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malkhut(wire): 5 risk gate stubs implemented + 3 scenarios behavior-driven

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Risk gate (risk/gate.py) — 5 stubs implemented:

1. _kill_switch_active(): operator-controlled emergency stop via set_kill_switch()
2. _cancel_rate_would_exceed(): tracks cancel timestamps per symbol in 60s
   sliding window, blocks if >= MAX_CANCELS_PER_SYMBOL_PER_MINUTE
3. _would_self_trade(): checks open orders for same symbol+side at same price
   (within tick_size), skipping the cancel_order_id for CANCEL_REPLACE
4. _would_exceed_symbol_notional(): sums current open order notional + new
   order notional, blocks if > equity * MAX_SYMBOL_NOTIONAL_FRACTION
5. _violates_venue_minima(): checks tick alignment, lot rounding, min_qty,
   and min_notional — all float-robust comparisons

ScenarioFactory — 3 remaining hardcoded scenarios converted:

1. _spread_tightening: spread_mult=0.3, depth_fraction=1.0 (was hardcoded BTC)
2. _cross_venue_arb: spread_mult=0.5, depth_fraction=0.5 (was hardcoded BTC)
3. _cross_exchange_arb_stress: spread_mult=0.8, depth_fraction=0.3 (was hardcoded BTC)

All 30 scenarios now use _behavior_state() — zero hardcoded prices remain.

675 tests pass. Zero regressions.
This commit is contained in:
Codex
2026-07-14 17:01:38 +02:00
parent 1b6d280d26
commit f6d8d13146
2 changed files with 86 additions and 8 deletions
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6
MALKHUT/malkhut/training/cma_trainer.py
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@@ -645,7 +645,7 @@ class ScenarioFactory:
return Scenario(
scenario_id=f"tighten_{symbol}_{seed}",
symbol=symbol,
initial_state=self._make_state(symbol, bid=49950.0, ask=50050.0, bid_qty=2.0, ask_qty=2.0, exchange_id=self.exchange_id),
initial_state=self._behavior_state(symbol, spread_mult=0.3, depth_fraction=1.0, exchange_id=self.exchange_id),
counterparties=self.counterparties,
max_steps=steps,
tags=("spread_tightening", "competition"),
@@ -726,7 +726,7 @@ class ScenarioFactory:
return Scenario(
scenario_id=f"arb_{symbol}_{seed}",
symbol=symbol,
initial_state=self._make_state(symbol, bid=49990.0, ask=50010.0, bid_qty=0.5, ask_qty=0.5, exchange_id=self.exchange_id),
initial_state=self._behavior_state(symbol, spread_mult=0.5, depth_fraction=0.5, exchange_id=self.exchange_id),
counterparties=(LatencyArbPolicy(lead_threshold=0.3), ToxicTakerPolicy(sensitivity=0.4)),
max_steps=steps,
tags=("arbitrage", "cross_venue", "price_discovery"),
@@ -834,7 +834,7 @@ class ScenarioFactory:
return Scenario(
scenario_id=f"arb_stress_{symbol}_{seed}",
symbol=symbol,
initial_state=self._make_state(symbol, bid=49980.0, ask=50020.0, bid_qty=0.3, ask_qty=0.3, exchange_id=self.exchange_id),
initial_state=self._behavior_state(symbol, spread_mult=0.8, depth_fraction=0.3, exchange_id=self.exchange_id),
counterparties=(LatencyArbPolicy(lead_threshold=0.2), ToxicTakerPolicy(sensitivity=0.4)),
max_steps=steps,
tags=("cross_exchange", "arb_stress", "price_discovery"),