### Install Configuration File - Shell

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/trading/bitcoin-arbitrage/README.md

Copies the example configuration file to be edited for user preferences. This is a prerequisite for setting up the arbitrage bot.

```shell
cp arbitrage/config.py-example arbitrage/config.py
```

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

### Setup Development Environment and Dependencies

Source: https://github.com/ufund-me/qbot/blob/main/docs/tutorials_code/15.rl_learning/README.md

Commands to create a Python virtual environment and install the necessary dependencies for the reinforcement learning project.

```bash
# 虚拟环境
virtualenv -p python3.6 venv
source ./venv/bin/activate
# 安装库依赖
pip install -r requirements.txt
```

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

### Start Node.js Application

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/fund-strategies/README.md

Starts the Node.js development server. This command is used after installing dependencies.

```bash
npm start
```

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

### Setup and Run LightGBM Model on CSI500

Source: https://github.com/ufund-me/qbot/blob/main/pytrader/strategies/benchmarks/README.md

This snippet demonstrates how to set up a LightGBM model for the CSI500 benchmark. It includes installing dependencies, creating a new configuration file, modifying it for CSI500, and running the model either once or multiple times for summarized results.

```bash
cd examples/benchmarks/LightGBM
pip install -r requirements.txt

# create new config and set the benchmark to csi500
cp workflow_config_lightgbm_Alpha158.yaml workflow_config_lightgbm_Alpha158_csi500.yaml
sed -i "s/csi300/csi500/g"  workflow_config_lightgbm_Alpha158_csi500.yaml
sed -i "s/SH000300/SH000905/g"  workflow_config_lightgbm_Alpha158_csi500.yaml

# you can either run the model once
qrun workflow_config_lightgbm_Alpha158_csi500.yaml

# or run it for multiple times automatically and get the summarized results.
cd  ../../
python run_all_model.py run 3 lightgbm Alpha158 csi500  # for models with randomness.  please run it for 20 times.
```

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

### Client Setup

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/easytrader/docs/usage.md

This section covers how to import the library and set up different trading client types.

```APIDOC
## Client Setup

### Import Library

```python
import easytrader
```

### Set Trading Client Type

**Haitong Client**

```python
user = easytrader.use('htzq_client')
```

**Huatai Client**

```python
user = easytrader.use('ht_client')
```

**Guojin Client**

```python
user = easytrader.use('gj_client') 
```

**Universal Tonghuashun Client**

```python
user = easytrader.use('universal_client') 
```

Note: The Universal Tonghuashun client is a fallback for when brokers do not directly provide a Tonghuashun client. It supports trading through multiple brokers.

**Other Broker-Specific Tonghuashun Clients**

```python
user = easytrader.use('ths')
```

Note: These are versions modified by brokers based on Tonghuashun, such as Yinhe's Gemini (Tonghuashun version) or Guojin Securities' independent trading program (Kernew PC version).

**Xueqiu**

```python
user = easytrader.use('xq')
```
```

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

### Install xalpha

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/backtest/README.md

Methods to install the xalpha library via pip or from source.

```bash
pip install xalpha

git clone https://github.com/refraction-ray/xalpha.git
cd xalpha && python3 setup.py install
```

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

### Install EasyTrader Library

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/easytrader/docs/install.md

Installs the EasyTrader package using pip. Windows users may need to manually install missing dependencies if prompted.

```shell
pip install easytrader
```

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

### Clone and Install Project Dependencies

Source: https://github.com/ufund-me/qbot/blob/main/pytrader/frontend/readme.md

This snippet shows the commands to clone the Element Plus Admin project from GitHub, navigate into the project directory, and install all necessary dependencies using npm.

```bash
git clone https://github.com/hsiangleev/element-plus-admin.git
cd element-plus-admin
npm install
```

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

### Install Baostock Data Library

Source: https://github.com/ufund-me/qbot/blob/main/docs/tutorials_code/15.rl_learning/README.md

Installs the Baostock library using a specific mirror for faster package retrieval, used for fetching historical stock market data.

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

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

### Start Development Server

Source: https://github.com/ufund-me/qbot/blob/main/pytrader/frontend/readme.md

Commands to start the local development server for the Element Plus Admin project. After running this, the application can be accessed via a browser at http://localhost:3002.

```bash
npm run dev
```

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

### Configure for Quantitative Platforms

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/backtest/README.md

Setup commands to use xalpha within cloud-based quantitative research environments like JoinQuant.

```python
!pip3 install xalpha --user
import sys
sys.path.insert(0, "/home/jquser/.local/lib/python3.6/site-packages")
import xalpha as xa

# For development version
!git clone https://github.com/refraction-ray/xalpha.git
!cd xalpha && python3 setup.py develop --user
```

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

### Web Server API

Source: https://github.com/ufund-me/qbot/blob/main/qbot/plugins/investool/README.md

