Add offline workflow and coverage tests

pipeline/alpha/cli.py

@@ -160,7 +160,8 @@ def reversal_vol(data_path, output_dir, lookback, vol_window):
 @alpha.command("eval")
 @click.option("--alpha-path", required=True, help="Path to alpha parquet file")
 @click.option("--data-path", required=True, help="Path to data parquet (for price data)")
-def eval_(alpha_path, data_path):
+@click.option("--report-dir", default="reports", help="Directory to save JSON report")
+def eval_(alpha_path, data_path, report_dir):
     """Evaluate an alpha's performance (return, Sharpe, turnover).
 
     Alphas are interpreted as position WEIGHTS, not return predictors.
@@ -183,9 +184,9 @@ def eval_(alpha_path, data_path):
     click.echo("=" * 50)
 
     # Also dump JSON
-    os.makedirs("reports", exist_ok=True)
+    os.makedirs(report_dir, exist_ok=True)
     alpha_name = alpha_df["alpha_name"].iloc[0]
-    json_path = f"reports/{alpha_name}_eval.json"
+    json_path = os.path.join(report_dir, f"{alpha_name}_eval.json")
     with open(json_path, "w") as f:
         json.dump(metrics, f, indent=2)
     click.echo(f"\nReport saved: {json_path}")

pipeline/portfolio/simulator.py

@@ -166,13 +166,14 @@ class ReferenceSimulator(ExecutionSimulator):
         st = wide(data_df, "isST") if "isST" in data_df.columns else opn * 0.0
 
         symbols = sorted(set(tgt.columns) | set(opn.columns))
+        data_index = close.index
         tgt = tgt.reindex(columns=symbols)
-        opn = opn.reindex(columns=symbols)
+        opn = opn.reindex(index=data_index, columns=symbols)
         close = close.reindex(columns=symbols)
-        preclose = preclose.reindex(columns=symbols)
-        amount = amount.reindex(columns=symbols)
-        tstat = tstat.reindex(columns=symbols)
-        st = st.reindex(columns=symbols)
+        preclose = preclose.reindex(index=data_index, columns=symbols)
+        amount = amount.reindex(index=data_index, columns=symbols)
+        tstat = tstat.reindex(index=data_index, columns=symbols)
+        st = st.reindex(index=data_index, columns=symbols)
 
         sym_arr = np.asarray(symbols, dtype=object)
         n = len(symbols)