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Order Blocks Finder [TradingFinder] Major OB | Supply and Demand🔵 Introduction Drawing all order blocks on the path, especially in range-bound or channeling markets, fills the chart with lines, making it confusing rather than providing the trader with the best entry and exit points. 🔵 Reason for Indicator Creation For traders familiar with market structure and only need to know the main accumulation points (best entry or exit points), and primary order blocks that act as strong sources of power. 🟣 Important Note All order blocks, both ascending and descending, are identified and displayed on the chart when the structure of "BOS" or "CHOCH" is broken, which can also be identified with "MSS." 🔵 How to Use When the indicator is installed, it plots all order blocks (active order blocks) and continues until the price reaches them. This continuation happens in boxes to have a better view in the TradingView chart. Green Range : Ascending order blocks where we expect a price increase in these areas. Red Range : Descending order blocks where we expect a price decrease in these areas. 🔵 Settings Order block refine setting : When Order block refine is off, the supply and demand zones are the entire length of the order block (Low to High) in their standard state and cannot be improved. If you turn on Order block refine, supply and demand zones will improve using the error correction algorithm. Refine type setting : Improving order blocks using the error correction algorithm can be done in two ways: Defensive and Aggressive. In the Aggressive method, the largest possible range is considered for order blocks. 🟣 Important The main advantage of the Aggressive method is minimizing the loss of stops, but due to the widening of the supply or demand zone, the reward-to-risk ratio decreases significantly. The Aggressive method is suitable for individuals who take high-risk trades. In the Defensive method, the range of order blocks is minimized to their standard state. In this case, fewer stops are triggered, and the reward-to-risk ratio is maximized in its optimal state. It is recommended for individuals who trade with low risk. Show high level setting : If you want to display major high levels, set show high level to Yes. Show low level setting : If you want to display major low levels, set show low level to Yes. 🔵 How to Use The general view of this indicator is as follows. When the price approaches the range, wait for the price reaction to confirm it, such as a pin bar or divergence. If the price passes with a strong candle (spike), especially after a long-range or at the beginning of sessions, a powerful event is happening, and it is outside the credibility level. An Example of a Valid Zone An Example of Breakout and Invalid Zone. (My suggestion is not to use pending orders, especially when the market is highly volatile or before and after news.) After reaching this zone, expect the price to move by at least the minimum candle that confirmed it or a price ceiling or floor. 🟣 Important : These factors can be more accurately measured with other trend finder indicators provided. 🔵 Auxiliary Tools There is much talk about not using trend lines, candlesticks, Fibonacci, etc., in the web space. However, our suggestion is to create and use tools that can help you profit from this market. • Fibonacci Retracement • Trading Sessions • Candlesticks 🔵 Advantages • Plotting main OBs without additional lines; • Suitable for timeframes M1, M5, M15, H1, and H4; • Effective in Tokyo, Sydney, and London sessions; • Plotting the main ceiling and floor to help identify the trend.
Pine Script® göstergesi
TFlab tarafından
52
MTF Swing Highs and Lows w/ Supply and Demand ZonesI designed this indicator out of necessity for the Market structure/Price action trading strategy I use. I thought I'd share. :) For the fans of my Multi Timeframe Swing High and Low indicator, I have added Supply and Demand Zones! The Supply and Demand Zones are based on the Swing Highs and Lows of my MTF Swing Highs and Lows Indicator. The S/D Zones are created on the wicks of the Swing Highs and Lows. You can choose whether to display the Chart, Higher and/or Highest timeframes as in the chart below. You can also choose to display up to 3 S/D Zones from the past 3 Swing Highs and Lows. The default setting is to display 1 chart timeframe S/D Zone, 2 higher and 3 highest, as I found this to be most effective without cluttering the screen too much The Chart Timeframe S/D Zones have an orange border, higher timeframe have a blue border and the highest have a black border. Supply zones based on Swing Highs are red and Demand Zones based on Swing Lows are green. This indicator displays Swing Highs and Lows on 3 timeframes based on the Chart timeframe, as follows: Chart TF Higher TF Highest TF 1m 5m 15m 5m 15m 60m 15m 60m 240m 60m 240m Daily 240m Daily Weekly Daily Weekly Monthly You can change the font size of the labels as you'd prefer.
Pine Script® göstergesi
KaizenTraderB tarafından
18
Bagang Pivot Zones | Supply & Demand, Support & ResistanceBagang Pivot Zones detects imbalances from classic reversal and momentum price actions. Imbalances create pivot zones, a.k.a Supply & Demand / Support & Resistance / Orderblock zones. Use Cases 1. Traders using Supply & Demand theory can quickly pinpoint imbalance zones created by BUY-to-SELL and SELL-to-BUY candles. 2. Trend Following traders can systematically catch and follow a trend based on pivot zones analysis. 3. Breakout traders can easily target pivot zones’ breakout and breakdown. 4. Take the guesswork out of risk management: manage stop-loss precisely behind pivot zones. 5. Analyze contrary pivot zones to set realistic profit targets. Objectivity By only comparing OHLC values to identify notable price actions, Bagang Pivot Zones avoids derived calculations with subjective parameters. Chart Issue If the chart zooms out after adding an indicator, right-click the price scale and toggle "Scale price chart only” on.
Pine Script® göstergesi
tonkinai tarafından
29
RSI Based Automatic Supply and DemandA script that draws supply and demand zones based on the RSI indicator. For example if RSI is under 30 a supply zone is drawn on the chart and extended for as long as there isn't a new crossunder 30. Same goes for above 70. The threshold which by default is set to 30, which means 30 is added to 0 and subtracted from 100 to give us the classic 30/70 threshold on RSI, can be set in the indicator settings. By only plotting the Demand Below Supply Above indicator you get automatic SD level that is updated every time RSI reaches either 30 or 70. If you plot the Resistance Zone / Support Zone you get an indicator that extends the zone instead of overwrite the earlier zone. Due to the zone being extended the chart can get a bit messy if there isn't a clear range going on. There is also a "confirmation bars" setting where you can tell the script how many bars under over 30 / 70 you want before a zone is drawn. Here is an image of only using the "Demand Below / Supply Above" plot. As you can see, this could be useful "Price Flow" indicator, where we would only short if a zone appears below another zone, or long if two zones in a row are going up, like stairs.
