optimize— agent skill

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 TA-Lib for ALL indicators (never VectorBT built-in)
• Use OpenAlgo ta for specialty indicators (Supertrend, Donchian, etc.)
• 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

1. Never use icons/emojis in code or logger output
2. 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

Example Usage

/optimize ema-crossover RELIANCE NSE D /optimize rsi SBIN