43 posts tagged with "features"

A 30-second polling dashboard is fine until the moment your bot fills a trade at an unexpected price and you spend three poll cycles wondering whether the previous tick is real or just stale. VolatiCloud streams trade opens, closes, PnL changes, and runner status over a persistent WebSocket — every event arrives in under a second, and the equity curve redraws as soon as a position closes. No refresh, no polling, no waiting.

Connecting Claude to your trading account is the easy half. The hard half is knowing what to ask it — because most "AI for crypto" demos collapse the moment you want to do something real. This post is the practical half: five concrete workflows where the VolatiCloud MCP server turns multi-screen dashboard work into one conversation, plus copy-pasteable prompts and the trade-offs nobody warns you about.

Haven't connected MCP yet? Start with the MCP server setup guide — it covers configuration for Claude Code, Claude Desktop, and Cursor, and how the tool architecture works. Come back here once you're connected.

VolatiCloud's MCP server is live. Drop a four-line config into Claude Code, Claude Desktop, or Cursor and your AI assistant gets typed access to every operation in the VolatiCloud GraphQL API — list bots, run a backtest, fork a strategy, fetch live trades — directly from chat. No manual API calls, no copy-pasted tokens, no glue code, and no leaked credentials in .env files.

This post covers what MCP is, how the VolatiCloud server is built, and how to install and configure it in your editor. Once connected, see the companion post on practical AI trading workflows and example prompts.

You spend three days tuning an RSI mean-reversion strategy. The Sharpe ratio is finally where you wanted it. You change one parameter to test a hunch, hit save — and now the working version is gone, and you can't remember exactly what made it work. Every algorithmic trader has lived this moment. VolatiCloud's immutable strategy versioning makes it impossible: every save creates a new version, nothing is overwritten, and live bots stay pinned to the exact version they were created against.

Manually clicking through a dashboard to start 12 bots before the New York open is not algorithmic trading — it's manual trading with extra steps. VolatiCloud's API client system gives you OAuth 2.0 client_credentials authentication for the entire GraphQL surface, so any script, CI pipeline, or external dashboard can manage bots, trigger backtests, and pull live performance data without a browser in the loop.

A bot that misses a stop-loss because its container restarted is not just an inconvenience — it can convert a -3% planned exit into a -12% surprise. The hosting model under your bot is the second-most consequential decision in algorithmic trading after the strategy itself, and most traders pick it by accident on day one. This guide walks through the trade-offs between cloud-managed runners and self-hosted infrastructure so you can pick the right model deliberately.

Every algorithmic trading strategy has knobs. RSI period: 14? 21? 9? Entry threshold: 30, 25, 35? Stoploss: −5%, −7%, −10%? ROI target: 2%, 3%, 5%? Each combination produces a different equity curve, and the difference between defaults and optimized parameters can be the difference between a profitable strategy and a flat one. Manual trial and error doesn't scale: with five parameters and ten values each, you're looking at 100,000 combinations. Freqtrade's hyperopt — wrapped in VolatiCloud's UI — searches the parameter space intelligently with Bayesian optimization and converges on the high-performing region in hundreds of epochs, not hundreds of thousands.

A backtest gives you one number: 84% profit, 14% max drawdown, Sharpe of 1.62. One equity curve. One drawdown figure. But the order in which your trades happened to occur was just one of thousands of possible sequences — and most of those alternate sequences would have produced a different equity curve, sometimes dramatically different. Monte Carlo simulation runs the trades through thousands of randomized variations and shows you the full distribution of outcomes: the worst 5%, the median, the best 5%. The single backtest becomes a confidence band, and the question shifts from "how did it do?" to "how robust is it?"

A strategy that returns 84% over two years on paper means nothing if you can't say what range of outcomes you might actually experience when you run it live. The single number is a starting point — what comes next is rigorous backtesting against years of OHLCV data, multi-pair stress tests, and risk-adjusted metrics like Sharpe and Sortino. VolatiCloud runs that pipeline on Freqtrade's production-tested engine, returns results in seconds to minutes, and surfaces every metric you need to decide whether the strategy survives contact with reality.

At 3:14 AM, your bot crashes. The exchange API key rotated, or the venue pushed an auth change, or a transient network blip put the runner in an error state. The bot stops trading. You won't find out until you open your laptop the next morning — by which point the move you wanted to capture has already happened, and the position you wanted to exit is now 4% deeper underwater. That single failure mode is the reason VolatiCloud's alerting system exists, and it's the reason the design goal is narrow: notify you immediately when something critical breaks, and stay quiet the rest of the time.