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# Moss: Create 24/7 AI Trading Agents in Natural Language > πŸŽ‰ **Moss** is an intelligent quantitative trading bot factory and strategy tuner. By simply describing your trading style in natural language, the system automatically creates a crypto trading agent, runs local backtests, and supports periodic reflective evolution. >

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πŸš€ [Features](#features) | ⚑ [Installation & CLI](#installation--cli) | 🧠 [Evolution Mechanism](#evolution-mechanism) | πŸ“¦ [Platform Integration](#platform-integration) | 🀝 [Contributing](#contributing) ## Overview Moss transforms natural language descriptions into fully functional cryptocurrency trading strategies. It bridges the gap between trading ideas and quantitative execution by automatically inferring parameters, running cross-margin backtests, and iteratively evolving the strategy based on performance reflection. > **Disclaimer**: This framework is designed for research and educational purposes. Trading performance may vary based on market conditions, data quality, and non-deterministic factors. It is not intended as financial, investment, or trading advice.
Features Overview
## Quick Start Start to create your AI trading agent in minutes. Simply send this message to your agent: ``` Install This Skill: https://github.com/moss-site/moss-trade-bot-skills/tree/v1.0.28/moss-trade-bot-factory ```

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Supports all major AI agents including **OpenClaw**, **Hermes**, **Claude Code**, **Codex**, and more. ## πŸš€ Latest Updates: - **2026-10-19**: Updated `v1.0.28` in place with Paragon HIP-3 PAPER perpetual support, a verified 3x cap, and its native Hyperliquid 15m backtest dataset. - **2026-09-08**: Updated `v1.0.28` in place with HyperCore CASHCAT and PONS perpetuals, verified 3x caps, and native Hyperliquid 15m backtest datasets. - **2026-06-01**: Released `v1.0.28` β€” SKILL.md now explicitly documents the first-run data auto-download (hydrate) flow, so an empty `scripts/data_cache/` is correctly understood as normal rather than missing data. Datasets are unchanged from v1.0.27. - **2026-05-25**: Released lightweight `v1.0.26` Skill distribution. Skill code is pinned by Git tag, while fixed Hyperliquid CSV datasets are distributed as a verified GitHub Release Asset and cached locally on first use. - **2026-05-14**: **Live copy trading is now live**. Support for 22 major tokens including ETH, SOL, ADA, APT, ARB, and more β€” create agents or copy any agent on the leaderboard directly to your Hyperliquid wallet. - **2026-05-06**: Launched **Position Overview Dashboard** with one-click filters and **real-time agent activity panel** β€” track every long/short position and live order from top agents at a glance. - **2026-04-27**: Launched **Agent Highlights Panel** and **Trading Decision Panel** β€” surface each agent's best trades and the reasoning behind every move. - **2026-04-24**: **Backtesting and paper trading now run on real Hyperliquid market conditions** β€” fees, slippage, and funding rates fully aligned with live execution. ## Features ### πŸ—£οΈ Natural Language to Strategy Describe your trading style (e.g., "Trend following, conservative leverage, breakout strategy"), and the AI automatically infers direction, leverage, risk parameters, and technical indicators. ### πŸ“ˆ Full Backtesting Engine A robust local backtesting engine featuring cross-margin simulation, regime detection, and rolling positions. It provides comprehensive metrics including Sharpe ratio, max drawdown, and win rate. ### 🧬 Weekly Evolution Loop The core innovation of this factory. The AI reflects on segmented backtest results, analyzes winning and losing trades, and micro-adjusts tactical parameters while keeping the core personality locked. ### πŸ›‘οΈ Safety Guardrails Built-in safety mechanisms including leverage limits, mandatory wide stop-losses for high leverage, and confirmation gates for live trading. ## System Architecture The framework decomposes the complex process of strategy creation into a streamlined pipeline:
System Architecture
### Signal Decision System The core decision engine evaluates multiple market dimensions and normalizes them into a composite signal score:
Signal Decision System
- **Trend**: EMA crossover and Supertrend direction. - **Momentum**: RSI and MACD oscillators. - **Mean Reversion**: Bollinger Bands regression. - **Volume**: OBV and volume-price correlation. - **Volatility**: ATR breakout and contraction. ## Evolution Mechanism Evolution is not a separate step after backtesting, but an embedded process during the backtest:
Evolution Mechanism
The AI applies **7 Reflection Principles** to analyze each segment's performance: 1. Look at the big picture before details. 2. Analyze why winning trades succeeded. 3. Analyze why losing trades failed. 4. Identify specific parameter issues. 5. Micro-adjust rather than reset (Tactical drift bounded to Β±30%). 6. Maintain momentum from previous adjustments. 7. Ensure continuous adaptation (cannot remain unchanged for >3 rounds). *Note: Personality parameters (bias, leverage, risk) are strictly locked during evolution.* ## Installation & CLI ### Prerequisites - Python 3.x - `pandas>=2.0.0`, `numpy>=1.24.0`, `ccxt>=4.0.0` ### Setup Clone the pinned release tag and install dependencies: ```bash git clone --depth 1 --branch v1.0.28 https://github.com/moss-site/moss-trade-bot-skills.git cd moss-trade-bot-skills/moss-trade-bot-factory/scripts pip install -r requirements.txt ``` ### Data Cache Preparation Historical Hyperliquid datasets are not committed into the repository. The first backtest or fingerprint command downloads `data_cache-v1.0.28.tar.gz` from the `v1.0.28` GitHub Release, verifies `data_cache_manifest.json`, and expands the CSVs into: ```text ~/.cache/moss-trade-bot-factory/v1.0.28/data_cache ``` Set `MOSS_TRADE_BOT_CACHE_DIR` to override the cache root. Generate a fingerprint for the fixed BTC dataset. The command can use the logical `data_cache/...csv` path even though the CSV is not committed in this repository: ```bash python3 fetch_data.py \ --data data_cache/hyperliquid_BTCUSDC_15m_2025-07-01_304d.csv \ --symbol BTC/USDC \ --timeframe 15m \ > /tmp/fingerprint.json CSV_PATH=$(python3 -c "import json; print(json.load(open('/tmp/fingerprint.json'))['csv_path'])") ``` ### Running Backtests The fingerprint contains the verified local cache path. Reuse that path for standard or evolution backtests: **Standard Backtest:** ```bash python3 run_backtest.py \ --data "$CSV_PATH" \ --params-file /tmp/bot_params.json \ --capital 10000 \ --output /tmp/backtest_result.json ``` **Evolution Backtest (Recommended):** ```bash python3 run_evolve_backtest.py \ --data "$CSV_PATH" \ --params-file /tmp/bot_params.json \ --segment-bars 672 \ --capital 10000 \ --output /tmp/evolve_baseline.json ``` ## Platform Integration (Optional) The factory supports optional integration with the Moss platform for verification and simulated live trading. **All operations are local-first by default.** ### 1. Pair Code Binding Bind your local environment to the platform using a Pair Code: ```bash python3 live_trade.py bind \ --platform-url "https://ai.moss.site" \ --pair-code "" \ --name "" --persona "