Merge pull request #1 from SSMIF-Quant/feature/wrappers

added functions that should be implemented

Author: dradoncic

.github/workflows/lint.yml

@@ -24,12 +24,12 @@ jobs:
 
       - name: Check formatting
         run: |
-          black clickhouse/ --check
+          black dataloader/ --check
 
       - name: Run linter
         run: |
-          pylint --fail-under=9.5 clickhouse/
+          pylint --fail-under=9.5 dataloader/
 
       - name: Type check
         run: |
-          mypy clickhouse/
+          mypy dataloader/

dataloader/loader.py

@@ -24,7 +24,7 @@ class DataLoader:
     client: ClassVar[Client] = Manager.get_connection()
 
     @classmethod
-    def get_data(
+    def query(
         cls,
         source: str,
         columns_list: Optional[List[str]] = None,
@@ -165,7 +165,7 @@ def _resolve_columns(
         return ", ".join(selected_cols)
 
     @classmethod
-    def show_tables(cls) -> List[str]:
+    def tables(cls) -> List[str]:
         """
         Returns available tables.
         """
@@ -174,10 +174,102 @@ def show_tables(cls) -> List[str]:
         return df["name"].tolist()
 
     @classmethod
-    def show_table_column(cls, source: str) -> List[str]:
+    def fields(cls, source: str) -> List[str]:
         """
         Returns all columns for a given table.
         """
         query = f"SHOW COLUMNS FROM {cls.database}.{source}"
         df = cls.client.query(query)
         return df["field"].tolist()
+
+    @classmethod
+    def all(cls, source: str) -> pd.DataFrame:
+        """Fetch entire table."""
+        raise NotImplementedError
+
+    @classmethod
+    def columns(cls, source: str) -> pd.DataFrame:
+        """Select specific columns from source."""
+        raise NotImplementedError
+
+    @classmethod
+    def head(cls, source: str, n: int = 10) -> pd.DataFrame:
+        """Get first N rows."""
+        raise NotImplementedError
+
+    @classmethod
+    def paginate(cls, source: str, limit: int, offset: int) -> pd.DataFrame:
+        """Get paginated results."""
+        raise NotImplementedError
+
+    @classmethod
+    def filter(cls, source: str, **kwargs) -> pd.DataFrame:
+        """
+        Simple equality filters. Pass column=value pairs.
+
+        Example:
+            DataLoader.filter('equities', symbol='AAPL', date_start='2024-01-01')
+        """
+        raise NotImplementedError
+
+    @classmethod
+    def match_pattern(cls, source: str, pattern: str) -> List[str]:
+        """Get columns matching a pattern."""
+        raise NotImplementedError
+
+    @classmethod
+    def select_pattern(cls, source: str, pattern: str, **filters) -> pd.DataFrame:
+        """Select columns matching a pattern with optional filters."""
+        raise NotImplementedError
+
+    @classmethod
+    def date_range(
+        cls, source: str, start_date: str, end_date: str, **additional_filters
+    ) -> pd.DataFrame:
+        """Get data between two dates (YYYY-MM-DD format)."""
+        raise NotImplementedError
+
+    @classmethod
+    def first_date(cls, source: str) -> pd.Timestamp:
+        """Return the earliest date in the table."""
+        raise NotImplementedError
+
+    @classmethod
+    def last_date(cls, source: str) -> pd.Timestamp:
+        """Return the latest date in the table."""
+        raise NotImplementedError
+
+    @classmethod
+    def latest(cls, source: str, n: int = 1) -> pd.DataFrame:
+        """Return the last N rows per symbol or table."""
+        raise NotImplementedError
+
+    @classmethod
+    def describe(cls, source: str) -> pd.DataFrame:
+        """Return column types, non-null counts, basic stats."""
+        raise NotImplementedError
+
+    @classmethod
+    def column_types(cls, source: str) -> Dict[str, str]:
+        """Return data types for each column in a table."""
+        raise NotImplementedError
+
+    @classmethod
+    def stream(cls, source: str, batch_size: int = 10000):
+        """
+        Yield data in chunks of batch_size.
+        Example usage:
+            for df_chunk in DataLoader.stream('equities', 5000):
+                process(df_chunk)
+        """
+        raise NotImplementedError
+
+    @classmethod
+    def iter_chunks(cls, source: str, chunk_size: int = 10000):
+        """Alias for stream."""
+        return cls.stream(source, batch_size=chunk_size)
+
+    @classmethod
+    def batch_query(cls, sources: List[str], filters: Optional[Dict[str, Any]] = None):
+        """Query multiple tables or symbols in a single call."""
+        raise NotImplementedError