What Are Trading Signals and How Do They Work?
Trading signals serve as critical indicators, analyzing price, volume, historical data, and other market factors to assist investors in determining the opportune moments to enter or exit a position. These signals leverage various analysis techniques, including technical analysis, quantitative analysis, fundamental analysis, major economic indicators, and market sentiment.
Read More: A Deep Dive into Trading Signals
Operating on a mechanical basis, these signals generate buy or sell recommendations for the user's target assets. By doing so, trading signals empower investors to make decisions based on data and strategy, mitigating the influence of herd mentality or emotional biases.
How to Obtain Trading Signals
As Marco Santanche, quant strategist and author of Quant Evolution, points out, “Trading signals can be obtained using a variety of data sources. Many datasets, including the widely available open-high-low-close-volume data (OHLCV), allow us to calculate some indicator or signal to enter into a position. But the ongoing data revolution has prompted institutional investors to seek more sophisticated datasets, which can allow them to outperform peers by accessing unique information, such as insider transactions, earnings forecasts or announcements, web traffic, meteorological data, and more.”
A simple example of a trading signal can be obtained from reading the Moving Average Convergence Divergence (MACD). The indicator might trigger a long position if one moving average moves above the other and a short position otherwise.
“The key lies in processing the data effectively,” says Santanche. “Even when working with with basic datasets like OHLCV, there may be latent information that statistical calculations and adjustments can reveal.”
How to Test a Signal
Testing a signal before implementation is vital to ensure its effectiveness. Contrary to popular belief, running numerous backtests and selecting the top-performing one is not a sound strategy. “A backtest is not the right tool to check if a signal works,” notes Santanche.
Backtests, while demonstrating historical success, may lack a rationale for the future and can easily fall victim to overfitting. Instead, understanding the meaning of a signal and why it should work is crucial. To avoid errors like false positives (Type I) or false negatives (Type II), where the signal works in the past but not the future or vice versa, investors need to go beyond backtests.
Two primary paths are suggested by Santanche:
Mathematical Optimization: In some cases, problems may have an analytical solution that can be found through specific formulas or optimization routines, especially in strategies like time series modeling or statistical arbitrage.
Synthetic Data: Building large datasets of random data similar to the one being tested can help avoid overfitting and provide more reliable insights into a signal's effectiveness.
Common Trading Signals
Below are a list of common trading signals that traders monitor:
Relative Strength Index (RSI): RSI is a momentum oscillator that measures the speed and change of price movements. The index identifies overbought and oversold conditions to anticipate potential reversals in the market.
Read More: Trend Following ETFs: A Deep Dive
Moving Average (MA): MA is a trend-following indicator that smoothens price data, helping identify the direction of a trend. Traders use MA to identify potential buying (upward trend) or selling (downward trend) opportunities.
Moving Average Convergence Divergence (MACD): MACD is a trend-following momentum indicator that shows the relationship between two moving averages of an asset's price. It is typically used to identify potential trend reversals through crossovers between the MACD Line and the Signal Line.
Fibonacci Retracement: A popular tool that uses horizontal lines to indicate potential support or resistance levels based on key Fibonacci ratios, which help identify levels where the price may retrace before continuing its original trend.
Bollinger Bands: These bands consist of a middle band (N-period simple moving average) and upper/lower bands representing N standard deviations. Traders use Bollinger Bands to Identify volatility and potential overbought or oversold conditions, aiding in entry or exit points.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.
