VectorBT + OpenAlgo Python回测环境一键配置指南 | 量化交易开发

setup by marketcalls/vectorbt-backtesting-skills

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安装命令

npx skills add https://github.com/marketcalls/vectorbt-backtesting-skills --skill setup

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🇨🇳中文介绍

为 VectorBT + OpenAlgo 设置完整的 Python 回测环境。

参数

$0 = Python 版本（可选，默认：python3）。例如：python3.12，python3.13

步骤

步骤 1：检测操作系统

运行以下命令来检测操作系统：

uname -s 2>/dev/null || echo "Windows"

映射结果：

Darwin = macOS
Linux = Linux
MINGW* 或或 = Windows

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步骤 2：创建虚拟环境

在当前工作目录中创建一个 Python 虚拟环境：

macOS / Linux：

python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip

python -m venv venv
venv\Scripts\activate
pip install --upgrade pip

如果用户指定了 Python 版本参数，则使用该版本而不是 python3：

$PYTHON_VERSION -m venv venv

步骤 3：安装 TA-Lib 系统依赖

TA-Lib 需要在 pip install ta-lib 之前在操作系统级别安装一个 C 库。

brew install ta-lib

Linux (Debian/Ubuntu)：

sudo apt-get update
sudo apt-get install -y build-essential wget
wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar -xzf ta-lib-0.4.0-src.tar.gz
cd ta-lib/
./configure --prefix=/usr
make
sudo make install
cd ..
rm -rf ta-lib ta-lib-0.4.0-src.tar.gz

Linux (RHEL/CentOS/Fedora)：

sudo yum groupinstall -y "Development Tools"
wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar -xzf ta-lib-0.4.0-src.tar.gz
cd ta-lib/
./configure --prefix=/usr
make
sudo make install
cd ..
rm -rf ta-lib ta-lib-0.4.0-src.tar.gz

pip install ta-lib

如果失败，请从 https://github.com/cgohlke/talib-build/releases 下载相应的 .whl 文件并使用以下命令安装：

pip install TA_Lib-0.4.32-cp312-cp312-win_amd64.whl

步骤 4：安装 Python 包

安装所有必需的包（最新版本）：

pip install openalgo vectorbt plotly anywidget nbformat ta-lib pandas numpy yfinance python-dotenv tqdm scipy numba nbformat ipywidgets quantstats ccxt duckdb psutil

步骤 5：创建回测文件夹

仅创建顶层的回测目录。策略子文件夹是在生成回测脚本时（通过 /backtest 技能）按需创建的。

mkdir -p backtesting

请不要预先创建策略子文件夹。

步骤 6：配置 .env 文件

6a. 检查项目根目录下是否存在 .env.sample。 如果存在，则将其用作模板。

6b. 使用 AskUserQuestion 询问用户将回测哪些市场：

印度市场 (OpenAlgo) — 需要 OpenAlgo API 密钥
印度市场 (DuckDB) — 直接数据库加载，无需 API
美国市场 (yfinance) — 无需 API 密钥
加密货币市场 (CCXT) — 可选 API 密钥用于私有数据

6c. 如果用户选择了印度市场，询问他们的 OpenAlgo API 密钥：

询问："输入您的 OpenAlgo API 密钥（来自 OpenAlgo 仪表板）："
如果用户提供了密钥，将其存储在 .env 中
如果用户跳过，则写入一个占位符

6d. 如果用户选择了印度市场 (DuckDB)，询问 DuckDB 数据库路径：

询问："输入您的 DuckDB 数据库文件路径（例如，D:/data/market_data.duckdb）："
自动检测格式：如果数据库有包含 symbol, exchange, interval, timestamp 列的 market_data 表，则为 OpenAlgo Historify 格式（存储为 HISTORIFY_DB_PATH）。否则存储为 DUCKDB_PATH。
如果用户也有 OpenAlgo Historify，询问："这是 OpenAlgo Historify 数据库吗？(y/n)"

6e. 如果用户选择了加密货币市场，询问他们是否要配置交易所 API 密钥：

询问："您有用于身份验证数据的交易所 API 密钥吗？（可选 — 公开的 OHLCV 数据无需密钥即可使用）"
如果是，询问 API 密钥和密钥，存储在 .env 中
如果否，则在 .env 中留空

6f. 在项目根目录下写入 .env 文件。 使用此模板，填写用户提供的任何密钥/路径：

# Indian Markets (OpenAlgo)
OPENALGO_API_KEY={user_provided_key or "your_openalgo_api_key_here"}
OPENALGO_HOST=http://127.0.0.1:5000

