ml4t
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ml4t-kill-switch
Automated risk limits that halt trading when thresholds are breached. Use when deploying live strategies that need drawdown or loss-limit protection.
Overview
Automated risk limits that halt trading when thresholds are breached. Use when deploying live strategies that need drawdown or loss-limit protection.
Read full documentation
Source documentation, not instructions for this website. Review permissions before running any commands.
Kill Switch
A human monitoring a dashboard will not react fast enough to a flash crash. By the time you see the loss and decide to act, the drawdown has compounded. Automated kill switches are the last line of defense - they must be hard-coded, not ML-based, and not overridable without explicit manual intervention.
The Problem
Live trading systems face risks that backtests never encounter: data feed failures, exchange outages, runaway algorithms, and flash crashes. A strategy producing 100 orders per second during a data glitch can lose more in minutes than it earned in months. Manual monitoring fails because: (1) humans are slow, (2) losses compound nonlinearly, and (3) the worst events happen when attention is lowest. Kill switches must trigger automatically, flatten positions immediately, and require human approval to resume.
The Pattern
WRONG
# "I'll watch the dashboard and close positions if things go wrong"
import time
while True:
pnl = get_daily_pnl()
if pnl < -10000:
send_email("Loss alert") # arrives 5 min later, read at 9am
time.sleep(60)
# Meanwhile, the algo keeps trading during the 60s sleep
CORRECT
class KillSwitch:
"""Hard-coded risk limits. Automatic trigger, manual reset only."""
# -3% daily P&L, -15% from peak, 200% gross, 15% in a single name
THRESHOLDS = {"max_daily_loss": -0.03, "max_drawdown": -0.15,
"max_gross_leverage": 2.0, "max_position_pct": 0.15}
def __init__(self, reset_code, on_breach):
self.triggered = False
self.trigger_reason = None
self.reset_code = reset_code # from your secret store, not from source
self.on_breach = on_breach # cancel open orders, flatten, page on-call
def check(self, daily_pnl, drawdown, gross_lev, max_pos, position=0.0, qty=0.0):
"""Called BEFORE every order. False = block. Only reset() clears a latch."""
# Derive risk reduction from the order. A caller-supplied `reducing`
# flag is a claim, and one mislabelled order defeats the whole switch.
reducing = qty * position < 0 and abs(qty) <= abs(position) # no zero cross
if self.triggered:
return reducing
checks = {
"max_daily_loss": daily_pnl > self.THRESHOLDS["max_daily_loss"],
"max_drawdown": drawdown > self.THRESHOLDS["max_drawdown"],
"max_gross_leverage": gross_lev < self.THRESHOLDS["max_gross_leverage"],
"max_position_pct": max_pos < self.THRESHOLDS["max_position_pct"],
}
for name, passed in checks.items():
if not passed:
self.triggered = True
self.trigger_reason = f"{name}: threshold breached"
self.on_breach(name) # cancels, flattens - not the next order's job
return False # `reducing` was judged against the pre-flatten book
return True # safe to proceed
def reset(self, manual_approval_code: str):
"""Require explicit human approval to resume."""
if manual_approval_code == self.reset_code:
self.triggered = False
self.trigger_reason = None
Graduated Response
Not every breach requires full shutdown. Scale down gracefully with risk levels:
def risk_level(drawdown, realized_vol, target_vol=0.10):
vol_ratio = realized_vol / target_vol
if drawdown < -0.20 or vol_ratio > 3.0: return "halt" # flatten all
if drawdown < -0.15 or vol_ratio > 2.0: return "red" # 25% size
if drawdown < -0.10 or vol_ratio > 1.5: return "yellow" # 50% size
return "green" # full size
Guardrails
- Thresholds must be set BEFORE deployment, not adjusted during a drawdown
- The breaching order never executes; a latched switch passes later reducing orders
- Data feed failure is a trigger - no data means no trading, not "use stale prices"
Production Implementation
ml4t-live wraps any broker with pre-trade risk checks:
from ml4t.live import SafeBroker, LiveRiskConfig, AlpacaBroker
config = LiveRiskConfig(
execution_mode="shadow", # required: "shadow", "paper" or "live"
max_daily_loss=5_000.0,
max_drawdown_pct=0.15, # positive fraction below the high-water mark
max_position_value=50_000.0,
)
broker = SafeBroker(AlpacaBroker(api_key, secret_key), config)
