Volume Spread Analysis IQ [TradingIQ]Hello Traders!
🔹Volume Spread Analysis IQ
This indicator was most voted on for our indicator competition - so here it is! Hope you guys like it :D
Volume Spread Analysis IQ is a chart-reading tool built to help traders judge effort, result, and background context in a way that is visual and practical.
Instead of forcing you to interpret volume and spread in isolation, this indicator organizes what the bar is doing into a readable structure so you can quickly see when the market is showing:
low participation
high participation
narrow or wide spread
potential hidden strength
potential hidden weakness
contextual VSA signals such as No Demand, No Supply, Upthrusts, Shakeouts, and Stopping Volume
🔹Why Effort vs Result Matters in Volume Spread Analysis
The following information is relevant to VSA interpretation.
In any market, price movement is the visible outcome of an underlying battle between buyers and sellers. Volume represents the effort being applied in that battle, while the spread of the candle reflects the result of that effort.
When effort and result move together, the market is behaving efficiently. High effort producing a large price move suggests strong conviction and participation. In trending conditions this often confirms that the dominant side of the market is still in control.
However, when effort and result begin to diverge, it can reveal hidden information about what is happening beneath the surface.
For example:
High effort with very little upward progress may indicate that strong selling pressure is absorbing buyers. Even though buyers are active, their effort is not producing meaningful results. This type of imbalance can appear before weakness develops.
Likewise, high effort with very little downward progress can signal that sellers are being absorbed by hidden demand. Large amounts of selling activity fail to push price lower, suggesting accumulation may be taking place.
Low effort situations are also informative. A rally with very low effort often lacks participation and can signal weak demand, while a selloff with very little effort can suggest that selling pressure is fading.
From a structural perspective, the effort/result relationship helps traders distinguish between moves driven by genuine participation and moves that occur simply because the market is temporarily thin. This distinction can be important when evaluating breakouts, pullbacks, or potential reversals.
In short, effort tells you how hard the market is trying to move, while result tells you how successful that attempt actually was. When these two fall out of balance, it often reveals shifts in supply and demand before they become obvious on price alone.
🔹What the indicator shows🔹
🔸Background bias
Each candle is tinted to reflect the recent VSA background. This helps you judge whether the market is currently leaning strong, weak, or neutral based on the recent flow of bullish and bearish evidence.
🔸Effort vs. Result view
The lower panel converts both volume and spread into easy-to-read rankings from 1 to 10.
Effort represents how active the market is.
Result represents how much price actually moved.
🔸Per-candle labels
Optional candle labels show a simple readout for each bar:
R = Result rank
E = Effort rank
🔸Effort vs. Result summary table
A live table on the chart shows the current effort rank, result rank, and the current interpretation of their relationship.
🔸Key VSA event markers
The script marks classic VSA conditions directly on the chart when they appear in the proper context:
No Demand
No Supply
Upthrust
Shakeout
Stopping Volume
🔹How to read it
Effort asks: How much activity came into this bar?
Result asks: How much did price actually move?
Background asks: Is recent behavior supporting strength or weakness?
This combination helps separate bars that look dramatic from bars that are actually meaningful.
For example:
High effort with poor upward result can hint that buying is struggling
High effort with poor downward result can hint that selling is being absorbed
Low effort rallies can warn of weak demand
Low effort selloffs can suggest supply is drying up
🔹Signal overview
No Demand
Highlights weak upward bars with low participation.
No Supply
Highlights weak downward bars where selling pressure appears limited.
Upthrust
Marks a rejection bar that appears in weak background conditions and can warn of downside risk.
Shakeout
Marks a lower rejection bar that appears in strong background conditions and can suggest bullish intent.
Stopping Volume
Flags heavy selling activity that may be halting a move lower. Context matters. In strong background it can be bullish. In weak background it can simply pause price before weakness resumes.
🔹Why this indicator is useful
Many traders can see volume. Far fewer can quickly judge whether that volume actually meant anything.
This tool is designed to help with exactly that.
It gives you:
a cleaner way to read volume and spread together
fast recognition of effort versus result imbalance
background context instead of isolated signals
VSA-style event labeling without requiring a cluttered chart
friendly settings for newer users, plus advanced overrides for experienced users
🔹Best use cases
confirming whether breakouts have real participation
spotting weak rallies and weak selloffs
judging whether aggressive bars are efficient or wasteful
finding VSA-style reversal or continuation clues
adding context to your existing market structure, liquidity, or price action model
🔹Important note
This indicator is a chart-reading tool , not a promise of outcomes. VSA works best when signals are interpreted in context, not taken mechanically one by one.
Use the background, the effort/result relationship, and the signal location together.
Important consideration
We scoured the internet, books, you name it to find detailed information on VSA techniques. That said, information is sparse and conflicting depending on where you look. We relied mostly on gold standard literature. However, the information in that literature is far from objective.
Many descriptions are similar to…
“An upthrust is a bar that pushes up and then fails, showing rejection of higher prices, usually in a weak background.”
Coding this requires interpretation by the engineer - there aren’t exact rules to follow. This means the indicator’s presentation of an upthrust, shakeout, etc. might not always align with your definition of those events.
You can customize the settings to force the indicator to better match your interpretation.
🔹Inputs you can customize
The script includes simple user-friendly controls such as:
What counts as a small body
What counts as a long wick
How strict close location should be
How strict spread and volume classifications should be
How much background proof you want before the indicator leans strong or weak
Whether to use broader or more traditional No Demand / No Supply logic
Whether Shakeouts and Upthrusts should require clear trend alignment
Advanced users can also enable raw threshold overrides for finer control.
