SuperMind Local SDK

https://quant.10jqka.com.cn/view/help/
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### Example: Initializing TradeAPI and Running a Strategy

Source: https://quant.10jqka.com.cn/view/help/10

Demonstrates how to initialize the `TradeAPI` with a specified order policy (e.g., `MarketPolicy`) and then use the `research_trade` function to execute a strategy in a real trading environment. This example uses `signal_mode=False` for live trading and sets `recover_dt='today'` to resume from the current day.

```python
from tick_trade_api import TradeAPI
#Initializing TradeAPI requires specifying an order policy, MarketPolicy for market orders; LimitPolicy for limit orders
trade_api=TradeAPI('69271711',order_policy=MarketPolicy)

source_code='''
# Stock Strategy Template
def init(context):
    pass

# Execute before trading
def before_trading(context):
    pass

# Function to run at opening
def handle_bar(context, bar_dict):
    order_id = order('000001.SZ', 100)
    print(get_orders())
    try:
        cancel_order(order_id)
    except:
        print('Cancel order failed')
    print(get_open_orders())
    print(get_tradelogs())
    print(context.portfolio.stock_account)
    print(context.portfolio.positions)
'
rtrade = research_trade(
    'Research Environment Strategy',
     source_code,
     frequency='MINUTE',
     trade_api=trade_api,
     signal_mode=False,
     recover_dt='today'
)
```

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

### Get Factor Data API Example

Source: https://quant.10jqka.com.cn/view/help/22

Example usage of a hypothetical function `get_sfactor_data` to retrieve factor data. This function is used after a factor has been 'factorized' and stored in the system.

```Python
start = '2017-01-01'
end = '2018-10-10'
stocks_list = ['000001.SZ', '000002.SZ']
factors = ['trix']

# Assuming get_sfactor_data is defined elsewhere and accessible
# factor_data = get_sfactor_data(start, end, stocks_list, factors)
# print(factor_data.head())
print("Example call: get_sfactor_data('2017-01-01', '2018-10-10', ['000001.SZ','000002.SZ'], ['trix'])")

```

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

### Example: Portfolio Optimization Strategy

Source: https://quant.10jqka.com.cn/view/help/4

Demonstrates a complete portfolio optimization strategy using the Quant API, including initialization, constraints, and execution.

```APIDOC
## Example: Portfolio Optimization Strategy

### Description
This example demonstrates a comprehensive portfolio optimization strategy. It initializes an optimizer, sets benchmark and transaction costs, adds stock weight and tracking error constraints, and then executes the optimization to determine optimal stock weights for weekly rebalancing. The strategy focuses on selecting 50 stocks from the CSI 500 index.

### Method
This is an illustrative example of code that would interact with the API endpoints.

### Endpoint
N/A (Illustrative Code)

### Code Snippet
```python
import random
from quantapi import QuantAPI # Assuming a client library exists

# Initialize API client
api = QuantAPI(api_key='YOUR_API_KEY')

def init(context):
    # Set benchmark
    context.benchmark = '000905.SH'
    set_benchmark(context.benchmark)

    # Set transaction costs and slippage
    set_commission(PerShare(type='stock', cost=0.0002))
    set_slippage(PriceSlippage(0.002))
    set_volume_limit(0.25, 0.5)

    # Select 50 random stocks from CSI 500
    index_stocks = get_index_stocks('000905.SH', '2017-12-21')
    stocks = random.sample(list(index_stocks), 50)
    
    # Prepare initial holdings and weights for optimizer
    context.security = {stock: 0 for stock in stocks}
    context.hold = {stock: 0 for stock in stocks}
    context.weight_constraints = {stock: [0.0, 100.0] for stock in stocks}

    # Schedule weekly optimization
    run_weekly(func=optimize_portfolio, date_rule=1, reference_security='000001.SZ')

def optimize_portfolio(context, bar_dict):
    trade_date = get_datetime().strftime('%Y%m%d')

    # 1. Initialize Portfolio Optimizer via API
    optimizer_response = api.initialize_optimizer(
        stock_return=context.security,
        trade_date=trade_date,
        return_expect=True,
        opt_focus='UI',
        benchmark=context.benchmark,
        period='w',
        holds=context.hold,
        long_short='long-only'
    )
    optimizer_id = optimizer_response['optimizer_id']