# A breach latches and blocks risk-increasing orders; flatten with
# await broker.close_all_positions() from your own breach handler.
Checklist
- All thresholds defined and documented before deployment
- Kill switch runs pre-trade (before every order submission)
- Automatic trigger, manual-only reset with approval code
- Data feed failure triggers halt (not stale-price trading)
- Monthly fire drill: simulate a breach and verify the system flattens
File metadata
name: ml4t-kill-switch description: "Automated risk limits that halt trading when thresholds are breached. Use when deploying live strategies that need drawdown or loss-limit protection." when_to_use: "Use when building live trading systems or production risk management" dependencies: [risk-metrics] metadata: book_chapters: "19, 25" library: "ml4t-live" paths: ["**/*portfolio*.py", "**/*position*.py", "**/*risk*.py", "**/*optim*.py", "**/*exposure*.py", "**/*kill*.py", "**/*stress*.py"]
View original text
---
name: ml4t-kill-switch
description: "Automated risk limits that halt trading when thresholds are breached. Use when deploying live strategies that need drawdown or loss-limit protection."
when_to_use: "Use when building live trading systems or production risk management"
dependencies: [risk-metrics]
metadata:
book_chapters: "19, 25"
library: "ml4t-live"
paths: ["**/*portfolio*.py", "**/*position*.py", "**/*risk*.py", "**/*optim*.py", "**/*exposure*.py", "**/*kill*.py", "**/*stress*.py"]
---
# Kill Switch
A human monitoring a dashboard will not react fast enough to a flash crash. By the time you see the loss and decide to act, the drawdown has compounded. Automated kill switches are the last line of defense - they must be hard-coded, not ML-based, and not overridable without explicit manual intervention.
## The Problem
Live trading systems face risks that backtests never encounter: data feed failures, exchange outages, runaway algorithms, and flash crashes. A strategy producing 100 orders per second during a data glitch can lose more in minutes than it earned in months. Manual monitoring fails because: (1) humans are slow, (2) losses compound nonlinearly, and (3) the worst events happen when attention is lowest. Kill switches must trigger automatically, flatten positions immediately, and require human approval to resume.
## The Pattern
### WRONG
```python
# "I'll watch the dashboard and close positions if things go wrong"
import time
while True:
pnl = get_daily_pnl()
if pnl < -10000:
send_email("Loss alert") # arrives 5 min later, read at 9am
time.sleep(60)
# Meanwhile, the algo keeps trading during the 60s sleep
```
### CORRECT
```python
class KillSwitch:
"""Hard-coded risk limits. Automatic trigger, manual reset only."""
# -3% daily P&L, -15% from peak, 200% gross, 15% in a single name
THRESHOLDS = {"max_daily_loss": -0.03, "max_drawdown": -0.15,
"max_gross_leverage": 2.0, "max_position_pct": 0.15}
def __init__(self, reset_code, on_breach):
self.triggered = False
self.trigger_reason = None
self.reset_code = reset_code # from your secret store, not from source
self.on_breach = on_breach # cancel open orders, flatten, page on-call
def check(self, daily_pnl, drawdown, gross_lev, max_pos, position=0.0, qty=0.0):
"""Called BEFORE every order. False = block. Only reset() clears a latch."""
# Derive risk reduction from the order. A caller-supplied `reducing`
# flag is a claim, and one mislabelled order defeats the whole switch.
reducing = qty * position < 0 and abs(qty) <= abs(position) # no zero cross
if self.triggered:
return reducing
checks = {
"max_daily_loss": daily_pnl > self.THRESHOLDS["max_daily_loss"],
"max_drawdown": drawdown > self.THRESHOLDS["max_drawdown"],
"max_gross_leverage": gross_lev < self.THRESHOLDS["max_gross_leverage"],
"max_position_pct": max_pos < self.THRESHOLDS["max_position_pct"],
}
for name, passed in checks.items():
if not passed:
self.triggered = True
self.trigger_reason = f"{name}: threshold breached"
self.on_breach(name) # cancels, flattens - not the next order's job
return False # `reducing` was judged against the pre-flatten book
return True # safe to proceed
def reset(self, manual_approval_code: str):
"""Require explicit human approval to resume."""