Pine Script® göstergesi
shtcoinr tarafından
111
FVG Supply and DemandThis indicator combines powerful tools into one: • Supply & Demand Zones built from swing highs/lows with ATR-based zone width, POI markers, and Break-of-Structure (BOS) detection. • Volumized Fair Value Gaps (FVGs) showing bullish/bearish gaps, total volume inside the gap, volume distribution, optional zone-combining, and auto-cleanup. • Swing TSL Line and manage bar color. It helps visualize key imbalance areas, institutional zones, and price reaction points. Credits to the Author. ⚠️ Disclaimer This indicator is provided for educational and analytical purposes only. It does not provide trading advice. Past results do not guarantee future outcomes. Use responsibly and in conjunction with your market analysis.
Pine Script® göstergesi
vinothblr2015 tarafından
MA Crossover with Demand/Supply Zones + Stop Loss/Take ProfitStop Loss and Take Profit Inputs: Added stopLossPerc and takeProfitPerc as inputs to allow the user to define the stop loss and take profit levels as a percentage of the entry price. Stop Loss and Take Profit Calculation: For long positions, the stop loss is calculated as strategy.position_avg_price * (1 - stopLossPerc), and the take profit is calculated as strategy.position_avg_price * (1 + takeProfitPerc). For short positions, the stop loss is calculated as strategy.position_avg_price * (1 + stopLossPerc), and the take profit is calculated as strategy.position_avg_price * (1 - takeProfitPerc). Exit Strategy: Added strategy.exit to define the stop loss and take profit levels for each trade. The from_entry parameter ensures that the exit is tied to the specific entry order. Flexibility: The stop loss and take profit levels are dynamic and adjust based on the entry price of the trade. How It Works: When a buy signal is generated (MA crossover near a demand zone), the strategy enters a long position and sets a stop loss and take profit level based on the input percentages. When a sell signal is generated (MA crossunder near a supply zone), the strategy enters a short position and sets a stop loss and take profit level based on the input percentages. The trade will exit automatically if either the stop loss or take profit level is hit. Example: If the entry price for a long position is $100, and the stop loss is set to 1% while the take profit is set to 2%: Stop loss level = 100 ∗ ( 1 − 0.01 ) = 100∗(1−0.01)=99 Take profit level = 100 ∗ ( 1 + 0.02 ) = 100∗(1+0.02)=102 Notes: You can adjust the stopLossPerc and takeProfitPerc inputs to suit your risk management preferences. Always backtest the strategy to ensure the stop loss and take profit levels are appropriate for your trading instrument and timeframe.
Pine Script® stratejisi
Saifinayat tarafından
Supply & DemandWe can think of imbalanced as a signal of a huge order being filled. For those who do not know what imbalanced candle are, an imbalanced candles are formed when the price move with force in a direction. Taking the last 3 candles, when the wicks the of 1st and 3rd candle does not fully overlap the middle one, an imbalanced candle is formed. Usually when a huge hands place its order it never gets filled entirely and the price usually comes back to this zone to fulfil the remaining order. This indicator highlight range defined by previous high and low pivot right before an imbalanced candle. Zones highlighted become zones to watch to enter a trade and become either supply or demand zone.
Pine Script® göstergesi
frozon tarafından
4
TRI - Multi-Timeframe FVGTRI - MULTI-TIMEFRAME FAIR VALUE GAPS v1.0.0 DESCRIPTION: Advanced multi-timeframe Fair Value Gap (FVG) indicator that displays FVG zones from higher timeframes on your current chart. Supports automatic or manual timeframe selection with comprehensive visualization and alert system. KEY FEATURES: Multi-timeframe FVG detection - view FVG from any higher timeframe Automatic timeframe selection - configure different FVG timeframes for each chart timeframe Automatic mitigation detection - zones change color when price mitigates them Configurable FVG threshold - filter out small gaps Customizable visualization - colors, borders, labels, text colors Smart zone inclusion - larger zones automatically remove smaller included zones Memory efficient - automatic cleanup of expired zones HOW IT WORKS: A Fair Value Gap (FVG) is detected when there's a 3-candle pattern with a gap between candle 1 and candle 3, indicating institutional order flow imbalances. Bullish FVG occurs when candle 3's low is above candle 1's high (gap up), creating a demand zone shown in green. Bearish FVG occurs when candle 3's high is below candle 1's low (gap down), creating a supply zone shown in red. The indicator uses request.security() to fetch data from the selected higher timeframe, detects FVG patterns on that timeframe, and displays them on your current chart. FVG zones remain active until price closes through them (mitigation), then change color and remain visible for a configurable number of bars before disappearing. TIMEFRAME CONFIGURATION: Configure different FVG timeframes based on current chart timeframe: 1m-5m charts → Default 4h FVG 15m charts → Default 4h FVG 30m-1h charts → Default 4h FVG 4h charts → Default 4h FVG Daily charts → Default Daily FVG Weekly charts → Default Weekly FVG Monthly charts → Default Monthly FVG All timeframes are configurable via input settings. BEST USE: Works on all timeframes and asset classes. Particularly useful for intraday traders who want to see higher timeframe FVG zones on their lower timeframe charts. FVG zones often act as support/resistance and are frequently filled by price returning to rebalance the imbalance. Use them to identify potential entry/exit points, stop-loss placement, and institutional order flow areas.
