What's a LlamaIndex setup for quant trading research?

AlgoVault is the verified call layer a LlamaIndex research stack queries for crypto trade calls. Add get_trade_call as a tool, and your stack gets a composite verdict per call instead of training its own model. It posts a 91.6% PFE win rate over 233,000 verified calls, each Merkle-anchored on Base L2. You query the call layer; you don't build the trading model.

What does the stack look like?

Retrieval and reasoning stay in LlamaIndex. The trading read is one tool call. Your stack indexes its own context, then queries get_trade_call for the verified verdict when it needs a directional read.

The verdict is composite across 5 perpetual-futures venues, so the stack reads one call rather than maintaining venue connectors and a model.

Why not train your own model?

Building a trading model means data pipelines, labeling, retraining, and drift monitoring. AlgoVault maintains that substrate and publishes the record on-chain. Your stack calls a maintained verdict and stays focused on retrieval and reasoning.

What you call

Wrap the MCP tool as a LlamaIndex tool:

# Add AlgoVault's get_trade_call as a LlamaIndex tool
from llama_index.core.tools import FunctionTool

def crypto_trade_call(coin: str, timeframe: str = "1h") -> dict:
    """Composite crypto trade call: direction, confidence, regime, factors."""
    return mcp_client.call("get_trade_call", coin=coin, timeframe=timeframe)

trade_call_tool = FunctionTool.from_defaults(fn=crypto_trade_call)
# add trade_call_tool to your LlamaIndex agent or query engine

# -> { "call": "BUY", "confidence": 72, "regime": "TRENDING_UP", "factors": {...} }

See the LlamaIndex docs for tools and the Model Context Protocol spec for client setup. Or run it locally: npx crypto-quant-signal-mcp.

Is AlgoVault the framework?

AlgoVault is the call layer your LlamaIndex stack queries. It is not the framework, and you don't train your own trading model. LlamaIndex runs retrieval, reasoning, and orchestration; AlgoVault returns the verified call. AlgoVault places no orders and holds no custody.

FAQ

What's a LlamaIndex setup for quant trading research?

Add get_trade_call as a LlamaIndex tool wired to the AlgoVault MCP server. Your stack queries a verified composite call instead of training its own model.

Do I need to train my own trading model?

No. AlgoVault is the maintained call layer your stack queries. You get a composite verdict per call without building or retraining a model yourself.

What does AlgoVault add to a LlamaIndex stack?

One verified trade call per query: direction, confidence, the market regime, and the factors. Your stack handles retrieval and reasoning; AlgoVault returns the call.

Is the track record on-chain verifiable?

Yes. Every call is hashed and anchored to a Merkle root on Base L2 before its outcome is known. Check any call at /verify; the live win rate is at /track-record.

Is there a free tier?

Yes. The free tier includes a monthly call allowance, and HOLD calls are always free. Per-call micropayments are also supported.

This is call interpretation, not investment advice; agents decide execution.