backtest

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

Read the vectorbt-expert skill rules for reference patterns

Create backtesting/{strategy_name}/ directory if it doesn't exist (on-demand)

Create a .py file in backtesting/{strategy_name}/ named {symbol}_{strategy}_backtest.py

Use the matching template from rules/assets/{strategy}/backtest.py as the starting point

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)

Plot equity curve + drawdown using Plotly (template="plotly_dark")

Export trades to CSV

Never use icons/emojis in code or logger output

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