> ## Documentation Index
> Fetch the complete documentation index at: https://systematica.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Spread

> systematica.api.models.spread

## `RollingSpreadModel`

```python theme={null}
RollingSpreadModel(
    window: int = 365,
    minp: int = None,
    upper: float = 0.2,
    lower: float = -0.2,
)
```

Rolling divergence spread model.

The spread represents the relative performance of both instruments.
Whenever the two instruments move apart, the spread increases and may reach
a certain threshold where we take a long position in the underperformer and
a short position in the overachiever. This threshold is set as percentage
point. All of this is done in a rolling manner to reduce noise.

Method generated by attrs for class RollingSpreadModel.

### Ancestors

* `systematica.models.base.BaseStatArb`
* `abc.ABC`

### Static methods

#### `get_feature_expression`

```python theme={null}
get_feature_expression() ‑> systematica.generic.base.FeatureExpression
```

Returns the expression model for rolling spread strategy.

**Returns**:

| Type               | Description                                                               |
| ------------------ | ------------------------------------------------------------------------- |
| `StatArbExprModel` | The expression model containing the PTS expression and included packages. |

### Instance variables

* `lower: float`: Lower threshold. Defaults to -0.2.

* `minp: int`: Mininim period. Defaults to `None`, which means no minimum period is applied.

* `upper: float`: Upper threshold. Defaults to 0.2.

* `window: int`: The size of the rolling window. Defaults to `365`.

### Methods

#### `plot`

```python theme={null}
plot(
    self,
    data: vectorbtpro.data.base.Data,
    s1: str,
    s2: str,
    use_close: bool = True,
    clean: bool = True,
) ‑> vectorbtpro.utils.figure.Figure
```

Plot rolling spread scores with entry and exit signals.

**Parameters**:

| Name        | Type       | Default | Description                                                                      |
| ----------- | ---------- | ------- | -------------------------------------------------------------------------------- |
| `data`      | `vbt.Data` | `--`    | Input data containing time series data.                                          |
| `s1`        | `str`      | `--`    | Column name for the first symbol.                                                |
| `s2`        | `str`      | `--`    | Column name for the second symbol.                                               |
| `use_close` | `bool`     | `True`  | Use close price if set to `True`. Otherwise, use open price. Defaults to `True`. |
| `clean`     | `bool`     | `True`  | Whether to clean the entry and exit signals. Defaults to `True`.                 |

**Returns**:

| Type         | Description                  |
| ------------ | ---------------------------- |
| `vbt.Figure` | Figure object with the plot. |

## `RollingZscoreModel`

```python theme={null}
RollingZscoreModel(
    use_returns: bool = False,
    window: int = 365,
    minp: int = None,
    upper_alpha: float = 0.05,
    lower_alpha: float = 0.05,
)
```

Rolling divergence spread model.

The spread represents the relative performance of both instruments. Whenever
the two instruments move apart, the spread increases and may reach a certain
threshold where we take a long position in the underperformer and a short
position in the overachiever.

This threshold is set to a number of standard deviations from the mean.
All of this is done in a rolling manner, as the linear relationship between
the instruments is always changing.  We will use the prediction error of
the ordinary least squares (OLS), which is the difference between the true
value and the predicted value.

Method generated by attrs for class RollingZscoreModel.

### Ancestors

* `systematica.models.base.BaseStatArb`
* `abc.ABC`

### Static methods

#### `get_feature_expression`

```python theme={null}
get_feature_expression() ‑> systematica.generic.base.FeatureExpression
```

Returns the expression model for rolling spread strategy.

**Returns**:

| Type               | Description                                                               |
| ------------------ | ------------------------------------------------------------------------- |
| `StatArbExprModel` | The expression model containing the PTS expression and included packages. |

### Instance variables

* `lower_alpha: float`: Lower tail probability used to compute percent point function (inverse of `cdf`) at q of the given RV. Defaults to 0.05.

* `minp: int`: Mininim period. Defaults to `None`, which means no minimum period is applied.

* `upper_alpha: float`: Upper tail probability used to compute percent point function (inverse of `cdf`) at q of the given RV. Defaults to 0.05.

* `use_returns: bool`: Flag to explicitly use returns as input if `True`. Otherwise, use log prices Defaults to \`False.

* `window: int`: The size of the rolling window. Defaults to `365`.

### Methods

#### `plot`

```python theme={null}
plot(
    self,
    data: vectorbtpro.data.base.Data,
    s1: str,
    s2: str,
    use_close: bool = True,
    clean: bool = True,
) ‑> vectorbtpro.utils.figure.Figure
```

Plot rolling spread z-scores with entry and exit signals.

**Parameters**:

| Name        | Type       | Default | Description                                                                      |
| ----------- | ---------- | ------- | -------------------------------------------------------------------------------- |
| `data`      | `vbt.Data` | `--`    | Input data containing time series data.                                          |
| `s1`        | `str`      | `--`    | Column name for the first symbol.                                                |
| `s2`        | `str`      | `--`    | Column name for the second symbol.                                               |
| `use_close` | `bool`     | `True`  | Use close price if set to `True`. Otherwise, use open price. Defaults to `True`. |
| `clean`     | `bool`     | `True`  | Whether to clean the entry and exit signals. Defaults to `True`.                 |

**Returns**:

| Type         | Description                  |
| ------------ | ---------------------------- |
| `vbt.Figure` | Figure object with the plot. |