Pandas TA

https://www.pandas-ta.dev/api/
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Pandas TA is a standard and simply structured Python library that provides a Pandas DataFrame...
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### Create Z Candles (Python)

Source: https://www.pandas-ta.dev/api/candle

The `cdl_z` function generates candlesticks based on a rolling Z-score calculation, providing a volatility-adjusted view. It requires open, high, low, and close series. Optional parameters include `length` for the rolling window and `full` to apply it to the entire DataFrame.

```python
cdl_z(
    open_: Series,
    high: Series,
    low: Series,
    close: Series,
    length: Int = None,
    full: bool = None,
    ddof: Int = None,
    offset: Int = None,
    **kwargs: DictLike,
) -> DataFrame
```

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

### CDL_PATTERN

Source: https://www.pandas-ta.dev/api/candle

This function acts as a wrapper for various candle pattern detection algorithms from the TA Lib library. It can identify a single pattern or all supported patterns.

```APIDOC
## POST /websites/pandas-ta_dev_api/cdl_pattern

### Description
This function wraps TA Lib candle patterns.

### Method
POST

### Endpoint
/websites/pandas-ta_dev_api/cdl_pattern

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **open_** (Series) - Required - `open` Series
- **high** (Series) - Required - `high` Series
- **low** (Series) - Required - `low` Series
- **close** (Series) - Required - `close` Series
- **name** (str | List[str]) - Optional - Pattern name or a list of pattern names. Default: `"all"`
- **scalar** (float) - Optional - Scalar. Default: `100`
- **offset** (int) - Optional - Post shift. Default: `0`
- **fillna** (value) - Optional - Replaces `na`'s with `value`.

### Request Example
```json
{
  "open_": [/* Series data */],
  "high": [/* Series data */],
  "low": [/* Series data */],
  "close": [/* Series data */],
  "name": "all",
  "scalar": 100,
  "offset": 0,
  "fillna": null
}
```

### Response
#### Success Response (200)
- **DataFrame** - DataFrame containing columns for the detected pattern(s).

#### Response Example
```json
{
  "result": {
    "pattern1": [/* Series data */],
    "pattern2": [/* Series data */]
  }
}
```
```

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

### Detect Various Candle Patterns (Python)

Source: https://www.pandas-ta.dev/api/candle

The `cdl_pattern` function acts as a wrapper for TA-Lib's extensive candle pattern recognition capabilities. Users can specify a single pattern name or a list of names to detect. It requires OHLC price series and returns a DataFrame with columns for each detected pattern.

```python
cdl_pattern(
    open_: Series,
    high: Series,
    low: Series,
    close: Series,
    name: str | List[str] = "all",
    scalar: IntFloat = None,
    offset: Int = None,
    **kwargs: DictLike,
) -> DataFrame
```

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

### Identify Doji Candle Pattern (Python)

Source: https://www.pandas-ta.dev/api/candle

The `cdl_doji` function identifies Doji candles, which are characterized by a small body relative to the previous bars' high-low range. It requires open, high, low, and close price series as input. Parameters like length and factor can be adjusted to fine-tune the detection.

```python
cdl_doji(
    open_: Series,
    high: Series,
    low: Series,
    close: Series,
    length: Int = None,
    factor: IntFloat = None,
    scalar: IntFloat = None,
    asint: bool = None,
    offset: Int = None,
    **kwargs: DictLike,
) -> Series
```

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

### Identify Inside Bar Candle Pattern (Python)

Source: https://www.pandas-ta.dev/api/candle

The `cdl_inside` function detects Inside Bars, where the current candle's high and low are contained within the previous candle's high and low. It takes open, high, low, and close series. The `asbool` parameter can return results as booleans.

```python
cdl_inside(
    open_: Series,
    high: Series,
    low: Series,
    close: Series,
    asbool: bool = None,
    scalar: IntFloat = None,
    offset: Int = None,
    **kwargs: DictLike,
) -> Series
```

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

### Heikin Ashi Candles (ha) Function Signature - Python

Source: https://www.pandas-ta.dev/api/candle

Defines the function signature for the Heikin Ashi Candles (ha) indicator in Pandas-TA. It takes open, high, low, and close price series as required arguments, with an optional offset parameter. It returns a Pandas DataFrame.

```python
ha(
    open_: Series,
    high: Series,
    low: Series,
    close: Series,
    offset: Int = None,
    **kwargs: DictLike,
) -> DataFrame
```