This API endpoint allows you to start the QBot web server. You can specify a configuration file path.

```APIDOC
## POST /ufund-me/qbot/webserver

### Description
Starts the QBot web server. It's recommended to increase the reverse proxy timeout for this service as the stock screening process can be time-consuming.

### Method
POST

### Endpoint
/ufund-me/qbot/webserver

### Parameters
#### Query Parameters
- **config** (string) - Optional - Path to the configuration file. Defaults to `./config.toml` in the current directory.

### Request Example
```bash
./investool webserver --config /path/to/your/config.toml
```

### Response
#### Success Response (200)
- **status** (string) - Server status message (e.g., "Web server started successfully.").

#### Response Example
```json
{
  "status": "Web server started successfully on port 8080."
}
```
```

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

### Install FinRL Library

Source: https://github.com/ufund-me/qbot/blob/main/docs/notebook/Stock_NeurIPS2018_SB3.ipynb

Installs the FinRL library directly from its GitHub repository using pip. This command ensures that the latest version of the library and its dependencies are fetched and installed.

```bash
!pip install git+https://github.com/AI4Finance-Foundation/FinRL.git
```

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

### Start Investool Web Server

Source: https://github.com/ufund-me/qbot/blob/main/qbot/plugins/investool/README.md

Launches the web server component of the tool. It reads configuration from config.toml by default, and users should ensure reverse proxy timeouts are adjusted for high-load scenarios.

```bash
./investool webserver
```

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

### Configure and Start Online Trading Engine

Source: https://github.com/ufund-me/qbot/blob/main/pytrader/README.md

Demonstrates how to initialize the MainEngine for online trading using the Eastmoney broker. It sets up logging and enables automatic strategy reloading.

```python
import easyquant
from easyquant import DefaultLogHandler

broker = 'eastmoney'
need_data = 'account.json'
log_handler = DefaultLogHandler(name='测试', log_type='file', filepath='logs.log')

m = easyquant.MainEngine(broker, need_data, quotation='online', bar_type="1m", log_handler=log_handler)
m.is_watch_strategy = True
m.load_strategy()
m.start()
```

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

### Install Node.js Dependencies

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/fund-strategies/README.md

Installs project dependencies using npm. Requires a Node.js development environment.

```bash
npm install
```

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

### Exportor CLI Examples

Source: https://github.com/ufund-me/qbot/blob/main/qbot/plugins/investool/README.md

Demonstrates various command-line invocations of the investool exportor tool to export stock data into different file formats and with custom parameters.

```bash
./investool -l error exportor -f ./stocks.json

```

```bash
./investool -l error exportor -f ./stocks.csv

```

```bash
./investool -l error exportor -f ./stocks.xlsx

```

```bash
./investool -l error exportor -f ./stocks.png

```

```bash
./investool -l error exportor -f ./stocks.all

```

```bash
./investool -l error exportor -f ./stocks.xlsx --filter.min_roe=6 --checker.min_roe=6

```

```bash
./investool -l error exportor -f ./stocks.xlsx --filter.special_security_name_abbr_list 福莱特 --filter.special_security_name_abbr_list 旗滨集团 --disable_check

```

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

### Import Libraries and Setup

Source: https://github.com/ufund-me/qbot/blob/main/docs/notebook/choose_stock.ipynb

Imports necessary Python libraries for data analysis, plotting, and financial data retrieval. It also sets up plotting configurations and initializes the Tushare API.

```python
%matplotlib inline
%matplotlib widget
import backtrader.plot
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [15, 8]
import tushare as ts
from datetime import datetime
import backtrader as bt
import pandas as pd
import os
import numpy as np
```

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

### Qbot CLI Get Account Information

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/easytrader/docs/usage.md

This command retrieves various account details, such as balance, holdings, and other relevant variables, from the Qbot system.

```bash
python cli.py --get balance
```

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

### Example Tushare API Usage Parameters

Source: https://github.com/ufund-me/qbot/blob/main/docs/02-经典策略/01-股票/量化一-均值策略.md

Illustrates the parameters required for fetching historical data using the Tushare API. This includes the stock code (e.g., '600515.SH'), the start date in 'YYYYMMDD' format, and the end date in 'YYYYMMDD' format. These parameters are essential for specifying the data range for analysis.

```python
ts_code='600515.SH'
start_date='20190101'
end_date='20191231'
```

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

### Qbot CLI Help

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/easytrader/docs/usage.md

This command displays the help information for the Qbot command-line interface, listing all available commands and their options.

```bash
python cli.py --help
```

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

### Install Python Dependencies - Shell

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/trading/bitcoin-arbitrage/README.md

Installs Python 3 and necessary packages like pip and python-nose on Debian-based systems. For the XMPPMessager observer, sleekxmpp is also installed via pip3.

```shell
sudo apt-get install python3 python3-pip python-nose
```

```shell
pip3 install sleekxmpp
```

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

### Build Documentation Locally

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/backtest/README.md

Commands to generate HTML documentation from the source files.

```bash
cd doc
make html
```

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

### Get Daily Financial Data with xalpha

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/backtest/doc/source/quickstart.md

