### Install and Setup Bullet-Trade for Backtesting

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/backtest.md

Installs the necessary environment and copies the example configuration file. Ensure you activate the virtual environment before proceeding.

```bash
python -m venv .venv
# macos/linux
source .venv/bin/activate
# windows (cmd)
.venv\Scripts\activate.bat
pip install -e ".[dev]"
# macos/linux
cp env.backtest.example .env
# windows (cmd)
copy env.backtest.example .env
```

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

### Copy example .env file (Windows cmd)

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/python-setup.md

On Windows command prompt, copy an example environment file named 'env.example' to '.env' to start configuring your settings.

```bash
copy env.example .env
```

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

### Copy example .env file (macOS/Linux)

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/python-setup.md

On macOS or Linux, copy an example environment file named 'env.example' to '.env' to start configuring your settings.

```bash
cp env.example .env
```

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

### Install BulletTrade

Source: https://context7.com/bullettrade/bullet-trade/llms.txt

Instructions for installing BulletTrade, including setting up a virtual environment and installing the package in regular or development mode.

```bash
python -m venv .venv
source .venv/bin/activate
pip install bullet-trade
pip install -e ".[dev]"
cp env.example .env
bullet-trade --version
```

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

### Install Project Dependencies

Source: https://github.com/bullettrade/bullet-trade/blob/main/tests/unit/test_dividend_data_consistency.md

Install the project's development dependencies and specific libraries required for each data provider.

```bash
pip install -e ".[dev]"
pip install jqdatasdk  # 如果测试 jqdata
pip install xtquant    # 如果测试 miniqmt
pip install tushare    # 如果测试 tushare
```

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

### Remote QMT Server Setup

Source: https://context7.com/bullettrade/bullet-trade/llms.txt

Instructions for setting up a remote Bullet Trade server on a Windows machine with QMT installed. Supports single or multiple accounts.

```bash
# ── 远程 server 端启动（Windows + QMT 机器）──
# .env 配置：
# QMT_DATA_PATH=C:\国金QMT交易端\userdata_mini
# QMT_ACCOUNT_ID=123456
# QMT_SERVER_TOKEN=secret

bullet-trade --env-file .env server \
    --listen 0.0.0.0 --port 58620 \
    --enable-data --enable-broker

# 多账户（股票+期货）
bullet-trade server --listen 0.0.0.0 --port 58620 \
    --accounts main=123456 hedge=654321:future \
    --enable-data --enable-broker
```

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

### Install BulletTrade Package

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/quickstart.md

Install the BulletTrade library using pip after activating your virtual environment.

```bash
pip install bullet-trade
```

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

### Parameter Optimization Output Example

Source: https://context7.com/bullettrade/bullet-trade/llms.txt

Example of the console output during parameter optimization, showing top-performing parameter sets sorted by risk-return ratio.

```text
# 优化输出示例（按收益回撤比降序，控制台显示 Top 10）：
参数优化Top建议（按收益回撤比降序）：
 lookback  hold_days  top_n  threshold  收益回撤比  策略年化收益  最大回撤  夏普比率
       25          5      2       0.02       2.35        28.5%   -12.1%     1.82
       20          5      3       0.01       2.18        25.2%   -11.6%     1.65
       30          7      2       0.03       2.05        22.8%   -11.1%     1.58
```

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

### Install xtquant Separately

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/python-setup.md

If needed, the 'xtquant' library can be installed independently. However, using the 'bullet-trade[qmt]' installation is generally recommended for simplicity.

```bash
pip install xtquant
```

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

### Parameter Configuration Example

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/optimize.md

Configure the parameter grid for optimization in a JSON file. Parameter names must match the global variable names in the strategy file.

```json
{
    "param_grid": {
        "lookback": [10, 15, 20, 25, 30],
        "hold_days": [3, 5, 7, 10],
        "top_n": [1, 2, 3, 5]
    }
}
```

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

### Check Python and Pip Installation

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/python-setup.md

Verify that Python and pip are installed and accessible from the command line. This is a prerequisite before creating a virtual environment.

```bash
python --version
```

```bash
pip --version
```

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

### Install BulletTrade with QMT Extension

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/python-setup.md

Install BulletTrade with the 'qmt' extension, which includes dependencies for direct QMT connection or running the BulletTrade server.

```bash
pip install "bullet-trade[qmt]"
```

