Arsenal Quant Project
БесплатноНе проверенHigh-performance decision oracle for autonomous DeFi agents. Computes MEV risk, Impermanent Loss, and Slippage with a 6-filter sequential decision matrix. Nativ
Описание
High-performance decision oracle for autonomous DeFi agents. Computes MEV risk, Impermanent Loss, and Slippage with a 6-filter sequential decision matrix. Native L402 Lightning monetization.
README
The Risk-Validation Layer for Autonomous AI Agents (DeFAI)
Arsenal-Quant-Project MCP server smithery badge
Method and raw results are published — backtest script · result data (180 days of Binance ETH/USDC daily closes): 🔬 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. 📐 This engine measures; it does not forecast. No predictive-accuracy figure is claimed — read the published result files and judge the method for yourself.
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).
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.
Two ways to call it
1. MCP JSON-RPC — the endpoint advertised on the MCP registry
POST https://api.arsenal-quant.com/mcp
{"jsonrpc":"2.0","id":1,"method":"tools/call",
"params":{"name":"evaluate_pool",
"arguments":{"apy":0.20,"price_ratio":0.85,"days_held":30}}}
Standard MCP handshake: initialize → tools/list → tools/call. Available over
streamable HTTP and stdio.
2. REST convenience route — no MCP client required
GET https://api.arsenal-quant.com/mcp/evaluate?apy=0.20&price_ratio=0.85&days_held=30
Both routes run the same calculation and the same quota. Note that
/mcp/evaluate is GET-only: a POST to that path returns 405 Allow: GET,
because JSON-RPC belongs on /mcp.
Engine 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": "<HMAC-SHA256 hex — illustrative placeholder, yours will differ>",
"layer": "FREE"
}
layer reports how the call was served: FREE while inside the free quota,
PREMIUM once an L402 payment has been verified. The call shown above is
served as FREE.
Access and Pricing
- Free tier —
evaluate_pool: 100 calls per IP per day, custom parameters included. No Lightning wallet is needed to use the engine. - Beyond the free quota: an L402 Lightning micro-payment. The amount is set by
server configuration and is currently 150 sats per evaluation. Read it from
the
WWW-Authenticateheader or fromerror.data.price_satsin the 402 response rather than hard-coding it. GET /mcp/audit/latest: 3 free calls per IP per hour, then L402 — this route is what keeps the Lightning rail live and demonstrable.
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 requires a wallet key with send permission to pay an invoice.
# Use a DEDICATED wallet funded with a small working balance, and never the key
# of a wallet holding significant funds. Keep it in the environment, never in code.
LNBITS_PAYMENT_KEY = os.getenv("LNBITS_PAYMENT_KEY")
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_PAYMENT_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.md
- MCP auto-discovery card:
https://api.arsenal-quant.com/.well-known/mcp/server-card.json
Why pay per call?
This engine does not prevent losses, and it makes no claim about how much money it saves you. What it does is compute — deterministically, in $\mathcal{O}(1)$, with an HMAC signature over the result — whether a position sits above or below its breakeven boundary. What you pay for is a reproducible, auditable number your agent can act on, priced per call so it can be budgeted like any other input.
Установка Arsenal Quant Project
У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.
▸ github.com/Faouzi122/Arsenal-Quant-ProjectFAQ
Arsenal Quant Project MCP бесплатный?
Да, Arsenal Quant Project MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для Arsenal Quant Project?
Нет, Arsenal Quant Project работает без API-ключей и переменных окружения.
Arsenal Quant Project — hosted или self-hosted?
Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.
Как установить Arsenal Quant Project в Claude Desktop, Claude Code или Cursor?
Открой Arsenal Quant Project на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
Похожие MCP
Fetch
Web content fetching and conversion for efficient LLM usage.
AWS KB Retrieval
Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.
автор: modelcontextprotocolSpring AI MCP Server
Provides auto-configuration for setting up an MCP server in Spring Boot applications.
llm-analysis-assistant
A very streamlined mcp client that supports calling and monitoring stdio/sse/streamableHttp, and can also view request responses through the /logs page. It also
автор: xuzexin-hzCompare Arsenal Quant Project with
Не уверен что выбрать?
Найди свой стек за 60 секунд
Автор?
Embed-бейдж для README
Похожее
Все в категории ai