Fetches daily historical data for a given financial instrument code. Supports stocks, funds, ETFs, bonds, currencies, and indices from various markets (China, Hong Kong, US) and sources like investing.com, FT, and S&P. The function returns a pandas DataFrame. Optional parameters like 'prev', 'start', and 'end' can be used to specify the date range.

```python
>>> xa.get_daily("EUR/CNY", prev=5) # 人民币中间价数据
        date   close
4 2020-03-30  7.8288
3 2020-03-31  7.8088
2 2020-04-01  7.8090
1 2020-04-02  7.7678
0 2020-04-03  7.7081
```

```python
>>> xa.get_daily("currencies/usd-cnh") # 英为离岸人民币
     date    open   close    high     low percent
260 2019-04-05  6.7167  6.7122  6.7194  6.7033  -0.07%
259 2019-04-08  6.7100  6.7173  6.7278  6.7082   0.08%
258 2019-04-09  6.7172  6.7195  6.7241  6.7127   0.03%
257 2019-04-10  6.7188  6.7187  6.7270  6.7164  -0.01%
..
3   2020-03-31  7.1133  7.0940  7.1177  7.0796  -0.29%
2   2020-04-01  7.0932  7.1230  7.1336  7.0772   0.41%
1   2020-04-02  7.1216  7.0929  7.1422  7.0846  -0.42%
0   2020-04-03  7.0919  7.1108  7.1197  7.0857   0.25%
```

```python
>>> xa.get_daily("FT-ZGLD:SWX:CHF", start="2020-03-01") # ft.com 基金数据
         date    open   close    high     low
24 2020-03-02  470.10  465.30  472.05  463.75
23 2020-03-03  465.95  477.20  477.85  465.30
22 2020-03-04  477.05  478.00  480.85  475.35
21 2020-03-05  476.75  479.15  481.30  476.75
..
3  2020-03-31  475.70  474.00  475.70  469.80
2  2020-04-01  466.90  468.10  470.80  464.10
1  2020-04-02  468.45  476.75  479.80  467.40
0  2020-04-03  477.80  481.00  483.00  477.55
```

```python
>>> xa.get_daily("HK00700", prev=5, end="2018-08-08") # 雪球港股数据
          date      open     close      high       low  percent
198 2018-08-03  347.2475  348.8432  353.6300  345.6519     1.39
199 2018-08-06  356.8213  352.0344  356.8213  349.0426     0.91
200 2018-08-07  354.0289  356.0235  357.4196  347.6464     1.13
201 2018-08-08  365.5972  363.0043  365.5972  359.6136     1.96
```

```python
>>> xa.get_daily("SH000050", prev=10) # 雪球A股指数数据
         date       open    close       high        low  percent
3  2020-03-25  2203.7900  2206.63  2216.0700  2188.3800     2.41
4  2020-03-26  2187.4600  2193.85  2208.5900  2179.9800    -0.58
5  2020-03-27  2219.5900  2203.25  2232.4400  2202.3000     0.43
6  2020-03-30  2172.8685  2188.88  2196.6724  2168.4372    -0.65
7  2020-03-31  2207.6900  2176.42  2208.7200  2173.4300    -0.57
8  2020-04-01  2170.1100  2171.34  2203.6400  2168.4400    -0.23
9  2020-04-02  2160.1300  2204.57  2204.5700  2159.0300     1.53
10 2020-04-03  2197.7113  2195.55  2210.2124  2188.8462    -0.41
```

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

### Initialize and Summarize Fund Portfolio

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/backtest/doc/samples/mul.ipynb

Demonstrates how to load transaction data from a CSV file, initialize a fund portfolio object, and generate a summary of current holdings and performance.

```python
import xalpha as xa
import pandas as pd

xa.set_display("notebook")
path = "../../tests/demo.csv"
read = xa.record(path)
sysopen = xa.mul(status=read.status)
summary = sysopen.combsummary().sort_values(by="基金现值", ascending=False)
```

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

### Build and Preview Project

Source: https://github.com/ufund-me/qbot/blob/main/pytrader/frontend/readme.md

Commands to build the Element Plus Admin project for production and preview the built application locally. This is typically done before deploying the application.

```bash
npm run build
npm run preview
```

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

### Configure and Execute Backtest and Analysis in Qlib

Source: https://github.com/ufund-me/qbot/blob/main/docs/notebook/workflow_by_code.ipynb

This Python snippet defines the configuration for a backtesting and analysis process, including executor, strategy, and backtest parameters. It then loads a trained model and generates prediction and portfolio analysis records.

```python
###################################
# prediction, backtest & analysis
###################################
port_analysis_config = {
    "executor": {
        "class": "SimulatorExecutor",
        "module_path": "qlib.backtest.executor",
        "kwargs": {
            "time_per_step": "day",
            "generate_portfolio_metrics": True,
        },
    },
    "strategy": {
        "class": "TopkDropoutStrategy",
        "module_path": "qlib.contrib.strategy.signal_strategy",
        "kwargs": {
            "model": model,
            "dataset": dataset,
            "topk": 50,
            "n_drop": 5,
        },
    },
    "backtest": {
        "start_time": "2017-01-01",
        "end_time": "2020-08-01",
        "account": 100000000,
        "benchmark": benchmark,
        "exchange_kwargs": {
            "freq": "day",
            "limit_threshold": 0.095,
            "deal_price": "close",
            "open_cost": 0.0005,
            "close_cost": 0.0015,
            "min_cost": 5,
        },
    },
}

# backtest and analysis
with R.start(experiment_name="backtest_analysis"):
    recorder = R.get_recorder(recorder_id=rid, experiment_name="train_model")
    model = recorder.load_object("trained_model")

    # prediction
    recorder = R.get_recorder()
    ba_rid = recorder.id
    sr = SignalRecord(model, dataset, recorder)
    sr.generate()

    # backtest & analysis
    par = PortAnaRecord(recorder, port_analysis_config, "day")
    par.generate()
```

