AKQuant A-Share Backtesting

A-share quantitative trading backtesting using AKQuant (Rust engine) and AKShare data. Use when user asks to "backtest a stock strategy", "test trading algorithm on Chinese stocks", "analyze stock performance", "run double MA strategy", or "optimize trading parameters". Supports double MA, RSI, and custom strategies for A-shares.

High-performance quantitative backtesting for Chinese stocks using Rust-powered AKQuant framework.

When to Use This Skill

Use this skill when you need to:

• Backtest trading strategies - "Backtest double MA strategy on 平安银行"
• Analyze stock performance - "How would a momentum strategy perform on 茅台?"
• Optimize trading parameters - "Find best MA periods for 宁德时代"
• Validate trading ideas - "Test if RSI works on Chinese tech stocks"
• Compare strategies - "Which performs better: MA crossover or RSI?"

Quick Examples

Example 1: Quick Backtest

User says: "Backtest double MA strategy on 贵州茅台"

Actions:

bash

python3 scripts/run_backtest.py 600519 10 30

Result: Returns total return, trade count, equity curve

Example 2: Strategy Comparison

User says: "Compare 5-day vs 20-day MA on 平安银行"

Actions:

bash

# Fast MA = 5, Slow MA = 20
python3 scripts/run_backtest.py 000001 5 20

# Compare with default 10/30
python3 scripts/run_backtest.py 000001 10 30

Result: Compare returns to find optimal parameters

Example 3: Research Workflow

User says: "Analyze which tech stocks performed best with momentum strategy in 2024"

Actions:

1. Test on multiple stocks: python3 scripts/run_backtest.py 300750 10 30 (宁德时代)
2. Test: python3 scripts/run_backtest.py 002594 10 30 (比亚迪)
3. Compare results and identify patterns

Step-by-Step Instructions

Step 1: Choose Stock Symbol

Common A-Share Symbols:

Symbol	Company	Sector
600519	贵州茅台	消费
000001	平安银行	金融
300750	宁德时代	新能源
002594	比亚迪	汽车
000858	五粮液	消费

Find symbol: Use AKShare or search "股票代码 + 公司名称"

Step 2: Select Strategy Parameters

Double MA Strategy (金叉买入，死叉卖出):

bash

python3 scripts/run_backtest.py <symbol> <fast_period> <slow_period>

Recommended combinations:

• Conservative: 20 / 60 (fewer trades, longer trends)
• Balanced: 10 / 30 (moderate frequency)
• Aggressive: 5 / 20 (more trades, shorter trends)

Step 3: Analyze Results

Key metrics to review:

• 总收益率 - Overall strategy performance
• 交易次数 - Frequency (lower = less commission)
• 最大回撤 - Risk measure (if implemented)
• 胜率 - % of profitable trades

Interpretation:

text

Return > 0%    → Strategy beats buy-and-hold
Return < 0%    → Strategy underperforms
Trade count > 20 → Consider commission impact

Available Strategies

Built-in Strategy: Double MA

Logic: Fast MA crosses above slow MA → Buy; Crosses below → Sell

Code example:

python

from double_ma_strategy import run_double_ma_backtest

result = run_double_ma_backtest(
    symbol="000001",
    fast_period=10,
    slow_period=30,
    initial_capital=100000,
    start_date="20240101",
    end_date="20241231"
)

print(f"Return: {result['return_pct']:.2f}%")
print(f"Trades: {len(result['trades'])}")

Custom Strategy Development

RSI Strategy Template:

python

import akquant as aq

class RsiStrategy:
    def __init__(self, period=14, oversold=30, overbought=70):
        self.rsi = aq.RSI(period)
        self.oversold = oversold
        self.overbought = overbought
        
    def on_bar(self, bar):
        self.rsi.update(bar['close'])
        
        if self.rsi.value < self.oversold:
            return 'BUY'  # 超卖买入
        elif self.rsi.value > self.overbought:
            return 'SELL'  # 超买卖出
        return 'HOLD'

