joinquant-docs

JoinQuant strategy development

Based on the offline documentation in this directory, write Python strategies for the JoinQuant official website (backtesting / simulation / research). When answering API questionsyou must first consult the local documentation, and must not fabricate function signatures or parameters from memory.

Differences from jqdatasdk：jqdata Used in the official website strategy environment;jqdatasdk is a local Python library, with slightly different APIs, and cannot be used in official website backtesting/simulation/research.

Documentation consultation process

Determine the question type, and locate files according to reference.md locate files
Use Grep/Read to search forthe target function name or Chinese keyword (e.g., get_price、order_target、市盈率）
cross-validation: for API behavior consult api.md, for fields/table structures consult data/*.md, for pitfalls consult faq.md
When giving an answercite the calling methods, parameters, return values, and example code from the documentation

需要写策略框架？     → api.md「开始写策略」「策略程序架构」
需要查数据 API？      → api.md「数据获取函数」+ data/ 对应品种文档
需要下单/持仓？      → api.md「交易函数」「对象」
需要财务/估值数据？  → data/Stock.md（run_query + valuation/fundamentals 表）
需要行业/概念选股？  → data/plateData.md + api.md get_industry_stocks 等
需要技术指标？       → data/technicalanalysis.md（from jqlib.technical_analysis import *）
需要 Alpha 因子？    → data/Alpha101.md、data/Alpha191.md
需要自定义因子？     → fator.md（jqfactor.Factor、calc_factors）
需要因子看板数据？   → data/factor_values.md

Strategy skeleton

Minimal runnable structure:

# 导入聚宽函数库
import jqdata

def initialize(context):
    g.security = '000001.XSHE'
    set_benchmark('000300.XSHG')
    set_option('use_real_price', True)  # 开启动态复权（真实价格），建议开启
    run_daily(trade, time='open')       # 或 time='every_bar' / '9:30'

def trade(context):
    security = g.security
    close_data = attribute_history(security, 5, '1d', ['close'])
    MA5 = close_data['close'].mean()
    current_price = close_data['close'][-1]
    cash = context.portfolio.available_cash

    if current_price > 1.01 * MA5:
        order_value(security, cash)
    elif current_price < MA5 and context.portfolio.positions[security].closeable_amount > 0:
        order_target(security, 0)

Lifecycle functions

Function	Description
`initialize(context)`	Global initialization, runs only once; use `g` to store global variables
`run_daily/weekly/monthly(func, ...)`	Scheduled tasks;`func` func must bea global function, and cannot be a class method
`handle_data(context, data)`	Driven by the backtest frequency;not recommended to be mixed with run_daily
`before_trading_start`	Before market open (9:00)
`after_trading_end`	After market close (15:30)

APIs marked with ♠ are supported only inbacktest/simulation, and cannot be called in the research module.jqdata The module can be used in both the research and backtest environments.

Security code specifications

Market	Suffix	Example
Shanghai Stock Exchange	`.XSHG`	`600519.XSHG`
Shenzhen Stock Exchange	`.XSHE`	`000001.XSHE`
China Financial Futures Exchange	`.CCFX`	`IC9999.CCFX`
Dalian Commodity Exchange	`.XDCE`	`A9999.XDCE`
Shanghai Futures Exchange	`.XSGE`	`AU9999.XSGE`
Zhengzhou Commodity Exchange	`.XZCE`	`CY8888.XZCE`
OTC funds	`.OF`	`519671.OF`

For futures strategies, set run_daily 's reference_security to the corresponding main contract (e.g., IF9999.CCFX), to match the night session opening time.

