### Minimal Backtest Example in QTEasy

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/back_testing/2. run_backtest.md

This example shows the basic setup for running a backtest. Ensure your data source and Operator are configured before execution.

```python
import qteasy as qt

# 假设已配置数据源与 Operator
op = qt.Operator(strategies='dma', signal_type='PT', run_freq='d')
qt.configure(asset_pool='000001.SZ', invest_start='2020-01-01', invest_end='2023-12-31')
result = qt.run(op, mode=1)
```

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

### Import QTEASY and check version

Source: https://github.com/shepherdpp/qteasy/blob/master/README.md

Import the QTEASY library and display its current version. This is a basic step to verify the installation and start using the library.

```python
import qteasy as qt
qt.__version__
```

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

### QTEASY configuration file example

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/tutorials/1-get-started.md

An example of a correctly configured `qteasy.cfg` file, including settings for tushare token and database connection.

```default
tushare_token = xxxxxxxxxxxxxxxxxxxxx

local_data_source = database
local_db_host = localhost
local_db_port = 3306
local_db_user = user_name
local_db_password = pass_word
local_db_name = qt_db
```

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

### Install qteasy with Pip Mirrors

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/faq.md

Use a mirror source to speed up installation in regions with restricted network access.

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

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

### Install QTEASY

Source: https://github.com/shepherdpp/qteasy/wiki/1-‐-安装方法及初始配置

Install the qteasy package using pip within an activated virtual environment.

```bash
pip install qteasy
```

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

### Install TA-Lib for QTEASY

Source: https://github.com/shepherdpp/qteasy/blob/master/README.md

Install the TA-Lib API for use with QTEASY, which enables all built-in trading strategies. Note that this also requires a C-language TA-Lib installation.

```bash
pip install 'qteasy[talib]'
```

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

### Example of Correct qteasy.cfg Configuration

Source: https://github.com/shepherdpp/qteasy/wiki/1-‐-安装方法及初始配置

This example shows the correct format for configuring Tushare token and MySQL database connection details in qteasy.cfg. Note that angle brackets should be omitted.

```ini
tushare_token = 2dff3f034aa966479c81e4b4b0736fb081b740abb2xxxxxxxxxxxxxxxxxxxxx

local_data_source = database

local_db_host = localhost
local_db_port = 3306
local_db_user = user_name
local_db_password = pass_word
local_db_name = qt_db
```

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

### Install QTEASY with Feather support

Source: https://github.com/shepherdpp/qteasy/blob/master/README.md

Install QTEASY along with the 'pyarrow' library for Feather file support. This is useful for efficient local data storage.

```bash
pip install 'qteasy[feather]'
```

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

### Configure qteasy.cfg

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/faq.md

Examples of correct configuration syntax for database connections in qteasy.cfg.

```default
local_db_port = 3306
```

```default
tushare_token = 2dff3f034aa966479c81e4b4b0736fb081b740abb2xxxxxxxxxxxxxxxxxxxxx

local_data_source = database

local_db_host = localhost
local_db_port = 3306
local_db_user = user_name
local_db_password = pass_word
local_db_name = ts_db
```

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

### Manually install TA-Lib

Source: https://github.com/shepherdpp/qteasy/blob/master/README.md

Manually install the TA-Lib Python package. This is an alternative to using the QTEASY-specific installation command.

```bash
pip install ta-lib
```

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

### Install pymysql

Source: https://github.com/shepherdpp/qteasy/wiki/1-‐-安装方法及初始配置

Install the pymysql library, which is required for MySQL database support in QTEASY.

```bash
pip install pymysql
```

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

### Get QTEASY Root Path

Source: https://github.com/shepherdpp/qteasy/wiki/1-‐-安装方法及初始配置

Print the root directory path of the qteasy installation.

```python
import qteasy as qt
print(qt.QT_ROOT_PATH)
```

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

### Display Startup Settings

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/api/api_reference.md

Prints the startup settings stored in QTEasy's configuration file.

```APIDOC
## GET /start_up_settings

### Description
Prints the startup settings stored in QTEasy's configuration file.

### Method
GET

### Endpoint
/start_up_settings

### Response
#### Success Response (200)
- **None** - This function does not return any value, but prints settings to the console.

### Response Example
```
Start up settings:
--------------------
local_data_source = file
local_data_file_type = csv
local_data_file_path = data/
```
```

