Harry Dunn Volatility BandsEnter strike price and 2 percentage numbers to automatically calculate and draw volatility bands on chart.
Göstergeler ve stratejiler
Level Founder indicatorQuesto strumento, ideato per l'individuazione dei livelli orizzontali sensibili si prepone l'obiettivo di semplificare la lettura tecnica dei grafici. Alla base di questo indicatore c'è il concetto di volatilità, inteso come scontro tra domanda ed offerta, come escursione delle forze nel campo di battaglia fino alla determinazione del prezzo finale di ogni candela. Di fatto, andando a cogliere quella che è la volatilità candela per candela, l'indicatore la calcola in termini assoluti rendendola un numericamente comparabile, in un range tra 0 e 100. Quando questo valore tocca i 100 si genera un picco di volatilità, il quale va ad identificare un punto di attenzione sul grafico di uno strumento. In corrispondenza di questi picchi si osserva dove la battaglia tra compratori e venditori si è conclusa, ovvero dove domanda ed offerta si sono incontrati per definire un prezzo: la chiusura di candela. In corrispondenza di tale prezzo si ha, quindi, un accordo certo tra domanda ed offerta dopo un periodo di contrattazione volatile, andando a certificare quello che è un livello di prezzo "sudato" per un determinato sottostante. Tale soglia si traduce in un livello orizzontale sensibile, che in futuro (avendo il mercato memoria degli scontri passati) potrà comportarsi da supporto o da resistenza, a seconda della situazione. In breve quindi, si traccia una linea orizzontale in corrispondenza delle chiusure di candela che condividono un picco sull'indicatore "Level Founder Indicator". Funziona su ogni time-frame e sottostante.
N.B. A ridosso di questi livelli si possono cercare pattern per l'operatività oppure cercare delle rotture di questi livelli per delle conferme/inversioni, spaziando dal trading intraday all'investimento di lungo periodo.
ENGLISH VERSION:
This tool, designed to identify sensitive horizontal levels, aims to simplify the technical reading of charts. This indicator is based on the concept of volatility, understood as the clash between supply and demand, the oscillation of forces on the battlefield until the final price of each candlestick is determined. By capturing the volatility candlestick by candlestick, the indicator calculates it in absolute terms, making it numerically comparable, within a range between 0 and 100. When this value reaches 100, a volatility spike is generated, which identifies a point of focus on an instrument's chart. At these peaks, we observe where the battle between buyers and sellers has concluded, that is, where supply and demand have met to define a price: the candlestick's close. At this price, therefore, a definite agreement between supply and demand occurs after a period of volatile trading, certifying what is a "hard-earned" price level for a given underlying asset. This threshold translates into a sensitive horizontal level, which in the future (given the market's memory of past clashes) could act as support or resistance, depending on the situation. In short, a horizontal line is drawn at the candlestick closes that share a peak on the "Level Founder Indicator." It works on any timeframe and underlying asset.
N.B.: Near these levels, you can look for trading patterns or look for breakouts of these levels for confirmations/reversals, ranging from intraday trading to long-term investing.
Quarter Strength Table (3M) [CHE] Quarter Strength Table (3M) — quarterly seasonality overview for the current symbol
Is there seasonality in certain assets? Some YouTubers claim there is—can you test it yourself?
Summary
This indicator builds a compact table that summarizes quarterly seasonality from three-month bars. It aggregates the simple return of each historical quarter, counts observations, computes the average return and the win rate for each quarter, and flags the historically strongest quarter. The output is a five-column table rendered on the chart, designed for quick comparison rather than signal generation. Because it processes only confirmed higher-timeframe bars, results are stable once a quarter has closed.
Motivation: Why this design?
Seasonality tools often mix intraperiod estimates with live bars, which can lead to misleading flips and inconsistent statistics. The core idea here is to restrict aggregation to completed three-month bars only and to deduplicate events by timestamp. This avoids partial information and double counting, so the table reflects a consistent, closed-bar history.
What’s different vs. standard approaches?
Baseline: Typical seasonality studies that compute monthly or quarterly stats directly on the chart timeframe or update on live higher-timeframe bars.
Architecture differences:
Uses explicit higher-timeframe requests for open, close, time, and calendar month from three-month bars.
Confirms the higher-timeframe bar before recording a sample; deduplicates by the higher-timeframe timestamp.
Keeps fixed arrays of length four for the four quarters; renders a fixed five-by-five table with zebra rows.
Practical effect: Once a quarter closes, counts and averages are stable. The “Best” column marks the highest average quarter so you can quickly identify the historically strongest period.
How it works (technical)
On every chart bar, the script requests three-month open, close, time, and the calendar month derived from that bar’s time. When the three-month bar is confirmed, it computes the simple return for that bar and maps the month to a quarter index between zero and three. A guard stores the last seen three-month timestamp to avoid duplicate writes. Per quarter, it accumulates the sum of returns, the number of samples, and the number of positive samples. From these, it derives average return and win rate. The table header is created once on the first bar; content updates only on the last visible chart bar for efficiency. No forward references are used, and lookahead is disabled in all higher-timeframe requests to avoid peeking.
Parameter Guide
Percent — Formats values as percentages. Default: true. Trade-off: Easier visual comparison; disable if you prefer raw unit returns.
Decimals — Number of digits shown. Default: two. Bounds: zero to six. Trade-off: More digits improve precision but reduce readability.
Show table — Toggles table rendering. Default: true. Trade-off: Disable when space is limited or for batch testing.
Reading & Interpretation
The table shows rows for Q1 through Q4 and columns for Count, Avg Ret, P(win), and Best.
Count: Number of completed three-month bars observed for that quarter.
Avg Ret: Average simple return across all samples in that quarter.
P(win): Share of samples with a positive return.
Best: An asterisk marks the quarter with the highest average return among those with at least one sample.
Use the combination of average and win rate to judge both magnitude and consistency. Low counts signal limited evidence.
Practical Workflows & Combinations
Trend following filter: Favor setups when the upcoming or active quarter historically shows a positive average and a stable win rate. Combine with structure analysis such as higher highs and higher lows to avoid fighting dominant trends.
Exits and risk: When entering during a historically weak quarter, consider tighter risk controls and quicker profit taking.
Multi-asset and multi-timeframe: The default settings work across most liquid symbols. For assets with sparse history, treat results as low confidence due to small sample sizes.
Behavior, Constraints & Performance
Repaint and confirmation: Aggregation occurs only when the three-month bar is confirmed; values do not change afterward for that bar. During an open quarter, no new sample is added.