    # 2. Add Constraints via API
    api.add_constraint(optimizer_id, con_type='stock_weight', args=context.weight_constraints)
    api.add_constraint(optimizer_id, con_type='tracking_error', args=(-0.02, 0.02))

    # 3. Get Optimized Weights via API
    weights_response = api.get_optimized_weights(optimizer_id)
    stock_weight = weights_response['optimized_weights']

    # 4. Execute Trades based on optimized weights
    for stock, weight in stock_weight.items():
        order_target_percent(stock, weight / 100)

```
```

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

### Get Fund Net Value Data: get_extras

Source: https://quant.10jqka.com.cn/view/help/4

Retrieves net value data for multiple funds. It accepts a list of security codes, start and end dates, and specific fields like 'unit_net_value', 'acc_net_value', or 'pre_net_value'. The function can return data as a dictionary of DataFrames or a Panel object, controlled by the `is_panel` parameter. Note that `start_date` and `count` cannot be used simultaneously.

```python
get_extras(
    security_list,
    start_date,
    end_date,
    fields,
    count=None,
    is_panel=False,
)
```

```python
# Get unit net value and cumulative net value for H300 ETF and CSI 500 ETF on specified dates, returning dictionary format
data = get_extras(['510300.SH', '510500.SH'], start_date='20230801', end_date='20230805', fields=['unit_net_value', 'acc_net_value'])
```

```python
# Get unit net value and cumulative net value for H300 ETF and CSI 500 ETF on specified dates, returning Panel format
panel_data = get_extras(['510300.SH', '510500.SH'], start_date='20230801', end_date='20230805', fields=['unit_net_value', 'acc_net_value'], is_panel=True)
```

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

### Factor Analysis Parameter Setup - Python

Source: https://quant.10jqka.com.cn/view/help/14

Defines key parameters for factor analysis and backtesting, including backtest dates, benchmark index, stock pool, number of quantiles for grouping, rebalancing period, and rebalancing frequency.

```python
start_date = '2021-05-22'      #回测开始时间
end_date = '2021-06-21'        #回测结束时间
benchindex = '000300.SH'       #基准指数设置
stockpool = '000905.SH'            #股票池设置
quantiles=3                    #因子分组数量
periods=1                      #调仓周期
frequency='daily'              #调仓频率'daily','weekly','monthly'一周按5个交易日计算，一月按21个交易日计算
```

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

### Get Historical Tick Data: get_tick (Python)

Source: https://quant.10jqka.com.cn/view/help/4

Retrieves historical market snapshot data for specified securities within a given date range. It requires securities codes, start and end dates, and a list of fields. The function returns a pandas DataFrame. This function also supports fetching intra-day tick data and may time out for current day data if the response exceeds six seconds.

```python
data = get_tick('000001.SZ', '20230801 09:23', '20230801 09:30', ['current'])
```

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

### Get Dominant Futures Contract Code

Source: https://quant.10jqka.com.cn/view/help/4

Retrieves the contract code for the dominant futures contract of a specified type on a given date. An optional sequence number can be provided to get other dominant contracts. The function returns a string representing the futures contract code.

```python
get_futures_dominate(symbol, date=None, seq=0)
```

```python
get_futures_dominate('RB', '20230801', 0)
```

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

### Python: Initialize Stock and Futures Accounts

Source: https://quant.10jqka.com.cn/view/help/12

Shows how to initialize both stock and futures accounts simultaneously within the 'init' function using set_subportfolios. This allows for trading both asset classes with their respective initial capital allocations.

```python
def init(context):
  #设置子账户,股票账户50万，期货账户50万.
  set_subportfolios([{'cash':500000,'type':'stock'},{'cash':500000,'type':'future'}])
```

