optimize
starsforks
How to install optimize?
marketcalls/vectorbt-backtesting-skills/optimize
$npx -y skills add marketcalls/vectorbt-backtesting-skills --skill optimizeOptimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.
SKILL.md
| 1 | Create a parameter optimization script for a VectorBT strategy. |
| 2 | |
| 3 | ## Arguments |
| 4 | |
| 5 | Parse `$ARGUMENTS` as: strategy symbol exchange interval |
| 6 | |
| 7 | - `$0` = strategy name (e.g., ema-crossover, rsi, donchian). Default: ema-crossover |
| 8 | - `$1` = symbol (e.g., SBIN, RELIANCE, NIFTY). Default: SBIN |
| 9 | - `$2` = exchange (e.g., NSE, NFO). Default: NSE |
| 10 | - `$3` = interval (e.g., D, 1h, 5m). Default: D |
| 11 | |
| 12 | If no arguments, ask the user which strategy to optimize. |
| 13 | |
| 14 | ## Instructions |
| 15 | |
| 16 | 1. Read the vectorbt-expert skill rules for reference patterns |
| 17 | 2. Create `backtesting/{strategy_name}/` directory if it doesn't exist (on-demand) |
| 18 | 3. Create a `.py` file in `backtesting/{strategy_name}/` named `{symbol}_{strategy}_optimize.py` |
| 19 | 4. The script must: |
| 20 | - Load `.env` from project root using `find_dotenv()` and fetch data via OpenAlgo `client.history()` |
| 21 | - If user provides a DuckDB path, load data directly via `duckdb.connect(path, read_only=True)`. See vectorbt-expert `rules/duckdb-data.md`. |
| 22 | - If `openalgo.ta` is not importable (standalone DuckDB), use inline `exrem()` fallback. |
| 23 | - **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" |
| 24 | - **Always use OpenAlgo ta** for specialty indicators (Supertrend, Donchian, etc.) - no TA-Lib equivalent exists |
| 25 | - Use `ta.exrem()` to clean signals (always `.fillna(False)` before exrem) |
| 26 | - Define sensible parameter ranges for the chosen strategy |
| 27 | - Use loop-based optimization to collect multiple metrics per combo |
| 28 | - Track: total_return, sharpe_ratio, max_drawdown, trade_count for each combination |
| 29 | - Use `tqdm` for progress bars |
| 30 | - **Indian delivery fees**: `fees=0.00111, fixed_fees=20` for delivery equity |
| 31 | - Find best parameters by total return AND by Sharpe ratio |
| 32 | - Print top 10 results for both criteria |
| 33 | - Generate Plotly heatmap of total return across parameter grid (`template="plotly_dark"`) |
| 34 | - Generate Plotly heatmap of Sharpe ratio across parameter grid |
| 35 | - **Fetch NIFTY benchmark** and compare best parameters vs benchmark |
| 36 | - **Print Strategy vs Benchmark comparison table** |
| 37 | - **Explain results** in plain language for normal traders |
| 38 | - Save results to CSV |
| 39 | 4. Never use icons/emojis in code or logger output |
| 40 | 5. For futures symbols, use lot-size-aware sizing: |
| 41 | - NIFTY: `min_size=65, size_granularity=65` |
| 42 | - BANKNIFTY: `min_size=30, size_granularity=30` |
| 43 | |
| 44 | ## Default Parameter Ranges |
| 45 | |
| 46 | | Strategy | Parameter 1 | Parameter 2 | |
| 47 | |----------|------------|-------------| |
| 48 | | ema-crossover | fast EMA: 5-50 | slow EMA: 10-60 | |
| 49 | | rsi | window: 5-30 | oversold: 20-40 | |
| 50 | | donchian | period: 5-50 | - | |
| 51 | | supertrend | period: 5-30 | multiplier: 1.0-5.0 | |
| 52 | |
| 53 | ## Example Usage |
| 54 | |
| 55 | `/optimize ema-crossover RELIANCE NSE D` |
| 56 | `/optimize rsi SBIN` |
People who liked this also viewed
6 picksType
- azure-costAzure cost management: query costs, forecast spending, optimize to reduce waste. WHEN: \"Azure costs\", \"Azure bill\", \"cost breakdown\", \"how much am I spending\", \"forecast spending\", \"optimizSkillsJul 2026
- pricingWhen the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increaseSkillsJul 2026
- stripe-integrationMulti-harness agentic plugin marketplace for Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, and Gemini CLISkillsJul 2026
- startup-financial-modelingMulti-harness agentic plugin marketplace for Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, and Gemini CLISkillsJul 2026
- tushare-finance专为 Claude Code 设计的 Tushare Pro 金融数据技能包，支持 220+ 个接口，用自然语言获取 A 股行情、财务数据、宏观经济等金融数据。SkillsJul 2026
- azure-pricingFetches real-time Azure retail pricing using the Azure Retail Prices API (prices.azure.com) and estimates Copilot Studio agent credit consumption. Use when the user asks about the cost of any Azure seSkillsJul 2026
