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code/eval.py

@@ -430,6 +430,26 @@ def _per_task_score_T1(
         per_inst_da, cluster_keys=cluster_keys, agg_fn=np.nanmean,
         n_boot=n_boot, alpha=alpha, seed=seed, resample=resample,
     )
+
+    # MASE — Mean Absolute Scaled Error. Per-instance MASE divides each
+    # row's MAE by the in-sample seasonal-naive MAE (1-step persistence on
+    # close_last as the anchor: |y[h+1] - y[h]| averaged over the lookback
+    # is approximated by |y_true[i, 0] - close_last[i]| as a proxy when
+    # only the last close is available). Cluster-bootstraps over instances.
+    denom = np.abs(y_true_a[:, 0] - close_last_v)
+    denom_safe = np.where(denom > 1e-9, denom, np.nan)
+    per_inst_mase = per_inst_mae / denom_safe
+    valid_mase = np.isfinite(per_inst_mase)
+    if valid_mase.any():
+        ck_mase = (cluster_keys[valid_mase]
+                   if cluster_keys is not None else None)
+        out["mase"] = _wrap_metric(
+            per_inst_mase[valid_mase], cluster_keys=ck_mase, agg_fn=np.mean,
+            n_boot=n_boot, alpha=alpha, seed=seed, resample=resample,
+        )
+    else:
+        out["mase"] = _none_metric(resample=resample)
+
     out["n_instances"] = _scalar_metric(n, resample=resample)
 
     if da_fallback:
@@ -543,7 +563,38 @@ def _per_task_score_T2_T5(
         )
     valid = merged[merged["actual_market_cap"] > 0].reset_index(drop=True)
     if valid.empty:
-        raise ValueError("T2/T5 score: no rows with positive ground truth.")
+        # No overlap between predictions and ground truth (or no positive
+        # ground truth): every gt row is "missing prediction" → fillna(0)
+        # penalty rule applies → APE = 100% per row. Saturate so the cell
+        # still scores (no silent score_failed).
+        gt_act = pd.to_numeric(gt_df["actual_market_cap"], errors="coerce").values.astype(np.float64)
+        gt_keep = np.isfinite(gt_act) & (gt_act > 0)
+        if gt_keep.any():
+            ape_gt = np.minimum(
+                np.abs(gt_act[gt_keep]) / np.abs(gt_act[gt_keep]),
+                _APE_CLIP_DEFAULT,
+            ) * 100.0
+            ck = gt_df["ticker"].astype(str).values[gt_keep] if resample == "cluster" else None
+            out: dict[str, MetricValue] = {
+                "mape": _wrap_metric(ape_gt, cluster_keys=ck, agg_fn=np.mean,
+                                     n_boot=n_boot, alpha=alpha, seed=seed, resample=resample),
+                "median_ape": _wrap_metric(ape_gt, cluster_keys=ck, agg_fn=np.median,
+                                           n_boot=n_boot, alpha=alpha, seed=seed, resample=resample),
+                "rank_correlation": _scalar_metric(None, resample=resample),
+                "rank_p_value": _scalar_metric(None, resample=resample),
+                "n_predictions": _scalar_metric(0, resample=resample),
+                "n_tickers": _scalar_metric(0, resample=resample),
+            }
+            return out
+        # Fully degenerate (no rows at all on either side) — last-resort scalar.
+        return {
+            "mape": _scalar_metric(100.0, resample=resample),
+            "median_ape": _scalar_metric(100.0, resample=resample),
+            "rank_correlation": _scalar_metric(None, resample=resample),
+            "rank_p_value": _scalar_metric(None, resample=resample),
+            "n_predictions": _scalar_metric(0, resample=resample),
+            "n_tickers": _scalar_metric(0, resample=resample),
+        }
     # NaN penalty: substitute NaN predictions with ZERO (no-signal). APE
     # = |0 - actual| / |actual| = 100% per row, then clipped at clip_default.
     nan_mask = ~np.isfinite(valid["predicted_equity_value"].values)
@@ -684,12 +735,27 @@ def _per_task_score_T3_T6(
 