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

### Configure and Run Backtest

Source: https://github.com/ufund-me/qbot/blob/main/docs/02-经典策略/01-股票/多因子选股.md

This snippet initializes the backtesting environment with parameters such as strategy ID, time range, initial capital, commission rates, and slippage. It uses the run function to execute the strategy in a specified mode.

```python
run(strategy_id='strategy_id',
    filename='main.py',
    mode=MODE_BACKTEST,
    token='token_id',
    backtest_start_time='2017-07-01 08:00:00',
    backtest_end_time='2017-10-01 16:00:00',
    backtest_adjust=ADJUST_PREV,
    backtest_initial_cash=10000000,
    backtest_commission_ratio=0.0001,
    backtest_slippage_ratio=0.0001)
```

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

### Initialize and Configure Xueqiu Follower

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/easytrader/docs/usage.md

This section covers initializing a follower for Xueqiu (snowball) portfolios. It includes logging in using Xueqiu cookies and configuring the follower to track a specific portfolio ID. For Xueqiu, which uses percentage-based rebalancing, `total_assets` or `initial_assets` must be set to define the portfolio's capital. The `adjust_sell` parameter can be used to refine sell order quantities based on actual holdings.

```python
xq_follower = easytrader.follower('xq')
```

```python
xq_follower.login(cookies='雪球 cookies，登陆后获取，获取方式见 https://smalltool.github.io/2016/08/02/cookie/')
```

```python
xq_follower.follow(xq_user, 'xq组合ID，类似ZH123456', total_assets=100000)
```

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

### GET /thsauto/cancel

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/trading/thsauto/README.md

Cancels an existing active order.

```APIDOC
## GET /thsauto/cancel

### Description
Cancels a pending order based on the provided entrustment number.

### Method
GET

### Endpoint
/thsauto/cancel

### Parameters
#### Query Parameters
- **entrust_no** (string) - Required - The entrustment number of the order to cancel

### Response
#### Success Response (200)
- **message** (string) - Confirmation message

#### Response Example
{
  "status": "success",
  "message": "Order cancelled successfully"
}
```

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

### GET /thsauto/buy

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/trading/thsauto/README.md

Executes a buy order for a specified stock.

```APIDOC
## GET /thsauto/buy

### Description
Places a buy order for a specific stock using the provided price and quantity.

### Method
GET

### Endpoint
/thsauto/buy

### Parameters
#### Query Parameters
- **stock_no** (string) - Required - The stock code
- **price** (float) - Required - The buy price
- **amount** (int) - Required - The number of shares

### Response
#### Success Response (200)
- **entrust_no** (string) - The order entrustment number

#### Response Example
{
  "status": "success",
  "entrust_no": "12345678"
}
```

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

### Create a Trading Environment

Source: https://github.com/ufund-me/qbot/blob/main/docs/notebook/Stock_NeurIPS2018_SB3.ipynb

Sets up a custom trading environment using the FinRL library. This environment simulates stock market conditions for backtesting trading strategies.

```python
from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv

# Define environment parameters
stock_list = ['AAPL', 'MSFT', 'GOOG']
initial_capital = 100000

# Create the environment
env = StockTradingEnv(df=None, stock_list=stock_list, initial_capital=initial_capital,  
                      hmax=100,  # Max number of shares to trade each time
                      reward_scaling=1e-4)

# Access environment details
obs = env.reset()
print(f'Observation Space: {env.observation_space}')
print(f'Action Space: {env.action_space}')
```

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

### GET /thsauto/balance

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/trading/thsauto/README.md

Retrieves the current balance information for the connected trading account.

```APIDOC
## GET /thsauto/balance

### Description
Fetches the current capital and balance details from the trading account.

### Method
GET

### Endpoint
/thsauto/balance

### Response
#### Success Response (200)
- **balance** (object) - Account balance details

#### Response Example
{
  "status": "success",
  "data": {
    "total_asset": 100000.00,
    "available_cash": 50000.00
  }
}
```

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

### Acquire Market Data with easyquotation

Source: https://context7.com/ufund-me/qbot/llms.txt

Demonstrates how to initialize a data source and fetch real-time stock quotes or K-line data using the easyquotation library.

```python
import easyquotation

# Use sina source for real-time quotes
quotation = easyquotation.use("sina")
stock_info = quotation.real(["000001"])

# Fetch daily K-line data
daykline = easyquotation.use("daykline")
kline_data = daykline.real(["000001"])
```