Pine Script® göstergesi
riccardo_tempesta tarafından
Fear & Greed Index (Zeiierman)█ Overview The Fear & Greed Index is an indicator that provides a comprehensive view of market sentiment. By analyzing various market factors such as market momentum, stock price strength, stock price breadth, put and call options, junk bond demand, market volatility, and safe haven demand, the Index can depict the overall emotions driving market behavior, categorizing them into two main sentiments: Fear and Greed. Fear: Indicates a market scenario where investors are scared, possibly leading to a sell-off or a stagnant market. In such conditions, the indicator helps in identifying potential buying opportunities as assets may be undervalued. Greed: Represents a state where investors are overly confident and buying aggressively, which can lead to inflated asset prices. The indicator in such cases can signal overbought conditions, advising caution or potential short opportunities. █ How It Works The Fear & Greed Index is an aggregate of seven distinct indicators, each gauging a specific dimension of stock market activity. These indicators include market momentum, stock price strength, stock price breadth, put and call options, junk bond demand, market volatility, and safe haven demand. The Index assesses the deviation of each individual indicator from its average, in relation to its typical fluctuations. In compiling the final score, which ranges from 0 to 100, the Index assigns equal weight to each indicator. A score of 100 denotes the highest level of Greed, while a score of 0 represents the utmost level of fear. S&P 500's Momentum: The Index monitors the S&P 500's position relative to its 125-day moving average. Positive momentum (price above the average) signals growing confidence among investors (Greed), while negative momentum (price below the average) indicates rising fear. Stock Price Strength: By comparing the number of stocks hitting 52-week highs to those at 52-week lows on the NYSE, the Index gauges market breadth. An extreme number of highs indicates Greed, whereas an extreme number of lows suggests Fear. Stock Price Breadth (Market Volume): Using the McClellan Volume Summation Index, which considers the volume of advancing versus declining stocks, the Index assesses whether the market is broadly participating in a trend, or if a smaller subset of stocks is driving it. Put and Call Options: The put/call ratio helps gauge investor sentiment. A rising ratio, particularly above 1, indicates increasing fear, as more investors are buying puts to protect against a decline. A falling ratio suggests growing confidence. Market Volatility (VIX): The VIX measures expected market volatility. Higher values generally indicate Fear, while lower values point to Greed. The Fear & Greed Index compares the VIX to its 50-day moving average to understand its trend. Safe Haven Demand: The performance of stocks versus bonds over a 20-day period helps understand where investors are putting their money. Bonds outperforming stocks is a sign of Fear, while the opposite suggests Greed. Junk Bond Demand: By comparing the yields on junk bonds to safer investment-grade bonds, the Index gauges risk appetite. A narrower yield spread suggests Greed (investors are taking more risk), while a wider spread indicates Fear. The Fear & Greed Index combines these components, scales, and averages them to produce a single value between 0 (Extreme Fear) and 100 (Extreme Greed). █ How to Use The Fear & Greed Index serves as a tool to evaluate the prevailing sentiments in the market. Investors, often driven by emotions, can react impulsively, and sentiment indicators like the Fear & Greed Index aim to highlight these emotional states, helping investors recognize personal biases that might impact their investment choices. When integrated with fundamental analysis and additional analytical instruments, the Index becomes a valuable resource for understanding and interpreting market moods and tendencies. The Fear & Greed Index operates on the principle that excessive fear can result in stocks trading well below their intrinsic values, while uncontrolled Greed can push prices above what they should be. ----------------- Disclaimer The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs. My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Pine Script® göstergesi
Zeiierman tarafından
18
Credit Spread RegimeThe Credit Market as Economic Barometer Credit spreads are among the most reliable leading indicators of economic stress. When corporations borrow money by issuing bonds, investors demand a premium above the risk-free Treasury rate to compensate for the possibility of default. This premium, known as the credit spread, fluctuates based on perceptions of economic health, corporate profitability, and systemic risk. The relationship between credit spreads and economic activity has been studied extensively. Two papers form the foundation of this indicator. Pierre Collin-Dufresne, Robert Goldstein, and Spencer Martin published their influential 2001 paper in the Journal of Finance, documenting that credit spread changes are driven by factors beyond firm-specific credit quality. They found that a substantial portion of spread variation is explained by market-wide factors, suggesting credit spreads contain information about aggregate economic conditions. Simon Gilchrist and Egon Zakrajsek extended this research in their 2012 American Economic Review paper, introducing the concept of the Excess Bond Premium. They demonstrated that the component of credit spreads not explained by default risk alone is a powerful predictor of future economic activity. Elevated excess spreads precede recessions with remarkable consistency. What Credit Spreads Reveal Credit spreads measure the difference in yield between corporate bonds and Treasury securities of similar maturity. High yield bonds, also called junk bonds, carry ratings below investment grade and offer higher yields to compensate for greater default risk. Investment grade bonds have lower yields because the probability of default is smaller. The spread between high yield and investment grade bonds is particularly informative. When this spread widens, investors are demanding significantly more compensation for taking on credit risk. This typically indicates deteriorating economic expectations, tighter financial conditions, or increasing risk aversion. When the spread narrows, investors are comfortable accepting lower premiums, signaling confidence in corporate health. The Gilchrist-Zakrajsek research showed that credit spreads contain two distinct components. The first is the expected default component, which reflects the probability-weighted cost of potential defaults based on corporate