     if merged.empty:
         # No (ticker, fiscal_year, field) overlap between predictions and
-        # ground truth. Emit None-valued metrics so downstream consumers
-        # detect "not applicable" via ``value is None`` rather than NaN.
-        out["overall_mape"] = _none_metric(resample=resample)
+        # ground truth: every y_true row is "missing" → fillna(0) penalty
+        # rule applies → APE = min(|0 - actual| / |actual|, clip) on
+        # |actual| ≥ 1.0 rows. Treat as a 100%-saturation failure so the
+        # cell still scores (no silent score_failed).
+        gt_act = pd.to_numeric(gt["value"], errors="coerce").values.astype(np.float64)
+        gt_keep = np.isfinite(gt_act) & (np.abs(gt_act) >= 1.0)
+        if gt_keep.any():
+            ape_gt = np.minimum(
+                np.abs(gt_act[gt_keep]) / np.abs(gt_act[gt_keep]),
+                _APE_CLIP_DEFAULT,
+            ) * 100.0  # =100% on every row (predict-zero penalty)
+            ck = gt["ticker"].astype(str).values[gt_keep] if resample == "cluster" else None
+            out["overall_mape"] = _wrap_metric(
+                ape_gt, cluster_keys=ck, agg_fn=np.mean,
+                n_boot=n_boot, alpha=alpha, seed=seed, resample=resample,
+            )
+        else:
+            out["overall_mape"] = _scalar_metric(100.0, resample=resample)
         out["per_field_mape"] = _none_metric(resample=resample)
         if task == "T3":
-            out["balance_equation_accuracy"] = _none_metric(resample=resample)
+            out["balance_equation_accuracy"] = _scalar_metric(0.0, resample=resample)
         out["success_rate"] = _scalar_metric(0.0, resample=resample)
         return out
 
@@ -888,7 +954,31 @@ def _per_task_score_T4(
             "This is a loader/preprocessing bug — fix at data source."
         )
     if merged.empty:
-        raise ValueError("T4 score: no overlap between predictions and ground truth.")
+        # No overlap between predictions and ground truth: fillna(0)
+        # penalty → MAE = mean(|actual_return_pct|) using gt rows.
+        gt_act = pd.to_numeric(gt_df["actual_return_pct"], errors="coerce").values.astype(np.float64)
+        gt_keep = np.isfinite(gt_act)
+        if gt_keep.any():
+            abs_err_gt = np.abs(gt_act[gt_keep])  # |0 - actual| = |actual|
+            ck = (
+                gt_df["scenario_id"].astype(str).values[gt_keep]
+                if resample == "cluster" and "scenario_id" in gt_df.columns else None
+            )
+            return {
+                "return_mae_pct": _wrap_metric(abs_err_gt, cluster_keys=ck, agg_fn=np.mean,
+                                               n_boot=n_boot, alpha=alpha, seed=seed, resample=resample),
+                "directional_accuracy": _scalar_metric(0.0, resample=resample),
+                "ci_calibration_95": _none_metric(resample=resample),
+                "n_predictions": _scalar_metric(0, resample=resample),
+                "n_scenarios": _scalar_metric(0, resample=resample),
+            }
+        return {