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

### Get Historical Data with Specific Htypes

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/api/history_data.md

Use special htypes to retrieve specific data, such as index weighting information. This example fetches the weight data for '000001.SZ' within the 'HS300' index.

```python
>>> # 使用特殊的htypes，可以获取特定的数据，如指数权重数据，下面的代码获取000001.SZ在HS300指数重的权重数据，单位为百分比
>>> qt.get_history_data(htype_names='wt_id:000300.SH', shares='000001.SZ, 000002.SZ', start='20191225', end='20200105')
{'000001.SZ':
            wt_idx:000300.SH
2020-01-02        1.1714
2020-01-03        1.1714,
'000002.SZ':
            wt_idx:000300.SH
2020-01-02        1.3595
2020-01-03        1.3595
}
```

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

### Get HistoryPanel Segment

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/api/HistoryPanel.md

Illustrates how to extract a specific date range segment from a HistoryPanel using the .isegment() method with start and end indices. The method returns a new HistoryPanel containing data only for the specified date range.

```python
>>> hp = HistoryPanel(np.array([[[10, 20, 30, 40, 50]]*10]*3),
...                          levels=['000001', '000002', '000003'],
...                          rows=pd.date_range('2015-01-05', periods=10),
...                          columns=['open', 'high', 'low', 'close', 'volume'])
>>> hp.isegment(2, 5)
```

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

### Get Adjusted Closing Price using DataType

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/manage_data/02. datatypes.md

Demonstrates how to use a predefined DataType object ('close|b') to fetch the adjusted closing price for a specific stock within a date range. Ensure the qteasy library is installed and the QT_DATA_SOURCE is configured.

```python
import qteasy
from qteasy.datatypes import DataType

# Get the adjusted closing price for Gree Electric Appliances from 2025-02-01 to 2025-02-27
close_b = DataType(name='close|b', asset_type='E', freq='D')
close_b.get_data_from_source(
        datasource=qteasy.QT_DATA_SOURCE, 
        symbols='000651.SZ',
        starts='2025-02-01', 
        ends='2025-02-27',
)
```

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

### Import QTEASY and Print Version

Source: https://github.com/shepherdpp/qteasy/wiki/1-‐-安装方法及初始配置

Import the qteasy library and print its version. This action also triggers the initial setup and creates a configuration file if it doesn't exist.

```python
import qteasy as qt
print(qt.__version__)
```

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

### View Current QTEasy Settings

Source: https://github.com/shepherdpp/qteasy/blob/master/README.md

Use `qt.start_up_settings()` to view the current configuration of QTEasy. This helps verify that your settings are correctly applied.

```python
qt.start_up_settings()
```

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

### Install TA-Lib Python Wrapper

Source: https://github.com/shepherdpp/qteasy/wiki/1-‐-安装方法及初始配置

After installing the C language TA-Lib, install its Python wrapper using pip.

```bash
pip install TA-Lib
```

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

### qteasy.run() - Unified Entry Point

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/api/use_qteasy.md

The `run` function serves as the unified entry point for QTEASY operations. It executes a given Operator based on the current configuration, supporting various modes like backtesting, optimization, or live trading. Temporary configuration overrides can be applied via keyword arguments.

```APIDOC
## POST /api/run

### Description
Starts QTEASY configuration and runs a given Operator in different modes (backtesting, optimization, live trading).

### Method
POST

### Endpoint
/api/run

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **op** (Operator) - Required - An Operator object with configured strategy and running parameters.
- **kwargs** (dict) - Optional - Temporary configuration items to override global settings, such as `mode`, `asset_pool`, `invest_start`, `invest_end`.

### Request Example
```json
{
  "op": {
    "strategy_name": "dma"
  },
  "mode": "BACKTEST_MODE",
  "invest_start": "2023-01-01",
  "invest_end": "2023-12-31"
}
```

### Response
#### Success Response (200)
- **result** (dict or list) - The structure of the returned data depends on the `mode`.
  - For `mode == 1` (backtesting), typically returns a dictionary with backtesting results.
  - For `mode == 2` (optimization), typically returns a list of results for multiple parameter sets.
  - For live trading and other modes, refer to `Operator.run()` documentation.

#### Response Example
```json
{
  "result": {
    "total_return": 0.15,
    "sharpe_ratio": 1.2,
    "drawdown": 0.05
  }
}
```
```

