AKQuant A-Share Backtesting

---

name: akquant-backtest description: 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.

---

AKQuant A-Share Backtesting

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:

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:

# 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):

python3 scripts/run_backtest.py   

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:

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:

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:

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:

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

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

# Current prices
df = ak.stock_zh_a_spot_em()

Troubleshooting

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

Cause: Dependencies not installed Solution:

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

   # 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

# 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

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

# 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