Vectorbt-backtesting-skills optimize

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
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