marketcalls/strategy-compare
Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.
Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.
npx skills add https://github.com/marketcalls/vectorbt-backtesting-skills/tree/main/skills/strategy-compareCompare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.
This repo contains 5 individual skills — each has its own dedicated page.
Quick backtest a strategy on a symbol. Creates a complete .py script with data fetch, signals, backtest, stats, and plots.
Optimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.
Quickly fetch data and print key backtest stats for a symbol with a default EMA crossover strategy. No file creation needed - runs inline in a notebook cell or prints to console.
Set up the Python backtesting environment. Detects OS, creates virtual environment, installs dependencies (openalgo, ta-lib, vectorbt, plotly), and creates the backtesting folder structure.
VectorBT backtesting expert. Use when user asks to backtest strategies, create entry/exit signals, analyze portfolio performance, optimize parameters, fetch historical data, use VectorBT/vectorbt, compare strategies, position sizing, equity curves, drawdown charts, or trade analysis. Also triggers for openalgo.ta helpers (exrem, crossover, crossunder, flip, donchian, supertrend).
Skills for agents such as Codex, Antigravity, and Claude
Analyze decision patterns and communication styles with this operational framework based on Donald Trump's negotiation models and public records.
Build a weekly cadence of customer touchpoints using Opportunity Solution Trees, assumption mapping, and interview snapshots. Use when the user mentions "continuous discovery", "opportunity solution tree", "weekly interviews", "assumption testing", "discovery habits", "product trio", or "outcome-based roadmap". Also trigger when setting up regular customer feedback loops, prioritizing which experiments to run, or connecting discovery insights to delivery work. Covers experience mapping, co-creation, and prioritizing opportunities. For interview technique, see mom-test. For team structure, see inspired-product.
Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.
Codex skill: topview-skill
Everything needed to grow a garden — validators, CI, MCP server, Obsidian plugin, and Claude skill