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Quickly fetch data and print key backtest stats for a symbol with a default EMA crossover strategy. No file creation needed - runs inline in a notebook cell or prints to console.
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| name | quick-stats |
| description | Quickly fetch data and print key backtest stats for a symbol with a default EMA crossover strategy. No file creation needed - runs inline in a notebook cell or prints to console. |
| argument-hint | [symbol] [exchange] [interval] |
| allowed-tools | Read, Bash, Glob, Grep |
Generate a quick inline backtest and print stats. Do NOT create a file - output code directly for the user to run or execute in a notebook.
$0 = symbol (e.g., SBIN, RELIANCE). Default: SBIN$1 = exchange. Default: NSE$2 = interval. Default: DGenerate a single code block the user can paste into a Jupyter cell or run as a script. The code must:
ta.exrem() (always .fillna(False) before exrem)fees=0.00111, fixed_fees=20symbol="NIFTY", exchange="NSE_INDEX")Symbol: SBIN | Exchange: NSE | Interval: D
Strategy: EMA 10/20 Crossover
Period: 2023-01-01 to 2026-02-27
Fees: Delivery Equity (0.111% + Rs 20/order)
-------------------------------------------
Total Return: 45.23%
Sharpe Ratio: 1.45
Sortino Ratio: 2.01
Max Drawdown: -12.34%
Win Rate: 42.5%
Profit Factor: 1.67
Total Trades: 28
-------------------------------------------
Benchmark (NIFTY): 32.10%
Alpha: +13.13%
template="plotly_dark")/quick-stats RELIANCE
/quick-stats HDFCBANK NSE 1h
Quick backtest a strategy on a symbol. Creates a complete .py script with data fetch, signals, backtest, stats, and plots.
VectorBT backtesting expert. Use when user asks to backtest strategies, create entry/exit signals, analyze portfolio performance, optimize parameters, fetch historical data, use VectorBT/vectorbt, compare strategies, position sizing, equity curves, drawdown charts, or trade analysis. Also triggers for openalgo.ta helpers (exrem, crossover, crossunder, flip, donchian, supertrend).
Optimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.
Set up the Python backtesting environment. Detects OS, creates virtual environment, installs dependencies (openalgo, ta-lib, vectorbt, plotly), and creates the backtesting folder structure.
Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.