🔹Closing Notes
And that’s about it!
This script might receive updates in the future if the community asks for it - stay tuned!
Thank you TradingView as always!
Indicatore
Profit Loss Display UniversalUniversal Profit Loss Display
is a chart overlay for quickly visualizing projected reward, risk, and reference levels from the current price.
The script lets you select:
- one main profit target
- one main stop loss level
- four additional reference sources
The sources can be any element of another indicator displayed on the current chart (using the TradingView's source parameter.
**How it works**
The indicator compares each selected level to the current price and estimates the move using your configured position base. This makes it useful for planning trades around fixed targets, stops, VWAPs, pivots, bands, or any custom level source.
For each active level, it displays the move from current price as price points, dollar value, or both. The profit target and stop loss also get dedicated value labels, and the script calculates a live reward-to-risk ratio in `R` format.
**Key features**
- Configurable `input.source` levels for profit target, stop loss, and 4 extra references
- Display mode selector: `Price Points`, `$ Value`, or `Both`
- Automatic leverage detection by asset class, with manual override
- Dollar projection based on chunk size and leverage
- Clean on-chart labels that only appear when a source is different from `close`
- Reward-to-risk ratio displayed directly on the chart
- Bottom-right info table showing active chunk size and leverage
**Best use cases**
- Pre-trade reward/risk planning
- Visual target and stop mapping
- Comparing multiple nearby levels from current price
- Quick dollar impact estimation without manual calculation
**Notes**
This is a planning helper, not a strategy or execution tool. Dollar projections do not include fees, spread, slippage, funding, or partial exits.
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**How To Use**
1. Add the indicator to your chart.
2. Set your main `Profit Target` and `Stop Loss Limit`.
These are the two primary levels used for the projected profit/loss labels and the `R` ratio.
3. Optionally assign `Extra Source 1-4`.
Use these for additional levels you want to monitor, such as VWAPs, pivots, moving averages, bands, or custom plotted values.
4. Choose your `Display Mode`.
- `Both` shows price distance and dollar value
- `Price Points` shows only the raw move from current price
- `$ Value` shows only the projected dollar impact
5. Set your `Chunk Size`.
This is the base position size used in the calculation.
6. Set `Leverage X`.
- Leave it at `0` to use automatic leverage by asset class
Automatic Leverage Rules
If Leverage X is set to 0, the script uses these defaults:
crypto: 5
forex: 30
futures: 10
cfd: 10
stock: 5
index: 20
fund: 5
anything else: 10
A small table in the bottom-right corner shows the active chunk size and leverage.
- Enter a manual value if you want to override auto leverage
7. Turn `Show Labels?` on or off as needed.
**What You Will See**
- A source label for the profit target
- A source label for the stop loss
- Source labels for up to 4 extra levels
- A dedicated profit value label
- A dedicated stop-loss value label
- An `R:x.x` reward-to-risk ratio label
- A bottom-right table showing current chunk size and leverage
**Important Behavior**
- Labels only appear when the selected source is different from `close`
- `close` acts as the default “off” state for any source input
- Labels are updated on the latest bar only
- The ratio is based on distance from current price to target and stop
**Practical Example**
If you are planning a trade:
- set `Profit Target` to your take-profit level
- set `Stop Loss Limit` to your invalidation level
- set `Chunk Size` to your standard trade allocation
- choose a leverage value or leave auto mode enabled
- use the extra sources for nearby confluence or reaction zones
This gives you an immediate view of:
- potential upside
- potential downside
- reward-to-risk ratio
- relative distance to other important levels
**Notes**
This script is a visual planning tool. It does not place trades and does not account for fees, slippage, spread, funding, or scaling in and out.
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NOA Trading Sessions ProNOA Trading Sessions Pro
This indicator plots up to four custom trading sessions on your chart and calculates real-time statistics for volume, range, and volatility. It is built to provide the exact metrics used on institutional trading desks to measure market context.
Core Mechanics
You can define the start and end times, IANA timezone, and colors for four different sessions (for example: Tokyo, London, Pre-Market, New York). The script plots the open, high, low, and close levels of the active session, along with an optional mid-line or session VWAP.
A data box anchors below each session. To prevent the text from overlapping with price bars or volume profiles, the script uses a dynamic ATR offset to push the label into empty chart space. This keeps the chart readable on any timeframe or asset class.
Reading the Data Like an Institutional Trader
Retail traders often trade patterns; institutional traders trade statistics. The data box provides the context needed to understand if a move is legitimate, exhausted, or a trap.
Range vs. Average Range: The indicator tracks the current session range and compares it to the historical average for that specific time of day. If the London session typically moves 80 ticks and currently sits at 30 ticks, the market is compressing and a move is likely pending.
Relative Volume (RVol): Volume validates price. The script calculates a volume multiplier comparing the current session to its historical average. If price breaks a session high on 0.5x volume, it is likely a liquidity sweep or a false breakout. If it breaks on 1.5x volume or higher, institutions are actively participating and the breakout is valid.
Delta: This measures the net directional push from the session open. Comparing the Delta to the total Range tells you how directional the session is versus how much it is just chopping back and forth.
VWAP: The Volume Weighted Average Price is the institutional baseline. Price extending far from VWAP on low relative volume is a prime condition for mean reversion. Price holding above VWAP on high relative volume signals sustained institutional accumulation.