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

### Implement Trading Strategy with init() and handle_bar() in Python

Source: https://quant.10jqka.com.cn/view/help/12

This Python code demonstrates the basic structure for implementing a trading strategy. The `init()` function is used for initializations, and `handle_bar()` is called for each time step (day or minute) to define trading logic based on historical data and market conditions. It includes logic for calculating moving averages and placing buy/sell orders.

```python
#初始化账户   
def init(context):
    #设置要交易的合约(AuT+D合约)   
    g.contract = 'AUTD'
    subscribe('AUTD')

#设置买卖条件，每个交易频率（日/分钟/tick）调用一次   
def handle_bar(context, bar_dict):
    #获取合约过去20日的收盘价数据   
    close = history_td(g.contract, fields=['close'], bar_count=20, fre_step='1d', is_panel=0)
    #计算五日均线价格   
    MA5 = close[g.contract].values[-5:].mean()
    #计算二十日均线价格   
    MA20 = close[g.contract].values.mean()
    #如果五日均线大于二十日均线   
    if MA5 > MA20:
        #计算所有可用资金可开仓手数(1000为合约乘数, 8.4%为保证金比例)   
        num=int(context.portfolio.accounts['METAL'].available_cash /(close[g.contract].values[-1]*1000*0.084))
        if num > 0:
            #使用所有可用资金开多进场   
            order_td(g.contract, num, 'buy', 'open')
            #记录这次开仓   
            log.info("买入 {0}手 {1}".format(num, g.contract))
    #如果五日均线小于二十日均线，并且目前有头寸   
    if (MA5 < MA20) and (context.portfolio.accounts['METAL'].positions[g.contract].buy_quantity > 0):
        #获取可平持仓   
        num = context.portfolio.accounts['METAL'].positions[g.contract].buy_quantity
        #平多离场   
        order_td(g.contract, num, 'sell', 'close', close_today=True)
        #记录这次平仓   
        log.info("卖出 {0}手 {1}".format(num, g.contract))

```

```python
#初始化账户   
def init(context):
    #设置要交易的合约(AuT+D合约)   
    g.contract = 'AUTD'
    subscribe('AUTD')

#设置买卖条件，每个交易频率（日/分钟/tick）调用一次   
def handle_bar(context, bar_dict):
    #获取合约过去20日的收盘价数据   
    close = history_td(g.contract, fields=['close'], bar_count=20, fre_step='1d', is_panel=0)
    #计算五日均线价格   
    MA5 = close[g.contract].values[-5:].mean()
    #计算二十日均线价格   
    MA20 = close[g.contract].values.mean()
    #如果五日均线大于二十日均线   
    if MA5 > MA20:
        #计算所有可用资金可开仓手数(1000为合约乘数, 9.8%为保证金比例)   
        num=int(context.portfolio.accounts['METAL'].available_cash /(close[g.contract].values[-1]*1000*0.098))
        if num > 0:
            #使用所有可用资金开多进场   
            order_td(g.contract, num, 'buy', 'open')
            #记录这次开仓   
            log.info("买入 {0}手 {1}".format(num, g.contract))
    #如果五日均线小于二十日均线，并且目前有头寸   
    if (MA5 < MA20) and (context.portfolio.accounts['METAL'].positions[g.contract].buy_quantity > 0):
        #获取可平持仓   
        num = context.portfolio.accounts['METAL'].positions[g.contract].buy_quantity
        #平多离场   
        order_td(g.contract, num, 'sell', 'close', close_today=True)
        #记录这次平仓   
        log.info("卖出 {0}手 {1}".format(num, g.contract))


    margin=context.portfolio.accounts['METAL'].margin
    log.info(margin)
    record(margin=margin)

```

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

### Get Concept Stocks

Source: https://quant.10jqka.com.cn/view/help/4

Retrieves the constituent stocks for a given concept code on a specific date.

```APIDOC
## GET /get_concept_stocks

### Description
Retrieves the constituent stocks for a given concept code on a specific date. This is useful for identifying which stocks belong to a particular thematic concept.

### Method
GET

### Endpoint
/get_concept_stocks

### Parameters
#### Query Parameters
- **symbol** (str) - Required - The concept code or concept index code (e.g., '886031.TI').
- **date** (Optional[str]) - Optional - The query date in 'YYYYMMDD' format (e.g., '20230801'). Defaults to the previous trading day.

### Request Example
```python
# Get stocks for concept '886031.TI' on August 1, 2023
stock_list = get_concept_stocks('886031.TI', date='20230801')
print(stock_list)
```

### Response
#### Success Response (200)
- **list** - A list of stock codes (strings) that are constituents of the specified concept on the given date.

#### Response Example
```
['300235.SZ', '002722.SZ', '300229.SZ', '688609.SH', ...]
```
```

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

### Downloading Simulation/Real Trading Client

Source: https://quant.10jqka.com.cn/view/help/14

Download link for the client that supports both simulation and real trading. It's recommended to test strategies with simulated trading before going live.

```url
https://download.10jqka.com.cn/index/download/id/709
```

