Sharing in-depth machine learning, quantitative, and technical market knowledge.
| Platform |  | Pricing | Freemium | Publishes | Daily |
|---|---|---|---|---|---|
| Issues | 1595 | Founded | 5 years ago | Last Issue | 5 days ago |
| Active | |||||
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Histogram-Based Gradient Boosting Regression Tree for Time Series Forecasting
Presenting and Coding a Machine Learning Model on Time Series
This article will discuss a machine learning model referred to as Histogram-Based Gradient Boosting Regressor. We will download a time series from an online source, transform it...
Yang-Zhang Volatility in Python
How to Code Yang-Zhang Volatility For Time Series Analysis
The Yang-Zhang volatility estimator is a measure of historical volatility that combines the advantages of both the Rogers-Satchell and Garman-Klass estimators. It is particularly u...
Different Ways to Normalize Time Series
Time series data has one job: describe how something changes over time. But raw values rarely play nice. Units vary. Magnitudes drift. Outliers throw elbows. Before modeling or comparing signals, you usually need to normalize the data. The...
Moon Phases in Trading - A Deep Dive
Among the more unconventional trading techniques some people use is moon phases, which is based on the belief that lunar cycles influence financial markets. This idea stems from financial astrology, which asserts that celestial bodies impac...
Rest Your Eyes and Listen to the Market
Using Soundwaves to Understand the Market
Modern trading is brutally visual. Screens glow all day with flickering candles, flashing indicators, heat maps, and numbers fighting for attention. We stare, squint, zoom in, zoom out. After hours...
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Founder of Quant Atlas - Systematic Market Forecasts www.quant-atlas.com
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