The Volatility Heatmap
The text color in the data box shifts automatically based on how the current range compares to the historical average range. This helps identify exhaustion and expansion in real time.
Gray (Under 70%): The session is in a compression phase. Price is chopping in a tight range. Expect mean reversion and avoid trading breakouts.
White (70% to 99%): Normal distribution. The session is developing as statistically expected.
Yellow (100% to 119%): Exhaustion. The session has met its historical daily target. Late breakouts at this stage have a high probability of failure, and institutional traders are likely taking profit.
Red (120%+): Expansion. This is a statistical outlier or a trend day, usually driven by high-impact news. Mean reversion strategies will fail here; you only trade with the momentum.
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Indicatore
Risk AwarenessRisk Awareness - Liquidation Level Indicator
A clean, professional tool for displaying liquidation prices on leveraged positions. Designed for traders who need instant visibility of their risk levels without chart clutter.
KEY FEATURES
Real-time liquidation price calculation for long and short positions
Adjustable leverage from 1x to 200x
Fire engine red (long) and lime green (short) color-coded levels
Two label modes: Compact (minimal) and Detailed (full info)
Horizontal lines extending left from current price
Optional P&L tracking and display
Background alerts when approaching liquidation
Customizable maintenance margin and liquidation fee parameters
HOW IT WORKS
The indicator calculates liquidation prices using the standard formula:
Long Liquidation = Entry Price x (1 - 1/Leverage - Liquidation Fee + Maintenance Margin)
Short Liquidation = Entry Price x (1 + 1/Leverage + Liquidation Fee - Maintenance Margin)
Default parameters (0.5% maintenance margin, 0.5% liquidation fee) are calibrated for major crypto futures exchanges like Binance, Bybit, and OKX.
DISPLAY MODES
Compact Mode: Shows only leverage and price (e.g., "40x: 48750.00")
Detailed Mode: Shows full information including percentage distance and optional P&L
CUSTOMIZATION OPTIONS
Position Settings: Adjust leverage, toggle long/short, select entry price source
Custom Parameters: Fine-tune maintenance margin and liquidation fee for your specific exchange
Visual Settings: Colors, line width, label size, historical bands, disclaimer display
Alert Settings: Set distance threshold for liquidation warnings
Risk Management: Track unrealized P&L based on position size
ALERTS
Built-in alert conditions for:
Price crossing liquidation levels
Approaching liquidation threshold
Critical loss levels (50%+)
IMPORTANT DISCLAIMER
This indicator provides ESTIMATED liquidation levels for Tier 1 positions (small to medium size). Actual liquidation prices may vary due to:
- Position size tiers (larger positions = higher maintenance margins)
- Accumulated funding rates
- Market volatility and order book depth
- Cross margin vs isolated margin mode
- Exchange-specific liquidation engines
Always verify liquidation prices on your exchange platform before trading. This tool is for educational and risk awareness purposes only.
IDEAL FOR
Crypto futures traders on Binance, Bybit, OKX, and similar platforms
Day traders managing leveraged positions
Swing traders monitoring overnight risk
Anyone trading with leverage who needs clear visual risk management
PRO TIPS
Use Compact mode with Tiny/Small label size for minimal chart clutter
Enable the Info Table for detailed metrics when needed
Set Alert Distance to 1-2% for advance warning before liquidation
Toggle "Show Historical Bands" OFF (default) for cleaner charts
Adjust Custom Parameters if trading on exchanges with different fee structures
Stay aware. Trade smart. Manage your risk. Indicatore
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Evo Market Decision Engine (EMDE)Evo Market Decision Engine (EMDE) is an experimental market-reading tool designed to combine multiple layers of analysis into a single visual and decision-oriented indicator.
The idea behind this tool is simple: to provide a working framework capable of combining structure, flow, momentum, market regime, sentiment, multi-timeframe alignment, and probabilistic reading in order to produce a more synthetic view of the current market context.
The indicator includes:
- a kernel-smoothed price base
- price excess detection
- buyer / seller flow reading
- pivot-based structural analysis
- flow toxicity and market regime measures
- an adaptive scoring engine
- a multi-timeframe filter
- a simulated “footprint” reading block
- an NQ100 sentiment model based on major large-cap stocks
- a full dashboard with probability, bias, filters, and signals
The goal is not to provide an absolute truth, but to offer a decision-support engine capable of synthesizing multiple signals into a single analytical framework.
Important: I do not recommend following the positions or signals generated by this tool as-is.
I mainly used it as a working base, idea lab, and study framework, not as a ready-to-trade turnkey tool.
I am sharing it anyway because it may be useful for some of you, whether to understand the logic, study the code structure, modify certain blocks, or use it as a starting point to develop something else.
The source code is open, so everyone can do whatever they want with it: study it, adapt it, improve it, simplify it, or simply use it as inspiration. Indicatore
Strategia
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Market Microstructure AnalyticsThe Hidden Toll on Every Trade
Every time you buy or sell a financial instrument, you pay a cost that never appears on your brokerage statement. It is not a commission. It is not a fee. It is the spread between the price at which someone is willing to sell to you and the price at which someone is willing to buy from you. That gap, measured in ticks, basis points, or fractions of a percent, is the bid-ask spread. Over a single trade it looks small. Over thousands of trades, across a year, for a fund managing billions, it compounds into one of the most significant sources of performance drag in all of finance.
