Algorithmic Trading Masterclass

Introduction to Algorithmic Trading

What is Algorithmic Trading?

Algorithmic trading (algo trading) uses computer programs to execute trades based on predefined instructions (algorithms) at speeds and frequencies impossible for human traders. These algorithms analyze market data, identify trading opportunities, and execute orders without human intervention.

Price Movement Simulation $154.32 +1.2%

Key Advantages

Speed & Efficiency

Execute trades in milliseconds, capitalizing on opportunities that disappear in seconds.

Emotionless Trading

Removes psychological factors that often lead to poor trading decisions.

Backtesting

Test strategies on historical data before risking real capital.

24/7 Monitoring

Continuously scan markets for opportunities without fatigue.

Common Algorithm Types

Mean Reversion

Based on the concept that prices tend to revert to their historical mean over time. Identifies overbought/oversold conditions using statistical measures like Bollinger Bands or Z-scores.

                                                # Python pseudo-code

                                                if current_price < mean_price - (std_dev * 2):

                                                    buy() # 2 standard deviations below mean

                                                elif current_price > mean_price + (std_dev * 2):

                                                    sell() # 2 standard deviations above mean

Momentum Trading

Capitalizes on the continuation of existing market trends. Uses indicators like RSI, MACD, or moving average crossovers to identify and ride trends.

                                                # Momentum strategy example

                                                short_ma = calculate_ma(prices, 10)

                                                long_ma = calculate_ma(prices, 50)

                                                if short_ma > long_ma and rsi < 70:

                                                    buy() # Uptrend confirmed, not overbought

                                                elif short_ma < long_ma and rsi > 30:

                                                    sell() # Downtrend confirmed, not oversold

Market Making

Provides liquidity by continuously placing both buy and sell orders, profiting from the bid-ask spread. Requires sophisticated risk management to avoid adverse selection.

                                                # Basic market making logic

                                                bid_price = current_price - spread_offset

                                                ask_price = current_price + spread_offset

                                                if inventory < max_position:

                                                    place_order('BID', bid_price, quantity)

                                                if inventory > -max_position:

                                                    place_order('ASK', ask_price, quantity)

Backtesting Example

Momentum Strategy (2018-2023)

+142.7% Return

Annual Return

19.2%

Max Drawdown

-24.3%

Sharpe Ratio

1.8

Win Rate

58.4%

Note: Past performance is not indicative of future results. Backtest results often look better than live performance due to factors like market impact, slippage, and changing market regimes.

Course Progress

Completion 12%

Introduction

Completed

Market Basics

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Strategy Design

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Backtesting

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Resources

Instructor

Dr. Jonathan Quant

Former Head of Quant Trading at Goldman Sachs

PhD in Financial Mathematics from MIT

15+ years in algorithmic trading

Managed $2B+ in algorithmic strategies