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### Verify Installation Output

Source: https://www.ricequant.com/doc/rqamsc/manual-rqamsc

Example of successful installation output showing the installed packages.

```text
Successfully installed brotli-1.1.0 certifi-2024.7.4 charset-normalizer-3.3.2 colorama-0.4.6 et-xmlfile-1.1.0 idna-3.7 msgpack-1.0.8
 numpy-1.24.4 openpyxl-3.1.5 orjson-3.10.6 pandas-2.0.3 python-dateutil-2.9.0.post0 python-rapidjson-1.18 pytz-2024.1 pyyaml-6.0.1 r
equests-2.32.3 rqamsc-0.4.1 rqdatac-3.0.12.2+12.ga61fb64d rqdatac-fund-1.0.35 six-1.16.0 tqdm-4.66.4 tzdata-2024.1 urllib3-2.2.2 win-inet-pton-1.1.0 xlrd-1.2.0
```

--------------------------------

### Environment Setup and Execution Commands

Source: https://www.ricequant.com/doc/rqalpha-plus/doc/advance-tutorial

Commands for installing dependencies and running the strategy script.

```bash
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple xlrd
rqalpha-plus run -f holding_target_position_simplified.py
```

--------------------------------

### Get Specific Return Examples

Source: https://www.ricequant.com/doc/rqdata/python/risk-factors-mod

Examples demonstrating how to retrieve specific returns for single stocks, multiple stocks, and using different industry mappings.

```python
In [16]: get_specific_return('600705.XSHG','20170302','20170307' )
Out[16]:
order_book_id  600705.XSHG
date
2017-03-02       -0.012205
2017-03-03        0.004288
2017-03-06       -0.005601
2017-03-07        0.022702
```

```python
In [29]: get_specific_return(['600705.XSHG','600100.XSHG'],'20170302','20170307' )
Out[29]:
order_book_id  600100.XSHG  600705.XSHG
date
2017-03-02        0.000004    -0.012205
2017-03-03       -0.004658     0.004288
2017-03-06       -0.003140    -0.005601
2017-03-07        0.013050     0.022702
```

```python
In [29]: rqdatac.get_specific_return('600705.XSHG','20170302','20170307',industry_mapping='citics_2019' )
Out[29]:
order_book_id  600705.XSHG
date
2017-03-02       -0.015470
2017-03-03        0.005021
2017-03-06       -0.003329
2017-03-07        0.022044
```

--------------------------------

### Get Trading Parameters Examples

Source: https://www.ricequant.com/doc/rqdata/python/futures-mod

Examples for retrieving trading parameters for a single contract on a specific day or over a date range.

```python
futures.get_trading_parameters('IF2312')
```

```python
futures.get_trading_parameters('CU2312',start_date=20231201,end_date=20231205)
```

--------------------------------

### Verify Upgrade Output

Source: https://www.ricequant.com/doc/rqamsc/manual-rqamsc

Example of successful upgrade output showing the uninstallation of the old version and installation of the new one.

```text
Installing collected packages: rqamsc
  Attempting uninstall: rqamsc
    Found existing installation: rqamsc 0.4.0
    Uninstalling rqamsc-0.4.0:
      Successfully uninstalled rqamsc-0.4.0
Successfully installed rqamsc-0.4.1
```

--------------------------------

### SDK Installation and Data Management

Source: https://www.ricequant.com/doc/rqsdk/manual-rqsdk

Essential commands for installing the SDK, downloading sample data, and updating production data.

```bash
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple rqsdk
```

```bash
rqsdk download-data --sample
```

```bash
rqsdk update-data --minbar 000001.XSHE
```

```bash
rqalpha-plus examples
```

--------------------------------

### Install RQData Packages

Source: https://www.ricequant.com/doc/rqdata/python/manual

Commands to install the rqdatac client and optional data packages.

```sh
pip install rqdatac
```

```sh
pip install "rqdatac[fund]"
```

```sh
pip install "rqdatac[fund,news]"
```

--------------------------------

### Perform Factor Analysis Example

Source: https://www.ricequant.com/doc/quant/factor-system

Example demonstrating how to calculate and analyze the 'close' price factor using the沪深 300 index components.

```python
[In]
from rqfactor import *
from rqfactor.notebook import *
f = Factor('close')
df = execute_factor(f, index_components('000300.XSHG', '20170101'), '20170101', '20170601')
result = factor_analysis(df, 5)
result.ic
[Out]
2017-01-04   -0.269962
2017-01-11   -0.163985
2017-01-18    0.103013
2017-01-25    0.096184
2017-02-08   -0.297142
2017-02-15    0.101017
2017-02-22    0.079381
2017-03-01    0.269773
2017-03-08    0.203213
2017-03-15    0.136387
2017-03-22   -0.218898
2017-03-29   -0.167801
2017-04-07   -0.067472
2017-04-14    0.190367
2017-04-21    0.070248
2017-04-28   -0.049433
2017-05-08    0.121866
2017-05-15    0.228014
2017-05-22   -0.193295
2017-05-31    0.292108
dtype: float64
```

