Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· Developer Tools
Trading DevBox
Trading strategy development sandbox. User describes trading intent in natural language, agent writes a Python backtest strategy and returns results.
skill openclawclawhub Free
Trading strategy development sandbox. User describes trading intent in natural language, agent writes a Python backtest strategy and returns results.
β¬ 0 downloads β
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- uu-z
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Trading DevBox
Help users develop and backtest trading strategies from natural language descriptions.
When to Use
User describes a trading idea or intent (e.g. "SOL θ· 10% δΉ°ε ₯οΌζΆ¨ 30% ζ’η") User asks to write, backtest, or optimize a trading strategy User mentions keywords: ηη₯, εζ΅, backtest, strategy, trading
Workflow
Parse the user's trading intent into structured parameters: Asset (e.g. SOL, BTC, ETH) Entry condition (e.g. price drops 10%) Exit condition (e.g. take profit at 30%, stop loss at 5%) Timeframe (e.g. 1h, 4h, 1d) Confirm the parsed parameters with the user before proceeding. Generate a Python backtest strategy using backtrader: mkdir -p /tmp/trading-devbox && cat > /tmp/trading-devbox/strategy.py << 'PYEOF' import backtrader as bt import sys import json class UserStrategy(bt.Strategy): params = dict( entry_drop_pct=10, take_profit_pct=30, stop_loss_pct=5, ) def __init__(self): self.order = None self.buy_price = None def next(self): if self.order: return if not self.position: # entry: price dropped by entry_drop_pct from recent high high = max(self.data.close.get(size=20) or [self.data.close[0]]) drop = (high - self.data.close[0]) / high * 100 if drop >= self.p.entry_drop_pct: self.order = self.buy() self.buy_price = self.data.close[0] else: pnl = (self.data.close[0] - self.buy_price) / self.buy_price * 100 if pnl >= self.p.take_profit_pct or pnl <= -self.p.stop_loss_pct: self.order = self.sell() if __name__ == '__main__': print(json.dumps({"status": "ok", "message": "Strategy generated"})) PYEOF python3 /tmp/trading-devbox/strategy.py Report the result to the user in a clear format.
Response Format
Always respond in the user's language. Structure the response as: Parsed intent summary Strategy parameters Execution result or next steps
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md