_data** (pandas.DataFrame) - Required - The input data. Required input column is `volume`. The index is of type `pandas.DatetimeIndex`.
- **period** (int) - Optional - The past periods to be used for the calculation of the indicator. Default is 5.
- **fill_missing_values** (bool) - Optional - If set to True, missing values in the input data are being filled. Default is True.

### Methods
#### getTiValue(date=None)

##### Description
Returns the Technical Indicator value for a given date. If the date is None, it returns the most recent entry.

##### Parameters
- **date** (str) - Optional - A date string, in the same format as the format of the `input_data` index. Default is None.

##### Returns
- The value of the Technical Indicator for the given date. If none value found for the given date, returns None.
- **Return type**: [float] or None

#### getTiData()

##### Description
Returns the Technical Indicator values for the whole period.

##### Returns
- The Technical Indicator values.
- **Return type**: pandas.DataFrame

#### getTiGraph()

##### Description
Generates a plot customized for each Technical Indicator.

##### Returns
- The generated plot.
- **Return type**: matplotlib.pyplot

#### getTiSignal()

##### Description
Calculates and returns the trading signal for the calculated technical indicator.

##### Returns
- The calculated trading signal.
- **Return type**: {(‘hold’, 0), (‘buy’, -1), (‘sell’, 1)}
```

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

### WilliamsR Indicator - Get Technical Indicator Graph

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Generates and returns a plot customized for the Williams %R Technical Indicator.

```APIDOC
## GET /api/indicators/williamsr/graph

### Description
Generates a plot customized for the Williams %R Technical Indicator.

### Method
GET

### Endpoint
/api/indicators/williamsr/graph

### Response
#### Success Response (200)
- **plot** (matplotlib.pyplot) - The generated plot.

#### Response Example
(Image data or plot object representation)
```

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

### Stochastic Momentum Index (SMI) - Get Indicator Graph

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Generates and returns a plot customized for the Stochastic Momentum Index.

```APIDOC
## GET /api/indicators/smi/graph

### Description
Generates a plot customized for the Stochastic Momentum Index.

### Method
GET

### Endpoint
/api/indicators/smi/graph

### Response
#### Success Response (200)
- **graph** (matplotlib.pyplot) - The generated plot.

#### Response Example
(Image data or plot object representation)
```

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

### POST /getTiSimulation

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Executes a trading simulation based on technical indicator signals, managing long and short positions with exposure constraints.

```APIDOC
## POST /getTiSimulation

### Description
Executes a trading simulation based on the trading signals produced by the technical indicator, by applying an Active trading strategy. It manages long and short positions and calculates performance metrics.

### Method
POST

### Endpoint
/getTiSimulation

### Parameters
#### Request Body
- **close_values** (pandas.DataFrame) - Required - The close prices of the stock, for the whole simulation period. Index is of type DateTimeIndex. Contains one column `close`.
- **max_exposure** (float) - Optional - Maximum allowed exposure for all opened positions. Defaults to None.
- **short_exposure_factor** (float) - Optional - The exposure factor when a new short position is opened. Defaults to 1.5.

### Response
#### Success Response (200)
- **Returns** (tuple) - A tuple containing a pandas.DataFrame (simulation details), a dict (statistics), and a matplotlib.pyplot (graph).

#### Error Handling
- **WrongTypeForInputParameter**: Raised if an input argument has the wrong type.
- **WrongValueForInputParameter**: Raised if an unsupported value is passed for an input argument.
- **NotValidInputDataForSimulation**: Raised if invalid `close_values` are passed.
```

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

### POST /getTiSimulation

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Executes a trading simulation based on technical indicator signals, managing long and short positions with configurable exposure limits.

```APIDOC
## POST /getTiSimulation

### Description
Executes a trading simulation based on the trading signals produced by the technical indicator, by applying an Active trading strategy. It tracks long and short positions and calculates performance metrics.

