BestHub
Sign in
Tagged articles
8 articles
Page 1 of 1
Data Party THU
Data Party THU
Aug 30, 2025 · Big Data

Which Time‑Series Smoothing Method Is Right for Your Data? A Deep Dive into Six Techniques

Noise in time‑series data hampers analysis, so this article systematically examines six widely used smoothing techniques—moving average, exponential moving average, Savitzky‑Golay, LOESS, Gaussian filter, and Kalman filter—detailing their principles, key parameters, performance traits, suitable scenarios, and a quantitative RPR evaluation metric.

Exponential Moving AverageKalman FilterLOESS
0 likes · 15 min read
Which Time‑Series Smoothing Method Is Right for Your Data? A Deep Dive into Six Techniques
Model Perspective
Model Perspective
Jun 2, 2022 · Fundamentals

Mastering Moving Average Methods: Simple, Weighted, and Trend Techniques

This article explains the moving average method for time‑series analysis, covering simple, weighted, and trend moving averages, their formulas, appropriate use cases, selection of parameters, and how they help reveal long‑term trends by smoothing out periodic and irregular fluctuations.

moving averagesimple moving averagetrend analysis
0 likes · 5 min read
Mastering Moving Average Methods: Simple, Weighted, and Trend Techniques
MaGe Linux Operations
MaGe Linux Operations
Dec 27, 2017 · Fundamentals

Visualizing Stock Data and Building K‑Line Charts with Python

This guide walks you through importing stock data, cleaning column names, visualizing price and volume trends, creating candlestick (K‑line) charts, analyzing relative changes, exploring correlations, and implementing a simple moving‑average trading strategy using pandas, matplotlib, and numpy.

Data visualizationK-LineMatplotlib
0 likes · 14 min read
Visualizing Stock Data and Building K‑Line Charts with Python
MaGe Linux Operations
MaGe Linux Operations
Nov 15, 2017 · Fundamentals

Master Stock Market Data Analysis with Python: Moving Averages Explained

This tutorial walks through using Python and pandas to fetch Yahoo Finance data, visualize stock prices with line and candlestick charts, and apply moving‑average techniques—including 20‑day, 50‑day, and 200‑day averages—to identify trends and build simple trading signals, all while emphasizing that the content is for educational purposes only and not investment advice.

Pythondata analysisfinance
0 likes · 13 min read
Master Stock Market Data Analysis with Python: Moving Averages Explained
MaGe Linux Operations
MaGe Linux Operations
May 16, 2017 · Fundamentals

Build a Simple Moving‑Average Stock Strategy on Ricequant in Minutes

This step‑by‑step guide shows how to implement, backtest, and run a single‑stock 5‑day versus 30‑day moving‑average trading strategy on the Ricequant platform, covering code setup, cash handling, order execution, and both daily and minute‑level simulations.

Algorithmic TradingPythonQuantitative Trading
0 likes · 10 min read
Build a Simple Moving‑Average Stock Strategy on Ricequant in Minutes