--------------------------------

### Examples of index_weights Usage

Source: https://www.ricequant.com/doc/rqdata/python/indices-mod

Examples showing how to retrieve index weights for a specific date or a date range.

```python
[In]
index_weights('000016.XSHG', '20160801')
[Out]
Order_book_id
600000.XSHG    0.03750
600010.XSHG    0.00761
600016.XSHG    0.05981
600028.XSHG    0.01391
600029.XSHG    0.00822
600030.XSHG    0.03526
600036.XSHG    0.04889
600050.XSHG    0.00998
600104.XSHG    0.02122
```

```python
[In]
index_weights('000016.XSHG', start_date='20250101',end_date='20250330')
[Out]
                                    weight
date	        order_book_id
2025-01-02	    600028.XSHG	        0.01227
                600030.XSHG	        0.03585
                600031.XSHG	        0.01231
                600036.XSHG	        0.06125
                600048.XSHG	        0.00801
...	...	...
2025-03-28	    688012.XSHG	        0.01142
                688041.XSHG	        0.01909
                688111.XSHG	        0.01027
                688256.XSHG	        0.01970
                688981.XSHG	        0.02495
2800 rows × 1 columns
```

--------------------------------

### Initialize CustomizedBenchmark

Source: https://www.ricequant.com/doc/rqamsc/api-rqamsc

Examples showing different ways to initialize a custom benchmark object.

```python
import datetime
from rqamsc import CustomizedBenchmark

customized_benchmark = CustomizedBenchmark(
    name='多时段自定义权重基准',
    type='composite',
    weights=[
        {
            'start_date': datetime.date(2015, 1, 1),
            'weights': [
                {'order_book_id': '000001.XSHE', 'weight': 0.5},
                {'order_book_id': '000002.XSHE', 'weight': 0.5}
            ]
        }
    ]
)
```

--------------------------------

### Retrieve Repo Fixing Rates Example

Source: https://www.ricequant.com/doc/rqdata/python/macro-economy

Example usage for fetching repo rates between specific dates.

```python
[In] rqdatac.econ.get_fixing_repo_rate(20250311,20250313)
[Out]
	        FR001	FR007	FR014	FDR001	FDR007	FDR014
date						
2025-03-11	0.0179	0.0183	0.0193	0.0178	0.0182	0.0190
2025-03-12	0.0178	0.0183	0.0193	0.0177	0.0181	0.0186
2025-03-13	0.0178	0.0182	0.0192	0.0178	0.0181	0.0185
```

--------------------------------

### Retrieve ETF Cash Components Examples

Source: https://www.ricequant.com/doc/rqdata/python/fund-mod

Examples for retrieving cash difference data for single or multiple ETFs.

```python
In[]:fund.get_etf_cash_components('510050.XSHG','20191201','20191205')
Out[]:

order_book_id date cash_component est_cash_component max_cash_ratio nav_per_basket pre_date
510050.XSHG 2019-12-02 55959.24 31237.24 0.5 2646969.24 2019-11-29
            2019-12-03 31488.64 35832.64 0.5 2608899.64 2019-12-02
            2019-12-04 34927.55 33264.55 0.5 2617784.55 2019-12-03
            2019-12-05 33230.56 35727.56 0.5 2610131.56 2019-12-04
```

```python
In[]:fund.get_etf_cash_components(['510050.XSHG','510300.XSHG'],'20191201','20191205')
Out[]:

order_book_id date cash_component est_cash_component max_cash_ratio nav_per_basket pre_date
510050.XSHG 2019-12-02 55959.24 31237.24 0.5 2646969.24 2019-11-29
            2019-12-03 31488.64 35832.64 0.5 2608899.64 2019-12-02
            2019-12-04 34927.55 33264.55 0.5 2617784.55 2019-12-03
            2019-12-05 33230.56 35727.56 0.5 2610131.56 2019-12-04
510300.XSHG 2019-12-02 -34311.25 -29329.25 0.5 3501800.75 2019-11-29
            2019-12-03 -28545.40 -30993.40 0.5 3508327.60 2019-12-02
            2019-12-04 -30828.28 -29276.28 0.5 3522019.72 2019-12-03
            2019-12-05 -29934.94 -32790.94 0.5 3520765.06 2019-12-04
```

--------------------------------

### Dominant Contract Switching Example

Source: https://www.ricequant.com/doc/rqdata/cli/manual

Example output showing the dominant contract for specific dates.

```json
[
  {
    "date": "2024-01-18",
    "dominant": "IF2401"
  },
  {
    "date": "2024-01-19",
    "dominant": "IF2402"
  },
  {
    "date": "2024-01-22",
    "dominant": "IF2402"
  }
]
```

