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ค้นหาในสคริปต์สำหรับ "东方财富的AI短线宝坑爹"

Well Rounded Moving AverageIntroduction There are tons of filters, way to many, and some of them are redundant in the sense they produce the same results as others. The task to find an optimal filter is still a big challenge among technical analysis and engineering, a good filter is the Kalman filter who is one of the more precise filters out there. The optimal filter theorem state that : The optimal estimator has the form of a linear observer , this in short mean that an optimal filter must use measurements of the inputs and outputs, and this is what does the Kalman filter. I have tried myself to Kalman filters with more or less success as well as understanding optimality by studying Linear–quadratic–Gaussian control, i failed to get a complete understanding of those subjects but today i present a moving average filter (WRMA) constructed with all the knowledge i have in control theory and who aim to provide a very well response to market price, this mean low lag for fast decision timing and low overshoots for better precision. Construction An good filter must use information about its output, this is what exponential smoothing is about, simple exponential smoothing (EMA) is close to a simple moving average and can be defined as : output = output(1) + α(input - output(1)) where α (alpha) is a smoothing constant, typically equal to 2/(Period+1) for the EMA. This approach can be further developed by introducing more smoothing constants and output control (See double/triple exponential smoothing - alpha-beta filter) . The moving average i propose will use only one smoothing constant, and is described as follow : a = nz(a ) + alpha*nz(A ) b = nz(b ) + alpha*nz(B ) y = ema(a + b,p1) A = src - y B = src - ema(y,p2) The filter is divided into two components a and b (more terms can add more control/effects if chosen well) , a adjust itself to the output error and is responsive while b is independent of the output and is mainly smoother, adding those components together create an output y , A is the output error and B is the error of an exponential moving average. Comparison There are a lot of low-lag filters out there, but the overshoots they induce in order to reduce lag is not a great effect. The first comparison is with a least square moving average, a moving average who fit a line in a price window of period length . Lsma in blue and WRMA in red with both length = 100 . The lsma is a bit smoother but induce terrible overshoots ZLMA in blue and WRMA in red with both length = 100 . The lag difference between each moving average is really low while VWRMA is way more precise. Hull MA in blue and WRMA in red with both length = 100 . The Hull MA have similar overshoots than the LSMA. Reduced overshoots moving average (ROMA) in blue and WRMA in red with both length = 100 . ROMA is an indicator i have made to reduce the overshoots of a LSMA, but at the end WRMA still reduce way more the overshoots while being smoother and having similar lag. I have added a smoother version, just activate the extra smooth option in the indicator settings window. Here the result with length = 200 : This result is a little bit similar to a 2 order Butterworth filter. Our filter have more overshoots which in this case could be useful to reduce the error with edges since other low pass filters tend to smooth their amplitude thus reducing edge estimation precision. Conclusions I have presented a well rounded filter in term of smoothness/stability and reactivity. Try to add more terms to have different results, you could maybe end up with interesting results, if its the case share them with the community :) As for control theory i have seen neural networks integrated to Kalman flters which leaded to great accuracy, AI is everywhere and promise to be a game a changer in real time data smoothing. So i asked myself if it was possible for a neural networks to develop pinescript indicators, if yes then i could be replaced by AI ? Brrr how frightening. Thanks for reading :)
อินดิเคเตอร์ Pine Script®
โดย alexgrover
9
BTC 5-MA Multi Cross Strategy By Hardik Prajapati Ai TradelabThis strategy is built around the five most powerful and commonly used moving averages in crypto trading — 5, 20, 50, 100, and 200-period SMAs (Simple Moving Averages) — applied on a 1-hour Bitcoin chart. Core Idea: The strategy aims to identify strong bullish trends by confirming when the price action crosses above all key moving averages. This alignment of multiple MAs indicates momentum shift and helps filter out false breakouts. ⸻ ⚙️ How It Works: 1. Calculates 5 Moving Averages: • 5 MA → Short-term momentum (fastest signal) • 20 MA → Near-term trend confirmation • 50 MA → Mid-term trend filter • 100 MA → Long-term trend foundation • 200 MA → Macro-trend direction (strongest support/resistance) 2. Buy Condition (Entry): • A Buy is triggered when: • The price crosses above the 5 MA, and • The closing price remains above all other MAs (20, 50, 100, 200) This signals that momentum is aligned across all time horizons — a strong uptrend confirmation. 3. Sell Condition (Exit): • The position is closed when price crosses below the 20 MA, showing weakness in short-term momentum. 4. Visual Signals: • 🟢 BUY triangle below candles → Entry signal • 🔴 SELL triangle above candles → Exit signal • Colored MAs plotted for trend clarity. ⸻ 📈 Recommended Usage: • Chart: BTC/USDT • Timeframe: 1 Hour • Type: Trend-following crossover strategy • Ideal for: Identifying major breakout moves and confirming trend reversals. ⸻ ⚠️ Notes: • This script is meant for educational and backtesting purposes only. • Always apply additional confirmation tools (like RSI, Volume, or VIX-style filters) before live trading. • Works best during trending markets; may produce whipsaws in sideways zones.
กลยุทธ์ Pine Script®
โดย hpstudio3d
Round Number Analyzer v3Round Number Analyzer v3 is an indicator designed to analyze how price interacts with round number levels (levels spaced at fixed intervals in points or pips). The indicator does not generate entry/exit signals, but provides detailed statistics to better understand market dynamics around these key levels. ✨ Key Features Cross Counting: detects every time the price crosses a round number level (up = Long, down = Short). Continuations & Reversals: classifies each cross as: Continuation: the move continues in the same direction as the previous sequence. Reversal: the move changes direction compared to the previous sequence. Sequence Classification (L1…L5+): each level is labelled based on its position within the consecutive cross sequence: L1 = first level of the sequence, L2 = second consecutive, … L5+ = fifth or higher. Comprehensive Stats Table (top right corner): Total crosses (Long, Short, Totals). Total continuations + breakdown by L1…L5+. Total reversals + breakdown by L1…L5+. Percentages calculated against the proper denominator, displayed directly inside the cells next to the absolute values. Date range of analysis (user-defined). Customizable Step: Works in both points and pips, making the indicator suitable for indices and forex. ⚙️ Main Inputs Start date / End date → sets the analysis period. Step mode → Points or Pips. Step value → distance between round levels. Pip size → pip size (default = 0.0001, typical for forex). 📈 How to Interpret A high continuation percentage after L1–L2 suggests the market tends to extend multiple times beyond the first breakout levels. Higher reversal percentages at advanced levels (L4–L5+) may signal trend exhaustion. The analysis helps estimate the probability of continuation or reversal depending on how many consecutive levels have already been crossed. 🔎 Practical Applications Support for breakout or mean-reversion strategies. Comparative analysis across different markets (e.g. indices vs forex) or different time periods. 📝 Notes The indicator is timeframe-robust, as it accounts for multiple steps within the same candle, ensuring results do not depend on the selected timeframe (except for TradingView’s historical data limits). It does not provide automatic trading signals, but serves as a quantitative analysis tool to refine your strategies. --- Round Number Analyzer v3 è un indicatore pensato per analizzare come il prezzo interagisce con i livelli di round number (livelli a distanza fissa in punti o pips). L’indicatore non genera segnali di ingresso/uscita, ma fornisce statistiche dettagliate utili per comprendere la dinamica del mercato attorno a questi livelli. ✨ Funzionalità principali Conteggio dei Cross: rileva ogni volta che il prezzo attraversa un livello round (verso l’alto = Long, verso il basso = Short). Continuations & Reversals: classifica ogni attraversamento come: Continuation: il movimento prosegue nella stessa direzione della sequenza precedente. Reversal: il movimento inverte la direzione rispetto alla sequenza precedente. Classificazione per sequenza (L1…L5+): ogni livello è etichettato in base alla sua posizione nella sequenza di cross consecutivi: L1 = primo livello della sequenza, L2 = secondo consecutivo, … L5+ = quinto o superiore. Statistiche complete in tabella (in alto a destra): Cross totali (Long, Short, Totals). Continuations totali + breakdown per L1…L5+. Reversals totali + breakdown per L1…L5+. Percentuali calcolate sul denominatore corretto, mostrate direttamente dentro le celle accanto ai valori assoluti. Date range di analisi (impostabile dall’utente). Step personalizzabile: puoi lavorare sia in punti che in pips, così l’indicatore è adatto sia per indici che per forex. ⚙️ Input principali Start date / End date → imposta l’intervallo temporale di analisi. Step mode → punti o pips. Step value → ampiezza tra i livelli round. Pip size → dimensione del pip (default = 0.0001, tipico per il forex). 📈 Come interpretarlo Una percentuale di continuation molto alta dopo L1–L2 indica che il mercato tende a proseguire più volte oltre i primi livelli di breakout. Percentuali di reversal più elevate nei livelli avanzati (L4–L5+) possono suggerire esaurimento della spinta. L’analisi permette di stimare la probabilità che un movimento in corso continui o si inverta in base a quanti livelli sono già stati attraversati consecutivamente. 🔎 Applicazioni pratiche Supporto per strategie di breakout o mean reversion. Analisi comparativa tra mercati (es. indici vs forex) o tra periodi temporali diversi. 📝 Note L’indicatore è timeframe-robust: il conteggio tiene conto di multipli step dentro la stessa candela, così i risultati non dipendono dal timeframe scelto (salvo i limiti di caricamento storico di TradingView). Non fornisce segnali operativi automatici, ma è un tool di analisi quantitativa per affinare le proprie strategie.
