Quantitativeanalysis — אינדיקטורים ואסטרטגיות

Business Predictability | Robinhodl21 Have you ever wondered why a company beats earnings estimates yet its stock barely moves—or even drops? It might be because the market already expected a beat. Companies that consistently outperform forecasts tend to attract higher expectations over time, so another “+20 % surprise” may no longer surprise anyone. In other cases, investors may weigh sales growth more heavily than earnings, especially in growth sectors where top-line momentum matters more than margin control. This indicator was built to explore exactly that dynamic. It helps you quantify how predictable a business truly is, how consistently it beats (or misses) expectations, and how well management seems to understand and guide its own performance. It’s not a timing tool, but a quality lens for long-term stock pickers who want to identify stable, well-managed companies with disciplined forecasting and execution. What the indicator is its is designed to quantify how often and how well a company beats-or-misses expectations (earnings and sales) over multiple years, then map that into a “predictability” and “quantile” score that you can use to compare across stocks. Its core logic combines deviation from estimates, rolling history, and statistical ranking to highlight companies where the management and the business appear to be aligned, stable and reliable. Key features: (• Choice of financial data frequency: you can select FQ (quarterly) or FY (annual) mode so the indicator adapts to your preferred horizon. (• Deviation calculation: earnings surprise and/or sales surprise can be combined via a weighted setup so you pick which metric drives the score. (• History buffer: you choose how many “commit points” (i.e., past surprises) to include in the statistics and quantile calculations. (• Quantile ranking: the tool computes how the company’s recent deviation stacks up versus its own history; in FY-mode we still use quarterly density for statistical robustness. (• Predictability & volatility metrics: beyond the quantile, you get a predictability score (low recent deviation + low volatility) and a simple “moat” / management-quality overlay via the SLOAN ratio. (• Status and CI table: the indicator comes with a visualization panel summarizing mean surprise, standard deviation, sample length, and your computed quantile and predictability grades. (• Future box: optional forward-map showing the next earnings date, estimated deltas and flagged surprises. What it is not It is not a timing indicator (i.e., it won’t tell you when to buy or sell precisely). It does not predict short-term price movements. Instead, it is tuned for fundamental stock picking: look for companies that repeatedly deliver surprise results, for which you believe management and business model give an advantage. Use it to add an extra dimension of “earnings surprise stability & management forecasting quality” to your dashboard. My usage case I developed this indicator as part of a broader portfolio strategy: I screen for companies that are both highly predictable (i.e., rarely miss) and have the capacity to beat earnings by a meaningful margin, because I believe this reflects strong business execution and good internal alignment. Over time I plan to expand the dashboard with more indicators geared toward company quality and moat (quantitative metrics built from financial statement data). This version is still work in progress (there may be bugs), so consider the output as one more input—do not rely on it exclusively. Important caveats The code is relatively computation-intensive, especially with large lookback windows and quarterly frequency. On my Mac Pro it runs smoothly—but depending on your device and market data feed you may experience slower performance. Also: synchronising earnings release timing and sales release timing across companies is tricky—sometimes data lags or is updated later, so there may be discrepancies. Because of this the indicator’s output should be treated as a guide rather than a guarantee. Empirical background The academic literature supports the idea that consistent surprises and management execution can matter—but the relationship is complex. For example, research on post-earnings-announcement drift (PEAD) shows that markets often under-react to surprise earnings and that returns continue to drift in the direction of surprise for weeks or months. At the same time, studies such as Skinner & Sloan (2000) show that when you control for growth expectations the relation of surprise to future returns becomes weaker. In other words: just beating earnings by 20 % repeatedly does not guarantee outsized share-price gains, because market expectations adjust, estimates bake in the beat and other factors (discount rates, fundamentals) still dominate. Final word Use it as part of your fundamental stock-analysis toolkit to gauge how well a company consistently delivers relative to expectations, how volatile those surprises are, and whether you think management has a competitive edge in forecasting or executing. As mentioned, this is a work in progress and should not be your only tool—but used wisely, it can add a meaningful extra dimension to your decision-making. I’ll continue to improve it and add new quality-and-moat oriented indicators in future releases.