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

### Initialize Snowball Portfolio Simulation

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/easytrader/docs/other/xueqiu.md

Demonstrates how to initialize the `easytrader` library for simulating trades with a Snowball (Xueqiu) portfolio. It shows how to set initial assets based on the portfolio's net value.

```python
import easytrader

# Initialize with default initial assets
# For custom initial assets, use:
easytrader.use('xq', initial_assets=1000000.0)
```

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

### Get Bar Data

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/backtest/doc/samples/newparadigm.ipynb

Fetches bar data (e.g., minute, hour) for indices.

```APIDOC
## Get Bar Data for Indices

### Description
Fetches bar data (e.g., minute, hour) for indices.

### Method
GET

### Endpoint
/api/indices/bar/{symbol}

### Parameters
#### Path Parameters
- **symbol** (string) - Required - The index symbol (e.g., indices/germany-30).

#### Query Parameters
- **interval** (integer) - Required - The interval in seconds (e.g., 3600 for hourly data).

### Request Example
```python
xa.get_bar("indices/germany-30", interval=3600)
```

### Response
#### Success Response (200)
- **timestamp** (integer) - The Unix timestamp of the bar.
- **open** (float) - The opening price.
- **high** (float) - The highest price.
- **low** (float) - The lowest price.
- **close** (float) - The closing price.
- **volume** (integer) - The trading volume.

#### Response Example
```json
{
  "timestamp": 1678886400,
  "open": 15000.50,
  "high": 15050.75,
  "low": 14980.25,
  "close": 15020.00,
  "volume": 100000
}
```
```

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

### Get Real-time Data

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/backtest/doc/source/quickstart.md

Retrieves real-time quote data for a specified financial instrument.

```APIDOC
## GET /api/data/rt

### Description
Fetches real-time quote data for a specified financial instrument.

### Method
GET

### Endpoint
/api/data/rt

### Parameters
#### Query Parameters
- **code** (string) - Required - The unique identifier for the financial instrument (e.g., "SH501018", "indices/germany-30").

### Request Example
```python
import xalpha as xa

# Get real-time data for a Chinese fund
rt_data_cn = xa.get_rt("SH501018")
print(rt_data_cn)

# Get real-time data for a German index
rt_data_de = xa.get_rt("indices/germany-30")
print(rt_data_de)
```

### Response
#### Success Response (200)
- **JSON Object** - A JSON object containing real-time quote information, typically including 'name', 'current', 'percent', 'currency', and 'market'.

#### Response Example
```json
{
  "name": "南方原油LOF",
  "current": 0.826,
  "percent": -0.48,
  "current_ext": null,
  "currency": "CNY",
  "market": "CN"
}
```
```

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

### Upgrade EasyTrader Library

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/easytrader/docs/install.md

Updates the existing EasyTrader installation to the latest version available on PyPI.

```shell
pip install easytrader -U
```

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

### Initialize Xalpha Library

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/backtest/doc/samples/newparadigm.ipynb

Imports the library and sets up the global local cache backend. It is recommended to use the xa.meth pattern for function calls to ensure dynamic features function correctly.

```python
import xalpha as xa
xa.set_backend(backend="csv", path="../../../lof/data", precached="20170101")
```

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

### GET /fund/managers

Source: https://github.com/ufund-me/qbot/blob/main/qbot/plugins/investool/statics/html/fund_managers.html

Retrieves a paginated list of fund managers based on various performance and profile filters.

```APIDOC
## GET /fund/managers

### Description
Retrieves a filtered list of fund managers with pagination support.

### Method
GET

### Endpoint
/fund/managers

### Parameters
#### Query Parameters
- **page_num** (integer) - Optional - Current page number
- **page_size** (integer) - Optional - Number of records per page
- **sort** (string) - Optional - Sorting criteria
- **min_working_years** (integer) - Optional - Minimum years of experience
- **min_yieldse** (float) - Optional - Minimum annualized yield percentage
- **max_current_fund_count** (integer) - Optional - Maximum number of funds managed
- **min_scale** (float) - Optional - Minimum management scale in billions
- **fund_type** (string) - Optional - Category of funds managed

### Response
#### Success Response (200)
- **Managers** (array) - List of manager objects
- **Pagination** (object) - Pagination metadata

#### Response Example
{
  "Managers": [{
    "Name": "John Doe",
    "WorkingYears": 10,
    "Score": 85,
    "CurrentBestReturn": 15.5
  }],
  "Pagination": {
    "PageNum": 1,
    "PagesCount": 5
  }
}
```

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

### Execute Backtest Strategy

Source: https://github.com/ufund-me/qbot/blob/main/docs/notebook/Pairs_Trading.ipynb

Example usage of the Multidata_Strategy_runner to instantiate and run a trading strategy, followed by plotting the results.

```python
strategy_runner = Multidata_Strategy_runner(strategy=PairTradingStrategy, ts_code=bank_choose[0], ts_code1=bank_choose[1], start_date=start_date, end_date=end_date, pro=True)
cerebro, strat = strategy_runner.run()
strategy_runner.plot()
```