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

### Get Orders

Source: https://quant.10jqka.com.cn/view/help/4

Retrieves details for specified orders or all orders for a given instrument on the current day.

```APIDOC
## GET /get_orders

### Description
Retrieves details for specified orders or all orders for a given instrument on the current day.

### Method
GET

### Endpoint
/get_orders

### Parameters
#### Path Parameters
None

#### Query Parameters
- **order_id** (str) - Optional - The specific order ID to retrieve.
- **order_book_id** (str) - Optional - The instrument code to filter orders by.

### Request Example
```json
{
  "get_orders": {
    "order_id": "aade994938b64871a4191971e8501eaf",
    "order_book_id": "000001.SZ"
  }
}
```

### Response
#### Success Response (200)
- **orders** (list) - A list of Order objects. Each object contains details like order ID, symbol, creation time, type, amount, filled amount, status, etc. Returns None if there are no orders for the day.

#### Response Example
```json
{
  "orders": [
    {
      "order_id": "aade994938b64871a4191971e8501eaf",
      "symbol": "000001.SZ",
      "created": "2024-11-18T09:31:00",
      "order_type": "LONG",
      "limit_price": 0,
      "amount": 8500,
      "filled_amount": 8500,
      "type": "MARKET",
      "transaction_cost": 19.8204275,
      "avg_price": 11.659075,
      "status": "FILLED",
      "datetime": "2024-11-18T09:31:00",
      "offset_flag": null
    }
  ]
}
```
```

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

### Portfolio Optimizer Application Example

Source: https://quant.10jqka.com.cn/view/help/4

Demonstrates a Python strategy using the Portfolio Optimizer to select 50 stocks from CSI 500, simulate its performance with weekly rebalancing, and apply stock weight and tracking error constraints. It includes initialization of the trading environment, commission, slippage, and volume limits.

```python
# From CSI 500 randomly select 50 stocks, simulate CSI 500 trend, weekly rebalancing
# Advantages of Stratified Sampling method:
# 1. When the number of index constituents is large, full replication requires significant capital.
# 2. Many stocks in the index have low trading volume, resulting in high transaction costs, and it may be impossible to fully replicate the constituent stocks of CSI 500.
import random
def init(context):
    # Set benchmark return: CSI 500 Index
    set_benchmark('000905.SH')
    # Print logs
    log.info('Strategy started, initialization function runs only once globally')
    # Set stock transaction commission to 0.02% (commission is deducted after buy and sell, excluding taxes, taxes are deducted after selling)
    set_commission(PerShare(type='stock',cost=0.0002))
    # Set stock trading slippage of 0.5%, indicating that the buy price is the actual price multiplied by 1.005, and the sell price is the actual price multiplied by 0.995
    set_slippage(PriceSlippage(0.002))
    # Set maximum transaction proportion per day to 25%, and per minute to 50%
    # When running daily frequency, if the order quantity exceeds 25% of the actual daily transaction volume, all orders will not be executed.
    # When running at minute frequency, if the order quantity exceeds 50% of the current minute's actual transaction volume, all orders will not be executed.
    set_volume_limit(0.25,0.5)
    # Randomly select 50 stocks from CSI 500
    stocks=random.sample(list(get_index_stocks('000905.SH','2017-12-21')), 50)
    # Construct stock list in dict format for subsequent use of the portfolio optimizer
    context.security={ia:0 for ia in stocks} 
    # Construct initial stock weights
    context.hold={ia:0 for ia in stocks}
    # Stock weight limits
    context.weight={ia :[0.0,100.0] for ia in stocks}
    # Weekly rebalancing
    run_weekly(func=optmize, date_rule=1, reference_security='000001.SZ')

def optmize(context, bar_dict):
    time=get_datetime().strftime('%Y%m%d')
    # Construct portfolio optimizer, setting the optimization target to maximize utility, using historical returns as expected returns
    opt=OptimizePort(context.security, '2017-12-21', True, opt_focus='UI', benchmark='000905.SH', period='w', holds=context.hold, long_short='long-only')
    opt.add_constraint('stock_weight',context.weight)
    # Add tracking error constraint
    opt.add_constraint('tracking_error',(-0.02,0.02))
    # opt.add_constraint('industry','industry_neutralize')
    # Use portfolio optimizer to solve
    stock_weight = opt.optimized_weight()
    # Adjust weights according to the portfolio optimizer results
    for ia in stock_weight.keys():
        order_target_percent(ia, stock_weight[ia]/100)