fundamentals. The second is the excess bond premium, which captures additional compensation demanded beyond expected defaults. This excess premium rises when investor risk appetite declines and financial conditions tighten. The Implementation Approach This indicator uses actual option-adjusted spread data from the Federal Reserve Economic Database (FRED), available directly in TradingView. The ICE BofA indices represent the industry standard for measuring corporate bond spreads. The primary data sources are FRED:BAMLH0A0HYM2, the ICE BofA US High Yield Index Option-Adjusted Spread, and FRED:BAMLC0A0CM, the ICE BofA US Corporate Index Option-Adjusted Spread for investment grade bonds. These indices measure the spread of corporate bonds over Treasury securities of similar duration, expressed in basis points. Option-adjusted spreads account for embedded options in corporate bonds, providing a cleaner measure of credit risk than simple yield spreads. The methodology developed by ICE BofA is widely used by institutional investors and central banks for monitoring credit conditions. The indicator offers two modes. The HY-IG excess spread mode calculates the difference between high yield and investment grade spreads, isolating the pure compensation for below-investment-grade credit risk. This measure is less affected by broad interest rate movements. The HY-only mode tracks the absolute high yield spread, capturing both credit risk and the overall level of risk premiums in the market. Interpreting the Regimes Credit conditions are classified into four regimes based on Z-scores calculated from the spread proxy. The Stress regime occurs when spreads reach extreme levels, typically above a Z-score of 2.0. At this point, credit markets are pricing in significant default risk and economic deterioration. Historically, stress regimes have coincided with recessions, financial crises, and major market dislocations. The 2008 financial crisis, the 2011 European debt crisis, the 2016 commodity collapse, and the 2020 pandemic all triggered credit stress regimes. The Elevated regime, between Z-scores of 1.0 and 2.0, indicates above-normal risk premiums. Credit conditions are tightening. This often occurs in the build-up to stress events or during periods of uncertainty. Risk management should be heightened, and exposure to credit-sensitive assets may be reduced. The Normal regime covers Z-scores between -1.0 and 1.0. This represents typical credit conditions where spreads fluctuate around historical averages. Standard investment approaches are appropriate. The Low regime occurs when spreads are compressed below a Z-score of -1.0. Investors are accepting below-average compensation for credit risk. This can indicate complacency, strong economic confidence, or excessive risk-taking. While often associated with favorable conditions, extremely tight spreads sometimes precede sudden reversals. Credit Cycle Dynamics Beyond static regime classification, the indicator tracks the direction and acceleration of spread movements. This reveals where credit markets stand in the credit cycle. The Deteriorating phase occurs when spreads are elevated and continuing to widen. Credit conditions are actively worsening. This phase often precedes or coincides with economic downturns. The Recovering phase occurs when spreads are elevated but beginning to narrow. The worst may be over. Credit conditions are improving from stressed levels. This phase often accompanies the early stages of economic recovery. The Tightening phase occurs when spreads are low and continuing to compress. Credit conditions are very favorable and improving further. This typically occurs during strong economic expansions but may signal building complacency. The Loosening phase occurs when spreads are low but beginning to widen from compressed levels. The extremely favorable conditions may be normalizing. This can be an early warning of changing sentiment. Relationship to Economic Activity The predictive power of credit spreads for economic activity is well-documented. Gilchrist and Zakrajsek found that the excess bond premium predicts GDP growth, industrial production, and unemployment rates over horizons of one to four quarters. When credit spreads spike, the cost of corporate borrowing increases. Companies may delay or cancel investment projects. Reduced investment leads to slower growth and eventually higher unemployment. The transmission mechanism runs from financial conditions to real economic activity. Conversely, tight credit spreads lower borrowing costs and encourage investment. Easy credit conditions support economic expansion. However, excessively tight spreads may encourage over-leveraging, planting seeds for future stress. Practical Application For equity investors, credit spreads provide context for market risk. Equities and credit often move together because both reflect corporate health. Rising credit spreads typically accompany falling stock prices. Extremely wide spreads historically have coincided with equity market bottoms, though timing the reversal remains challenging. For fixed income investors, spread regimes guide sector allocation decisions. During stress regimes, flight to quality favors Treasuries over corporates. During low regimes, spread compression may offer limited additional return for credit risk, suggesting caution on high yield. For macro traders, credit spreads complement other indicators of financial conditions. Credit stress often leads equity volatility, providing an early warning signal. Cross-asset strategies may use credit regime as a filter for position sizing. Limitations and Considerations FRED data updates with a lag, typically one business day for the ICE BofA indices. For intraday trading decisions, more current proxies may be necessary. The data is most reliable on daily timeframes. Credit spreads can remain at extreme levels for extended periods. Mean reversion signals indicate elevated probability of normalization but do not guarantee timing. The 2008 crisis saw spreads remain elevated for many months before normalizing. The indicator is calibrated for US credit markets. Application to other regions would require different data sources such as European or Asian credit indices. The relationship between spreads and subsequent economic activity may vary across market cycles and structural regimes. References Collin-Dufresne, P., Goldstein, R.S., and Martin, J.S. (2001). The Determinants of Credit Spread Changes. Journal of Finance, 56(6), 2177-2207. Gilchrist, S., and Zakrajsek, E. (2012). Credit Spreads and Business Cycle Fluctuations. American Economic Review, 102(4), 1692-1720. Krishnamurthy, A., and Muir, T. (2017). How Credit Cycles across a Financial Crisis. Working Paper, Stanford University.