For decades, institutional traders have measured this cost obsessively. Research desks at hedge funds and investment banks have dedicated entire teams to understanding when spreads are wide, why they widen, who is causing them to widen, and what that signal implies about the near-term behaviour of a market. Retail traders, however, have had almost no access to this kind of analysis. The reason is simple: measuring the bid-ask spread in real time requires access to the order book, tick-by-tick trade data, and quote data that most platforms either do not provide or lock behind expensive data terminals.
This indicator changes that. Using only the OHLCV data that every chart on TradingView already contains, it reconstructs spread estimates and liquidity conditions through seven statistically validated models drawn directly from the academic market microstructure literature. It cannot replicate what a full order book feed provides, and the documentation is explicit about where the approximations are. But it gets considerably closer than anything available to the typical chart-based trader, and on short intraday charts it delivers information that is genuinely useful for both execution decisions and regime assessment.
What Market Microstructure Actually Measures
Market microstructure is the academic field that studies how prices are formed at the level of individual transactions. Its central question is not where a price will go tomorrow but how the mechanics of trading itself affect price formation right now. Two papers published decades apart established the framework this indicator builds on.
The first was by Roll (1984), who noticed something elegant: in an efficient market, the prices of consecutive trades should not be correlated with each other, because any predictability would be arbitraged away. But if you look at actual trade-by-trade price changes, you consistently find negative autocorrelation. Prices bounce back and forth. The reason, Roll argued, is the bid-ask spread itself. Buyers trade at the ask and sellers at the bid, so consecutive trades alternate between two price levels. This bouncing creates a predictable negative covariance in price changes, and the size of that covariance is directly related to the size of the spread. From this insight he derived the formula S = 2 times the square root of the negative covariance of consecutive price changes. If you observe a series of trades and measure how negatively they correlate with each other, you can back out the spread without ever seeing a quote.
The second foundational contribution came from Kyle (1985), who approached the problem from a completely different angle. He asked: if a market contains some traders who have private information about the true value of an asset, how do their orders affect price? His answer was the lambda coefficient, a measure of how much the price moves per unit of net order flow. A high lambda means the market is thin and informed: each additional unit of buying or selling pushes the price significantly. A low lambda means the market absorbs flow without moving much. Lambda is not just a spread measure; it is a measure of how much information asymmetry exists in the market at any given moment. This is the adverse selection component of the spread, and it is arguably the most strategically useful signal the indicator produces.
The Spread Estimators
The first layer of computation produces four distinct estimates of the bid-ask spread, each using a different statistical approach.
The Roll (1984) estimator is the oldest and most widely cited. It computes the rolling covariance between a price change and the price change that came before it, then takes two times the square root of the negative of that covariance. One important detail: Roll's model is defined in terms of absolute price changes, not log-returns. Using log-returns introduces a scaling distortion tied to the price level of the asset, which biases the spread estimate upward at high prices. This implementation correctly uses delta-P throughout.
The Corwin-Schultz (2012) estimator takes a fundamentally different approach. Rather than looking at the serial structure of price changes, it uses the high-low range of a bar. The core insight is that the high price of any trading period is most likely a transaction that occurred at the ask, while the low price is most likely a transaction at the bid. If you look at a two-period window, the combined high-low range reflects the true price variance over those two periods plus the spread component. A single-period range conflates variance and spread; the two-period structure allows them to be separated algebraically. The resulting formula involves a decomposition using the constant k = 3 minus 2 times the square root of 2, which emerges from the statistical properties of the high-low range under continuous diffusion. Corwin and Schultz (2012) validated this estimator extensively against actual quoted spreads across thousands of US equities and found it performs well both in cross-section and over time.
The Abdi-Ranaldo (2017) estimator is the most recent of the three and, in empirical tests, the most stable. For each bar, it computes a quantity called c, defined as the log of the close price minus the average of the log-high and log-low. This is the signed deviation of the close price from the geometric midpoint of the bar's range, expressed in log-space. Abdi and Ranaldo proved that the expected value of the product of c at time t and c at time t plus one equals negative one quarter of the spread squared. This means that by measuring how negatively c correlates with the next period's c, you can recover the spread. The estimator inherits much of the intuition of Roll but anchors itself to the intrabar price range rather than the close-to-close change, which tends to reduce noise substantially. To handle cases where the high and low are identical, which occurs on 1-tick bars or extremely liquid instruments, the implementation excludes invalid pairs from the covariance calculation rather than substituting zeros, which would bias the estimate toward zero.
The effective spread proxy takes yet another approach. Rather than estimating the quoted spread, it attempts to estimate the effective spread, which is the actual cost paid by a specific trade. The formula is two times the trade direction multiplied by the distance between the transaction price and the quote midpoint. Trade direction is approximated using the tick rule, which assigns a positive sign to transactions at prices higher than the previous price and a negative sign to those at lower prices, carrying the previous sign forward when the price is unchanged. This classification method was formalised by Lee and Ready (1991) and remains the standard approach for assigning direction when quote data is unavailable. The bar midpoint substitutes for the true quote midpoint, which introduces a systematic upward bias because the high and low of a bar are extreme transaction prices, not quotes. The effective spread proxy is therefore most reliable as a relative indicator of whether transaction costs are rising or falling, rather than as an absolute estimate of the quoted spread.
