Data Analysis Methodology

Full data analysis workflow: data exploration, technical indicator calculation, statistical modeling, visualization

Methodology Overview

Using synthetic stock data to demonstrate a complete data analysis workflow, focusing on methodology and technical skills rather than specific trading strategies.

Step 1: Data Exploration & Cleaning

Statistical description and quality check of raw data

Statistical Summary

Mean

176.54

Std Dev

21.50

Min

144.24

Max

221.77

Skewness

0.1302

Kurtosis

-1.1825

N = 200 trading days | Synthetic data (seed=42)

Step 2: Technical Indicator Calculation & Visualization

Calculate and visualize common technical analysis indicators

Price

Volume

Step 3: Statistical Modeling

Apply statistical methods for in-depth analysis

Return Distribution

Normal Distribution Fit: μ=0.0019, σ=0.0111

Correlation Matrix

Linear Regression

y = 0.0121x + 0.001828

R² = 0.0001

Hypothesis Testing

H₀

μ = 0

H₁

μ ≠ 0

t-statistic2.4147

p-value0.0157

Mean0.001900

Std Dev0.011100

Skewness-0.1093

Kurtosis-0.8461

p < 0.05 → Reject H₀ (significant)

Step 4: Analysis Conclusions & Decision Framework

Synthesize multi-indicator signals into analysis conclusions

Signal Summary

RSI

65.1Neutral

MACD

4.4000Bullish

Trend (20d)

7.87%Bullish

Methodology Summary

Step 1: Data Exploration & Cleaning

Step 2: Technical Indicator Calculation & Visualization

Step 3: Statistical Modeling

Step 4: Analysis Conclusions & Decision Framework