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Merged
BobbyAxerol merged 3 commits into from
Jul 23, 2026
Merged

Dev #26

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618bbf1
docs: add phase 14 performance debt roadmap
vcmbob Jul 23, 2026
5096777
bench: add phase 14 service loop profile
vcmbob Jul 23, 2026
f451e03
perf: add phase 14c prepared scoring controls
vcmbob Jul 23, 2026
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8 changes: 8 additions & 0 deletions backends/native_event.py
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Original file line number Diff line number Diff line change
Expand Up @@ -416,6 +416,7 @@ def run_basket(
slot_size: Optional[Union[float, Dict[str, float]]] = None,
min_qty: Optional[Union[float, Dict[str, float]]] = None,
min_notional: Optional[Union[float, Dict[str, float]]] = None,
market_arrays: Optional[PreparedMarketArrays] = None,
) -> BacktestResultV2:
"""
Build frozen basket orders from a scalar signal and execute them.
Expand Down Expand Up @@ -445,6 +446,7 @@ def run_basket(
leverage=leverage,
fee_rate=fee_rate,
symbols=symbols,
market_arrays=market_arrays,
instruments=instruments,
qty_step=qty_step,
lot_size=lot_size,
Expand All @@ -469,6 +471,7 @@ def run_stat_arb_pair_arbitrage(
funding_rate: Union[float, pd.Series, Dict] = 0.0,
contract_size: Optional[Union[float, Dict[str, float]]] = None,
leverage: Optional[Union[float, Dict[str, float]]] = None,
market_arrays: Optional[PreparedMarketArrays] = None,
) -> BacktestResultV2:
"""
Execute a Phase D stat-arb pair through the frozen basket planner.
Expand Down Expand Up @@ -529,6 +532,7 @@ def run_stat_arb_pair_arbitrage(
leverage=leverage,
fee_rate=fee_rates,
symbols=symbols,
market_arrays=market_arrays,
)
funding_dict = prepare_funding(stat_funding if self.config.use_funding else 0.0, symbols, idx)
roles = self._stat_arb_roles(spec)
Expand Down Expand Up @@ -586,6 +590,7 @@ def run_basis_arbitrage(
contract_size: Optional[Union[float, Dict[str, float]]] = None,
leverage: Optional[Union[float, Dict[str, float]]] = None,
hedge_ratios: Optional[Dict[str, pd.Series]] = None,
market_arrays: Optional[PreparedMarketArrays] = None,
) -> BacktestResultV2:
"""
Execute a minimal native-event USDM linear basis arbitrage backtest.
Expand Down Expand Up @@ -624,6 +629,7 @@ def run_basis_arbitrage(
leverage=leverage,
fee_rate=fee_rates,
symbols=symbols,
market_arrays=market_arrays,
)

funding_dict = prepare_funding(basis_funding if self.config.use_funding else 0.0, symbols, idx)
Expand Down Expand Up @@ -680,6 +686,7 @@ def run_package_arbitrage(
contract_size: Optional[Union[float, Dict[str, float]]] = None,
leverage: Optional[Union[float, Dict[str, float]]] = None,
hedge_ratios: Optional[Dict[str, pd.Series]] = None,
market_arrays: Optional[PreparedMarketArrays] = None,
) -> BacktestResultV2:
"""
Execute Phase G package-style advanced arbitrage specs.
Expand Down Expand Up @@ -725,6 +732,7 @@ def run_package_arbitrage(
leverage=leverage,
fee_rate=fee_rates,
symbols=symbols,
market_arrays=market_arrays,
)

funding_dict = prepare_funding(package_funding if self.config.use_funding else 0.0, symbols, idx)
Expand Down
191 changes: 130 additions & 61 deletions backends/native_portfolio.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,10 +48,12 @@ class NativePortfolioConfig:
execution: ExecutionConfig = field(default_factory=ExecutionConfig)
fee_rate: float = 0.0
use_funding: bool = True
report_level: str = "full"

def __post_init__(self) -> None:
if float(self.fee_rate) < 0.0:
raise ValueError("fee_rate must be >= 0")
object.__setattr__(self, "report_level", _normalize_report_level(self.report_level))


