Automatic Closest FVG with BPRFair Value Gaps are a hugely popular concept and because of that there are numerous indicators available. This one however, was designed to automate the process of actually using them in trading.
Designed with lower time frame entries in mind (though will work on HTF just as well), this indicator automatically draws the closest, non-mitigated FVG, to the current price, cutting out the work of looking for what FVG is relevant.
The indicator also has an option to show when the current nearest pair of FVGs form a BPR or 'balanced price range'.
There are various option for what counts as mitigation, including no mitigation at all, and when mitigated an FVG is no longer considered for proximity searching.
在脚本中搜索"liquidity"
Mark LevelsMark Levels is marking liquidity pools by drawing lines on their pivots and labelling them so that you can instantly detect them on your realtime chart
It supports:
- marking previous and current day lows and highs
- marking previous and current week lows and highs
- marking previous and current month lows and highs
- marking equal lows and highs
technically it re-builds them on the last bar or as soon as new realtime bar is updated. it looks with 1k bars back to find higher timeframe ranges and find lows and highs there
Adjustments:
- changing the line style of the group
- changing the lines color and the labels on the groups
- currently pools are split on 2 groups Period Liquidity and Equal Pivots Liquidity.
FX Sessions (DTS)FX Sessions (DST-Safe)
This indicator highlights the four main Forex trading sessions — Sydney, Tokyo, London, and New York — using the local timezone of each market.
• DST handled automatically: Sessions shift correctly when London or New York move clocks forward/back.
• Clear visualization: Light background shading for each session, with the London–New York overlap emphasized for peak liquidity.
• Customizable: Toggle individual sessions, labels, and the on-chart legend table.
• Intraday focus: Works best on lower timeframes (1m–1h) for identifying active trading hours and volatility windows.
Use this tool to instantly spot when liquidity and volatility are likely to increase, so you know where to focus your trading.
Key Indicators Dashboard (KID)Key Indicators Dashboard (KID) — Comprehensive Market & Trend Metrics
📌 Overview
The Key Indicators Dashboard (KID) is an advanced multi-metric market analysis tool designed to consolidate essential technical, volatility, and relative performance data into a single on-chart table. Instead of switching between multiple indicators, KID centralizes these key measures, making it easier to assess a stock’s technical health, volatility state, trend status, and relative strength at a glance.
🛠 Key Features
⦿ Average Daily Range (ADR %): Measures average daily price movement over a specified period. It is calculated by averaging the daily price range (high - low) over a set number of days (default 20 days).
⦿ Average True Range (ATR): Measures volatility by calculating the average of a true range over a specific period (default 14). It helps traders gauge the typical extent of price movement, regardless of the direction.
⦿ ATR%: Expresses the Average True Range as a percentage of the price, which allows traders to compare the volatility of stocks with different prices.
⦿ Relative Strength (RS): Compares a stock’s performance to a chosen benchmark index (default NIFTYMIDSML400) over a specific period (default 50 days).
⦿ RS Score (IBD-style): A normalized 1–100 rating inspired by Investor’s Business Daily methodology.
How it works: The RS Score is based on a weighted average of price changes over 3 months (40%), 6 months (20%), 9 months (20%), and 12 months (20%).
The raw value is converted into a percentage return, then normalized over the past 252 trading days so the lowest value maps to 1 and the highest to 100.
This produces a percentile-style score that highlights the strongest stocks in relative terms.
⦿ Relative Volume (RVol): Compares a stock's current volume to its average volume over a specific period (default 50). It is calculated by dividing the current volume by the average historical volume.
⦿ Average ₹ Volume (Turnover): Represents the total monetary value of shares traded for a stock. It's calculated by multiplying a day's closing price by its volume, with the final value converted to crores for clarity. This metric is a key indicator of a stock's liquidity and overall market interest.
⦿ Moving Average Extension: Measures how far a stock's current price has moved from from a selected moving average (EMA or SMA). This deviation is normalized by the stock's volatility (ATR%), with a default threshold of 6 ATR used to indicate that the stock is significantly extended and is marked with a selected shape (default Red Flag).
⦿ 52-Weeks High & Low: Measures a stock's current price in relation to its highest and lowest prices over the past year. It calculates the percentage a stock is below its 52-week high and above its 52-week low.
⦿ Market Capitalization: Market Cap represents the total value of all outstanding.
⦿ Free Float: It is the value of shares readily available for public trading, with the Free Float Percentage showing the proportion of shares available to the public.
⦿ Trend: Uses Supertrend indicator to identify the current trend of a stock's price. A factor (default 3) and an ATR period (default 10) is used to signal whether the trend is up or down.
⦿ Minervini Trend Template (MTT): It is a set of technical criteria designed to identify stocks in strong uptrends.
Price > 50-DMA > 150-DMA > 200-DMA
200-DMA is trending up for at least 1 month
Price is at least 30% above its 52-week low.
Price is within at least 25 percent of its 52-week high
Table highlights when a stock meets all above criteria.
⦿ Sector & Industry: Display stock's sector and industry, provides categorical classification to assist sector-based analysis. The sector is a broad economic classification, while the industry is a more specific group within that sector.
⦿ Moving Averages (MAs): Plot up to four customizable Moving Averages on a chart. You can independently set the type (Simple or Exponential), the source price, and the length for each MA to help visualize a stock's underlying trend.
MA1: Default 10-EMA
MA2: Default 20-EMA
MA3: Default 50-EMA
MA4: Default 200-EMA
⦿ Moving Average (MA) Crossover: It is a trend signal that occurs when a shorter-term moving average crosses a longer-term one. This script identifies these crossover events and plots a marker on the chart to visually signal a potential change in trend direction.
User-configurable MAs (short and long).
A bullish crossover occurs when the short MA crosses above the long MA.
A bearish crossover occurs when the short MA crosses below the long MA.
⦿ Inside Bar (IB): An Inside Bar is a candlestick whose entire price range is contained within the range of the previous bar. This script identifies this pattern, which often signals consolidation, and visually marks bullish and bearish inside bars on the chart with distinct colors and labels.
⦿ Tightness: Identifies periods of low volatility and price consolidation. It compares the price range over a short lookback period (default 3) to the average daily range (ADR). When the lookback range is smaller than the ADR, the indicator plots a marker on the chart to signal consolidation.
⦿ PowerBar (Purple Dot): Identifies candles with a strong price move on high volume. By default, it plots a purple dot when a stock moves up or down by at least 5% and has a minimum volume of 500,000. More dots indicate higher volatility and liquidity.
⦿ Squeezing Range (SQ): Identifies periods of low volatility, which can often precede a significant price move. It checks if the Bollinger Bands have narrowed to a range that is smaller than the Average True Range (ATR) for a set number of consecutive bars (default 3).
(UpperBB - LowerBB) < (ATR × 2)
⦿ Mark 52-Weeks High and Low: Marks and labels a stock's 52-Week High and Low prices directly on the chart. It draws two horizontal lines extending from the candles where the highest and lowest prices occurred over the past year, providing a clear visual reference for long-term price extremes.
⏳PineScreener Filters
The indicator’s alert conditions act as filters for PineScreener.
Price Filter: Minimum and maximum price cutoffs (default ₹25 - ₹10000).
Daily Price Change Filter: Minimum and maximum daily percent change (default -5% and 5%).
🔔 Built-in Alerts
Supports alert creation for:
ADR%, ATR/ATR %, RS, RS Rating, Turnover
Moving Average Crossover (Bullish/Bearish)
Minervini Trend Template
52-Week High/Low
Inside Bars (Bullish/Bearish)
Tightness
Squeezing Range (SQ)
⚙️ Customizable Visualization
Switchable between vertical or horizontal layout.
Works in dark/light mode
User-configurable to toggle any indicator ON or OFF.
User-configurable Moving (EMA/SMA), Period/Lengths and thresholds.