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

### Install TA-Lib Dependency

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/faq.md

Instructions for installing the C-based TA-Lib library on various operating systems before installing the Python wrapper.

```bash
$ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
$ brew install ta-lib
```

```bash
$ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
$ arch -arm64 brew install ta-lib
```

```bash
$ tar -xzf ta-lib-0.4.0-src.tar.gz
$ cd ta-lib/
$ ./configure --prefix=/usr
$ make
$ sudo make install
```

```bash
$ pip install ta-lib
```

```bash
$ arch -arm64 python -m pip install --no-cache-dir ta-lib
```

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

### Install PyArrow manually

Source: https://github.com/shepherdpp/qteasy/blob/master/README.md

Manually install the PyArrow library, which is used for Feather file operations. This can be done after QTEASY is installed.

```bash
pip install pyarrow
```

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

### Install Plotly for Interactive Charts

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/getting_started.md

Install Plotly for basic interactive chart functionality. For enhanced interactivity in Notebooks (FigureWidget), install ipywidgets and anywidget.

```bash
pip install plotly
```

```bash
pip install ipywidgets anywidget
```

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

### Install PyTables with Conda

Source: https://github.com/shepherdpp/qteasy/blob/master/README.md

Install the PyTables library using Conda, which is required for HDF file operations. This library cannot be automatically installed via pip.

```bash
conda install pytables
```

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

### Run Strategy Optimization with Grid Search

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/optimization/2. run_optimization.md

This example demonstrates how to run strategy optimization using the 'grid' method. It initializes a 'dma' strategy operator, configures backtesting parameters, and then executes the optimization with specified sample and output counts. Ensure necessary parameters like asset pool and investment period are configured.

```python
import qteasy as qt

op = qt.Operator(strategies='dma', signal_type='PT', run_freq='d')
# dma 策略带 short_period、long_period 等可调参数
qt.configure(asset_pool='000001.SZ', invest_start='2020-01-01', invest_end='2023-12-31')
result = qt.run(op, mode=2, opti_method='grid', opti_sample_count=100, opti_output_count=10)
```

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

### Manage qteasy startup settings

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/api/api_reference.md

Functions to view, update, and remove startup configuration settings.

```pycon
>>> import qteasy as qt
>>> qt.start_up_settings()
Start up settings:
--------------------
local_data_source = file
local_data_file_type = csv
local_data_file_path = data/
```

```pycon
>>> import qteasy as qt
>>> qt.start_up_settings()
Start up settings:
--------------------
local_data_source = file
local_data_file_type = csv
local_data_file_path = data/
```

```pycon
>>> qt.update_start_up_setting(local_data_source='database', local_data_file_type='feather')
Start up settings updated successfully!
```

```pycon
>>> qt.start_up_settings()
Start up settings:
--------------------
local_data_source = file
local_data_file_type = feather
local_data_file_path = data/
```

```pycon
>>> import qteasy as qt
>>> qt.start_up_settings()
Start up settings:
--------------------
local_data_source = file
local_data_file_type = csv
local_data_file_path = data/
```

```pycon
>>> qt.remove_start_up_setting('local_data_source')
Start up settings removed:  ('local_data_source',)
```

```pycon
>>> qt.start_up_settings()
Start up settings:
--------------------
local_data_file_type = csv
local_data_file_path = data/
```

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

### Operator Strategy Management Example

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/references/3-back-test-strategy.md

Demonstrates clearing existing strategies, adding a new strategy with specific parameters, and then printing the Operator's strategies and IDs. It also shows verbose operator information.

```python
op.clear_strategies()
op.add_strategy(stg, pars=(26, 35, 189), opt_tag=1)
print(f'Operator strategies: {op.strategies}')
print(f'Strategy IDs are: {op.strategy_ids}')
op.info(verbose=True)
```