class NativePortfolioBackend:
Expand Down Expand Up @@ -94,6 +96,7 @@ def run_signals(
slot_size: Optional[Union[float, Dict[str, float]]] = None,
min_qty: Optional[Union[float, Dict[str, float]]] = None,
min_notional: Optional[Union[float, Dict[str, float]]] = None,
report_level: Optional[str] = None,
) -> BacktestResultV2:
idx = validate_datetime(datetime_index)
if positions is None and raw_signal_matrix is None:
Expand Down Expand Up @@ -260,9 +263,18 @@ def run_signals(
liquidated=bool(liq_flag),
liquidation_bar=int(liq_idx),
quantity_constraints=constraints.as_dict(),
report_level=self.config.report_level if report_level is None else report_level,
)
spec = PortfolioDomainSpec(mode=portfolio_mode, sizing_mode=sizing_mode)
result.metadata["portfolio_contract_report"] = validate_portfolio_result_contract(result, spec, tolerance=1e-8)
if result.metadata.get("report_level") == "minimal":
result.metadata["portfolio_contract_report"] = {
"status": "skipped",
"passed": None,
"reason": "report_level='minimal' omits heavy audit reports; rerun with report_level='full' for contract validation",
"spec": {"mode": portfolio_mode, "sizing_mode": sizing_mode},
}
else:
result.metadata["portfolio_contract_report"] = validate_portfolio_result_contract(result, spec, tolerance=1e-8)
return result

def prepare_market_arrays(
Expand Down Expand Up @@ -445,7 +457,9 @@ def _build_result(
liquidated: bool,
liquidation_bar: int,
quantity_constraints: Dict[str, Dict[str, float]],
report_level: str,
) -> BacktestResultV2:
level = _normalize_report_level(report_level)
equity = pd.Series(equity_arr, index=idx, name="equity")
close_report = pd.DataFrame(closes_m, index=idx, columns=symbol_list, copy=False)
target_units_report = pd.DataFrame(target_m, index=idx, columns=symbol_list, copy=False)
Expand All @@ -458,38 +472,6 @@ def _build_result(
accepted_notional_arr = pos_arr * closes_m * cs_row
target_notional = pd.DataFrame(target_notional_arr, index=idx, columns=symbol_list, copy=False)
accepted_notional = pd.DataFrame(accepted_notional_arr, index=idx, columns=symbol_list, copy=False)
funding_rates = pd.DataFrame(funding_m, index=idx, columns=symbol_list, copy=False)
risk_vol_report = pd.DataFrame(risk_vol, index=idx, columns=symbol_list, copy=False)

exposure_report = self._build_exposure_report(
accepted_notional_arr=accepted_notional_arr,
target_notional_arr=target_notional_arr,
equity_arr=equity_arr,
idx=idx,
leverages=leverages,
maintenance_ratio=maintenance_ratio,
betas=betas,
)
risk_contribution_report = pd.DataFrame(np.abs(accepted_notional_arr) * risk_vol, index=idx, columns=symbol_list, copy=False)
exposure_report.attrs["risk_contribution_report"] = risk_contribution_report
symbol_pnl_report = self._build_symbol_pnl_report(
idx=idx,
symbols=symbol_list,
accepted_units_arr=pos_arr,
closes_arr=closes_m,
funding_rates_arr=funding_m,
is_funding_bar=is_funding_bar,
contract_sizes=contract_sizes,
fee_arr=fee_arr,
)
rebalance_report = self._build_rebalance_report(
idx=idx,
symbols=symbol_list,
target_units_arr=target_m,
accepted_units_arr=pos_arr,
closes_arr=closes_m,
contract_sizes=contract_sizes,
)

positions = pd.DataFrame(pos_arr, index=idx, columns=[f"Position_{s}" for s in symbol_list], copy=False)
closes = pd.DataFrame(closes_m, index=idx, columns=[f"Close_{s}" for s in symbol_list], copy=False)
Expand All @@ -498,7 +480,14 @@ def _build_result(
prev_units = np.vstack([np.zeros((1, len(symbol_list)), dtype=np.float64), pos_arr[:-1]])
funding_cost_arr = prev_units * closes_m * cs_row * funding_m
funding_cost_arr = np.where(is_funding_bar.reshape(-1, 1).astype(bool), funding_cost_arr, 0.0).sum(axis=1)
margin = exposure_report[["initial_margin", "maintenance_margin"]].copy()
abs_accepted = np.abs(accepted_notional_arr)
margin = pd.DataFrame(
{
"initial_margin": (abs_accepted / leverages.reshape(1, -1)).sum(axis=1),
"maintenance_margin": abs_accepted.sum(axis=1) * float(maintenance_ratio),
},
index=idx,
)
diagnostics = pd.DataFrame(
{
"turnover": turnover_arr,
Expand All @@ -515,6 +504,95 @@ def _build_result(
where=equity_arr[:-1] != 0.0,
)

