Generate complete VectorBT backtesting scripts with data fetch, signals, stats, and plots.
Works with
Supports 10+ pre-built strategies (EMA crossover, RSI, Donchian, Supertrend, MACD, SDA2, momentum, and more) with template-based script generation
Fetches data from OpenAlgo API or loads directly from DuckDB; auto-detects Historify vs custom formats
Uses TA-Lib for standard indicators and OpenAlgo ta for specialty indicators (Supertrend, Donchian, Ichimoku); includes signal deduplication via ex
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionbacktestExecute the skills CLI command in your project's root directory to begin installation:
Fetches backtest from marketcalls/vectorbt-backtesting-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate backtest. Access via /backtest in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create a complete VectorBT backtest script for the user.
Parse $ARGUMENTS as: strategy symbol exchange interval
$0 = strategy name (e.g., ema-crossover, rsi, donchian, supertrend, macd, sda2, momentum)$1 = symbol (e.g., SBIN, RELIANCE, NIFTY). Default: SBIN$2 = exchange (e.g., NSE, NFO). Default: NSE$3 = interval (e.g., D, 1h, 5m). Default: DIf no arguments, ask the user which strategy they want.
backtesting/{strategy_name}/ directory if it doesn't exist (on-demand).py file in backtesting/{strategy_name}/ named {symbol}_{strategy}_backtest.pyrules/assets/{strategy}/backtest.py as the starting point.env from the project root using find_dotenv() (walks up from script dir automatically)client.history() from OpenAlgoduckdb.connect(path, read_only=True) instead of OpenAlgo API. Auto-detect format: Historify (market_data table, epoch timestamps) vs custom (ohlcv table, date+time). See vectorbt-expert rules/duckdb-data.md.openalgo.ta is not importable (standalone DuckDB), use inline exrem() fallback.ta.exrem() to clean duplicate signals (always .fillna(False) before exrem)vbt.Portfolio.from_signals() with min_size=1, size_granularity=1fees=0.00111, fixed_fees=20 for delivery equitysymbol="NIFTY", exchange="NSE_INDEX")pf.stats()quantstats is availabletemplate="plotly_dark")min_size=65, size_granularity=65 (effective 31 Dec 2025)min_size=30, size_granularity=30fees=0.00018, fixed_fees=20 for F&O futures| Strategy | Keyword | Template |
|---|---|---|
| EMA Crossover | ema-crossover |
assets/ema_crossover/backtest.py |
| RSI | rsi |
assets/rsi/backtest.py |
| Donchian Channel | donchian |
assets/donchian/backtest.py |
| Supertrend | supertrend |
assets/supertrend/backtest.py |
| MACD Breakout | macd |
assets/macd/backtest.py |
| SDA2 | sda2 |
assets/sda2/backtest.py |
| Momentum | momentum |
assets/momentum/backtest.py |
| Dual Momentum | dual-momentum |
assets/dual_momentum/backtest.py |
| Buy & Hold | buy-hold |
assets/buy_hold/backtest.py |
| RSI Accumulation | rsi-accumulation |
assets/rsi_accumulation/backtest.py |
symbol="NIFTY", exchange="NSE_INDEX")^NSEI for India, ^GSPC (S&P 500) for US markets/backtest ema-crossover RELIANCE NSE D
/backtest rsi SBIN
/backtest supertrend NIFTY NFO 5m
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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I recommend backtest for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
backtest fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
backtest is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added backtest from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
backtest reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend backtest for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for backtest matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: backtest is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in backtest — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
backtest has been reliable in day-to-day use. Documentation quality is above average for community skills.
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