Pine Script® göstergesi
EdgeTools tarafından
2
VPA ANALYSIS VPA Analysis provide the indications for various conditions as per the Volume Spread Analysis concept. The various legends are provided below LEGEND DETAILS UT1 - Upthrust Bar: This will be widespread Bar on high Volume closing on the low. This normally happens after an up move. Here the smart money move the price to the High and then quickly brings to the Low trapping many retail trader who rushed into in order not to miss the bullish move. This is a bearish Signal UT2 -Upthrust Bar Confirmation: A widespread Down Bar following a Upthrust Bar. This confirms the weakness of the Upthrust Bar. Expect the stock to move down Confirms . This is a Bearish Signal PUT - Pseudo Upthrust: An Upthrust Bar in bar action but the volume remains average. This still indicates weakness. Indicate Possible Bearishness PUC -Pseudo Upthrust Confirmation: widespread Bar after a pseudo–Upthrust Bar confirms the weakness of the Pseudo Upthrust Bar Confirms Bearishness BC - Buying Climax: A very wide Spread bar on ultra-High Volume closing at the top. Such a Bar indicates the climatic move in an uptrend. This Bar traps many retailers as the uptrend ends and reverses quickly. Confirms Bearishness TC - Trend Change: This Indicates a possible Trend Change in an uptrend. Indicates Weakness SEC- Sell Condition: This bar indicates confluence of some bearish signals. Possible end of Uptrend and start of Downtrend soon. Bearish Signal UT - Upthrust Condition: When multiple bearish signals occur, the legend is printed in two lines. The Legend “UT” indicates that an upthrust condition is present. Bearish Signal ND - No demand in uptrend: This bar indicates that there is no demand. In an uptrend this indicates weakness. Bearish Signal ND - No Demand: This bar indicates that there is no demand. This can occur in any part of the Trend. In all place other than in an uptrend this just indicates just weakness ED - Effort to Move Down: Widespread Bar closing down on High volume or above average volume . The smart money is pushing the prices down. Bearish Signal EDF - Effort to Move Down Failed: Widespread / above average spread Bar closing up on High volume or above average volume appearing after ‘Effort to move down” bar. This indicates that the Effort to move the pries down has failed. Bullish signal SV - Stopping Volume: A high volume medium to widespread Bar closing in the upper middle part in a down trend indicates that smart money is buying. This is an indication that the down trend is likely to end soon. Indicates strength ST1 - Strength Returning 1: Strength seen returning after a down trend. High volume adds to strength. Indicates Strength ST2 - Strength Returning 2: Strength seen returning after a down trend. High volume adds to strength. BYC - Buy Condition: This bar indicates confluence of some bullish signals Possible end of downtrend and start of uptrend soon. Indicates Strength EU - Effort to Move Up: Widespread Bar closing up on High volume or above average volume . The smart money is pushing the prices up. Bullish Signal EUF - Effort to Move Up Failed: Widespread / above average spread Bar closing down on High volume or above average volume appearing after ‘Effort to move up” bar. This indicates that the Effort to move the pries up has failed. Bearish Signal LVT- Low Volume Test: A low volume bar dipping into previous supply area and closing in the upper part of the Bar. A successful test is a positive sign. Indicates Strength ST(after a LVT ) - Strength after Successful Low Volume Test: An up Bar closing near High after a Test confirms strength. Bullish Signal RUT - Reverse Upthrust Bar: This will be a widespread Bar on high Volume closing on the high is a Down Trend. Here the buyers have become active and move the prices from the low to High. The down Move is likely to end and up trend likely to start soon. indicates Strength NS - No supply Bar: This bar indicates that there is no supply. This is a sign of strength especially in a down trend. Indicates strength ST - Strength Returns: When multiple bullish signals occur, the legend is printed in two lines. The Legend “ST” indicates that an condition of strength other than the condition mentioned in the second line is present. Bullish Signals BAR COLORS Green- Bullish / Strength Red - Bearish / weakness Blue / White - Sentiment Changing from bullish to Bearish and Vice Versa
Pine Script® göstergesi
karthikmarar tarafından
19
Polynomial Regression Bands + Channel [DW]This is an experimental study designed to calculate polynomial regression for any order polynomial that TV is able to support. This study aims to educate users on polynomial curve fitting, and the derivation process of Least Squares Moving Averages (LSMAs). I also designed this study with the intent of showcasing some of the capabilities and potential applications of TV's fantastic new array functions. Polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as a polynomial of nth degree (order). For clarification, linear regression can also be described as a first order polynomial regression. The process of deriving linear, quadratic, cubic, and higher order polynomial relationships is all the same. In addition, although deriving a polynomial regression equation results in a nonlinear output, the process of solving for polynomials by least squares is actually a special case of multiple linear regression. So, just like in multiple linear regression, polynomial regression can be solved in essentially the same way through a system of linear equations. In this study, you are first given the option to smooth the input data using the 2 pole Super Smoother Filter from John Ehlers. I chose this specific filter because I find it provides superior smoothing with low lag and fairly clean cutoff. You can, of course, implement your own filter functions to see how they compare if you feel like experimenting. Filtering noise prior to regression calculation can be useful for providing a more stable estimation since least squares regression can be rather sensitive to noise. This is especially true on lower sampling lengths and higher degree polynomials since the regression output becomes more "overfit" to the sample data. Next, data arrays are populated for the x-axis and y-axis values. These are the main datasets utilized in the rest of the calculations. To keep the calculations more numerically stable for higher periods and orders, the x array is filled with integers 1 through the sampling period rather than using current bar numbers. This process can be thought of as shifting the origin of the x-axis as new data emerges. This keeps the axis values significantly lower than the 10k+ bar values, thus maintaining more numerical stability at higher orders and sample lengths. The data arrays are then used to create a pseudo 2D matrix of x power sums, and a vector of x power*y sums. These matrices are a representation the system of equations that need to be solved in order to find the regression coefficients. Below, you'll see some examples of the pattern of equations used to solve for our coefficients represented in augmented matrix form. For example, the augmented matrix for the system equations required to solve a second order (quadratic) polynomial regression by least squares is formed like this: (∑x^0 ∑x^1 ∑x^2 | ∑(x^0)y) (∑x^1 ∑x^2 ∑x^3 | ∑(x^1)y) (∑x^2 ∑x^3 ∑x^4 | ∑(x^2)y) The augmented matrix for the third order (cubic) system is formed like this: (∑x^0 ∑x^1 ∑x^2 ∑x^3 | ∑(x^0)y) (∑x^1 ∑x^2 ∑x^3 ∑x^4 | ∑(x^1)y) (∑x^2 ∑x^3 ∑x^4 ∑x^5 | ∑(x^2)y) (∑x^3 ∑x^4 ∑x^5 ∑x^6 | ∑(x^3)y) This pattern continues for any n ordered polynomial regression, in which the coefficient matrix is a n + 1 wide square matrix with the last term being ∑x^2n, and the last term of the result vector being ∑(x^n)y. Thanks to this pattern, it's rather convenient to solve the for our regression coefficients of any nth degree polynomial by a number of different methods. In this script, I utilize a process known as LU Decomposition to solve for the regression coefficients. Lower-upper (LU) Decomposition is a neat form of matrix manipulation that expresses a 2D matrix as the product of lower and upper triangular matrices. This decomposition method is incredibly handy for solving systems of equations, calculating determinants, and inverting matrices. For a linear system Ax=b, where A is our coefficient matrix, x is our vector of unknowns, and b is our vector of results, LU Decomposition turns our system into LUx=b. We can then factor this into two separate matrix equations and solve the system using these two simple steps: 1. Solve Ly=b for y, where y is a new vector of unknowns that satisfies the equation, using forward substitution. 