Higher-timeframe usage: All higher-timeframe requests disable lookahead and rely on confirmation to mitigate repaint.
Resources: Declared `max_bars_back` is two thousand. Arrays are fixed at length four. The script updates the table only on the last visible bar to reduce work.
Known limits: Averages can be affected by outliers and structural market changes. Limited history reduces reliability. Corporate actions and contract rolls may influence returns depending on the symbol’s data source. This is a visualization and not a trading system.
Sensible Defaults & Quick Tuning
Starting values: Percent true; Decimals two; Show table true.
If numbers feel noisy: Decrease decimals to one to reduce visual clutter.
If you need raw values: Turn off Percent to display unit returns.
If the table overlaps price: Toggle Show table off when annotating, or reposition via your chart’s table controls.
What this indicator is—and isn’t
This is a historical summary of quarterly behavior. It visualizes evidence and helps frame expectations. It is not predictive, does not generate trade signals, and does not manage positions or risk. Always combine with market structure, liquidity considerations, and independent risk controls.
Inputs with defaults
Percent: true, boolean.
Decimals: two, integer between zero and six.
Show table: true, boolean.
Pine version: v6
Overlay: true
Primary outputs: Table with five columns and five rows.
Metrics/functions used: Higher-timeframe data requests, table rendering, arrays, bar state checks, month mapping.
Special techniques: Closed-bar aggregation, deduplication by higher-timeframe timestamp, zebra row styling.
Performance/constraints: Two thousand bars back, small fixed loops, higher-timeframe requests without lookahead.
Compatibility/assets/timeframes: Works on time-based charts across most assets with sufficient history.
Limitations/risks: Sample size sensitivity, regime shifts, data differences across venues.
Debug/diagnostics: (Unknown/Optional)
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
fartbombLinReg fit (history): solid line showing the best-fit linear trend over the last len bars.
Projected offset (visual): same line shifted h bars right so you can see direction.
Future projection (segments): the actual next-h forecast, drawn from the last bar.
Forecast made h bars ago: circle at each bar’s target showing what was predicted h bars earlier.
H / X markers: a hit if that earlier forecast fell inside the bar’s high–low range.
Vertical Lines at 10:00 & 11:30Sales-Style Description
This script is a simple but powerful TradingView add-on that automatically marks your chart with clear, bold vertical lines at exactly 10:00 AM and 11:30 AM every day. No more manually drawing lines or setting reminders — it does the work for you.
Always on time: It tracks the market clock in real-time and drops a line the moment your chart hits those times.
Clean visibility : The lines are bright blue (#2962FF), solid, and drawn with thickness level 3, so they stand out against any background or chart theme.
Automatic housekeeping: It keeps your workspace clean by automatically deleting old lines once you reach a set limit, so your chart never gets cluttered.
Customizable : You can change the time zone, thickness, and the number of days’ worth of lines to keep.
Set it and forget it: Once added to your chart, it runs quietly in the background — you’ll always know when the 10:00 and 11:30 sessions hit without lifting a finger.
Quick Valuation V.1.0 (Ibo)This Pine Script indicator performs a Quick Discounted Cash Flow (DCF)-style Valuation to estimate the intrinsic value of a stock.
It calculates a projected Fair Value and a Margin of Safety based on user inputs or automatically pulled financial data from TradingView (like revenue, growth, margin, and exit P/E). It also automatically computes a Discount Rate using a modified CAPM model.
Key Features
Valuation Output: Calculates a target Fair Value and the resulting Margin of Safety.
Data Flexibility: Automatically pulls essential fundamentals (Revenue, Margins, Shares Outstanding, etc.) but allows the user to override any value (revenue, growth, P/E, shares, etc.) via the settings.
Automated Discount Rate: Calculates the Discount Rate (Cost of Equity) using the current 10-Year Real Yield and a computed or user-defined Beta.
Clear Display: Presents all input metrics, calculated values, and data sources (TradingView or User Input) in a neat table on the chart.
Synthetic Implied APROverview
The Synthetic Implied APR is an artificial implied APR, designed to imitate the implied APR seen when trading cryptocurrency funding rates. It combines real-time funding rates with premium data to calculate an artificial market expectation of the annualized funding rate.
The (actual) implied APR is the market's expectation of the annualized funding rate. This is dependent on bid/ask impacts of the implied APR, something which is currently unavailable to fetch with TradingView. In essence, an implied APR of X% means traders believe that asset's funding fees to average X% when annualized.
What's important to understand, is that the actual value of the synthetic implied APR is not relevant. We only simply use its relative changes when we trade (i.e if it crosses above/below its MA for a given weight). Even for the same asset, the implied APRs will change depending on days to maturity.
How it calculates
The synthetic implied APR is calculated with these steps:
Collects premium data from perpetual futures markets using optimized lower timeframe requests (check my 'Predicted Funding Rates' indicator)
Calculates the funding rate by adding the premium to an interest rate component (clamped within exchange limits)
Derives the underlying APR from the 8-hour funding rate (funding rate × 3 × 365)
Apply a weighed formula that imitates both the direction (underlying APR) with the volatility of prices (from the premium index and funding)
premium_component = (prem_avg / 50 ) * 365
weighedprem = (weight * fr) + ((1 - weight) * apr) + (premium_component * 0.3)
impliedAPR = math.avg(weighedprem, ta.sma(apr, maLength))
How to use it: Generally
Preface: Funding rates are an indication of market sentiment
If funding is positive, generally the market is bullish as longs are willing to pay shorts funding
If funding is negative, generally the market is bearish as shorts are willing to pay longs funding
So, this script can be used like a typical oscillator:
Bullish: If implied APR > MA OR if implied APR MA is green
Bearish: If implied APR < MA OR if implied APR MA is red
The components:
Synthetic Implied APR: The main metric. At current setting of 0.7, it imitates volatility
Weight: The higher the value, the smoother the synthetic implied APR is (and MA too). This value is very important to the imitation. At 0.7, it imitates the actual volatility of the implied APR. At weight = 1, it becomes very smooth. Perfect for trading
Synthetic Implied APR Moving Average: A moving average of the Synthetic implied APR. Can choose from multiple selections, (SMA, EMA, WMA, HMA, VWMA, RMA)
How to use it: Trading Funding
When trading funding there're multiple ways to use it with different settings
Trade funding rates with trend changes
Settings: Weight = 1
Method 1: When the implied APR MA turns green, long funding rates (or short if red)
Method 2: When the implied APR crosses above the MA, long funding rates (or short when crosses below)
Trade funding rates with MA pullbacks
Settings: Weight = 0.7, timeframe 15m
In an uptrend: When implied APR crosses below then above the script, long funding opportunity
In an downtrend: When implied APR crosses above then below the script, shortfunding opportunity
You can determine the trend with the method before, using a weight of 1
To trade funding rates, it's best to have these 3 scripts at these settings:
Predicted Funding Rates: This allows you to see the predicted funding rates and see if they've maxxed out for added confluence too (+/-0.01% usually for Binance BTC futures)
Synthetic implied APR: At weight 1, the MA provides a good trend (whether close above/below or colour change)
Synthetic implied APR: At weight 0.7, it provides a good imitation of volatility
How to use it: Trading Futures
When trading futures:
You can determine roughly what the trend is, if the assumption is made that funding rates can help identify trends if used as a sentiment indicator. It should be supplemented with traditional trend trading methods
To prevent whipsaws, weight should remain high
Long trend: When the implied APR MA turns green OR when it crosses above its MA
Short trend: When the implied APR MA turns red OR when it below above its MA
Why it's original
This indicator introduces a unique synthetic weighting system that combines funding rates, underlying APR, and premium components in a way not found in existing TradingView scripts. Trading funding rates is a niche area, there aren't that many scripts currently available. And to my knowledge, there's no synthetic implied APR scripts available on TradingView either. So I believe this script to be original in that sense.
