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).

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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

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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:

1. MCP Client (in‑agent) – packages the model prompt, tool calls & contextual memory into a typed frame.

2. MCP Server – validates frame, resolves data adapters, returns structured payload.

3. Adapters – market‑data, on‑chain, file‑system, news, custom DB.

4. Cognitive Loop – Planning → Strategy → Risk → Execution → Reflection.

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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()

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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})

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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 ctx

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6 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]

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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"

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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;
}

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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")

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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)

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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.

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12 Extensibility Roadmap

1. Plug‑in Solana program registry – publish strategy hashes + proofs.

2. zk‑Rollup risk attestations – provide cryptographic guarantees.

3. Multi‑model cockpit – ensemble GPT‑4o + local Llama 3.

4. Reinforcement‑learning policy gradient executor.