2. Solve Ux=y for x using backward substitution. This gives us the values of our original unknowns - in this case, the coefficients for our regression equation. After solving for the regression coefficients, the values are then plugged into our regression equation: Y = a0 + a1*x + a1*x^2 + ... + an*x^n, where a() is the ()th coefficient in ascending order and n is the polynomial degree. From here, an array of curve values for the period based on the current equation is populated, and standard deviation is added to and subtracted from the equation to calculate the channel high and low levels. The calculated curve values can also be shifted to the left or right using the "Regression Offset" input Changing the offset parameter will move the curve left for negative values, and right for positive values. This offset parameter shifts the curve points within our window while using the same equation, allowing you to use offset datapoints on the regression curve to calculate the LSMA and bands. The curve and channel's appearance is optionally approximated using Pine's v4 line tools to draw segments. Since there is a limitation on how many lines can be displayed per script, each curve consists of 10 segments with lengths determined by a user defined step size. In total, there are 30 lines displayed at once when active. By default, the step size is 10, meaning each segment is 10 bars long. This is because the default sampling period is 100, so this step size will show the approximate curve for the entire period. When adjusting your sampling period, be sure to adjust your step size accordingly when curve drawing is active if you want to see the full approximate curve for the period. Note that when you have a larger step size, you will see more seemingly "sharp" turning points on the polynomial curve, especially on higher degree polynomials. The polynomial functions that are calculated are continuous and differentiable across all points. The perceived sharpness is simply due to our limitation on available lines to draw them. The approximate channel drawings also come equipped with style inputs, so you can control the type, color, and width of the regression, channel high, and channel low curves. I also included an input to determine if the curves are updated continuously, or only upon the closing of a bar for reduced runtime demands. More about why this is important in the notes below. For additional reference, I also included the option to display the current regression equation. This allows you to easily track the polynomial function you're using, and to confirm that the polynomial is properly supported within Pine. There are some cases that aren't supported properly due to Pine's limitations. More about this in the notes on the bottom. In addition, I included a line of text beneath the equation to indicate how many bars left or right the calculated curve data is currently shifted. The display label comes equipped with style editing inputs, so you can control the size, background color, and text color of the equation display. The Polynomial LSMA, high band, and low band in this script are generated by tracking the current endpoints of the regression, channel high, and channel low curves respectively. The output of these bands is similar in nature to Bollinger Bands, but with an obviously different derivation process. By displaying the LSMA and bands in tandem with the polynomial channel, it's easy to visualize how LSMAs are derived, and how the process that goes into them is drastically different from a typical moving average. The main difference between LSMA and other MAs is that LSMA is showing the value of the regression curve on the current bar, which is the result of a modelled relationship between x and the expected value of y. With other MA / filter types, they are typically just averaging or frequency filtering the samples. This is an important distinction in interpretation. However, both can be applied similarly when trading. An important distinction with the LSMA in this script is that since we can model higher degree polynomial relationships, the LSMA here is not limited to only linear as it is in TV's built in LSMA. Bar colors are also included in this script. The color scheme is based on disparity between source and the LSMA. This script is a great study for educating yourself on the process that goes into polynomial regression, as well as one of the many processes computers utilize to solve systems of equations. Also, the Polynomial LSMA and bands are great components to try implementing into your own analysis setup. I hope you all enjoy it! -------------------------------------------------------- NOTES: - Even though the algorithm used in this script can be implemented to find any order polynomial relationship, TV has a limit on the significant figures for its floating point outputs. This means that as you increase your sampling period and / or polynomial order, some higher order coefficients will be output as 0 due to floating point round-off. There is currently no viable workaround for this issue since there isn't a way to calculate more significant figures than the limit. However, in my humble opinion, fitting a polynomial higher than cubic to most time series data is "overkill" due to bias-variance tradeoff. Although, this tradeoff is also dependent on the sampling period. Keep that in mind. A good rule of thumb is to aim for a nice "middle ground" between bias and variance. If TV ever chooses to expand its significant figure limits, then it will be possible to accurately calculate even higher order polynomials and periods if you feel the desire to do so. To test if your polynomial is properly supported within Pine's constraints, check the equation label. If you see a coefficient value of 0 in front of any of the x values, reduce your period and / or polynomial order. - Although this algorithm has less computational complexity than most other linear system solving methods, this script itself can still be rather demanding on runtime resources - especially when drawing the curves. In the event you find your current configuration is throwing back an error saying that the calculation takes too long, there are a few things you can try: -> Refresh your chart or hide and unhide the indicator. The runtime environment on TV is very dynamic and the allocation of available memory varies with collective server usage. By refreshing, you can often get it to process since you're basically just waiting for your allotment to increase. This method works well in a lot of cases. -> Change the curve update frequency to "Close Only". If you've tried refreshing multiple times and still have the error, your configuration may simply be too demanding of resources. v4 drawing objects, most notably lines, can be highly taxing on the servers. That's why Pine has a limit on how many can be displayed in the first place. By limiting the curve updates to only bar closes, this will significantly reduce the runtime needs of the lines since they will only be calculated once per bar. Note that doing this will only limit the visual output of the curve segments. It has no impact on regression calculation, equation display, or LSMA and band displays. -> Uncheck the display boxes for the drawing objects. If you still have troubles after trying the above options, then simply stop displaying the curve - unless it's important to you. As I mentioned, v4 drawing objects can be rather resource intensive. So a simple fix that often works when other things fail is to just stop them from being displayed. -> Reduce sampling period, polynomial order, or curve drawing step size. If you're having runtime errors and don't want to sacrifice the curve drawings, then you'll need to reduce the calculation complexity. If you're using a large sampling period, or high order polynomial, the operational complexity becomes significantly higher than lower periods and orders. When you have larger step sizes, more historical referencing is used for x-axis locations, which does have an impact as well. By reducing these parameters, the runtime issue will often be solved. Another important detail to note with this is that you may have configurations that work just fine in real time, but struggle to load properly in replay mode. This is because the replay framework also requires its own allotment of runtime, so that must be taken into consideration as well. - Please note that the line and label objects are reprinted as new data emerges. That's simply the nature of drawing objects vs standard plots. I do not recommend or endorse basing your trading decisions based on the drawn curve. That component is merely to serve as a visual reference of the current polynomial relationship. No repainting occurs with the Polynomial LSMA and bands though. Once the bar is closed, that bar's calculated values are set. So when using the LSMA and bands for trading purposes, you can rest easy knowing that history won't change on you when you come back to view them. - For those who intend on utilizing or modifying the functions and calculations in this script for their own scripts, I included debug dialogues in the script for all of the arrays to make the process easier. To use the debugs, see the "Debugs" section at the bottom. All dialogues are commented out by default. The debugs are displayed using label objects. By default, I have them all located to the right of current price. If you wish to display multiple debugs at once, it will be up to you to decide on display locations at your leisure. When using the debugs, I recommend commenting out the other drawing objects (or even all plots) in the script to prevent runtime issues and overlapping displays.
