Beginner Explanation
Imagine you’re trying to predict the weather. You look at past weather reports to see patterns, like how many sunny days came before a rainy day. Technical variables in finance work similarly; they are numbers calculated from past prices and trading volumes of stocks. By analyzing these numbers, traders try to guess whether a stock’s price will go up or down in the future, just like predicting if it will rain tomorrow based on past weather patterns.Technical Explanation
Technical variables are metrics used in technical analysis to forecast future price movements based on historical data. Common examples include moving averages, relative strength index (RSI), and Bollinger Bands. For instance, a simple moving average (SMA) can be calculated using Python as follows: “`python import pandas as pd data = pd.read_csv(‘stock_data.csv’) # Calculate the 20-day moving average data[‘SMA_20’] = data[‘Close’].rolling(window=20).mean() “` Technical variables help traders identify trends and reversals, making them crucial for developing trading strategies.Academic Context
Technical variables are grounded in the Efficient Market Hypothesis and behavioral finance. They are used extensively in quantitative finance to model and predict market behavior. Key papers include ‘The Efficient Market Hypothesis’ by Eugene Fama and ‘Technical Analysis of Stock Trends’ by Edwards and Magee, which discuss the efficacy of technical indicators in forecasting. The mathematical foundation involves time series analysis and statistical methods, where models like ARIMA or GARCH can be applied to evaluate the significance of these variables in price prediction.Code Examples
Example 1:
import pandas as pd
data = pd.read_csv('stock_data.csv')
# Calculate the 20-day moving average
data['SMA_20'] = data['Close'].rolling(window=20).mean()View Source: https://arxiv.org/abs/2511.16657v1