The core of quantitative trading is making investment decisions based on data and rules, not emotions and intuition.

AI allows you to quickly iterate strategy ideas without spending a lot of time on code details.

1. Basic Concepts of Quantitative Trading

Strategy Types:

Trend Following: Buy when prices rise, sell when they fall (e.g., moving average strategy)

Mean Reversion: Bet on prices reverting to historical averages (e.g., Bollinger Bands strategy)

Arbitrage Strategy: Exploit price differences across markets/instruments (e.g., ETF arbitrage)

Machine Learning Strategy: Use AI to predict price direction

Key Metrics:

Sharpe Ratio: Risk-adjusted return (>1 is good)

Maximum Drawdown: Historical maximum loss (<20% is acceptable)

Annualized Return: Actual investment return

2. Environment Setup

bash
Install necessary libraries
pip install pandas numpy matplotlib backtrader akshare yfinance
Or use Cursor/ChatGPT to generate the complete environment configuration

3. Acquiring and Processing Data with AI

python
Let ChatGPT write data acquisition code for you
import akshare as ak
import pandas as pd
A-share data (AKShare free)
def get_stock_data(symbol, start_date, end_date):
    df = ak.stock_zh_a_hist(
        symbol=symbol,
        start_date=start_date,
        end_date=end_date,
        adjust='qfq'  # Forward-adjusted
    )
    df['date'] = pd.to_datetime(df['日期'])
    df = df.rename(columns={'收盘': 'close', '开盘': 'open', 
                             '最高': 'high', '最低': 'low', '成交量': 'volume'})
    return df.set_index('date')
Ask ChatGPT: "Write a function to get historical data of CSI 300 constituent stocks"

4. Designing Trading Strategies with AI

Strategy 1: Dual Moving Average Golden Cross Strategy (Beginner-friendly)

python
Tell ChatGPT: "Implement a dual moving average strategy: buy when 5-day MA crosses above 20-day MA, sell when it crosses below"
def dual_ma_strategy(df, short_window=5, long_window=20):
    df['ma_short'] = df['close'].rolling(window=short_window).mean()
    df['ma_long'] = df['close'].rolling(window=long_window).mean()
    
    # Signal generation
    df['signal'] = 0
    df.loc[df['ma_short'] > df['ma_long'], 'signal'] = 1   # Buy signal
    df.loc[df['ma_short'] < df['ma_long'], 'signal'] = -1  # Sell signal
    
    # Trading points (where signal changes)
    df['position'] = df['signal'].diff()
    return df

Optimizing Strategy with ChatGPT

I have a dual moving average strategy with the following backtest results:
Annualized Return: 12%
Sharpe Ratio: 0.8
Maximum Drawdown: -28%
Please analyze the issues with this strategy and suggest:
How to add a stop-loss mechanism to reduce maximum drawdown
Whether volume filtering can be added to improve signal quality
Direction for parameter optimization (parameter ranges for short/long moving averages)

5. Backtesting Framework (Backtrader)

python
import backtrader as bt
class DualMAStrategy(bt.Strategy):
    params = (('short_period', 5), ('long_period', 20))
    
    def __init__(self):
        self.ma_short = bt.indicators.SMA(period=self.params.short_period)
        self.ma_long = bt.indicators.SMA(period=self.params.long_period)
        self.crossover = bt.indicators.CrossOver(self.ma_short, self.ma_long)
    
    def next(self):
        if self.crossover > 0:  # Golden cross
            self.buy()
        elif self.crossover < 0:  # Death cross
            self.sell()
Ask ChatGPT to write the complete backtesting code, including: initial capital, commission, performance report

6. Risk Control (Most Important Part)

Add the following risk controls to the existing strategy:
Stop-loss: Force close position if loss exceeds 5%
Position sizing: Each trade uses no more than 20% of total capital
Maximum holdings: Hold no more than 5 stocks simultaneously
Drawdown protection: Liquidate all positions when account drawdown exceeds 15% and wait
Implement with Backtrader and display these risk control metrics in the backtest report

7. Important Notes (Very Important)

Overfitting Risk: Good backtest performance does not guarantee future profitability Trading Costs: A-share commission + stamp duty is about 0.15%, affecting high-frequency strategies Market Impact: Institutional-level strategies may not be replicable in personal accounts Capital Management: Any strategy carries loss risk; only invest money you can afford to lose

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Further Reading

AI Stock Analysis Tools Comparison

Common Mistakes in Quantitative Strategies