Common API quick reference

Market quotes and history

get_price(security, start_date, end_date, frequency='daily', fields=None, fq='pre')
attribute_history(security, count, unit, fields)  # 回测环境，不含当天
history(count, unit, field, security_list, df=True)
get_bars(security, count, unit, fields, include_now=True)

Universe and sectors

get_all_securities(types=['stock'], date=None)   # date 防未来函数
get_index_stocks('000300.XSHG', date=None)
get_industry_stocks('C15', date=None)
get_concept_stocks('GN036', date=None)
set_universe([...])  # 设置后 history 可不传 security_list

Financial data (SQL query)

from jqdata import *
q = query(valuation).filter(valuation.code == '000001.XSHE')
df = get_fundamentals(q, date='2015-10-15')

# 或 run_query（单次最多 4000 行，不可连表）
df = finance.run_query(query(finance.STK_XXX).filter(...).limit(4000))

Trading

order(security, amount)              # 按股数，正买负卖
order_value(security, value)       # 按金额
order_target(security, amount)       # 调到目标股数
order_target_value(security, value)  # 调到目标市值
order_target_percent(security, percent)  # 调到目标仓位比例

The buy quantity for A-shares must be an integer multiple of 100 (200 minimum for STAR Market); when selling all positions there is no limit. A maximum of 10,000 orders per day.

Technical indicators

from jqlib.technical_analysis import *
# check_date 策略中建议用 context.current_dt，避免盘中取当日收盘指标产生未来数据
result = MACD(security_list, check_date=context.current_dt, SHORT=12, LONG=26, MID=9)

Custom factors

from jqfactor import Factor, calc_factors

class MyFactor(Factor):
    name = 'my_factor'
    max_window = 5
    dependencies = ['close']
    def calc(self, data):
        return data['close'].mean()

factors = calc_factors(securities, [MyFactor()], start_date, end_date)

See fator.md and data/Alpha101.md、data/Alpha191.md。

Key precautions

Prevent look-ahead bias

get_all_securities(date=...)、get_index_stocks(..., date=...) etc. must be passedthe historical point-in-time's date, and future dates cannot be used
history / attribute_history When fetching daily data withdoes not include the current day; to get the current day's data you need to fetch minute-level data
Technical indicator check_date Returns the closing value only when precise to the date; calling intraday on the current day will produce future data
Financial data is processed by announcement date by default; note get_fundamentals of date parameter meaning

Run frequency

Prefer to use run_daily, avoid mixing with handle_data mixed use
run_daily(func, time='every_bar') frequency is consistent with the backtest settings
run_weekly/monthly of force=False can avoid nearest execution when registered late

Data query limits

run_query / get_fundamentals returns at most 4000 rows
run_query Join queries are not supported
Default market data isforward-adjusted；fq=None is unadjusted

Environment and product distinction

Product	Use case
`jqdata`(official website)	Backtest, simulation, research
`jqdatasdk`(local)	Local quantitative research,cannotbe imported in official website strategies

Choose documentation by scenario

Scenario	Read first	Further reference
Beginners writing their first strategy	`api.md`"Start Writing Strategies"	`api.md`"Strategy Program Architecture"
Stock selection + financials	`data/Stock.md`	`api.md` get_fundamentals
Index constituent stock strategies	`data/index.md`	`api.md` get_index_stocks
Industry/concept rotation	`data/plateData.md`	`api.md` get_industry_stocks
Futures strategies	`data/Future.md`	`api.md`"Futures Strategy Dedicated Functions"
Options strategies	`data/Option.md`	`api.md` options-related
Funds/ETFs	`data/fund.md`	`data/OTCfund.md`
Convertible bonds	`data/bond.md`	—
Macro data	`data/macroData.md`	—
Technical analysis indicators	`data/technicalanalysis.md`	—
Factor stock selection	`fator.md` + `data/factor_values.md`	`data/Alpha101.md`
Margin trading and securities lending	`data/Stock.md` margin trading and securities lending chapter	`api.md` margin trading and securities lending dedicated functions
Errors/data questions	`faq.md`	`api.md`"Notes"

Strategy examples

For complete examples, see api.md "Strategy Examples" at the end: moving average strategy, multi-stock positions, chasing rallies strategy, Halloween effect, etc.

Documentation updates

The local data dictionary is synchronized from the official website by script:

bun scripts/get-joinquant-docs.ts
# 强制覆盖已有 md：FORCE_UPDATE=1 bun scripts/get-joinquant-docs.ts

Additional resources

Complete documentation index:reference.md
Platform API full text:api.md
FAQ:faq.md
Factor analysis:fator.md