### Method
POST

### Endpoint
/getTiSimulation

### Parameters
#### Request Body
- **close_values** (pandas.DataFrame) - Required - The close prices of the stock for the simulation period. Index is DateTimeIndex, contains column 'close'.
- **max_exposure** (float) - Optional - Maximum allowed exposure for all opened positions. Defaults to None.
- **short_exposure_factor** (float) - Optional - The exposure factor when a new short position is opened. Defaults to 1.5.

### Response
#### Success Response (200)
- **Returns** (tuple) - A tuple containing a pandas.DataFrame (simulation details), a dict (statistics), and a matplotlib.pyplot (graph).

### Errors
- **WrongTypeForInputParameter**: Input argument has wrong type.
- **WrongValueForInputParameter**: Unsupported value for input argument.
- **NotValidInputDataForSimulation**: Invalid close_values passed for the simulation.
```

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

### Get Technical Indicator Value

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Retrieves the Technical Indicator value for a specific date or the most recent entry if no date is provided.

```APIDOC
## GET /api/indicators/ti_value

### Description
Returns the Technical Indicator value for a given date. If the date is None, it returns the most recent entry.

### Method
GET

### Endpoint
/api/indicators/ti_value

### Parameters
#### Query Parameters
- **date** (str) - Optional - A date string, in the same format as the format of the `input_data` index.

### Response
#### Success Response (200)
- **value** (float or None) - The value of the Technical Indicator for the given date. If no value is found for the given date, returns None.

#### Response Example
{
  "value": 123.45
}
```

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

### PriceRateOfChange Class Initialization

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Initializes the PriceRateOfChange indicator with input data and configuration parameters.

```APIDOC
## class tti.indicators.PriceRateOfChange

### Description
Initializes the Price Rate Of Change Technical Indicator.

### Parameters
- **input_data** (pandas.DataFrame) - Required - The input data containing a 'close' column with a pandas.DatetimeIndex.
- **period** (int) - Optional - The past periods to be used for calculation (default=25).
- **fill_missing_values** (bool) - Optional - Whether to fill missing values in input data (default=True).
```

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

### Moving Average Indicator

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Implementation of the Moving Average technical indicator, including methods to get data, graph, and signals.

```APIDOC
## Class tti.indicators.MovingAverage

### Description
Moving Average Technical Indicator class implementation.

### Parameters
#### Constructor Parameters
- **input_data** (pandas.DataFrame) - Required - The input data. Required input column is `close`. The index is of type `pandas.DatetimeIndex`.
- **period** (int) - Optional, default=20 - The past periods to be used for the calculation of the moving average. 5-13 days are for Very Short Term, 14-25 days are for Short Term, 26-49 days are for Minor Intermediate, 50-100 days are for Intermediate and 100-200 days are for Long Term.
- **ma_type** (str) - Optional, default='simple' - The type of the calculated moving average. Supported values are `simple`, `exponential`, `time_series`, `triangular` and `variable`.
- **fill_missing_values** (bool) - Optional, default=True - If set to True, missing values in the input data are being filled.

### Methods
#### getTiData()

##### Description
Returns the Technical Indicator values for the whole period.

##### Returns
- **ti_data** (pandas.DataFrame) - The Technical Indicator values. Index is of type `pandas.DatetimeIndex`. It contains one column, the `ma`.

#### getTiGraph()

##### Description
Generates a plot customized for each Technical Indicator.

##### Returns
- **plot** (matplotlib.pyplot) - The generated plot.

#### getTiSignal()

##### Description
Calculates and returns the trading signal for the calculated technical indicator.

##### Returns
- **signal** ({'hold', 0}, {'buy', -1}, {'sell', 1}) - The calculated trading signal.

### Raises
- **WrongTypeForInputParameter** - Input argument has wrong type.
- **WrongValueForInputParameter** - Unsupported value for input argument.
- **NotEnoughInputData** - Not enough data for calculating the indicator.
- **TypeError** - Type error occurred when validating the `input_data`.
- **ValueError** - Value error occurred when validating the `input_data`.
```

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

### POST /getTiSimulation

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Executes a trading simulation based on technical indicator signals, managing long and short positions with optional exposure constraints.