The Liquidity Metrics
The second layer moves beyond spread estimation into broader liquidity measurement. The key distinction is this: the spread tells you what it costs to execute one trade right now. Liquidity metrics tell you something about the structure of the market, how deep it is, how much information is embedded in the current order flow, and how efficiently prices are absorbing volume.
The Amihud (2002) illiquidity ratio is the most widely used liquidity measure in the academic asset pricing literature. Its construction is conceptually simple: it divides the absolute value of a log return by the dollar volume of trading in the same period. What this measures is price impact per dollar traded. If a stock moves one percent and 10 million dollars changed hands, the ratio is small. If the same one percent move happened on only 50,000 dollars of volume, the ratio is large, indicating a thin market where small amounts of capital move prices significantly. Unlike the spread measures, which capture the cost of a single round trip, the Amihud ratio captures market depth. This implementation uses dollar volume rather than share or contract volume, which is the correct specification for comparability across instruments at different price levels. The ratio is scaled by a factor of 100 million for display purposes; its absolute level is asset-dependent and should always be interpreted relative to the instrument's own history.
Kyle lambda, estimated here via ordinary least squares regression of price changes on signed volume, is the most theoretically sophisticated metric in the indicator. Each bar's signed volume is the total volume signed by the tick rule direction: positive if the bar closed higher than the previous bar, negative if it closed lower. The regression coefficient from regressing price changes on this signed volume is the lambda estimate. A high positive lambda means prices are moving more than expected for the amount of flow being absorbed, which is the signature of informed trading. When lambda rises, someone in the market likely knows something that others do not, and market makers are widening their spreads in response. The critical implementation detail here is that the volume must not be normalised before the regression. Normalising the signed volume changes the regression coefficient from a price-impact-per-share measure to a dimensionless sensitivity measure, which is a different quantity and does not correspond to Kyle's original model.
The Parkinson (1980) range-based volatility estimator serves a supporting role: it estimates intrabar variance from the high-low range using the formula sigma-squared equals one over four times the natural log of two, multiplied by the square of the log ratio of high to low. This estimator is approximately five times more statistically efficient than the classic close-to-close variance estimator for the same number of observations (Parkinson 1980). Its role in this indicator is to help decompose the high-low range: the range reflects both volatility and the spread, and the ratio of the composite spread estimate to the Parkinson volatility tells you which component is dominant at any given time.
The Composite and the Regime System
Having computed multiple independent estimates of the spread, the natural question is how to combine them. Simple averaging is theoretically suboptimal when the estimators have different levels of noise. The precision-weighted composite assigns each estimator a weight inversely proportional to its robust variance, so that noisier estimators contribute less to the final reading.
The key word is robust. Rather than computing standard rolling variance, which is dominated by extreme observations and can make a normally well-behaved estimator look unreliable for weeks after a single outlier bar, this implementation uses a variance estimator based on the Median Absolute Deviation, or MAD. The MAD is the median of the absolute deviations from the rolling median. Multiplied by the consistency factor 1.4826, it provides an equivalent to the standard deviation that is resistant to outliers with a breakdown point of 0.5, meaning up to half the observations in a window can be extreme values without corrupting the estimate. This approach follows Rousseeuw and Croux (1993), who established the formal properties of MAD-based scale estimators.
Two further safeguards stabilise the weights. A ridge regularisation term, set to five percent of the mean robust variance across active estimators, prevents any weight from exploding toward infinity when an estimator is temporarily near-constant. And a weight cap, set by default at 70 percent of the total, prevents any single estimator from dominating the composite during regimes where it happens to be locally smooth. The live weights are displayed in the dashboard so the user can always see how the composite is currently distributed.
The regime detection system answers the question of whether the current spread level is historically unusual. This is done through a robust z-score: the composite spread is compared to its rolling median, and the deviation is normalised by the MAD. The result is a standardised score that tells you how many robust standard deviations the current spread is from its recent typical level. A score of two or above signals a statistically unusual widening event. The same procedure is applied independently to the Amihud illiquidity ratio and to the absolute value of Kyle lambda.
These three scores are then combined into the Liquidity Stress Index, computed as their equal-weighted average after each component has been winsorised at plus or minus three robust standard deviations. The winsorisation prevents a single extreme reading in one dimension from overwhelming the composite. Each component is then winsorised before averaging to prevent a single extreme dimension from dominating. The result is mapped to a zero-to-100 scale using the hyperbolic tangent function, where 50 represents neutral conditions, readings in the 65 to 80 range indicate elevated stress, and readings above 80 indicate severe stress across multiple liquidity dimensions simultaneously.
Practical Use Cases
For a retail trader, the most immediately useful output is the composite spread and its regime classification. When the composite spread widens and the regime indicator shifts to Elevated or Stress, entering a new position becomes more expensive than usual. On illiquid instruments this widening can be dramatic, consuming a significant fraction of the expected profit in transaction costs before the trade even begins. Conversely, when spreads are compressed, the market is functioning efficiently and execution is cheap. Timing entries and exits around spread conditions is a simple, evidence-based way to reduce the invisible drag that erodes returns over time.
The spread trend indicator, which compares a five-period exponential moving average of the composite spread against a twenty-period average, provides a simple directional signal. A widening trend often precedes a period of higher volatility, lower liquidity, or increased uncertainty. This does not tell you which direction the price will move, but it tells you that the environment is becoming less predictable and more costly to trade, which is operationally important information.