--------------------------------

### Retrieve ETF Components Example

Source: https://www.ricequant.com/doc/rqdata/python/fund-mod

Example usage of retrieving component data for a specific ETF on a given date.

```python
In [10]: fund.get_etf_components('510050.XSHG',trading_date=20190117)
Out[10]:

 trading_date  order_book_id  stock_code  stock_amount  cash_substitute  cash_substitute_proportion  fixed_cash_substitute
0  2019-01-17  510050.XSHG    600000      5600.0              允许                               0.1    NaN
1  2019-01-17  510050.XSHG    600016      11900.0            允许                               0.1    NaN
2  2019-01-17  510050.XSHG    600019      4300.0              允许                               0.1    NaN
3  2019-01-17  510050.XSHG    600028      5800.0              允许                               0.1    NaN
4  2019-01-17  510050.XSHG    600029      1600.0              允许                               0.1    NaN
5  2019-01-17  510050.XSHG    600030      3800.0              允许                               0.1    NaN
6  2019-01-17  510050.XSHG    600036      4900.0              允许                               0.1    NaN
7  2019-01-17  510050.XSHG    600048      3400.0              允许                               0.1    NaN
8  2019-01-17  510050.XSHG    600050      4500.0              允许                               0.1    NaN
...
```

--------------------------------

### Example Data Output

Source: https://www.ricequant.com/doc/rqdata/cli/manual

A sample JSON response format for general queries.

```json
[
  {
    "current_year": "0.5969",
    "month": "0.4378",
    "order_book_id": "000001.XSHE",
    "today": "0.5969",
    "tradedate": "2024-01-02",
    "week": "0.5559",
    "year": "0.472"
  }
]
```

--------------------------------

### Example: Get Institutional Investor Transaction Status

Source: https://www.ricequant.com/doc/rqdata/python/fund-mod

Example usage for querying transaction status for institutional investors within a specific date range.

```python
In [18]: fund.get_transaction_status('040001',start_date='2020-01-15',end_date='2020-01-25',investor='institution')
Out[18]:
                         subscribe_status redeem_status issue_status subscribe_upper_limit subscribe_lower_limit redeem_lower_limit
order_book_id datetime
040001        2020-01-15             Open          Open        Close                  None                     1                  1
              2020-01-16          Limited          Open        Close                 1e+06                     1                  1
              2020-01-17          Limited          Open        Close                 1e+06                     1                  1
              2020-01-18          Limited          Open        Close                 1e+06                     1                  1
              2020-01-19          Limited          Open        Close                 1e+06                     1                  1
              2020-01-20          Limited          Open        Close                 1e+06                     1                  1
              2020-01-21          Limited          Open        Close                 1e+06                     1                  1
              2020-01-22             Open          Open        Close                  None                     1                  1
              2020-01-23             Open          Open        Close                  None                     1                  1
              2020-01-24             Open          Open        Close                  None                     1                  1
              2020-01-25             Open          Open        Close                  None                     1                  1
```

--------------------------------

### Example: Get Retail Investor Transaction Status

Source: https://www.ricequant.com/doc/rqdata/python/fund-mod

Example usage for querying transaction status for retail investors within a specific date range.

```python
In [14]: fund.get_transaction_status('040001',start_date='2020-11-01',end_date='2020-11-10',investor='retail')
Out[14]:
                         subscribe_status redeem_status issue_status subscribe_upper_limit subscribe_lower_limit redeem_lower_limit
order_book_id datetime
040001        2020-11-01             Open          Open        Close                  None                     1                  1
              2020-11-02             Open          Open        Close                  None                     1                  1
              2020-11-03             Open          Open        Close                  None                     1                  1
              2020-11-04             Open          Open        Close                  None                     1                  1
              2020-11-05             Open          Open        Close                  None                     1                  1
              2020-11-06             Open          Open        Close                  None                     1                  1
              2020-11-07             Open          Open        Close                  None                     1                  1
              2020-11-08             Open          Open        Close                  None                     1                  1
              2020-11-09             Open          Open        Close                  None                     1                  1
              2020-11-10             Open          Open        Close                  None                     1                  1
```

--------------------------------

### Initialize RQAMSC and List Products

Source: https://www.ricequant.com/doc/rqamsc/manual-rqamsc

Initializes the client with credentials and retrieves a list of products in the current workspace.

```python
import rqamsc

rqamsc.init(username="your_user_name", password="your_password", uri="https://www.ricequant.com", ssl_verify=True)
# 私有化部署用户初始化可能为下方代码
# rqamsc.init(username="your_user_name", password="your_password", uri="http://internal.rqams.com", ssl_verify=False)

# 现在尝试获取一下当前空间下的所有产品
print(rqamsc.list_products())
```

