spb/tools-spboucher-ai

1{2  "slug": "vectorbt",3  "name": "vectorbt",4  "aliases": [5    "VectorBT",6    "vectorbtpro"7  ],8  "vendor": {9    "name": "Oleg Polakow",10    "slug": "oleg-polakow",11    "website": "https://vectorbt.dev"12  },13  "homepage_url": "https://vectorbt.dev/",14  "docs_url": "https://vectorbt.dev/",15  "repo_url": "https://github.com/polakowo/vectorbt",16  "license_url": "https://github.com/polakowo/vectorbt/blob/master/LICENSE.md",17  "product_type": "library",18  "product_status": "active",19  "summary": "Python library for vectorized backtesting and portfolio analysis built on pandas, NumPy and Numba, with a commercial PRO edition",20  "description": "vectorbt is a Python library by Oleg Polakow for backtesting trading strategies as array operations rather than event loops. Signals, orders and portfolio values are represented as pandas and NumPy arrays and compiled with Numba, which makes it practical to test thousands of parameter combinations in one pass and to inspect results with interactive Plotly charts. It covers indicator construction, signal generation, portfolio simulation from signals or orders, and performance statistics such as drawdowns and trade records. The open-source edition is distributed under Apache-2.0 with a Commons Clause that forbids selling the software, and a separately licensed vectorbt PRO continues development with additional features. The vectorized model trades realism for speed: path-dependent logic and complex order handling are harder to express than in event-driven engines.",21  "capabilities": [22    "Vectorized simulation of many parameter sets at once",23    "Portfolio construction from signals, orders or holdings",24    "Built-in indicators and an indicator factory",25    "Trade, drawdown and return statistics",26    "Interactive Plotly visualizations",27    "Numba-accelerated computation on pandas data"28  ],29  "workflows": [30    "Sweep moving-average windows across a universe and compare equity curves",31    "Simulate a signal-based portfolio and export trade records to pandas"32  ],33  "advantages": [34    "Fast parameter sweeps on a single machine",35    "Stays within the pandas and NumPy ecosystem"36  ],37  "limitations": [38    "Vectorized model makes path-dependent strategies awkward",39    "Open-source license includes a Commons Clause restricting resale",40    "Newer features land in the paid PRO edition"41  ],42  "prerequisites": [43    "Python with pandas, NumPy and Numba",44    "Price data from an external source"45  ],46  "audiences": [47    "Quantitative researchers",48    "Python-savvy retail traders"49  ],50  "alternatives": [51    "zipline-reloaded",52    "quantconnect",53    "pandas"54  ],55  "comparison_notes": "Zipline Reloaded and QuantConnect's LEAN are event-driven engines that step through time; vectorbt computes whole strategies as array operations, which is faster for sweeps but less faithful for order-level logic.",56  "categories": [57    {58      "slug": "financial-analysis-investing",59      "primary": true60    }61  ],62  "tags": [63    "backtesting",64    "python",65    "pandas",66    "numba",67    "open-source",68    "time-series"69  ],70  "use_cases": [71    "backtest-trading-strategies",72    "analyze-intraday-data"73  ],74  "operating_systems": [75    "macos",76    "linux",77    "windows"78  ],79  "execution_modes": [80    "local"81  ],82  "interfaces": [83    "library"84  ],85  "integrations": [86    "pandas",87    "NumPy",88    "Numba",89    "Plotly",90    "yfinance",91    "ccxt"92  ],93  "data_formats": [94    "pandas DataFrame",95    "CSV",96    "HDF5"97  ],98  "languages": [99    "Python"100  ],101  "source_model": "source-available",102  "license_spdx": "Apache-2.0 WITH Commons-Clause",103  "pricing_model": [104    "free",105    "paid"106  ],107  "self_hostable": true,108  "sources": [109    {110      "url": "https://vectorbt.dev/",111      "type": "official_site",112      "supports": [113        "name",114        "summary",115        "capabilities"116      ],117      "checked_at": "2026-11-02",118      "title": "vectorbt documentation"119    },120    {121      "url": "https://github.com/polakowo/vectorbt",122      "type": "repository",123      "supports": [124        "product_status",125        "languages"126      ],127      "checked_at": "2026-11-02",128      "title": "polakowo/vectorbt"129    },130    {131      "url": "https://github.com/polakowo/vectorbt/blob/master/LICENSE.md",132      "type": "license_page",133      "supports": [134        "license_spdx",135        "source_model"136      ],137      "checked_at": "2026-11-02",138      "title": "LICENSE.md — Apache 2.0 with Commons Clause"139    },140    {141      "url": "https://vectorbt.pro/",142      "type": "official_site",143      "supports": [144        "pricing_model"145      ],146      "checked_at": "2026-11-02",147      "title": "vectorbt PRO"148    }149  ],150  "confidence": "high",151  "depth": "standard",152  "deployment_models": [],153  "featured": false,154  "discovery_method": "editorial-research",155  "editorial_status": "reviewed",156  "visibility": "public",157  "author": "batch-F",158  "pricing_url": "https://vectorbt.pro/"159}160