skilly. Buy ad slot
All skills
Data Analysis · Business · Python / AGENT SKILL

backtest

marketcalls/vectorbt-backtesting-skills
3.2K installs 205 GitHub stars
0

Generate a complete Python script to backtest a trading strategy on a symbol.
Quick backtest a strategy on a symbol. Creates a complete .py script with data fetch, signals, backtest, stats, and plots.

BEFORE YOU INSTALL

Understand the trade-offs.

SECURITY REVIEW

Not yet assessed

Review the original instructions and requested permissions before installing.

No security review is available for this catalog entry yet.

SKILL QUALITY

Not yet assessed

How clearly the skill guides your agent, how complete its workflow is, and how you can check the outcome.

No quality assessment is available for this catalog entry yet.

The full skill.

Original instructions from the publisher’s SKILL.md

Create a complete VectorBT backtest script for the user.

## Arguments

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: D

If no arguments, ask the user which strategy they want.

## Instructions

1. Read the vectorbt-expert skill rules for reference patterns
2. Create `backtesting/{strategy_name}/` directory if it doesn't exist (on-demand)
3. Create a `.py` file in `backtesting/{strategy_name}/` named `{symbol}_{strategy}_backtest.py`
4. Use the matching template from `rules/assets/{strategy}/backtest.py` as the starting point
5. The script must:
   - Load `.env` from the project root using `find_dotenv()` (walks up from script dir automatically)
   - Fetch data via `client.history()` from OpenAlgo
   - If user provides a DuckDB path, load data directly via `duckdb.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`.
   - If `openalgo.ta` is not importable (standalone DuckDB), use inline `exrem()` fallback.
   - **Use OpenAlgo ta for ALL indicators by default** (EMA, SMA, RSI, MACD, BBands, ATR, ADX, STDDEV, MOM, and 90+ more) - `from openalgo import ta`
   - **Only use TA-Lib if the user explicitly says "talib"/"TA-Lib"** in their request; specialty indicators (Supertrend, Donchian, Ichimoku, HMA, KAMA, ALMA, ZLEMA, VWMA) always come from OpenAlgo ta regardless, since TA-Lib has no equivalent
   - Use `ta.exrem()` to clean duplicate signals (always `.fillna(False)` before exrem)
   - Run `vbt.Portfolio.from_signals()` with `min_size=1, size_granularity=1`
   - **Indian delivery fees**: `fees=0.00111, fixed_fees=20` for delivery equity
   - Fetch NIFTY benchmark via OpenAlgo (`symbol="NIFTY", exchange="NSE_INDEX"`)
   - Print full `pf.stats()`
   - **Print Strategy vs Benchmark comparison table** (Total Return, Sharpe, Sortino, Max DD, Win Rate, Trades, Profit Factor)
   - **Explain the backtest report** in plain language for normal traders
   - Generate the OpenStatz interactive dashboard tearsheet via `ostz.dashboard(...)` if `openstatz` is available - a self-contained offline HTML file, no server needed (always use OpenStatz, never QuantStats; never the legacy `ostz.reports.html` static report). **Set `strategy_returns.name` (e.g. `"EMA 20/50 Crossover - SBIN"`) and `benchmark.name` before calling `dashboard()`** - that name, not the `title=` argument, is what the tearsheet shows as the strategy header/column/legend (see the openstatz-tearsheet rule)
   - Plot equity curve + drawdown using Plotly (`template="plotly_dark"`)
   - Export trades to CSV
5. Never use icons/emojis in code or logger output
6. For futures symbols (NIFTY, BANKNIFTY), use lot-size-aware sizing:
   - NIFTY: `min_size=65, size_granularity=65` (effective 31 Dec 2025)
   - BANKNIFTY: `min_size=30, size_granularity=30`
   - Use `fees=0.00018, fixed_fees=20` for F&O futures

## Available Strategies

| 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` |

## Benchmark Rules

- Default: NIFTY 50 via OpenAlgo (`symbol="NIFTY", exchange="NSE_INDEX"`)
- If user specifies a different benchmark, use that instead
- For yfinance: use `^NSEI` for India, `^GSPC` (S&P 500) for US markets
- Always compare: Total Return, Sharpe, Sortino, Max Drawdown

## Example Usage

`/backtest ema-crossover RELIANCE NSE D`
`/backtest rsi SBIN`
`/backtest supertrend NIFTY NFO 5m`