wqbkit 0.4.3

WorldQuant Brain Alpha Research Automation Toolkit

wqbkit

WorldQuant Brain (WQB) Alpha Research Automation Toolkit

English | 中文

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Overview

wqbkit is a Python toolkit that automates alpha research workflows on the WorldQuant Brain (WQB) platform. It covers the entire alpha lifecycle — from simulation and scoring to correlation analysis, genetic expression evolution, and Osmosis competition allocation.

Key Features

Module	Description
AlphaBaseCore	WQB authentication, HTTP request wrapper with 429 retry
AlphaDbCore	AlphaBaseCore + PostgreSQL ORM + PnL cache + token extraction
AlphaCalcCorr	Multi-metric correlation engine (self / ppac / prod / self_web)
AlphaGenerator	Expression factory (0/1/2/3-order) with operator/field validation
AlphaMachine	Genetic iteration pipeline — pruning, deduplication, next-generation
AlphaSimulator	Multi-threaded batch simulation scheduler with queue-based results
SuperAlphaSimulator	Dedicated simulator for SUPER-type alphas
AlphaDyeing	SUPER alpha construction and combo template management
Osmosis V3	OsmosisAlphaSelectorV3 + OsmosisAllocatorV3 + OsmosisClearV3 + OsmosisRunnerV3
sc_send	Bark (iOS) push notification for long-running jobs

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Installation

pip install wqbkit

Prerequisites

• Python >= 3.10
• PostgreSQL (optional, for local alpha metadata caching)
• wqb SDK — WorldQuant Brain official Python client

pip install wqb

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Quick Start

from wqbkit import AlphaDbCore, AlphaCalcCorr, AlphaGenerator, OsmosisRunnerV3

# Initialize core (auto-loads .env if present)
core = AlphaDbCore()

# Correlation analysis
calcor = AlphaCalcCorr()
self_corr = calcor.calc_corr(alpha_id, calc_type='self')

# Generate expressions
generator = AlphaGenerator()
new_exprs = generator.second_order_factory(
    expression="ts_mean(close, 20)",
    region="USA",
    atom=True
)

# Osmosis V3 pipeline
runner = OsmosisRunnerV3()
runner.run(update=True, dry_run=False)

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Configuration

Create a .env file in your project root directory (the directory above wqbkit/ in editable-install mode):

# Database (optional)
DB_HOST=localhost
DB_PORT=5432
DB_NAME=WorldQuant
DB_USER=your_db_user
DB_PASSWORD=your_db_password

# WQB Credentials
WQB_USERNAME=your_wqb_username
WQB_PASSWORD=your_wqb_password

# Retry
MAX_RETRIES=5
RETRY_DELAY_BASE=2

# Bark Push (optional)
BARK_KEY=your_bark_device_key

wqbkit automatically loads .env on import via python-dotenv.

Runtime Paths

Logs and temporary data are written to your project root directory, never inside the package:

Type	Default Path	Configurable via
Logs	{project_root}/logs/	config.LOGS_DIR
Correlation cache	{project_root}/data/correlation/	config.DATA_DIR / "correlation"
Osmosis data	{project_root}/data/Osmosis/	config.DATA_DIR / "Osmosis"

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API Reference

Core Classes

from wqbkit import (
    AlphaBaseCore,          # WQB session + HTTP + retry
    AlphaDbCore,            # AlphaBaseCore + DB + PnL cache
    AlphaCalcCorr,          # Correlation analysis engine
    AlphaGenerator,         # Expression factory
    AlphaSimulator,         # Multi-threaded simulation scheduler
    AlphaMachine,           # Genetic iteration pipeline
    AlphaDyeing,            # SUPER alpha builder
    SuperAlphaSimulator,    # SUPER alpha simulator
    OsmosisAlphaSelectorV3, # Osmosis V3 selector
    OsmosisAllocatorV3,     # Osmosis V3 allocator
    OsmosisClearV3,         # Osmosis V3 score clearer
    OsmosisRunnerV3,        # Osmosis V3 end-to-end runner
    sc_send,                # Push notification helper
    schemas,                # Data models: FactorData, TaskData, SimulationData
)

