Delete trading_data.py

trading_data.py

@@ -1,77 +0,0 @@
-from datasets import DatasetBuilder, DownloadManager, DatasetInfo
-import datasets
-import os
-import pandas as pd
-
-class TradingDataset(datasets.GeneratorBasedBuilder):
-    # Replace 'your_dataset_name' with an actual name for your dataset
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="all",      version=datasets.Version("1.0.0")), 
-        datasets.BuilderConfig(name="stocks",   version=datasets.Version("1.0.0")),
-        datasets.BuilderConfig(name="etfs",     version=datasets.Version("1.0.0"))
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description="This is my custom dataset.",
-
-            # datasets.features.FeatureConnectors
-            features=datasets.Features({
-                "File": datasets.Value("string"),
-                "Date": datasets.Value("datetime"),
-                "Open": datasets.Value("float64"),
-                "High": datasets.Value("float64"),
-                "Low": datasets.Value("float64"),
-                "Close": datasets.Value("float64"),
-                "Adj Close": datasets.Value("float64"),
-                "Volume": datasets.Value("float64"),
-            }),
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll get used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="https://huggingface.co/datasets/sebdg/trading_data/",
-            citation="Your Citation Here",
-        )
-
-    def _split_generators(self, dl_manager: DownloadManager):
-        """Returns SplitGenerators."""
-        print('Split generators')
-        # If your dataset is hosted online, use the DownloadManager to download and extract it
-        # For local data, you can skip the DownloadManager and use the local paths directly
-        
-        # For example, if your dataset is online:
-        # downloaded_files = dl_manager.download_and_extract("Your dataset URL")
-        # For local files, directly point to the file paths
-        urls_to_download = {"data_file": "path/to/your/local/file.csv"}
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": downloaded_files["data_file"],
-                    "split": "train",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-        # Load the CSV file
-        print('Yielding examples')
-        data = pd.read_csv(filepath)
-        for id, row in data.iterrows():
-            yield id, {
-                "File": row["File"],  # Adjust field names based on your CSV
-                "Date": row["Date"],
-                "Open": row["Open"],
-                "High": row["High"],
-                "Low": row["Low"],
-                "Close": row["Close"],
-                "Adj Close": row["Adj Close"],
-                "Volume": row["Volume"],
-            }