marketcalls/vectorbt-backtesting-skills
6 skills10.9k installs1.1k starsGitHub
Install
npx skills add https://github.com/marketcalls/vectorbt-backtesting-skillsSkills in this repo
1BacktestThe backtest skill creates complete VectorBT backtest scripts from strategy keywords, symbols, exchanges, and intervals passed as arguments. It reads vectorbt-expert rules, creates backtesting/{strategy}/ directories on demand, and starts from rules/assets templates for ema-crossover, rsi, donchian, supertrend, macd, sda2, momentum, dual-momentum, buy-hold, and rsi-accumulation strategies. Scripts load .env via find_dotenv, fetch data through OpenAlgo client.history, or read DuckDB paths with Historify versus custom table detection per duckdb-data.md. Indicators use TA-Lib for standard metrics and OpenAlgo ta for specialty ones, always cleaning signals with ta.exrem and fillna(False). Portfolio runs use from_signals with min_size and size_granularity, Indian delivery fees 0.00111 plus fixed_fees 20, and NIFTY benchmark via NSE_INDEX by default. Output prints pf.stats, a strategy versus benchmark table, plain-language report, optional QuantStats HTML, Plotly equity and drawdown charts, and CSV trade export without emojis in code or logs. Futures symbols apply lot-size-aware sizing for NIFTY and BANKNIFTY.2.5kinstalls2Vectorbt ExpertPython with vectorbt pandas numpy plotly Data sources OpenAlgo Indian markets DuckDB direct database yfinance US Global CCXT Crypto custom providers DuckDB support supports both custom DuckDB and OpenAlgo Historify format API keys loaded from single root env via python dotenv find_dotenv never hardcode keys Technical indicators TA Lib ALWAYS never use VectorBT built in indicators Specialty indicators openalgo ta for Supertrend Donchian Ichimoku HMA KAMA ALMA ZLEMA VWMA Signal cleaning openalgo ta for exrem crossover crossunder flip Fee model Indian market standard STT statutory charges Rs 20 order Benchmark NIFTY 50 via OpenAlgo NSE_INDEX by default Charts Plotly with template plotly_dark Environment variables loaded from single env at project root via find_dotenv walks up from script dir Scripts go in backtesting strategy_name directories created on demand not pre created Never use icons emojis in code or logger output 1 ALWAYS use TA Lib for ALL technical indicators EMA SMA RSI MACD BBANDS ATR ADX STDDEV MOM NEVER use vbt MA run vbt RSI run or any VectorBT built in2.2kinstalls3OptimizeThe optimize skill scaffolds VectorBT parameter optimization scripts for Indian market strategies. It parses strategy, symbol, exchange, and interval arguments, creates backtesting directories, and writes optimize.py files loading data via OpenAlgo client.history() or DuckDB. Scripts use TA-Lib indicators, OpenAlgo ta signals, ta.exrem() with fillna(False), loop-based combo testing with tqdm, and Indian delivery fees fees=0.00111 plus fixed_fees=20. Each combo records total_return, sharpe_ratio, max_drawdown, and trade_count, prints top ten by return and Sharpe, saves CSV, renders Plotly dark heatmaps, fetches NIFTY benchmark, and explains results in plain language for traders tuning ema-crossover, rsi, donchian, or supertrend strategies. Loop-based VectorBT optimization with tqdm. OpenAlgo history() or DuckDB data load. TA-Lib plus OpenAlgo ta with exrem cleaning. Tracks return, Sharpe, drawdown, trade_count. Plotly heatmaps and NIFTY benchmark table. VectorBT optimization scaffolder for Indian equities. User asks to optimize VectorBT strategy parameters.1.7kinstalls4Strategy CompareThis skill generates a strategy comparison script that backtests multiple trading strategies (e.g. ema-crossover, rsi, donchian, supertrend) on a single symbol and produces a side-by-side stats table with key metrics, NIFTY benchmark comparison, and overlaid equity curve plots. It fetches data via OpenAlgo or DuckDB, applies TA-Lib indicators with signal cleaning via exrem(), calculates Indian delivery fees, and explains which strategy performed best. The script outputs CSV results and Plotly visualizations to help traders evaluate strategy relative performance.1.6kinstalls5Quick Statsquick-stats is an agent skill from marketcalls/vectorbt-backtesting-skills that 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. Generate a quick inline backtest and print stats. Do NOT create a file - output code directly for the user to run or execute in a notebook. ## Arguments - `$0` = symbol (e.g., SBIN, RELIANCE). Default: SBIN - `$1` = exchange. Default: NSE - `$2` = interval. Default: D ## Instructions Generate a single code block the user can paste into a Jupyte Developers invoke quick-stats during ship/testing work for testing & qa tasks. The skill documents triggers, prerequisites, and step-by-step workflows grounded in SKILL.md. Compatible with Claude Code, Cursor, and Codex agent runtimes that load marketplace skills.1.5kinstalls6SetupThe setup skill bootstraps a Python backtesting environment for VectorBT plus OpenAlgo across macOS, Linux, and Windows. It detects OS, creates a venv, installs TA-Lib C library prerequisites via brew or source compile, then pip installs openalgo, vectorbt, plotly, ta-lib, pandas, duckdb, quantstats, and ccxt. It creates a backtesting/ folder, configures .env for OpenAlgo API keys, DuckDB paths, Historify DB, and optional crypto exchange keys based on user market selection, and verifies imports. Important notes forbid global installs, require sudo on Linux TA-Lib builds, and never commit .env secrets. Use when initializing VectorBT backtesting workspaces for Indian, US, or crypto markets. Agents should follow the SKILL.md workflow end to end, grounding classification in documented commands, file paths, prerequisites, and troubleshooting notes rather than improvising steps. Set up Python VectorBT backtesting environment with TA-Lib, OpenAlgo, DuckDB, and market data dependencies. Invoke when User sets up VectorBT backtesting environment, installs ta-lib, or configures OpenAlgo DuckDB. Best for Quant developers initializing VectorBT backtesting for Indian, US, or crypto markets. Exp.1.4kinstalls