```APIDOC
## POST /getTiSimulation

### Description
Executes trading simulation based on the trading signals produced by the technical indicator, by applying an Active trading strategy. It tracks long and short positions and calculates performance metrics.

### Method
POST

### Endpoint
/getTiSimulation

### Parameters
#### Request Body
- **close_values** (pandas.DataFrame) - Required - The close prices of the stock, for the whole simulation period. Index is of type DateTimeIndex. Contains one column `close`.
- **max_exposure** (float) - Optional - Maximum allowed exposure for all opened positions. Defaults to None.
- **short_exposure_factor** (float) - Optional - The exposure factor when a new short position is opened. Defaults to 1.5.

### Response
#### Success Response (200)
- **Returns** (tuple) - A tuple containing a pandas.DataFrame (simulation details), a dict (statistics), and a matplotlib.pyplot (graph).

### Errors
- **WrongTypeForInputParameter**: Input argument has wrong type.
- **WrongValueForInputParameter**: Unsupported value for input argument.
- **NotValidInputDataForSimulation**: Invalid close_values passed for the simulation.
```

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

### Trading Simulation with Accumulation Distribution Line

Source: https://context7.com/vsaveris/trading-technical-indicators/llms.txt

Runs backtesting simulations using the built-in trading engine. Evaluates indicator performance by simulating trades based on signals. Requires pandas and AccumulationDistributionLine.

```python
import pandas as pd
from tti.indicators import AccumulationDistributionLine

df = pd.read_csv('./data/SCMN.SW.csv', parse_dates=True, index_col=0)

# Create indicator
adl = AccumulationDistributionLine(input_data=df)

# Run trading simulation
simulation_data, statistics, simulation_graph = adl.getTiSimulation(
    close_values=df[['close']],  # Must be DataFrame with 'close' column
    max_exposure=None,           # None = unlimited, or set max capital at risk
    short_exposure_factor=1.5    # Multiplier for short position exposure
)

# View simulation results
print("Simulation Data (last 5 days):\n", simulation_data.tail())
# Columns: signal, open_trading_action, stock_value, exposure,
#          portfolio_value, earnings, balance

# View simulation statistics
print("\nSimulation Statistics:")
for key, value in statistics.items():
    print(f"  {key}: {value}")
# Output includes:
#   number_of_trading_days: 5651
#   number_of_buy_signals: 4767
#   number_of_sell_signals: 601
#   last_stock_value: 475.5
#   last_exposure: 22340.73
#   last_open_long_positions: 40
#   last_open_short_positions: 0
#   last_earnings: 19817.21
#   final_balance: 38837.21
```

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

### Create and Use Moving Averages (SMA, EMA, TSMA, TMA, VMA)

Source: https://context7.com/vsaveris/trading-technical-indicators/llms.txt

Instantiate various Moving Average types (simple, exponential, time-series, triangular, variable) with specified periods. Retrieve indicator data, trading signals, and current values. Different periods are suitable for different analysis timeframes.

```python
import pandas as pd
from tti.indicators import MovingAverage

df = pd.read_csv('./data/stock_data.csv', parse_dates=True, index_col=0)

# Simple Moving Average (SMA)
sma = MovingAverage(input_data=df, period=20, ma_type='simple')

# Exponential Moving Average (EMA) - more weight to recent prices
ema = MovingAverage(input_data=df, period=20, ma_type='exponential')

# Time-Series Moving Average (TSMA)
tsma = MovingAverage(input_data=df, period=20, ma_type='time_series')

# Triangular Moving Average (TMA) - double-smoothed
tma = MovingAverage(input_data=df, period=20, ma_type='triangular')

# Variable Moving Average (VMA) - volatility-adjusted
vma = MovingAverage(input_data=df, period=20, ma_type='variable')

# Get calculated values
print("SMA values:\n", sma.getTiData().tail())
# Output column: ma-simple (or ma-exponential, etc.)