--------------------------------

### Initialize Product object

Source: https://www.ricequant.com/doc/rqamsc/api-rqamsc

Demonstrates the construction of a Product object with valuation and exchange rate settings.

```python
import datetime

from bson import ObjectId

from rqamsc import Product, ValuationSettings, ETFValuationAccordingField, ExchangeRateSettings, ExchangeRateType

product_doc = {
    "_id": ObjectId("6177c9ea528f3ac1ce662abb"),
    "name": "300估值因子增强_347418",
    "data_source": "trade_and_valuation_report",
    "start_date": datetime.datetime(2019, 1, 3),
    "trading_start_date": datetime.datetime(2019, 1, 3),
    "investment_category": "equity",
    "strategy_category": "index_enhanced",
    "realtime_period_type": "daytime",
    "benchmark": {
        "type": "index",
        "id": "000300.XSHG"
    },
    "calendar": "exchange",
    "accounts": [
        {
            "name": "300估值因子增强_托管账户",
            "is_custodian": True,
            "account_number": "3eb0b699-7ca8-481a-883a-25ff81ea8ad0",
            "broker": "ricequant"
        },
        {
            "name": "300估值因子增强_交易账户",
            "is_custodian": False,
            "account_number": "8e6827c0-3bdf-4c17-b7fb-e643af1cb6da",
            "broker": "ricequant"
        }
    ],
    "fee_settings": {},
    "user_id": 12345,
    "workspace_id": ObjectId("5f19620f7e8e904a613f5482"),
    "auto_equity": True,
    "unit_policy": "auto_prev_unit_net_value",
    "full_name": "300估值因子增强_347418_全名",
    "create_time": datetime.datetime(2021, 10, 26),
    "fund_code": "",
    "manager": "",
    "invest_advisor": "",
    "invest_manager": "",
    "maturity_date": datetime.datetime(2999, 12, 31),
    "label": "paper",
    "valuation_settings": ValuationSettings(etf=ETFValuationAccordingField.iopv),
    "exchange_rate_settings": ExchangeRateSettings(HKD=ExchangeRateType.sh)
}
```

--------------------------------

### Example: Get Index Weights for a Specific Date

Source: https://www.ricequant.com/doc/rqdata/python/ricequant-index

Example usage of index_weights to retrieve constituent data for the Ricequant Small Market Value Concept Index.

```python
[In]
index_weights('866002.RI', '20160801')
[Out]
Order_book_id
601700.XSHG    0.000572
002756.XSHE    0.000572
002265.XSHE    0.000572
 ...
000851.XSHE    0.000572
002362.XSHE    0.000572
```

--------------------------------

### Basic Strategy Initialization and Execution

Source: https://www.ricequant.com/doc/quant/strategy-sample

Demonstrates a basic strategy structure using init for data filtering and handle_bar for order execution.

```python
def init(context):
    stocks = all_instruments('CS').order_book_id
    fundamental_df=get_factor(stocks,['pe_ratio','revenue'])
    fundamental_df = fundamental_df[(fundamental_df['pe_ratio'] > 55) & (fundamental_df['pe_ratio'] < 60)]
    fundamental_df = fundamental_df.sort_values(by='revenue', ascending=False).head(10).reset_index(1,drop=True)

    context.fundamental_df = fundamental_df

    logger.info(context.fundamental_df)
    update_universe(context.fundamental_df.index.values)

    context.stocks = context.fundamental_df.index.values
    stocks_number = len(context.stocks)
    context.average_percent = 0.99 / stocks_number
    logger.info("Calculated average percent for each stock is: %f" % context.average_percent)
    context.fired = False


def handle_bar(context, bar_dict):
    if not context.fired:
        for stock in context.stocks:
            order_target_percent(stock, context.average_percent)
            logger.info("Bought: " + str(context.average_percent) + " % for stock: " + str(stock))
        context.fired = True
```

--------------------------------

### Get Industry Inclusion/Exclusion Dates

Source: https://www.ricequant.com/doc/rqdata/python/stock-hk

Retrieves the start and end dates for when stocks were included or excluded from a specific industry.

```python
get_industry_change(industry, source='citics_2019', level=None, market='hk')
```

```python
[In]
get_industry_change(industry='原材料业', level=1,source='hsi',market='hk')
[Out]
               start_date cancel_date
order_book_id
01812.XHKG 2013-09-09 2200-12-31
00347.XHKG 2013-09-09 2200-12-31
00358.XHKG 2013-09-09 2200-12-31
01787.XHKG 2018-09-28 2200-12-31
00338.XHKG 2013-09-09 2200-12-31
... ... ...
09879.XHKG 2024-03-21 2200-12-31
06616.XHKG 2021-07-16 2200-12-31
02237.XHKG 2022-07-18 2200-12-31
02881.XHKG 2024-06-18 2200-12-31
02610.XHKG 2025-03-25 2200-12-31
```