# DuckDB Data Sources (direct database loading - fastest)
# Custom DuckDB (user-created with OHLCV table)
DUCKDB_PATH={user_provided_path or ""}
# OpenAlgo Historify DuckDB (market_data table with epoch timestamps)
HISTORIFY_DB_PATH={user_provided_path or ""}

# Crypto Markets (CCXT) - Optional
CRYPTO_API_KEY={user_provided_key or ""}
CRYPTO_SECRET_KEY={user_provided_key or ""}

6g. 将 .env 添加到 .gitignore（如果存在的话）（切勿提交机密信息）：

脚本使用 find_dotenv() 自动向上查找单个根目录下的 .env，因此子目录中不需要副本。

grep -qxF '.env' .gitignore 2>/dev/null || echo '.env' >> .gitignore

步骤 7：验证安装

运行快速验证：

python -c "
import vectorbt as vbt
import openalgo
import plotly
import talib
import duckdb
import anywidget
import nbformat
import quantstats as qs
from dotenv import load_dotenv
print('All packages installed successfully')
print(f'  vectorbt: {vbt.__version__}')
print(f'  plotly: {plotly.__version__}')
print(f'  duckdb: {duckdb.__version__}')
print(f'  nbformat: {nbformat.__version__}')
print(f'  quantstats: {qs.__version__}')
print(f'  TA-Lib: available')
print(f'  python-dotenv: available')
"

如果 TA-Lib 导入失败，请通知用户需要先安装 C 库（参见步骤 3）。

步骤 8：打印摘要

打印摘要，显示：

检测到的操作系统
使用的 Python 版本
虚拟环境路径
已安装的包及其版本
创建的回测文件夹（策略子文件夹由 /backtest 按需创建）
.env 文件状态（已配置密钥 / 占位符）— 项目根目录下的单个文件
提醒："如果您跳过了配置，请运行 cp .env.sample .env 并填写 API 密钥"

切勿全局安装包 — 始终使用虚拟环境
在 Linux 上安装 TA-Lib C 库需要管理员/sudo 权限
在 macOS 上，必须安装 Homebrew 才能使用 brew install ta-lib
如果用户已有虚拟环境，请在创建新环境前询问
backtesting/ 文件夹是所有生成的回测脚本的保存位置
切勿提交 .env 文件 — 它们包含机密信息。始终使用 .gitignore。
如果用户在设置过程中提供了 API 密钥，请直接将其写入 .env — 不要要求用户手动编辑文件
python-dotenv 已包含在 pip 安装中，所有脚本必须使用它来加载 .env

🇺🇸English

Set up the complete Python backtesting environment for VectorBT + OpenAlgo.

Arguments

$0 = Python version (optional, default: python3). Examples: python3.12, python3.13

Steps

Step 1: Detect Operating System

Run the following to detect the OS:

uname -s 2>/dev/null || echo "Windows"

Map the result:

Darwin = macOS
Linux = Linux
MINGW* or CYGWIN* or Windows = Windows

Print the detected OS to the user.

Step 2: Create Virtual Environment

Create a Python virtual environment in the current working directory:

macOS / Linux:

python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip

Windows:

python -m venv venv
venv\Scripts\activate
pip install --upgrade pip

If the user specified a Python version argument, use that instead of python3:

$PYTHON_VERSION -m venv venv

Step 3: Install TA-Lib System Dependency

TA-Lib requires a C library installed at the OS level BEFORE pip install ta-lib.

macOS:

brew install ta-lib

Linux (Debian/Ubuntu):

sudo apt-get update
sudo apt-get install -y build-essential wget
wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar -xzf ta-lib-0.4.0-src.tar.gz
cd ta-lib/
./configure --prefix=/usr
make
sudo make install
cd ..
rm -rf ta-lib ta-lib-0.4.0-src.tar.gz

Linux (RHEL/CentOS/Fedora):

sudo yum groupinstall -y "Development Tools"
wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar -xzf ta-lib-0.4.0-src.tar.gz
cd ta-lib/
./configure --prefix=/usr
make
sudo make install
cd ..
rm -rf ta-lib ta-lib-0.4.0-src.tar.gz

Windows:

pip install ta-lib

If that fails, download the appropriate .whl file from https://github.com/cgohlke/talib-build/releases and install with:

pip install TA_Lib-0.4.32-cp312-cp312-win_amd64.whl

Step 4: Install Python Packages

Install all required packages (latest versions):

pip install openalgo vectorbt plotly anywidget nbformat ta-lib pandas numpy yfinance python-dotenv tqdm scipy numba nbformat ipywidgets quantstats ccxt duckdb psutil

Step 5: Create Backtesting Folder

Create only the top-level backtesting directory. Strategy subfolders are created on-demand when a backtest script is generated (by the /backtest skill).

mkdir -p backtesting

Do NOT pre-create strategy subfolders.