+            "return_mae_pct": _scalar_metric(0.0, resample=resample),
+            "directional_accuracy": _scalar_metric(0.0, resample=resample),
+            "ci_calibration_95": _none_metric(resample=resample),
+            "n_predictions": _scalar_metric(0, resample=resample),
+            "n_scenarios": _scalar_metric(0, resample=resample),
+        }
     # NaN-prediction penalty: substitute with 0.0 (no-signal); MAE = |actual|.
     nan_mask = ~np.isfinite(merged["predicted_return_pct"].values)
     merged.loc[nan_mask, "predicted_return_pct"] = 0.0
@@ -971,7 +1061,46 @@ def _per_task_score_T7(
             y_true, on="address", how="inner", suffixes=("_pred", "_actual"),
         )
     if merged.empty:
-        raise ValueError("T7 score: no overlapping addresses between y_true / y_pred.")
+        # No overlapping addresses: fillna(0) penalty per gt rent + price
+        # column. Saturates to 100% APE per row.
+        out: dict[str, MetricValue] = {
+            "n_predictions": _scalar_metric(0, resample=resample),
+        }
+        for target, actual_cands in [
+            ("rent", ["rent", "rentEstimate", "rent_estimate"]),
+            ("price", ["price", "lastSalePrice", "last_sale_price"]),
+        ]:
+            actual_col = next((c for c in actual_cands if c in y_true.columns), None)
+            if actual_col is None:
+                out[f"{target}_MAPE"] = _scalar_metric(float("nan"), resample=resample)
+                out[f"{target}_median_APE"] = _scalar_metric(float("nan"), resample=resample)
+                out[f"{target}_n_valid"] = _scalar_metric(0, resample=resample)
+                continue
+            gt_act = pd.to_numeric(y_true[actual_col], errors="coerce").values.astype(np.float64)
+            gt_keep = np.isfinite(gt_act) & (np.abs(gt_act) > 0)
+            if gt_keep.any():
+                ape_gt = np.minimum(
+                    np.abs(gt_act[gt_keep]) / np.abs(gt_act[gt_keep]),
+                    _APE_CLIP_DEFAULT,
+                ) * 100.0
+                ck = (
+                    y_true["address"].astype(str).values[gt_keep]
+                    if resample == "cluster" and "address" in y_true.columns else None
+                )
+                out[f"{target}_MAPE"] = _wrap_metric(
+                    ape_gt, cluster_keys=ck, agg_fn=np.mean,
+                    n_boot=n_boot, alpha=alpha, seed=seed, resample=resample,
+                )
+                out[f"{target}_median_APE"] = _wrap_metric(
+                    ape_gt, cluster_keys=ck, agg_fn=np.median,
+                    n_boot=n_boot, alpha=alpha, seed=seed, resample=resample,
+                )
+                out[f"{target}_n_valid"] = _scalar_metric(int(gt_keep.sum()), resample=resample)
+            else:
+                out[f"{target}_MAPE"] = _scalar_metric(100.0, resample=resample)
+                out[f"{target}_median_APE"] = _scalar_metric(100.0, resample=resample)
+                out[f"{target}_n_valid"] = _scalar_metric(0, resample=resample)
+        return out
 
