refactor: migrate database schema from TIMESTAMP to TIMESTAMPTZ for better timezone handling

scripts/benchmark_metrics_compute.py

@@ -8,18 +8,19 @@ from time import perf_counter
 
 from arbitrade.config.settings import Settings
 from arbitrade.metrics import MetricsCalculator
-from arbitrade.storage.db import DuckDBStore
+from arbitrade.storage.pg_store import PgStore
 
 
-def _python_scan_compute(store: DuckDBStore) -> tuple[float, float | None, float | None]:
-    with store.connect() as conn:
-        trade_rows = conn.execute("""
-            SELECT started_at, finished_at, realized_pnl
-            FROM trades
-            WHERE finished_at IS NOT NULL
-            """).fetchall()
-        sql_d = "SELECT detected_at FROM opportunities"
-        orows = conn.execute(sql_d).fetchall()
+async def _python_scan_compute(store: PgStore) -> tuple[float, float | None, float | None]:
+    sql_s = """
+        SELECT started_at, finished_at, realized_pnl
+        FROM trades
+        WHERE finished_at IS NOT NULL
+    """
+    sql_d = "SELECT detected_at FROM opportunities"
+    async with store.pool.acquire() as conn:
+        trade_rows = await conn.fetch(sql_s)
+        orows = await conn.fetch(sql_d)
 
     realized = sum(float(row[2]) for row in trade_rows if row[2] is not None)
     durations = [
@@ -41,7 +42,7 @@ def _python_scan_compute(store: DuckDBStore) -> tuple[float, float | None, float
     return realized, avg_duration, opm
 
 
-def _seed_dataset(store: DuckDBStore) -> None:
+async def _seed_dataset(store: PgStore) -> None:
     now = datetime.now(UTC)
 
     trade_rows: list[tuple[object, ...]] = []
@@ -66,7 +67,8 @@ def _seed_dataset(store: DuckDBStore) -> None:
     opportunity_rows: list[tuple[object, ...]] = []
     for i in range(5000):
         detected_at = now + timedelta(milliseconds=200 * i)
-        opportunity_rows.append((detected_at, "USD->BTC->ETH->USD", 2.5, 1.2, 0.03, bool(i % 2)))
+        opportunity_rows.append(
+            (detected_at, "USD->BTC->ETH->USD", 2.5, 1.2, 0.03, bool(i % 2)))
 
     order_rows: list[tuple[object, ...]] = []
     for i in range(3500):
@@ -87,11 +89,11 @@ def _seed_dataset(store: DuckDBStore) -> None:
             )
         )
 
-    with store.connect() as conn:
-        conn.execute("DELETE FROM trades")
-        conn.execute("DELETE FROM opportunities")
-        conn.execute("DELETE FROM orders")
-        conn.executemany(
+    async with store.pool.acquire() as conn:
+        await conn.execute("DELETE FROM trades")
+        await conn.execute("DELETE FROM opportunities")
+        await conn.execute("DELETE FROM orders")
+        await conn.executemany(
             """
             INSERT INTO trades (
                 trade_ref,
@@ -107,7 +109,7 @@ def _seed_dataset(store: DuckDBStore) -> None:
             """,
             trade_rows,
         )
-        conn.executemany(
+        await conn.executemany(
             """
             INSERT INTO opportunities (
                 detected_at,
@@ -120,7 +122,7 @@ def _seed_dataset(store: DuckDBStore) -> None:
             """,
             opportunity_rows,
         )
-        conn.executemany(
+        await conn.executemany(
             """
             INSERT INTO orders (
                 trade_ref,
@@ -141,28 +143,28 @@ def _seed_dataset(store: DuckDBStore) -> None:
         )
 
 
-def main() -> int:
+async def main() -> int:
     db_path = Path(gettempdir()) / "arbitrade_metrics_bench.duckdb"
     settings = Settings(_env_file=None, DUCKDB_PATH=db_path)
-    store = DuckDBStore(settings)
+    store = PgStore(settings)
     store.migrate()
-    _seed_dataset(store)
+    await _seed_dataset(store)
 
     calculator = MetricsCalculator(store)
 
     for _ in range(3):
-        _python_scan_compute(store)
-        calculator.compute()
+        await _python_scan_compute(store)
+        await calculator.compute()
 
     runs = 20
     start = perf_counter()
     for _ in range(runs):
-        _python_scan_compute(store)
+        await _python_scan_compute(store)
     python_ms = (perf_counter() - start) * 1000.0 / runs
 
     start = perf_counter()
     for _ in range(runs):
-        calculator.compute()
+        await calculator.compute()
     sql_ms = (perf_counter() - start) * 1000.0 / runs
 
     speedup = (python_ms / sql_ms) if sql_ms > 0.0 else 0.0