อินดิเคเตอร์ Pine Script®
โดย steviatrader
PongExperience PONG! The classic arcade game, now on your charts! With this indicator, you can finally achieve your lifelong dream of beating the Markets. . . at PONG! Pong is jam-packed with features! Such as: 2 Paddles A moving dot Floating numbers The idea of a net This indicator is solely a visualization, it serves simply as an exercise to depict what is capable through PineScript. It can be used to re-skin other indicators or data, but on its own, is not intended as a market indicator. With that out of the way... > PONG The Pong indicator is a recreation of the classic arcade game Pong developed to pit the markets against the cold hard logic of a CPU player. Given the lack of interaction that is capable, the game is not played in the typical sense, by a player and computer or 2 players. This version of Pong uses the chart price movements to control the "Market" Paddle, and it is contrasted by a (not AI) "CPU" Paddle, which is controlled by its own set of logic. > Market Paddle The Market Paddle is controlled by a data source which can be input by the user. By default (Auto Mode), the Market Paddle is controlled through a fixed length Donchian channel range, pinning the range high to 100 and range low to 0. As seen below. This can be altered to use data from different symbols or indicators, and can optionally be smoothed using multiple types of Moving Averages. In the chart below, you can see how the RSI indicator is imported and smoothed to control the Market Paddle. Note: The Market Paddle follows the moving average. If not desired, simply set the "Smoothing" input to "NONE". > CPU Paddle In simple terms, the CPU Paddle is a handicapped Aimbot. Its logic is, more or less, "move directly towards the ball's vertical location". If it were allowed to have full range of the screen, it would be impossible for it to lose a point. Due to this, we must slow it down to "play fair"... as fair as that may be. The CPU Paddle is allowed to move at a rate specified by a certain Percent of its vertical width. By default, this is set to 2%. Each update, the CPU Paddle can advance up or down 2% of its vertical width. The directional movement is determined based on the angle of the ball, and it's current position relative to the CPU Paddle's position. Given that it is not a direct follow, it may at times seem more... "human". When a point is scored, the CPU paddle maintains its position, similar to the original Pong game, the paddles were controlled solely by the raw output of the controllers and did not reset. > Ball At the start of each point, the ball begins at the center of the screen and moves in a randomly determined angle at its base speed. The direction is determined by the player who scored the last point. The loser of the last point "serves" the ball. Given the circumstances, serving is a gigantic advantage. So the loser serving is just another place where the Market is given an advantage. The ball's base speed is 1, it will move 1 (horizontal) bar on each update of the script. This speed can "technically" increase to infinity over time, if given the perfect rally. This is due to the hit logic as described below. Note: The minimum ball speed is also 1. > Bonk Math When the ball hits a paddle, essentially 3 outcomes can occur, each resulting in the ball's direction being changed from positive to negative. Action A: Its angle is doubled, and its speed is doubled. Action B: Its angle is reversed, and its speed is decreased if it is going faster than base speed. Action C: Its angle is preserved, and its speed is preserved. "Basic Bounce" Each paddle is segmented into 3 zones, with the higher and lower tips (20%) of the paddles producing special actions. The central 60% of each paddle produces a basic bounce. The special actions are determined by the trajectory of the ball and location on the paddle. > Custom Mode As stated above, the script loads in "Auto Mode" by default. While this works fine to simply watch the gameplay, the Custom Mode unlocks the ability to visualize countless possibilities of indicators and analyses playing Pong! In the chart below, we have set up the game to use the NYSE TICK Index as our Market Player. The NYSE TICK Index shows the number of NYSE stocks trading on an uptick minus those on a downtick. Its values fluctuate throughout the day, typically ranging between +1000 and -1000. Therefore, we have set up Pong to use Ticker USI:TICK and set the Upper Boundary to 1000 and Lower Boundary to -1000. With this method, the paddle is directly controlled by the overall (NYSE) market behaviors. As seen in a chart earlier, you can also take advantage of the Custom Mode to overlay Pong onto traditional oscillators for use anywhere! > Styles This version of Pong comes stocked with 5 colorways to suit your chart vibes! > Pro Tips & Additional Information - This game has sound! For the full experience, set alerts for this indicator and a notification sound will play on each hit!* *Due to server processing, the notification sounds are not precisely played at each hit. :( - In auto mode, decreasing the length used will give an advantage to the market, as its actions become more sporadic over this window. - The CPU logic system actually allows the market to have a "technical" edge, since the Market Paddle is not bound to any speed, and is solely controlled by the raw market movements/data input. - This type of visualization only works on live charts, charts without updates will not see any movement. - Indicator sources can only be imported from other indicators on the same chart. - The base screen resolution is 159 bars wide, with the height determined by the boundaries. - When using a symbol and an outside source, be mindful that the script is attempting to pull the source from the input symbol. Data can appear wonky when not considering the interactions of these inputs. There are many small interesting details that can't be seen through the description. For example, the mid-line is made from a box. This is because a line object would not appear on top of the box used for the screen. For those keen eye'd coders, feel free to poke around in the source code to make the game truly custom. Just remember: The market may never be fair, but now at least it can play Pong! Enjoy!