For professional traders and systematic strategy developers, the Kyle lambda signal has specific applications. When lambda is elevated relative to its own history, which the dashboard displays as the adverse selection z-score, it indicates that price changes are disproportionate to the measured order flow. This is consistent with the presence of informed traders, a phenomenon central to the theoretical work of Kyle (1985) and Glosten and Milgrom (1985). Elevated adverse selection is one of the clearest early warning signs of an impending directional move driven by asymmetric information, such as pre-announcement positioning, earnings whispers, or macroeconomic data leakage.
The Amihud illiquidity ratio is particularly valuable for cross-asset comparisons and for monitoring the liquidity conditions of a specific instrument over time. Portfolio managers can use it to time their entries and exits in less liquid securities: entering when illiquidity is below its historical median and exiting before a period of known low liquidity such as a holiday period or low-volume session. Research by Amihud and Mendelson (1986) established that expected returns are positively correlated with illiquidity, meaning that investors demand higher compensation for holding assets where transaction costs are high. The illiquidity z-score in this indicator allows that premium to be tracked in real time.
The spread-to-volatility ratio is a metric that practitioners familiar with the work of Corwin and Schultz (2012) will recognise immediately. It expresses the composite spread as a percentage of the Parkinson volatility estimate. When this ratio is high, the spread accounts for a large fraction of the observed price range, which typically indicates a market where market makers are cautious and price discovery is slow. When it is low, the price range is driven primarily by genuine information, not by the mechanics of the spread. This ratio is useful for distinguishing between a volatile and actively traded market, which is generally healthy, and a wide-spread market that looks volatile but is actually just illiquid.
The Liquidity Stress Index in its scaled zero-to-100 form provides an accessible summary for traders who do not want to track multiple metrics simultaneously. During normal market conditions the reading sits near 50. When all three components, the spread, the illiquidity ratio, and the adverse selection estimate, are simultaneously elevated relative to their own histories, the index rises sharply. The historical examples of this pattern occurring together include the flash crash of May 2010, the August 2015 China-driven volatility spike, the COVID-19 crash of March 2020, and various cryptocurrency deleveraging events. In each case, the simultaneous widening of spreads, collapse of market depth, and spike in price impact coefficients preceded the most severe price dislocations by enough time to be actionable.
Configuration and Settings
The Estimation Window controls the rolling window for all covariance and liquidity calculations. A shorter window, around 10 to 20 bars, makes the estimators more responsive to recent changes but increases noise. A longer window, around 50 bars, produces smoother estimates that better reflect structural conditions but lag more. The default of 20 is a reasonable starting point for most intraday timeframes.
The EMA Smoothing parameter applies an exponential moving average to each raw spread estimate before it is used in the composite and displayed on the chart. This reduces bar-to-bar noise without introducing the same lag that a longer estimation window would create. Setting it to 1 disables smoothing entirely, which is useful for research purposes but not for trading.
The Regime Window determines how far back the robust z-scores look when assessing whether current conditions are unusual. A setting of 100 means the indicator asks whether the current spread is unusual relative to the last 100 bars. For daily charts, 100 bars is approximately five months of trading. For tick charts, it represents the most recent 100 tick bars. This parameter should be set large enough to capture at least one full market cycle of the relevant timeframe.
The Maximum Composite Weight prevents any single estimator from being assigned more than the specified fraction of total weight. The default of 70 percent is conservative; in practice, during regimes where all three estimators agree and produce similar variances, the weights tend to distribute fairly evenly. The cap becomes most important when one estimator is temporarily quiet and its MAD-based variance falls to near zero, which would otherwise assign it almost all the weight.
The LSI Winsorisation Cap limits the influence of extreme readings in any single component before they contribute to the Liquidity Stress Index. At the default of three robust standard deviations, a reading of ten, which would represent a truly exceptional event, contributes the same as a reading of three. This prevents a single data anomaly or calculation artifact from permanently elevating the stress index.
Structural Limitations
No representation is made that these outputs are equivalent to actual exchange quote data. They are not. TradingView provides bars, not tick-by-tick trades, and the academic models on which this indicator is based were developed for transaction-level data. The Roll estimator assumes that each observation is a single trade; when a bar aggregates hundreds or thousands of trades, the covariance structure it observes is a convolution of many individual trade-level covariances, and the result understates the true spread. This bias grows with bar duration and trade frequency. On 1-tick or 5-tick bars the bias is minimal; on daily bars it can be substantial.
The tick rule classification, which assigns trade direction to bars and underpins both the Kyle lambda and effective spread estimates, was designed for individual trades. Applied to the close price of aggregated bars, it misclassifies a material fraction of bars. Ellis, Michaely and O'Hara (2000) documented misclassification rates of 30 to 50 percent on daily stock data. On short intraday bars the performance is better, but it never reaches the accuracy achievable with actual quote data.
The rolling MAD computation is a streaming approximation to the exact finite-window MAD. In a stationary process the difference is negligible and the heavy-tail robustness property is preserved. In rapidly changing regimes the approximation introduces a small second-order error that does not materially affect the interpretation of the outputs.
The effective spread proxy suffers from a systematic upward bias because it uses the bar midpoint rather than the true quote midpoint. This bias is largest when the intrabar range is wide relative to the actual spread, which is precisely when the estimate is most needed. On very short tick bars the range collapses toward the actual spread and the bias diminishes, but on longer bars the effective spread reading should be treated as an upper bound rather than a point estimate.
