Trading Researcher (The Scholar)

This skill allows OpenClaw to autonomously learn new trading strategies, convert them into executable Python code, and validate them.

🧠 Core Capabilities

1. Research (/research learn)

Searches the web for high-quality resources on a specific trading strategy (e.g., "Vegas Tunnel", "Turtle Trading").

• Action: Uses web_search to find tutorials/PDFs.
• Action: Uses web_fetch to extract the core logic (Entry, Exit, Risk).
• Output: A structured summary of the strategy's rules.

2. Codify (/research codify)

Converts the researched logic into a standardized Python class compatible with the Babata Bot framework.

• Input: The logic summary from step 1.
• Output: A class StrategyName(BaseStrategy): ... Python file.

3. Backtest (/research backtest)

Runs a historical simulation of the codified strategy.

• Input: The Python strategy file + Timeframe + Duration (e.g., "3 months").
• Action: Fetches deep history from MT5.
• Output: Win rate, Drawdown, Profit Factor.

🚀 Usage Examples

# 1. Learn a new strategy
/research learn "Naked K Price Action Pinbar Strategy"

# 2. Turn it into code
/research codify "Pinbar Strategy"

# 3. Prove it works (3 months)
/research backtest "Pinbar Strategy" --duration "90d"

🛠️ Integration with Babata Bot

Strategies approved by the backtest are moved to the strategies/ folder in the main bot, allowing the "Meta-Strategy" selector to use them.