---

name: rqalpha description: RQAlpha — open-source event-driven backtesting framework by RiceQuant, supporting A-shares and futures with modular architecture. version: 1.0.0 homepage: https://github.com/ricequant/rqalpha metadata: {"clawdbot":{"emoji":"🍚","requires":{"bins":["python3"]}}}

RQAlpha (RiceQuant Backtesting Framework)

RQAlpha is an open-source, event-driven backtesting framework developed by RiceQuant. It provides a complete solution for strategy development, backtesting, and paper trading for China A-shares and futures markets. Highly modular with a plugin (mod) system.

Docs: https://rqalpha.readthedocs.io/

Installation

pip install rqalpha

# Download built-in bundle data (A-share daily data)
rqalpha download-bundle

Strategy Structure

def init(context):
    """Called once at strategy start — set up subscriptions and parameters."""
    context.stock = '000001.XSHE'
    context.fired = False

def handle_bar(context, bar_dict):
    """Called on every bar — main trading logic."""
    if not context.fired:
        order_shares(context.stock, 1000)
        context.fired = True

def before_trading(context):
    """Called before market open each day."""
    pass

def after_trading(context):
    """Called after market close each day."""
    pass

Running a Backtest

Command Line

rqalpha run \
    -f strategy.py \
    -s 2024-01-01 \
    -e 2024-06-30 \
    --account stock 100000 \
    --benchmark 000300.XSHG \
    --plot

Python API

from rqalpha.api import *
from rqalpha import run_func

config = {
    "base": {
        "start_date": "2024-01-01",
        "end_date": "2024-06-30",
        "accounts": {"stock": 100000},
        "benchmark": "000300.XSHG",
        "frequency": "1d",
    },
    "extra": {
        "log_level": "warning",
    },
    "mod": {
        "sys_analyser": {"enabled": True, "plot": True},
    },
}

result = run_func(init=init, handle_bar=handle_bar, config=config)
print(result)

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Symbol Format

Market	Suffix	Example
Shanghai A-shares	.XSHG	600000.XSHG (SPDB)
Shenzhen A-shares	.XSHE	000001.XSHE (Ping An Bank)
Index	.XSHG/.XSHE	000300.XSHG (CSI 300)
Futures	.XSGE/.XDCE/.XZCE/.CCFX	IF2401.CCFX (CSI 300 futures)

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Order Functions

# Buy/sell by share count
order_shares('000001.XSHE', 1000)       # Buy 1000 shares
order_shares('000001.XSHE', -500)       # Sell 500 shares

# Buy by lot (1 lot = 100 shares)
order_lots('000001.XSHE', 10)           # Buy 10 lots (1000 shares)

# Buy by value
order_value('000001.XSHE', 50000)       # Buy 50,000 CNY worth

# Buy by percentage of portfolio
order_percent('000001.XSHE', 0.5)       # Buy 50% of portfolio value

# Target position
order_target_value('000001.XSHE', 100000)   # Adjust to 100,000 CNY
order_target_percent('000001.XSHE', 0.3)    # Adjust to 30% of portfolio

# Cancel order
cancel_order(order_id)

Data Query Functions

def handle_bar(context, bar_dict):
    # Current bar data
    bar = bar_dict['000001.XSHE']
    price = bar.close
    volume = bar.volume
    dt = bar.datetime

    # Historical data (returns DataFrame)
    prices = history_bars('000001.XSHE', bar_count=20, frequency='1d',
                          fields=['close', 'volume', 'open', 'high', 'low'])

    # Check if stock is tradable
    tradable = is_valid_price(bar.close)

    # Check if suspended
    suspended = is_suspended('000001.XSHE')

Portfolio & Position

def handle_bar(context, bar_dict):
    # Portfolio info
    cash = context.portfolio.cash                    # Available cash
    total = context.portfolio.total_value            # Total portfolio value
    market_value = context.portfolio.market_value    # Position market value
    pnl = context.portfolio.pnl                      # Total PnL
    returns = context.portfolio.daily_returns        # Daily return

    # Position info
    positions = context.portfolio.positions
    for stock, pos in positions.items():
        print(f'{stock}: quantity={pos.quantity}, '
              f'sellable={pos.sellable}, '
              f'avg_price={pos.avg_price:.2f}, '
              f'market_value={pos.market_value:.2f}, '
              f'pnl={pos.pnl:.2f}')

