Matching Historical Price Patterns with a 5-Feature Input and Lorentzian Kernel to Classify Trend Direction Markets rarely repeat, but they sometime echo familiar setups. This article shows how to turn those echoes into trading signals.

Ehlers Smoothing and a 98-Lag Autocorrelation Map to Inspect Repeating Price Behaviors Price action carries structural memory. Patterns echo across time. Some fade. Others repeat. Understanding these echoes gives you a tactical understanding. In this

Optimal ATR Multiples by Clustering EMA-Based Performance Metrics for Deriving Buy and Sell Signals with What if your trend indicator could tune itself with no optimization, no backtesting, or no overfitting? That’s the idea behind the

Time-Aware Regression for Detecting and Segmenting Structured Trend Shifts Linear regressions can reveal much more than a single best-fit line. When anchored to time and applied in segments, they can expose how market trends evolve structurally.

A line-break band system that encodes directional strength using count-based alpha and ATR-driven volatility offsets TrendWave Bands don’t try to predict market direction. They react to it cleanly, visually, and in real time.  We combine

Map support and resistance zones using ATR and Zigzag-based Fibonacci fans anchored to key price pivots. Most traders draw Fibonacci levels by hand. That may be a problem.  Without a clear rule for where to start,

Manual trend lines are subjective and slow. This algorithmic method finds direction, strength, and confluence Most trend channels anchor to arbitrary points, assume symmetry, and fail to adapt when the market shifts.  Traders rely on

Building a Dynamic Volume Profile Oscillator to Quantify Price Dislocation Relative to VWAP We present a way to measure how far price moves from where most volume actually traded and whether that move was normal or

Volatility Signature Analysis for Intraday Noise Decomposition and to Detect Market Microstructure Distortion in Python Volatility is one of the most important concepts in markets. We use it to manage risk, forecast returns, and price options. 

A sign-based framework to detect non-random behavior in stock returns using binary return patterns over rolling windows Most people assume markets are random. After all, that’s what the Chicago School teaches — and what much of modern finance

How to identify predictability zones and structural regime shifts - like momentum, mean-reversion, or randomness - using variance ratios and autocorrelation across multiple timeframes Finding consistent alpha in the market is hard. Traders could rely on intuition, but this

Not All Factors Are Created Equal - How to Find, Validate, and Test the Best Alpha Factors Using Alphalens in Python Finding consistent alpha in the market is hard. Traders could rely on intuition, but this might not

Efficient Estimation of bid-ask spreads from open, high, low, and close prices using moment conditions, rolling windows, and generalized method of moments weighting Most traders underestimate how much bid-ask spreads eat into their profits. Even

Using the Merton Model to estimate credit risk, simulate default probabilities, and assess financial stability Traditional credit ratings are slow to react. The market moves faster. Investors and risk analysts need a real-time measure of

Deriving Risk-neutral Price Forecasts, Estimating Expected Price Distributions and Foward-looking Market Implied Probabilities Most traders rely on past price movements to predict future trends. But we can actually extract forward-looking prices directly from options prices.

Use the Fama-French 5-Factor Model plus momentum to identify undervalued stocks, analyze alpha, and measure return contributions in Python Markets aren’t always efficient. Stocks can be mispriced for extended periods. This creates opportunities for traders who

Using the Merton Model to estimate credit risk, simulate default probabilities, and assess financial stability There are investors such as Joel Greenblatt who note the great importance of ROE is a metric for identifying undervalued

Automating Currency Arbitrage Triangular Currency Arbitrage exploits price discrepancies across three currency pairs. Traders can profit by executing a series of currency exchanges that loop back to the original currency to take advantage of small

Volatility Predictions with External Variables and Confidence Intervals Volatility forecasting is highly beneficial when pricing derivatives and or making risk management decisions. In this article, we apply a hybrid GARCH-VAR model specifically designed for forecasting rolling

Estimating Instrinsic Value Programmatically with Yahoo Finance Calculating the fair value of stocks is an essential part of an investor’s due diligence toolkit. The Discounted Cash Flow model allows analysts to estimate a company’s intrinsic

Signal Generation Using Bollinger Bands and Keltner Channels As a trader, one of the patterns to look for is a volatility squeeze. This occurs when market volatility tightens, often shown by the Bollinger Bands and

