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

• 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}_optimize.py

• 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

• Never use icons/emojis in code or logger output

• 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