Arsenal Decision Engine
This server acts as a Decision Intelligence Layer, transforming economic uncertainty into actionable signals across financial domains. All outputs include confidence scores and mandatory human review thresholds for autonomous execution.
Sentiment Analysis: Analyzes market psychology (fear/greed) from raw text, outputting signals like
EXECUTE,HEDGE, orDELAY.Maritime Freight Risk: Evaluates disruption risk and True Price per corridor (e.g., Suez), with corridor-to-corridor price comparison.
DeFi Arbitrage Evaluation: Computes net return vs. flash loan cost using a 6-filter matrix to recommend action.
MEV Protection: Analyzes Uniswap V2 transactions for sandwich attack risk, returning ROI-based capital protection signals using O(1) mathematical determinism.
Agent Circuit Breaker: Terminates or monitors runaway agent processes by PID (
TERMINATE_AND_REGROUPorMONITOR) to prevent token drain from infinite loops.Cache Manager: Enables/disables semantic caching to reduce redundant vector DB calls, optimizing cost and performance.
Live Market Pulse: Access a real-time event feed for market signals.
Access is available via a pay-per-decision model (L402 Lightning Network protocol) or subscription-based API keys.
Enables Google ADK agents to auto-discover and use MCP tools for economic decision signals, including market psychology, maritime freight, and DeFi arbitrage.
Provides a Tool wrapper for LangChain agents to analyze market psychology, maritime freight risk, and DeFi arbitrage via the Arsenal Decision Engine.
Arsenal Decision Engine 🛡️
The Risk-Validation Layer for Autonomous AI Agents (DeFAI)
Validation on 180-Day Binance ETH/USDC Data (
07_Backtest_Engine/): 🔬 Breakeven Corridor is a deterministic algebraic boundary (where IL = accumulated yield). Any position whose price ratio stays within[lower_be, upper_be]has R_net > 0 by mathematical definition — not a probabilistic model. 🎯 82% to 97% Mid-Checkpoint Predictive Accuracy — the risk level signal correctly predicted final position health (positive/negative R_net) across all tested APY × holding-period scenarios.
Mission
Transform DeFi uncertainty into deterministic, actionable risk metrics for autonomous agents. We do not run stateful trading bots or generate speculative prediction signals; we provide a stateless risk middleware layer that agents query before deploying or maintaining standard constant-product / full-range LP positions.
Built for agents, priced for agents. Pay per decision via Lightning Network (L402).
Related MCP server: riskstate-mcp
What This Engine Does
Before an autonomous agent deploys capital or adjusts a standard constant-product / full-range LP position (such as Uniswap V2 or full-range V3), it submits the pool parameters (APY, price ratio, days held) to our API. The engine computes the exact mathematical risk, the net return ($R_{net}$), and the dynamic Breakeven Corridor bounds.
No LLMs. No hallucinations. Pure algebraic calculation.
Complexity: $\mathcal{O}(1)$ time and memory.
Latency: $< 15\text{ms}$ local execution.
Agent Request (HTTP GET)
https://api.arsenal-quant.com/mcp/evaluate?apy=0.20&price_ratio=0.85&days_held=30Engine Response (JSON Contract)
{
"impermanent_loss_pct": 0.3292,
"accumulated_yield_pct": 1.6438,
"r_net_pct": 1.3146,
"il_to_yield_ratio": 0.2,
"risk_level": "LOW",
"breakeven_corridor": {
"lower_ratio": 0.6941,
"upper_ratio": 1.4407,
"interpretation": "Position remains profitable if price ratio stays within [0.6941, 1.4407]"
},
"inputs": {
"apy": 0.2,
"price_ratio": 0.85,
"days_held": 30
},
"source": "Arsenal Decision Engine v2.0",
"oracle_signature": "b5dce8268fe762fa66ffccc083b02e9b65801888cdf76004ff22b648ea80869b",
"layer": "PREMIUM"
}API Pricing (Dynamic L402)
Free Tier: First 3 requests per IP/hour are free.
Low/Moderate Risk Positions: 50 Sats per evaluation.
High/Critical Risk Positions: 500 Sats per evaluation.
Python Integration Example
import urllib.request
import urllib.error
import json
import re
import os
API_URL = "https://api.arsenal-quant.com/mcp/evaluate?apy=0.20&price_ratio=0.85&days_held=30"
LNBITS_URL = "https://demo.lnbits.com"
LNBITS_ADMIN_KEY = os.getenv("LNBITS_ADMIN_KEY", "your_key_here")
def query_risk_oracle():
req = urllib.request.Request(API_URL, method="GET")
req.add_header("x-agent-id", "autonomous-lp-bot")
try:
with urllib.request.urlopen(req) as resp:
return json.loads(resp.read().decode('utf-8'))
except urllib.error.HTTPError as e:
if e.code == 402:
auth_header = e.headers.get("WWW-Authenticate")
macaroon = re.search(r'token="([^"]+)"', auth_header).group(1)
invoice = re.search(r'invoice="([^"]+)"', auth_header).group(1)
pay_req = urllib.request.Request(
f"{LNBITS_URL}/api/v1/payments",
data=json.dumps({"out": True, "bolt11": invoice}).encode(),
headers={"X-Api-Key": LNBITS_ADMIN_KEY, "Content-Type": "application/json"}
)
with urllib.request.urlopen(pay_req) as pay_resp:
preimage = json.loads(pay_resp.read().decode())["preimage"]
retry_req = urllib.request.Request(API_URL, method="GET")
retry_req.add_header("Authorization", f"L402 {macaroon}:{preimage}")
retry_req.add_header("x-agent-id", "autonomous-lp-bot")
with urllib.request.urlopen(retry_req) as final_resp:
return json.loads(final_resp.read().decode('utf-8'))
else:
raise
if __name__ == "__main__":
evaluation = query_risk_oracle()
print(f"Risk Level : {evaluation['risk_level']}")
print(f"R_net : {evaluation['r_net_pct']:+.4f}%")
print(f"Breakeven : [{evaluation['breakeven_corridor']['lower_ratio']}, {evaluation['breakeven_corridor']['upper_ratio']}]")Developer Integration
Integration cookbook & MCP guides:
COOKBOOK.mdMCP auto-discovery card:
https://api.arsenal-quant.com/.well-known/mcp/server-card.json
Why L402? (Proof of Savings)
If this engine protects your agent from a $50,000 Impermanent Loss wipeout, a 500 Satoshi ($0.30) deterministic risk-validation call is not a cost — it is a mathematical insurance policy.
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