if manual_approval_code == self.reset_code:
self.triggered = False
self.trigger_reason = None
```
## Graduated Response
Not every breach requires full shutdown. Scale down gracefully with risk levels:
```python
def risk_level(drawdown, realized_vol, target_vol=0.10):
vol_ratio = realized_vol / target_vol
if drawdown < -0.20 or vol_ratio > 3.0: return "halt" # flatten all
if drawdown < -0.15 or vol_ratio > 2.0: return "red" # 25% size
if drawdown < -0.10 or vol_ratio > 1.5: return "yellow" # 50% size
return "green" # full size
```
## Guardrails
- Thresholds must be set BEFORE deployment, not adjusted during a drawdown
- The breaching order never executes; a latched switch passes later reducing orders
- Data feed failure is a trigger - no data means no trading, not "use stale prices"
## Production Implementation
`ml4t-live` wraps any broker with pre-trade risk checks:
```python
from ml4t.live import SafeBroker, LiveRiskConfig, AlpacaBroker
config = LiveRiskConfig(
execution_mode="shadow", # required: "shadow", "paper" or "live"
max_daily_loss=5_000.0,
max_drawdown_pct=0.15, # positive fraction below the high-water mark
max_position_value=50_000.0,
)
broker = SafeBroker(AlpacaBroker(api_key, secret_key), config)
# A breach latches and blocks risk-increasing orders; flatten with
# await broker.close_all_positions() from your own breach handler.
```
## Checklist
- [ ] All thresholds defined and documented before deployment
- [ ] Kill switch runs pre-trade (before every order submission)
- [ ] Automatic trigger, manual-only reset with approval code
- [ ] Data feed failure triggers halt (not stale-price trading)
- [ ] Monthly fire drill: simulate a breach and verify the system flattens
Review the source
Price & running costs
- Get the skill
- Price unconfirmed
- Run it
- Requirements have not been confirmed. Check the source for agent, API and service charges.
- License
- Apache-2.0
- Price unconfirmed
- We have not confirmed a price for this skill. Existing source and install links remain available.
Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: Apache-2.0
- Financial research output is not financial advice; require human review before any live investment decision
- Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
- Low GitHub adoption signal
- AI review approval is missing
- Financial research output is not financial advice; require human review before any live investment decision.
- This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
- Quality score needs review
- GitHub adoption: 20 GitHub stars
- Stars/forks activity: 20 stars, 11 forks; issue activity unavailable in current metadata
- Review status: AI review approval is missing
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Start with one small task
- 1Read the source. Confirm the input, expected output, dependencies and permissions.
- 2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
- 3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Source & usage notes
IndexedStatic Checked
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
- Source repository
- ml4t/skills
- License
- Apache-2.0
- Version
- Unknown
- Last GitHub push
- Sep 29, 2026
- Registry updated
- Sep 29, 2026
- Instruction path
- portfolio/kill-switch/SKILL.md @ f0ea01919e0c
Version reported in registry metadata; check source releases before relying on it.
Quality
54/100
Needs review
Trust
63/100
Sandbox only
Audit
74/100
Risky
- Financial research output is not financial advice; require human review before any live investment decision
- Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
- Low GitHub adoption signal
- AI review approval is missing
- Financial research output is not financial advice; require human review before any live investment decision.
- This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
- Quality score needs review
- GitHub adoption: 20 GitHub stars
- Stars/forks activity: 20 stars, 11 forks; issue activity unavailable in current metadata
- Review status: AI review approval is missing
- Verified installs
- —
- Outcomes
- —
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
Agent access
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
More details
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"category": "finance",
"url": "https://www.openagentskill.com/skills/ml4t-ml4t-kill-switch",
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"documentation": "Usable metadata, review docs",
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"Financial research output is not financial advice; require human review before any live investment decision.",
"This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.",
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"No OpenAgentSkill engagement data yet",
"Audit risk risky exceeds max_risk=medium",
"High-risk permission hints: Secrets or environment access",
"Financial research output is not financial advice; require human review before any live investment decision",
"Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required"
],
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"Audit: 74/100 Risky",
"Safety: 50/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
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"endpoints": {
"web": "https://www.openagentskill.com/skills/ml4t-ml4t-kill-switch",
"api": "https://www.openagentskill.com/api/agent/skills/ml4t-ml4t-kill-switch",
"audit": "https://www.openagentskill.com/skills/ml4t-ml4t-kill-switch/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=ml4t-ml4t-kill-switch&task=Use%20ml4t-kill-switch%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20ml4t-kill-switch%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20ml4t-kill-switch%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/ml4t-ml4t-kill-switch/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/ml4t-ml4t-kill-switch"
}
}For the creator
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- Creator
- ml4t
- Source
- ml4t/skills
- Indexed by
- OpenAgentSkill community index
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