> ## 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.

# Cv

> systematica.walk_forward.cv

## `WalkForwardCV`

```python theme={null}
WalkForwardCV(
    id: str = None,
    loader_or_data: Callable = <function load_clean_data>,
    timeframe: str = '1d',
    start: str = None,
    end: str = None,
    s1: str = 'BTCUSDT',
    s2: str = 'ETHUSDT',
    suffix: str = 'h5',
    use_close: bool = True,
    freq: str = None,
    signal_model: str = None,
    long_entries: float = nan,
    long_exits: float = nan,
    short_entries: float = nan,
    short_exits: float = nan,
    clean: bool = True,
    select_symbols: str | int | List[str | int] = None,
    portfolio_config: Dict[str, Any] = _Nothing.NOTHING,
    model_params: Dict[str, Any] = _Nothing.NOTHING,
    metrics: str | List[str] = None,
    metrics_kwargs: Dict[str, Any] = _Nothing.NOTHING,
    use_rolling: bool = False,
    storage: str | optuna.storages._base.BaseStorage = None,
    pruner: optuna.pruners._base.BasePruner = <optuna.pruners._successive_halving.SuccessiveHalvingPruner object>,
    sampler: optuna.samplers._base.BaseSampler = <optuna.samplers._tpe.sampler.TPESampler object>,
    study_name: str = None,
    direction: str | List[str] = 'maximize',
    load_if_exists: bool = False,
    n_trials: int = 100,
    n_completed_trials: int = None,
    timeout: float = None,
    n_jobs: int = 1,
    catch: Iterable[Type[Exception]] | Type[Exception] = _Nothing.NOTHING,
    callbacks: Iterable[Callable[[optuna.study.study.Study, optuna.trial._frozen.FrozenTrial], None]] | None = None,
    gc_after_trial: bool = False,
    show_progress_bar: bool = False,
    verbose: bool = True,
    tracker: Type = None,
    model: Any = None,
    preprocess_func: Callable = None,
)
```

Analyzes a statistical arbitrage model using **cross-validation**.

Method generated by attrs for class WalkForwardCV.

### Ancestors

* `systematica.walk_forward.base.BaseWalkForward`
* `abc.ABC`
* `systematica.generic.base.Model`
* `systematica.generic.base.Feature`

### Descendants

* `systematica.api.analytics.arbitrage_index.ArbitrageIndexCV`
* `systematica.api.analytics.meta_model.MetaModelCV`
* `systematica.api.analytics.momentum.AverageMomentumFactorCV`
* `systematica.api.analytics.momentum.MomentumFactorCV`
* `systematica.api.analytics.ou_process.OUProcessCV`
* `systematica.api.analytics.range_breakout.RangeBreakoutCV`
* `systematica.api.analytics.volatility.VolatilityFactorCV`
* `systematica.api.analytics.volume_profile.VolumeProfileFactorCV`

### Static methods

#### `check_is_valid`

```python theme={null}
check_is_valid(
    model: Any,
)
```

Validates `CV` model.

**Raises**:

| Type             | Description                                          |
| ---------------- | ---------------------------------------------------- |
| `AttributeError` | If the model does not use `splitter` as a parameter. |

#### `reshape_nested_dataframe`

```python theme={null}
reshape_nested_dataframe(
    df: pandas.core.frame.DataFrame,
    index: pandas.core.indexes.base.Index,
) ‑> pandas.core.frame.DataFrame
```

Reshape a DataFrame by transposing, unstacking, reindexing, and restacking.

**Parameters**:

| Name | Type           | Default | Description                                                             |
| ---- | -------------- | ------- | ----------------------------------------------------------------------- |
| `df` | `pd.DataFrame` | `--`    | The DataFrame to reshape. Must have a MultiIndex with a 'symbol' level. |

**Returns**:

| Type           | Description             |
| -------------- | ----------------------- |
| `pd.DataFrame` | The reshaped DataFrame. |

### Instance variables

* `index_train: pandas.core.indexes.base.Index`: Computes and returns the train set index of the dataset.  The train set index is derived from the end bounds values returned by the `splitter`.

* `index_test: pandas.core.indexes.base.Index`: Computes and returns the test set index of the dataset.  The test set index is derived from the end bounds values returned by the `splitter`.

* `all_portfolio_metrics: List[str]`: Get available cross-validation metrics.

* `close_reset_stacked_by_set`:

* `high_reset_stacked_by_set`:

* `low_reset_stacked_by_set`:

* `model_output_reset_stacked_by_set`:

* `open_reset_stacked_by_set`:

* `preprocess_data_reset_stacked_by_set`:

* `price_reset_stacked_by_set`:

* `returns_reset_stacked_by_set`:

* `split_preprocess_data`:

* `splitter`:

### Methods

#### `get_splits`

```python theme={null}
get_splits(
    self,
    df: pandas.core.series.Series | pandas.core.frame.DataFrame,
) ‑> pandas.core.frame.DataFrame
```

Splits based on the cross-validation splitter.