⦿ (Optional) : For horizontal table orientation increase Top Margin to 16% in Chart (Canvas) settings to avoid chart overlapping with table.
⚡ Add this script to your chart and start making smarter trade decisions today! 🚀
FA Dashboard: Valuation, Profitability & SolvencyFundamental Analysis Dashboard: A Multi-Dimensional View of Company Quality
This script presents a structured and customizable dashboard for evaluating a company’s fundamentals across three key dimensions: Valuation, Profitability, and Solvency & Liquidity.
Unlike basic fundamental overlays, this dashboard consolidates multiple financial indicators into visual tables that update dynamically and are grouped by category. Each ratio is compared against configurable thresholds, helping traders quickly assess whether a company meets certain value investing criteria. The tables use color-coded checkmarks and fail marks (✔️ / ❌) to visually signal pass/fail evaluations.
▶️ Key Features
Valuation Ratios:
Earnings Yield: EBIT / EV
EV / EBIT and EV / FCF: Enterprise value metrics for profitability
Price-to-Book, Free Cash Flow Yield, PEG Ratio
Profitability Ratios:
Return on Invested Capital (ROIC), ROE, Operating, Net & Gross Margins, Revenue Growth
Solvency & Liquidity Ratios:
Debt to Equity, Debt to EBITDA, Current Ratio, Quick Ratio, Altman Z-Score
Each of these metrics is calculated using request.financial() and can be viewed using either annual (FY) or quarterly (FQ) data, depending on user preference.
🧠 How to Use
Add the script to any stock chart.
Select your preferred data period (FY or FQ).
Adjust thresholds if desired to match your personal investing strategy.
Review the visual dashboard to see which metrics the company passes or fails.
💡 Why It’s Useful
This tool is ideal for traders or long-term investors looking to filter stocks using fundamental criteria. It draws inspiration from principles used by Benjamin Graham, Warren Buffett, and Joel Greenblatt, offering a fast and informative way to screen quality businesses.
This is not a repackaged built-in or autogenerated script. It’s a custom-built, interactive tool tailored for fundamental analysis using official financial data provided via Pine Script’s request.financial().
RSI Support & Resistance Breakouts with OrderblocksThis tool is an overly simplified method of finding market squeeze and breakout completely based on a dynamic RSI calculation. It is designed to draw out areas of price levels where the market is pushing back against price action leaving behind instances of short term support and resistance levels you otherwise wouldn't see with the common RSI.
It uses the changes in market momentum to determine support and resistance levels in real time while offering price zone where order blocks exist in the short term.
In ranging markets we need to know a couple things.
1. External Zone - It's important to know where the highs and lows were left behind as they hold liquidity. Here you will have later price swings and more false breakouts.
2. Internal Zone - It's important to know where the highest and lowest closing values were so we can see the limitations of that squeeze. Here you will find the stronger cluster of orders often seen as orderblocks.
In this tool I've added a 200 period Smoothed Moving Average as a trend filter which causes the RSI calculation to change dynamically.
Regular Zones - without extending
The Zones draw out automatically but are often too small to work with.
To solve this problem, you can extend the zones into the future up to 40 bars.
This allows for more visibility against future price action.
--------------------------------------------
Two Types of Zones
External Zones - These zones give you positioning of the highest and lowest price traded within the ranging market. This is where liquidity will be swept and often is an ultimate breaking point for new price swings.
How to use them :
External Zones - External zones form at the top of a pullback. After this price should move back into its impulsive wave.
During the next corrective way, if price breaches the top of the previous External Zone, this is a sign of trend weakness. Expect a divergence and trend reversal.
Internal Zones - (OrderBlocks) Current price will move in relation to previous internal zones. The internal zone is where a majority of price action and trading took place. It's a stronger SQUEEZE area. Current price action will often have a hard time closing beyond the previous Internal Zones high or low. You can expect these zones to show you where the market will flip over. In these same internal zones you'll find large rejection candles.
**Important Note** Size Doesn't Matter
The size of the internal zone does not matter. It can be very small and still very powerful.
Once an internal zone has been hit a few times, its often not relevant any longer.
Order Block Zone Examples
In this image you can see the Internal Zone that was untouched had a STRONG price reaction later on.
Internal Zones that were touched multiple times had weak reactions later as price respected them less over time.
Zone Overlay Breakdown
The Zones form and update in real time until momentum has picked up and price begins to trend. However it leaves behind the elements of the inducement area and all the key levels you need to know about for future price action.
Resistance Fakeout : Later on after the zone has formed, price will return to this upper zone of price levels and cause fakeouts. A close above this zone implies the market moves long again.
Midline Equilibrium : This is simply the center of the strongest traded area. We can call this the Point of Control within the orderblock. If price expands through both extremes of this zone multiple times in the future, it eliminates the orderblock.
Support Fakeout : Just like its opposing brother, price will wick through this zone and rip back causing inducement to trap traders. You would need a clear close below this zone to be in a bearish trend.
BARCOLOR or Candle Color: (Optional)
Bars are colored under three conditions
Bullish Color = A confirmed bullish breakout of the range.
Bearish Color = A confirmed bearish breakout of the range.
Squeeze Color = Even if no box is formed a candle or candles can have a squeeze color. This means the ranging market happened within the high and low of that singular candle.
Fair Value Gap Finder [Find Better Trades]Fair Value Gap Finder (FVG) – Spot Institutional Imbalances
📈 Identify Key Market Imbalances
The Fair Value Gap Finder automatically detects price inefficiencies where aggressive buying or selling has created an imbalance in liquidity. These gaps, often left by institutional traders, can serve as key areas for price to revisit before continuing its trend.
🔍 How It Works:
Highlights bullish Fair Value Gaps (FVGs) in green, signaling potential support zones.
Highlights bearish Fair Value Gaps (FVGs) in red, signaling potential resistance zones.
Uses ATR-based filtering to eliminate small, insignificant gaps, focusing only on high-probability setups.
Alerts included! Get notified when a valid Fair Value Gap is detected.
📊 How to Trade Using FVGs:
✅ For Buy Trades: Wait for price to return to a bullish FVG and confirm support before entering long.
✅ For Sell Trades: Wait for price to revisit a bearish FVG and confirm resistance before entering short.
✅ Use with candlestick patterns, trend analysis, or volume for additional confirmation.
⚙️ Customizable Settings:
Adjust the ATR Multiplier to control how large a gap must be before triggering a signal.
Enable alerts to stay informed in real time when new FVGs appear.
💡 Why Use This Indicator?
Fair Value Gaps are widely used by professional traders to spot areas of liquidity, making them valuable for scalping, swing trading, and institutional-style trading.
🚀 Add it to your TradingView chart and start trading with precision!
Quantitative Easing and Tightening PeriodsQuantitative Easing (QE) and Quantitative Tightening (QT) periods based on historical events from the Federal Reserve:
Quantitative Easing (QE) Periods:
QE1:
Start: November 25, 2008
End: March 31, 2010
Description: The Federal Reserve initiated QE1 in response to the financial crisis, purchasing mortgage-backed securities and Treasuries.
QE2:
Start: November 3, 2010
End: June 29, 2011
Description: QE2 involved the purchase of $600 billion in U.S. Treasury bonds to further stimulate the economy.
QE3:
Start: September 13, 2012
End: October 29, 2014
Description: QE3 was an open-ended bond-buying program with monthly purchases of $85 billion in Treasuries and mortgage-backed securities.
QE4 (COVID-19 Pandemic Response):
Start: March 15, 2020
End: March 10, 2022
Description: The Federal Reserve engaged in QE4 in response to the economic impact of the COVID-19 pandemic, purchasing Treasuries and MBS in an effort to provide liquidity.
