aetherbot trading

    1

    A paper-first crypto trading simulator with non-negotiable risk gates and honest buy-and-hold backtesting.

    $15

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    aetherbot trading

    Example session with this skill installed

    Run a backtest on btc_usdt_15m.csv using the default RsiEmaCross strategy and show me how it performed against buy-and-hold.

    • Read your context and instructions
    • Compiled the aetherbot trading

    I have executed the paper-run simulation using the RsiEmaCross strategy against the provided BTC/USDT 15m fixture. The RiskManager processed all signals, enforcing the default 10% stake and 0.10 stoploss limits.

    Backtest Result: BTC/USDT (15m)

    MetricValue
    Initial Balance10,000.00 USDT
    Final Balance9,991.75 USDT
    Total P&L-8.25 USDT (-0.08%)
    Total Trades4
    Win Rate25.0%
    Max Drawdown0.12%
    Buy-and-Hold P&L+12.40 USDT (+0.12%)
    Benchmark VerdictFAIL (Underperformed Buy-and-Hold)

    Veto Log Summary

    • 2 signals skipped: Max open trades (1) reached.
    • 0 signals vetoed: Daily loss limit not triggered.
    • 0 signals vetoed: Max drawdown limit not triggered.

    Next steps

    1. Review the RsiEmaCross entry thresholds in config.py to reduce late entries.
    2. Adjust the minimal_roi table to capture smaller price movements during low volatility.
    3. Run the simulation again with --stake-pct 5 to see how lower exposure affects the drawdown.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Paper-first crypto trading with AetherBot's real risk rules — a dry-run simulator, RiskManager veto gates, the RsiEmaCross strategy, and honest backtests against buy-and-hold. Paper trading only; never live, never a promise of returns. The pack's own fixture backtest loses money and says so. Includes config defaults, position sizer, pure-Python TA, paper runner and CLI. Experimental / educational. The source repo stays free on GitHub.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Trading & Investing.

    Educational paper trading only. No live trading code path, CCXT exchange wrappers, or persistence layer included. Requires CSV candle data for simulation.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before buying a skill.

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    Security scan passed 4 days ago

    Software
    Python

    Permissions

    Terminal / Shell
    Read Files
    Write Files
    Listed4 days ago

    What's inside

    Frequently Asked Questions

    This skill provides a 'RiskManager' veto layer that sits between your strategy and the simulation. It enforces hard limits on daily losses, drawdown, and trade frequency, ensuring your strategy remains disciplined even during volatile market conditions.

    The skill is built for Python 3.10+ environments and is designed to be compatible with any AI agent capable of executing local Python scripts or interacting with CLI-based tools. It requires no external API keys or exchange connections to function.

    While the skill comes with a bundled RSI/EMA strategy, it is designed to be a flexible engine. You can import your own historical OHLCV data in CSV format to test how various indicator combinations would have performed in the past.

    You will receive the complete Python source code for the simulation engine, the RiskManager module, pre-configured YAML settings files, and a sample BTC/USDT dataset to start testing immediately.

    No, this skill is strictly a 'paper-first' simulator designed for educational and strategy validation purposes. It does not include exchange connectors (like CCXT) or live execution code to prevent accidental financial risk.

    Standard backtests often ignore the simplicity of just holding an asset. This skill automatically calculates the 'Buy and Hold' return for your specific data period, forcing you to see if your active strategy is actually adding value or just underperforming the market.

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