RiskState

Pre-trade risk layer for crypto. $BTC & $ETH exposure governance. Spot, perps & DeFi aware. For AI agents, traders, allocators. Free beta.

[RiskState @riskstate_ai]

Introducing RiskState — risk governance for autonomous crypto agents.

Your agent decides WHAT to trade.
RiskState tells it HOW MUCH it can risk.

→ 30+ real-time data sources
→ 5-level policy engine (BLOCK → EXPANSION)
→ Deterministic & auditable (SHA-256)
→ BTC + ETH + DeFi lending aware
→ Any LLM, any framework, any execution layer

Free beta API live → https://t.co/K2J74nXlCB

[RiskState @riskstate_ai]

✅16.6% Max drawdown vs 40.4% Unmanaged exposure

[Likido.eth @Likido_defi]

I backtested @riskstate_ai product — and published everything.

"Backtesting the Policy, Not the Signal": an exposure-matched audit of RiskState's sizing output, on 275 live policy decisions + an 8-year multi-regime reconstruction (2018–2026).

▸ 16.6% vs 40.4% max drawdown vs unmanaged exposure
▸ 3.4% vs 23.8% weekly deep-tail frequency
▸ Stored historical decisions — zero look-ahead, seeded, reproducible, CIs on everything

And the part nobody else publishes: at the 7-day horizon our dynamic sizing doesn't yet beat a constant cap — so we pre-registered the fix (realized-vol-first sizing) for score_v4, with shadow telemetry already running in production.

Risk engines that only publish wins aren't publishing research.

https://t.co/2cMKroPoZr

[RiskState @riskstate_ai]

New in the RiskState API:
POST /v2/risk-distribution — loss quantiles, expected shortfall and P(loss > X), regime-conditioned and byte-reproducible.

Public docs synced → https://t.co/GUiSCQw5Pq

[RiskState @riskstate_ai]

bitcoin:native Historic bottom zone
☑️ MVRV 1.19 (Bottom 1.0-1.2)
☑️ 200w MA as key structural support

⚠️ MACRO Risk-off

[RiskState @riskstate_ai]

bitcoin:native H2 2026
A map with two roads, conditioned by bottom formation

[Likido.eth @Likido_defi]

https://t.co/R0LuoD4bxk

[RiskState @riskstate_ai]

bitcoin:native BLOCK DEFENSIVE
check the risk policy before placing orders
Visualizer or API https://t.co/INY9apOtrq

[RiskState @riskstate_ai]

$ETH risk permissions — Apr 15

Policy: CAUTIOUS L3
Max size: 54% (spot)
Binding: Cycle (VALUE_ZONE)
Direction: NEUTRAL

Structural downgrade LIGHT active. Cap reduced -10%.

[RiskState @riskstate_ai]

$BTC risk permissions — Apr 15

Policy: CAUTIOUS L3
Max size: 42% (spot)
Binding: Cycle (POST-PEAK)
Direction: LONG_PREFERRED

Composite 64 — signals lean bullish but cycle phase caps exposure.

[RiskState @riskstate_ai]

$ETH signal: STRUCTURAL DOWNGRADE LIGHT

3 of 7 signals active:
- Below 200D SMA (hard)
- Supply inflationary +0.85%/yr (soft)
- Dominance <15% (soft)

Policy cap reduced -10%.
Max exposure: 54% spot.

Full reset requires 200D reclaim + supply flip.

[RiskState @riskstate_ai]

$BTC signal: OI z-score +2.28

Leverage at 30-day highs — 2.28σ above mean. Amplified volatility risk on any directional move.

Set against: funding -0.41%, 4,805 BTC outflow, F&G 23. Contrarian setup forming, but OI extremes cap exposure.

Policy: CAUTIOUS L3, 42% max.

[RiskState @riskstate_ai]

Every week you see a new "AI trading agent" repo with 500+ stars.

Signal detection, execution, backtesting — all covered.

- Position sizing when your signals conflict?
- Risk limits when volatility spikes?
- What happens to your agent when the market drops 15% in a week?

That's the layer nobody builds. And it's the one that matters most when the market turns.

https://t.co/pg1dd8G8T6

[Likido.eth @Likido_defi]

https://t.co/iyxi9YM6Je

[RiskState @riskstate_ai]

@crypto_kovri Check it out https://t.co/HeTLCwDwmd

[RiskState @riskstate_ai]

best piece i've read on agentic trading architecture this year. "if the model can see the rule, it can optimize around it" is the insight most builders miss — they put risk limits in the prompt and call it governance.

the separation between planning and constraint layer is exactly right. the model proposes, a deterministic system adjudicates. that's the only way hard constraints stay hard.

one thing i'd push further: the constraint layer itself should be dynamic. exposure caps that adapt to live volatility, macro regime, funding extremes — not just static user-set thresholds. the boundary needs to move with the market, not just hold a fixed line.

been building exactly this separation. great to see the architecture formalized.

[RiskState @riskstate_ai]

Genuine question for anyone building AI trading agents or bots:

What determines your max position size? Is it fixed? % of portfolio? Some function of volatility?

And when the regime shifts — do you adjust automatically or manually?

Curious how people handle this in production, not in backtests.
https://t.co/pg1dd8G8T6

[RiskState @riskstate_ai]

Great product. "set risk limits" is the right idea — but static limits set once at deploy don't adapt. what happens when volatility spikes or macro turns risk-off while your agent is running? a dynamic policy layer that adjusts limits in real-time based on live market conditions would make the arena a lot safer. Check us out!

[RiskState @riskstate_ai]

How a risk engine reacts when its own signals disagree: it doesn't pick a winner, it cuts size.

BTC and ETH both flagged DIVERGENT today — trend active, RSI overbought. Same formula, smaller exposure.

Determinism over opinion.

[Likido.eth @Likido_defi]

https://t.co/cShW6LQDm2

[RiskState @riskstate_ai]

@garrytan Absolutely! Check out our risk governor for AI agents https://t.co/Awe64xHQpT

[RiskState @riskstate_ai]

Most trading agents blow up the same way: they get the direction right and the size wrong.

POST-PEAK, low volatility, longs crowded. Everything looks quiet. That's exactly when over-sizing kills you — the move comes from compression, not from signal.

The model that picks trades should never set its own limits.

That's why we built a separate policy engine — deterministic, 5 levels, live market conditions.

API docs: https://t.co/pg1dd8G8T6