อินดิเคเตอร์ Pine Script®
โดย SamRecio
2
Small Business Economic Conditions - Statistical Analysis ModelThe Small Business Economic Conditions Statistical Analysis Model (SBO-SAM) represents an econometric approach to measuring and analyzing the economic health of small business enterprises through multi-dimensional factor analysis and statistical methodologies. This indicator synthesizes eight fundamental economic components into a composite index that provides real-time assessment of small business operating conditions with statistical rigor. The model employs Z-score standardization, variance-weighted aggregation, higher-order moment analysis, and regime-switching detection to deliver comprehensive insights into small business economic conditions with statistical confidence intervals and multi-language accessibility. 1. Introduction and Theoretical Foundation The development of quantitative models for assessing small business economic conditions has gained significant importance in contemporary financial analysis, particularly given the critical role small enterprises play in economic development and employment generation. Small businesses, typically defined as enterprises with fewer than 500 employees according to the U.S. Small Business Administration, constitute approximately 99.9% of all businesses in the United States and employ nearly half of the private workforce (U.S. Small Business Administration, 2024). The theoretical framework underlying the SBO-SAM model draws extensively from established academic research in small business economics and quantitative finance. The foundational understanding of key drivers affecting small business performance builds upon the seminal work of Dunkelberg and Wade (2023) in their analysis of small business economic trends through the National Federation of Independent Business (NFIB) Small Business Economic Trends survey. Their research established the critical importance of optimism, hiring plans, capital expenditure intentions, and credit availability as primary determinants of small business performance. The model incorporates insights from Federal Reserve Board research, particularly the Senior Loan Officer Opinion Survey (Federal Reserve Board, 2024), which demonstrates the critical importance of credit market conditions in small business operations. This research consistently shows that small businesses face disproportionate challenges during periods of credit tightening, as they typically lack access to capital markets and rely heavily on bank financing. The statistical methodology employed in this model follows the econometric principles established by Hamilton (1989) in his work on regime-switching models and time series analysis. Hamilton's framework provides the theoretical foundation for identifying different economic regimes and understanding how economic relationships may vary across different market conditions. The variance-weighted aggregation technique draws from modern portfolio theory as developed by Markowitz (1952) and later refined by Sharpe (1964), applying these concepts to economic indicator construction rather than traditional asset allocation. Additional theoretical support comes from the work of Engle and Granger (1987) on cointegration analysis, which provides the statistical framework for combining multiple time series while maintaining long-term equilibrium relationships. The model also incorporates insights from behavioral economics research by Kahneman and Tversky (1979) on prospect theory, recognizing that small business decision-making may exhibit systematic biases that affect economic outcomes. 2. Model Architecture and Component Structure The SBO-SAM model employs eight orthogonalized economic factors that collectively capture the multifaceted nature of small business operating conditions. Each component is normalized using Z-score standardization with a rolling 252-day window, representing approximately one business year of trading data. This approach ensures statistical consistency across different market regimes and economic cycles, following the methodology established by Tsay (2010) in his treatment of financial time series analysis. 2.1 Small Cap Relative Performance Component The first component measures the performance of the Russell 2000 index relative to the S&P 500, capturing the market-based assessment of small business equity valuations. This component reflects investor sentiment toward smaller enterprises and provides a forward-looking perspective on small business prospects. The theoretical justification for this component stems from the efficient market hypothesis as formulated by Fama (1970), which suggests that stock prices incorporate all available information about future prospects. The calculation employs a 20-day rate of change with exponential smoothing to reduce noise while preserving signal integrity. The mathematical formulation is: Small_Cap_Performance = (Russell_2000_t / S&P_500_t) / (Russell_2000_{t-20} / S&P_500_{t-20}) - 1 This relative performance measure eliminates market-wide effects and isolates the specific performance differential between small and large capitalization stocks, providing a pure measure of small business market sentiment. 2.2 Credit Market Conditions Component Credit Market Conditions constitute the second component, incorporating commercial lending volumes and credit spread dynamics. This factor recognizes that small businesses are particularly sensitive to credit availability and borrowing costs, as established in numerous Federal Reserve studies (Bernanke and Gertler, 1995). Small businesses typically face higher borrowing costs and more stringent lending standards compared to larger enterprises, making credit conditions a critical determinant of their operating environment. The model calculates credit spreads using high-yield bond ETFs relative to Treasury securities, providing a market-based measure of credit risk premiums that directly affect small business borrowing costs. The component also incorporates commercial and industrial loan growth data from the Federal Reserve's H.8 statistical release, which provides direct evidence of lending activity to businesses. The mathematical specification combines these elements as: Credit_Conditions = α₁ × (HYG_t / TLT_t) + α₂ × C&I_Loan_Growth_t where HYG represents high-yield corporate bond ETF prices, TLT represents long-term Treasury ETF prices, and C&I_Loan_Growth represents the rate of change in commercial and industrial loans outstanding. 2.3 Labor Market Dynamics Component The Labor Market Dynamics component captures employment cost pressures and labor availability metrics through the relationship between job openings and unemployment claims. This factor acknowledges that labor market tightness significantly impacts small business operations, as these enterprises typically have less flexibility in wage negotiations and face greater challenges in attracting and retaining talent during periods of low unemployment. The theoretical foundation for this component draws from search and matching theory as developed by Mortensen and Pissarides (1994), which explains how labor market frictions affect employment dynamics. Small businesses often face higher search costs and longer hiring processes, making them particularly sensitive to labor market conditions. The component is calculated as: Labor_Tightness = Job_Openings_t / (Unemployment_Claims_t × 52) This ratio provides a measure of labor market tightness, with higher values indicating greater difficulty in finding workers and potential wage pressures. 2.4 Consumer Demand Strength Component Consumer Demand Strength represents the fourth component, combining consumer sentiment data with retail sales growth rates. Small businesses are disproportionately affected by consumer spending patterns, making this component crucial for assessing their operating environment. The theoretical justification comes from the permanent income hypothesis developed by Friedman (1957), which explains how consumer spending responds to both current conditions and future expectations. The model weights consumer confidence and actual spending data to provide both forward-looking sentiment and contemporaneous demand indicators. The specification is: Demand_Strength = β₁ × Consumer_Sentiment_t + β₂ × Retail_Sales_Growth_t where β₁ and β₂ are determined through principal component analysis to maximize the explanatory power of the combined measure. 2.5 Input Cost Pressures Component Input Cost Pressures form the fifth component, utilizing producer price index data to capture inflationary pressures on small business operations. This component is inversely weighted, recognizing that rising input costs negatively impact small business profitability and operating conditions. Small businesses typically have limited pricing power and face challenges in passing through cost increases to customers, making them particularly vulnerable to input cost inflation. The theoretical foundation draws from cost-push inflation theory as described by Gordon (1988), which explains how supply-side price pressures affect business operations. The model employs a 90-day rate of change to capture medium-term cost trends while filtering out short-term volatility: Cost_Pressure = -1 × (PPI_t / PPI_{t-90} - 1) The negative weighting reflects the inverse relationship between input costs and business conditions. 