```

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

### Get Futures Contract Information

Source: https://quant.10jqka.com.cn/view/help/4

Fetches detailed information for a specific futures contract on a given date.

```APIDOC
## GET /websites/quant_10jqka_cn_view_help/get_futures_info

### Description
Fetches detailed information for a specific futures contract on a given date.

### Method
GET

### Endpoint
/websites/quant_10jqka_cn_view_help/get_futures_info

### Parameters
#### Query Parameters
- **symbol** (string, Required) - The futures contract symbol (e.g., 'RB' for Rebar).
- **date** (string, Optional) - Query date in 'YYYYMMDD' format. Defaults to the previous trading day.

### Response
#### Success Response (200)
- **dict** - A dictionary containing futures contract details:
    - 'symbol' (string): Contract symbol.
    - 'name' (string): Contract name.
    - 'commission' (float): Commission rate.
    - 'margin_rate' (float): Margin rate.
    - 'contract_multiplier' (float): Contract multiplier.
    - 'exchange' (string): Exchange name.
    - 'unit' (float): Trading unit.

### Response Example
```json
{
  "symbol": "RB",
  "name": "螺纹钢",
  "commission": 0.0001,
  "margin_rate": 0.05,
  "contract_multiplier": 10.0,
  "exchange": "上海期货交易所",
  "unit": 1.0
}
```
```

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

### Initialize Strategy with Context

Source: https://quant.10jqka.com.cn/view/help/4

The `init` function is called once at the beginning of a backtest or simulation. It's used to set up initial account information, strategy parameters, and global variables.

```python
from mindgo_api import *

def init(context):
    # Set the target security (Ping An Bank), be careful not to overwrite system variables when naming
    context.stock = '000001.SZ'

```

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

### Get Trade Logs

Source: https://quant.10jqka.com.cn/view/help/4

Retrieves details for all executed trades for specified orders or instruments on the current day.

```APIDOC
## GET /get_tradelogs

### Description
Retrieves details for all executed trades for specified orders or instruments on the current day.

### Method
GET

### Endpoint
/get_tradelogs

### Parameters
#### Path Parameters
None

#### Query Parameters
- **order_id** (str) - Optional - The specific order ID to retrieve trades for.
- **order_book_id** (str) - Optional - The instrument code to filter trades by.

### Request Example
```json
{
  "get_tradelogs": {
    "order_id": "97ae3e30206d4d3fa5d28a15d0b08018",
    "order_book_id": "000001.SZ"
  }
}
```

### Response
#### Success Response (200)
- **trades** (list) - A list of Trade objects. Each object contains details like instrument, execution time, order ID, price, quantity, commission, etc. Returns None if there are no trades for the day.

#### Response Example
```json
{
  "trades": [
    {
      "order_book_id": "000001.SZ",
      "trading_datetime": "2024-11-19T09:31:00",
      "datetime": "2024-11-19T09:31:00",
      "order_id": "97ae3e30206d4d3fa5d28a15d0b08018",
      "last_price": 11.779375,
      "last_quantity": 8400,
      "commission": 19.78935,
      "tax": 0,
      "transaction_cost": 19.78935,
      "side": "BUY",
      "position_effect": null,
      "exec_id": "e4815a0f45954586b221d4a2ae4530d1",
      "frozen_price": 11.75,
      "close_today_amount": 0,
      "pnl": null
    }
  ]
}
```
```

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

### Get Optimized Portfolio Weights

Source: https://quant.10jqka.com.cn/view/help/4

Calculates and returns the optimized portfolio weights based on the configured optimizer and constraints.

```APIDOC
## GET /api/portfolio/optimizer/{optimizer_id}/weights

### Description
Calculates and returns the optimized portfolio weights based on the configured optimizer and constraints.

### Method
GET

### Endpoint
/api/portfolio/optimizer/{optimizer_id}/weights

### Parameters
#### Path Parameters
- **optimizer_id** (str) - Required - The ID of the portfolio optimizer.

### Request Example
```json
{
  "optimizer_id": "opt_12345"
}
```

### Response
#### Success Response (200)
- **optimized_weights** (dict) - A dictionary where keys are stock codes and values are their optimized weights (as percentages).

#### Response Example
```json
{
  "optimized_weights": {
    "000001.SZ": 10.5,
    "000002.SZ": 15.2,
    "000003.SZ": 8.3
  }
}
```
```

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

### SFTP Command Help

Source: https://quant.10jqka.com.cn/view/help/20

Displays a list of available commands within the SFTP client. This is useful for quickly referencing command syntax and functionality during an SFTP session.

```bash
sftp> help
```