--------------------------------

### 定义 init 初始化函数

Source: https://www.ricequant.com/doc/rqalpha-plus/api/callback

在策略启动时触发一次，用于初始化上下文数据。

```python
init(context)
```

```python
def init(context):
    # cash_limit的属性是根据用户需求自己定义的，你可以定义无限多种自己随后需要的属性，ricequant的系统默认只是会占用context.portfolio的关键字来调用策略的投资组合信息
    context.cash_limit = 5000
```

--------------------------------

### Complete Strategy Implementation

Source: https://www.ricequant.com/doc/rqalpha-plus/doc/quick-start

Integrates initialization and handle_bar logic into a full strategy script.

```python
import talib


def init(context):
    context.stock = "000001.XSHE"


def handle_bar(context, bar_dict):
    prices = history_bars(context.stock, 100, '1d', 'close')
    macd, macd_signal, _ = talib.MACD(prices, 12, 26, 9)

    if macd[-1] > macd_signal[-1] and macd[-2] < macd_signal[-2]:
        order_target_percent(context.stock, 1)

    if macd[-1] < macd_signal[-1] and macd[-2] > macd_signal[-2]:
        if get_position(context.stock).quantity > 0:
            order_target_percent(context.stock, 0)
```

--------------------------------

### Get Southbound Eligible Securities

Source: https://www.ricequant.com/doc/rqdata/python/stock-hk

Retrieves list of stocks eligible for Southbound trading. Requires the rqdatac_hk package to be installed and imported.

```python
hk.get_southbound_eligible_secs(trading_type='sh', date=None, start_date=None, end_date=None, market='hk')
```

--------------------------------

### Initialize Client

Source: https://www.ricequant.com/doc/rqamsc/api-rqamsc

Authenticates the user and establishes a connection to the specified RQAMS URI.

```python
rqamsc.init(username, password, uri="https://www.ricequant.com", ssl_verify=True)
```

--------------------------------

### Optimizer Initialization Example

Source: https://www.ricequant.com/doc/rqalpha-plus/doc/example

A basic initialization function for an optimizer, setting up a monthly rebalance schedule.

```python
def init(context):
    scheduler.run_monthly(rebalance, 1)
```

--------------------------------

### Install rqalpha-mod-ams

Source: https://www.ricequant.com/doc/rqamsc/api-rqamsc

Install the required module for AMS integration.

```bash
pip install rqalpha-mod-ams>=1.1.1 --extra-index-url https://rquser:ricequant99@py.ricequant.com/simple/
```

--------------------------------

### Futures Instrument Data Example

Source: https://www.ricequant.com/doc/rqdata/cli/manual

Example output format for a futures instrument query.

```json
[
  {
    "contract_multiplier": "10.0",
    "de_listed_date": "2024-03-14",
    "end_delivery_date": "2024-03-19",
    "exchange": "DCE",
    "industry_name": "油脂",
    "listed_date": "2023-03-15",
    "margin_rate": "0.2",
    "market_tplus": "0.0",
    "maturity_date": "2024-03-14",
    "order_book_id": "A2403",
    "product": "Commodity",
    "round_lot": "1.0",
    "start_delivery_date": "2024-03-19",
    "symbol": "豆一2403",
    "trading_code": "a2403",
    "trading_hours": "21:01-23:00,09:01-10:15,10:31-11:30,13:31-15:00",
    "type": "Future",
    "underlying_order_book_id": "",
    "underlying_symbol": "A"
  }
]
```

--------------------------------

### Python 策略 Hello World

Source: https://www.ricequant.com/doc/quant/backtest

展示了一个简单的买入并持有策略，通过 init 初始化状态并在 handle_bar 中执行交易逻辑。

```python
# 可以自己import我们平台支持的第三方python模块，比如pandas、numpy等。

# 在这个方法中编写任何的初始化逻辑。context对象将会在你的算法策略的任何方法之间做传递。
def init(context):
    context.s1 = "000001.XSHE"
    # order是否被发送出去
    context.fired = False


# 你选择的证券的数据更新将会触发此段逻辑，例如日或分钟历史数据切片或者是实时数据切片更新
def handle_bar(context, bar_dict):
    # 开始编写你的主要的算法逻辑

    # bar_dict[order_book_id] 可以拿到某个证券的bar信息
    # context.portfolio 可以拿到现在的投资组合信息

    # 使用order_shares方法进行下单

    # TODO: 开始编写你的算法吧！
    if not context.fired:
     # order_percent并且传入1代表买入该股票并且使其占有投资组合的100%
        order_percent(context.s1, 1)
        context.fired = True
```