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

### Restart Tonghuashun Client (HTTP API)

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/trading/thsauto/README.md

Restarts the Tonghuashun client application. This is a GET request to the specified API endpoint.

```http
http://192.168.0.116:5000/thsauto/client/restart
```

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

### Initialize Pyecharts Online Mode

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/backtest/doc/samples/schedulestudy.ipynb

Initializes the pyecharts library for online rendering. This is a setup step and typically only needs to be run once.

```python
import sys

sys.path.insert(0, "../../")
from pyecharts import online

online()
#  演示必要的准备代码，使用该库时不需重复此单元格命令
```

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

### Configure Incremental IO with SQL Backend

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/backtest/doc/samples/info.ipynb

Demonstrates how to use a SQLAlchemy engine as a backend for incremental IO. This allows fund data to be stored and retrieved from a relational database instead of local files.

```python
from sqlalchemy import create_engine

engine = create_engine("mysql://user:pass@host/db_name?charset=utf8")
sqlconf = {"save": True, "fetch": True, "path": engine, "form": "sql"}
zzhli = xa.indexinfo("1399922", **sqlconf)
```

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

### Executing Fund Estimation Analysis

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/backtest/doc/samples/netvalueestimation.ipynb

Example usage of the estimation functions to generate a comparative dataframe for various fund holdings.

```python
cache_table = {}
cache_data(cache_table, holdings_160216_19s4, holdings_162411_19s4, holdings_501018_19s3, holdings_501018_19s4, holdings_501018_bc_cash, ["F501018", "USD/CNY", "2111", "F160216", "F162411"])

nfyydf = estimate_table("20190101", "20200305", ("real", "", {"F501018": 100}), ("fund_est", "USD/CNY", holdings_501018_19s4), ("fund_est_2111", "2111", holdings_501018_19s4), ("bc_est", "USD/CNY", holdings_501018_bc_cash), ("bc_est_2111", "2111", holdings_501018_bc_cash), _default=cache_table)

nfyydf.iloc[-20:]
```

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

### Multi-User and Multi-Strategy Following

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/easytrader/docs/usage.md

This section demonstrates how to follow multiple strategies for multiple users simultaneously, specifying the total assets for each user.

```APIDOC
## POST /ufund-me/qbot/multi-follow

### Description
Follow multiple strategies for multiple users concurrently. This endpoint allows specifying lists of users, strategies, and their corresponding total asset values.

### Method
POST

### Endpoint
/ufund-me/qbot/multi-follow

### Parameters
#### Request Body
- **users** (array of strings) - Required - A list of user identifiers.
- **strategies** (array of strings) - Required - A list of strategy identifiers, corresponding to the users.
- **total_assets** (array of numbers) - Required - A list of total asset values, corresponding to the users and strategies.

### Request Example
```json
{
  "users": ["user1_id", "user2_id"],
  "strategies": ["strategyA", "strategyB"],
  "total_assets": [50000, 75000]
}
```

### Response
#### Success Response (200)
- **message** (string) - A confirmation message indicating that multiple strategies are being followed.

#### Response Example
```json
{
  "message": "Successfully started following multiple strategies for multiple users."
}
```
```

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

### Set Benchmark for SSE 50 Index (Extended)

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/backtest/doc/samples/schedulestudy.ipynb

Sets the cash information as a benchmark for the SSE 50 index, starting from '2011-01-01'.

```python
sz50.bcmkset(xa.cashinfo(), start="2011-01-01")
```

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

### Initialize Python Data Structures

Source: https://github.com/ufund-me/qbot/blob/main/docs/notebook/pandas.ipynb

Demonstrates the syntax for creating lists, dictionaries, and tuples in Python. It highlights the mutability of lists and dictionaries versus the immutability of tuples.

```python
is_list = ['a', 1, False, 1.0]
is_dict = {'a': 1, 1: 'b'}
is_tuple = (1, 2, 1, 'a')
```

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

### Command Line Interface (CLI) - Get Information

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/easytrader/docs/usage.md

This section explains how to retrieve account information such as balance and holdings using the CLI.

```APIDOC
## CLI Get Information

### Description
Retrieve various account information using the command line interface. This includes fetching account balances, current holdings, and other relevant variables.

### Command
```bash
python cli.py --get <info_type>
```

### Parameters
#### Command Line Arguments
- **--get** (string) - Required - The type of information to retrieve (e.g., 'balance', 'positions').

### Example
```bash
python cli.py --get balance
```

### Response
- The command will output the requested information directly to the console.
```

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

### Get Intraday Bar Data

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/backtest/doc/source/quickstart.md

