Optimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.

Create a parameter optimization script for a VectorBT strategy. Arguments Parse $ARGUMENTS as: strategy symbol exchange interval $0 = strategy name (e.g., ema crossover, rsi, donchian). Default: ema crossover $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 to optimize. 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} optimize.py 4. The script must: Load .env from project root using find dotenv() and fetch data via OpenAlgo client.history() If user provides a DuckDB path, load data directly via duckdb.connect(path, read only=True) . 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 (never VectorBT built in). Only switch to TA Lib if the user explicitly says "talib"/"TA Lib" Always use OpenAlgo ta for specialty indicators (Supertrend, Donchian, etc.) no TA Lib equivalent exists Use ta.exrem() to clean signals (always .fillna(False) before exrem) Define sensible parameter ranges for the chosen strategy Use loop based optimization to collect multiple metrics per combo Track: total return, sharpe ratio, max drawdown, trade count for each combination Use tqdm for progress bars Indian delivery fees : fees=0.00111, fixed fees=20 for delivery equity Find best parameters by total return AND by Sharpe ratio Print top 10 results for both criteria Generate Plotly heatmap of total return across parameter grid ( template="plotly dark" ) Generate Plotly heatmap of Sharpe ratio across parameter grid Fetch NIFTY benchmark and compare best parameters vs benchmark Print Strategy vs Benchmark comparison table Explain results in plain language for normal traders Save results to CSV 4. Never use icons/emojis in code or logger output 5. For futures symbols, use lot size aware sizing: NIFTY: min size=65, size granularity=65 BANKNIFTY: min size=30, size granularity=30 Default Parameter Ranges Strategy Parameter 1 Parameter 2 ema crossover fast EMA: 5 50 slow EMA: 10 60 rsi window: 5 30 oversold: 20 40 donchian period: 5 50 supertrend period: 5 30 multiplier: 1.0 5.0 Example Usage /optimize ema crossover RELIANCE NSE D /optimize rsi SBIN