DCA with the Money Supply Index DCA with the Money Supply Index (MSI) by zdmre This strategy is based on the Money Supply Index (MSI) by zdmre and enhances it with two functional options for users: a DCA (Dollar-Cost Averaging) approach and a signal-based buy/sell mode. It’s designed to help traders and investors make data-driven, disciplined entry decisions based on monetary supply trends. 🧠 Concept Overview The Money Supply Index (MSI) provides insight into how liquidity (money supply) influences market movements. This strategy builds upon that foundation by allowing users to either: Accumulate positions over time using DCA, based on favorable MSI conditions. Execute a single buy and sell trade, optimized for bull market conditions. ⚙️ Inputs Explained General Parameters Start Bar Index / Stop Bar Index Defines the range of bars (historical data) for backtesting or strategy visualization. Long DCA Activates the DCA mode. If unchecked, the strategy operates in single-entry/single-exit signal mode. Trading Signal Enables signal-based entries and exits when the MSI reaches predefined thresholds. DCA Parameters Entry Value The MSI value that triggers a DCA buy event. When the MSI crosses below this value, the strategy considers it a favorable moment to deploy the saved capital. Saved Amount The amount of money set aside regularly (e.g., monthly) for investment. This simulates the DCA effect by accumulating capital and deploying it when conditions are optimal. Data Inputs Money Supply The data source for the Money Supply Index (default: ECONOMICS:USM2). Relational Symbol The market instrument to compare against the money supply (default: NASDAQ_DLY:NDX). This allows the strategy to measure liquidity impact on a specific market. Chart Display Options You can toggle these metrics on the chart for better visualization: Entry Price (green) – The price level of executed buys. Cash Balance (yellow) – Remaining uninvested capital. Invested Capital (red) – Total amount currently invested. Current Value (blue) – The current valuation of the investment. Profit (purple) – The total realized and unrealized profit. Trades on Chart / Signal Labels / Quantity – Enables trade markers, signal text, and position size visualization. 📈 How the Strategy Works 1️⃣ DCA Mode In DCA mode, the strategy simulates periodic savings and only invests when the MSI indicates favorable liquidity conditions (based on the Entry Value). This approach aims to achieve the best possible average entry price over time — a powerful strategy for long-term investors seeking stable accumulation with reduced emotional bias. 2️⃣ Signal-Based Mode In signal mode (with DCA disabled), the strategy performs one buy and one sell trade based on MSI turning points. It’s most effective during bull markets, where liquidity expansion supports upward momentum. This mode helps identify high-probability entry and exit zones rather than averaging in continuously. 💡 Additional Notes This strategy includes helpful metrics to monitor your personal investment performance — showing invested capital, cash reserves, and profit in real-time. The goal is to combine macroeconomic insight (money supply) with disciplined execution and capital management. ⚠️ Disclaimer This strategy is for educational and research purposes only. It does not constitute financial advice. Always conduct your own analysis before making investment decisions.

S&P 500 & Normalized CAPE Z-Score Analyzer This macro-focused indicator visualizes the historical valuation of the U.S. equity market using the CAPE ratio (Shiller P/E), normalized over its long-term average and standard deviations. It helps traders and investors identify overvaluation and undervaluation zones over time, combining both statistical signals and historical context. 💡 Why It’s Useful This indicator is ideal for macro traders and long-term investors looking to contextualize equity valuations across decades. It helps identify statistical extremes in valuation by referencing the standard deviation of the CAPE ratio relative to its long-term mean. The overlay of S&P 500 price with valuation zones provides a visual confirmation tool for macro decisions or timing insights. It includes: ✅ Three display modes: -S&P 500 (color-coded by CAPE valuation zone) -Normalized CAPE (vs. long-term mean) -CAPE Z-Score (standardized measure) 🎯 How to Interpret Dynamic coloring of the S&P 500 price based on CAPE valuation: 🔴 Z > +2σ → Highly Overvalued 🟠 Z > +1σ → Overvalued ⚪ -1σ < Z < +1σ → Neutral 🟢 Z < -1σ → Undervalued ✅ Z < -2σ → Strong Buy Zone -Live valuation label showing the current CAPE, Z-score, and zone. -Macro event shading: major historical events (e.g. Great Depression, Oil Crisis, Dot-com Bubble, COVID Crash) are shaded on the chart for context. ✅ Built-in alerts: CAPE > +2σ → Potential risk zone CAPE < -2σ → Potential opportunity zone 📊 Use Cases This indicator is ideal for: 🧠 Macro traders seeking long-term valuation extremes. 📈 Portfolio managers monitoring systemic valuation risk. 🏛️ Long-term investors timing strategic allocation shifts. 🧪 How It Works CAPE ratio (Shiller PE) is retrieved from Quandl (MULTPL/SHILLER_PE_RATIO_MONTH). The script calculates the long-term average and standard deviation of CAPE. The Z-score is computed as: (CAPE - Mean) / Standard Deviation Users can switch between: S&P 500 chart, color-coded by CAPE valuation zones. Normalized CAPE, centered around zero (historic mean). CAPE Z-score, showing statistical positioning directly. Visual bands represent +1σ, +2σ, -1σ, -2σ thresholds. You can switch between modes using the “Display” dropdown in the settings panel. 📊 Data Sources CAPE: MULTPL/SHILLER_PE_RATIO_MONTH via Quandl S&P 500: Monthly close prices of SPX (TradingView data) All data updated on monthly resolution This is not a repackaged built-in or autogenerated script. It’s a custom-built and interactive indicator designed for educational and analytical use in macroeconomic valuation studies.