--------------------------------

### Transaction Volume Limit Example

Source: https://www.ricequant.com/doc/rqoptimize/api/optimizer

Example of setting a transaction volume upper limit.

```python
cons = [TransactionVolumeLimit(current_holding=current_holding_num, upper_limit=0.01, cash=1000000, window=1,
stock_list=['300482.XSHE', '300659.XSHE', '000001.XSHE', '600004.XSHG', '603369.XSHG', '600529.XSHG'])]
```

--------------------------------

### Install rqamsc SDK

Source: https://www.ricequant.com/doc/rqamsc/manual-rqamsc

Use pip to install the rqamsc package from the Ricequant private repository.

```bash
pip install --extra-index-url https://rquser:ricequant99@py.ricequant.com/simple/ rqamsc
```

--------------------------------

### Transaction Market Value Limit Example

Source: https://www.ricequant.com/doc/rqoptimize/api/optimizer

Example of setting a transaction market value limit.

```python
cons = [TransactionMarketValueLimit (current_holding=current_holding_num, upper_limit=0.05, cash=1000000, window=1,
stock_list=['300482.XSHE', '300659.XSHE', '000001.XSHE', '600004.XSHG', '603369.XSHG', '600529.XSHG'])]
```

--------------------------------

### Example: MinStyleDeviation Usage

Source: https://www.ricequant.com/doc/rqoptimize/api/optimizer

Demonstrates setting target style exposures and applying priority weights.

```python
target_style = pd.Series({'size': 0, 'beta': 1, 'book_to_price': 0, 'earnings_yield': 0, 'growth': 0, 'leverage': 0,'liquidity': 0, 'momentum': 0, 'non_linear_size': 0, 'residual_volatility': 0})
objective = MinStyleDeviation({'size': 0, 'beta': 1, 'book_to_price': 0, 'earnings_yield': 0, 'growth': 0, 'leverage': 0, 'liquidity':0,'momentum': 0, 'non_linear_size': 0, 'residual_volatility': 0}, relative=True, priority=target_style)
```

--------------------------------

### Initialize Strategy with File Data

Source: https://www.ricequant.com/doc/quant/strategy-api

Demonstrates loading a CSV file into a pandas DataFrame during strategy initialization.

```python
import pandas as pd
from six import BytesIO

# 在这个方法中编写任何的初始化逻辑。context对象将会在你的算法策略的任何方法之间做传递。
def init(context):
    # 由于保存的是原数据文件，因此需要用BytesIO进行转换
    body = get_file('day_px.csv')
    data=pd.read_csv(BytesIO(body))
    logger.info(data)
```

--------------------------------

### Install third-party libraries

Source: https://www.ricequant.com/doc/quant/research

Installs external Python packages directly within an IPython Notebook cell.

```bash
pip install QuantLib --user
```

--------------------------------

### Convert Product Data

Source: https://www.ricequant.com/doc/rqamsc/api-rqamsc

Demonstrates how to instantiate a Product object from a dictionary and convert it back to a dictionary.

```python
# 使用 from_doc 方法将dict数据转化为产品对象
product = Product.from_doc(product_doc)
# 即可调用对象属性
product_id = product.id  # ObjectId("6177c9ea528f3ac1ce662abb")    note: _id被转换为id
trading_start_date = product.trading_start_date  # datetime.datetime(2019, 1, 3)

# 产品对象也可以使用 to_dict 方法转化为字典
product.to_dict()  # 输出结果即与product_doc一致    note: 对象中的id字段在字典中key仍为id
```

--------------------------------

### Index Component Limit Example

Source: https://www.ricequant.com/doc/rqoptimize/api/optimizer

Example of setting a hard constraint on the weight of the CSI 300 index.

```python
cons = [IndexComponentLimit(index_code = '000300.XSHG',upper_limit = 0.3,lower_limit = 0.1, hard=True)]
```

--------------------------------

### Retrieve Data in Online Environments

Source: https://www.ricequant.com/doc/rqdata/python/manual

Usage example for Ricequant's online IPython Notebook or backtesting environment where initialization is pre-handled.

```python
get_price('000001.XSHE', start_date=20150101, end_date=20150201)
```