A Close Look at the Current Economic Landscape and Health Economic recession indicators are flashing warning signs. Top analysts, investors, and policymakers are debating whether we are currently in or heading into a recession. Recent

Using Llama to analyze Earnings Transcripts, 10-Q Fillings and Quarterly Investor Presentations In this article, we explore the implementation of Meta’s Llama 3.1 for analyzing earnings announcements using Groq’s fast inference capabilities. Llama 3.1, the

Setting Volatility-Based Stop Losses with Optimized Paramaters While knowing when to enter a trade position matters, knowing when to exit if things go wrong is even more important. Without stop-losses, recovering from losses requires more

Probability Of Price Changes Occurring Conditional On Initial Price Changes Conditional probability helps us understand the likelihood of an event occurring, given that another event has already occurred. In the context of assets prices, this

Portfolio optimization across multiple factors – Dividends, Returns, Volatility, CVaR, and more Multi-objective Portfolio optimization  considers multiple objectives simultaneously, such as maximizing returns, minimizing risk, and enhancing diversification, to build a portfolio that aligns with

Yield Data Retrieval, Curve Interpretation, Spreads, Correlations, and Regime Shifts The yield curve, a plot of Treasury bond yields across different maturities, serves as a crucial economic indicator. It reflects investor expectations about future interest

A Comprehensive Guide for Automating IV Analysis in Python Implied Volatility (IV) shows the market’s expectations of future price movements for an underlying asset. This article will show you how to automate Implied Volatility Analysis

Identify and Trade Cointegrated Pairs Pairs trading is a market-neutral trading strategy that involves matching a long position with a short position in two assets with a high correlation. This strategy aims to exploit relative

How Bootstrap and Historical Simulations Help Predict Future Market Prices

Exploring the Application of YOLO for predicting Market Ups and Downs Predicting market movements is crucial for traders and analysts. Traditional methods often fall short due to their manual and error-prone nature. The application of

Accelerating Business Knowledge Acquisition For Investments Financial due diligence is crucial for making informed investment decisions. Traditionally, this process involved labor-intensive manual analysis of financial documents. However, integrating Large Language Models (LLMs) can transform financial

Implementation of a Fine-tuned YOLOv8 for Stock Price Patterns Identification Candlestick pattern recognition is a useful tool in technical analysis for traders. However, the manual identification of these patterns is time-consuming and prone to human

This article demonstrates how to leverage data sources, such as the FMP API and web scraping techniques, to efficiently retrieve and analyze the portfolio holdings and trading activity of large institutional investors. By processing this

Who, What, and When: Transactional-Level Analysis of Senators’ Trades with Accuracy Measuremenets and Trends Through the FMP API and the SEC website Members of the U.S. Congress trade stocks, potentially leading to conflicts of interest. For

Advanced Strategy to Account for Correlations, Risk, and Returns in your Portfolio Leveraging Hierarchical Structures Portfolio managers have traditionally used Markowitz’s Mean-Variance Optimization, the backbone of modern portfolio theory. Yet, as the investment landscape changes,

End-to-end Implementation with buy and sell signals. Indicators include VPT, VROC, CMF, Klinger Oscillator and VROC In this article, we delve into the practical application of various key volume indicators, providing a complete end-to-end implementation in

End-to-end Implementation with buy and sell signals. Indicators include Awesome Oscillator, RSI, ROC, CCI and Stochastic Oscillator Momentum trading, which hinges on the continuation of existing market trends, has always been very popular among financial

End-to-end Implementation with buy and sell signals Trend indicators, a key component in technical analysis, offer insights into market directions and strength, allowing traders to make informed decisions. While the theoretical understanding of these indicators

End-to-end Implementation with buy and sell signals. Indicators include Keltner Channels, Relative Volatility Index, and more Understanding volatility is crucial in the financial markets. It not only signals potential risks but also opens up opportunities for

Exploring Different Moving Average Methods and Time Frame Variations for Strategic Advantage Optimization Moving Averages are cornerstone concepts in technical analysis, yet their simplicity belies a deeper utility in trading. Moving averages, in their simplest

