optimize

---name: optimizedescription: Optimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.argument-hint: [strategy] [symbol] [exchange] [interval]allowed-tools: Read, Write, Edit, Bash, Glob, Grep---

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
5. Never use icons/emojis in code or logger output
6. 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