--------------------------------

### 配置 Copilot 指令

Source: https://www.ricequant.com/doc/rqsdk/manual-rqsdk

在 .github/copilot-instructions.md 中设置项目上下文、组件信息及代码风格要求。

```markdown
# RQSDK 开发指导

## 项目上下文

本项目使用 Ricequant SDK (RQSDK) 进行量化开发。

## 主要组件

- **RQData**: 金融数据 API，用于获取股票、期货等市场数据
- **RQAlpha Plus**: 策略回测引擎，支持日线、分钟线、tick 回测
- **RQFactor**: 因子编写及检验工具
- **RQOptimizer**: 股票多因子优化器

## 文档参考

官方文档索引：https://www.ricequant.com/doc/document-index.txt

## 代码风格要求

- 使用 Python 3.6+ 语法
- 导入模块时使用 `import rqdatac` 和 `from rqalpha.api import *`
- 策略函数包括 `init`, `handle_bar`, `before_trading`, `after_trading`
- 使用 `context` 对象存储策略状态
- 使用 `bar_dict` 获取行情数据
```

--------------------------------

### Configure Incremental Backtesting via Command Line

Source: https://www.ricequant.com/doc/rqalpha-plus/doc/advance-tutorial

Specify storage folder and strategy ID for incremental backtesting.

```bash
# 在当前目录的/persist下查看是否有文件名为2的文件夹，若有则读取文件内容，在本地保存回测结果的基础上运行增量回测，若没有则在当前目录/persist下生成一个命名为2的文件夹保存本次回测的结果
--persist-folder . --strategy-id 2
```

--------------------------------

### Retrieve US Treasury Yields Example

Source: https://www.ricequant.com/doc/rqdata/python/macro-economy

Example usage for fetching specific tenors of US Treasury yields.

```python
[In] rqdatac.econ.get_us_treasury_yield(start_date=None, end_date=None, tenor=["3M", "2Y", "10Y", "30Y"]) 
[Out]
	        3M 2Y 10Y 30Y
date						
2026-03-16 0.0372 0.0368 0.0423 0.0486
2026-03-17 0.0372 0.0368 0.0420 0.0485
2026-03-18 0.0373 0.0376 0.0426 0.0488
2026-03-19 0.0373 0.0379 0.0425 0.0483
2026-03-20 0.0374 0.0388 0.0439 0.0496
... ... ... ... ...
2026-06-08 0.0380 0.0415 0.0456 0.0503
2026-06-09 0.0379 0.0413 0.0453 0.0501
2026-06-10 0.0379 0.0413 0.0455 0.0503
2026-06-11 0.0378 0.0405 0.0445 0.0495
2026-06-12 0.0378 0.0409 0.0448 0.0497
```

--------------------------------

### Initialize CustomizedBenchmark from dictionary

Source: https://www.ricequant.com/doc/rqamsc/api-rqamsc

Use the from_doc method to convert a dictionary configuration into a CustomizedBenchmark object.

```python
import datetime
from rqamsc import CustomizedBenchmark

customized_benchmark_doc = {
    "name": "多时段自定义权重基准",
    "type": "composite",
    "weights": [
        {
            "start_date": "2015-01-01",
            "weights": [
                {
                    "order_book_id": "000001.XSHE",
                    "weight": 0.5
                },
                {
                    "order_book_id": "000002.XSHE",
                    "weight": 0.5
                }
            ]
        }
    ]
}
# 使用 from_doc 可将dict数据转换为相应对象
customized_benchmark = CustomizedBenchmark.from_doc(customized_benchmark_doc)
```

--------------------------------

### Check Upload Parameters

Source: https://www.ricequant.com/doc/rqamsc/api-rqamsc

View help documentation for the upload command.

```bash
rqalpha upload -h
```

--------------------------------

### Download sample data

Source: https://www.ricequant.com/doc/rqalpha-plus/doc/quick-start

Downloads a sample dataset to the local machine to enable quick testing of backtesting functionality.

```shell
rqsdk download-data --sample
```

--------------------------------

### Custom Stock Pool Limit Example

Source: https://www.ricequant.com/doc/rqoptimize/api/optimizer

Example of setting a 10%-30% weight constraint for a custom stock list.

```python
cons = [StockPoolLimit(stock_list=['300357.XSHE', '001914.XSHE', '002901.XSHE', '002552.XSHE', '300482.XSHE'],
upper_limit=0.3, lower_limit=0.1, hard=True)]
```

--------------------------------

### 更新 SDK 版本

Source: https://www.ricequant.com/doc/rqsdk/manual-rqsdk

更新 rqsdk 及本地安装的各产品版本，执行前请确保所有 Python 程序已停止。

```bash
rqsdk update
```

--------------------------------

### rqamsc.get_customized_instrument_price()

Source: https://www.ricequant.com/doc/rqamsc/api-rqamsc

Gets price history for a custom instrument.

```APIDOC
## rqamsc.get_customized_instrument_price(customized_ins_id)

### Description
Retrieves the price history for a specific custom instrument.

### Parameters
- **customized_ins_id** (str) - Required - The ID of the custom instrument.

### Returns
- **List[Dict]** - A list of price records containing 'date' and 'value'.
```

