📰 Dev.to · Ayrat Murtazin

Detect key price levels automatically using local extrema and kernel density estimation in pandas.

Decompose price series into time-frequency components and generate low-noise trading signals using PyWavelets.

Use implied volatility from options chains to detect 5–7% mispricings on Polymarket prediction markets.

Combine Hidden Markov Models and K-Means clustering with PCA to detect equity, credit, and volatility regimes in Python.

How adaptive bandwidth selection in local linear regression generates cleaner trend signals on high-momentum growth stocks.

Detecting statistical jumps in Bitcoin returns and building a contrarian strategy with rolling volatility bands.

Combining Hidden Markov Models with K-Means clustering to identify risk-on, risk-off, and transition regimes across SPY, IWM, HYG, LQD, and VIX.

Build a Monte Carlo simulation engine to project probabilistic price ranges using historical volatility.

A practical guide to combining SMA windows for cleaner signals and better trend detection.

The 50-day weighted moving average delivered the best risk-adjusted returns in my backtesting analysis.

Build a multi-asset price simulator that captures realistic market behavior across 4 asset classes using self-exciting jump processes.

1. Introduction Forecasting financial markets is a sophisticated fusion of quantitative...