TRADING BLOG

Quantitative Trading.
Deep Expertise.

Expert guides on TradingView indicators, Pine Script development, De Casteljau mathematics, Kalman filtering, machine learning signals, and market structure analysis.

LEVEL
FEATUREDTRADINGVIEW GUIDESBEGINNER8 min read

Non-Repainting TradingView Indicators: The Complete Guide (2026)

Most TradingView indicators repaint — meaning they change past signals after the fact, making backtest results look far better than live results. Here's how to spot repainting and what to look for.

Read article →1 March 2026
MARKET GUIDESBEGINNER10 min read

Best TradingView Indicators for Gold (XAUUSD) in 2026

Gold is one of the most traded instruments in the world and one of the hardest to trade consistently. Here are the indicator types that work best on XAUUSD — and what to look for in 2026.

Read →5 Mar 2026
MATHEMATICS & METHODSADVANCED12 min read

The De Casteljau Algorithm in Trading: Bézier Curves for Price Projection

The De Casteljau algorithm is a cornerstone of computer graphics, used to draw smooth curves. Applied to financial markets, it becomes a powerful tool for projecting the smooth path of price — filtering noise and anticipating acceleration.

Read →8 Mar 2026
MATHEMATICS & METHODSINTERMEDIATE9 min read

Kalman Filter in Trading: How to Eliminate Market Noise from Your Charts

Developed by Rudolf Kálmán for NASA's Apollo program, the Kalman filter is arguably the most mathematically sophisticated noise-reduction tool available to traders. Here's how it works and why it outperforms moving averages for signal clarity.

Read →10 Mar 2026
MATHEMATICS & METHODSINTERMEDIATE8 min read

Hurst Exponent: How to Identify Trending vs Ranging Markets Before You Trade

The Hurst Exponent is one of the most powerful and underused tools in quantitative trading. Developed by hydrologist Harold Hurst in 1951, it measures the long-term memory of a time series — in trading terms, whether a market is trending, random, or mean-reverting.

Read →11 Mar 2026
MACHINE LEARNINGADVANCED11 min read

Machine Learning Trading Signals: How an 8-Model Ensemble Works

Single-model machine learning trading systems are brittle — they overfit to the conditions they were trained on and fail when those conditions change. Ensemble methods combine multiple models to produce more robust, generalised signals. Here's how it works.

Read →12 Mar 2026
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