Pine Script v6 introduced the built-in variables bid and ask, which return the current best bid and ask prices from a connected broker feed when accessed on the 1-tick timeframe via request.security(syminfo.tickerid, "1T", bid) and request.security(syminfo.tickerid, "1T", ask). This is a genuine improvement over bar-based proxies for the single most recent bar. However, these variables carry three constraints that prevent them from replacing the statistical estimators in this indicator. First, they carry no historical record: the values exist only at the current bar and return na on all prior bars, which makes it impossible to compute rolling covariances, MAD-based z-scores, or any of the regime detection logic that requires a lookback window. Second, the data is only available through a live broker connection on TradingView. Users on free accounts, paper trading environments, or instruments not covered by their connected broker will receive na throughout. Third, instrument coverage is uneven: major forex pairs, selected cryptocurrency pairs on exchanges such as Binance, and equities through brokers such as Interactive Brokers are generally supported, but futures, CFDs on many instruments, and equities through data-only feeds often return no data. The statistical estimators in this indicator therefore remain the primary analytical engine. If a broker connection is active, the live bid-ask spread retrieved via these built-in variables can serve as a real-time reference point to validate whether the rolling estimates are in a plausible range for the current session, but it cannot contribute to the historical signal calculations.
None of the outputs should be used as the sole basis for any trading decision.
References
Abdi, F. & Ranaldo, A. (2017) A Simple Estimation of Bid-Ask Spreads from Daily Close, High, and Low Prices. Review of Financial Studies, 30(12).
Amihud, Y. (2002) Illiquidity and Stock Returns: Cross-Section and Time-Series Effects. Journal of Financial Markets, 5(1), 31-56.
Amihud, Y. & Mendelson, H. (1986) Asset Pricing and the Bid-Ask Spread. Journal of Financial Economics, 17(2).
Corwin, S.A. & Schultz, P. (2012) A Simple Way to Estimate Bid-Ask Spreads from Daily High and Low Prices. Journal of Finance, 67(2).
Ellis, K., Michaely, R. & O'Hara, M. (2000) The Accuracy of Trade Classification Rules: Evidence from Nasdaq. Journal of Financial and Quantitative Analysis, 35(4).
Glosten, L.R. & Milgrom, P.R. (1985) Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders. Journal of Financial Economics, 14(1).
Hasbrouck, J. (2009) Trading Costs and Returns for U.S. Equities: Estimating Effective Costs from Daily Data. Journal of Finance, 64(3).
Kyle, A.S. (1985) Continuous Auctions and Insider Trading. Econometrica, 53(6).
Lee, C.M.C. & Ready, M.J. (1991) Inferring Trade Direction from Intraday Data. Journal of Finance, 46(2).
Parkinson, M. (1980) The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53(1).
Roll, R. (1984) A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market. Journal of Finance, 39(4).
Rousseeuw, P.J. & Croux, C. (1993) Alternatives to the Median Absolute Deviation. Journal of the American Statistical Association, 88(424).
Indicatore
Smart Position Sizer [QuantRegime]## Overview
The Smart Position Sizer calculates the exact position size for any trade based on your account size, risk percentage, and stop loss distance. It answers the question every trader should ask before every trade: **"How many shares/contracts/coins should I buy?"**
Most traders either skip this calculation (and blow up) or do it manually (and make mistakes). This indicator does it instantly, on any market, on any timeframe.
## How It Works
**The core formula:**
$$\text{Position Size} = \frac{\text{Account} \times \text{Risk \%}}{\text{Stop Loss Distance}}$$
But unlike a simple calculator, this indicator:
1. **Automatically calculates stop loss distance** using 5 different methods
2. **Shows entry, SL, and 3 take-profit levels** directly on your chart
3. **Displays exact dollar risk, position value, and implied leverage**
4. **Adjusts in real-time** as price and volatility change
## 5 Stop Loss Methods
| Method | Best For | How It Works |
|--------|----------|-------------|
| **ATR** | Most traders | Places SL at N × ATR from entry. Adapts to current volatility. |
| **Fixed Points** | Scalpers | Fixed pip/point distance. Consistent mechanical stop. |
| **Fixed %** | Swing traders | SL at a fixed % below/above entry. Simple and predictable. |
| **Recent Swing** | Price action traders | Uses the most recent swing high/low as the stop level. |
| **Manual Price** | Specific levels | You type in the exact SL price (e.g., below a key support). |
## The Dashboard
The dashboard shows everything you need at a glance:
- **Direction** — Long or Short (auto-detected or manual)
- **Position Size** — How many units/shares/contracts to buy
- **Position Value** — Total dollar value of the position
- **Risk Amount** — Exact dollars at risk if stopped out
- **Stop Loss** — Price level and % distance
- **ATR** — Current average true range and % of price
- **TP1, TP2, TP3** — Take profit targets with R:R ratio and dollar profit
## Chart Lines
The indicator draws directly on your chart:
- **White solid line** — Entry price
- **Red dashed line** — Stop loss level
- **Cyan dotted lines** — 3 take profit levels with R:R labels
## Who This Is For
- **Crypto traders** — Know exactly how many coins to buy for your risk tolerance
- **Stock traders** — Calculate share count for any position
- **Forex traders** — Get lot sizes based on pip risk
- **Futures traders** — Contract sizing with proper risk management
## Settings Guide
### For Conservative Traders (1% risk)
- Risk Per Trade: 1%
- ATR Multiplier: 2.0-2.5
- TP1: 1.5R, TP2: 2.5R, TP3: 4R
### For Moderate Traders (2% risk)
- Risk Per Trade: 2%
- ATR Multiplier: 1.5-2.0
- TP1: 1R, TP2: 2R, TP3: 3R
### For Aggressive (scalping, 3%+)
- Risk Per Trade: 3%
- SL Method: Fixed Points (tight)
- TP1: 1R, TP2: 1.5R, TP3: 2R
## Important Notes
- **Update your account size** as it changes — this is not connected to your broker
- Position sizes are calculated based on the current bar's close price
- The "Auto" direction uses a 21 EMA — override manually if you have a specific bias
- Works on any market with proper syminfo (stocks, crypto, forex, futures, indices)
## Risk Management Rules
This indicator enforces proper risk management by design:
- Never risk more than your set % per trade
- ATR-based stops adapt to current market volatility
- The R:R display helps you only take trades with favorable risk/reward
> "The goal of a successful trader is to make the best trades. Money is secondary." — Alexander Elder
## Disclaimer
This indicator is for educational and analytical purposes only. It does not constitute financial advice. Position sizing calculations depend on accurate input of your account size and do not account for slippage, gaps, or execution differences. Always verify calculations before placing trades. Indicatore
Indicatore
Daily Bias Evaluator Clean + Current Prediction FIXEDThis indicator is a daily bias evaluator designed to estimate whether the next trading day is more likely to take the Previous Day High (PDH), the Previous Day Low (PDL), or remain neutral. It is built for daily-chart use and combines a rule-based scoring model with historical outcome tracking.