Retrieves intraday bar data (minute, 5-minute, hourly, weekly) for a specified financial instrument.

```APIDOC
## GET /api/data/bar

### Description
Fetches intraday bar data for a specified financial instrument at a given interval.

### Method
GET

### Endpoint
/api/data/bar

### Parameters
#### Query Parameters
- **code** (string) - Required - The unique identifier for the financial instrument (e.g., "LK", "commodities/brent-oil").
- **interval** (integer) - Required - The interval for the bar data in seconds (e.g., 60 for minute, 3600 for hourly).
- **prev** (integer) - Optional - The number of previous bars to retrieve.
- **start** (string) - Optional - The start date/time for the data.
- **end** (string) - Optional - The end date/time for the data.

### Request Example
```python
import xalpha as xa

# Get hourly data for a US stock
data_hourly = xa.get_bar("LK", interval=3600, prev=12)
print(data_hourly)

# Get minute data for Brent oil
data_minute = xa.get_bar("commodities/brent-oil", interval=60)
print(data_minute)
```

### Response
#### Success Response (200)
- **DataFrame** - A pandas DataFrame containing the intraday bar data with columns like 'date', 'open', 'high', 'low', 'close', 'volume', etc.

#### Response Example
```
                  date    open  high   low   close    volume  turnoverrate  percent
0  2020-04-03 00:30:00  7.3001  7.60  6.71  7.0497  30253047         12.58    -3.23
1  2020-04-03 01:30:00  7.0500  7.20  6.51  6.5450  17016394          7.09    -7.16
... (more data)
```
```

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

### Direct Binance and CCXT Trading

Source: https://context7.com/ufund-me/qbot/llms.txt

Demonstrates how to place market and limit orders on Binance directly and how to use the CCXT unified interface for multi-exchange trading, including fetching positions and executing trades based on signals.

```python
binance.order_market_buy(symbol="BTCUSDT", quantity=0.001)
binance.order_limit_sell(symbol="BTCUSDT", quantity=0.001, price="50000")
```

```python
ccxt_account = {
    "apikey": "your_api_key",
    "secretkey": "your_secret_key"
}
ccxt_opts = {"platform": "币安Binance", "strategy": "macd"}
ccxt_engine = CcxtTradeEngine(ccxt_account, ccxt_opts)
position = ccxt_engine.get_positions("BTC")
ccxt_engine.execute_trade(signal="buy", symbol="BTC/USDT", amount=0.001)
import pandas as pd
df = pd.DataFrame({"macd": [-0.1, 0.05, 0.1]})  # 示例数据
signal = ccxt_engine.check_trade_signals(df)
```

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

### Kill Tonghuashun Client (HTTP API)

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/trading/thsauto/README.md

Forcefully closes the Tonghuashun client application. This is a GET request to the specified API endpoint.

```http
http://192.168.0.116:5000/thsauto/client/kill
```

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

### Qbot CLI Login

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/easytrader/docs/usage.md

This command logs into the Qbot system using the specified trading client (e.g., 'yh') and prepares configuration from a JSON file. Upon successful login, an `account.session` file is generated to store the user object.

```bash
python cli.py --use yh --prepare gf.json
```

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

### Query Filled Orders (HTTP API)

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/trading/thsauto/README.md

Retrieves a list of orders that have been successfully filled. This is a GET request to the specified API endpoint.

```http
http://192.168.0.116:5000/thsauto/orders/filled
```

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

### Initialize Database Engines and Load SQL Data

Source: https://github.com/ufund-me/qbot/blob/main/docs/notebook/新闻分析.ipynb

This snippet uses a custom setting module to initialize database engines for stock and news databases. It then reads the 'tb_cnstock' table from both sources into pandas DataFrames.

```python
from setting import get_engine
import pandas as pd

first = get_engine('db_stock')
df = pd.read_sql('tb_cnstock', first)

second = get_engine('db_news')
df2 = pd.read_sql('tb_cnstock', second)
```

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

### Configure Backtesting Parameters in Backtrader

Source: https://github.com/ufund-me/qbot/blob/main/docs/README.md

This snippet demonstrates how to initialize a backtesting environment using the Backtrader framework, including setting the asset symbol, time range, and broker commission rates.

```python
symbol = "华正新材(603186)"
Starting_Portfolio_Value = 10000.00
Startdate = datetime.datetime(2010, 1, 1)
Enddate = datetime.datetime(2020, 4, 21)
# 设置佣金为0.001, 除以100去掉%号
cerebro.broker.setcommission(commission=0.001)
```

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

### Fix ImportError for Matplotlib in Backtrader

Source: https://github.com/ufund-me/qbot/blob/main/docs/tutorials_code/README.md

Resolves the 'ImportError: cannot import name warnings' error by installing a specific compatible version of the matplotlib library.

```bash
pip install matplotlib==3.2.2
```