2.6 Monetary Policy Impact Component Monetary Policy Impact represents the sixth component, incorporating federal funds rates and yield curve dynamics. Small businesses are particularly sensitive to interest rate changes due to their higher reliance on variable-rate financing and limited access to capital markets. The theoretical foundation comes from monetary transmission mechanism theory as developed by Bernanke and Blinder (1992), which explains how monetary policy affects different segments of the economy. The model calculates the absolute deviation of federal funds rates from a neutral 2% level, recognizing that both extremely low and high rates can create operational challenges for small enterprises. The yield curve component captures the shape of the term structure, which affects both borrowing costs and economic expectations: Monetary_Impact = γ₁ × |Fed_Funds_Rate_t - 2.0| + γ₂ × (10Y_Yield_t - 2Y_Yield_t) 2.7 Currency Valuation Effects Component Currency Valuation Effects constitute the seventh component, measuring the impact of US Dollar strength on small business competitiveness. A stronger dollar can benefit businesses with significant import components while disadvantaging exporters. The model employs Dollar Index volatility as a proxy for currency-related uncertainty that affects small business planning and operations. The theoretical foundation draws from international trade theory and the work of Krugman (1987) on exchange rate effects on different business segments. Small businesses often lack hedging capabilities, making them more vulnerable to currency fluctuations: Currency_Impact = -1 × DXY_Volatility_t 2.8 Regional Banking Health Component The eighth and final component, Regional Banking Health, assesses the relative performance of regional banks compared to large financial institutions. Regional banks traditionally serve as primary lenders to small businesses, making their health a critical factor in small business credit availability and overall operating conditions. This component draws from the literature on relationship banking as developed by Boot (2000), which demonstrates the importance of bank-borrower relationships, particularly for small enterprises. The calculation compares regional bank performance to large financial institutions: Banking_Health = (Regional_Banks_Index_t / Large_Banks_Index_t) - 1 3. Statistical Methodology and Advanced Analytics The model employs statistical techniques to ensure robustness and reliability. Z-score normalization is applied to each component using rolling 252-day windows, providing standardized measures that remain consistent across different time periods and market conditions. This approach follows the methodology established by Engle and Granger (1987) in their cointegration analysis framework. 3.1 Variance-Weighted Aggregation The composite index calculation utilizes variance-weighted aggregation, where component weights are determined by the inverse of their historical variance. This approach, derived from modern portfolio theory, ensures that more stable components receive higher weights while reducing the impact of highly volatile factors. The mathematical formulation follows the principle that optimal weights are inversely proportional to variance, maximizing the signal-to-noise ratio of the composite indicator. The weight for component i is calculated as: w_i = (1/σᵢ²) / Σⱼ(1/σⱼ²) where σᵢ² represents the variance of component i over the lookback period. 3.2 Higher-Order Moment Analysis Higher-order moment analysis extends beyond traditional mean and variance calculations to include skewness and kurtosis measurements. Skewness provides insight into the asymmetry of the sentiment distribution, while kurtosis measures the tail behavior and potential for extreme events. These metrics offer valuable information about the underlying distribution characteristics and potential regime changes. Skewness is calculated as: Skewness = E / σ³ Kurtosis is calculated as: Kurtosis = E / σ⁴ - 3 where μ represents the mean and σ represents the standard deviation of the distribution. 3.3 Regime-Switching Detection The model incorporates regime-switching detection capabilities based on the Hamilton (1989) framework. This allows for identification of different economic regimes characterized by distinct statistical properties. The regime classification employs percentile-based thresholds: - Regime 3 (Very High): Percentile rank > 80 - Regime 2 (High): Percentile rank 60-80 - Regime 1 (Moderate High): Percentile rank 50-60 - Regime 0 (Neutral): Percentile rank 40-50 - Regime -1 (Moderate Low): Percentile rank 30-40 - Regime -2 (Low): Percentile rank 20-30 - Regime -3 (Very Low): Percentile rank < 20 3.4 Information Theory Applications The model incorporates information theory concepts, specifically Shannon entropy measurement, to assess the information content of the sentiment distribution. Shannon entropy, as developed by Shannon (1948), provides a measure of the uncertainty or information content in a probability distribution: H(X) = -Σᵢ p(xᵢ) log₂ p(xᵢ) Higher entropy values indicate greater unpredictability and information content in the sentiment series. 3.5 Long-Term Memory Analysis The Hurst exponent calculation provides insight into the long-term memory characteristics of the sentiment series. Originally developed by Hurst (1951) for analyzing Nile River flow patterns, this measure has found extensive application in financial time series analysis. The Hurst exponent H is calculated using the rescaled range statistic: H = log(R/S) / log(T) where R/S represents the rescaled range and T represents the time period. Values of H > 0.5 indicate long-term positive autocorrelation (persistence), while H < 0.5 indicates mean-reverting behavior. 3.6 Structural Break Detection The model employs Chow test approximation for structural break detection, based on the methodology developed by Chow (1960). This technique identifies potential structural changes in the underlying relationships by comparing the stability of regression parameters across different time periods: Chow_Statistic = (RSS_restricted - RSS_unrestricted) / RSS_unrestricted × (n-2k)/k where RSS represents residual sum of squares, n represents sample size, and k represents the number of parameters. 4. Implementation Parameters and Configuration 4.1 Language Selection Parameters The model provides comprehensive multi-language support across five languages: English, German (Deutsch), Spanish (Español), French (Français), and Japanese (日本語). This feature enhances accessibility for international users and ensures cultural appropriateness in terminology usage. The language selection affects all internal displays, statistical classifications, and alert messages while maintaining consistency in underlying calculations. 4.2 Model Configuration Parameters Calculation Method: Users can select from four aggregation methodologies: - Equal-Weighted: All components receive identical weights - Variance-Weighted: Components weighted inversely to their historical variance - Principal Component: Weights determined through principal component analysis - Dynamic: Adaptive weighting based on recent performance Sector Specification: The model allows for sector-specific calibration: - General: Broad-based small business assessment - Retail: Emphasis on consumer demand and seasonal factors - Manufacturing: Enhanced weighting of input costs and currency effects - Services: Focus on labor market dynamics and consumer demand - Construction: Emphasis on credit conditions and monetary policy Lookback Period: Statistical analysis window ranging from 126 to 504 trading days, with 252 days (one business year) as the optimal default based on academic research. Smoothing Period: Exponential moving average period from 1 to 21 days, with 5 days providing optimal noise reduction while preserving signal integrity. 4.3 Statistical Threshold Parameters Upper Statistical Boundary: Configurable threshold between 60-80 (default 70) representing the upper significance level for regime classification. Lower Statistical Boundary: Configurable threshold between 20-40 (default 30) representing the lower significance level for regime classification. Statistical Significance Level (α): Alpha level for statistical tests, configurable between 0.01-0.10 with 0.05 as the standard academic default. 4.4 Display and Visualization Parameters Color Theme Selection: Eight professional color schemes optimized for different user preferences and accessibility requirements: - Gold: Traditional financial industry colors - EdgeTools: Professional blue-gray scheme - Behavioral: Psychology-based color mapping - Quant: Value-based quantitative color scheme - Ocean: Blue-green maritime theme - Fire: Warm red-orange theme - Matrix: Green-black technology theme - Arctic: Cool blue-white theme Dark Mode Optimization: Automatic color adjustment for dark chart backgrounds, ensuring optimal readability across different viewing conditions. Line Width Configuration: Main index line thickness adjustable from 1-5 pixels for optimal visibility. Background Intensity: Transparency control for statistical regime backgrounds, adjustable from 90-99% for subtle visual enhancement without distraction. 4.5 Alert System Configuration Alert Frequency Options: Three frequency settings to match different trading styles: - Once Per Bar: Single alert per bar formation - Once Per Bar Close: Alert only on confirmed bar close - All: Continuous alerts for real-time monitoring Statistical Extreme Alerts: Notifications when the index reaches 99% confidence levels (Z-score > 2.576 or < -2.576). Regime Transition Alerts: Notifications when statistical boundaries are crossed, indicating potential regime changes. 5. Practical Application and Interpretation Guidelines 5.1 Index Interpretation Framework The SBO-SAM index operates on a 0-100 scale with statistical normalization ensuring consistent interpretation across different time periods and market conditions. Values above 70 indicate statistically elevated small business conditions, suggesting favorable operating environment with potential for expansion and growth. Values below 30 indicate statistically reduced conditions, suggesting challenging operating environment with potential constraints on business activity. The median reference line at 50 represents the long-term equilibrium level, with deviations providing insight into cyclical conditions relative to historical norms. The statistical confidence bands at 95% levels (approximately ±2 standard deviations) help identify when conditions reach statistically significant extremes. 