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

### get_history_data Example

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/api/HistoryPanel.md

Example of how to use get_history_data with special htypes to retrieve specific data like index weights.

```APIDOC
## get_history_data Example

### Description
This example demonstrates fetching index weight data for specific stocks within a given date range using the `get_history_data` function with the `htype_names` parameter.

### Method
```python
qt.get_history_data(htype_names='wt_id:000300.SH', shares='000001.SZ, 000002.SZ', start='20191225', end='20200105')
```

### Response Example
```json
{
  "000001.SZ": {
    "wt_idx:000300.SH": {
      "2020-01-02": 1.1714,
      "2021-01-03": 1.1714
    }
  },
  "000002.SZ": {
    "wt_idx:000300.SH": {
      "2020-01-02": 1.3595,
      "2021-01-03": 1.3595
    }
  }
}
```
```

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

### Run Backtest with Visualization and Trade Log

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/tutorials/3-start-first-strategy.md

Execute the trading strategy using 'qt.run()' with visualization and trade log enabled to analyze performance.

```python
res=qt.run(op, visual=True, trade_log=True)
```

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

### Blender Expression Example

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/references/4-build-in-strategy-blender.md

A practical example demonstrating how to set up and use blender expressions with multiple trading strategies.

```APIDOC
## Blender Expression Example

This example illustrates how to create an Operator with multiple identical strategies, set different parameters for each, and then combine their signals using a blender expression.

### Setup

```python
# Initialize an Operator with five identical DMA strategies
# Each strategy will generate different signals due to distinct parameters
op = qt.Operator('dma, dma, dma, dma, dma')

# Set unique parameters for each strategy instance
op.set_parameter(stg_id=0, pars=(132, 200, 24))
op.set_parameter(stg_id=1, pars=(124, 187, 51))
op.set_parameter(stg_id=2, pars=(103, 81, 16))
op.set_parameter(stg_id=3, pars=(48, 111, 148))
op.set_parameter(stg_id=4, pars=(104, 127, 58))
```

### Weighted Average Combination

One method to combine signals is through weighted averaging. Assign different weights to each strategy's output:

- Strategy 0: Weight 0.8
- Strategy 1: Weight 1.2
- Strategy 2: Weight 2.0
- Strategy 3: Weight 0.5
- Strategy 4: Weight 1.5

To implement this, you would set the blender expression using `op.set_blender()` with a string representing the weighted sum, for example:

```python
# Example of setting a weighted average blender expression
# Note: The actual expression string would be constructed based on the weights and signal names (s0, s1, etc.)
# op.set_blender("0.8*s0 + 1.2*s1 + 2.0*s2 + 0.5*s3 + 1.5*s4") 
```
```

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

### POST /qt/configure

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/references/3-back-test-strategy.md

Configures the qteasy running environment variables, including backtesting parameters, asset pools, and investment settings.

```APIDOC
## POST /qt/configure

### Description
Sets the environment variables for qteasy, such as backtesting dates, initial capital, asset pools, and trading rules.

### Method
POST

### Parameters
#### Request Body
- **mode** (int) - Optional - 1 for backtesting mode
- **benchmark_asset** (string) - Optional - Asset code for performance benchmarking
- **asset_pool** (string) - Optional - Trading asset combination
- **asset_type** (string) - Optional - Type of assets (e.g., 'IDX')
- **trade_batch_size** (float) - Optional - Minimum trading batch size
- **invest_start** (string) - Optional - Start date of investment (YYYYMMDD)
- **invest_end** (string) - Optional - End date of investment (YYYYMMDD)
- **invest_cash_amounts** (list) - Optional - Initial investment amounts
- **visual** (boolean) - Optional - Whether to output visual charts

### Request Example
{
  "mode": 1,
  "benchmark_asset": "000300.SH",
  "asset_pool": "000300.SH",
  "asset_type": "IDX",
  "trade_batch_size": 0.01,
  "invest_start": "20100105",
  "invest_end": "20201231",
  "invest_cash_amounts": [1000000],
  "visual": true
}
```

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

### Initialize Operator and Run Backtest

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/examples/Example_02_Alpha选股策略.md