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

### Initialize Portfolio Optimizer

Source: https://quant.10jqka.com.cn/view/help/4

Constructs a portfolio optimizer object. It requires stock return data, trade date, and optionally takes parameters for return expectation calculation, optimization focus, benchmark, risk aversion, transaction costs, rebalancing period, current holdings, and long/short strategy.

```python
OptimizePort(stock_return, trade_date, return_expect, opt_focus='UI', benchmark='000905.SH', loss_aversion=0.5, cost=0.003, period='d', holds=0, long_short='long-only')
```

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

### Get Dominant Futures Contract Code

Source: https://quant.10jqka.com.cn/view/help/4

Retrieves the code of the dominant futures contract for a given symbol and date.

```APIDOC
## GET /websites/quant_10jqka_cn_view_help/get_futures_dominate

### Description
Retrieves the code of the dominant futures contract for a given symbol and date.

### Method
GET

### Endpoint
/websites/quant_10jqka_cn_view_help/get_futures_dominate

### Parameters
#### Query Parameters
- **symbol** (string, Required) - The futures contract symbol (e.g., 'RB' for Rebar).
- **date** (string, Optional) - Query date in 'YYYYMMDD' format. Defaults to the previous trading day.
- **seq** (integer, Optional) - Sequence number for dominant contracts. Defaults to 0 for the main dominant contract.

### Response
#### Success Response (200)
- **string** - The dominant futures contract code (e.g., 'RB2310').

### Response Example
```json
"RB2310"
```
```

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

### Initialize Portfolio Optimizer

Source: https://quant.10jqka.com.cn/view/help/4

Initializes the portfolio optimizer with specified parameters for constructing an optimized investment portfolio.

```APIDOC
## POST /api/portfolio/optimizer/initialize

### Description
Initializes the portfolio optimizer with specified parameters for constructing an optimized investment portfolio.

### Method
POST

### Endpoint
/api/portfolio/optimizer/initialize

### Parameters
#### Request Body
- **stock_return** (pd.Series or dict) - Required - Expected stock returns. For pd.Series, index is stock code, value is return. For dict, key is stock code, value is return.
- **trade_date** (str) - Required - The date for optimization (e.g., '2022-12-14').
- **return_expect** (bool) - Required - Method for calculating expected returns. `True` uses historical average, `False` uses provided `stock_return`.
- **opt_focus** (str) - Optional - Optimization focus. Options: 'UI' (Utility Maximization, default), 'IR' (Information Ratio), 'MVO' (Maximum Sharpe Ratio).
- **benchmark** (str) - Optional - Benchmark index. Supported: '000905.SH' (default), '000300.SH', '000016.SH', '000906.SH', '000010.SH', '000001.SH'.
- **loss_aversion** (float) - Optional - Coefficient of loss aversion. Defaults to 0.5.
- **cost** (float) - Optional - Transaction cost. Defaults to 0.003.
- **period** (str) - Optional - Rebalancing period. Options: 'd' (daily, default), 'w' (weekly), 'm' (monthly).
- **holds** (pd.Series or dict or int) - Optional - Current holdings. For pd.Series/dict, index/key is stock code, value is weight. `0` for no holdings (default).
- **long_short** (str) - Optional - Specifies if short selling is allowed. Options: 'long-only' (default), 'short-long'.

### Request Example
```json
{
  "stock_return": {"000001.SZ": 0.01, "000002.SZ": 0.015},
  "trade_date": "2022-12-14",
  "return_expect": false,
  "opt_focus": "UI",
  "benchmark": "000905.SH",
  "loss_aversion": 0.5,
  "cost": 0.003,
  "period": "d",
  "holds": 0,
  "long_short": "long-only"
}
```

### Response
#### Success Response (200)
- **optimizer_id** (str) - Unique identifier for the initialized optimizer.

#### Response Example
```json
{
  "optimizer_id": "opt_12345"
}
```
```