Notes
Because it depends on my triangular weighting algos, optimal accuracy is found on timeframes that are 4H or less. On higher timeframes, the accuracy drops off. Best timeframes for intraday trading using this are 15m or 1 hour
The higher the timeframe, the lower the MA one should use. At 1 hour, 200 or higher is best. At say, 4h, length of 50 is best
Only works for coins that have a Binance premium index
Inputs
Funding Period - Select between "1 Hour" or "8 Hour" funding cycles. 8 hours is standard for Binance
Table - Toggle the information dashboard on/off to show or hide real-time metrics including funding rate, premium, and APR value
Weight - Controls the balance between funding rate (higher values = smoother) and APR (lower values = more responsive) in the calculation, ranging from 0.0 to 1.0. Default is 0.7, this imitates the volatility
Auto Timeframe Implied Length - Automatically calculates optimal smoothing length based on your chart timeframe for consistent behavior across different time periods
Manual Implied Length - Sets a fixed smoothing length (in bars) when auto mode is disabled, with lower values being more responsive and higher values being smoother
Show Implied APR MA - Displays an additional moving average line of the Synthetic Implied APR to help identify trend direction and crossover signals
MA Type for Implied APR - Selects the calculation method (SMA, EMA, WMA, HMA, VWMA, or RMA) for the moving average, each offering different responsiveness and lag characteristics
MA Length for Implied APR - Sets the lookback period (1-500 bars) for the moving average, with shorter lengths providing more signals and longer lengths filtering noise
Show Underlying APR - Displays the raw APR calculation (without synthetic weighting) as a reference line to compare against the main indicator
Bullish Color - Sets the color for positive values in the table and rising MA line
Bearish Color - Sets the color for negative values in the table and falling MA line
Table Background - Customizes the background color and transparency of the information dashboard
Table Text Color - Sets the color for label text in the left column of the information table
Table Text Size - Controls the font size of table text with options from Tiny to Huge
ICT Venom Trading Model [TradingFinder] SMC NY Session 2025SetupIntroduction
The ICT Venom Model is one of the most advanced strategies in the ICT framework, designed for intraday trading on major US indices such as US100, US30, and US500. This model is rooted in liquidity theory, time and price dynamics, and institutional order flow.
The Venom Model focuses on detecting Liquidity Sweeps, identifying Fair Value Gaps (FVG), and analyzing Market Structure Shifts (MSS). By combining these ICT core concepts, traders can filter false breakouts, capture sharp reversals, and align their entries with the real institutional liquidity flow during the New York Session.
Key Highlights of ICT Venom Model :
Intraday focus : Optimized for US indices (US100, US30, US500).
Time element : Critical window is 08:00–09:30 AM (Venom Box).
Liquidity sweep logic : Price grabs liquidity at 09:30 AM open.
Confirmation tools : MSS, CISD, FVG, and Order Blocks.
Dual setups : Works in both Bullish Venom and Bearish Venom conditions.
At its core, the ICT Venom Strategy is a framework that explains how institutional players manipulate liquidity pools by engineering false breakouts around the initial range of the market. Between 08:00 and 09:30 AM New York time, a range called the “Venom Box” is formed.
This range acts as a trap for retail traders, and once the 09:30 AM market open occurs, price usually sweeps either the high or the low of this box to collect stop-loss liquidity. After this liquidity grab, the market often reverses sharply, giving birth to a classic Bullish Venom Setup or Bearish Venom Setup
The Venom Model (ICT Venom Trading Strategy) is not just a pattern recognition tool but a precise institutional trading model based on time, liquidity, and market structure. By understanding the Initial Balance Range, watching for Liquidity Sweeps, and entering trades from FVG zones or Order Blocks, traders can anticipate market reversals with high accuracy. This strategy is widely respected among ICT followers because it offers both risk management discipline and clear entry/exit conditions. In short, the Venom Model transforms liquidity manipulation into actionable trading opportunities.
Bullish Setup :
Bearish Setup :
🔵 How to Use
The ICT Venom Model is applied by observing price behavior during the early hours of the New York session. The first step is to define the Initial Range, also called the Venom Box, which is formed between 08:00 and 09:30 AM EST. This range marks the high and low points where institutional traders often create traps for retail participants. Once the official market opens at 09:30 AM, price usually sweeps either the top or bottom of this box to collect liquidity.
After this liquidity grab, the market tends to reverse in alignment with the true directional bias. To confirm the setup, traders look for signals such as a Market Structure Shift (MSS), Change in State of Delivery (CISD), or the appearance of a Fair Value Gap (FVG). These elements validate the reversal and provide precise levels for trade execution.
🟣 Bullish Setup
In a Bullish Venom Setup, the market first sweeps the low of the Venom Box after 09:30 AM, triggering sell-side liquidity collection. This downward move is often sharp and deceptive, designed to stop out retail long positions and attract new sellers. Once liquidity is taken, the market typically shifts direction, forming an MSS or CISD that signals a reversal to the upside.