Configures the strategy parameters and executes the backtest using the qt.run function.

```python
>>> import numpy as np
>>> alpha = AlphaPS(pars=[],
...                 name='AlphaPS',
...                 description='本策略每隔1个月定时触发计算SHSE.000300成份股的过去的EV/EBITDA并选取EV/EBITDA大于0的股票',
...                 data_types=[DataType('total_mv', asset_type='E'),
...                             DataType('total_liab'),
...                             DataType('c_cash_equ_end_period'),
...                             DataType('ebitda')],
...                 window_length=10)  
>>> op = qt.Operator(alpha, signal_type='PS')
>>> qt.run(op=op,
...        mode=1,
...        asset_type='E',
...        asset_pool=shares,
...        invest_start='20160405',
...        invest_end='20210201',
...        trade_batch_size=100,
...        sell_batch_size=1,
...        trade_log=True)
```

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

### Install TA-Lib C library on Linux

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/tutorials/1-get-started.md

Download, configure, compile, and install the TA-Lib C library from source on Linux systems.

```bash
tar -xzf ta-lib-0.4.0-src.tar.gz
cd ta-lib/
./configure --prefix=/usr
make
sudo make install
```

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

### Configure for Path A: E + simulator

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/tutorials/8-live-trade-risk-and-broker-walkthrough.md

Sets up the qteasy configuration for simulated live trading using asset type 'E' and a simulator broker. Ensure live trading parameters are correctly configured.

```python
import qteasy as qt

qt.configure(
    mode=0,
    asset_type='E',
    live_trade_broker_type='simulator',
    live_price_acquire_channel='eastmoney',
    live_price_acquire_freq='15MIN',
)
```

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

### Update QTEASY startup settings

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/tutorials/1-get-started.md

Use `update_start_up_setting()` to modify QTEASY's startup configuration. Settings will take effect on the next import.

```python
qt.update_start_up_setting(tushare_token='你的tushare token', local_data_source='database', local_db_host='localhost', local_db_port=3306, local_db_user='user_name', local_db_password='pass_word', local_db_name='qt_db')
qt.start_up_settings()
```

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

### 查看qteasy配置

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/references/2-get-history-data.md

使用qt.configuration查看当前配置变量，使用qt.QT_DATA_SOURCE查看当前数据源设置。

```python
qt.configuration(config_key='local_data_source, local_data_file_type, local_data_file_path', default=False)
qt.QT_DATA_SOURCE
```

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

### Install TA-Lib C library on Mac OS

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/tutorials/1-get-started.md

Use Homebrew to install the TA-Lib C library on macOS. For Apple Silicon chips, use the `arch -arm64` command.

```bash
brew install ta-lib
```

```bash
arch -arm64 brew install ta-lib
```

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

### Backtesting Result Output with Custom Settings

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/examples/Example_04_多因子选股.md

Example output from a QTEasy backtesting run using custom operator settings, demonstrating performance over a different investment period.

```bash
         ====================================
         |                                  |
         |       BACK TESTING RESULT        |
         |                                  |
         ====================================
    
    qteasy running mode: 1 - History back testing
    time consumption for operate signal creation: 0.0 ms
    time consumption for operation back looping:  8 sec 335.0 ms
    
    investment starts on      2016-04-05 00:00:00
    ends on                   2021-02-01 00:00:00
    Total looped periods:     4.8 years.
    
    -------------operation summary:------------
    Only non-empty shares are displayed, call 
    "loop_result["oper_count"]" for complete operation summary
    
              Sell Cnt Buy Cnt Total Long pct Short pct Empty pct
    
```

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

### Configure and Run DMA Strategy Backtest

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/getting_started.md

Set up QTEasy configuration parameters for backtesting, including asset pool, investment amounts, dates, costs, and visualization options. Then, create an Operator with the 'dma' strategy and run the backtest.

```python
>>> # 设置qteasy的运行配置参数
>>> qt.configure(
...     asset_pool='000300.SH',  # 交易资产池包括沪深300指数
...     asset_type='IDX',  # 投资资产类型为IDX-指数
...     invest_cash_amounts=[100000],  # 回测初始投资金额为十万元
...     invest_start='20150101',  # 回测投资初始日期
...     invest_end='20241231',  # 回测投资结束日期
...     cost_rate_buy=0.0003,  # 交易费率：买入手续费万分之三
...     cost_rate_sell=0.0001,  # 交易费率：卖出手续费万分之一
...     visual=True,  # 是否输出回测结果可视化图表：是
...     trade_log=True,  # 是否输出回测记录：是
... )
>>> op = qt.Operator(strategies='dma')  # 创建一个交易员对象，执行一个DMA交易策略
>>> op.set_parameter('dma', par_values=(20, 60, 10))  # 设置交易策略的参数
>>> res = qt.run(op, mode=1)  # 启动交易，运行模式为1（回测交易）
```