     out: dict[str, MetricValue] = {
         "n_predictions": _scalar_metric(int(len(merged)), resample=resample),

data/processed/daily/columns.json

@@ -0,0 +1,156 @@
+{
+  "target": [
+    "close"
+  ],
+  "endogenous": [
+    "open",
+    "high",
+    "low",
+    "volume",
+    "adj_close"
+  ],
+  "exogenous_fundamental": [
+    "shares_outstanding",
+    "derived_market_cap",
+    "derived_pe",
+    "derived_ev",
+    "derived_ev_to_revenue",
+    "derived_ev_to_ebitda",
+    "derived_fcf_yield",
+    "derived_pb",
+    "derived_debt_to_equity",
+    "derived_effective_tax_rate",
+    "derived_cost_of_debt",
+    "derived_beta",
+    "derived_wacc",
+    "derived_gross_margin",
+    "derived_ebitda_margin",
+    "derived_net_margin",
+    "derived_cogs_pct",
+    "derived_rev_growth_yoy",
+    "derived_current_ratio"
+  ],
+  "exogenous_statement": [
+    "stmt_revenue",
+    "stmt_net_income",
+    "stmt_ebit",
+    "stmt_gross_profit",
+    "stmt_operating_income",
+    "stmt_basic_eps",
+    "stmt_tax_provision",
+    "stmt_pretax_income",
+    "stmt_interest_expense",
+    "stmt_operating_cashflow",
+    "stmt_capex",
+    "stmt_total_assets",
+    "stmt_total_liabilities",
+    "stmt_total_debt",
+    "stmt_total_equity",
+    "stmt_cash",
+    "stmt_shares_outstanding",
+    "stmt_shares_issued",
+    "stmt_accounts_receivable",
+    "stmt_inventory",
+    "stmt_current_assets",
+    "stmt_ppe_net",
+    "stmt_goodwill",
+    "stmt_accounts_payable",
+    "stmt_current_liabilities",
+    "stmt_lt_debt",
+    "stmt_cogs",
+    "stmt_operating_expenses",
+    "stmt_financing_cashflow",
+    "stmt_ebitda",
+    "stmt_free_cashflow",
+    "stmt_tax_rate",
+    "stmt_revenue_ttm",
+    "stmt_net_income_ttm",
+    "stmt_ebit_ttm",
+    "stmt_gross_profit_ttm",
+    "stmt_operating_income_ttm",
+    "stmt_basic_eps_ttm",
+    "stmt_operating_cashflow_ttm",
+    "stmt_capex_ttm",
+    "stmt_cogs_ttm",
+    "stmt_operating_expenses_ttm",
+    "stmt_financing_cashflow_ttm",
+    "stmt_ebitda_ttm",
+    "stmt_free_cashflow_ttm"
+  ],
+  "exogenous_macro": [
+    "fred_FEDFUNDS",
+    "fred_SOFR",
+    "fred_DGS2",
+    "fred_DGS10",
+    "fred_DGS30",
+    "fred_T10Y3M",
+    "fred_T10Y2Y",
+    "fred_MORTGAGE30US",
+    "fred_SP500",
+    "fred_NASDAQCOM",
+    "fred_DJIA",
+    "fred_VIXCLS",
+    "fred_DCOILWTICO",
+    "fred_DHHNGSP",
+    "fred_DTWEXBGS",
+    "fred_DEXUSEU",
+    "fred_DEXJPUS",
+    "fred_DEXUSUK",
+    "fred_DEXCHUS",
+    "fred_CPIAUCSL",
+    "fred_CPILFESL",
+    "fred_PPIACO",
+    "fred_T10YIE",
+    "fred_T5YIE",
+    "fred_PCEPI",
+    "fred_UNRATE",
+    "fred_ICSA",
+    "fred_PAYEMS",
+    "fred_JTSJOL",
+    "fred_CES0500000003",
+    "fred_BAMLH0A0HYM2",
+    "fred_BAMLC0A0CM",
+    "fred_TEDRATE",
+    "fred_STLFSI2",
+    "fred_NFCI",
+    "fred_INDPRO",
+    "fred_RSAFS",
+    "fred_UMCSENT",
+    "fred_TOTALSA",
+    "fred_PERMIT",
+    "fred_CSUSHPISA",
+    "fred_HOUST",
+    "fred_M2SL",
+    "fred_BOGMBASE",
+    "fred_WALCL",
+    "fred_BUSLOANS"
+  ],
+  "exogenous_commodity": [
+    "eia_crude_oil_crude_exports_weekly",
+    "eia_crude_oil_crude_imports_weekly",
+    "eia_crude_oil_crude_production_monthly",
+    "eia_crude_oil_crude_reserves_annual",
+    "eia_crude_oil_crude_spot_daily",
+    "eia_natural_gas_natural_gas_futures_weekly",
+    "eia_natural_gas_natural_gas_spot_weekly"
+  ],
+  "context_filing": [
+    "nearest_filing_type",
+    "nearest_filing_date",
+    "nearest_filing_path",
+    "days_since_filing"
+  ],
+  "context_real_estate": [],
+  "metadata": [
+    "ticker",
+    "date",
+    "sector",
+    "exchange",
+    "in_russell_2000",
+    "lower_end_russell2000",
+    "small_cap_outside",
+    "industry",
+    "fullTimeEmployees",
+    "label"
+  ]
+}

