MCP Protocol
Trade Mind – Detailed Project Overview
Mission Build an open, modular AI‑trading stack that can reason over heterogeneous data (market, on‑chain, news) and act safely on Solana & other venues via the Model Context Protocol (MCP).
1 High‑Level Value Proposition
Challenge
Solution (Trade Mind)
Tool + data fragmentation
Unified context through MCP hub & client SDKs
Slow manual research
Autonomous, iterative Research Agent with planning → search → reflection
Fragile strategies
Pluggable Strategy Engine with back‑test & on‑chain simulation
Hidden tail‑risk
Rule‑ & ML‑based Risk Control at pre‑trade & post‑trade layers
Ops overhead
Container‑first dev‑ops, auto‑scaling executors, Solana program registry
2 System Architecture
┌──────────────┐ MCP ┌────────────────┐
│ AI / UX Host │◄──────────────│ MCP Server(s) │
└──────────────┘ │ + adapters │
▲ ▲ ▲ └────────────────┘
│ │ │ ┌─────────────────────────────────┐
│ │ │ │ Local / Remote Data Sources │
│ │ └────────│ • Market WS • SQL • Parquet │
│ └──────────│ • Solana RPC • S3 / IPFS │
│ └─────────────────────────────────┘
│ ▼ ▲
│ Strategy Execution
▼
┌───────────────┐ … dotted arrows = MCP frames …
│ Reflection │
└───────────────┘The diagram matches the neon purple→green gradient of your brand style (see delivered asset). Key tiers:
MCP Client (in‑agent) – packages the model prompt, tool calls & contextual memory into a typed frame.
MCP Server – validates frame, resolves data adapters, returns structured payload.
Adapters – market‑data, on‑chain, file‑system, news, custom DB.
Cognitive Loop – Planning → Strategy → Risk → Execution → Reflection.
3 Model Context Protocol (MCP)
A thin, language‑agnostic framing of context → request → response with JSON schemas.
3.1 Minimal Frame Schema (YAML)
Frame:
id: uuid
agent:
name: string
version: semver
timestamp: iso8601
context:
goal: string # e.g. "Generate order proposals"
history: array<object> # event log excerpts
request:
type: enum[DATA,EXEC]
body: any # adapter‑specific
signature: string # Ed25519 (optional)3.2 MCP Server – TypeScript/Node Example
// src/server.ts
import fastify from "fastify";
import Ajv from "ajv";
import { frameSchema } from "./schemas";
import { routeFrame } from "./router";
const app = fastify();
const ajv = new Ajv();
const validate = ajv.compile(frameSchema);
app.post("/frame", async (req, res) => {
const frame = req.body as any;
if (!validate(frame)) {
return res.code(400).send({ errors: validate.errors });
}
const result = await routeFrame(frame);
return res.send(result);
});
app.listen({ port: 8080 }).then(() =>
console.log("🚀 MCP Server running @ http://localhost:8080")
);3.3 Python MCP Client Helper
# trademind/mcp.py
import httpx, uuid, datetime as dt
SERVER = "http://localhost:8080/frame"
def send_frame(goal: str, req_type: str, body: dict):
frame = {
"id": str(uuid.uuid4()),
"agent": {"name": "trade-mind", "version": "0.1.0"},
"timestamp": dt.datetime.utcnow().isoformat(),
"context": {"goal": goal, "history": []},
"request": {"type": req_type, "body": body},
}
r = httpx.post(SERVER, json=frame, timeout=15)
r.raise_for_status()
return r.json()4 Data Integration Layer
4.1 Market Data (WebSocket)
# adapters/market_ws.py
import asyncio, websockets, json
ENDPOINT = "wss://stream.binance.com:9443/ws/btcusdt@kline_1m"
async def stream():
async with websockets.connect(ENDPOINT) as ws:
while msg := await ws.recv():
data = json.loads(msg)
yield {
"t": data["E"],
"open": float(data["k"]["o"]),
"close": float(data["k"]["c"]),
}4.2 On‑Chain Signals (Solana web3.js)
import { Connection, PublicKey } from "@solana/web3.js";
const conn = new Connection("https://api.mainnet-beta.solana.com");
export async function getAccountBalance(addr: string) {
const lamports = await conn.getBalance(new PublicKey(addr));
return lamports / 1e9; // SOL
}4.3 Telegram / X News Scraper (Python)
from telethon import TelegramClient
from datetime import datetime, timedelta
client = TelegramClient("session", api_id, api_hash)
news = []
async def fetch(channel="Cointelegraph"):
async for msg in client.iter_messages(channel, limit=100):
if datetime.utcnow() - msg.date < timedelta(hours=12):
news.append({"text": msg.text, "ts": msg.date})5 Context Orchestrator
Plans multi‑step agent chains and attaches the right adapter calls.
# orchestrator.py
from mcp import send_frame
def build_context(goal):
history = retrieve_memory(goal)
return {"goal": goal, "history": history}
def orchestrate(goal, steps):
ctx = build_context(goal)
for step in steps:
response = send_frame(goal, step["type"], step["body"])
ctx["history"].append(response)
return ctx6 Strategy Engine – Example Momentum Strategy
import pandas as pd
def momentum(df: pd.DataFrame, lookback=20):
df["ret"] = df.close.pct_change()
df["signal"] = df.ret.rolling(lookback).mean()
return df["signal"].iloc[-1]7 Risk Control Module
MAX_DRAWDOWN = 0.12
MAX_LEVERAGE = 3.0
def check_risk(portfolio, proposed):
if portfolio.drawdown > MAX_DRAWDOWN:
return False, "DD too high"
if portfolio.leverage + proposed.leverage > MAX_LEVERAGE:
return False, "Leverage limit"
return True, "OK"8 Execution Layer – Place Order on Serum (TypeScript)
import { Market, OpenOrders } from "@project-serum/serum";
async function placeOrder(marketPk, payer, side, price, size) {
const market = await Market.load(conn, marketPk, {}, programID);
const tx = await market.makePlaceOrderTransaction(conn, {
owner: payer.publicKey,
payer: payer.publicKey,
side,
price,
size,
orderType: "limit",
});
const sig = await conn.sendTransaction(tx, [payer]);
await conn.confirmTransaction(sig);
return sig;
}9 Reflection & Learning Loop
# reflection.py
from sklearn.linear_model import LinearRegression
def log_episode(trade, outcome):
db.insert("episodes", {**trade, **outcome})
def retrain():
df = db.read("episodes")
X = df[["signal", "volatility"]]
y = df["pnl"]
model = LinearRegression().fit(X, y)
model.save("models/pnl_reg.pkl")10 Autonomous Research Pipeline
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
TEMPLATE = "You are a crypto researcher… {question}"
llm = OpenAI(temperature=0)
prompt = PromptTemplate.from_template(TEMPLATE)
chain = LLMChain(llm=llm, prompt=prompt)
q = "What are the catalysts for SOL in the next 3 months?"
raw_answer = chain.run(q)
print(raw_answer)11 Deployment Topologies
Single‑host Lab – Docker‑Compose with
mcp-server,orchestrator,strategy,db.Hybrid Cloud – MCP servers next to data sources; stateless client hosted in Lambda / Cloud Run.
Fully Decentralised – MCP frame relayed through Solana program for on‑chain audit trail.
12 Extensibility Roadmap
Plug‑in Solana program registry – publish strategy hashes + proofs.
zk‑Rollup risk attestations – provide cryptographic guarantees.
Multi‑model cockpit – ensemble GPT‑4o + local Llama 3.
Reinforcement‑learning policy gradient executor.
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