Quantitative Tightening (QT) Periods:
QT1:
Start: October 1, 2017
End: August 1, 2019
Description: The Federal Reserve began shrinking its balance sheet in 2017, gradually reducing its holdings of U.S. Treasuries and mortgage-backed securities. This period ended in August 2019 when the Fed decided to stop reducing its balance sheet.
QT2:
Start: June 1, 2022
End: Ongoing (as of March 2025)
Description: The Federal Reserve started QT again in June 2022, reducing its holdings of U.S. Treasuries and MBS in response to rising inflation. The Fed has continued this tightening cycle.
These periods are key moments in U.S. monetary policy, where the Fed either injected liquidity into the economy (QE) or reduced its balance sheet by not reinvesting maturing securities (QT). The exact dates and nature of these policies may vary based on interpretation and adjustments to the Fed's actions during those times.
ATR 3x Multiplier StrategyBeta version
Volatility and Candle Spikes in Trading
Volatility
Volatility refers to the degree of variation in the price of a financial asset over time. It measures how much the price fluctuates and is often associated with risk and uncertainty in the market. High volatility means larger price swings, while low volatility indicates more stable price movements.
Key aspects of volatility:
Measured using indicators like Average True Range (ATR), Bollinger Bands, and Implied Volatility (IV).
Influenced by factors such as market news, economic events, and liquidity.
Higher volatility increases both risk and potential profit opportunities.
Candle Spikes
A candle spike (or wick) refers to a sudden price movement that forms a long shadow or wick on a candlestick chart. These spikes can indicate strong buying or selling pressure, liquidity hunts, or stop-loss triggers.
Types of candle spikes:
Bullish Spike (Long Lower Wick): Indicates buyers rejected lower prices, pushing the price higher.
Bearish Spike (Long Upper Wick): Suggests sellers rejected higher prices, pushing the price lower.
Stop-Loss Hunt: Market makers may trigger stop-losses by creating artificial spikes before reversing the price.
News-Induced Spikes: Economic data releases or unexpected events can cause sudden price jumps.
Understanding volatility and candle spikes can help traders manage risk, spot entry/exit points, and avoid false breakouts. 🚀📈
Electronic Trading Hours Session/CandlesThis indicator visually distinguishes the electronic trading session, spanning from the prior day's close (e.g., 5:00 PM EST) through the overnight period until the next day's opening bell (e.g., 9:30 AM EST).
It can be customized to highlight this period with a shaded zone or colored candles depending on the trader’s preference.
The overnight levels that create the opening range gap often act as critical zones of liquidity.
The indicator provides a clear visual cue of potential price magnets that smart money (institutional traders) may target during the opening bell session to trigger liquidity sweeps.
BPR [TakingProphets]The BPR (Balanced Price Range) Indicator by Taking Prophets is built for traders who follow ICT (Inner Circle Trader) concepts and smart money strategies. In ICT methodology, a Balanced Price Range (BPR) occurs when price rapidly moves in one direction, creating an imbalance that often gets revisited before price continues its trend. These areas represent inefficiencies in the market where liquidity was not properly distributed, making them key zones for potential retracements and trade setups.
How the Indicator Works:
🔹 Automatically Detects BPRs – No need to manually mark imbalances; the indicator highlights them for you.
🔹 Helps Identify Smart Money Footprints – Spot areas where price is likely to retrace and rebalance liquidity.
🔹 Customizable Sensitivity – Adjust detection parameters based on your preferred trading style.
🔹 Works Across All Markets – Apply it to Forex, Futures, Crypto, and Stocks on TradingView.
🔹 Clean and Intuitive Interface – Designed to be simple yet powerful for both new and experienced traders.
Forex Hammer and Hanging Man StrategyThe strategy is based on two key candlestick chart patterns: Hammer and Hanging Man. These chart patterns are widely used in technical analysis to identify potential reversal points in the market. Their relevance in the Forex market, known for its high liquidity and volatile price movements, is particularly pronounced. Both patterns provide insights into market sentiment and trader psychology, which are critical in currency trading, where short-term volatility plays a significant role.
1. Hammer:
• Typically occurs after a downtrend.
• Signals a potential trend reversal to the upside.
• A Hammer has:
• A small body (close and open are close to each other).
• A long lower shadow, at least twice as long as the body.
• No or a very short upper shadow.
2. Hanging Man:
• Typically occurs after an uptrend.
• Signals a potential reversal to the downside.
• A Hanging Man has:
• A small body, similar to the Hammer.
• A long lower shadow, at least twice as long as the body.
• A small or no upper shadow.
These patterns are a manifestation of market psychology, specifically the tug-of-war between buyers and sellers. The Hammer reflects a situation where sellers tried to push the price down but were overpowered by buyers, while the Hanging Man shows that buyers failed to maintain the upward movement, and sellers could take control.
Relevance of Chart Patterns in Forex
In the Forex market, chart patterns are vital tools because they offer insights into price action and market sentiment. Since Forex trading often involves large volumes of trades, chart patterns like the Hammer and Hanging Man are important for recognizing potential shifts in market momentum. These patterns are a part of technical analysis, which aims to forecast future price movements based on historical data, relying on the psychology of market participants.
Scientific Literature on the Relevance of Candlestick Patterns
1. Behavioral Finance and Candlestick Patterns:
Research on behavioral finance supports the idea that candlestick patterns, such as the Hammer and Hanging Man, are relevant because they reflect shifts in trader psychology and sentiment. According to Lo, Mamaysky, and Wang (2000), patterns like these could be seen as representations of collective investor behavior, influenced by overreaction, optimism, or pessimism, and can often signal reversals in market trends.
2. Statistical Validation of Chart Patterns:
Studies by Brock, Lakonishok, and LeBaron (1992) explored the profitability of technical analysis strategies, including candlestick patterns, and found evidence that certain patterns, such as the Hammer, can have predictive value in financial markets. While their study primarily focused on stock markets, their findings are generally applicable to the Forex market as well.
3. Market Efficiency and Candlestick Patterns:
The efficient market hypothesis (EMH) posits that all available information is reflected in asset prices, but some studies suggest that markets may not always be perfectly efficient, allowing for profitable exploitation of certain chart patterns. For instance, Jegadeesh and Titman (1993) found that momentum strategies, which often rely on price patterns and trends, could generate significant returns, suggesting that patterns like the Hammer or Hanging Man may provide a slight edge, particularly in short-term Forex trading.
Testing the Strategy in Forex Using the Provided Script
The provided script allows traders to test and evaluate the Hammer and Hanging Man patterns in Forex trading by entering positions when these patterns appear and holding the position for a specified number of periods. This strategy can be tested to assess its performance across different currency pairs and timeframes.
1. Testing on Different Timeframes:
• The effectiveness of candlestick patterns can vary across different timeframes, as market dynamics change with the level of detail in each timeframe. Shorter timeframes may provide more frequent signals, but with higher noise, while longer timeframes may produce more reliable signals, but with fewer opportunities. This multi-timeframe analysis could be an area to explore to enhance the strategy’s robustness.
2. Exit Strategies:
• The script incorporates an exit strategy where positions are closed after holding them for a specified number of periods. This is useful for testing how long the reversal patterns typically take to play out and when the optimal exit occurs for maximum profitability. It can also help to adjust the exit logic based on real-time market behavior.
Conclusion
The Hammer and Hanging Man patterns are widely recognized in technical analysis as potential reversal signals, and their application in Forex trading is valuable due to the market’s high volatility and liquidity. This strategy leverages these candlestick patterns to enter and exit trades based on shifts in market sentiment and psychology. Testing and optimization, as offered by the script, can help refine the strategy and improve its effectiveness.
For further refinement, it could be valuable to consider combining candlestick patterns with other technical indicators or using multi-timeframe analysis to confirm patterns and increase the probability of successful trades.
References:
• Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. The Journal of Finance, 55(4), 1705-1770.
• Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. The Journal of Finance, 47(5), 1731-1764.
• Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65-91.
This provides a theoretical basis for the use of candlestick patterns in trading, supported by academic literature and research on market psychology and efficiency.
Financials Score All Description of the "Financials Score All" Script
This Pine Script calculates a financial score for a specific stock, based on various financial metrics. The purpose is to provide a comprehensive numerical score that reflects the financial health of the stock. The score is calculated using multiple financial indicators, including profitability, valuation, debt management, and liquidity. Here’s a breakdown of what each part of the script does:
period = input.string('FQ', 'Period', options= )
FQ refers to Quarterly financial data.
FY refers to Fiscal Year financial data.
Financial Metrics:
The script uses various financial metrics to calculate the score. These are obtained via request.financial, which retrieves financial data for the stock from TradingView's database. Below are the metrics used:
opmar (Operating Margin): Measures the company's profitability as a percentage of revenue.
eps (Earnings Per Share): Represents the portion of a company's profit allocated to each outstanding share.
eps_ttm (Earnings Per Share – Trailing Twelve Months): EPS over the most recent 12 months.
pe_ratio (Price-to-Earnings Ratio): A measure of the price investors are willing to pay for a stock relative to its earnings.
pb_ratio (Price-to-Book Ratio): A valuation ratio comparing a company’s market value to its book value.
de_ratio (Debt-to-Equity Ratio): A measure of the company’s financial leverage, showing how much debt it has compared to shareholders' equity.
roe_pb (Return on Equity Adjusted to Book): Measures the company's profitability relative to its book value.
fcf_per_share (Free Cash Flow per Share): Represents the free cash flow available for dividends, debt reduction, or reinvestment, per share.
pfcf_ratio (Price-to-Free-Cash-Flow Ratio): A measure comparing a company’s market value to its free cash flow.
current_ratio (Current Ratio): A liquidity ratio that measures a company's ability to pay short-term obligations with its current assets.
RSI Calculation:
The script calculates the Relative Strength Index (RSI) for the stock using an 8-period lookback:
rsi = ta.rsi(close, 8)
Score Calculation:
The script calculates a total score by adding points based on the values of the financial metrics. Each metric is checked against a condition, and if the condition is met, the score is incremented:
If the Operating Margin (opmar) is greater than 20, the score is incremented by 20 points.
If Earnings Per Share (EPS) is positive, 10 points are added.
If the P/E ratio is between 0 and 20, 10 points are added.
If the P/B ratio is less than 3, 10 points are added.
If the Debt-to-Equity ratio is less than 0.8, 10 points are added.
If the Return on Equity Adjusted to Book is greater than 10, 10 points are added.
If the P/FCF ratio is between 0 and 15, 10 points are added.
If the Current Ratio is greater than 1.61, 10 points are added.
If the RSI is less than 35, 10 points are added.
The score is accumulated based on these conditions and stored in the total_score variable.
Displaying the Total Score:
Finally, the total score is plotted on the chart:
Summary of How It Works:
This script calculates a financial score for a stock using a variety of financial indicators. Each metric has a threshold, and when the stock meets certain criteria (for example, a good operating margin, a healthy debt-to-equity ratio, or a low P/E ratio), points are added to the overall score. The result is a single numerical value that reflects the financial health of the stock.
This score can help traders or investors identify companies with strong financials, or serve as a comparison tool between different stocks based on their financial health.
Generally >60 is the best stocks for med and long term trades
Mean Price
^^ Plotting switched to Line.
This method of financial time series (aka bars) downsampling is literally, naturally, and thankfully the best you can do in terms of maximizing info gain. You can finally chill and feed it to your studies & eyes, and probably use nothing else anymore.
(HL2 and occ3 also have use cases, but other aggregation methods? Not really, even if they do, the use cases are ‘very’ specific). Tho in order to understand why, you gotta read the following wall, or just believe me telling you, ‘I put it on my momma’.
The true story about trading volumes and why this is all a big misdirection
Actually, you don’t need to be a quant to get there. All you gotta do is stop blindly following other people’s contextual (at best) solutions, eg OC2 aggregation xD, and start using your own brain to figure things out.
Every individual trade (basically an imprint on 1D price space that emerges when market orders hit the order book) has several features like: price, time, volume, AND direction (Up if a market buy order hits the asks, Down if a market sell order hits the bids). Now, the last two features—volume and direction—can be effectively combined into one (by multiplying volume by 1 or -1), and this is probably how every order matching engine should output data. If we’re not considering size/direction, we’re leaving data behind. Moreover, trades aren’t just one-price dots all the time. One trade can consume liquidity on several levels of the order book, so a single trade can be several ticks big on the price axis.
You may think now that there are no zero-volume ticks. Well, yes and no. It depends on how you design an exchange and whether you allow intra-spread trades/mid-spread trades (now try to Google it). Intra-spread trades could happen if implemented when a matching engine receives both buy and sell orders at the same microsecond period. This way, you can match the orders with each other at a better price for both parties without even hitting the book and consuming liquidity. Also, if orders have different sizes, the remaining part of the bigger order can be sent to the order book. Basically, this type of trade can be treated as an OTC trade, having zero volume because we never actually hit the book—there’s no imprint. Another reason why it makes sense is when we think about volume as an impact or imbalance act, and how the medium (order book in our case) responds to it, providing information. OTC and mid-spread trades are not aggressive sells or buys; they’re neutral ticks, so to say. However huge they are, sometimes many blocks on NYSE, they don’t move the price because there’s no impact on the medium (again, which is the order book)—they’re not providing information.
... Now, we need to aggregate these trades into, let’s say, 1-hour bars (remember that a trade can have either positive or negative volume). We either don’t want to do it, or we don’t have this kind of information. What we can do is take already aggregated OHLC bars and extract all the info from them. Given the market is fractal, bars & trades gotta have the same set of features:
- Highest & lowest ticks (high & low) <- by price;
- First & last ticks (open & close) <- by time;
- Biggest and smallest ticks <- by volume.*
*e.g., in the array ,
2323: biggest trade,
-1212: smallest trade.
Now, in our world, somehow nobody started to care about the biggest and smallest trades and their inclusion in OHLC data, while this is actually natural. It’s the same way as it’s done with high & low and open & close: we choose the minimum and maximum value of a given feature/axis within the aggregation period.
So, we don’t have these 2 values: biggest and smallest ticks. The best we can do is infer them, and given the fact the biggest and smallest ticks can be located with the same probability everywhere, all we can do is predict them in the middle of the bar, both in time and price axes. That’s why you can see two HL2’s in each of the 3 formulas in the code.
So, summed up absolute volumes that you see in almost every trading platform are actually just a derivative metric, something that I call Type 2 time series in my own (proprietary ‘for now’) methods. It doesn’t have much to do with market orders hitting the non-uniform medium (aka order book); it’s more like a statistic. Still wanna use VWAP? Ok, but you gotta understand you’re weighting Type 1 (natural) time series by Type 2 (synthetic) ones.
How to combine all the data in the right way (khmm khhm ‘order’)
Now, since we have 6 values for each bar, let’s see what information we have about them, what we don’t have, and what we can do about it:
- Open and close: we got both when and where (time (order) and price);
- High and low: we got where, but we don’t know when;
- Biggest & smallest trades: we know shit, we infer it the way it was described before.'
By using the location of the close & open prices relative to the high & low prices, we can make educated guesses about whether high or low was made first in a given bar. It’s not perfect, but it’s ultimately all we can do—this is the very last bit of info we can extract from the data we have.
There are 2 methods for inferring volume delta (which I call simply volume) that are presented everywhere, even here on TradingView. Funny thing is, this is actually 2 parts of the 1 method. I wonder how many folks see through it xD. The same method can be used for both inferring volume delta AND making educated guesses whether high or low was made first.