5.2 Regime Classification System The model employs a seven-level regime classification system based on percentile rankings: Very High Regime (P80+): Exceptional small business conditions, typically associated with strong economic growth, easy credit availability, and favorable regulatory environment. Historical analysis suggests these periods often precede economic peaks and may warrant caution regarding sustainability. High Regime (P60-80): Above-average conditions supporting business expansion and investment. These periods typically feature moderate growth, stable credit conditions, and positive consumer sentiment. Moderate High Regime (P50-60): Slightly above-normal conditions with mixed signals. Careful monitoring of individual components helps identify emerging trends. Neutral Regime (P40-50): Balanced conditions near long-term equilibrium. These periods often represent transition phases between different economic cycles. Moderate Low Regime (P30-40): Slightly below-normal conditions with emerging headwinds. Early warning signals may appear in credit conditions or consumer demand. Low Regime (P20-30): Below-average conditions suggesting challenging operating environment. Businesses may face constraints on growth and expansion. Very Low Regime (P0-20): Severely constrained conditions, typically associated with economic recessions or financial crises. These periods often present opportunities for contrarian positioning. 5.3 Component Analysis and Diagnostics Individual component analysis provides valuable diagnostic information about the underlying drivers of overall conditions. Divergences between components can signal emerging trends or structural changes in the economy. Credit-Labor Divergence: When credit conditions improve while labor markets tighten, this may indicate early-stage economic acceleration with potential wage pressures. Demand-Cost Divergence: Strong consumer demand coupled with rising input costs suggests inflationary pressures that may constrain small business margins. Market-Fundamental Divergence: Disconnection between small-cap equity performance and fundamental conditions may indicate market inefficiencies or changing investor sentiment. 5.4 Temporal Analysis and Trend Identification The model provides multiple temporal perspectives through momentum analysis, rate of change calculations, and trend decomposition. The 20-day momentum indicator helps identify short-term directional changes, while the Hodrick-Prescott filter approximation separates cyclical components from long-term trends. Acceleration analysis through second-order momentum calculations provides early warning signals for potential trend reversals. Positive acceleration during declining conditions may indicate approaching inflection points, while negative acceleration during improving conditions may suggest momentum loss. 5.5 Statistical Confidence and Uncertainty Quantification The model provides comprehensive uncertainty quantification through confidence intervals, volatility measures, and regime stability analysis. The 95% confidence bands help users understand the statistical significance of current readings and identify when conditions reach historically extreme levels. Volatility analysis provides insight into the stability of current conditions, with higher volatility indicating greater uncertainty and potential for rapid changes. The regime stability measure, calculated as the inverse of volatility, helps assess the sustainability of current conditions. 6. Risk Management and Limitations 6.1 Model Limitations and Assumptions The SBO-SAM model operates under several important assumptions that users must understand for proper interpretation. The model assumes that historical relationships between economic variables remain stable over time, though the regime-switching framework helps accommodate some structural changes. The 252-day lookback period provides reasonable statistical power while maintaining sensitivity to changing conditions, but may not capture longer-term structural shifts. The model's reliance on publicly available economic data introduces inherent lags in some components, particularly those based on government statistics. Users should consider these timing differences when interpreting real-time conditions. Additionally, the model's focus on quantitative factors may not fully capture qualitative factors such as regulatory changes, geopolitical events, or technological disruptions that could significantly impact small business conditions. The model's timeframe restrictions ensure statistical validity by preventing application to intraday periods where the underlying economic relationships may be distorted by market microstructure effects, trading noise, and temporal misalignment with the fundamental data sources. Users must utilize daily or longer timeframes to ensure the model's statistical foundations remain valid and interpretable. 6.2 Data Quality and Reliability Considerations The model's accuracy depends heavily on the quality and availability of underlying economic data. Market-based components such as equity indices and bond prices provide real-time information but may be subject to short-term volatility unrelated to fundamental conditions. Economic statistics provide more stable fundamental information but may be subject to revisions and reporting delays. Users should be aware that extreme market conditions may temporarily distort some components, particularly those based on financial market data. The model's statistical normalization helps mitigate these effects, but users should exercise additional caution during periods of market stress or unusual volatility. 6.3 Interpretation Caveats and Best Practices The SBO-SAM model provides statistical analysis and should not be interpreted as investment advice or predictive forecasting. The model's output represents an assessment of current conditions based on historical relationships and may not accurately predict future outcomes. Users should combine the model's insights with other analytical tools and fundamental analysis for comprehensive decision-making. The model's regime classifications are based on historical percentile rankings and may not fully capture the unique characteristics of current economic conditions. Users should consider the broader economic context and potential structural changes when interpreting regime classifications. 7. Academic References and Bibliography Bernanke, B. S., & Blinder, A. S. (1992). The Federal Funds Rate and the Channels of Monetary Transmission. American Economic Review, 82(4), 901-921. Bernanke, B. S., & Gertler, M. (1995). Inside the Black Box: The Credit Channel of Monetary Policy Transmission. Journal of Economic Perspectives, 9(4), 27-48. Boot, A. W. A. (2000). Relationship Banking: What Do We Know? Journal of Financial Intermediation, 9(1), 7-25. Chow, G. C. (1960). Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3), 591-605. Dunkelberg, W. C., & Wade, H. (2023). NFIB Small Business Economic Trends. National Federation of Independent Business Research Foundation, Washington, D.C. Engle, R. F., & Granger, C. W. J. (1987). Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276. Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417. Federal Reserve Board. (2024). Senior Loan Officer Opinion Survey on Bank Lending Practices. Board of Governors of the Federal Reserve System, Washington, D.C. Friedman, M. (1957). A Theory of the Consumption Function. Princeton University Press, Princeton, NJ. Gordon, R. J. (1988). The Role of Wages in the Inflation Process. American Economic Review, 78(2), 276-283. Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384. Hurst, H. E. (1951). Long-term Storage Capacity of Reservoirs. Transactions of the American Society of Civil Engineers, 116(1), 770-799. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291. Krugman, P. (1987). Pricing to Market When the Exchange Rate Changes. In S. W. Arndt & J. D. Richardson (Eds.), Real-Financial Linkages among Open Economies (pp. 49-70). MIT Press, Cambridge, MA. Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91. Mortensen, D. T., & Pissarides, C. A. (1994). Job Creation and Job Destruction in the Theory of Unemployment. Review of Economic Studies, 61(3), 397-415. Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423. Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance, 19(3), 425-442. Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed.). John Wiley & Sons, Hoboken, NJ. U.S. Small Business Administration. (2024). Small Business Profile. Office of Advocacy, Washington, D.C. 8. Technical Implementation Notes The SBO-SAM model is implemented in Pine Script version 6 for the TradingView platform, ensuring compatibility with modern charting and analysis tools. The implementation follows best practices for financial indicator development, including proper error handling, data validation, and performance optimization. The model includes comprehensive timeframe validation to ensure statistical accuracy and reliability. The indicator operates exclusively on daily (1D) timeframes or higher, including weekly (1W), monthly (1M), and longer periods. This restriction ensures that the statistical analysis maintains appropriate temporal resolution for the underlying economic data sources, which are primarily reported on daily or longer intervals. When users attempt to apply the model to intraday timeframes (such as 1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 2-hour, 4-hour, 6-hour, 8-hour, or 12-hour charts), the system displays a comprehensive error message in the user's selected language and prevents execution. This safeguard protects users from potentially misleading results that could occur when applying daily-based economic analysis to shorter timeframes where the underlying data relationships may not hold. The model's statistical calculations are performed using vectorized operations where possible to ensure computational efficiency. The multi-language support system employs Unicode character encoding to ensure proper display of international characters across different platforms and devices. The alert system utilizes TradingView's native alert functionality, providing users with flexible notification options including email, SMS, and webhook integrations. The alert messages include comprehensive statistical information to support informed decision-making. The model's visualization system employs professional color schemes designed for optimal readability across different chart backgrounds and display devices. The system includes dynamic color transitions based on momentum and volatility, professional glow effects for enhanced line visibility, and transparency controls that allow users to customize the visual intensity to match their preferences and analytical requirements. The clean confidence band implementation provides clear statistical boundaries without visual distractions, maintaining focus on the analytical content.