Traders then wait for price to retrace into a Fair Value Gap or a demand-side Order Block created during the reversal leg. This retracement offers the ideal entry point for long positions. Stop-loss placement should be just below the liquidity sweep low, while profit targets are set at the Venom Box high and, if momentum continues, at higher session or daily highs.
🟣 Bearish Setup
In a Bearish Venom Setup, the process is similar but reversed. After the Initial Range is defined, if price breaks above the Venom Box high following the 09:30 AM open, it signals a false breakout designed to collect buy-side liquidity. This move usually traps eager buyers and clears out stop-losses above the high.
After the liquidity sweep, confirmation comes through an MSS or CISD pointing to a reversal downward. At this stage, traders anticipate a retracement into a Fair Value Gap or a supply-side Order Block formed during the reversal. Short entries are taken within this zone, with stop-loss positioned just above the liquidity sweep high. The logical profit targets include the Venom Box low and, in stronger bearish momentum, deeper session or daily lows.
🔵 Settings
Refine Order Block : Enables finer adjustments to Order Block levels for more accurate price responses.
Mitigation Level OB : Allows users to set specific reaction points within an Order Block, including: Proximal: Closest level to the current price. 50% OB: Midpoint of the Order Block. Distal: Farthest level from the current price.
FVG Filter : The Judas Swing indicator includes a filter for Fair Value Gap (FVG), allowing different filtering based on FVG width: FVG Filter Type: Can be set to "Very Aggressive," "Aggressive," "Defensive," or "Very Defensive." Higher defensiveness narrows the FVG width, focusing on narrower gaps.
Mitigation Level FVG : Like the Order Block, you can set price reaction levels for FVG with options such as Proximal, 50% OB, and Distal.
CISD : The Bar Back Check option enables traders to specify the number of past candles checked for identifying the CISD Level, enhancing CISD Level accuracy on the chart.
🔵 Conclusion
The ICT Venom Model is more than just a reversal setup; it is a complete intraday trading framework that blends liquidity theory, time precision, and market structure analysis. By focusing on the Initial Range between 08:00 and 09:30 AM New York time and observing how price reacts at the 09:30 AM open, traders can identify liquidity sweeps that reveal institutional intentions.
Whether in a Bullish Venom Setup or a Bearish Venom Setup, the model allows for precise entries through Fair Value Gaps (FVGs) and Order Blocks, while maintaining clear risk management with well-defined stop-loss and target levels.
Ultimately, the ICT Venom Model provides traders with a structured way to filter false moves and align their trades with institutional order flow. Its strength lies in transforming liquidity manipulation into actionable opportunities, giving intraday traders an edge in timing, accuracy, and consistency. For those who master its logic, the Venom Model becomes not only a strategy for entry and exit, but also a deeper framework for understanding how liquidity truly drives price in the New York session.
Normalized WMA Oscillator | OquantNormalized WMA Oscillator | Oquant
The Normalized WMA Oscillator is a trend-momentum indicator designed to help traders visualize the relative position of a Weighted Moving Average (WMA) within its recent price range.
What is a WMA and How It Works:
A Weighted Moving Average (WMA) is a type of moving average that gives more weight to recent price data, making it more responsive to price changes compared to a simple moving average. Each price point in the lookback period is multiplied by a weighting factor, with the most recent prices having the highest weights. The WMA helps traders identify potential trends more quickly.
This indicator applies min-max normalization to the standard WMA, scaling its values between 0 and 1 over a configurable lookback period. This allows traders to see whether the WMA is near its recent highs, lows, or midpoint, regardless of the absolute price level.
Key Features:
WMA Source Input: Choose price source for wma calculation.
Customizable WMA Length: Adjust the sensitivity of the WMA.
Min-Max Normalization Length: Smooth the scaling of WMA values between 0 and 1.
Signal Thresholds: Configurable upper and lower thresholds to indicate potential entries.
Visual Alerts: Color-coded oscillator and candles plot for bullish (green) and bearish (purple) signals.
Alerts Ready: Built-in alert conditions for crossovers and crossunders of the oscillator.
How It Works:
Calculate the WMA on the selected source.
Normalize its value using the minimum and maximum WMA values over the specified lookback period.
Generate long signals when the normalized WMA moves above the upper threshold, and short signals when it moves below the lower threshold.
Plot the oscillator and candles in green for bullish signals and purple for bearish signals.
Inputs:
Source: Data used for WMA calculation.
WMA Length: Period for Weighted Moving Average.
Min-Max Length: Lookback period for min-max scaling.
Upper Threshold: Level above which a long signal is considered.
Lower Threshold: Level below which a short signal is considered.
⚠️ Disclaimer: This indicator is intended for educational and informational purposes only. Trading/investing involves risk, and past performance does not guarantee future results. Always test and evaluate indicators/strategies before applying them in live markets. Use at your own risk.
Power Hour Breakout Signals [LuxAlgo]The Power Hour Breakout tool helps traders identify key price levels from the Power Hour and spot breakouts from those levels easily. This tool features Power Hour extensions, Fibonacci levels, and session break marks for the trader's convenience.
🔶 USAGE
The Power Hour is defined as the last hour of the trading session and is set by default from 3:00 p.m. to 4:00 p.m. New York time. During this period, volume and volatility enter the market. Traders using higher timeframes may use this period to enter or exit positions by placing MOC (Market on Close) orders.
This tool highlights the Power Hour and the top and bottom price levels. Each time prices break out from these levels, a signal is displayed on the chart.
We can use the Power Hour to gauge market sentiment:
Bullish sentiment: Price trades above the Power Hour.
Mixed sentiment: Price trades within the Power Hour.
Bearish sentiment: Price trades below the Power Hour.
🔹 Displaying Power Hours and Breakouts
By default, all detected Power Hours are displayed. Traders can manually adjust this number by disabling the "Display All" parameter in the Settings panel.
Breakouts are displayed by default, too, but this feature can be disabled as well.
The chart above shows different configurations of these parameters.
🔹 Power Hour Extensions
Traders can use Power Hour extensions as potential targets for breakout signals.
In the settings panel, traders can select the percentage of the Power Hour price range to use for each extension. For example, 100% uses the full range, 200% uses the range twice, and so on.
As seen on the chart, traders can configure different percentages for the top and bottom extensions.
🔹 Fibonacci Levels
Traders can display default or custom Fibonacci levels on the Power Hour range to identify retracement opportunities and evaluate market movement strength. Each level can be enabled or disabled, as well as customized by level, color, and line style.