metadata = {
"backend": "native_portfolio",
"mode": mode,
"asset_type": asset_type,
"hedge_type": hedge_type,
"engine": "native_portfolio_v1",
"report_level": level,
"initial_buying_power": self.config.account.initial_capital * float(np.mean(leverages)),
"funding_rate_unit": "per_event",
"target_units_report": target_units_report,
"accepted_units_report": accepted_units_report,
"beta": {s: float(betas[j]) for j, s in enumerate(symbol_list)},
"fee_series": fees,
"turnover_series": turnover,
"fee_total": float(np.sum(fee_arr)),
"turnover_total": float(np.sum(turnover_arr)),
"fee_rate_oneway": float(self.config.fee_rate),
"contract_size": {s: float(contract_sizes[j]) for j, s in enumerate(symbol_list)},
"quantity_constraints": quantity_constraints,
}
omitted = []
if level in {"full", "standard"}:
funding_rates = pd.DataFrame(funding_m, index=idx, columns=symbol_list, copy=False)
exposure_report = self._build_exposure_report(
accepted_notional_arr=accepted_notional_arr,
target_notional_arr=target_notional_arr,
equity_arr=equity_arr,
idx=idx,
leverages=leverages,
maintenance_ratio=maintenance_ratio,
betas=betas,
)
symbol_pnl_report = self._build_symbol_pnl_report(
idx=idx,
symbols=symbol_list,
accepted_units_arr=pos_arr,
closes_arr=closes_m,
funding_rates_arr=funding_m,
is_funding_bar=is_funding_bar,
contract_sizes=contract_sizes,
fee_arr=fee_arr,
)
metadata.update(
{
"target_notional_report": target_notional,
"accepted_notional_report": accepted_notional,
"exposure_report": exposure_report,
"funding_rates_report": funding_rates,
"symbol_pnl_report": symbol_pnl_report,
}
)
if level == "full":
risk_vol_report = pd.DataFrame(risk_vol, index=idx, columns=symbol_list, copy=False)
risk_contribution_report = pd.DataFrame(np.abs(accepted_notional_arr) * risk_vol, index=idx, columns=symbol_list, copy=False)
exposure_report.attrs["risk_contribution_report"] = risk_contribution_report
rebalance_report = self._build_rebalance_report(
idx=idx,
symbols=symbol_list,
target_units_arr=target_m,
accepted_units_arr=pos_arr,
closes_arr=closes_m,
contract_sizes=contract_sizes,
)
metadata.update(
{
"risk_volatility_report": risk_vol_report,
"risk_contribution_report": risk_contribution_report,
"kernel_symbol_pnl": pd.DataFrame(sym_pnl_arr, index=idx, columns=symbol_list, copy=False),
"rebalance_report": rebalance_report,
}
)
else:
omitted.extend(["risk_volatility_report", "risk_contribution_report", "kernel_symbol_pnl", "rebalance_report"])
else:
omitted.extend(
[
"target_notional_report",
"accepted_notional_report",
"exposure_report",
"funding_rates_report",
"risk_volatility_report",
"risk_contribution_report",
"symbol_pnl_report",
"kernel_symbol_pnl",
"rebalance_report",
]
)
metadata["reports_omitted"] = tuple(omitted)

return BacktestResultV2(
equity=equity,
returns=pd.Series(returns_arr, index=idx, name="returns"),
Expand All @@ -529,33 +607,7 @@ def _build_result(
funding=pd.Series(funding_cost_arr, index=idx, name="funding"),
margin=margin,
diagnostics=diagnostics,
metadata={
"backend": "native_portfolio",
"mode": mode,
"asset_type": asset_type,
"hedge_type": hedge_type,
"engine": "native_portfolio_v1",
"initial_buying_power": self.config.account.initial_capital * float(np.mean(leverages)),
"funding_rate_unit": "per_event",
"target_units_report": target_units_report,
"accepted_units_report": accepted_units_report,
"target_notional_report": target_notional,
"accepted_notional_report": accepted_notional,
"exposure_report": exposure_report,
"risk_volatility_report": risk_vol_report,
"risk_contribution_report": risk_contribution_report,
"beta": {s: float(betas[j]) for j, s in enumerate(symbol_list)},
"symbol_pnl_report": symbol_pnl_report,
"kernel_symbol_pnl": pd.DataFrame(sym_pnl_arr, index=idx, columns=symbol_list, copy=False),
"rebalance_report": rebalance_report,
"fee_series": fees,
"turnover_series": turnover,
"fee_total": float(np.sum(fee_arr)),
"turnover_total": float(np.sum(turnover_arr)),
"fee_rate_oneway": float(self.config.fee_rate),
"contract_size": {s: float(contract_sizes[j]) for j, s in enumerate(symbol_list)},
"quantity_constraints": quantity_constraints,
},
metadata=metadata,
)

@staticmethod
Expand Down Expand Up @@ -713,3 +765,20 @@ def _portfolio_mode_id(mode: str) -> int:
"beta_neutral": 5,
}
return mapping[mode]


def _normalize_report_level(report_level: str) -> str:
level = str(report_level or "full").lower().strip()
aliases = {
"audit": "full",
"complete": "full",
"default": "full",
"lite": "standard",
"light": "minimal",
"optimizer": "minimal",
"scoring": "minimal",
}
level = aliases.get(level, level)
if level not in {"full", "standard", "minimal"}:
raise ValueError("report_level must be one of 'full', 'standard', or 'minimal'")
return level
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