#quant articles | Hashnode

PMPrasanth Madhurapantulaiwtlp.hashnode.devJun 9 · 3 min read

Learn quantitative finance with Python from scratch

Quantitative finance has a reputation for being gatekept behind expensive certificates and heavy math. Some of the math is real. But the fastest way in is not a reading list, it is building the core m

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AMAyrat Murtazinalgoedge-insights.hashnode.devApr 28 · 12 min read

Building a Statistical Arbitrage Strategy from Scratch in Python

Statistical arbitrage sits at the intersection of statistics, signal processing, and market microstructure. Rather than predicting the future outright, it identifies systematic deviations from an estimated fair value and exploits the mean-reversion o...

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AMAyrat Murtazinalgoedge-insights.hashnode.devApr 28 · 11 min read

Backtesting Trading Indicators with Lumibot: A Mean Reversion Strategy

Mean reversion is one of the oldest and most empirically grounded ideas in quantitative finance: prices that deviate significantly from their historical average tend to snap back. When combined with a rigorous backtesting framework, this intuition be...

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AMAyrat Murtazinalgoedge-insights.hashnode.devApr 27 · 11 min read

Top 36 Moving Average Methods For Stock Prices in Python [2/4]

Most moving averages suffer from a fundamental tradeoff: a short window reacts quickly to price changes but produces noisy signals, while a long window is smooth but lags behind the market. The Fractal Adaptive Moving Average (FRAMA) resolves this by...

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AMAyrat Murtazinalgoedge-insights.hashnode.devApr 19 · 12 min read

Plotting Smarter Stock Entries & Exits with K-Reversal in Python

Most retail traders lose edge not because their strategy is wrong, but because their entry and exit timing is imprecise. The K-Reversal Indicator addresses this directly — it is a momentum oscillator designed to identify when price sentiment has shif...

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AMAyrat Murtazinalgoedge-insights.hashnode.devApr 19 · 11 min read

Charting Market Rhythms: The Rolling Hurst Exponent in Python

Most market indicators tell you what price is doing — momentum, overbought, oversold. The Hurst exponent tells you something deeper: how price is behaving structurally. Is it trending persistently, reverting to a mean, or wandering randomly? That dis...

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AMAyrat Murtazinalgoedge-insights.hashnode.devApr 18 · 11 min read

Future Stock Price Movements with Historical & Implied Volatility using Python and Monte Carlo

Forecasting future stock prices is not about prediction — it is about quantifying the distribution of possible outcomes. Monte Carlo simulation, powered by Geometric Brownian Motion (GBM), gives you exactly that: a probabilistic map of where a stock ...

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AMAyrat Murtazinalgoedge-insights.hashnode.devApr 18 · 12 min read

Visualizing Expected Stock Price Movement with Python and Volatility Multipliers

Understanding where a stock price might go — and with what probability — is one of the most practical problems in quantitative finance. Rather than guessing direction, professional traders quantify uncertainty. They build probability distributions ar...

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AMAyrat Murtazinalgoedge-insights.hashnode.devApr 17 · 11 min read

Why the 50-Day WMA Dominates: A Quantitative Risk-Return Analysis in Python

Moving averages are among the oldest tools in technical analysis, but most traders use them without ever measuring whether one window length actually outperforms another on a risk-adjusted basis. This article treats that question as a proper quantita...

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AMAyrat Murtazinalgoedge-insights.hashnode.devApr 17 · 11 min read

Unlocking Market Insights with Python: Analyzing Rolling Z-Scores in Stock Trading

Mean reversion is one of the oldest and most robust ideas in quantitative finance: prices that deviate significantly from their recent average tend to revert. The rolling z-score is a clean, interpretable way to quantify that deviation in real time. ...

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Trending tags this week

Learn quantitative finance with Python from scratch

Building a Statistical Arbitrage Strategy from Scratch in Python

Backtesting Trading Indicators with Lumibot: A Mean Reversion Strategy

Top 36 Moving Average Methods For Stock Prices in Python [2/4]

Plotting Smarter Stock Entries & Exits with K-Reversal in Python

Charting Market Rhythms: The Rolling Hurst Exponent in Python

Future Stock Price Movements with Historical & Implied Volatility using Python and Monte Carlo

Visualizing Expected Stock Price Movement with Python and Volatility Multipliers

Why the 50-Day WMA Dominates: A Quantitative Risk-Return Analysis in Python

Unlocking Market Insights with Python: Analyzing Rolling Z-Scores in Stock Trading

#quant

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#quant

Explore Hashnode

Trending tags this week

Learn quantitative finance with Python from scratch

Building a Statistical Arbitrage Strategy from Scratch in Python

Backtesting Trading Indicators with Lumibot: A Mean Reversion Strategy

Top 36 Moving Average Methods For Stock Prices in Python [2/4]

Plotting Smarter Stock Entries & Exits with K-Reversal in Python

Charting Market Rhythms: The Rolling Hurst Exponent in Python

Future Stock Price Movements with Historical & Implied Volatility using Python and Monte Carlo

Visualizing Expected Stock Price Movement with Python and Volatility Multipliers

Why the 50-Day WMA Dominates: A Quantitative Risk-Return Analysis in Python

Unlocking Market Insights with Python: Analyzing Rolling Z-Scores in Stock Trading