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

### Get All Tradable Futures Contracts

Source: https://quant.10jqka.com.cn/view/help/4

Retrieves a list of all tradable futures contracts for a specified underlying symbol on a given date.

```APIDOC
## GET /websites/quant_10jqka_cn_view_help/get_future_code

### Description
Retrieves a list of all tradable futures contracts for a specified underlying symbol on a given date.

### Method
GET

### Endpoint
/websites/quant_10jqka_cn_view_help/get_future_code

### Parameters
#### Query Parameters
- **underlying_symbol** (string, Required) - The underlying symbol for the futures contracts (e.g., 'RB').
- **date** (string, Optional) - The date for which to retrieve contracts, in 'YYYYMMDD' format.

### Response
#### Success Response (200)
- **list** - A list of futures contract codes (e.g., ['RB1809', 'RB1810', ...]).

### Response Example
```json
[
  "RB1809",
  "RB1810",
  "RB1811",
  "RB1812",
  "RB1901",
  "RB1902",
  "RB1903",
  "RB1904",
  "RB1905",
  "RB1906",
  "RB1907",
  "RB1908"
]
```
```

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

### Python: Initialize Futures Account

Source: https://quant.10jqka.com.cn/view/help/12

Demonstrates how to initialize a futures trading account within the 'init' function using set_subportfolios. This is crucial for trading futures instruments and requires specifying the initial cash and account type.

```python
def init(context):
  #设置子账户,股票账户0万，期货账户50万.
  set_subportfolios([{'cash':0,'type':'stock'},{'cash':500000,'type':'future'}])
```

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

### Get Statistics

Source: https://quant.10jqka.com.cn/view/help/4

Retrieves the number of stocks within various price change ranges for a specific date or the current day.

```APIDOC
## GET /api/get_stats

### Description
Used to obtain the number of stocks within various price change intervals for daily or real-time market data. You can specify the date for the statistics.

### Method
GET

### Endpoint
/api/get_stats

### Parameters
#### Query Parameters
- **date** (Optional[str]) - Optional - The date for which to retrieve statistics. Defaults to the current day.

### Request Example
```python
from mindgo_api import *
data = get_stats('20230810')
```

### Response
#### Success Response (200)
- **list** - A list where each element represents the count of stocks within a specific price change interval.

#### Response Example
```json
[
  10, 2, 5, 13, 13, 26, 76, 113, 418, 1175, 222, 1706, 737, 245, 106, 77, 28, 18, 8, 3, 31
]
```

### Notes
- There are 21 price change intervals: (-infinity, -9%), [-9%, -8%), ..., [-1%, 0%), [0%], (0%, 1%], ..., (9%, +infinity).
```

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

### Python: Handling Potential Issues with Initial Holdings in Live Trading

Source: https://quant.10jqka.com.cn/view/help/10

Illustrates a common issue where strategies may encounter errors due to initial holdings in a live trading account that are not present in the backtesting environment. The provided code snippet shows a potential conflict when deleting stock information from `context.information` if the stock was not initially bought within the strategy. It suggests using `dict.pop()` as a safer alternative to `del` for handling dictionary keys that might not exist.

```python
import time

def init(context):
    g.information = {}
    g.symbols = ['000001.SZ','600519.SH']

def handle_bar(context):
    for symbol in g.symbols:
        order(symbol,100)
    for symbol in list(context.portfolio.positions):
        g.information[symbol] = 1
    time.sleep(3)
    for symbol in list(context.portfolio.positions):
        print(g.infomation[symbol])
```

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

### Get Open Orders

Source: https://quant.10jqka.com.cn/view/help/4

Retrieves details for all open (unfulfilled) orders for specified instruments or all open orders on the current day.

```APIDOC
## GET /get_open_orders

### Description
Retrieves details for all open (unfulfilled) orders for specified instruments or all open orders on the current day.

### Method
GET

### Endpoint
/get_open_orders

### Parameters
#### Path Parameters
None

#### Query Parameters
- **order_id** (str) - Optional - The specific order ID to retrieve.
- **order_book_id** (str) - Optional - The instrument code to filter open orders by.

### Request Example
```json
{
  "get_open_orders": {
    "order_id": "aade994938b64871a4191971e8501eaf",
    "order_book_id": "000001.SZ"
  }
}
```

### Response
#### Success Response (200)
- **orders** (list) - A list of Order objects representing open orders. Each object contains details similar to `get_orders`. Returns None if there are no open orders for the day.

#### Response Example
```json
{
  "orders": [
    {
      "order_id": "aade994938b64871a4191971e8501eaf",
      "symbol": "000001.SZ",
      "created": "2024-11-18T09:31:00",
      "order_type": "LONG",
      "limit_price": 0,
      "amount": 8500,
      "filled_amount": 8500,
      "type": "MARKET",
      "transaction_cost": 19.8204275,
      "avg_price": 11.659075,
      "status": "FILLED",
      "datetime": "2024-11-18T09:31:00",
      "offset_flag": null
    }
  ]
}
```
```