# Get trading signal
signal = sma.getTiSignal()
print(f"Signal: {signal}")
# Returns ('buy', -1) when close > MA
# Returns ('hold', 0) otherwise

# Compare multiple MAs
print(f"SMA: {sma.getTiValue()[0]:.2f}")
print(f"EMA: {ema.getTiValue()[0]:.2f}")
```

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

### VolatilityChaikins Class Initialization

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Initializes the Volatility Chaikins indicator with input data and configuration parameters.

```APIDOC
## Class: tti.indicators.VolatilityChaikins

### Description
Initializes the Volatility Chaikins Technical Indicator class.

### Parameters
- **input_data** (pandas.DataFrame) - Required - Input data containing 'high' and 'low' columns with a DatetimeIndex.
- **ema_period** (int) - Optional - Periods for EMA calculation (default=10).
- **change_period** (int) - Optional - Period for calculating change (default=10).
- **fill_missing_values** (bool) - Optional - Whether to fill missing values (default=True).
```

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

### TimeSeriesForecast Calculation Example

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/examples/indicators/README.txt

Illustrates the calculation of the TimeSeriesForecast technical indicator. The calculation time is 0.0 seconds and a graph is generated.

```python
Example code execution for technical indicator: TimeSeriesForecast

- Indicator calculated in: 0.0 seconds.

- Graph ./figures/example_TimeSeriesForecast.png saved.

- Technical Indicator data:
                  tsf
date
2012-01-03       NaN
2012-01-04       NaN
2012-01-05       NaN
2012-01-06       NaN
2012-01-09       NaN
...              ...
2012-09-06  143.0036
2012-09-07  143.2993
2012-09-10  142.5787
2012-09-11  141.7865
2012-09-12  141.1675

[176 rows x 1 columns]

- Technical Indicator value at 2012-08-15 00:00:00 : [137.5237]

- Technical Indicator value at 2012-09-12 00:00:00 : [141.1675]

- Technical Indicator signal: ('hold', 0)
```

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

### POST /getTiSimulation

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Executes a trading simulation based on technical indicator signals, applying an active strategy to open and close long and short positions.

```APIDOC
## POST /getTiSimulation

### Description
Executes a trading simulation based on the trading signals produced by the technical indicator. It manages long and short positions and tracks portfolio performance metrics.

### Method
POST

### Endpoint
/getTiSimulation

### Parameters
#### Request Body
- **close_values** (pandas.DataFrame) - Required - The close prices of the stock for the simulation period. Index is DateTimeIndex, contains column 'close'.
- **max_exposure** (float) - Optional - Maximum allowed exposure for all opened positions. Defaults to None.
- **short_exposure_factor** (float) - Optional - The exposure factor when a new short position is opened. Defaults to 1.5.

### Response
#### Success Response (200)
- **DataFrame** (pandas.DataFrame) - Contains simulation details: signal, open_trading_action, stock_value, exposure, portfolio_value, earnings, balance.
- **Statistics** (dict) - Contains simulation statistics: number_of_trading_days, number_of_buy_signals, number_of_ignored_buy_signals, number_of_sell_signals, number_of_ignored_sell_signals, last_stock_value, last_exposure, last_open_long_positions, last_open_short_positions, last_portfolio_value, last_earnings, final_balance.
- **Graph** (matplotlib.pyplot) - A graph displaying stock price, exposure, and balance.
```

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

### Stochastic Momentum Index (SMI) - Get Indicator Data

Source: https://github.com/vsaveris/trading-technical-indicators/blob/master/docs/source/tti.indicators.md

Retrieves the full Technical Indicator values for the entire calculated period.

```APIDOC
## GET /api/indicators/smi/data

### Description
Returns the Technical Indicator values for the whole period.

### Method
GET

### Endpoint
/api/indicators/smi/data

### Response
#### Success Response (200)
- **data** (pandas.DataFrame) - The Technical Indicator values. Index is of type pandas.DatetimeIndex. It contains one column, the `smi`.

#### Response Example
```json
{
  "data": {
    "2023-01-01": 0.75,
    "2023-01-02": 0.80,
    "2023-01-03": 0.78
  }
}
```
```