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

### GET /cashflow

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/manage_data/08. data_tables_40.md

获取上市公司的现金流量表数据，包含经营活动、投资活动及筹资活动等详细现金流指标。

```APIDOC
## GET /cashflow

### Description
获取上市公司的现金流量表数据，包含经营活动、投资活动及筹资活动等详细现金流指标。

### Method
GET

### Endpoint
/cashflow

### Parameters
#### Query Parameters
- **ts_code** (varchar) - Required - 证券代码
- **end_date** (date) - Required - 报告期

### Response
#### Success Response (200)
- **ts_code** (varchar) - 证券代码
- **end_date** (date) - 报告期
- **ann_date** (date) - 公告日期
- **f_ann_date** (date) - 实际公告日期
- **comp_type** (varchar) - 公司类型
- **report_type** (varchar) - 报表类型
- **end_type** (varchar) - 报告期类型
- **net_profit** (double) - 净利润
- **finan_exp** (double) - 财务费用
- **c_fr_sale_sg** (double) - 销售商品、提供劳务收到的现金
- **recp_tax_rends** (double) - 收到的税费返还
- **n_depos_incr_fi** (double) - 客户存款和同业存放款项净增加额
- **n_incr_loans_cb** (double) - 向中央银行借款净增加额
- **n_inc_borr_oth_fi** (double) - 向其他金融机构拆入资金净增加额
- **prem_fr_orig_contr** (double) - 收到原保险合同保费取得的现金
- **n_incr_insured_dep** (double) - 保户储金净增加额
- **n_reinsur_prem** (double) - 收到再保业务现金净额
- **n_incr_disp_tfa** (double) - 处置交易性金融资产净增加额
- **ifc_cash_incr** (double) - 收取利息和手续费净增加额
- **n_incr_disp_faas** (double) - 处置可供出售金融资产净增加额
- **n_incr_loans_oth_bank** (double) - 拆入资金净增加额
- **n_cap_incr_repur** (double) - 回购业务资金净增加额
- **c_fr_oth_operate_a** (double) - 收到其他与经营活动有关的现金
- **c_inf_fr_operate_a** (double) - 经营活动现金流入小计
- **c_paid_goods_s** (double) - 购买商品、接受劳务支付的现金
- **c_paid_to_for_empl** (double) - 支付给职工以及为职工支付的现金
- **c_paid_for_taxes** (double) - 支付的各项税费
- **n_incr_clt_loan_adv** (double) - 客户贷款及垫款净增加额
- **n_incr_dep_cbob** (double) - 存放央行和同业款项净增加额
- **c_pay_claims_orig_inco** (double) - 支付原保险合同赔付款项的现金
- **pay_handling_chrg** (double) - 支付手续费的现金
- **pay_comm_insur_plcy** (double) - 支付保单红利的现金
- **oth_cash_pay_oper_act** (double) - 支付其他与经营活动有关的现金
- **st_cash_out_act** (double) - 经营活动现金流出小计
- **n_cashflow_act** (double) - 经营活动产生的现金流量净额
- **oth_recp_ral_inv_act** (double) - 收到其他与投资活动有关的现金
- **c_disp_withdrwl_invest** (double) - 收回投资收到的现金
- **c_recp_return_invest** (double) - 取得投资收益收到的现金
- **n_recp_disp_fiolta** (double) - 处置固定资产、无形资产和其他长期资产收回的现金净额
- **n_recp_disp_sobu** (double) - 处置子公司及其他营业单位收到的现金净额
- **stot_inflows_inv_act** (double) - 投资活动现金流入小计
- **c_pay_acq_const_fiolta** (double) - 购建固定资产、无形资产和其他长期资产支付的现金
- **c_paid_invest** (double) - 投资支付的现金
- **n_disp_subs_oth_biz** (double) - 取得子公司及其他营业单位支付的现金净额
- **oth_pay_ral_inv_act** (double) - 支付其他与投资活动有关的现金
- **n_incr_pledge_loan** (double) - 质押贷款净增加额
- **stot_out_inv_act** (double) - 投资活动现金流出小计
- **n_cashflow_inv_act** (double) - 投资活动产生的现金流量净额
```