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

### Fund Trading and Backtesting with xalpha

Source: https://context7.com/ufund-me/qbot/llms.txt

Explains how to manage fund trading records and perform backtesting analysis using the xalpha library. It covers creating trade records, simulating operations like subscriptions and redemptions, and calculating performance metrics.

```python
from xalpha import trade, itrade
from xalpha.record import record
import pandas as pd
from xalpha.info import fundinfo

status = pd.DataFrame({
    'date': pd.to_datetime(['2023-01-05', '2023-02-10', '2023-03-15']),
    '000001': [1000, 500, -200]  # 正数为买入金额，负数为卖出份额
})

fund = fundinfo("000001")  # 华夏成长基金
t = trade(fund, status)

print(t.cftable)
report = t.dailyreport()
xirr_rate = t.xirrrate()
print(f"年化收益率: {xirr_rate:.2%}")
unit_cost = t.unitcost()
print(f"单位成本: {unit_cost:.4f}")
t.v_tradevolume(freq="M")
t.v_tradecost()

it = itrade("SH510300", status)
it_report = it.dailyreport()
```

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

### Build Docker Image for Fund Strategy

Source: https://github.com/ufund-me/qbot/blob/main/pyfunds/fund-strategies/README.md

Builds a Docker image for the fund strategy application. This command should be run from the project's root directory.

```bash
docker build -t fund_strategy .
```

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

### Trading Operations

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/easytrader/docs/usage.md

This section details various trading functions, including getting account balance, positions, placing orders, and canceling orders.

```APIDOC
## Trading Operations

### 1. Get Account Balance

```python
user.balance
```

**Return Example:**

```json
[{'reference_market_value': 21642.0, 'available_funds': 28494.21, 'currency': '0', 'total_assets': 50136.21, 'stock_reference_profit_loss': -90.21, 'fund_balance': 28494.21, 'fund_account': 'xxx'}]
```

### 2. Get Positions

```python
user.position
```

**Return Example:**

```json
[{'buy_frozen': 0, 'trading_market': '沪A', 'sell_frozen': '0', 'reference_market_price': 4.71, 'reference_market_value': 10362.0, 'reference_cost_price': 4.672, 'reference_profit_loss': 82.79, 'current_holdings': 2200, 'profit_loss_percentage': '0.81%', 'shareholder_code': 'xxx', 'stock_balance': 2200, 'stock_available': 2200, 'security_code': '601398', 'security_name': '工商银行'}]
```

### 3. Buy Order

```python
user.buy('162411', price=0.55, amount=100)
```

**Return Example:**

```json
{'entrust_no': 'xxxxxxxx'}
```

Note: System can be configured to return trade confirmations. If not configured, the default return is `{"message": "success"}`.

### 4. Sell Order

```python
user.sell('162411', price=0.55, amount=100)
```

**Return Example:**

```json
{'entrust_no': 'xxxxxxxx'}
```

### 5. One-Click IPO Subscription

```python
user.auto_ipo()
```

### 6. Cancel Order

```python
user.cancel_entrust('entrust_no obtained from buy/sell')
```

**Return Example:**

```json
{'message': 'Order cancellation successful'}
```

### 7. Query Today's Trades

```python
user.today_trades
```

**Return Example:**

```json
[{'buy_sell_flag': 'Buy', 'trading_market': '深A', 'entrust_seq': '12345', 'trade_price': 0.626, 'trade_amount': 100, 'trade_date': '20170313', 'trade_time': '09:50:30', 'trade_amount_total': 62.60, 'shareholder_code': 'xxx', 'security_code': '162411', 'security_name': '华宝油气'}]
```

### 8. Query Today's Entrusts

```python
user.today_entrusts
```

**Return Example:**

```json
[{'buy_sell_flag': 'Buy', 'trading_market': '深A', 'entrust_price': 0.627, 'entrust_seq': '111111', 'entrust_amount': 100, 'entrust_date': '20170313', 'entrust_time': '09:50:30', 'trade_amount': 100, 'cancel_amount': 0, 'status_description': 'Filled', 'shareholder_code': 'xxxxx', 'security_code': '162411', 'security_name': '华宝油气'},
 {'buy_sell_flag': 'Buy', 'trading_market': '深A', 'entrust_price': 0.6, 'entrust_seq': '1111', 'entrust_amount': 100, 'entrust_date': '20170313', 'entrust_time': '09:40:30', 'trade_amount': 0, 'cancel_amount': 100, 'status_description': 'Cancelled', 'shareholder_code': 'xxx', 'security_code': '162411', 'security_name': '华宝油气'}]
```

### 9. Query Today's IPO Subscription Data

```python
from easytrader.utils.stock import get_today_ipo_data
ipo_data = get_today_ipo_data()
print(ipo_data)
```

**Return Example:**

```json
[{'stock_code': 'stock_code', 'stock_name': 'stock_name', 'price': issue_price, 'apply_code': 'apply_code'}]
```

### 10. Refresh Data

```python
user.refresh()
```

### 11. Xueqiu Portfolio Weight Adjustment

```python
user.adjust_weight('stock_code', target_weight)
```

Example: `user.adjust_weight('000001', 10)` adjusts the holding weight of Ping An Bank to 10% in the portfolio.

```

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

### Cancel Order (HTTP API)

Source: https://github.com/ufund-me/qbot/blob/main/qbot/engine/trade/trading/thsauto/README.md

Cancels a previously placed order using its entrustment number. This is a GET request to the specified API endpoint.

```http
http://192.168.0.116:5000/thsauto/cancel?entrust_no=2060704404
```