Imagine and/or find the cases on your charts to understand faster:
* Close > open means we have an up bar and probably the volume is positive, and probably high was made later than low.
* Close < open means we have a down bar and probably the volume is negative, and probably low was made later than high.
Now that’s the point when you see that these 2 mentioned methods are actually parts of the 1 method:
If close = open, we still have another clue: distance from open/close pair to high (HC), and distance from open/close pair to low (LC):
* HC < LC, probably high was made later.
* HC > LC, probably low was made later.
And only if close = open and HC = LC, only in this case we have no clue whether high or low was made earlier within a bar. We simply don’t have any more information to even guess. This bar is called a neutral bar.
At this point, we have both time (order) and price info for each of our 6 values. Now, we have to solve another weighted average problem, and that’s it. We’ll weight prices according to the order we’ve guessed. In the neutral bar case, open has a weight of 1, close has a weight of 3, and both high and low have weights of 2 since we can’t infer which one was made first. In all cases, biggest and smallest ticks are modeled with HL2 and weighted like they’re located in the middle of the bar in a time sense.
P.S.: I’ve also included a "robust" method where all the bars are treated like neutral ones. I’ve used it before; obviously, it has lesser info gain -> works a bit worse.
Price and OI ChangePrice and OI Change
Description:
The "Price and OI Change" indicator provides insights into market dynamics by analyzing the price and open interest (OI) changes over a 7-day period. This indicator is designed for use with both spot and futures markets, including cryptocurrencies.
Key Features:
Price and OI Change Calculation: Computes the 7-day change in price and open interest to help identify market trends and shifts.
Market Conditions Visualization: Differentiates market conditions by changing the background color based on:
Leverage-Driven Market: Blue background indicates increasing prices and OI, suggesting a bullish trend driven by leverage.
Spot-Driven Market: Green background shows increasing prices but decreasing OI, indicating a bullish trend driven by spot market activity.
Leverage Sell-Off: Orange background reveals decreasing prices with increasing OI, signaling a potential liquidation phase.
Deleveraging Sell-Off: Red background reflects decreasing prices and OI, indicating a bearish market with reduced leverage.
Top 3 BTC Futures Average OI: Displays the average open interest for the top 3 BTC futures contracts from major exchanges (Binance, OKX, Bybit). This helps gauge overall market sentiment and liquidity.
Visualization Tools: Includes optional plotting of open interest data and average OI for better visualization of market conditions.
Usage:
Traders and Analysts: Use the background color changes and average OI to make informed decisions about market entry and exit points.
Futures Traders: Track OI changes in major BTC futures to assess market strength and potential liquidity issues.
Relative Equal Highs/LowsThis Pine script indicator is designed to create a visual representation of the relative equal highs & lows formed and automatically removed mitigated ones. Unlike indicators designed to show exact equal high/lows this indicator allows a small, configurable degree of variance between price to identify areas where price stops.
Relevance:
Relative Equal highs and lows can serve as valuable tools in identifying potential shifts in trend direction. They come into play when the price hits a support or resistance level and can’t advance further, signaling a possible reversal or pivot point. When the price sufficiently retreats from these levels, relative equal highs and lows can also indicate liquidity draws where buy/sell stops might be positioned, in accordance with SMC/ICT concepts.
How It Works:
The indicator tracks all unmitigated highs & lows within the chart’s present timeframe, limited to the user-defined max bars lookback for optimal performance. If the prices are within the configured variance they are marked as relatively equal and at that point are visually identified by a horizontal line, which connects the two (or more) points of price. Depending on configuration of the indicator, a line is rendered from the 1st, last or both values within the relatively equal range of price. A unique feature of this indicator is its ability to remove the line once the price mitigates the relative equal high/low by falling below the lows or rising above highs. This ensures the chart remains uncluttered and highlights only the currently relevant levels, setting it apart from other indicators providing similar functionality.
Configurability:
The indicator offers five style settings for complete customization of the lines that represent equal highs/lows. These settings include line style, color, and width, along with an option to extend the lines to the right of the chart for enhanced visibility of equal high/low levels. To optimize performance, the indicator allows users to configure the lookback length, determining how far back the price history should be examined. In most instances, the default setting of 500 bars proves more than adequate. Additionally, you can set thresholds via separate configs for stocks & indices that will determine if the price is relatively equal and lastly allow you to configure where the indicator line should be drawn, the first, last or all the values.
Additional notes:
This uses a different approach then my “equal highs/lows” indicator to identify price levels and because it focuses specifically on relative as opposed to exact values it is entirely different and may show “weaker”, but still important levels of liquidity. This indicator is more suited for analysis of stocks and indices or higher-timeframes where price-action rarely forms exact equal values instead more frequently forming almost equal values. My other indicator is more suited for smaller (15m or less) timeframe on indices where exact equal prices are often identical. Depending on situation different indicators should be used.
Open Intrest / Volume / Liquidations (Suite) [BigBeluga]This indicator is a suite of tools that aims to provide traders with efficient metrics to analyze the market in a different way, such as various types of Open Interest, Intraday Volume, and Liquidations.
This indicator can both save time and also provide a different approach to the usual price action trading style.
🔶 FEATURES
The indicator contains the following features:
Open Interest Suite
- Delta OI
- Net longs and shorts
- OI Relative Strength Index
Intraday Volume Suite
- Bullish and Bearish LTF Volume
- CVD
- Delta Volume
Liquidations Suite
- Long and Short Liquidations
- Cumulative Liquidations
🔶 EXAMPLE OF SUITE
In the example above, we can see how we can plot long and short positions, both opening and closing out.
This can give a unique way to view which side is the strongest but also which side has the most resting liquidity.
For example, if more longs are entering the market, it also means more liquidity for longs and vice versa.
Or, for example, plotting the delta OI will allow the user to see big percentages in change and spot big areas of position closing out.
This presents a fascinating method for observing numerous positions closing out in conjunction with a surge of liquidations, which could indicate a potential reversal in price.
Here, we can see a basic example of using intraday volume on a 1m LTF.
With this, we are able to see both bullish and bearish volume of the same candle, very useful to see both volumes traded in the same candle.
Using the CVD to see the overall direction based purely on the volume and spot divergence, for example, the price in an uptrend but CVD going down, indicating weak shorts in the market or trapped shorts.
Or simply view liquidations happening in the market in a very different way, both long and short liquidation at the same time + the option to use multi-timeframe liquidations.
🔶 CONCLUSION
The idea of this script is to provide a set of tools in a unique script to optimize time and analyze the market in both a quick way and in a different way than usual.
Day/Week/Month Metrics (Zeiierman)█ Overview
The Day/Week/Month Metrics (Zeiierman) indicator is a powerful tool for traders looking to incorporate historical performance into their trading strategy. It computes statistical metrics related to the performance of a trading instrument on different time scales: daily, weekly, and monthly. Breaking down the performance into daily, weekly, and monthly metrics provides a granular view of the instrument's behavior.
The indicator requires the chart to be set on a daily timeframe.
█ Key Statistics
⚪ Day in month
The performance of financial markets can show variability across different days within a month. This phenomenon, often referred to as the "monthly effect" or "turn-of-the-month effect," suggests that certain days of the month, especially the first and last days, tend to exhibit higher than average returns in many stock markets around the world. This effect is attributed to various factors including payroll contributions, investment of monthly dividends, and psychological factors among traders and investors.
⚪ Edge
The Edge calculation identifies days within a month that consistently outperform the average monthly trading performance. It provides a statistical advantage by quantifying how often trading on these specific days yields better returns than the overall monthly average. This insight helps traders understand not just when returns might be higher, but also how reliable these patterns are over time. By focusing on days with a higher "Edge," traders can potentially increase their chances of success by aligning their strategies with historically more profitable days.