อินดิเคเตอร์ Pine Script®
โดย EdgeTools
ORB Breakout Strategy with reversalORB 1,5,15,30,60min with reversals, its my first strategy Im not 100% sure it works well. Im not a programmer nor a profitable trader. Max stoploss in points sets maximum fixed stoploss Stop offset sets additional points below/above signal bar RR Ratio is pretty self explanatory, it sets target based on stoploss American session is time when positions can be opened ORB SessionIs basically almost the same but when the time runs it closes all positions\ ORB candle timeframe is the time which orb is measured Enable reverse position enables reversing positions on stoploss of first position, stoploss of reverse position is based on max stoploss and target is set by RR times max stoploss Im sharing this to share this with my friends, discuss some things and dont have to test it manually. I made it all myself and with help of AI Sorry for bad english
กลยุทธ์ Pine Script®
โดย Ciuchers
EMA Cross Alert V666 [noFuck]EMA Cross Alert — What it does EMA Cross Alert watches three EMAs (Short, Mid, Long), detects their crossovers, and reports exactly one signal per bar by priority: EARLY > Short/Mid > Mid/Long > Short/Long. Optional EARLY mode pings when Short crosses Long while Mid is still between them—your polite early heads-up. Why you might like it Three crossover types: s/m, m/l, s/l EARLY detection: earlier hints, not hype One signal per bar: less noise, more focus Clear visuals: tags, big cross at signal price, EARLY triangles Alert-ready: dynamic alert text on bar close + static alertconditions for UI Inputs (plain English) Short/Mid/Long EMA length — how fast each EMA reacts Extra EMA length (visual only) — context EMA; does not affect signals Price source — e.g., Close Show cross tags / EARLY triangles / large cross — visual toggles Enable EARLY signals (Short/Long before Mid) — turn early pings on/off Count Mid EMA as "between" even when equal (inclusive) — ON: Mid counts even if exactly equal to Short or Long; OFF (default): Mid must be strictly between them Enable dynamic alerts (one per bar close) — master alert switch Alert on Short/Mid, Mid/Long, Short/Long, EARLY — per-signal alert toggles Quick tips Start with defaults; if you want more EARLY on smooth/low-TF markets, turn “inclusive” ON Bigger lengths = calmer trend-following; smaller = faster but choppier Combine with volume/structure/risk rules—the indicator is the drummer, not the whole band Disclaimer Alerts, labels, and triangles are not trade ideas or financial advice. They are informational signals only. You are responsible for entries, exits, risk, and position sizing. Past performance is yesterday; the future is fashionably late. Credits Built with the enthusiastic help of Code Copilot (AI)—massively involved, shamelessly proud, and surprisingly good at breakfasting on exponential moving averages.
อินดิเคเตอร์ Pine Script®
โดย sichernich
Crypto Perp Calc v1Advanced Perpetual Position Calculator for TradingView Description A comprehensive position sizing and risk management tool designed specifically for perpetual futures trading. This indicator eliminates the confusion of calculating leveraged positions by providing real-time position metrics directly on your chart. Key Features: Interactive Price Selection: Click directly on chart to set entry, stop loss, and take profit levels Accurate Lot Size Calculation: Instantly calculates the exact position size needed for your margin and leverage Multiple Entry Support: DCA into positions with up to 3 entry points with customizable allocation Multiple Take Profit Levels: Scale out of positions with up to 3 TP targets Comprehensive Risk Metrics: Shows dollar P&L, account risk percentage, and liquidation price Visual Risk/Reward: Color-coded boxes and lines display your trade setup clearly Real-time Info Table: All critical position data in one organized panel Perfect for traders using perpetual futures who need precise position sizing with leverage. --------- How to Use Quick Start (3 Clicks) 1. Add the indicator to your chart 2. Click three times when prompted: First click: Set your entry price Second click: Set your stop loss Third click: Set your take profit 3. Read the TOTAL LOTS value from the info table (highlighted in yellow) 4. Use this lot size in your exchange when placing the trade Detailed Setup Step 1: Configure Your Account Enter your account balance (total USDT in account) Set your margin amount (how much USDT to risk on this trade) Choose your leverage (1x to 125x) Select Long or Short position Step 2: Set Price Levels Main levels use interactive clicking (Entry, SL, TP) For multiple entries or TPs, use the settings panel to manually input prices and percentages Step 3: Read the Results The info table shows: TOTAL LOTS - The position size to enter on your exchange Margin Used - Your actual capital at risk Notional - Total position value (margin × leverage) Max Risk - Dollar amount you'll lose at stop loss Total Profit - Dollar amount you'll gain at take profit R:R Ratio - Risk to reward ratio Account Risk - Percentage of account at risk Liquidation - Price where position gets liquidated Step 4: Advanced Features (Optional) Multiple Entries (DCA): Enable "Use Multiple Entries" Set up to 3 entry prices Allocate percentage for each (must total 100%) See individual lot sizes for each entry Multiple Take Profits: Enable "Use Multiple TPs" Set up to 3 TP levels Allocate percentage to close at each level (must total 100%) View profit at each target Visual Elements Blue lines/labels: Entry points Red lines/labels: Stop loss Green lines/labels: Take profit targets Colored boxes: Visual risk (red) and reward (green) zones Info table: Can be positioned anywhere on screen Alerts Set price alerts for: Entry zones reached Stop loss approached Take profit levels hit Works with TradingView's alert system Tips for Best Results Always verify the lot size matches your intended risk Check the liquidation price stays far from your stop loss Monitor the account risk percentage (recommended: keep under 2-3%) Use the warning indicators if risk exceeds margin For quick trades, use single entry/TP; for complex strategies, use multiple levels Example Workflow Find your trade setup using your analysis Add this indicator and click to set levels Check risk metrics in the table Copy the TOTAL LOTS value Enter this exact position size on your exchange Set alerts for key levels if desired This tool bridges the gap between TradingView charting and exchange execution, ensuring your position sizing is always accurate when trading with leverage. Disclaimer, this was coded with help of AI, double check calculations if they are off.
อินดิเคเตอร์ Pine Script®
โดย bujayjay
P/B Ratio (Per Share) vs Median + Bollinger Band- 📝 This indicator highlights potential buying opportunities by analyzing the Price-to-Book (P/B) ratio in relation to Bollinger Bands and its historical median. - 🎯 The goal is to provide a visually intuitive signal for value-oriented entries, especially when valuation compression aligns with historical context. - 💡 Vertical green shading is applied when the P/B ratio drops below the lower Bollinger Band, which is calculated directly from the P/B ratio itself — not price. This condition often signals the ticker may be oversold. - 🟢 Lighter green appears when the ratio is below the lower band but above the median, suggesting a possible shorter-term entry with slightly more risk. - 🟢 Darker green appears when the ratio is both below the lower band and below the median, pointing to a potentially stronger, longer-term value entry. - ⚠️ This logic was tested using 1 and 2-day time frames. It may not be as helpful in longer time frames, as the financial data TradingView pulls in begins in Q4 2017. - ⚠️ Note: This script relies on financial data availability through TradingView. It may not function properly with certain tickers — especially ETFs, IPOs, or thinly tracked assets — where P/S ratio data is missing or incomplete. - ⚠️ This indicator will not guarantee successful results. Use in conjunction with other indicators and do your due diligence. - 🤖 This script was iteratively refined with the help of AI to ensure clean logic, minimalist design, and actionable signal clarity. - 📢 Idea is based on the script "Historical PE ratio vs median" by haribotagada - 💬 Questions, feedback, or suggestions? Drop a comment — I’d love to hear how you’re using it or what you'd like to see changed.