Osmosis V3

from wqbkit import OsmosisAlphaSelectorV3, OsmosisAllocatorV3, OsmosisRunnerV3

# 1. Select alphas
selector = OsmosisAlphaSelectorV3()
df = selector.select(region="USA")

# 2. Allocate scores
allocator = OsmosisAllocatorV3()
df = allocator.allocate(df, method="mixed")
allocator.update_osmosis_points(df, dry_run=False)

# 3. Or run the full pipeline
runner = OsmosisRunnerV3()
runner.run(update=True, dry_run=False)

Correlation Analysis

from wqbkit import AlphaCalcCorr

calcor = AlphaCalcCorr()

# Compute correlations
corr = calcor.calc_corr(alpha_id, calc_type='self')     # self-correlation
corr = calcor.calc_corr(alpha_id, calc_type='ppac')     # ppac correlation
corr = calcor.calc_corr(alpha_id, calc_type='prod')     # prod correlation

Alpha Simulation

from wqbkit import AlphaSimulator

sim = AlphaSimulator()

# Single alpha simulation
result = sim.simulate(expression="rank(close)", region="USA", universe="TOP3000")

# Batch simulation from database queue
sim.run_batch_simulation(task_id=123)

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Project Structure

wqbkit/
├── app/
│   ├── core/          # AlphaBaseCore, AlphaDbCore, decorators, logger, URLs
│   ├── database/      # SQLAlchemy models, AlphaDBManager, schemas
│   └── utils/         # Token extraction helpers
└── modules/
    ├── regular_alpha/ # AlphaSimulator, AlphaMachine, AlphaGenerator
    ├── super_alpha/   # SuperAlphaSimulator, AlphaDyeing, SuperAlphaCreator
    ├── correlation/   # AlphaCalcCorr
    ├── message/       # Bark push notifications
    └── competitions/
        └── Osmosis/   # Osmosis V3 toolkit

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License

MIT

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概述

wqbkit 是一个用于自动化 WorldQuant Brain 平台 Alpha 研究的 Python 工具包，覆盖 Alpha 全生命周期：模拟、评分、去相关、遗传迭代进化、Osmosis 竞赛分配。

核心功能

模块	说明
AlphaBaseCore	WQB 认证、HTTP 封装、429 重试
AlphaDbCore	AlphaBaseCore + PostgreSQL ORM + PnL 缓存 + token 提取
AlphaCalcCorr	多指标相关性引擎（self / ppac / prod / self_web）
AlphaGenerator	表达式工厂（0/1/2/3 阶），带算子/字段校验
AlphaMachine	遗传迭代管线：剪枝、去重、下一代
AlphaSimulator	多线程批量模拟调度器，队列式结果处理
SuperAlphaSimulator	SUPER 类型 Alpha 专用模拟器
AlphaDyeing	SUPER Alpha 构造与 combo 模板管理
Osmosis V3	OsmosisAlphaSelectorV3 + OsmosisAllocatorV3 + OsmosisClearV3 + OsmosisRunnerV3
sc_send	Bark (iOS) 推送通知，用于长时任务提醒

快速开始

from wqbkit import AlphaDbCore, AlphaCalcCorr, AlphaGenerator, OsmosisRunnerV3

core = AlphaDbCore()
calcor = AlphaCalcCorr()
generator = AlphaGenerator()
runner = OsmosisRunnerV3()
runner.run(update=True, dry_run=False)

配置说明

在项目根目录创建 .env：

WQB_USERNAME=your_username
WQB_PASSWORD=your_password

日志和运行时数据统一写入项目根目录的 logs/ 和 data/ 下，不会写入包内部。