Update trading_data.py

trading_data.py

@@ -0,0 +1,77 @@
+from datasets import DatasetBuilder, DownloadManager, DatasetInfo
+import datasets
+import os
+import pandas as pd
+
+class TradingDataset(DatasetBuilder):
+    # 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"],
+            }