For example, as we can see on the chart, prices attempt to break out at the Power Hour top level, then retrace to the 0.618 Fibonacci level, and then rise to the 200% Power Hour top extension.
🔶 SETTINGS
Display Last X Power Hours: Select how many Power Hours to display or enable the Display All feature.
Power Hour (NY Time): Choose a custom Power Hour in New York time.
🔹 Breakouts
Breakouts: Enable or disable breakouts.
Bullish Breakout: Select color for bullish breakouts.
Bearish Breakout: Select color for bearish breakouts.
🔹 Extensions
Top Extension: Enable or disable the top extension and choose the percentage of Power Hour to use.
Bottom extension: Enable or disable the bottom extension and choose the percentage of Power Hour to use.
🔹 Fibonacci Levels
Display Fibonacci: Enable or disable Fibonacci levels.
Reverse: Reverse Fibonacci levels.
Levels, Colors & Style
Display Labels: Enable or disable labels and choose text size.
🔹 Style
Power Hour Colors
Extension Transparency: Choose the extension's transparency. 0 is solid, and 100 is fully transparent.
Session Breaks: Enable or disable session breaks.
Quantile-Based Adaptive Detection🙏🏻 Dedicated to John Tukey. He invented the boxplot, and I finalized it.
QBAD (Quantile-Based Adaptive Detection) is ‘the’ adaptive (also optionally weighted = ready for timeseries) boxplot with more senseful fences. Instead of hardcoded multipliers for outer fences, I base em on a set of quantile-based asymmetry metrics (you can view it as an ‘algorithmic’ counter part of central & standardized moments). So outer bands are Not hardcoded, not optimized, not cross-validated etc, simply calculated at O(nlogn).
You can use it literally everywhere in any context with any continuous data, in any task that requires statistical control, novelty || outlier detection, without worrying and doubting the sense in arbitrary chosen thresholds. Obviously, given the robust nature of quantiles, it would fit best the cases where data has problems.
The thresholds are:
Basis: the model of the data (median in our case);
Deviations: represent typical spread around basis, together form “value” in general sense;
Extensions: estimate data’s extremums via combination of quantile-based asymmetry metrics without relying on actual blunt min and max, together form “range” / ”frame”. Datapoints outside the frame/range are novelties or outliers;
Limits: based also on quantile asymmetry metrics, estimate the bounds within which values can ‘ever’ emerge given the current data generating process stays the same, together form “field”. Datapoints outside the field are very rare, happen when a significant change/structural break happens in current data-generating process, or when a corrupt datapoint emerges.
…
The first part of the post is for locals xd, the second is for the wanderers/wizards/creators/:
First part:
In terms of markets, mostly u gotta worry about dem instruments that represent crypto & FX assets: it’s either activity hence data sources there are decentralized, or data is fishy.
For a higher algocomplexity cost O(nlong), unlike MBAD that is 0(n), this thing (a control system in fact) works better with ishy data (contaminated with wrong values, incomplete, missing values etc). Read about the “ breakdown point of an estimator ” if you wanna understand it.
Even with good data, in cases when you have multiple instruments that represent the same asset, e.g. CL and BRN futures, and for some reason you wanna skip constructing a proper index of em (while you should), QBAD should be better put on each instrument individually.
Another reason to use this algo-based rather than math-based tool, might be in cases when data quality is all good, but the actual causal processes that generate the data are a bit inconsistent and/or possess ‘increased’ activity in a way. SO in high volatility periods, this tool should provide better.
In terms of built-ins you got 2 weightings: by sequence and by inferred volume delta. The former should be ‘On’ all the time when you work with timeseries, unless for a reason you want to consciously turn it off for a reason. The latter, you gotta keep it ‘On’ unless you apply the tool on another dataset that ain’t got that particular additional dimension.
Ain’t matter the way you gonna use it, moving windows, cumulative windows with or without anchors, that’s your freedom of will, but some stuff stays the same:
Basis and deviations are “value” levels. From process control perspective, if you pls, it makes sense to Not only fade or push based on these levels, but to also do nothing when things are ambiguous and/or don’t require your intervention
Extensions and limits are extreme levels. Here you either push or fade, doing nothing is not an option, these are decisive points in all the meanings
Another important thing, lately I started to see one kind of trend here on tradingview as well and in general in near quant sources, of applying averages, percentiles etc ‘on’ other stationary metrics, so called “indicators”. And I mean not for diagnostic or development reasons, for decision making xd
This is not the evil crime ofc, but hillbilly af, cuz the metrics are stationary it means that you can model em, fit a distribution, like do smth sharper. Worst case you have Bayesian statistics armed with high density intervals and equal tail intervals, and even some others. All this stuff is not hard to do, if u aint’t doing it, it’s on you.
So what I’m saying is it makes sense to apply QBAD on returns ‘of your strategy’, on volume delta, but Not on other metrics that already do calculations over their own moving windows.
...
Second part:
Looks like some finna start to have lil suspicions, that ‘maybe’ after all math entities in reality are more like blueprints, while actual representations are physical/mechanical/algorithmic. Std & centralized moments is a math entity that represents location, scale & asymmetry info, and we can use it no problem, when things are legit and consistent especially. Real world stuff tho sometimes deviates from that ideal, so we need smth more handy and real. Add to the mix the algo counter part of means: quantiles.
Unlike the legacy quantile-based asymmetry metrics from the previous century (check quantile skewness & kurtosis), I don’t use arbitrary sets of quantiles, instead we get a binary pattern that is totally geometric & natural (check the code if interested, I made it very damn explicit). In spirit with math based central & standardized moments, each consequent pair is wider empathizing tail info more and more for each higher order metric.
Unlike the classic box plot, where inner thresholds are quartiles and the rest are based on em, here the basis is median (minimises L1), I base inner thresholds on it, and we continue the pattern by basing the further set of levels on the previous set. So unlike the classic box plot, here we have coherency in construction, symmetry.
Another thing to pay attention to, tho for some reason ain’t many talk about it, it’s not conceptually right to think that “you got data and you apply std moments on it”. No, you apply it to ‘centered around smth’ data. That ‘smth’ should minimize L2 error in case of math, L1 error in case of algo, and L0 error in case of learning/MLish/optimizational/whatever-you-cal-it stuff. So in the case of L0, that’s actually the ‘mode’ of KDE, but that’s for another time. Anyways, in case of L2 it’s mean, so we center data around mean, and apply std moments on residuals. That’s the precise way of framing it. If you understand this, suddenly very interesting details like 0th and 1st central moments start to make sense. In case of quantiles, we center data around the median, and do further processing on residuals, same.