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

### CDL_INSIDE

Source: https://www.pandas-ta.dev/api/candle

Identifies an 'Inside' candle, which is defined as a candle whose trading range (high to low) is contained entirely within the trading range of the previous candle.

```APIDOC
## POST /websites/pandas-ta_dev_api/cdl_inside

### Description
Attempts to identify an "Inside" candle which is smaller than it's previous candle.

### Method
POST

### Endpoint
/websites/pandas-ta_dev_api/cdl_inside

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **open_** (Series) - Required - `open` Series
- **high** (Series) - Required - `high` Series
- **low** (Series) - Required - `low` Series
- **close** (Series) - Required - `close` Series
- **asbool** (bool) - Optional - Return booleans. Default: `False`
- **scalar** (float) - Optional - Scalar. Default: `100`
- **offset** (int) - Optional - Post shift. Default: `0`
- **fillna** (value) - Optional - Replaces `na`'s with `value`.

### Request Example
```json
{
  "open_": [/* Series data */],
  "high": [/* Series data */],
  "low": [/* Series data */],
  "close": [/* Series data */],
  "asbool": false,
  "scalar": 100,
  "offset": 0,
  "fillna": null
}
```

### Response
#### Success Response (200)
- **Series** - 1 column identifying inside candles.

#### Response Example
```json
{
  "result": [/* Series data */]
}
```
```

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

### CDL_DOJI

Source: https://www.pandas-ta.dev/api/candle

Identifies a 'Doji' candle, characterized by a small body relative to its high-low range. It compares the candle's body to the average range of the preceding ten bars.

```APIDOC
## POST /websites/pandas-ta_dev_api/cdl_doji

### Description
Attempts to identify a "Doji" candle which is shorter than 10% of the average of the 10 previous bars High-Low range.

### Method
POST

### Endpoint
/websites/pandas-ta_dev_api/cdl_doji

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **open_** (Series) - Required - `open` Series
- **high** (Series) - Required - `high` Series
- **low** (Series) - Required - `low` Series
- **close** (Series) - Required - `close` Series
- **length** (int) - Optional - The period. Default: `10`
- **factor** (float) - Optional - Doji value. Default: `100`
- **scalar** (float) - Optional - Scalar. Default: `100`
- **asint** (bool) - Optional - Returns as `Int`. Default: `True`
- **offset** (int) - Optional - Post shift. Default: `0`
- **naive** (bool) - Optional - Prefills potential Doji; bodies that are less than a percentage, `factor`, of it's High-Low range. Default: `False`
- **fillna** (value) - Optional - Replaces `na`'s with `value`.

### Request Example
```json
{
  "open_": [/* Series data */],
  "high": [/* Series data */],
  "low": [/* Series data */],
  "close": [/* Series data */],
  "length": 10,
  "factor": 100,
  "scalar": 100,
  "asint": true,
  "offset": 0,
  "naive": false,
  "fillna": null
}
```

### Response
#### Success Response (200)
- **Series** - 1 column containing the Doji candle identification.

#### Response Example
```json
{
  "result": [/* Series data */]
}
```
```

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

### CDL_Z

Source: https://www.pandas-ta.dev/api/candle

Generates candlesticks based on a rolling Z-score calculation. This method can utilize the full DataFrame length or a specified period for the Z-score.

```APIDOC
## POST /websites/pandas-ta_dev_api/cdl_z

### Description
Creates candlesticks using a rolling Z Score.

### Method
POST

### Endpoint
/websites/pandas-ta_dev_api/cdl_z

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **open_** (Series) - Required - `open` Series
- **high** (Series) - Required - `high` Series
- **low** (Series) - Required - `low` Series
- **close** (Series) - Required - `close` Series
- **length** (int) - Optional - The period. Default: `10`
- **full** (bool) - Optional - Apply `length` to whole DataFrame. Default: `False`
- **ddof** (int) - Optional - By default, uses Pandas `ddof=1`. For Numpy calculation, use `0`. Default: `1`
- **offset** (int) - Optional - Post shift. Default: `0`
- **naive** (bool) - Optional - If `True`, prefills potential Doji less than the length if it less than a percentage of it's High-Low range. Default: `False`
- **fillna** (value) - Optional - Replaces `na`'s with `value`.

### Request Example
```json
{
  "open_": [/* Series data */],
  "high": [/* Series data */],
  "low": [/* Series data */],
  "close": [/* Series data */],
  "length": 10,
  "full": false,
  "ddof": 1,
  "offset": 0,
  "naive": false,
  "fillna": null
}
```

### Response
#### Success Response (200)
- **DataFrame** - DataFrame with 4 columns representing the Z-score candlesticks.

#### Response Example
```json
{
  "result": {
    "z_open": [/* Series data */],
    "z_high": [/* Series data */],
    "z_low": [/* Series data */],
    "z_close": [/* Series data */]
  }
}
```
```
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