Pine Script® göstergesi
DonovanWall tarafından
42
RSI Analytic Volume Matrix [RAVM] Overview RSI Analytic Volume Matrix is an overlay indicator that turns classic RSI into a multi-layered market-reading engine. Instead of treating RSI 30 and 70 as simple buy/sell lines, RAVM combines RSI geometry (angle and acceleration), statistical volume analysis, and a 5×5 VSA-inspired matrix to describe what is really happening inside each candle. The script is designed as an educational and analytical tool. It does not generate trading signals. Instead, it helps you read the market context, understand where the pressure is coming from (buyers vs. sellers), and see how price, momentum, and volume interact in real time. Concept & Philosophy RAVM is built around a hierarchical logic and a few core ideas: • Hierarchical State Machine: First, RSI defines a context (where we are in the 0–100 range). Then the geometric engine evaluates the angle-of-turn of RSI using a Z-Score. Only after a meaningful geometric event is detected does the system promote a bar to a potential setup (warning vs. confirmed). • Geometric Primacy: The angle and acceleration of RSI (RSI geometry) are more important than the raw RSI level itself. RAVM uses a geometric veto: if the geometric trigger is not confirmed, the confidence score is capped below 50%, even if volume looks interesting. • RSI Beyond 30 and 70: Being above 70 or below 30 is not treated as an automatic overbought/oversold signal. RAVM treats those zones as contextual factors that contribute only a partial portion of the final score, alongside geometry, total volume expansion, buy/sell balance, and delta power. • Volume Decomposition: Volume is decomposed into total, buy-side, sell-side, and delta components. Each of these is normalized with a Z-Score over a shared statistical window, so RSI geometry and volume live in the same statistical context. • Educational Scoring Pipeline: RAVM builds a 0–100 "Quantum Score" for each detected setup. The score expresses how strong the story is across four dimensions: geometry (RSI angle-of-turn), total volume expansion, which side is driving that volume (buyers vs. sellers), and the power of delta. The score is designed for learning and weighting, not for mechanical trade entries. • VSA Matrix Engine: A 5×5 matrix combines momentum states and volume dynamics. Each cell corresponds to an interpreted VSA-style scenario (Absorption, Distribution, No Demand, Stopping Volume, Strong Reversal, etc.), shown both as text and as a heatmap dashboard on the chart. How RAVM Works 1. RSI Context & Geometry RAVM starts with a classic RSI, but it does not stop at simple level checks. It computes the velocity and acceleration of RSI and normalizes them via a Z-Score to produce an Angle-of-Turn metric (Z-AoT). This Z-AoT is then mapped into a 0–1 intensity value called MSI (Momentum Shift Intensity). The script monitors both classic RSI zones (around 30 and 70) and geometric triggers. Entering the lower or upper zone is treated as a contextual event only. A setup becomes "confirmed" when a significant geometric turn is detected (based on Z-AoT thresholds). Otherwise, the bar is at most a warning. 2. Volume & Statistical Engine The volume engine can work in two modes: a geometric approximation (based on candle structure) or a more precise intrabar mode using up/down volume requests. In both cases, RAVM builds a volume packet consisting of: • Total volume • Buy-side volume • Sell-side volume • Delta (buy – sell) Each of these series is normalized using a Z-Score over the same statistical window that is used for RSI geometry. This allows RAVM to answer questions such as: Is total volume exceptional on this bar? Is the expansion mostly coming from buyers or from sellers? Is delta unusually strong or weak compared to recent history? 3. Scoring System (Quantum Score) For each bar where a setup is active, RAVM computes a 0–100 score intended as an educational confidence measure. The scoring pipeline follows this sequence: A. RSI Geometry (MSI): Measures the strength of the RSI angle-of-turn via Z-AoT. This has geometric primacy over simple level checks. B. RSI Zone Context: Being below 30 or above 70 contributes only a partial bonus to the score, reflecting the idea that these zones are context, not automatic signals. Mildly supportive zones (e.g., RSI below 50 for bullish contexts) can also contribute with lower weight. C. Total Volume Expansion: A normalized Volume Power term expresses how exceptional the total volume is relative to its recent distribution. If there is no meaningful volume expansion, the score remains modest even if RSI geometry looks interesting. D. Which Side Is Driving the Volume: RAVM then checks whether the expansion is primarily on the buy side or the sell side, using Z-Score statistics for buy and sell volume separately. This stage does not yet rely on delta as a power metric; it simply answers the question: "Is this expansion mostly driven by buyers, sellers, or both?" E. Delta as Final Power: Only at the final stage does the script bring in delta and its Z-Score as a measure of how one-sided the pressure really is. A strong negative delta during a bullish context, for example, can highlight absorption, while a strong positive delta against a bearish context can highlight distribution or a buying climax. If a setup is not geometrically confirmed (for example, a simple entry into RSI 30/70 without a strong geometric turn), RAVM caps the final score below 50%. This "Geometric Veto" enforces the idea that RSI geometry must confirm before a scenario can be considered high-confidence. 