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

### Strategy File Example

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/optimize.md

Define your trading strategy logic in a Python file. Ensure global variables used for optimization are initialized with default values.

```python
from jqdata import *

def initialize(context):
    set_benchmark('000300.XSHG')
    set_option('use_real_price', True)
    
    # 这些参数将被优化器覆盖
    g.lookback = 20        # 回看天数
    g.hold_days = 5        # 持有天数
    g.top_n = 3            # 选股数量
    
    run_daily(trade, '10:00')

def trade(context):
    # 策略逻辑...
    pass
```

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

### Local QMT Live Trading Setup

Source: https://context7.com/bullettrade/bullet-trade/llms.txt

Configuration and command to run live trading using the local QMT broker. Requires setting environment variables for data path and account ID.

```bash
# ── 本地 QMT 实盘 ──
# .env 配置（同一台 Windows 机器）：
# DEFAULT_DATA_PROVIDER=qmt
# DEFAULT_BROKER=qmt
# QMT_DATA_PATH=C:\国金QMT交易端\userdata_mini
# QMT_ACCOUNT_ID=123456

bullet-trade live strategies/demo_strategy.py --broker qmt
```

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

### Run Local QMT Strategy

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/config.md

Command to run a live strategy with the QMT broker in a local setup.

```bash
bullet-trade live strategies/demo_strategy.py --broker qmt
```

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

### Run a Minimal Backtest

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/quickstart.md

Execute a backtest for a demo strategy with specified start and end dates. Ensure your strategy file is correctly located.

```bash
bullet-trade backtest strategies/demo_strategy.py --start 2024-01-01 --end 2024-06-01
```

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

### Example Strategy Test Output

Source: https://github.com/bullettrade/bullet-trade/blob/main/tests/strategies/README.md

This is an example of the detailed output generated after testing a strategy, including performance metrics and a pass/fail status.

```text
============================================================
测试策略: strategy.joinquant.EPO
文件路径: /path/to/tests/strategies/strategy.joinquant.EPO.py
============================================================

策略配置:
  回测期间: 2024-01-01 ~ 2024-12-31
  初始资金: 1,000,000
  运行频率: daily
  基准指数: 000300.XSHG

回测结果:
  总收益率: 15.30%
  年化收益率: 15.30%
  基准收益率: 8.50%
  阿尔法: 0.0680
  贝塔: 0.7500
  夏普比率: 1.45
  最大回撤: -18.20%
  胜率: 62.00%

============================================================
策略 strategy.joinquant.EPO 测试通过 ✓
============================================================

```

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

### Successful Test Run Output Example

Source: https://github.com/bullettrade/bullet-trade/blob/main/tests/unit/README_dividend_test.md

Example output demonstrating a successful execution of all dividend data consistency tests.

```text
bullet-trade/tests/unit/test_dividend_data_consistency.py::test_golden_dividends_format PASSED         [ 16%]
bullet-trade/tests/unit/test_dividend_data_consistency.py::test_provider_dividends_match_golden[jqdata] PASSED [ 33%]
bullet-trade/tests/unit/test_dividend_data_consistency.py::test_provider_dividends_match_golden[miniqmt] PASSED [ 50%]
bullet-trade/tests/unit/test_dividend_data_consistency.py::test_provider_dividends_match_golden[tushare] PASSED [ 66%]
bullet-trade/tests/unit/test_dividend_data_consistency.py::test_cross_provider_consistency PASSED      [ 83%]
bullet-trade/tests/unit/test_dividend_data_consistency.py::test_dividend_cash_calculation PASSED       [100%]

============================== 6 passed in 12.34s ==============================
```

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

### Verify BulletTrade Installation

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/python-setup.md

Check if BulletTrade is installed correctly by running its help and version commands. Successful output indicates the environment is ready.

```bash
bullet-trade --help
```

```bash
bullet-trade --version
```

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

### Start BulletTrade Research Environment

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/research.md

Launches the BulletTrade research environment, which typically opens in a web browser. Use the --diagnose flag for troubleshooting.

```bash
bullet-trade lab
```

```bash
bullet-trade lab --diagnose
```

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

### Remote qmt-remote Client Setup

Source: https://context7.com/bullettrade/bullet-trade/llms.txt

Configuration and command to connect a client (macOS/Linux compatible) to a remote QMT server using qmt-remote. Requires server details and token.

```bash
# ── 远程 qmt-remote 客户端（macOS/Linux 可用）──
# .env 配置：
# DEFAULT_DATA_PROVIDER=qmt-remote
# DEFAULT_BROKER=qmt-remote
# QMT_SERVER_HOST=10.0.0.8
# QMT_SERVER_PORT=58620
# QMT_SERVER_TOKEN=secret
# QMT_SERVER_ACCOUNT_KEY=main   # 多账户时才需要

bullet-trade live strategies/demo_strategy.py --broker qmt-remote
```