⚪ Month
Historically, the stock market has exhibited seasonal trends, with certain months showing distinct patterns of performance. One of the most well-documented patterns is the "Sell in May and go away" phenomenon, suggesting that the period from November to April has historically brought significantly stronger gains in many major stock indices compared to the period from May to October. This pattern highlights the potential impact of seasonal investor sentiment and activities on market performance.
⚪ Day in week
Various studies have identified the "day-of-the-week effect," where certain days of the week, particularly Monday and Friday, show different average returns compared to other weekdays. Historically, Mondays have been associated with lower or negative average returns in many markets, a phenomenon often linked to the settlement of trades from the previous week and negative news accumulation over the weekend. Fridays, on the other hand, might exhibit positive bias as investors adjust positions ahead of the weekend.
⚪ Week in month
The performance of markets can also vary within different weeks of the month, with some studies suggesting a "week of the month effect." Typically, the first and the last week of the month may show stronger performance compared to the middle weeks. This pattern can be influenced by factors such as the timing of economic reports, monthly investment flows, and options and futures expiration dates which tend to cluster around these periods, affecting investor behavior and market liquidity.
█ How It Works
⚪ Day in Month
For each day of the month (1-31), the script calculates the average percentage change between the opening and closing prices of a trading instrument. This metric helps identify which days have historically been more volatile or profitable.
It uses arrays to store the sum of percentage changes for each day and the total occurrences of each day to calculate the average percentage change.
⚪ Month
The script calculates the overall gain for each month (January-December) by comparing the closing price at the start of a month to the closing price at the end, expressed as a percentage. This metric offers insights into which months might offer better trading opportunities based on historical performance.
Monthly gains are tracked using arrays that store the sum of these gains for each month and the count of occurrences to calculate the average monthly gain.
⚪ Day in Week
Similar to the day in the month analysis, the script evaluates the average percentage change between the opening and closing prices for each day of the week (Monday-Sunday). This information can be used to assess which days of the week are typically more favorable for trading.
The script uses arrays to accumulate percentage changes and occurrences for each weekday, allowing for the calculation of average changes per day of the week.
⚪ Week in Month
The script assesses the performance of each week within a month, identifying the gain from the start to the end of each week, expressed as a percentage. This can help traders understand which weeks within a month may have historically presented better trading conditions.
It employs arrays to track the weekly gains and the number of weeks, using a counter to identify which week of the month it is (1-4), allowing for the calculation of average weekly gains.
█ How to Use
Traders can use this indicator to identify patterns or trends in the instrument's performance. For example, if a particular day of the week consistently shows a higher percentage of bullish closes, a trader might consider this in their strategy. Similarly, if certain months show stronger performance historically, this information could influence trading decisions.
Identifying High-Performance Days and Periods
Day in Month & Day in Week Analysis: By examining the average percentage change for each day of the month and week, traders can identify specific days that historically have shown higher volatility or profitability. This allows for targeted trading strategies, focusing on these high-performance days to maximize potential gains.
Month Analysis: Understanding which months have historically provided better returns enables traders to adjust their trading intensity or capital allocation in anticipation of seasonally stronger or weaker periods.
Week in Month Analysis: Identifying which weeks within a month have historically been more profitable can help traders plan their trades around these periods, potentially increasing their chances of success.
█ Settings
Enable or disable the types of statistics you want to display in the table.
Table Size: Users can select the size of the table displayed on the chart, ranging from "Tiny" to "Auto," which adjusts based on screen size.
Table Position: Users can choose the location of the table on the chart
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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!
Temporary imbalances 2.0 This indicator attempts to calculate potential points of imbalance and equilibrium based on VWAPs and modified moving averages. The idea is to determine if there has been a change in volume and perform the calculation from that point It uses the standard deviation to determine the significant imbalance threshold. Candles with bullish imbalances are highlighted in green, while candles with bearish imbalances are highlighted in red.
"It also features a set of VWAPs and modified moving averages that you can enable or disable."
When you activate the 'Show Anchor VWAP' option, it will add five modified VWAPs.
Practical Significance:
The Anchored VWAP is a volume-weighted average price that serves as a dynamic reference to assess the average price during specific moments of market imbalance.
During a bullish imbalance, the anchor_vwap reflects the VWAP at that moment, emphasizing price behavior during that specific period.
Similarly, in a bearish imbalance, the anchor_vwap provides the associated VWAP for that condition, highlighting price movements during the imbalance phase.
How to Use:
The anchor_vwap can be employed to contextualize the volume-weighted average price during critical moments associated with significant changes in market imbalance.
By analyzing price behavior during and after periods of imbalance, the Anchored VWAP can help better understand market dynamics and identify potential areas of support or resistance.
Show VWAP Percent Imbalance"
Definition: Represents the Volume Weighted Average Price (VWAP) adjusted by the volume-weighted average of the price multiplied by volume, with a focus on conditions where the percentage volume variation surpasses a predefined threshold.
Calculation: Utilizes the simple moving average weighted of the product of the volume-weighted average price and volume only when the percentage volume variation exceeds a specific threshold.
Interpretation: Provides insight into the volume-weighted price trend during conditions where the percentage volume variation exceeds a predefined limit.
The "showDeltaVWAP" is a toggleable setting that you can turn on or off. When activated, it displays special lines on the chart. Let's understand what these lines represent:
Delta Anchor VWAP:
A green line (Delta Anchor VWAP) represents a measure of market volume imbalance.
Delta2 Anchor VWAP:
A red line (Delta2 Anchor VWAP) shows another perspective of volume imbalance.
VWAP Delta Volume:
A light blue line (VWAP Delta Volume) displays a volume-weighted average of price.
VWAP Delta Volume2:
An orange line (VWAP Delta Volume2) shows another view of the volume-weighted average of price.
Delta3 Anchor VWAP:
A light blue line (Delta3 Anchor VWAP) represents a combination of the previous measures.
Delta4 Anchor VWAP:
A purple line (Delta4 Anchor VWAP) is another combination, providing an overall view.
These lines are based on different conditions and calculations related to trading volume. When you activate "showDeltaVWAP," these lines appear on the chart, aiding in better understanding market behavior.
"Show Faster Volatility" is an option that you can enable or disable. When activated (set to true), it displays special lines on the chart called "Faster Volatility VWAP," "Faster Volatility VWAP2," and "Faster Volatility VWAP3." Let's understand what these lines represent:
Faster Volatility VWAP:
A purple line (Faster Volatility VWAP) is a Volume Weighted Average Price (VWAP) that is calculated more quickly based on short-term price reversal patterns.
Faster Volatility VWAP2:
A light gray line (Faster Volatility VWAP2) is another Volume Weighted Average Price (VWAP) that is calculated even more quickly based on even shorter-term price reversal patterns.
Faster Volatility VWAP3:
A purple line (Faster Volatility VWAP3) is another Volume Weighted Average Price (VWAP) calculated rapidly based on even shorter-term price reversal patterns.
These lines are designed to indicate moments of possible exhaustion of volatility in the market, suggesting that there may be a subsequent increase in volatility. When you activate "Show Faster Volatility," these lines are displayed on the chart.
"Show Average VWAPs Imbalance" displays weighted averages of different Volume Weighted Average Prices (VWAPs) in relation to specific market conditions. Here's an explanation of each component:
Standard VWAP:
The blue line represents the standard VWAP, a volume-weighted average of asset prices over a specific period.
VWAP with Added Imbalance (avg_vwap2):
The pink line is a weighted average that adds an imbalance value to the standard VWAP. This component highlights periods of market imbalance.
VWAP with Balance (avg_vwap3):
The lilac line is a weighted average that adds balance based on the imbalance between uptrend and downtrend, reflecting changes in volume. This provides insights into supply and demand dynamics.
Overall Average of VWAPs (avg_vwaptl):
The violet line is a weighted average that incorporates both standard and adjusted VWAPs, offering an overview of market behavior under different considered conditions.