Scheduler

from rqalpha.api import *

def init(context):
    # Run function at specific time every trading day
    scheduler.run_daily(rebalance, time_rule=market_open(minute=5))
    # Run weekly (every Monday)
    scheduler.run_weekly(weekly_task, tradingday=1, time_rule=market_open(minute=5))
    # Run monthly (first trading day)
    scheduler.run_monthly(monthly_task, tradingday=1, time_rule=market_open(minute=5))

def rebalance(context, bar_dict):
    pass

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Mod System (Plugins)

RQAlpha's modular architecture allows extending functionality via mods:

config = {
    "mod": {
        "sys_analyser": {
            "enabled": True,
            "plot": True,
            "benchmark": "000300.XSHG",
        },
        "sys_simulation": {
            "enabled": True,
            "matching_type": "current_bar",    # Order matching: current_bar or next_bar
            "slippage": 0.01,                  # Slippage (CNY)
        },
        "sys_transaction_cost": {
            "enabled": True,
            "commission_rate": 0.0003,         # Commission rate
            "tax_rate": 0.001,                 # Stamp tax (sell only)
            "min_commission": 5,               # Minimum commission
        },
    },
}

Available Built-in Mods

Mod	Description
sys_analyser	Performance analysis and plotting
sys_simulation	Order matching simulation
sys_transaction_cost	Commission and tax calculation
sys_accounts	Account management
sys_benchmark	Benchmark tracking
sys_progress	Progress bar display
sys_risk	Risk management checks

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Advanced Examples

Dual Moving Average Crossover Strategy

import numpy as np
from rqalpha.api import *

def init(context):
    context.stock = '600000.XSHG'
    context.fast = 5
    context.slow = 20
    scheduler.run_daily(trade_logic, time_rule=market_open(minute=5))

def trade_logic(context, bar_dict):
    prices = history_bars(context.stock, context.slow + 1, '1d', fields=['close'])
    if len(prices) < context.slow:
        return

    closes = prices['close']
    fast_ma = np.mean(closes[-context.fast:])
    slow_ma = np.mean(closes[-context.slow:])

    pos = context.portfolio.positions.get(context.stock)
    has_position = pos is not None and pos.quantity > 0

    if fast_ma > slow_ma and not has_position:
        order_target_percent(context.stock, 0.9)
        logger.info(f'BUY: fast_ma={fast_ma:.2f} > slow_ma={slow_ma:.2f}')
    elif fast_ma < slow_ma and has_position:
        order_target_percent(context.stock, 0)
        logger.info(f'SELL: fast_ma={fast_ma:.2f} < slow_ma={slow_ma:.2f}')

def handle_bar(context, bar_dict):
    pass

Multi-Stock Equal-Weight Rebalancing

from rqalpha.api import *

def init(context):
    context.stocks = ['600000.XSHG', '000001.XSHE', '601318.XSHG',
                       '600036.XSHG', '000858.XSHE']
    scheduler.run_monthly(rebalance, tradingday=1, time_rule=market_open(minute=30))

def rebalance(context, bar_dict):
    # Sell stocks not in target list
    for stock in list(context.portfolio.positions.keys()):
        if stock not in context.stocks:
            order_target_percent(stock, 0)

    # Equal-weight allocation
    weight = 0.95 / len(context.stocks)
    for stock in context.stocks:
        if not is_suspended(stock):
            order_target_percent(stock, weight)
            logger.info(f'Rebalance: {stock} -> {weight:.1%}')

def handle_bar(context, bar_dict):
    pass

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Tips

• RQAlpha is a local-only framework — no cloud dependency, ideal for offline research.
• Use rqalpha download-bundle to get free built-in daily data for A-shares.
• The mod system allows plugging in custom data sources, brokers, and risk modules.
• For live trading, connect via rqalpha-mod-vnpy to use vn.py's broker gateways.
• Supports both daily and minute-level backtesting.
• Docs: https://rqalpha.readthedocs.io/

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社区与支持

由 大佬量化 (Boss Quant) 维护 — 量化交易教学与策略研发团队。

微信客服: bossquant1 · Bilibili · 搜索 大佬量化 on 微信公众号 / Bilibili / 抖音