อินดิเคเตอร์ Pine Script®
โดย traceockinga
P/E Ratio vs Median + Bollinger Band- 📝 This indicator highlights potential buying opportunities by analyzing the Price-to-Earnings (P/E) ratio in relation to Bollinger Bands and its historical median. - 🎯 The goal is to provide a visually intuitive signal for value-oriented entries, especially when valuation compression aligns with historical context. - 💡 Vertical green shading is applied when the P/E ratio drops below the lower Bollinger Band, which is calculated directly from the P/E ratio itself — not price. This condition often signals the ticker may be oversold. - 🟢 Lighter green appears when the ratio is below the lower band but above the median, suggesting a possible shorter-term entry with slightly more risk. - 🟢 Darker green appears when the ratio is both below the lower band and below the median, pointing to a potentially stronger, longer-term value entry. - ⚠️ This logic was tested using 1 and 2-day time frames. It may not be as helpful in longer time frames, as the financial data TradingView pulls in begins in Q4 2017. - ⚠️ Note: This script relies on financial data availability through TradingView. It may not function properly with certain tickers — especially ETFs, IPOs, or thinly tracked assets — where P/S ratio data is missing or incomplete. - ⚠️ This indicator will not guarantee successful results. Use in conjunction with other indicators and do your due diligence. - 🤖 This script was iteratively refined with the help of AI to ensure clean logic, minimalist design, and actionable signal clarity. - 📢 Idea is based on the script "Historical PE ratio vs median" by haribotagada - 💬 Questions, feedback, or suggestions? Drop a comment — I’d love to hear how you’re using it or what you'd like to see changed.
อินดิเคเตอร์ Pine Script®
โดย traceockinga
P/S Ratio vs Median + Bollinger Band- 📝 This indicator highlights potential buying opportunities by analyzing the Price-to-Sales (P/S) ratio in relation to Bollinger Bands and its historical median. - 🎯 The goal is to provide a visually intuitive signal for value-oriented entries, especially when valuation compression aligns with historical context. - 💡 Vertical green shading is applied when the P/S ratio drops below the lower Bollinger Band, which is calculated directly from the P/S ratio itself — not price. This condition often signals the ticker may be oversold. - 🟢 Lighter green appears when the ratio is below the lower band but above the median, suggesting a possible shorter-term entry with slightly more risk. - 🟢 Darker green appears when the ratio is both below the lower band and below the median, pointing to a potentially stronger, longer-term value entry. - ⚠️ This logic was tested using 1 and 2-day time frames. It may not be as helpful in longer time frames, as the financial data TradingView pulls in begins in Q4 2017. - ⚠️ Note: This script relies on financial data availability through TradingView. It may not function properly with certain tickers — especially ETFs, IPOs, or thinly tracked assets — where P/S ratio data is missing or incomplete. - ⚠️ This indicator will not guarantee successful results. Use in conjunction with other indicators and do your due diligence. - 🤖 This script was iteratively refined with the help of AI to ensure clean logic, minimalist design, and actionable signal clarity. - 📢 Idea is based on the script "Historical PE ratio vs median" by @haribotagada - 💬 Questions, feedback, or suggestions? Drop a comment — I’d love to hear how you’re using it or what you'd like to see changed.
อินดิเคเตอร์ Pine Script®
โดย traceockinga
Confluence StackPlease read the instructions below. The code was mostly written using AI so may contain errors. Happy trading all and good luck. ATB Richard INTENDED USE This indicator is designed for technical traders who want to move beyond simple buy/sell signals and gain a deeper understanding of the underlying market dynamics. It is ideal for trend followers, swing traders, and anyone looking to confirm the quality of a trend. WHO IS THIS FOR? Traders who want to differentiate between strong, sustainable trends and weak, unreliable moves. Analysts looking to identify high-conviction setups backed by multiple factors (e.g., momentum confirmed by volume). Discretionary traders who need a quick, visual tool to gauge market sentiment and avoid choppy conditions. WHY USE IT? Traditional indicators often give conflicting signals. The Confluence Stack solves this by aggregating multiple perspectives into one clear visual. It helps you answer not just "Is the market going up?" but "WHY is it going up, and how strong is the conviction?". This allows for more informed decision-making and helps filter out low-probability trades. DISCLAIMER AND LICENSE This script is for educational purposes only and is not a recommendation to buy or sell any financial instrument. All trading and investment decisions are the sole responsibility of the user. Trading involves significant risk. This source code is subject to the terms of the Mozilla Public License 2.0 at www.mozilla.org HOW TO USE THIS INDICATOR This indicator is designed to show the 'character' of a market move by grouping signals into distinct categories. Instead of seeing many individual signals, you see the strength of the underlying forces driving the price. 1. READ THE HEIGHT (Strength of Confluence) The total height of the stack shows the strength of agreement. A tall stack means many signals are aligned, indicating a high-conviction move. A short stack means weak agreement and a choppy, indecisive market. 2. READ THE COLOR (Character of the Move) The colors tell you WHY the market is moving. BLUE (Momentum): A stack of mostly blue shades indicates a trend driven by pure momentum. This is the 'speed' of the market. RSI (Relative Strength Index): Measures the magnitude of recent price gains versus losses. A smooth measure of trend strength. Stochastic Oscillator: Measures the current closing price's position within the recent high-low range. More sensitive to immediate price action. CCI (Commodity Channel Index): Measures the price's deviation from its moving average. Excels at identifying cyclical turns. MACD (Moving Average Convergence Divergence): A trend-following momentum indicator showing the relationship between two moving averages. Excellent for identifying the start and end of trends. YELLOW (Volume): The appearance of yellow shades confirms the move is supported by high market participation. This is the 'fuel' for the trend. Volume Ratio: A custom signal that triggers when buy or sell volume is unusually high compared to its recent average. CRV (Candle Range Volume): A custom signal that looks for candles with significant price range and volume. OBV (On-Balance Volume): A cumulative indicator that adds volume on up days and subtracts it on down days. It shows the long-term flow of money. FUCHSIA (Volatility): A fuchsia block signals a volatility breakout. This adds a sense of urgency and confirms the price is moving with exceptional force. Bollinger Bands: A signal triggers when the price closes outside of the upper or lower standard deviation bands. ORANGE (Price Action): An orange block is a pure price structure signal. It's a raw statement of intent from the market. Price Gap: A signal that triggers when there's a gap up or gap down between candles. 3. READ THE TRANSITION (Shift in Sentiment) The most important signal from the stacks is the flip from one side of the zero line to the other. Flipping from Negative to Positive: A bearish stack disappears and is replaced by a bullish stack. This indicates market sentiment is shifting from bearish to bullish. Flipping from Positive to Negative: A bullish stack disappears and is replaced by a bearish stack. This warns of a potential top or the start of a new downtrend. 4. FILTER FOR NOISE (Plot Threshold) In choppy markets, the stack can flicker with low signal counts (e.g., +1 or -1). To focus only on high-conviction moves, go to the indicator settings and increase the "Plot Threshold". A setting of 2 or 3 will hide all stacks that don't have at least 2 or 3 agreeing signals, effectively filtering out market noise and keeping your chart clean. 5. CUSTOMIZE YOUR SIGNALS (Enable/Disable) This indicator is fully customizable. In the settings, you can enable or disable each of the 9 indicators individually. For example, if you are a pure momentum trader, you could disable all Volume, Volatility, and Price Action signals to focus only on the blue stacks. Tailor it to fit your specific trading style. EXAMPLE INTERPRETATIONS Strong, Confirmed Trend: A tall stack of mostly blue (Momentum) and yellow (Volume) indicates a high-quality trend backed by both speed and market participation. Momentum-Only Trend: A tall stack of only blue is a strong momentum move, but the lack of yellow (Volume) is a warning that the move may lack the "fuel" to be sustained. Choppy/Indecisive Market: A short, mixed-color stack flickering around the zero line means the market is choppy with no clear conviction. It's often best to stay out. Volatility Breakout: A new stack that appears suddenly with a fuchsia (Bollinger Bands) block on its first bar suggests a volatility-driven breakout is initiating. Exhaustion Move: An orange (Price Gap) block appearing at the peak of a tall, long-standing stack can signal an exhaustion gap, potentially marking the end of the trend. Weakening Conviction (Divergence): If price makes a new high but the positive stack is visibly shorter than the stack at the previous price high, it suggests underlying conviction is weakening.
