An AI-powered trading assistant that converts natural language into backtested, deployable algorithmic trading bots across multiple asset classes.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install botspot-trading
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install botspot-trading using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is BotSpot Trading Strategy Builder?
BotSpot Trading Strategy Builder is a sophisticated integration for AI agents that bridges the gap between trading ideas and execution. By leveraging Openclaw Skills, this tool allows users to describe complex trading logic in plain English, which the AI then transforms into functional code. It connects directly to the BotSpot MCP server to provide a professional-grade environment for strategy generation, rigorous backtesting on historical data, and seamless deployment to major brokerages.
Whether you are targeting stocks, options, crypto, or futures, this skill provides the analytical horsepower to validate ideas before risking capital. It emphasizes data-driven decision-making by providing detailed tearsheets, equity curves, and SQL-based trade analysis, making it an essential addition to any developer's Openclaw Skills library.
BotSpot Trading Strategy Builder Use Cases
- Automating technical analysis strategies like moving average crossovers or RSI-based entries.
- Backtesting multi-year historical data to validate the Sharpe ratio and drawdown of a new idea.
- Deploying crypto trend-following bots across exchanges like Coinbase or Binance.
- Managing risk for options credit spreads using automated ATR position sizing.
- Browsing and analyzing top-performing community bots in the public marketplace.
How BotSpot Trading Strategy Builder Works
- Clarification: The AI agent asks for specific parameters including asset class, timeframe, and risk management rules.
- Generation: Using the generate_strategy tool, the AI writes the underlying code for the trading bot.
- Backtesting: The user initiates a backtest (typically 1-2 years) to simulate performance on real historical data.
- Monitoring: The agent tracks progress via backtest_status and retrieves artifacts like CSV trade logs and visual charts.
- Analysis: Performance metrics including total return and win rate are extracted using SQL queries on the trade data.
- Refinement: Based on results, the strategy is iteratively refined or optimized.
- Deployment: Once validated, the bot is deployed live to a connected broker such as Interactive Brokers or Charles Schwab.
BotSpot Trading Strategy Builder Setup
To integrate this capability into your environment, add the custom MCP connector. This Openclaw Skills compatible server uses OAuth for secure authentication.
# Connector Configuration
URL: https://mcp.botspot.trade/mcp
# Verification
# Use the get_account_status tool to verify your remaining limits
Navigate to your agent's Settings > Connectors and add the URL provided above to start building.
BotSpot Trading Strategy Builder Data Schema & Taxonomy
The skill organizes trading data into structured artifacts and series for deep analysis:
| Data Type | Description | Access Method |
|---|---|---|
| Strategy Code | Generated Python/Pine-like logic | generate_strategy |
| Trade Logs | Raw execution data in CSV format | query_csv |
| Visuals | Equity curves and performance charts | get_backtest_visuals |
| Metrics | Sharpe ratio, drawdown, and win rate | get_backtest_artifact |
| Account Info | Usage limits and subscription tier | get_account_status |
BotSpot Trading Strategy Builder Advanced Features
- Multi-Broker Support: Deploy a single strategy across 10+ brokers including Alpaca, Tradier, and NinjaTrader.
- SQL Analytics: Perform complex analysis on backtest results using the query_csv tool to find specific trade patterns.
- Marketplace Integration: Directly pull and analyze public bots from the community to benchmark your own performance.
- Dynamic Refinement: Use the refine_strategy hook to update logic based on backtest failures without starting from scratch.
- Real-time Visualization: Stream backtest chart series to visualize the equity curve development.
SKILL.md
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