Oth moment (I call it init) is always 1, tho it’s interesting to extrapolate backwards the sequence for higher order moments construction, to understand how we actually end up with this zero.
1st moment (I call it bias) of residuals would be zero if you match centering and residuals analysis methods. But for some reason you didn’t do that (e.g centered data around midhinge or mean and applied QBAD on the centered data), you have to account for that bias.
Realizing stuff > understanding stuff
Learning 2981234 human invented fields < realizing the same unified principles how the Universe works
∞
ATR Volatility and Trend AnalysisATR Volatility and Trend Analysis
Unlock the power of the Average True Range (ATR) with the ATR Volatility and Trend Analysis indicator. This comprehensive tool is designed to provide traders with a multi-faceted view of market dynamics, combining volatility analysis, dynamic support and resistance levels, and trend detection into a single, easy-to-use indicator.
How It Works
The ATR Volatility and Trend Analysis indicator is built upon the core concept of the ATR, a classic measure of market volatility. It expands on this by providing several key features:
Dynamic ATR Bands: The indicator plots three sets of upper and lower bands around the price. These bands are calculated by multiplying the current ATR value by user-defined multipliers. They act as dynamic support and resistance levels, widening during volatile periods and contracting during calm markets.
Volatility Breakout Signals: Identify potential breakouts with precision. The indicator generates a signal when the current ATR value surges above its own moving average by a specified threshold, indicating a significant increase in volatility that could lead to a strong price move.
Trend Detection: The indicator determines the market trend by analyzing both price action and ATR behavior. A bullish trend is signaled when the price is above its moving average and volatility is increasing. Conversely, a bearish trend is signaled when the price is below its moving average and volatility is increasing.
How to Use the ATR Multi-Band Indicator
Identify Support and Resistance: Use the ATR bands as key levels. Price approaching the outer bands may indicate overbought or oversold conditions, while a break of the bands can signal a strong continuation.
Confirm Breakouts: Look for a volatility breakout signal to confirm the strength behind a price move. A breakout from a consolidation range accompanied by a volatility signal is a strong indicator of a new trend.
Trade with the Trend: Use the background coloring and trend signals to align your trades with the dominant market direction. Enter long positions during confirmed bullish trends and short positions during bearish trends.
Set Up Alerts: The indicator includes alerts for band crosses, trend changes, and volatility breakouts, ensuring you never miss a potential trading opportunity.
What makes it different?
While many indicators use ATR, the ATR Volatility and Trend Analysis tool is unique in its integration of multiple ATR-based concepts into a single, cohesive system. It doesn't just show volatility; it interprets it in the context of price action to deliver actionable trend and breakout signals, making it a complete solution for ATR-based analysis.
Disclaimer
This indicator is designed as a technical analysis tool and should be used in conjunction with other forms of analysis and proper risk management.
Past performance does not guarantee future results, and traders should thoroughly test any strategy before implementing it with real capital.
BTC TOPperThe BTC TOPper indicator is a sophisticated technical analysis tool designed to identify critical price levels where Bitcoin's weekly Simple Moving Average (SMA) intersects with historically significant All-Time High (ATH) levels. This indicator is particularly valuable for long-term trend analysis and identifying potential reversal zones in Bitcoin's price action.
Key Features:
🔹 Weekly SMA Analysis: Uses a 200-period Simple Moving Average on weekly timeframe to smooth out short-term volatility and focus on long-term trends
🔹 Persistent Historical ATH Tracking: Automatically detects and "freezes" ATH levels that have been held for more than one year, creating persistent reference levels
🔹 Multi-Level Cross Detection: Tracks up to 10 different frozen ATH levels simultaneously, providing comprehensive historical context
🔹 Visual Cross Alerts: Highlights entire weeks with red background when the weekly SMA crosses any frozen ATH level, making signals impossible to miss
🔹 Advanced Smoothing Options: Includes optional secondary moving averages (SMA, EMA, SMMA, WMA, VWMA) with Bollinger Bands for enhanced analysis
🔹 Customizable Parameters: Adjustable SMA length, offset, and smoothing settings to fit different trading strategies
How It Works:
ATH Detection: Continuously monitors for new all-time highs
Level Freezing: After an ATH is held for 1+ year, it becomes a "frozen" historical level
Cross Monitoring: Watches for intersections between the 200-week SMA and any frozen ATH level
Signal Generation: Highlights the entire week when a cross occurs, providing clear visual alerts
Trading Applications:
Long-term Trend Analysis: Identify when Bitcoin approaches historically significant resistance levels
Reversal Zone Detection: Spot potential areas where price might reverse based on historical context
Support/Resistance Confirmation: Use frozen ATH levels as dynamic support and resistance zones
Market Structure Analysis: Understand how current price relates to historical market cycles
Best Practices:
Use on weekly timeframe for optimal results
Combine with other technical indicators for confirmation
Pay attention to multiple frozen levels clustering in the same price range
Consider market context and fundamentals alongside technical signals
Settings:
Length: 200 (default) - SMA period
Source: Close price
Smoothing: Optional secondary MA with multiple types available
Bollinger Bands: Optional volatility bands around secondary MA
This indicator is ideal for Bitcoin traders and analysts who want to understand the relationship between current price action and historical market structure, particularly useful for identifying potential major reversal zones based on historical ATH levels.
Seasonal Pattern DecoderSeasonal Pattern Decoder
The Seasonal Pattern Decoder is a powerful tool designed for traders and analysts who want to uncover and leverage seasonal tendencies in financial markets. Instead of cluttering your chart with complex visuals, this indicator presents a clean, intuitive table that summarizes historical monthly performance, allowing you to spot recurring patterns at a glance.
How It Works
The indicator fetches historical monthly data for any symbol and calculates the percentage return for each month over a specified number of years. It then organizes this data into a comprehensive table, providing a clear, year-by-year and month-by-month breakdown of performance.
Key Features
Historical Performance Table: Displays monthly returns for up to a user-defined number of years, making it easy to compare performance across different periods.
Color-Coded Heatmap: Each cell is colored based on the performance of the month. Strong positive returns are shaded in green, while strong negative returns are shaded in red, allowing for immediate visual analysis of monthly strength or weakness.
Annual Summary: A "Σ" column shows the total percentage return for each full calendar year.