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

### Client-Side Multi-Account Configuration

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/config.md

For multi-account setups, clients need to specify the active account key in their environment.

```env
QMT_SERVER_ACCOUNT_KEY=main
```

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

### Switch Data Provider Example

Source: https://context7.com/bullettrade/bullet-trade/llms.txt

Python code demonstrating how to query the current data provider and switch between JQData and MiniQMT using the Bullet Trade API.

```python
from bullet_trade.data.api import get_price, set_data_provider, get_data_provider

# 查询当前数据源
provider = get_data_provider()
print(f"当前数据源: {provider.name}")

# 切换到 JQData（需配置 JQDATA_USERNAME / JQDATA_PASSWORD）
set_data_provider('jqdata')
df_jq = get_price('601318.XSHG', '2025-01-01', '2025-06-30', fq=None)

# 切换到 MiniQMT（本地，需配置 QMT_DATA_PATH）
set_data_provider('qmt')
```

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

### Create .env file

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/python-setup.md

Example configuration for a .env file, used to store environment variables like account IDs and tokens. This file should be placed in the directory where BulletTrade commands are executed.

```env
QMT_ACCOUNT_ID=123456
QMT_SERVER_TOKEN=secret
```

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

### Demo Strategy Implementation

Source: https://context7.com/bullettrade/bullet-trade/llms.txt

A sample strategy demonstrating basic initialization, setting benchmarks, options, and daily execution of trading logic.

```python
from jqdata import *

def initialize(context):
    set_benchmark('000300.XSHG')
    set_option('use_real_price', True)
    g.target = ['000001.XSHE', '600000.XSHG']
    run_daily(market_open, time='10:00')

def market_open(context):
    for stock in g.target:
        df = get_price(stock, count=5, fields=['close'])
        if df['close'].iloc[-1] > df['close'].mean():
            order_target_value(stock, 50000)
        else:
            order_target_value(stock, 0)
```

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

### Get Split and Dividend Events

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/api.md

Retrieve a list of stock split and dividend events for a given security. For non-backtesting scenarios, explicit start and end dates must be provided. The output structure is standardized.

```python
get_split_dividend(security='000001.XSHE', start_date='2023-01-01', end_date='2023-12-31')
```

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

### 创建最简单的策略文件

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/beginner-route-a.md

创建一个名为 `my_first_strategy.py` 的 Python 文件，包含 `initialize` 和 `market_open` 函数，用于执行买入操作。

```python
from bullet_trade.core.api import *


def initialize(context):
    set_benchmark('000300.XSHG')
    g.stock = '510300.XSHG'
    g.has_bought = False
    run_daily(market_open, time='open')


def market_open(context):
    if not g.has_bought:
        order_value(g.stock, context.portfolio.available_cash)
        g.has_bought = True
        log.info(f"买入 {g.stock}，金额={context.portfolio.available_cash:.2f}")
```

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

### Initialize and Run Daily Strategy - Python

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/beginner-guide.md

This Python code snippet demonstrates initializing a strategy with a benchmark and scheduling a daily market open event. It's typical for strategies that primarily use price data, scheduling, and basic order functions, suitable for independent execution.

```python
from bullet_trade.core.api import *


def initialize(context):
    set_benchmark('000300.XSHG')
    run_daily(market_open, time='open')


def market_open(context):
    stocks = get_index_stocks('000300.XSHG')
    df = get_price(stocks[:10], end_date=context.previous_date, count=20, fields=['close'], panel=False)
    current = get_current_data()['000001.XSHG']
    if current.paused:
        return
    order_target_value('510300.XSHG', 100000)
```

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

### Upgrade Pip

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/python-setup.md

Upgrade the pip package installer within the activated virtual environment. It's recommended to do this before installing other packages.

```bash
python -m pip install --upgrade pip
```

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

### Run Backtest and Live Trading Commands

Source: https://github.com/bullettrade/bullet-trade/blob/main/bullet_trade/notebook/01.welcome.ipynb

Use these commands to initiate backtesting or live trading simulations for your strategies. Specify the strategy file path and date range for backtests, or the broker for live trading.

```bash
bullet-trade backtest strategies/demo_strategy.py --start 2025-01-01 --end 2025-06-01
```

```bash
bullet-trade live strategies/demo_strategy.py --broker qmt
```

