Getting Started with Algorithmic Trading

# Getting Started with Algorithmic Trading

Algorithmic trading can seem intimidating, but it doesn't have to be. This guide will walk you through building your first strategy from scratch.

What is Algorithmic Trading?

Simply put: using computer programs to execute trades based on predefined rules. Instead of manually clicking "buy" and "sell," you write code that does it for you.

Benefits

Your First Strategy: Simple Moving Average Crossover

This classic strategy is perfect for beginners:

**Buy Signal**: When fast moving average crosses above slow moving average **Sell Signal**: When fast moving average crosses below slow moving average

Python Implementation

import pandas as pd

def generate_signals(df, fast_window=10, slow_window=30): """Generate trading signals based on MA crossover""" # Calculate moving averages df['fast_ma'] = df['close'].rolling(window=fast_window).mean() df['slow_ma'] = df['close'].rolling(window=slow_window).mean()

# Generate signals df['signal'] = 0 df['signal'][fast_window:] = np.where( df['fast_ma'][fast_window:] > df['slow_ma'][fast_window:], 1, # Buy -1 # Sell )

# Identify crossover points df['positions'] = df['signal'].diff()

return df

Testing Your Strategy

Before risking real money, backtest on historical data:

def backtest_strategy(df, initial_capital=10000):
    """Simple backtest implementation"""
    positions = pd.DataFrame(index=df.index).fillna(0.0)
    positions['stock'] = 100 * df['signal']  # 100 shares per signal

portfolio = positions.multiply(df['close'], axis=0) pos_diff = positions.diff()

portfolio['holdings'] = (positions.multiply(df['close'], axis=0)).sum(axis=1) portfolio['cash'] = initial_capital - (pos_diff.multiply(df['close'], axis=0)).sum(axis=1).cumsum() portfolio['total'] = portfolio['cash'] + portfolio['holdings'] portfolio['returns'] = portfolio['total'].pct_change()

return portfolio

Key Metrics to Track

When evaluating your strategy:

Deploying with Provectus Quantus

Once you're satisfied with backtest results:

Step 1: Connect Your Broker Navigate to **Brokers** and link your trading account.

Step 2: Create a Schedule Set up automated execution: - Define your entry/exit conditions - Set position sizes - Configure risk limits

Step 3: Monitor Performance Track live results on the **Dashboard**: - Real-time P&L - Open positions - Order history

Common Beginner Mistakes

Next Steps

Resources

• [Python for Finance](https://www.python.org/)
• [QuantConnect](https://www.quantconnect.com/) - Free backtesting platform
• [Zipline](https://github.com/quantopian/zipline) - Open-source backtesting library

Conclusion

Algorithmic trading is a journey, not a destination. Start simple, test rigorously, and scale gradually. With Provectus Quantus, you have the infrastructure to automate your strategies safely and efficiently.

**Ready to get started?** Connect your broker and deploy your first strategy today.