data/processed/monthly/columns.json

@@ -0,0 +1,156 @@
+{
+  "target": [
+    "close"
+  ],
+  "endogenous": [
+    "open",
+    "high",
+    "low",
+    "volume",
+    "adj_close"
+  ],
+  "exogenous_fundamental": [
+    "shares_outstanding",
+    "derived_market_cap",
+    "derived_pe",
+    "derived_ev",
+    "derived_ev_to_revenue",
+    "derived_ev_to_ebitda",
+    "derived_fcf_yield",
+    "derived_pb",
+    "derived_debt_to_equity",
+    "derived_effective_tax_rate",
+    "derived_cost_of_debt",
+    "derived_beta",
+    "derived_wacc",
+    "derived_gross_margin",
+    "derived_ebitda_margin",
+    "derived_net_margin",
+    "derived_cogs_pct",
+    "derived_rev_growth_yoy",
+    "derived_current_ratio"
+  ],
+  "exogenous_statement": [
+    "stmt_revenue",
+    "stmt_net_income",
+    "stmt_ebit",
+    "stmt_gross_profit",
+    "stmt_operating_income",
+    "stmt_basic_eps",
+    "stmt_tax_provision",
+    "stmt_pretax_income",
+    "stmt_interest_expense",
+    "stmt_operating_cashflow",
+    "stmt_capex",
+    "stmt_total_assets",
+    "stmt_total_liabilities",
+    "stmt_total_debt",
+    "stmt_total_equity",
+    "stmt_cash",
+    "stmt_shares_outstanding",
+    "stmt_shares_issued",
+    "stmt_accounts_receivable",
+    "stmt_inventory",
+    "stmt_current_assets",
+    "stmt_ppe_net",
+    "stmt_goodwill",
+    "stmt_accounts_payable",
+    "stmt_current_liabilities",
+    "stmt_lt_debt",
+    "stmt_cogs",
+    "stmt_operating_expenses",
+    "stmt_financing_cashflow",
+    "stmt_ebitda",
+    "stmt_free_cashflow",
+    "stmt_tax_rate",
+    "stmt_revenue_ttm",
+    "stmt_net_income_ttm",
+    "stmt_ebit_ttm",
+    "stmt_gross_profit_ttm",
+    "stmt_operating_income_ttm",
+    "stmt_basic_eps_ttm",
+    "stmt_operating_cashflow_ttm",
+    "stmt_capex_ttm",
+    "stmt_cogs_ttm",
+    "stmt_operating_expenses_ttm",
+    "stmt_financing_cashflow_ttm",
+    "stmt_ebitda_ttm",
+    "stmt_free_cashflow_ttm"
+  ],
+  "exogenous_macro": [
+    "fred_FEDFUNDS",
+    "fred_SOFR",
+    "fred_DGS2",
+    "fred_DGS10",
+    "fred_DGS30",
+    "fred_T10Y3M",
+    "fred_T10Y2Y",
+    "fred_MORTGAGE30US",
+    "fred_SP500",
+    "fred_NASDAQCOM",
+    "fred_DJIA",
+    "fred_VIXCLS",
+    "fred_DCOILWTICO",
+    "fred_DHHNGSP",
+    "fred_DTWEXBGS",
+    "fred_DEXUSEU",
+    "fred_DEXJPUS",
+    "fred_DEXUSUK",
+    "fred_DEXCHUS",
+    "fred_CPIAUCSL",
+    "fred_CPILFESL",
+    "fred_PPIACO",
+    "fred_T10YIE",
+    "fred_T5YIE",
+    "fred_PCEPI",
+    "fred_UNRATE",
+    "fred_ICSA",
+    "fred_PAYEMS",
+    "fred_JTSJOL",
+    "fred_CES0500000003",
+    "fred_BAMLH0A0HYM2",
+    "fred_BAMLC0A0CM",
+    "fred_TEDRATE",
+    "fred_STLFSI2",
+    "fred_NFCI",
+    "fred_INDPRO",
+    "fred_RSAFS",
+    "fred_UMCSENT",
+    "fred_TOTALSA",
+    "fred_PERMIT",
+    "fred_CSUSHPISA",
+    "fred_HOUST",
+    "fred_M2SL",
+    "fred_BOGMBASE",
+    "fred_WALCL",
+    "fred_BUSLOANS"
+  ],
+  "exogenous_commodity": [
+    "eia_crude_oil_crude_exports_weekly",
+    "eia_crude_oil_crude_imports_weekly",
+    "eia_crude_oil_crude_production_monthly",
+    "eia_crude_oil_crude_reserves_annual",
+    "eia_crude_oil_crude_spot_daily",
+    "eia_natural_gas_natural_gas_futures_weekly",
+    "eia_natural_gas_natural_gas_spot_weekly"
+  ],
+  "context_filing": [
+    "nearest_filing_type",
+    "nearest_filing_date",
+    "nearest_filing_path",
+    "days_since_filing"
+  ],
+  "context_real_estate": [],
+  "metadata": [
+    "ticker",
+    "date",
+    "sector",
+    "exchange",
+    "in_russell_2000",
+    "lower_end_russell2000",
+    "small_cap_outside",
+    "industry",
+    "fullTimeEmployees",
+    "label"
+  ]
+}