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

### Strategy Entry and Global Objects

Source: https://context7.com/bullettrade/bullet-trade/llms.txt

Demonstrates how to import JoinQuant compatible APIs, use the global state container 'g', the logging object 'log', and the message sending function 'send_msg'. Includes setting benchmarks and options for real-time pricing.

```python
from jqdata import *

def initialize(context):
    # g：全局状态容器，可挂载任意可序列化属性
    g.target_ratio = 0.2
    g.stocks = ['000001.XSHE', '600000.XSHG']
    g.live_trade = True  # 标记实盘模式

    # log：支持 debug/info/warn/error/critical
    log.set_level('strategy', 'info')

    set_benchmark('000300.XSHG')
    set_option('use_real_price', True)

    run_daily(trade, time='10:00')

def trade(context):
    # send_msg：输出策略日志，配置 MESSAGE_KEY 后可推送企业微信
    send_msg(f"[交易] 当前净值: {context.portfolio.total_value:.2f}")

    for stock in g.stocks:
        df = get_price(stock, count=5, fields=['close'])
        last = df['close'].iloc[-1]
        mean = df['close'].mean()
        if last > mean:
            order_target_value(stock, context.portfolio.total_value * g.target_ratio)
            log.info(f"买入 {stock}, 最新价={last:.2f}, 5日均价={mean:.2f}")
        else:
            order_target_value(stock, 0)

# set_message_handler：注册自定义消息处理函数
def my_handler(msg):
    print(f"[自定义推送] {msg}")

set_message_handler(my_handler)
# 清除 handler：set_message_handler(None)
```

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

### Add New Strategy Steps (Bash & YAML)

Source: https://github.com/bullettrade/bullet-trade/blob/main/tests/strategies/README.md

Steps to integrate a new strategy: copy the file, configure parameters in `config.yaml`, and run the test.

```bash
# 1. 复制策略文件
cp ~/my_strategy.py tests/strategies/strategy.my_awesome.py

# 2. 编辑 config.yaml 添加配置
```yaml
strategy.my_awesome:
  start_date: '2023-01-01'
  end_date: '2023-12-31'
  capital_base: 100000
```

# 3. 运行测试
pytest tests/test_strategies.py -v -s -k "my_awesome"
```

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

### Check Python installation paths (Windows)

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/python-setup.md

If multiple Python installations exist, use 'where' to identify which 'python' and 'pip' executables are being used. This helps in resolving path issues.

```bash
where python
```

```bash
where pip
```

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

### Failed Test Run Output Example (Data Inconsistency)

Source: https://github.com/bullettrade/bullet-trade/blob/main/tests/unit/README_dividend_test.md

Example output for a failed test case, indicating data inconsistency between a provider and the golden standard. The error message specifies the provider, security, dividend event, and the discrepancy in values.

```text
FAILED bullet-trade/tests/unit/test_dividend_data_consistency.py::test_provider_dividends_match_golden[miniqmt]

AssertionError: miniqmt 601318.XSHG 第1个分红事件 (2024-07-26) bonus_pre_tax 不匹配: 期望 15.0, 实际 1.5
```

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

### Required Strategy Initialization Function (Python)

Source: https://github.com/bullettrade/bullet-trade/blob/main/tests/strategies/README.md

Defines the mandatory `initialize` function for strategy setup, which is called once before backtesting begins.

```python
def initialize(context):
    """策略初始化函数，回测开始前调用一次"""
    pass
```

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

### Run Remote Server

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/config.md

Command to start the Bullet Trade server, listening on all interfaces and enabling data and broker services.

```bash
bullet-trade --env-file .env server --listen 0.0.0.0 --port 58620 --enable-data --enable-broker
```

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

### Workflow: Migrate Strategy from JoinQuant (Bash & YAML)

Source: https://github.com/bullettrade/bullet-trade/blob/main/tests/strategies/README.md

Steps to migrate a strategy from JoinQuant: copy the strategy file, configure parameters in `config.yaml`, and run the test.

```bash
# 1. 复制策略文件（不修改代码）
cp ~/Downloads/my_joinquant_strategy.py tests/strategies/

# 2. 编辑 config.yaml 添加回测参数
vim tests/strategies/config.yaml

# 3. 运行测试
pytest tests/test_strategies.py -v -s -k "my_joinquant"
```

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

### Sample Backtesting Strategy

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/backtest.md

A basic Python strategy demonstrating initialization, setting a benchmark, enabling real price trading, and scheduling a daily market open function to execute trades based on price trends.

```python
from jqdata import *

def initialize(context):
    set_benchmark('000300.XSHG')
    set_option('use_real_price', True)
    g.target = ['000001.XSHE', '600000.XSHG']
    run_daily(market_open, time='10:00')

def market_open(context):
    for stock in g.target:
        df = get_price(stock, count=5, fields=['close'])
        if df['close'][-1] > df['close'].mean():
            order_target_value(stock, 10000)
```