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

### DMA Strategy Backtest Report Example

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/getting_started.md

Example output of a DMA strategy backtest, showing key performance indicators, operation summary, and investment details. This report is generated after running the backtest.

```default
====================================
|                                  |
|         BACKTEST REPORT          |
|                                  |
====================================
qteasy running mode: 1 - History back testing
time consumption for operate signal creation: 81.3 ms
time consumption for operation back testing:  4.9 ms
investment starts on      2015-01-05 15:00:00
ends on                   2024-12-30 15:00:00
Total looped periods:     10.0 years.
-------------operation summary:------------
Only non-empty shares are displayed, call 
"loop_result["oper_count"]" for complete operation summary
          Sell Cnt Buy Cnt Total Long pct Short pct Empty pct
000300.SH   340       41    381   44.7%     -0.0%     55.3%  

Total operation fee:     ¥    3,261.19
total investment amount: ¥  100,000.00
final value:              ¥  115,601.73
Total return:                    15.60%
Avg Yearly return:                1.46%
Skewness:                         -1.16
Kurtosis:                         16.95
Benchmark return:                 1.67%
Benchmark Yearly return:          0.17%

------strategy loop_results indicators------ 
alpha:                            0.006
Beta:                             0.975
Sharp ratio:                      0.113
Info ratio:                       0.001
250 day volatility:               0.130
Max drawdown:                    36.85%
    peak / valley:        2015-04-27 / 2018-11-27
    recovered on:         Not recovered!


==================END OF REPORT===================
```

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

### Configure QTEasy Backtesting Environment

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/references/3-back-test-strategy.md

Set up essential backtesting environment variables such as benchmark, asset pool, dates, initial investment, and visualization preferences. These settings persist until changed.

```python
qt.configure(
    mode=1,  # 设置运行模式为：1-回测模式
    benchmark_asset = '000300.SH',  # 设置交易评价基准（类型由系统根据代码自动推断）
    asset_pool = '000300.SH',  # 设置交易资产组合
    asset_type = 'IDX',  # 交易资产组合的资产类型
    trade_batch_size = 0.01,  # 设置允许最小交易批量（最小0.01）
    invest_start = '20100105',  # 设置交易开始日期
    invest_end = '20201231',  # 设置交易终止日期
    invest_cash_amounts = [1000000],  # 设置初始交易投资金额为十万元
    visual=True  # 输出可视化交易结果图表
)
```

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

### Get Data Source Overview

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/manage_data/03. datasource.md

Use the `overview()` method to analyze all data tables in the data source and get a summary of their information. This process may take time depending on the size of the tables.

```python
>>> overview = ds.overview()
Analyzing local data source tables... depending on size of tables, it may take a few minutes
[########################################]104/104-100.0%  A...zing completed!
Finished analyzing datasource: 
file://csv@qt_root/data/
3 table(s) out of 104 contain local data as summary below, to view complete list, print returned DataFrame
===============================tables with local data===============================
               Has_data Size_on_disk Record_count Record_start Record_end
table                                                                    
trade_calendar   True       1.8MB         70K          CFFEX        SZSE 
stock_basic      True       852KB          5K           None        None 
stock_daily      True      98.8MB        1.3M       20211112    20241231
```

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

### Configure Live Trading Environment

Source: https://github.com/shepherdpp/qteasy/blob/master/docs/source/live_trading/2-configuration-and-run.md

Use these templates to set up the live trading environment for stocks or funds using the simulator broker.

```python
import qteasy as qt

qt.configure(
    mode=0,
    asset_type='E',
    live_trade_broker_type='simulator',
    live_price_acquire_channel='eastmoney',
    live_price_acquire_freq='15MIN',
)
```

```python
import qteasy as qt

qt.configure(
    mode=0,
    asset_type='FD',
    live_trade_broker_type='simulator',
    live_price_acquire_channel='eastmoney',
    live_price_acquire_freq='15MIN',
)
```