Effective Lesser-Known Trading Indicators Implemented - Choppiness, Disparity, Awesome, Three Way, RVI and More Popular indicators often dominate discussions. Yet, lesser-known indicators provide unique market insights. This article explores these obscure but effective trading indicators. Each tool offers

Identifying Today’s Stock Pattern in Historical Data Considering Current Market Conditions and Visualizing Future Movements in Python Earnings announcements provide valuable insights into a company’s financial health and future prospects, swaying market sentiments and shaping

Identifying Today’s Stock Pattern in Historical Data Considering Current Market Conditions and Visualizing Future Movements in Python Previously, we showed how Dynamic Time Warping (DTW) could be used in mining patterns from recurring price movements in

Finding Today’s Stock Price Pattern in Historical Data Stock prices change frequently, creating complex data that’s hard to understand. However, through pattern mining in this data, we can uncover valuable insights into future price changes.

The Fundamentals, Adaptive and Dynamic, Advanced Weighting and From Niche to Noteworthy Moving averages are one of the most widely used technical indicators by traders and investors of all levels. They help to smooth out

The ‘Volume Ratio’ — a handy little metric to pinpoint unusual volume activity at any given moment. It offers insights into a stock’s momentum, and when paired with directional indicators, can provide further evidence of

Beyond Traditional Indicators for Insights Into Market Behavior Every trader dreads market crashes, those moments when the market unexpectedly plunges. One minute everything is smooth, and the next, portfolios turn red. Hours are spent analyzing

Buying Losers and Selling Winners In Python in the Modern Digital Era Have you ever overreacted to something, later reflecting and thinking, ‘Maybe I went a bit overboard’? Now, imagine the stock market — a

15 Time-varying Techniques for Proactive Risk Analytics in Python [Part 2/2] Building on the foundational principles of dynamic risk management discussed in our prior article, we now discuss specific techniques that address both the peaks

15 Time-varying Techniques for Proactive Risk Analytics in Python [Part 1/2] Dynamic risk management, at its core, is the identification, assessment, and prioritization of risks followed by coordinated and economical application of resources to minimize,

7 Essential Methods to Discover Key Price Levels in Stock Prices Support and resistance levels are foundational concepts in the world of trading, acting as critical signposts for making informed decisions. Traders use these levels

How Would Your Stocks React to the Next Market Drop? Estimating Portfolio Movements in Different Market Scenarios Traditionally, we’ve leaned on correlation matrices for understanding inter-asset dynamics. Yet, as market crashes of the past have

Towards Unlocking Market Signals for Clearer Trading Insights You know, when you look at how stocks move up and down, it can seem as unpredictable as ocean waves. However, just like scientists can predict wave

Can Volatility Analysis Unveil the Market’s Mood Swings? 1. Introduction We’ve all been there — staring at a stock chart, trying to make sense of those wild price jumps, or just wondering why there’s a

4 Methods for Quantifying the Collective Moves in Stocks An intriguing phenomenon, deeply rooted in behavioral finance, is the concept of ‘herding behavior’. This is where individual investors mimic the actions of the larger crowd, sometimes even

Implementation Risk Parity and Evaluation using Key Rolling Risk Metrics Instead of merely allocating assets based on potential returns or market capitalization, Risk Parity Rebalancing emphasizes creating a harmonious balance where each asset contributes equally

Implementation of the Simple, Yet Powerful K-Reversal Indicator Amid the vast arsenal of tools and techniques stands the K-Reversal Indicator — an unassuming, yet remarkably effective instrument. In this article, we not only demystify its

Charting Market Rhythms: The Rolling Hurst Exponent in Python Ever tried catching the rhythm of the stock market? If you have, you’d know trends aren’t just about rising and falling arrows on a graph. They’re

Price Movement Estimation Using Historical and Implied Volatility 1. Introduction Forecasting financial markets is a sophisticated fusion of quantitative precision and global economic nuance. In this quest, the Monte Carlo simulation stands out as a premier

Dynamic Price Movement Probability 1. Introduction When diving into the financial world, understanding stock price movements is a significant advantage. Here, we present an interactive method that offers insights into the likelihood of stock prices

Expected Stock Price Movement with Python Figure 1. Projected Stock Price Movement for ASML.AS over a 20-Day Horizon: Visualizing the Impact of Different Volatility Multipliers on Expected Price Ranges. Few concepts are as captivating as