4. Overlay UI & Smart Labels RAVM is an overlay indicator: all information is drawn directly on the price chart, not in a separate pane. When a setup is active, a smart label is attached to the bar, together with a vertical connector line. Each label shows: • Direction of the setup (bullish or bearish) • Trigger type (classic OS/OB vs. geometric/hidden) • Status (warning vs. confirmed) • Quantum Score as a percentage Confirmed setups use stronger colors and solid connectors, while warnings use softer colors and dotted connectors. The script also manages label placement to avoid overlap, keeping the chart clean and readable. In addition to labels, a dashboard table is drawn on the chart. It displays the currently active matrix scenario, the dominant bias, a short textual interpretation, the full 5×5 heatmap, and summary metrics such as RSI, MSI, and Volume Power. RSI Is Not Just 30 and 70 One of the central design decisions in RAVM is to treat RSI 30 and 70 as context, not as fixed buy/sell buttons. Many traders mechanically assume that RSI below 30 means "buy" and RSI above 70 means "sell". RAVM explicitly rejects this simplification. Instead, the script asks a series of deeper questions: How sharp is the angle-of-turn of RSI right now? Is total volume expanding or contracting? Is that expansion dominated by buyers or sellers? Is delta confirming the move, or is there a hidden absorption or distribution taking place? In the scoring logic, being in a lower or upper RSI zone contributes only part of the final score. Geometry, volume expansion, the buy/sell split, and delta power all have to align before a high-confidence scenario emerges. This makes RAVM much closer to a structured market-reading tool than a classic overbought/oversold indicator. Matrix User Manual – Reading the 5×5 Grid The heart of RAVM is its 5×5 matrix, where the vertical axis represents momentum states (M1–M5) and the horizontal axis represents volume dynamics (V1–V5). Each cell in this grid corresponds to a VSA-style scenario. The dashboard highlights the currently active cell and prints a textual description so you can read the story at a glance. 1. Confirmation Scenarios These scenarios occur when momentum direction and volume expansion are aligned: • Bullish Confirmation / Strong Reversal: Momentum is shifting strongly upward (often from a depressed RSI context), and expanded volume is driven mainly by buyers. Often seen as a strong bullish reversal or continuation signal from a VSA perspective. • Bearish Confirmation / Strong Drop: Momentum is turning decisively downward, and expanded volume is driven mainly by sellers. This maps to strong bearish continuation or sharp reversal patterns. 2. Absorption & Stopping Volume • Absorption: Total volume expands, but the dominant flow is opposite to the recent price move or the geometric bias. For example, heavy selling volume while the geometric context is bullish. This can indicate smart money quietly absorbing orders from the crowd. • Stopping Volume: Exceptionally high volume appears near the end of an extended move, while momentum begins to decelerate. Price may still print new extremes, but the effort vs. result relationship signals potential exhaustion and the possibility of a turn. 3. Distribution & Buying Climax • Distribution: Heavy buying volume appears within a bearish or topping context. Rather than healthy accumulation, this often represents larger players offloading inventory to late buyers. The matrix will typically flag this as a bearish-leaning scenario despite strong upside prints. • Buying Climax: A surge of buy-side volume near the end of a strong uptrend, with momentum starting to weaken. From a VSA point of view, this is often the last push where retail aggressively buys what smart money is selling. 4. No Demand & No Supply • No Demand: Price attempts to rise but does so on low, non-expansive volume. The market is not interested in following the move, and the lack of participation often precedes weakness or sideways action. • No Supply: Price tries to push lower on thin volume. Selling pressure is limited, and the lack of supply can precede stabilization or recovery if buyers step back in. 5. Trend Exhaustion • Uptrend Exhaustion: Momentum remains nominally bullish, but the quality of volume deteriorates (e.g., more effort, less net result). The matrix marks this as an uptrend losing internal strength, often after a series of aggressive moves. • Downtrend Exhaustion: Similar logic in the opposite direction: strong prior downtrend, but increasingly inefficient downside progress relative to the volume invested. This can precede accumulation or a relief rally. 6. Effort vs. Result Scenarios • Bullish Effort, Little Result: Buyers invest notable volume, but price progress is limited. This may reveal hidden selling into strength or a lack of follow-through from the broader market. • Bearish Effort, Little Result: Sellers push volume, but price does not decline proportionally. This can indicate absorption of selling pressure and potential underlying demand. 7. Neutral, Churn & Thin Markets • Neutral / Thin Market: Momentum and volume both remain muted. RAVM marks these as neutral cells where aggressive decision-making is usually less attractive and observing the broader structure is more important. • High Volume Churn / Volatility: Both sides are active with high volume but limited directional progress. This can correspond to battle zones, local ranges, or high volatility rotations where the main message is conflict rather than clear trend. Inputs & Options RAVM includes several input groups to adapt the tool to your preferences: • Localization: Multiple language options for all labels and dashboard text (e.g., English, Farsi, Turkish, Russian). • RSI Core Settings: RSI length, source, and upper/lower contextual zones (typically around 30 and 70). • Geometric Engine: Z-AoT sigma thresholds, confirmation ratios, and normalization window multiplier. These control how sensitive the script is to RSI angle-of-turn events. • Volume Engine: Choice between geometric approximation and intrabar up/down volume, Z-Score thresholds for volume expansion, and related parameters. • Visual Interface: Toggles for smart labels, dashboard table, font sizes, dashboard position, and color themes for bullish, bearish, and warning states. Disclaimer RSI Analytic Volume Matrix is provided for educational and research purposes only. It does not constitute financial advice and is not a signal generator. Any trading decisions you make based on this tool, or any other, are entirely your own responsibility. Always consider your own risk management rules and conduct your own analysis.
Pine Script® göstergesi
ata_sabanci tarafından
4