Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
BacktestBot
Backtest trading strategies against historical market data with performance analytics and risk metrics
skill openclawclawhub Free
Backtest trading strategies against historical market data with performance analytics and risk metrics
⬇ 0 downloads ★ 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
- 0.0.2
Provenance
- Publisher
- CollierKing
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
BacktestBot
Backtest trading strategies against historical market data with detailed performance analytics.
What it does
BacktestBot enables you to define, test, and evaluate trading strategies using historical data, including: Strategy definition — describe strategies in natural language or structured rules (entry/exit signals, position sizing, stop losses) Historical simulation — run strategies against years of tick or daily data across equities, options, futures, and crypto Performance metrics — Sharpe ratio, max drawdown, win rate, profit factor, CAGR, and trade-level breakdown Risk analysis — value-at-risk, correlation to benchmarks, worst-case drawdown periods, and tail risk metrics Comparison — test multiple strategy variants side-by-side and rank by risk-adjusted returns
Usage
Ask your agent to backtest strategies and analyze results: "Backtest a mean reversion strategy on SPY using RSI below 30 as entry over the last 5 years" "Compare buy-and-hold vs momentum rotation across the S&P 500 sectors since 2020" "What is the max drawdown if I use a 2% trailing stop on AAPL swing trades?" "Optimize the lookback period for my moving average crossover strategy on QQQ"
Configuration
Set the following environment variables: BACKTESTBOT_API_KEY — API key for BacktestBot. Used to authenticate requests for historical OHLCV data, strategy simulations, and performance metrics. BACKTESTBOT_DATA_DIR — (optional) local directory for cached historical data. Defaults to ~/.backtestbot/data.
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md