AVG Row: Calculates and displays the average return for each month across all the years shown in the table.
WR Row: Shows the "Win Rate" for each month, which is the percentage of time that month had a positive return. This is crucial for identifying high-probability seasonal trends.
How to Use
Add the "Seasonal Pattern Decoder" indicator to your chart. Note that it works best on Daily, Weekly, or Monthly timeframes. A warning message will be displayed on intraday charts.
In the indicator settings, adjust the "Lookback Period" to control how many years of historical data you want to analyze.
Use the "Show Years Descending" option to sort the table from the most recent year to the oldest.
The "Heat Range" setting allows you to adjust the sensitivity of the color-coding to fit the volatility of the asset you are analyzing.
This tool is ideal for confirming trading biases, developing seasonal strategies, or simply gaining a deeper understanding of an asset's typical behavior throughout the year.
## Disclaimer
This indicator is designed as a technical analysis tool and should be used in conjunction with other forms of analysis and proper risk management.
Past performance does not guarantee future results, and traders should thoroughly test any strategy before implementing it with real capital.
2MA Cross with Glow Effects 2MA Cross with Glow Effects
Overview
This indicator enhances the classic moving average crossover strategy with a dynamic and visually appealing "glow" effect. It plots two customisable moving averages on the chart and illuminates the area around them when a crossover occurs, providing a clear and intuitive signal for potential trend changes.
Features
Dual Moving Averages: Configure two independent moving averages to suit your trading style.
Multiple MA Types: Choose from a wide range of moving average types for each line, including:
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
WMA (Weighted Moving Average)
VWMA (Volume-Weighted Moving Average)
RMA (Relative Moving Average)
HMA (Hull Moving Average)
ALMA (Arnaud Legoux Moving Average)
LSMA (Least Squares Moving Average)
Customisable Appearance: Adjust the length, line width, and color for each moving average.
Unique Glow Effect: A configurable glow appears around the moving averages during a crossover, providing an unmistakable visual cue. You can control the intensity and width of this effect.
How It Works
The core of the indicator is the calculation of two moving averages based on the user's selected type and length. The script continuously monitors the relationship between these two MAs.
The "glow" is a sophisticated visual effect achieved by using Pine Script's `fill()` function to create a smooth, colored gradient around the MA lines. The glow is conditionally rendered:
When the first moving average (MA1) crosses above the second (MA2), MA1 will glow above its line.
When MA1 crosses below MA2, it will glow below its line.
The same logic is applied to MA2, creating a dual-glow effect that clearly shows which MA is dominant.
To ensure a consistent visual appearance across different chart timeframes, the indicator incorporates a `tfMultiplier` that automatically adjusts the glow's width.
How to Use
This indicator can be used in the same way as a standard moving average crossover strategy
Bullish Signal: Look for the shorter-period moving average to cross above the longer-period moving average. The glow effect will make this event highly visible.
Bearish Signal: Look for the shorter-period moving average to cross below the longer-period moving average.
Traders can use this for trend identification, entry/exit signals, and as a component of a more comprehensive trading system. For example, a common setup is using a 20-period EMA and a 50-period EMA to capture medium-term trends.
Disclaimer
This indicator is designed as a technical analysis tool and should be used in conjunction with other forms of analysis and proper risk management.
Past performance does not guarantee future results, and traders should thoroughly test any strategy before implementing it with real capital.
Normalized Portfolio TrackerThis script lets you create, visualize, and track a custom portfolio of up to 15 assets directly on TradingView.
It calculates a synthetic "portfolio index" by combining multiple tickers with user-defined weights, automatically normalizing them so the total allocation always equals 100%.
All assets are scaled to a common starting point, allowing you to compare your portfolio’s performance versus any benchmark like SPY, QQQ, or BTC.
🚀 Goal
This script helps traders and investors:
• Understand the combined performance of their portfolio.
• Normalize diverse assets into a single synthetic chart .
• Make portfolio-level insights without relying on external spreadsheets.
🎯 Use Cases
• Backtest your portfolio allocations directly on the chart.
• Compare your portfolio vs. benchmarks like SPY, QQQ, BTC.
• Track thematic baskets (commodities, EV supply chain, regional ETFs).
• Visualize how each component contributes to overall performance.
📊 Features
• Weighted Portfolio Performance : Combines selected assets into a synthetic value series.
• Base Price Alignment : Each asset is normalized to its starting price at the chosen date.
• Dynamic Portfolio Table : Displays symbols, normalized weights (%), equivalent shares (based on each asset’s start price, sums to 100 shares), and a total row that always sums to 100%.
• Multi-Asset Support : Works with stocks, ETFs, indices, crypto, or any TradingView-compatible symbol.
⚙️ Configuration
Flexible Portfolio Setup
• Add up to 15 assets with custom weight inputs.
• You can enter any arbitrary numbers (e.g. 30, 15, 55).
• The script automatically normalizes all weights so the total allocation always equals 100%.
Start Date Selection
• Choose any custom start date to normalize all assets.
• The portfolio value is then scaled relative to the main chart symbol, so you can directly compare portfolio performance against benchmarks like SPY or QQQ.
Chart Styles
• Candlestick chart
• Heikin Ashi chart
• Line chart
Custom Display
• Adjustable colors and line widths
• Optionally display asset list, normalized weights, and equivalent shares
⚙️ How It Works
• Fetch OHLC data for each asset.
• Normalizes weights internally so totals = 100%.
• Stores each asset’s base price at the selected start date.
• Calculates equivalent “shares” for each allocation.
• Builds a synthetic portfolio value series by summing weighted contributions.
• Renders as Candlestick, Heikin Ashi, or Line chart.
• Adds a portfolio info table for clarity.
⚠️ Notes
• This script is for visualization only . It does not place trades or auto-rebalance.
• Weight inputs are automatically normalized, so you don’t need to enter exact percentages.
Candle count, with simple numberWhat it does
Counts the length of same-color candle streaks (consecutive bullish or bearish bars) and prints the running number above each bar:
e.g., “1, 2, 3…”; when color flips, it restarts at “1”.
Prime numbers (2, 3, 5, 7, 11, 13) are emphasized by rendering one size step larger and with a user-selected color.
Labels are pinned to each bar (anchored by bar index and price), so they do not drift when you pan or zoom the chart.
How it works
Determines candle direction: bullish if close > open, bearish if close < open.
If the current bar has the same direction as the previous bar, the counter increments; otherwise it resets to 1.