--------------------------------

### Benchmark Information Examples

Source: https://www.ricequant.com/doc/rqpattr/api/pattr-api

Supported formats for the benchmark_info parameter.

```python
{'type': 'index', 'name': '上证 300', 'detail': '000300.XSHG'}
```

```python
{'type': 'mixed_index', 'name': '20% 上证 300 + 80% 上证综指', 'detail': {'000300.XSHG': 0.2, '000008.XSHG': 0.8}}
```

```python
{'type': 'yield_rate', 'name': '1 年期国债', 'detail': 'YIELD1Y'}
```

```python
{'type': 'cash', 'name': "零收益现金", 'detail': 0.0}
```

--------------------------------

### 交互式登录

Source: https://www.ricequant.com/doc/rqdata/cli/manual

在未设置环境变量时，通过交互式命令输入 License 进行登录。

```bash
rqdata auth login
```

--------------------------------

### rqdata macro get

Source: https://www.ricequant.com/doc/rqdata/cli/manual

Queries specific macroeconomic indicators by name.

```APIDOC
## CLI Command: rqdata macro get

### Description
Queries specific macroeconomic indicators based on provided factor names and date ranges.

### Parameters
#### Payload Parameters
- **factors** (string) - Required - Macroeconomic indicator name or array of names.
- **start_date** (string) - Required - Start date (YYYY-MM-DD).
- **end_date** (string) - Required - End date (YYYY-MM-DD).

### Request Example
```bash
rqdata macro get --payload '{"start_date": "2023-01-01", "end_date": "2024-12-31", "factors": "社会消费品零售总额:当期同比:月"}' --format json
```
```

--------------------------------

### Investment Overview Returns Series Return Example

Source: https://www.ricequant.com/doc/rqamsc/api-rqamsc

Provides a sample JSON response structure containing daily, weekly, and monthly return data for benchmarks and products.

```python
[
    {
        "id": "000300.XSHG",
        "type": "benchmark",
        "name": "沪深300",
        "daily": [
            {
                "date": "2025-07-01",
                "daily_returns": 0.0019565917768269436,
                "cumulative_returns": 0.0019565917768269436
            }
        ],
        "weekly": [
            {
                "date": "2025-07-01",
                "daily_returns": 0.0019565917768269436,
                "cumulative_returns": 0.0019565917768269436
            }
        ],
        "monthly": [
            {
                "date": "2025-07-01",
                "daily_returns": 0.0019565917768269436,
                "cumulative_returns": 0.0019565917768269436
            }
        ]
    },
    {
        "id": "67d945ed3fc21df7d82acc4b",
        "type": "product",
        "name": "测试产品A",
        "daily": [
            {
                "date": "2025-07-01",
                "cumulative_returns": -0.9934833520008146,
                "geometric_excess_cumulative_returns": -0.993496077522052,
                "daily_returns": -0.9934833520008146,
                "excess_earnings": -0.9954399437776416
            }
        ],
        "weekly": [
            {
                "date": "2025-07-01",
                "cumulative_returns": -0.9934833520008146,
                "geometric_excess_cumulative_returns": -0.993496077522052
            }
        ],
        "monthly": [
            {
                "date": "2025-07-01",
                "cumulative_returns": -0.9934833520008146,
                "geometric_excess_cumulative_returns": -0.993496077522052
            }
        ]
    },
]
```

--------------------------------

### 配置 config.yaml 文件

Source: https://www.ricequant.com/doc/rqamsc/api-rqamsc

定义估值表文件存放路径以及产品与估值表文件的映射关系。

```yaml
path: D:\估值表文件目录
product_vr_map:
  - product: 产品1 # AMS中产品名称
    full_name: xxxx一号证券投资基金委托资产估值表 # 注意：1.该字段与AMS产品的"产品全称"字段保持一致 2. 该字段包含于估值表文件名称中且区别与其他估值表文件名称
    is_overwrite: true # 是否采用估值表对账中自动覆盖方式（true则表示使用估值表文件作为产品当天的估值结果）
  - product: 产品2
    full_name: xxxx二号证券投资基金委托资产估值表
```

--------------------------------

### Get Current Workspace

Source: https://www.ricequant.com/doc/rqamsc/api-rqamsc

Returns the currently active workspace object.

```python
rqamsc.current_workspace()
```

--------------------------------

### Example: Retrieve Below-Expectation Data

Source: https://www.ricequant.com/doc/rqdata/python/alternative-data

Demonstrates how to query for stocks that performed below expectations within a specified timeframe.

```python
rqdatac.consensus.get_expect_prob('000001.XSHE',expect_prob='below',start_date=None,end_date=None)
```

--------------------------------

### Get Workspaces

Source: https://www.ricequant.com/doc/rqamsc/api-rqamsc

Retrieves a list of all workspaces the user owns or participates in.

```python
rqamsc.get_workspaces()
```
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