Visual Customization (Show Average VWAPs Imbalance):
Users have the option to show or hide these average lines on the chart, allowing for a clear visualization of market trends.
"Show Min Variation VWAP" is associated with the calculation and display of a smoothed version of the Volume Weighted Average Price (VWAP), taking into account the minimum price variation over a specific period.
"How Imbalance Anchor VWAP Calculated as the smoothed relationship between liquidity difference and maximum VWAP equilibrium" is associated with the calculation and display of a smoothed version of the Imbalance Anchor VWAP. Here is a detailed explanation:
Calculations and Smoothing:
The variable "smoothed_difference" represents the exponential moving average (EMA) of the difference between two variables related to liquidity.
"smoothed_difference2" is the division of "smoothed_difference" by the maximum variation of the VWAP Equilibrium.
"smoothed_difference3" involves additional manipulation of "smoothed_difference" and "vwap_delta3."
"smoothed_difference4" incorporates the previous results, adjusted by the value of the VWAP.
Visual Customization:
The user has the option to enable or disable the display on the chart.
The line is colored in a shade of green.
It provides a smoothed representation of the Imbalance Anchor VWAP.
The line is colored in a shade of blue, and the calculation involves the summation of moving averages (20, 50, 200). Afterward, there is division by 3. Additionally, there is the summation of moving averages (766, 866, 966), divided by 3. The final step is to add these results together and divide by 2. media name is Imbalance Value2
Show VWAP Equilibrium (Max Variation) Calculated as the difference between two VWAPs derived from the highest and lowest price changes
Show Equilibrium VWAP Calculated as the sum of VWAP and (sma200 - sma20)
calculate the difference between the media of 200 to 20
Show Equilibrium VWAP Calculated as the sum of VWAP and (766+866+966)/3 - (sma200 - sma20)
Show Equilibrium VWAP Standard Deviation Calculated as the Exponential Moving Average (EMA) of the Standard Deviation of SMA (sma200 + sma20 + sma8)/3
Show Equilibrium VWAP Delta Calculated as the ratio of the smoothed VWAP Delta Result componentes
Show Standard Deviation Equilibrium VWAP Delta: Calculated as the Standard Deviation between the Average of VWAP Delta Result Components and Their Smoothed Versions
This average attempts to calculate the equilibrium."
vwap_equilibrium:
Definition: Represents the Volume Weighted Average Price (VWAP) adjusted by the volume-weighted average of the price (hl2) multiplied by volume, focusing on periods of volume equilibrium.
Calculation: Utilizes the simple moving average weighted (sma) of the product of the volume-weighted average price and volume only when there is no volume imbalance.
Interpretation: This indicator provides a view of the volume-weighted price trend during moments when the market is in equilibrium, meaning there is no noticeable imbalance in volume conditions. The calculation of VWAP is adjusted to reflect market characteristics during periods of stability.
vwap_percent_condition:
Definition: Represents the Volume Weighted Average Price (VWAP) adjusted by the volume-weighted average of the price multiplied by volume, with a focus on conditions where the percentage volume variation surpasses a predefined threshold.
Calculation: Utilizes the simple moving average weighted of the product of the volume-weighted average price and volume only when the percentage volume variation exceeds a specific threshold.
Interpretation: Provides insight into the volume-weighted price trend during conditions where the percentage volume variation exceeds a predefined limit.
The objective of these two VWAPs is to calculate possible equilibrium points between buyers and sellers.
The indicator works for all timeframes This indicator can be adjusted according to the preferences and characteristics of the specific asset or market. It provides clear visual information and can be used as a complementary tool for technical analysis in trading strategies.
Interesting
Interesting
lookback period 7 , 12, 20,70,200, 500,766,866,966
imbalance threshold 2.4, 3.3 ,4.2
The objective of this indicator is to identify and highlight various points of imbalance and equilibrium.
Price & Volume Profile (Expo)█ Overview
The Price & Volume Profile provides a holistic perspective on market dynamics by simultaneously tracking price action and trading volume across a range of price levels. So it is not only a volume-based indicator but also a price-based one. In addition to illustrating volume distribution, it quantifies how frequently the price has fallen within a particular range, thus offering a holistic perspective on market dynamics.
This unique and comprehensive approach to market analysis by considering both price action and trading volume, two crucial dimensions of market activity. Its distinctive methodology offers several advantages:
Holistic Market View: By simultaneously tracking the frequency of specific price ranges (Price Profile) and the volume traded at those ranges (Volume Profile), this indicator provides a more complete picture of market behavior. It shows not only where the market is trading but also how much it's trading, reflecting both price acceptance levels and market participation intensity.
Point of Control (POC): The POC, as highlighted by this indicator, serves as a significant reference point for traders. It identifies the price level with the highest trading activity, thus indicating a strong consensus among market participants about the asset's fair value. Observing how price interacts with the POC can offer valuable insights into market sentiment and potential trend reversals.
Support and Resistance Levels: Price levels with high trading activity often act as support or resistance in future price movements. The indicator visually represents these levels, enabling traders to anticipate potential price reactions.
Price Profile
Price and Volume Profile
█ Calculations
The algorithm analyzes both trade frequency and volume across different price levels. It identifies these levels within the visible chart range, then examines each bar to determine if the selected price falls within these levels. If so, it increases a counter and adds the trading volume. This process repeats across the visible range and is visualized as a horizontal histogram, each bar representing a price level and the bar length reflecting trade frequency and volume. Additionally, it calculates the Point of Control (POC), signifying the price level with the highest activity.
In summary: The histogram presents a dual perspective - not only the traded volume at each price level but also the frequency of the price hitting each range. The longer the bar, the more times the price has frequented that specific range, revealing key insights into price behavior and acceptance levels. These frequently visited areas often emerge as strong support or resistance zones, helping traders navigate market movements.
Please note that the indicator adjusts to the visible price range, making it adaptable to changing market conditions. This dynamic analysis can provide more relevant and timely information than static indicators.
█ How to use
This indicator is beneficial for traders as it offers insights into the distribution of trading activity across different price levels. It helps identify key areas of support and resistance and gives a visual representation of market sentiment and liquidity.
The point of control (POC) , which is the price level with the highest traded volume or frequency count, becomes even more crucial in this context. It marks the price at which the most trading activity occurred, signaling a strong consensus among market participants about the asset's fair value. If the market price deviates significantly from the POC, it could suggest an overbought or oversold condition, potentially leading to a price reversion.
Fair Price Areas/gaps are specific price levels or zones where an asset has spent limited time in the past. These areas are considered interesting or significant because they may have an impact on future price action.
Similar to the concept of fair value gaps, which refers to discrepancies between an asset's market price and its estimated intrinsic value, Fair Price Areas/gaps focus on price levels that have been relatively underutilized in terms of trading activity. When an asset's price reaches a Fair Price Area/gap, traders and investors pay attention because they expect the price to react in some way. The rationale behind this concept is that price tends to gravitate towards areas where it has spent less time in the past, as the market perceives them as significant levels.
█ Settings
The indicator is customizable, allowing users to define the number of price levels (rows), the offset, the data source, and whether to display volume or frequency count. It also adjusts dynamically to the visible price range on the chart, ensuring that the analysis remains relevant and timely with changing market conditions.
Source: The price to use for the calculation. Typically, this is the closing price. By considering the user-selected Source (typically the closing price), the indicator determines the frequency with which the price lands within each designated price level (row) over the selected period. In essence, the indicator provides a count of bars where the Source price falls within each range, essentially creating a "Price Profile."
Row Size: The number of price levels (rows) to divide the visible price range into.
Display: Choose whether to display the number of bars ("Counter") or the total volume ("Volume") for each price level.
Offset: The distance of the histogram from the price chart.
Point of Control (POC): If enabled, the indicator will highlight the price level with the most activity.
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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!