The script analyzes the most recently completed daily candle as the setup day and evaluates factors such as rejection of PDH/PDL, bullish or bearish context, close location within the range, optional previous-week filter, continuation behavior, and a simple fair value gap factor. Based on these inputs, it generates a next-day bias: Likely PDH, Likely PDL, or Neutral.
It also evaluates historical predictions bar by bar and classifies outcomes as:
Correct
Wrong
Inside
Both
Neutral
Historical markers are plotted directly on the chart, and summary statistics are displayed in a table, including total evaluated cases and accuracy percentage.
This script is intended as a structured research and training tool for studying daily directional bias and previous-day liquidity behavior, not as a fully automated trading system.
Short description
Daily bias evaluator for predicting whether the next day is more likely to take PDH or PDL, with historical stats, markers, and rule-based scoring. Indicatore
Indicatore
Indicatore
Indicatore
Synapse_VSync_LibV-Sync (Volume Synchronization) is a multi-dimensional macro-confluence engine. It aggregates four objective market truths into a single synchronized bias (0.0 to 1.0) to filter signals and define market regime.
The Four Pillars of V-Sync
1. Base Volume (Temporal Flux)
Engine: Exponentially weighted volume flow.
Logic: up_volume / total_volume with a math.exp(-i/lookback) decay.
Utility: Capturing sustained momentum in raw participation. It filters out low-volume "fakeout" moves that lack broad participation.
2. Footprint (Order Flow Delta)
Engine: Micro-delta tracking (Institutional Tape).
Logic: Normalized ratio of aggressive buy orders vs sell orders, sourced from LTF footprint or synthetic body-to-wick estimation.
Utility: Identifying where "Smart Money" is actively committing capital in real-time.
3. TICK Data (Market Internals)
Engine: Exchange-wide breadth internals.
Index Mapping:
SPX/ES: NYSE:TICK
NQ/NDX: NASDAQ:TICKQ
Fidelity: Processes intrabar HT/LT extremes to capture high-speed institutional sweeps.
Commitment Levels: Benchmarked at 800 (MOO alignment), 1000 (Extreme), and 1200 (Climax).
4. Thermal Map (Structural Binning)
Engine: Range-based volume distribution (Heatmap).
Logic: 30-bin price-range analysis. Identifies if the current price is supported by "Buy Liquidity" below or capped by "Sell Liquidity" above.
Utility: Visualizing structural depth and identifying high-probability zones where price is likely to stick or bounce.
Interaction & Intelligence Modules
5. Interaction Tooltips
Engine: Dynamic string generator.
Logic: Aggregates pillars (V-Sync, TICK, Heatmap) and local interaction (Delta, OB Bias) into a human-readable forensic report.
Utility: Provides instant clarity on why a level is reacting (e.g., "Institutional Defense" vs "Passive Absorption").
6. Delta Aggregation (Defense vs Aggression)
Engine: Decaying session delta sum.
Logic: Tracks footprint delta at discrete price levels. Categorizes bias as:
Aggressive (A): Delta moves in the direction of the break (Push).
Defensive (D): Delta moves against the local price interaction (Absorption/Soaking).
Utility: Standardizing the interpretation of footprint across all Synapse indicators.
7. Universal Plot Auditing
Engine: Kinetic flux interaction logic.
Logic: Allows auditing of any technical plot line (Moving Averages, VWAP, Anchored Levels) for touches, cross-overs, and structural fidelity.
Utility: Enables the entire Synapse forensic suite to be applied to any existing indicator's data lines.
Library Architecture: Synapse_VSync_Lib
Key Functions
f_get_tick_source(): Auto-detects SPX vs NQ for correct internal sourcing.
f_calc_tick_extreme(): High-fidelity internal pressure tracking.
f_vsync_stack(): Blends all pillars into a weighted consensus.
HUD Representation
Indicators utilizing the full stack display V-STACK (instead of V-SYNC), signifying that Market Internals and Structural structural depth are being calculated alongside volume flow.
License: Open Source (MIT License) Libreria
