Finance — Indicators and Strategies

Momentum Regression [BackQuant]Momentum Regression The Momentum Regression is an advanced statistical indicator built to empower quants, strategists, and technically inclined traders with a robust visual and quantitative framework for analyzing momentum effects in financial markets. Unlike traditional momentum indicators that rely on raw price movements or moving averages, this tool leverages a volatility-adjusted linear regression model (y ~ x) to uncover and validate momentum behavior over a user-defined lookback window. Purpose & Design Philosophy Momentum is a core anomaly in quantitative finance — an effect where assets that have performed well (or poorly) continue to do so over short to medium-term horizons. However, this effect can be noisy, regime-dependent, and sometimes spurious. The Momentum Regression is designed as a pre-strategy analytical tool to help you filter and verify whether statistically meaningful and tradable momentum exists in a given asset. Its architecture includes: Volatility normalization to account for differences in scale and distribution. Regression analysis to model the relationship between past and present standardized returns. Deviation bands to highlight overbought/oversold zones around the predicted trendline. Statistical summary tables to assess the reliability of the detected momentum. Core Concepts and Calculations The model uses the following: Independent variable (x): The volatility-adjusted return over the chosen momentum period. Dependent variable (y): The 1-bar lagged log return, also adjusted for volatility. A simple linear regression is performed over a large lookback window (default: 1000 bars), which reveals the slope and intercept of the momentum line. These values are then used to construct: A predicted momentum trendline across time. Upper and lower deviation bands , representing ±n standard deviations of the regression residuals (errors). These visual elements help traders judge how far current returns deviate from the modeled momentum trend, similar to Bollinger Bands but derived from a regression model rather than a moving average. Key Metrics Provided On each update, the indicator dynamically displays: Momentum Slope (β₁): Indicates trend direction and strength. A higher absolute value implies a stronger effect. Intercept (β₀): The predicted return when x = 0. Pearson’s R: Correlation coefficient between x and y. R² (Coefficient of Determination): Indicates how well the regression line explains the variance in y. Standard Error of Residuals: Measures dispersion around the trendline. t-Statistic of β₁: Used to evaluate statistical significance of the momentum slope. These statistics are presented in a top-right summary table for immediate interpretation. A bottom-right signal table also summarizes key takeaways with visual indicators. Features and Inputs ✅ Volatility-Adjusted Momentum : Reduces distortions from noisy price spikes. ✅ Custom Lookback Control : Set the number of bars to analyze regression. ✅ Extendable Trendlines : For continuous visualization into the future. ✅ Deviation Bands : Optional ±σ multipliers to detect abnormal price action. ✅ Contextual Tables : Help determine strength, direction, and significance of momentum. ✅ Separate Pane Design : Cleanly isolates statistical momentum from price chart. How It Helps Traders 📉 Quantitative Strategy Validation: Use the regression results to confirm whether a momentum-based strategy is worth pursuing on a specific asset or timeframe. 🔍 Regime Detection: Track when momentum breaks down or reverses. Slope changes, drops in R², or weak t-stats can signal regime shifts. 📊 Trade Filtering: Avoid false positives by entering trades only when momentum is both statistically significant and directionally favorable. 📈 Backtest Preparation: Before running costly simulations, use this tool to pre-screen assets for exploitable return structures. When to Use It Before building or deploying a momentum strategy : Test if momentum exists and is statistically reliable. During market transitions : Detect early signs of fading strength or reversal. As part of an edge-stacking framework : Combine with other filters such as volatility compression, volume surges, or macro filters. Conclusion The Momentum Regression indicator offers a powerful fusion of statistical analysis and visual interpretation. By combining volatility-adjusted returns with real-time linear regression modeling, it helps quantify and qualify one of the most studied and traded anomalies in finance: momentum.

Rolling Log Returns [BackQuant]Rolling Log Returns The Rolling Log Returns indicator is a versatile tool designed to help traders, quants, and data-driven analysts evaluate the dynamics of price changes using logarithmic return analysis. Widely adopted in quantitative finance, log returns offer several mathematical and statistical advantages over simple returns, making them ideal for backtesting, portfolio optimization, volatility modeling, and risk management. What Are Log Returns? In quantitative finance, logarithmic returns are defined as: ln(Pₜ / Pₜ₋₁) or for rolling periods: ln(Pₜ / Pₜ₋ₙ) where P represents price and n is the rolling lookback window. Log returns are preferred because: They are time additive : returns over multiple periods can be summed. They allow for easier statistical modeling , especially when assuming normally distributed returns. They behave symmetrically for gains and losses, unlike arithmetic returns. They normalize percentage changes, making cross-asset or cross-timeframe comparisons more consistent. Indicator Overview The Rolling Log Returns indicator computes log returns either on a standard (1-period) basis or using a rolling lookback period , allowing users to adapt it to short-term trading or long-term trend analysis. It also supports a comparison series , enabling traders to compare the return structure of the main charted asset to another instrument (e.g., SPY, BTC, etc.). Core Features ✅ Return Modes : Normal Log Returns : Measures ln(price / price ), ideal for day-to-day return analysis. Rolling Log Returns : Measures ln(price / price ), highlighting price drift over longer horizons. ✅ Comparison Support : Compare log returns of the primary instrument to another symbol (like an index or ETF). Useful for relative performance and market regime analysis . ✅ Moving Averages of Returns : Smooth noisy return series with customizable MA types: SMA, EMA, WMA, RMA, and Linear Regression. Applicable to both primary and comparison series. ✅ Conditional Coloring : Returns > 0 are colored green ; returns < 0 are red . Comparison series gets its own unique color scheme. ✅ Extreme Return Detection : Highlight unusually large price moves using upper/lower thresholds. Visually flags abnormal volatility events such as earnings surprises or macroeconomic shocks. Quantitative Use Cases 🔍 Return Distribution Analysis : Gain insight into the statistical properties of asset returns (e.g., skewness, kurtosis, tail behavior). 📉 Risk Management : Use historical return outliers to define drawdown expectations, stress tests, or VaR simulations. 🔁 Strategy Backtesting : Apply rolling log returns to momentum or mean-reversion models where compounding and consistent scaling matter. 📊 Market Regime Detection : Identify periods of consistent overperformance/underperformance relative to a benchmark asset. 📈 Signal Engineering : Incorporate return deltas, moving average crossover of returns, or threshold-based triggers into machine learning pipelines or rule-based systems. Recommended Settings Use Normal mode for high-frequency trading signals. Use Rolling mode for swing or trend-following strategies. Compare vs. a broad market index (e.g., SPY or QQQ ) to extract relative strength insights. Set upper and lower thresholds around ±5% for spotting major volatility days. Conclusion The Rolling Log Returns indicator transforms raw price action into a statistically sound return series—equipping traders with a professional-grade lens into market behavior. Whether you're conducting exploratory data analysis, building factor models, or visually scanning for outliers, this indicator integrates seamlessly into a modern quant's toolbox.

Relative Fundamental Comparison When dealing with stocks, I like to review basic fundamentals of the company. This script displays the fundamental ratios of base chart stock with three other stocks (I can’t increase the number due to security function limitations). I found it particularly important when dealing with an unknown company. I quickly compare the company with other industry leaders to get a comparative fundamental review. I am very new to Pinescript, so waiting for your comments and review.

Relative Growth Screen Based on the Growth Range indicator published here: Instead of plotting, they are printed in color coded table. Colors say whether the growth rate of these factors are relatively higher or lower. Similar to quality screen, table positions can be customized. If you have big enough screen, you can fit both quality and growth screens this way: s3.tradingview.com