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

### Initialize Trade API in Python

Source: https://quant.10jqka.com.cn/view/help/10

Initializes the TradeAPI for simulated or live trading using a provided account ID. This is a prerequisite for interacting with the trading interface in a Python environment.

```python
from tick_trade_api.api import TradeAPI
trade_api = TradeAPI(account_id='84728199') #填入已登录的资金账号
```

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

### Get Tradable Options Contracts

Source: https://quant.10jqka.com.cn/view/help/4

Retrieves a list of all tradable ETF options contracts from Shanghai and Shenzhen exchanges for a given date.

```APIDOC
## GET /websites/quant_10jqka_cn_view_help/get_option_code

### Description
Retrieves a list of all tradable ETF options contracts from Shanghai and Shenzhen exchanges for a given date.

### Method
GET

### Endpoint
/websites/quant_10jqka_cn_view_help/get_option_code

### Parameters
#### Query Parameters
- **date** (string, Optional) - The query date in 'YYYYMMDD' format.

### Response
#### Success Response (200)
- **pandas.DataFrame** - A DataFrame containing tradable option contracts, with columns 'symbol' and 'month'.

### Response Example
```json
{
  "symbol": "90001832.SZ",
  "month": null
}
```
```

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

### Write a Stock Trading Strategy in Python

Source: https://quant.10jqka.com.cn/view/help/12

Example of a Python script for a stock trading strategy using moving averages. It defines init and handle_bar functions to initialize and execute trading logic based on 5-day and 20-day moving averages for a specific stock (600519.SH). The strategy buys when the 5-day MA crosses above the 20-day MA and sells when it crosses below. It utilizes functions like `history`, `order_target_percent`, and `log.info`.

```python
#初始化账户
def init(context):
    g.index='600519.SH'

def handle_bar(context,bar_dict):
    close = history(g.index, ['close'], 20, '1d', False, fq = 'pre', is_panel=0)
    MA5 = close['close'].values[-5:].mean()
    #计算二十日均线价格
    MA20 = close['close'].values.mean()
    #如果五日均线大于二十日均线
    if MA5 > MA20:
        #使用所有现金买入证券
        order_target_percent(g.index,1)
        #记录本次买入
        log.info("全仓买入{0}".format(g.index))
    #如果五日均线小于二十日均线
    if MA5 < MA20 :
        #卖出所有证券
        order_target_percent(g.index,0)
        #记录本次卖出
        log.info("全仓卖出{0}".format(g.index))


```

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

### Get All Securities Information

Source: https://quant.10jqka.com.cn/view/help/4

Retrieves a list of all listed securities and their basic information for a specified date. Supports filtering by security type.

```APIDOC
## GET /websites/quant_10jqka_cn_view_help/get_all_securities

### Description
Retrieves a list of all listed securities and their basic information for a specified date. Supports filtering by security type.

### Method
GET

### Endpoint
/websites/quant_10jqka_cn_view_help/get_all_securities

### Parameters
#### Query Parameters
- **ty** (string, Optional) - Security type filter. Possible values include 'stock', 'hstock', 'ustock', 'index', 'etf', 'lof', 'fja', 'fjb', 'qdii', 'ota', 'fund', 'futures', 'commodity_futures', 'bond_futures', 'option', 'cbond', 'forex', 'metal'. Defaults to all types.
- **date** (string, Optional) - Query date in 'YYYYMMDD' format. Defaults to the previous trading day.

### Response
#### Success Response (200)
- **pandas.DataFrame** - A DataFrame with security codes as the index and columns including name, listed_date, de_listed_date, type, etc.

### Response Example
```json
{
  "benchmark": null,
  "de_listed_date": "2200-01-01",
  "display_name": "*ST建艺",
  "end_date": "2200-01-01",
  "exchange": "深交所",
  "listed_date": "2016-03-11",
  "market1": "212100",
  "market2": "001001",
  "market_hq": "33",
  "min_round_lot": 0,
  "name": "stjy",
  "order_book_id": "002789.SZ",
  "parent": null,
  "price_limit": 0.00,
  "round_lot": 100,
  "start_date": "2016-03-11",
  "symbol_hq": "002789",
  "type": "stock",
  "underlying_symbol": null
}
```
```
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