Advanced Topics in Algorithmic Trading: Part 1

This course explores popular technical indicators used in quantitative trading, focusing on moving averages, MACD, Z-score, and RSI. These indicators help identify trends, detect overbought or oversold conditions, and refine trading strategies.

1. Moving Averages

A Simple Moving Average (SMA) is calculated by averaging prices over a set period. The look-back window length affects the responsiveness of the SMA:

• Longer look-back windows create smoother trends but introduce lag.

• Shorter look-back windows follow price movements more closely but generate more noise.

An alternative is the Exponential Moving Average (EMA), which applies greater weight to recent prices:

• The decay factor (λ) determines how much past data influences the EMA.

• Higher λ values create smoother EMAs but introduce lag.

• Lower λ values make the EMA track prices more closely but increase volatility.

2. MACD (Moving Average Convergence Divergence)

The MACD indicator measures the relationship between two EMAs:

• Fast EMA – Slow EMA = MACD Series.

• When the fast EMA crosses above the slow EMA, it indicates an uptrend.

• When the fast EMA crosses below, it signals a downtrend.

A third EMA, called the MACD Signal, smooths the MACD Series:

• MACD Divergence (MACD – MACD Signal) helps identify buy and sell opportunities.

• Reducing λ values makes the indicator react faster but increases false signals.

3. Price Z-Score

The Z-score quantifies how far the current price deviates from its historical average, helping to determine rich (overpriced) or cheap (underpriced) conditions:

• Z = (Current Price – EMA Price) / Volatility

• A high Z-score indicates overvaluation; a low Z-score suggests undervaluation.

• Increasing λ creates smoother, less volatile Z-scores.

• Lower λ allows the Z-score to track prices more closely but increases false signals.

4. Relative Strength Index (RSI)

The RSI measures price momentum and indicates overbought or oversold conditions:

• RSI = 100 – [100 / (1 + Average Gain / Average Loss)]

• RSI above 70 suggests overbought conditions (sell signal).

• RSI below 30 suggests oversold conditions (buy signal).

By adjusting λ, traders can fine-tune the RSI:

• Higher λ smooths RSI movements but delays signals.

• Lower λ makes RSI more sensitive to price changes but increases noise.

5. Trading Strategy Simulation

A basic RSI-based strategy is tested:

• Buy when RSI falls below a threshold (θ).

• Sell when RSI exceeds 1 – θ.

• The strategy maintains a long or short position based on RSI trends.

The Sharpe ratio comparison shows that:

• SPY ETF has a Sharpe ratio of 0.6.

• The RSI strategy achieved 0.8, suggesting better risk-adjusted returns.

• However, the strategy underperformed post-2017, indicating potential overfitting.

Conclusion

Technical indicators like SMA, EMA, MACD, Z-score, and RSI are valuable tools but require careful parameter tuning. They can improve trade timing, but overfitting risks mean strategies must be tested on new data to ensure effectiveness.