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optimize

该 Skill 根据策略、标的、交易所和周期参数,指导创建 VectorBT 参数优化脚本;脚本通过 OpenAlgo 获取数据,遍历参数组合,跟踪收益、夏普、回撤和交易次数,比较 NIFTY 基准,并输出热图、CSV 和交易者易懂的结果说明。

marketcalls
热度 18.34
最近更新 2026-10-19 04:12:10
分类:开发依赖:代码执行 / 读写文件 / 第三方 API应用场景:代码生成 / 财务 / 市场分析 / 数据处理 ...

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add marketcalls/vectorbt-backtesting-skills/optimize
Metadata
name
optimize
description
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