Technical Analysis vs. Fundamental Analysis in Algorithmic Trading: A Complete Guide

In algorithmic trading, data analysis is the foundation of any strategy. There are two primary approaches: technical analysis, which focuses on price movements and market patterns, and fundamental analysis, which evaluates an asset’s intrinsic value based on economic and financial data. Each has its place in the world of algo trading, and understanding how to use them can help you craft robust trading algorithms. In this article, we’ll cover the basics of each approach, their pros and cons, and how they can be applied individually or combined in an algorithmic trading strategy.

What is Technical Analysis?

Technical analysis (TA) examines historical price data and patterns to predict future price movements. TA-based strategies use various indicators (such as moving averages, RSI, and MACD) to identify trends, momentum, and potential entry/exit points.

Key Indicators in Technical Analysis:

1. Moving Averages (MA) – Smooth price data to identify trends.
2. Relative Strength Index (RSI) – Measures momentum and identifies overbought/oversold conditions.
3. MACD (Moving Average Convergence Divergence) – Shows trend direction and momentum.
4. Bollinger Bands – Assess volatility and identify reversal points.

What is Fundamental Analysis?

Fundamental analysis (FA) looks at financial statements, economic data, and news events to evaluate an asset’s intrinsic value. FA-based strategies are typically used for longer-term trades, as economic fundamentals change more slowly than market prices.

Common Data Used in Fundamental Analysis:

1. Economic Indicators – Interest rates, GDP growth, inflation.
2. Company Financials – Revenue, earnings, debt levels for stocks.
3. News Events – Earnings reports, geopolitical events, central bank announcements.

Comparing Technical and Fundamental Analysis in Algo Trading

When to Use Technical or Fundamental Analysis in Algo Trading

• Technical Analysis: Effective for short-term trading, such as high-frequency trading (HFT), day trading, and scalping. TA is useful when quick responses to price patterns are required, especially in markets like forex, where news and economic factors are priced in quickly.
• Fundamental Analysis: More suitable for medium to long-term trades, especially in stock markets where company earnings, growth potential, and economic outlooks affect prices over time. FA can be applied in event-driven trading algorithms, which react to specific news events (e.g., earnings announcements).
• Combining Both: Many algo traders blend TA and FA to create more robust systems. For instance, using technical analysis to time entries and exits based on fundamental insights can lead to strategies with higher reliability.

Example Strategy 1: Moving Average Crossover (Technical Analysis) in MQL4

This MQL4 example uses a moving average crossover strategy, a popular TA-based approach, where a shorter-period moving average crossing above a longer-period moving average triggers a buy signal, and vice versa.

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// Moving Average Crossover Strategy Example in MQL4
input int FastMAPeriod = 10;
input int SlowMAPeriod = 20;
input double LotSize = 0.1;
 
// Variables to track trade status
bool buyTradeOpen = false;
bool sellTradeOpen = false;
 
void OnTick() {
   double fastMA = iMA(NULL, 0, FastMAPeriod, 0, MODE_SMA, PRICE_CLOSE, 0);
   double slowMA = iMA(NULL, 0, SlowMAPeriod, 0, MODE_SMA, PRICE_CLOSE, 0);
 
   // Buy when fast MA crosses above slow MA
   if (fastMA > slowMA && !buyTradeOpen) {
       if (sellTradeOpen) CloseSell();
       OpenBuy();
   }
   // Sell when fast MA crosses below slow MA
   else if (fastMA < slowMA && !sellTradeOpen) {
       if (buyTradeOpen) CloseBuy();
       OpenSell();
   }
}
 
// Functions for opening and closing trades
void OpenBuy() { /*...*/ }
void OpenSell() { /*...*/ }
void CloseBuy() { /*...*/ }
void CloseSell() { /*...*/ }

Example Strategy 2: Economic News Impact (Fundamental Analysis) Pseudo-Code in MQL4

MQL4 doesn’t have direct access to economic data feeds, but you could use external sources, such as an API or news feed, to integrate FA into your algo. Here’s an outline of how a news-based algorithm might look in MQL4 using a hypothetical data feed:

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// Pseudo-code for a Fundamental News-Based Strategy
// (Requires external news feed or API integration)
 
double currentInterestRate;   // Hypothetical external variable for interest rate
double previousInterestRate;
 
void OnTick() {
   // Example: Buy when interest rate decreases (hypothetically indicating economic stimulus)
   if (currentInterestRate < previousInterestRate) { OpenBuy(); } else if (currentInterestRate > previousInterestRate) {
       OpenSell();
   }
}
 
// Functions for opening and closing trades
void OpenBuy() { /*...*/ }
void OpenSell() { /*...*/ }
void CloseBuy() { /*...*/ }
void CloseSell() { /*...*/ }

This approach could be expanded to respond to more complex events, such as changes in GDP or employment data, once the data is integrated into your MQL4 environment.

Combining Technical and Fundamental Analysis

For traders who prefer a combined approach, consider using a multi-layered algorithm:

• First Layer: Use fundamental analysis to define a market’s long-term direction (e.g., favouring long trades if the economic outlook is positive).
• Second Layer: Apply technical indicators to time entry and exit points.

For example, you could set a rule where the algorithm only takes buy signals if fundamental conditions (like a favourable interest rate) support growth.

Conclusion

Choosing between technical and fundamental analysis—or combining both—is essential for developing an algorithmic trading strategy suited to your objectives and time horizon. Technical analysis offers speed and precision for short-term trades, while fundamental analysis provides insight into long-term value. By understanding these approaches, you can make informed decisions on when to apply each, optimising your trading algorithms.

In the next article, we’ll explore specific technical indicators and their roles in algorithmic trading, diving deeper into calculation methods and coding examples in MQL4.