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Guard zero-equity weight target translation #123

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Merged
Pigbibi merged 1 commit into from
Jun 3, 2026
Merged

Guard zero-equity weight target translation #123

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36 changes: 34 additions & 2 deletions decision_mapper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -271,6 +271,28 @@ def _build_hold_current_value_decision(portfolio_inputs) -> StrategyDecision:
return StrategyDecision(positions=tuple(positions))


def _build_zero_equity_value_decision(decision: StrategyDecision) -> StrategyDecision:
positions: list[PositionTarget] = []
for position in decision.positions:
positions.append(
PositionTarget(
symbol=position.symbol,
target_value=0.0,

@chatgpt-codex-connector chatgpt-codex-connector Bot Jun 3, 2026

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P2 Badge Preserve holdings for blocked zero-equity plans

When a margin or loan-backed account reports total_equity <= 0 while still holding strategy symbols, this branch converts the blocked weight decision into zero-dollar targets. The generated plan does not carry a consumable no_execute guard for execution; the rebalance loop reads allocation["targets"] and sells whenever target_values[symbol] - market_values[symbol] is below the threshold (application/execution_service.py:606, application/execution_service.py:693). Previously this input failed before any executable plan was produced, but now it can submit liquidation sells for those symbols. For a no-execute fallback, keep current market values as targets or add an execution block that downstream code actually honors.

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role=position.role or _symbol_role(position.symbol),
order_preference=position.order_preference,
)
)
return StrategyDecision(
positions=tuple(positions),
budgets=decision.budgets,
risk_flags=tuple(dict.fromkeys((*decision.risk_flags, "no_execute"))),
diagnostics={
**dict(decision.diagnostics),
"execution_blocked_reason": "non_positive_total_equity",
},
)


def _normalize_to_value_target_decision(
decision: StrategyDecision,
*,
Expand All @@ -284,14 +306,24 @@ def _normalize_to_value_target_decision(
return decision, None

if target_mode == "weight" and not no_execute:
total_equity = float(portfolio_inputs.total_equity)
if total_equity <= 0.0:
return _build_zero_equity_value_decision(
decision,
), _build_weight_translation_annotations(
decision,
total_equity=total_equity,
liquid_cash=float(portfolio_inputs.liquid_cash),
runtime_metadata=runtime_metadata,
)
translated = translate_decision_to_target_mode(
decision,
target_mode="value",
total_equity=float(portfolio_inputs.total_equity),
total_equity=total_equity,
)
return translated, _build_weight_translation_annotations(
decision,
total_equity=float(portfolio_inputs.total_equity),
total_equity=total_equity,
liquid_cash=float(portfolio_inputs.liquid_cash),
runtime_metadata=runtime_metadata,
)
Expand Down
28 changes: 28 additions & 0 deletions tests/test_decision_mapper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -212,6 +212,34 @@ def test_applies_platform_reserved_cash_policy_to_weight_decision(self):
self.assertEqual(plan["execution"]["reserved_cash"], 1500.0)
self.assertEqual(plan["execution"]["investable_cash"], 2500.0)

def test_zero_equity_weight_targets_no_execute_instead_of_translation_error(self):
decision = StrategyDecision(
positions=(
PositionTarget(symbol="AAPL", target_weight=0.5),
PositionTarget(symbol="MSFT", target_weight=0.5),
),
diagnostics={"signal_description": "risk on"},
)
snapshot = PortfolioSnapshot(
as_of=datetime.now(timezone.utc),
total_equity=0.0,
buying_power=0.0,
positions=(),
metadata={"account_hash": "longbridge-zero"},
)

plan = map_strategy_decision_to_plan(
decision,
snapshot=snapshot,
strategy_profile="mega_cap_leader_rotation_top50_balanced",
)

self.assertEqual(plan["allocation"]["target_mode"], "value")
self.assertEqual(plan["allocation"]["targets"], {"AAPL": 0.0, "MSFT": 0.0})
self.assertEqual(plan["portfolio"]["total_equity"], 0.0)
self.assertEqual(plan["execution"]["trade_threshold_value"], 100.0)
self.assertEqual(plan["execution"]["investable_cash"], 0.0)

def test_carries_snapshot_manifest_diagnostics_to_execution(self):
decision = StrategyDecision(
positions=(),
Expand Down