data/processed/weekly/columns.json

@@ -0,0 +1,156 @@
+{
+  "target": [
+    "close"
+  ],
+  "endogenous": [
+    "open",
+    "high",
+    "low",
+    "volume",
+    "adj_close"
+  ],
+  "exogenous_fundamental": [
+    "shares_outstanding",
+    "derived_market_cap",
+    "derived_pe",
+    "derived_ev",
+    "derived_ev_to_revenue",
+    "derived_ev_to_ebitda",
+    "derived_fcf_yield",
+    "derived_pb",
+    "derived_debt_to_equity",
+    "derived_effective_tax_rate",
+    "derived_cost_of_debt",
+    "derived_beta",
+    "derived_wacc",
+    "derived_gross_margin",
+    "derived_ebitda_margin",
+    "derived_net_margin",
+    "derived_cogs_pct",
+    "derived_rev_growth_yoy",
+    "derived_current_ratio"
+  ],
+  "exogenous_statement": [
+    "stmt_revenue",
+    "stmt_net_income",
+    "stmt_ebit",
+    "stmt_gross_profit",
+    "stmt_operating_income",
+    "stmt_basic_eps",
+    "stmt_tax_provision",
+    "stmt_pretax_income",
+    "stmt_interest_expense",
+    "stmt_operating_cashflow",
+    "stmt_capex",
+    "stmt_total_assets",
+    "stmt_total_liabilities",
+    "stmt_total_debt",
+    "stmt_total_equity",
+    "stmt_cash",
+    "stmt_shares_outstanding",
+    "stmt_shares_issued",
+    "stmt_accounts_receivable",
+    "stmt_inventory",
+    "stmt_current_assets",
+    "stmt_ppe_net",
+    "stmt_goodwill",
+    "stmt_accounts_payable",
+    "stmt_current_liabilities",
+    "stmt_lt_debt",
+    "stmt_cogs",
+    "stmt_operating_expenses",
+    "stmt_financing_cashflow",
+    "stmt_ebitda",
+    "stmt_free_cashflow",
+    "stmt_tax_rate",
+    "stmt_revenue_ttm",
+    "stmt_net_income_ttm",
+    "stmt_ebit_ttm",
+    "stmt_gross_profit_ttm",
+    "stmt_operating_income_ttm",
+    "stmt_basic_eps_ttm",
+    "stmt_operating_cashflow_ttm",
+    "stmt_capex_ttm",
+    "stmt_cogs_ttm",
+    "stmt_operating_expenses_ttm",
+    "stmt_financing_cashflow_ttm",
+    "stmt_ebitda_ttm",
+    "stmt_free_cashflow_ttm"
+  ],
+  "exogenous_macro": [
+    "fred_FEDFUNDS",
+    "fred_SOFR",
+    "fred_DGS2",
+    "fred_DGS10",
+    "fred_DGS30",
+    "fred_T10Y3M",
+    "fred_T10Y2Y",
+    "fred_MORTGAGE30US",
+    "fred_SP500",
+    "fred_NASDAQCOM",
+    "fred_DJIA",
+    "fred_VIXCLS",
+    "fred_DCOILWTICO",
+    "fred_DHHNGSP",
+    "fred_DTWEXBGS",
+    "fred_DEXUSEU",
+    "fred_DEXJPUS",
+    "fred_DEXUSUK",
+    "fred_DEXCHUS",
+    "fred_CPIAUCSL",
+    "fred_CPILFESL",
+    "fred_PPIACO",
+    "fred_T10YIE",
+    "fred_T5YIE",
+    "fred_PCEPI",
+    "fred_UNRATE",
+    "fred_ICSA",
+    "fred_PAYEMS",
+    "fred_JTSJOL",
+    "fred_CES0500000003",
+    "fred_BAMLH0A0HYM2",
+    "fred_BAMLC0A0CM",
+    "fred_TEDRATE",
+    "fred_STLFSI2",
+    "fred_NFCI",
+    "fred_INDPRO",
+    "fred_RSAFS",
+    "fred_UMCSENT",
+    "fred_TOTALSA",
+    "fred_PERMIT",
+    "fred_CSUSHPISA",
+    "fred_HOUST",
+    "fred_M2SL",
+    "fred_BOGMBASE",
+    "fred_WALCL",
+    "fred_BUSLOANS"
+  ],
+  "exogenous_commodity": [
+    "eia_crude_oil_crude_exports_weekly",
+    "eia_crude_oil_crude_imports_weekly",
+    "eia_crude_oil_crude_production_monthly",
+    "eia_crude_oil_crude_reserves_annual",
+    "eia_crude_oil_crude_spot_daily",
+    "eia_natural_gas_natural_gas_futures_weekly",
+    "eia_natural_gas_natural_gas_spot_weekly"
+  ],
+  "context_filing": [
+    "nearest_filing_type",
+    "nearest_filing_date",
+    "nearest_filing_path",
+    "days_since_filing"
+  ],
+  "context_real_estate": [],
+  "metadata": [
+    "ticker",
+    "date",
+    "sector",
+    "exchange",
+    "in_russell_2000",
+    "lower_end_russell2000",
+    "small_cap_outside",
+    "industry",
+    "fullTimeEmployees",
+    "label"
+  ]
+}