THIS IS F*CKING INSANE
Kept the return, cut the crash 2.3x
QuantGPT ran one moving-average rule through one MCP endpoint. S&P 500, 1999 to 2026. Worst drawdown: -24.4% against -56.8% for buy and hold. Same 6.8% annual return. Sharpe 0.61 vs 0.44. 42 trades, gross of costs.
The model writes the rule. Everything after that is the server's job.
> SEND - model pushes the rule to the backtest server over MCP
> TEST - server replays it on nightly-refreshed history from 1998, delisted tickers included
> VERDICT - one of three doors: DATA VALIDATED, TRADE-OFF, NO EDGE
> SWEEP - one prompt, 5 lengths x 4 buffers, checks plateau vs lucky spike
> STRESS - futuretests replay holdings through past shocks: tariffs, oil, war
> JOURNAL - rechecks every finished test each time new bars land
Then 30 days on paper before any real capital moves.
No tool the model can call through MCP places, changes or cancels an order. The runner executes your code on your schedule, under your own keys, at your own broker. The model and the order book never share a path.
Take out the server and every backtest becomes a guess. Skip the paper window and the first real trade is also the first test.
Full call flow in the terminal below ↓
Spending tokens blind drains your whole budget
Tokdash v2.7.0 shipped Oct 24.
8 open-source GitHub repos for tracking token spend and capping agent budgets:
22.8k stars as of Oct 24. MIT and Apache-2.0. Pushed Oct 19–24.
MEASURE THE BURN
01. codeburn ★11.6k
▸ https://t.co/7JqpqGxGTI
→ spend by model, project, task
02. tokscale ★5.7k
▸ https://t.co/jUNpRpda2X
→ token and cost CLI across agents
03. Tokdash ★87
▸ https://t.co/orOp04Ma9l
→ local dashboard with quota resets
PRICE EACH CALL
04. genai-prices ★401
▸ https://t.co/vo7yQUpoAr
→ price table for LLM calls
CAP THE SPEND
05. floe-guard ★347
▸ https://t.co/mJ5dgXB9fV
→ hard stop before budget ceiling
06. cycles-server ★5
▸ https://t.co/tX6YrMkcs7
→ reserve budget before agent acts
07. ai-gateway ★2.3k
▸ https://t.co/GVfqDcSYpN
→ quotas and attribution at gateway
08. openmeter ★2.4k
▸ https://t.co/cd7PQmrRwB
→ usage metering and enforcement
pip install floe-guard
from floe_guard import BudgetGuard
guard = BudgetGuard(limit_usd=5.00)
the stack:
logs → count → price → guard → reserve → quota → meter
Wrong ceiling stops a live agent.
Not the model. The blind spend around it.
Full cost stack in the terminal ↓