Powertrend - Volume Range Filter Strategy [wbburgin]The Powertrend is a range filter that is based off of volume, instead of price. This helps the range filter capture trends more accurately than a price-based range filter, because the range filter will update itself from changes in volume instead of changes in price. In certain scenarios this means that the Powertrend will be more profitable than a normal range filter.
Essentials of the Strategy
This is a breakout strategy which works best on trending assets with high volume and liquidity. It should be used on middle to higher timeframes and can be used on all assets that have volume provided by the data source (stocks, crypto, forex). It is long-only as of now. It can work on lower timeframes if you optimize the strategy filters to make less trades or if your exchange/broker is low/no fees, provided that your exchange/broker has high liquidity and volume.
The strategy enters a long position if the range filter is trending upwards and the price crosses over the upper range band, which signifies a price-volume breakout. The strategy closes the long position if the range filter is trending downwards and the price crosses under the lower range band, which signifies a breakdown. Both these conditions can be altered by the three filter options in the settings. The default trend filter is not alterable because it helps prevent false entries and exits that are against the trend.
Settings
The Length setting is the lookback period for the range smoothing.
The ADX Filter setting enables you to turn on an ADX filter, which will halt entries and exits unless the ADX of your customizable length is above a ADX VWMA of that length.
The Range Supertrend setting creates a supertrend from the top and bottom ranges, which can be used to filter entries and exits. The length is customizable. The filter can show you whether the range is making higher highs and lower lows. Below is an example of the Range Supertrend being used as a filter and plotted on-chart:
The VWMA setting halts entries if they are below a customizable length VWMA.
Both the Range Supertrend and the VWMA can also be plotted separately without actually filtering the strategy, so that you can use them independently if you wish. You can turn off the bar color, the highlighting, and the labels if you wish in the settings. A note about the bar color: if the color changes but the strategy does not signal an exit or entry this means that the crossover was against the trend. In these circumstances it may be indicative of a pullback to enter or exit or to add onto your position.
About the Strategy Results Below
A range filter is normally composed of two components - the range filter itself and a smoothing function. In the development of this script I tested both normal and volume-based varieties of the range filter and the smoothing function:
Tests Performed
Volume-based Range x VWMA smoothing
Price-based Range x VWMA smoothing
Price-based Range x EMA smoothing
Volume-based Range x EMA smoothing (final result)
The highest-performing was a volume-based range filter and a normal EMA-based smoothing function, but that does not mean that this strategy will be profitable - exits are based off of signal reversion so I strongly encourage you to develop your own take profits/stop losses for the strategy if you think it may be a good fit for you. The results below are with a commission value of 0.05% (because I built the strategy first for equities), slippage of 3, so if your exchange/broker has a higher fee schedule, I recommend adding filters and/or moving to higher timeframes for the strategy. Additionally, I used 10% of equity in each trade, while using the Range Supertrend filter (the previous upload was unrealistic because it used 100% of equity - missed a 0, apologies, and added in slippage).
OverNightSession @joshuuuThis indicator highlights the Overnightsession (ONS), taught by TheCurrenyMerchant.
The Overnightsession is from 4-8 am UTC-5. This session can be used to form trades, e.g. after one side has been taken out.
It has the options to display Projection and the equilibrium level. Equilibrium level (50%) can be used to identify if price is currently in premium/discount of the range and the projections (standard deviations of the range) can be used to identify possible targets.
A classic setup he teaches is:
Price trades agressively out of the range taking liquidity. As soon as we trade above the high of the candle that took liquidity, that candle can be considered an orderblock, where the 50% level can be used for long setups.
⚠️ Open Source ⚠️
Coders and TV users are authorized to copy this code base, but a paid distribution is prohibited. A mention to the original author is expected, and appreciated.
⚠️ Terms and Conditions ⚠️
This financial tool is for educational purposes only and not financial advice. Users assume responsibility for decisions made based on the tool's information. Past performance doesn't guarantee future results. By using this tool, users agree to these terms.
Volume composition / quantifytools— Overview
While net volume is useful information, it can be a blunt data point. Volume composition breaks down the content of volume, allowing a more detailed look inside each volume node. Volume composition consists of the following information:
Total volume (buy and sell). By default gray node.
Dominating volume (buy or sell). By default dark green/dark red node.
Dominating active volume (buy or sell). By default light green/light red node.
Dominating volume as percentage of total volume.
Dominating active volume as percentage of total active volume.
Buy and sell volume is defined by volume associated with lower timeframe up/down moves. This classification is further broken down to passive/active, standing for decreasing/increasing volume, e.g. a move up with volume higher than previous bar volume = active buy volume, a move up with volume lower than previous bar volume = passive buy volume.
Volume data is fetched from a lower timeframe that is automatically adjusted to fit the timeframe you're using. By default, the following settings are applied:
Charts <= 30 min: 1 minute timeframe
Charts > 30 min & <= 3 hours : 5 minute timeframe
Charts > 3 hours & <= 8 hours : 15 minute timeframe
Charts > 8 hours & <= 1D: 1 hour timeframe
Charts > 1D & <= 3D : 2 hour timeframe
Charts > 3D: 4 hour timeframe
Timeframe settings can be changed via input menu. The lower the timeframe, the more precision you get but with the cost of less historical data and slower loading time. Users can also choose which source to use for determining buy/sell volume, e.g. using close as source, a close that is higher than previous close would be considered as buy volume. This could be replaced with OHLC4 for example, resulting in a volume direction based on OHLC average.
Volume composition of current chart can also be replaced with any other chart volume composition:
— Visuals
Breakdown of visual elements:
1. Symbol and timeframe used for volume composition calculations. By default the chart that is viewed and automatically selected lower timeframe.
2. Dominating volume threshold exceeded. Can be defined via input menu, 70% of total volume by default.
3. Dominating volume as percentage of total volume. Plotted below volume nodes, without % symbol.
4. Dominating active volume, + or - symbol, standing for buy and sell. Plotted below dominating volume percentage. When dominating volume and dominating active volume sides are in a disagreement (e.g. dominating volume is on buy side while dominating active volume is on sell side) this symbol will appear inside brackets, (+) or (-).
5. Dominating active volume as percentage of total active volume. Plotted below +/- symbol.
6. Dominating active volume threshold exceeded. Can be defined via input menu, 70% by default.
Dominating volume & active volume percentages can be rounded to single numbers to avoid clutter caused by overlapping values. The percentage values will be rounded to closest single number value, e.g. dominating volume percentage at 54% = 5, dominating volume percentage at 55% = 6.
Volume anomalies can be highlighted on the chart with a color for studying the events and their past implications in greater detail. Available anomalies for highlights are the following:
Buy volume threshold exceeded
Sell volume threshold exceeded
Active buy volume threshold exceeded
Active sell volume threshold exceeded
Volume & active volume divergence
— Practical guide
Volume is arguably one of the most important data points as it directly relates to liquidity. High volume can be an indication of strength (price likely to continue moving) or absorption (price likely to halt/turn). Same applies to active volume, but with an element of aggression. High active volume serves as an indication of exuberance or otherwise forceful transacting, like stop losses triggering. With these principles in mind, the composition of volume allows distinguishing potentially important events.
Example #1 : Identifying areas of trapped market participants
Often when volume spikes distinctively, we can make the case that price has found sufficient liquidity to halt/turn. Since we know which side was absorbed, in what quantity and type (passive/active), we can identify areas of trapped market participants. In such scenarios, the higher the dominant active volume and volume spike itself, the better.
Example #2 : Identifying a healthy trend
A healthy trend is one that has an active and consistent bid driving it. When this is the case, it can be seen in consistently supportive active volume.
Example #3 : Identifying inflection points
When dominant side of volume and dominant side of active volume diverge, something is up. A divergence often marks an area of indecision, hinting an imminent move one way or the other.
