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

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