Quant Crash Course

What is QuantConnect?

Think of QuantConnect as your coding playground for trading. It's a cloud platform where you can write, test, and actually trade strategies without worrying about servers, data feeds, or broker integrations.

Here's the deal: You write Python code, test it on years of market data, and if it works, you can flip a switch and trade it live. All in your browser.

Your First 5 Minutes

Let's get you from zero to your first working algorithm. No fluff, just the essentials.

Your First Real Algorithm

Alright, now that you've seen the basic structure, let's build something that actually does something interesting. We'll create a simple momentum strategy - winners keep winning and losers keep losing.

Remember: The strategy logic is everything. The Python syntax is just a tool to express your ideas.

from AlgorithmImports import *

class MyFirstStrategy(QCAlgorithm):
    
    def initialize(self):
        # Basic setup - when to trade and how much money
        self.set_start_date(2020, 1, 1)
        self.set_cash(100000)  # $100k to start
        
        # Add the stocks we want to trade
        self.spy = self.add_equity("SPY", Resolution.DAILY)  # S&P 500 ETF
        self.qqq = self.add_equity("QQQ", Resolution.DAILY)  # Nasdaq ETF
        
        # Set up momentum indicators (fancy way to measure "trending")
        self.spy_momentum = self.momentum("SPY", 20, Resolution.DAILY)
        self.qqq_momentum = self.momentum("QQQ", 20, Resolution.DAILY)
        
        # Rebalance monthly (first day of each month)
        self.schedule.on(
            self.date_rules.month_start("SPY"),
            self.time_rules.after_market_open("SPY", 30),
            self.rebalance
        )
    
    def rebalance(self):
        # Wait for indicators to have enough data
        if not (self.spy_momentum.is_ready and self.qqq_momentum.is_ready):
            return
            
        # Check which one is trending stronger
        spy_trend = self.spy_momentum.current.value
        qqq_trend = self.qqq_momentum.current.value
        
        # Put all our money in whichever is trending stronger
        if spy_trend > qqq_trend:
            self.set_holdings("SPY", 1.0)  # 100% SPY
            self.set_holdings("QQQ", 0.0)  # 0% QQQ
        else:
            self.set_holdings("SPY", 0.0)  # 0% SPY
            self.set_holdings("QQQ", 1.0)  # 100% QQQ
    
    def on_data(self, data):
        # We only trade monthly, so nothing to do here
        pass

Testing Your Strategy: Backtesting

Here's a key concept: before risking real money, you want to test your strategy on historical data. This is called backtesting.

Your momentum strategy? It gets tested on years of real market data automatically. You'll see metrics like:

• Total return: How much money you made/lost
• Win rate: Percentage of profitable trades
• Max drawdown: Worst losing streak

This way you know if your idea works before putting real money on the line.

One Important Thing: You Can Lose Money

Algorithmic trading isn't a get-rich-quick scheme. You can lose money, just like any other investment.

What's Next?

Ready to try it yourself? Here's your simple next steps:

That's It - You Know the Basics!

You've just learned the core concepts of algorithmic trading:

1. Algorithms can trade automatically based on rules you write
2. Backtesting lets you test strategies on historical data
3. QuantConnect makes it accessible without complex setup
4. You can start with fake money to learn safely

The beauty of QuantConnect is that it handles all the technical complexity. You just focus on the trading ideas.