**Returns**:

| Type           | Description                          |
| -------------- | ------------------------------------ |
| `pd.DataFrame` | DataFrame containing the split data. |

#### `set_index`

```python theme={null}
set_index(
    self,
    key: str,
    set_datetime_index: bool,
) ‑> None | pandas.core.indexes.datetimes.DatetimeIndex
```

Set valid datetime index

**Parameters**:

| Name                 | Type   | Default | Description                                                 |
| -------------------- | ------ | ------- | ----------------------------------------------------------- |
| `key`                | `str`  | `--`    | If `train`, extract train-set. If `test`, extract test-set. |
| `set_datetime_index` | `bool` | `--`    | If `True`, set end-test datetime index. Ignored otherwise.  |

**Returns**:

| Type                       | Description     |
| -------------------------- | --------------- |
| `None \| tp.DatetimeIndex` | Datetime index. |

#### `signals_reset_stacked_by_set`

```python theme={null}
signals_reset_stacked_by_set(
    self,
    validate_model: bool = True,
) ‑> pandas.core.series.Series
```

Resets the model output stacked by set and attaches bounds to the index.

This method uses the `splitter` to reset signals, stacking it by
set and attaching bounds to the index. it then select the test-set.

**Parameters**:

| Name             | Type   | Default | Description                                              |
| ---------------- | ------ | ------- | -------------------------------------------------------- |
| `validate_model` | `bool` | `True`  | Check if model parameters are valid. Defaults to `True`. |

**Returns**:

| Type        | Description           |
| ----------- | --------------------- |
| `pd.Series` | The signals test-set. |

#### `compare_train_test_split`

```python theme={null}
compare_train_test_split(
    self,
    train_test_registry: List[systematica.registries.base.Register] = None,
) ‑> pandas.core.frame.DataFrame
```

Compare train and test set across all splits.

Calculate all train/test comparison registered features.

**Parameters**:

| Name                      | Type                   | Default     | Description                                                                             |
| ------------------------- | ---------------------- | ----------- | --------------------------------------------------------------------------------------- |
| `train_test_stats_config` | `tp.List[StateConfig]` | \`\`None`.` | State representation config. if None, uses `TRAIN_TEST_STATS_CONFIG`. Defaults to None. |

**Returns**:

| Type           | Description        |
| -------------- | ------------------ |
| `pd.DataFrame` | Computed features. |

#### `get_annualized_return`

```python theme={null}
get_annualized_return(
    self,
    key: str = 'test',
    raw_output: bool = True,
    set_datetime_index: bool = True,
) ‑> pandas.core.series.Series | pandas.core.frame.DataFrame
```

Computes the annualized returns for the test set.

**Parameters**:

| Name                 | Type   | Default | Description                                                                         |
| -------------------- | ------ | ------- | ----------------------------------------------------------------------------------- |
| `key`                | `str`  | `test`  | If `train`, extract train-set. If `test`, extract test-set. Defaults to `test`.     |
| `raw_output`         | `bool` | `True`  | If `True`, returns raw results. Default is `True`, which returns processed results. |
| `set_datetime_index` | `bool` | `True`  | If `True`, set end-test datetime index. Ignored otherwise. Defaults to `True`.      |

**Returns**:

| Type        | Description                                                |
| ----------- | ---------------------------------------------------------- |
| `pd.Series` | Series containing the annualized returns for the test set. |

#### `get_portfolio_metrics`

```python theme={null}
get_portfolio_metrics(
    self,
    metrics: str | List[str] = None,
    metrics_kwargs: Dict[str, Any] = None,
    to_numpy: bool = False,
    validate_model: bool = True,
    portfolio_config: Dict[str, Any] = None,
) ‑> pandas.core.frame.DataFrame | numpy.ndarray
```

Computes portfolio performance metrics for different test sets.

**Parameters**:

| Name               | Type        | Default | Description                                                                                                   |
| ------------------ | ----------- | ------- | ------------------------------------------------------------------------------------------------------------- |
| `metrics`          | `str`       | `--`    | \| tp.List\[str], default None Portfolio performance metrics to compute. If `None`, uses the default metrics. |
| `metrics_kwargs`   | `tp.Kwargs` | `None`  | Additional keyword arguments for the metrics.                                                                 |
| `to_numpy`         | `bool`      | `False` | If `True`, returns a `np.ndarray`. Default is `False`, returning a `pd.DataFrame`.                            |
| `validate_model`   | `bool`      | `True`  | Check if model parameters are valid. Defaults to `True`.                                                      |
| `portfolio_config` | `tp.Kwargs` | `None`  | Additional configuration for the portfolio simulation. Defaults to `None`.                                    |

**Returns**:

| Type                       | Description                    |
| -------------------------- | ------------------------------ |
| `pd.DataFrame \| tp.Array` | Portfolio performance metrics. |