Technical Indicators Reference

Indicator	Usage	Signal
aq.SMA(n)	Trend following	Price > SMA → uptrend
aq.EMA(n)	Faster trend	More responsive than SMA
aq.RSI(n)	Momentum	<30 oversold, >70 overbought
aq.MACD()	Trend + momentum	Crossover signals
aq.BollingerBands(n, k)	Volatility	Price touches bands
aq.ATR(n)	Risk sizing	Position sizing based on volatility

Example:

python

import akquant as aq

# Multi-indicator strategy
sma = aq.SMA(20)
rsi = aq.RSI(14)

for price in prices:
    sma.update(price)
    rsi.update(price)
    
    # Buy: Price > SMA AND RSI < 40 (uptrend but not overbought)
    if price > sma.value and rsi.value < 40:
        signal = 'BUY'

Data Access via AKShare

Stock Historical Data

python

import akshare as ak

# Daily price data (qfq = 前复权)
df = ak.stock_zh_a_hist(
    symbol="000001",
    period="daily",
    start_date="20240101",
    end_date="20241231",
    adjust="qfq"
)

# Columns: 日期, 开盘, 收盘, 最高, 最低, 成交量

Real-time Quote

python

# Current prices
df = ak.stock_zh_a_spot_em()

Troubleshooting

Error: "ModuleNotFoundError: No module named 'akquant'"

Cause: Dependencies not installed Solution:

bash

source /root/.openclaw/venv/bin/activate
pip install akquant akshare pandas numpy

Error: "Stock symbol not found"

Cause: Wrong symbol format Solution:

• A-shares use 6-digit codes: 000001 (SZ), 600519 (SH), 300750 (创业板)
• Don't include exchange prefix (use 000001 not SZ000001)

Error: "No data returned"

Causes:

1. Invalid date range - Check start_date < end_date
2. Stock suspended - Some stocks have trading halts
3. Delisted stock - Verify stock is still trading
4. Network issue - AKShare requires internet connection

Strategy returns -100% (total loss)

Causes:

1. Wrong parameter order - fast_period should be < slow_period

   bash

   # Wrong: fast > slow
   python3 scripts/run_backtest.py 000001 30 10
   
   # Correct: fast < slow
   python3 scripts/run_backtest.py 000001 10 30
   

2. Too many trades - High commission costs
3. Wrong signal logic - Check buy/sell conditions

Slow performance

Solutions:

• Reduce date range (test 3 months instead of 1 year)
• Use fast_period >= 5 to reduce calculation
• AKQuant is Rust-based and fast; slowness usually comes from data fetching

Results inconsistent between runs

Cause: AKShare data updates (recent days) Solution:

• Use fixed date ranges for reproducibility
• Cache data locally if needed

Best Practices

Strategy Development Workflow

1. Start simple - Test MA crossover before complex strategies
2. Visualize - Plot equity curve if possible
3. Walk-forward test - Train on 2023, test on 2024
4. Transaction costs - Include 0.1% commission + 0.1% slippage
5. Risk management - Add stop-loss logic

Parameter Optimization

bash

# Test multiple combinations
for fast in 5 10 15; do
  for slow in 20 30 60; do
    echo "Testing $fast/$slow:"
    python3 scripts/run_backtest.py 000001 $fast $slow
  done
done

Avoid Overfitting

• Don't optimize too many parameters
• Test on out-of-sample data
• Simple strategies often outperform complex ones

Limitations & Warnings

• Data delay: AKShare has 15-minute delay - for backtesting only, not live trading
• Historical bias: Past performance ≠ future results
• Execution: Real-world fills may differ from backtest assumptions
• Survivorship: Delisted stocks not in current data
• Dividends: Adjusted prices used, but dividend timing affects returns

References

• AKQuant Cheat Sheet - Quick API reference
• AKShare Documentation - Data sources
• AKQuant GitHub - Official docs