bybit-futures_skill

This skill helps you build and operate a Bybit USDT perpetual futures trading system with risk controls, paper trading, backtesting, and WebSocket live

Python

2.5k

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill openclaw/skills --skill bybit-futures

_meta.json280 B
SKILL.md3.4 KB

Overview

This skill is a complete Bybit USDT perpetual futures trading system for building, testing, and running automated crypto futures strategies. It includes paper trading, live execution, risk management, WebSocket real-time data, and common strategy templates (EMA crossover, RSI mean reversion). Use it to prototype, backtest, and deploy production trading bots with configurable risk controls and Telegram alerts.

How this skill works

The system connects to Bybit via WebSocket v5 for millisecond tick data and candle feeds, computing signals only on closed candles while using ticks for precise stop-loss/take-profit management. Core modules include a risk manager for position sizing and limits, a paper-trading engine that mirrors live logic, a live execution module that places real orders, and a backtester for historical validation. State persistence and auto-reconnect keep the engine resilient, and Telegram notifications report events and errors.

When to use it

Building a crypto futures trading bot that targets Bybit USDT perpetuals
Validating strategies in paper trading before risking capital
Implementing strict risk rules: max position, leverage, stop-loss, daily drawdown
Backtesting strategies on historical 1h data (EMA/RSI templates provided)
Deploying a WebSocket-based real-time execution system with persistence and alerts

Best practices

Start in paper trading and validate signals, sl/ tp behavior, and state persistence
Configure conservative default risk settings (lower leverage, smaller position %) and tune after live performance
Run backtests over multiple market regimes before live deployment
Use systemd or another process manager for reliable uptime and auto-restarts
Keep API keys and Telegram credentials secure; rotate keys and limit permissions

Example use cases

Run EMA(12,26) crossover on ETH/USDT 1h as a live trading strategy with automatic SL/TP and size control
Backtest RSI(14) mean-reversion on SOL and alternative tokens to measure edge and win rate
Prototype a custom strategy using the provided template, validate in paper trading, then switch to live execution
Deploy the paper trading WebSocket service on a VPS with systemd for continuous monitoring and Telegram alerts
Enforce daily drawdown and max concurrent positions across multiple symbols to protect capital

FAQ

Yes. A custom strategy template and guidance are included; plug your signal logic into the strategy module and use the same risk and execution pipelines for testing and deployment.

Is switching from paper to live safe?

The live module mirrors paper-trading logic so behavior should match if config and API credentials are set correctly. Always run end-to-end checks with small sizes and maintain conservative risk limits when first going live.