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

### 运行最小实盘部署

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/beginner-route-a.md

使用 `bullet-trade live` 命令运行 `my_first_strategy.py` 策略，并指定券商为 `qmt`。

```bash
bullet-trade live my_first_strategy.py --broker qmt
```

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

### Get All Securities Information

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/api.md

Fetch information about all available securities of a specified type (e.g., 'stock'). In a backtesting context, this defaults to the current backtest date.

```python
get_all_securities(types='stock', date='2023-01-01')
```

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

### Get Last Price of a Symbol

Source: https://github.com/bullettrade/bullet-trade/blob/main/bullet_trade/notebook/04.joinquant_remote_live_trade.ipynb

Fetches the last traded price for a specified security symbol. Ensure the data client is initialized before calling this function.

```python
bt.get_data_client().get_last_price('600635.XSHG')
```

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

### 配置最小 .env 文件

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/beginner-route-a.md

在运行回测前，需要配置 `.env` 文件，指定默认数据提供商、QMT 数据路径和账户 ID。

```env
DEFAULT_DATA_PROVIDER=qmt
QMT_DATA_PATH=C:\QMT\userdata_mini
QMT_ACCOUNT_ID=123456
```

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

### Get Current Data Provider Name

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/data/DATA_PROVIDER_GUIDE.md

Retrieve the name of the currently active data provider using `get_data_provider()`. Useful for debugging or conditional logic.

```python
from bullet_trade.data.api import get_data_provider

provider = get_data_provider()
print(f"Current data provider: {provider.name}")
```

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

### Configure Data Provider for Backtesting

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/backtest.md

Set the default data provider and provide credentials if required. The `DEFAULT_DATA_PROVIDER` is mandatory.

```bash
# 数据源类型 (jqdata, tushare, qmt)
DEFAULT_DATA_PROVIDER=jqdata         # 必填，行情源
JQDATA_USERNAME=your_username       # 选填，按数据源需要
JQDATA_PASSWORD=your_password
```

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

### Minute-Line Backtesting Configuration

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/backtest.md

Command to run a backtest using minute-frequency data. Ensure your data source supports minute data and that `use_real_price=True` is set in your strategy.

```bash
bullet-trade backtest strategy.py --start 2024-01-01 --end 2024-01-31 --frequency minute
```

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

### Optional Strategy Functions (Python)

Source: https://github.com/bullettrade/bullet-trade/blob/main/tests/strategies/README.md

Includes optional functions for handling trading logic, pre-trading setup, and post-trading cleanup at different stages of the trading day.

```python
def handle_data(context, data):
    """每个交易bar调用（按frequency配置）"""
    pass

def before_trading_start(context):
    """每日交易开始前调用"""
    pass

def after_trading_end(context):
    """每日交易结束后调用"""
    pass

def process_initialize(context):
    """实盘/模拟盘初始化"""
    pass
```

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

### 配置最小 .env 文件用于实盘

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/beginner-route-a.md

在运行实盘前，配置 `.env` 文件，指定默认数据提供商、默认券商、QMT 数据路径和账户 ID。

```env
DEFAULT_DATA_PROVIDER=qmt
DEFAULT_BROKER=qmt
QMT_DATA_PATH=C:\QMT\userdata_mini
QMT_ACCOUNT_ID=123456
```

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

### Python: Get Current Tick Snapshot

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/tick.md

Fetches a single tick snapshot for a given security code. This method does not depend on the subscription status and can be used for debugging.

```python
get_current_tick('000001.XSHE')
```

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

### Get Index Constituent Stocks

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/api.md

Retrieve a list of stock codes that constitute a given index on a specific date. Defaults to the current backtest date if running in a backtesting environment.

```python
get_index_stocks(index_symbol='000300.XSHG', date='2023-01-01')
```

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

### 多账户配置

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/trade-support.md

当使用多个账户时，需要在 bt.configure 函数中指定 account_key。这允许策略区分和管理不同的交易账户。

```python
bt.configure(
    host="your.server.ip",
    port=58620,
    token="secret",
    account_key="main",
)
```

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

### Get Trading Days

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/api.md

Retrieve a list of valid trading days within a specified date range. In backtesting environments, the `end_date` is automatically truncated to the current backtest time.

```python
get_trade_days(start_date='2023-01-01', end_date='2023-12-31')
```

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

### Run Remote QMT-Remote Client Strategy

Source: https://github.com/bullettrade/bullet-trade/blob/main/docs/config.md

Command to run a live strategy using the qmt-remote broker, connecting to a remote server.

```bash
bullet-trade live strategies/demo_strategy.py --broker qmt-remote
```