อินดิเคเตอร์ Pine Script®
โดย richardjamescook123
Altcoin Market Share vs ETH/BTCIdea from x.com on X Each colored line represents the percentage share of different altcoin baskets (excluding stablecoins) or ETH relative to either the ETH or BTC market cap (can add more, e.g. SOL or create different dashboards with Memes, AI, DeFi, you name it) I know: At first glance, this may seem noisy and complex, but it all depends on the questions you want to answer. Once you define those, much of the noise becomes irrelevant, allowing you to simplify the analysis and focus only on what matters to you. What I’ve done here is provide a few initial insights that I found useful (will isolate a couple of them in future). This analysis doesn’t tell you which specific coins to buy, but rather provides a broad market overview as a foundation. It helps guide you toward areas of relative strength or weakness. I’ve included a lot of information here, but the key is to extract the signal from the noise by asking the right questions, for example: At what point do altcoins become overvalued or undervalued against Ethereum? However, when asking these questions, it's important to remember that an overvaluation or undervaluation of Ethereum relative to altcoins tells you little about its valuation against Bitcoin or USD. These are separate questions further down the process.
อินดิเคเตอร์ Pine Script®
โดย privatekapperaza
Long Elite Squeeze (LES) — H.H 22 Lindsay (AI)LES (Long Elite Squeeze) LES (Long Elite Squeeze) is a trading framework designed to capture the highest-probability long setups. It’s not just another signal script — it’s a structured system built to filter noise, manage risk, and keep you aligned with real momentum. 🔹 Core Logic Breakout Confirmation – Ensures moves have structure, not just random spikes. Relative Volume (RVOL) – Confirms participation and fuel behind the move. RSI Alignment – Avoids overextended traps and fakeouts. Squeeze Momentum – The backbone of LES. Signals fire only after a defined squeeze pattern shift (6+ dark green bars followed by a light green bar). 🔹 Trade Management Built In Automated Sell Signals – Trigger on either: 2 consecutive dark green bars on Squeeze Momentum WaveTrend cross down (only valid after a Buy signal — no random shorts) HUD Entry Checklist – Live conditions shown on chart. Status Tracker HUD – Flips between “Waiting for Entry” and “In Trade” for clear context. 🔹 Flexibility 3 switchable squeeze versions (V1, V2, V3) for different market conditions. Customizable EMA & ATR settings (with color options). Session-aware logic — filter signals to prime trading hours. 🔹 Blueprint & Credits LES is a fusion of proven concepts, standing on the shoulders of respected creators: -Squeeze Momentum – LazyBear -WaveTrend Oscillator – LazyBear -Relative Volume – LonesomeTheBlue Breakout/structural logic – refined from classic frameworks Their work laid the foundation — LES expands and integrates them into a complete trading system. ⚡ Why LES Stands Out LES wasn’t coded overnight. It’s the result of countless hours of live testing, rebuilding, and refining. Every feature earned its place by proving value in real trading, not theory. LES is more than an indicator. It’s a disciplined framework — crafted to turn chaos into structure, randomness into probability, and noise into clarity. ⚠️ Disclaimer: This is a trading framework, not financial advice. Performance depends on trader discipline, risk management, and market conditions.
อินดิเคเตอร์ Pine Script®
โดย ElitexFineFir
6
Alexmoku Genesis v0.2.6 — Runtime BulletproofAlexmoku Genesis v0.2.6 – Reinventing Ichimoku with Precision and Intelligence Built by Alex • Engineered for traders who demand more than lagging clouds 🔬 Overview Alexmoku Genesis is a ground-up reimagining of the traditional Ichimoku system — retaining its philosophical foundations of harmony, balance, and structure, while infusing it with modern logic, advanced volatility modeling, and AI-aligned signal classification. This is not your grandfather’s Ichimoku. Genesis transforms it into a forward-sensing, volatility-aware system that adapts in real time and guides discretionary and algorithmic traders alike with smarter signals, predictive structure, and built-in trade intelligence. ⚙️ Core Enhancements 📐 Volatility-Responsive Smoothing Tenkan-sen and Kijun-sen lengths are dynamically adjusted based on recent price volatility (ATR-based). Prevents overreaction in choppy markets and improves responsiveness during strong trends. 🌀 Adaptive Cloud Projection Span A and Span B are still projected 52 periods forward — but cloud width adapts to market rhythm, not just static values. 🕵️ Enhanced Chikou Span (CKS+) Chikou logic is upgraded to emphasize structure interaction, not just simple price overlay. Useful for identifying support/resistance echoes and divergence validation. 🌐 Multi-Timeframe Cloud Awareness (Genesis Core) Internal alignment checks between higher and lower timeframe Kumo positioning. Future module support for overlaying MTF clouds. 🔖 Signal Classifier Engine (Experimental) Each major structure event is tagged with signal intent: 📦 prep 🚀 launch 🔁 reversion 🪤 trap Enables faster discretionary reads or future automation. 🧠 Trust Score Logic (Coming soon) Future versions will assign numerical trust scores (1–10) to trade setups based on confluence. 🧰 Settings 🔧 Volatility Scaling Factor – Adjusts how reactive the indicator is to volatility. Default: 300. 🌫 Displacement (Kumo) – Cloud projection forward. Default (and required): 52. ⛅ Cloud Display Toggle – Show/hide Kumo visuals without affecting logic. ⚡️ Future expansion modules already scaffolded (MTF overlays, color-coded signal classifiers, Trust Score engine, etc.) 🧘 Philosophy Behind the System "This isn’t just about catching runners — it’s about becoming the kind of person who can trade with clarity, peace, and precision." Alexmoku Genesis reflects the W.E.A.L.T.H. System's holistic philosophy: Wealth Engineered Algorithmic Leveraged Trading Holistically Trading isn't just a strategy. It's a mirror. Genesis is designed to train both your execution and your awareness. ⚠️ Disclaimer This tool is designed for experienced technical traders and is still in active development. Use judgment and proper risk management. This is not financial advice.
อินดิเคเตอร์ Pine Script®
โดย Alex_A74