Advanced Volume-Driven Breakout Signals The "Advanced Volume-Driven Breakout Signals" indicator is a cutting-edge tool designed to help traders identify high-potential trading opportunities through sophisticated volume analysis techniques. This indicator integrates volume flow analysis, moving averages, and Relative Volume (RVOL) to provide a comprehensive view of market conditions, going beyond traditional Volume Spread Analysis (VSA) methods. Key Features: Volume Flow Analysis: Distinguishes bullish and bearish volume flows with distinct colors, making it easier to visualize market sentiment and potential breakout points. Volume Flow Moving Averages: Calculates moving averages for volume using various methods (SMA, EMA, WMA, HMA, VWMA), accommodating different trading strategies. This includes settings for adjusting the type of moving average and its period, as well as thresholds for high, medium, and low volume levels. Volume Spikes Detection: Identifies significant volume spikes based on user-defined multipliers and moving averages, highlighting unusual trading activity. Volume MA Cloud Settings: Computes general moving averages of volume to track trends and detect deviations. This feature includes options to select different moving average types and adjust thresholds for detecting high volume activity. Relative Volume (RVOL): Measures current volume relative to historical averages, triggering signals when RVOL exceeds predefined thresholds, indicating notable changes in trading activity. Entry Conditions: Provides clear long and short entry signals based on combined volume flow conditions and RVOL, offering actionable trading opportunities. Volume Visualization: — Bullish Volume Flow: Light and dark green bars indicate bullish volume flow. — Bearish Volume Flow: Light and dark red bars denote bearish volume flow. — High Volume Bars: Highlighted in yellow, and extreme volume bars in orange for additional context. These bars are plotted for visual aid and do not directly influence trade signals, focusing instead on the quality and strength of the volume flow. Alerts: Allows users to create alert notifications for long and short entry signals when the criteria are met, enabling traders to respond promptly to trading opportunities. Usage: Overlay: Apply the indicator directly to your price chart to visualise real-time signals and volume conditions. Customisable: Adjust settings for moving averages, RVOL, and other parameters to match your trading strategy and preferences. Comparison to VSA Scripts: The "Advanced Volume-Driven Breakout Signals" indicator extends beyond traditional VSA scripts by incorporating a wider range of analytical features. While VSA primarily focuses on volume spread patterns and price action, this indicator offers enhanced functionality with advanced RVOL metrics, customizable moving averages, and detailed volume spike detection, making it a more versatile tool for identifying breakout opportunities and managing trades. It is particularly effective when used alongside key levels and order blocks. Acknowledgements: Special thanks to @oh92 and @goofoffgoose for their invaluable scripts, which served as inspiration in the development of this advanced trading indicator. Notes: The script is continually evolving, with ongoing refinements aimed at enhancing accuracy and performance.

GARCH Volatility Estimation - The Quant Science The GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model is a statistical model used to forecast the volatility of a financial asset. This model takes into account the fluctuations in volatility over time, recognizing that volatility can vary in a heteroskedastic (i.e., non-constant variance) manner and can be influenced by past events. The general formula of the GARCH model is: σ²(t) = ω + α * ε²(t-1) + β * σ²(t-1) where: σ²(t) is the conditional variance at time t (i.e., squared volatility) ω is the constant term (intercept) representing the baseline level of volatility α is the coefficient representing the impact of the squared lagged error term on the conditional variance ε²(t-1) is the squared lagged error term at the previous time period β is the coefficient representing the impact of the lagged conditional variance on the current conditional variance In the context of financial forecasting, the GARCH model is used to estimate the future volatility of the asset. HOW TO USE This quantitative indicator is capable of estimating the probable future movements of volatility. When the GARCH increases in value, it means that the volatility of the asset will likely increase as well, and vice versa. The indicator displays the relationship of the GARCH (bright red) with the trend of historical volatility (dark red). USER INTERFACE Alpha: select the starting value of Alpha (default value is 0.10). Beta: select the starting value of Beta (default value is 0.80). Lenght: select the period for calculating values within the model such as EMA (Exponential Moving Average) and Historical Volatility (default set to 20). Forecasting: select the forecasting period, the number of bars you want to visualize data ahead (default set to 30). Design: customize the indicator with your preferred color and choose from different types of charts, managing the design settings.