Step 6: Configure .env File

6a. Check if.env.sample exists at the project root. If it does, use it as a template.

6b. Ask the user which markets they will be backtesting using AskUserQuestion:

Indian Markets (OpenAlgo) — requires OpenAlgo API key
Indian Markets (DuckDB) — direct database loading, no API needed
US Markets (yfinance) — no API key needed
Crypto Markets (CCXT) — optional API key for private data

6c. If the user selected Indian Markets , ask for their OpenAlgo API key:

Ask: "Enter your OpenAlgo API key (from the OpenAlgo dashboard):"
If the user provides a key, store it in .env
If the user skips, write a placeholder

6d. If the user selected Indian Markets (DuckDB) , ask for the DuckDB database path:

Ask: "Enter the path to your DuckDB database file (e.g., D:/data/market_data.duckdb):"
Auto-detect format: If the database has a market_data table with symbol, exchange, interval, timestamp columns, it is OpenAlgo Historify format (store as HISTORIFY_DB_PATH). Otherwise store as DUCKDB_PATH.
If the user also has OpenAlgo Historify, ask: "Is this an OpenAlgo Historify database? (y/n)"

6e. If the user selected Crypto Markets , ask if they want to configure exchange API keys:

Ask: "Do you have exchange API keys for authenticated data? (Optional — public OHLCV data works without keys)"
If yes, ask for API key and secret key, store in .env
If no, leave them blank in .env

6f. Write the.env file in the project root directory. Use this template, filling in any keys/paths the user provided:

# Indian Markets (OpenAlgo)
OPENALGO_API_KEY={user_provided_key or "your_openalgo_api_key_here"}
OPENALGO_HOST=http://127.0.0.1:5000

# DuckDB Data Sources (direct database loading - fastest)
# Custom DuckDB (user-created with OHLCV table)
DUCKDB_PATH={user_provided_path or ""}
# OpenAlgo Historify DuckDB (market_data table with epoch timestamps)
HISTORIFY_DB_PATH={user_provided_path or ""}

# Crypto Markets (CCXT) - Optional
CRYPTO_API_KEY={user_provided_key or ""}
CRYPTO_SECRET_KEY={user_provided_key or ""}

6g. Add.env to .gitignore if it exists (never commit secrets):

Scripts use find_dotenv() to automatically walk up and find the single root .env, so no copies are needed in subdirectories.

grep -qxF '.env' .gitignore 2>/dev/null || echo '.env' >> .gitignore

Step 7: Verify Installation

Run a quick verification:

python -c "
import vectorbt as vbt
import openalgo
import plotly
import talib
import duckdb
import anywidget
import nbformat
import quantstats as qs
from dotenv import load_dotenv
print('All packages installed successfully')
print(f'  vectorbt: {vbt.__version__}')
print(f'  plotly: {plotly.__version__}')
print(f'  duckdb: {duckdb.__version__}')
print(f'  nbformat: {nbformat.__version__}')
print(f'  quantstats: {qs.__version__}')
print(f'  TA-Lib: available')
print(f'  python-dotenv: available')
"

If TA-Lib import fails, inform the user that the C library needs to be installed first (see Step 3).

Step 8: Print Summary

Print a summary showing:

Detected OS
Python version used
Virtual environment path
Installed packages and versions
Backtesting folder created (strategy subfolders created on-demand by /backtest)
.env file status (configured with keys / placeholder) — single file at project root
Reminder: "Run cp .env.sample .env and fill in API keys if you skipped configuration"

Important Notes

Never install packages globally — always use the virtual environment
TA-Lib C library installation requires admin/sudo privileges on Linux
On macOS, Homebrew must be installed for brew install ta-lib
If the user already has a virtual environment, ask before creating a new one
The backtesting/ folder is where all generated backtest scripts will be saved
NEVER commit .env files — they contain secrets. Always use .gitignore.
If the user provides an API key during setup, write it directly to .env — do not ask them to edit the file manually
python-dotenv is included in the pip install and must be used by all scripts to load .env

Weekly Installs

366

Repository

marketcalls/vec…g-skills

GitHub Stars

104

First Seen

Feb 27, 2026

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