Volatility Clustering Trading Strategies with Python

Hands On Time Series Analysis with Bayesloop

I Optimized a Trading Strategy Using Sharpe Ratio and Bayesian Optimization

Here’s What I Found

How I Optimized a Bitcoin Rate of Change Trading Strategy Using Bayesian Optimization

Can AI Beat Buy & Hold on Bitcoin? I Tried It, and Here’s What I Found

Modeling Strategic Levers with DoWhy and Python

What causal inference can teach us about decision-making in sustainability-driven markets.

The Four Moving Averages Every Beginner Trader Should Understand

How simple smoothing techniques can reveal hidden structure in price movements and help traders see beyond daily noise.

Bollinger Band %B: The Secret Weapon Every Trader Needs to Know...

Simple & Effective Trading Strategies in Python

And when to use which.

Simulated Annealing in Stock Trading

The Mathematics Behind Optimal Trading Strategy Parameter Selection

Finding Big Money Options

Score option chains using open interest, OI/volume, and strike distance to highlight active contracts in Python.

Building an Adaptive Simple Moving Average Trading Strategy with Simulated Annealing

From Static to Adaptive: Improving Moving Average Strategies

How I Used KAMA and Simulated Annealing to Optimize a Stock Trading Strategy

Discover the surprising power of adaptive moving averages and Python to find better trades than traditional indicators

The SMC Edge with EODHD — Why Swings Win Over FVG

Backtesting Smart Money Concepts (SMC) Algo-Trading Strategies with EODHD Data for AAPL.US 2025

Momentum Asset Rotation Strategy

Select and Rotate Assets Weekly using a Momentum-Driven Ranking System with Cash Allocation

Can Supervised ML Classifiers Improve ROI of Pairs Trading Strategy?

Delve into ML-Powered Cointegrated Statistical Arbitrage with Z-Score, Backtesting, Sharpe Ratio and Max Drawdown KPIs in Python

Future Prices with NIG Distributions

Forward Daily, Weekly and Monthly Return Density Estimation Using Fat-Tailed Models with Skew and Kurtosis Adjustments

Extracting Market Crash Probabilities

Estimate risk-neutral crash odds using OTM options, spline smoothing, and Breeden-Litzenberger in Python.

Detecting Market Manipulation

Flag unstable and clean market zones by combining adaptive volatility, spectral, and prediction error signals

Self-Tuning Trading Signals with K-Means and SuperTrend

Self-Calibrated Buy and Sell Signals with K-Means Clustering and Optimal Volatility Settings Over Time

Appetite for Risk: Building an Intelligent Market Regime Detection System

Unleashing AI to Decode Market Moods and Seize Opportunities

RSI Trading Strategy Framework: A Comprehensive Backtesting Implementation with Bias Prevention

From a 5,394% “Profit” to a Genuinely Robust ML System

Measuring Market Breadth and Momentum

Detect S&P 500 breadth divergences, advances/declines, momentum returns percentiles in Python.

Regime-Based Tactical Allocation: A Volatility-Adaptive Trading Strategy

Classify Future Price Moves via Self-Supervised Pattern Recalls

Ultimate Portfolio Diversification using PCA Factor Models

Unraveling Asset Returns, Bucketing and Risk Management using Python