For values 2, 3, 5, 7, 11, 13 the number is highlighted (bigger + custom color).
Each number is drawn just above the bar’s High with a configurable offset.
The script does not repaint on history. During the live bar, the number updates in real time (as expected).
Settings
Digits size — Base text size (Tiny / Small / Normal / Large / Huge).
Prime numbers are automatically shown one step larger than the base size.
Offset above bar (ticks) — Vertical offset from the bar’s High, in instrument ticks.
Prime numbers color — Text color used specifically for prime numbers (non-prime digits are white).
How to read & use it
Rising momentum. Long streaks (e.g., 5–7+) suggest strong directional moves with few pullbacks.
Early pause/mean-reversion hints. After a long streak, the appearance of the opposite color (counter resets to “1”) often coincides with a pause or minor retrace.
Research & statistics. Quickly see which streak lengths are common on your market/timeframe (e.g., “How often do 3–5 bar runs occur?”).
Trade management. You can tie partial exits to specific streak lengths (2, 3, 5…) or reduce risk when the counter flips back to “1”.
Why it’s useful
Provides a clean, numeric view of momentum with zero smoothing or lag.
Works on any symbol and timeframe.
Prime-number emphasis makes important counts pop at a glance.
Pinned labels stay exactly above their bars, ensuring stable, readable visuals at any zoom level.
Notes
Doji bars (close == open) are treated as no direction and reset the streak.
This is a context tool, not a standalone buy/sell signal. Combine it with your entry/exit framework.
Very dense charts may hit platform label limits; the script raises the limit, but extremely long histories on very low timeframes can still be heavy.
VWAP Daily/Weekly/Monthly - Automatic AnchoredExplanation:
This script plots Volume-Weighted Average Price (VWAP) lines that are automatically anchored to the beginning of key timeframes — daily, weekly, and monthly. VWAP is a widely used trading indicator that shows the average price of an asset weighted by trading volume, making it useful for identifying fair value and institutional trading levels.
The “automatic anchored” feature means that you don’t have to manually select starting points. Instead, the script automatically resets the VWAP at the start of each day, week, or month, depending on the chosen setting. This ensures the VWAP always reflects the true average price for that period, providing traders with a consistent reference for support, resistance, and trend direction across multiple timeframes.
Notice:
On the chart, you may notice visible “jumps” in the VWAP lines. These are intentional. Each jump marks the reset point at the start of a new day, week, or month, depending on the selected setting. This design keeps the VWAP history from the previous period intact, allowing you to clearly see how price interacted with VWAP in past sessions.
By keeping these historical resets, you can easily compare short-term (daily) VWAP behavior against longer-term levels like weekly and monthly VWAP. This provides valuable context, helping you spot when price respects or diverges from fair value across different timeframes.
In short:
Daily VWAP resets at the start of each trading day.
Weekly VWAP resets at the beginning of each trading week.
Monthly VWAP resets at the start of each month.
This makes it easy to analyze how price interacts with VWAP levels across different time horizons without manual adjustments.
Market Structure DashboardThis indicator displays a **multi-timeframe dashboard** that helps traders track market structure across several horizons: Monthly, Weekly, Daily, H4, H1, M15, and M5.
It identifies the current trend (Bullish, Bearish, or Neutral) based on the progression of **swing highs and lows** (HH/HL, LH/LL).
For each timeframe, the dashboard shows:
* The **current structure** (Bullish, Bearish, Neutral) with a clear color code (green, red, gray).
* **Pivot information**:
* either the latest swing high/low values,
* or the exact date and time of their occurrence (user-selectable in the settings).
An integrated **alert system** notifies you whenever the market structure changes (e.g., "Daily: Neutral → Bullish").
### Key Features:
* Clear overview of multi-timeframe market structures.
* Customizable pivot info display (values or timestamps).
* Built-in alerts on trend changes.
* Compact and readable dashboard, displayed in the top-right corner of the chart.
This tool is ideal for traders who want to quickly assess the **overall market structure** across multiple timeframes and be instantly alerted to potential reversals.
Custom Period High LowSummary
I'm moving over from TradeStation and default Pre-Market Session there is 0800-0930. Default PMS on TradingView is 0400-0930. I find that the 0800-0930 High and Low are more accurate levels. This script addresses exactly that - it allows you to grab High and Low of any custom time slot.
This script started as Custom Pre-Market H/L, that's why the shading. Then I realized it can be used for any custom time period, so I renamed it to PERIOD H/L.
Limitations
Different tickers are provided by different exchanges, in different time zones. The end result is that the SAME session time (0800-0930) may shift for different tickers. Examples:
- SPY : 0800-0930 // no shift: NYSE, in NYC
- ES1!: 0900-1030 // shifted 1 hr ahead: CME, in Chicago
- NQ1!: 0900-1030 // shifted 1 hr ahead: CME, in Chicago
To see for yourself, set Time Zone config parameter to empty string for non-NYC tickers like ES1! or NQ1 and watch times for shaded and non-shaded areas.
Why TV chooses to go by the ticker's TZ, and not the TZ that's configured in the lower right corner of my TV screen - I have no idea. But asking for user's TZ is how you fix it.
If you know how I can get that value so I don't have to ask the user - let me know. I'm new to TV.
Hacks
You can use it more than once for, say, Opening Range Breakout. Configure your custom PMS for 0930-0945, change lines, remove area fill - and ta-da - you have High and Low for first 15 min! See release chart for the example.
EMA 50/200/100 [NevoxCore]⯁ OVERVIEW
EMA 50/200/100 is a clean EMA trio for trend mapping.
It highlights the classic 50/200 bias, keeps a constant EMA-100 anchor in white, plots cross dots, and can mark the first pullback back to a target EMA within an ATR tolerance.
Solid bias bar coloring (Nevox pink/orange or classic green/red) and compact visuals make it fast and reliable with no repainting.
⯁ HOW IT WORKS
Calculates Fast EMA 50, Slow EMA 200, and an always-on EMA 100 (white).
Bias = Fast vs. Slow: Fast > Slow → long regime; Fast < Slow → short regime.
Cross dots appear at confirmed 50/200 crosses (once per bar close).
First Pullback: after a cross, the script arms a window and marks the first return to the chosen EMA (100 or Fast) within ATR × tolerance.
Bar coloring is solid by regime (pink/orange by default, classic green/red when enabled).
No lookahead; signals confirm on bar close.
⯁ KEY FEATURES
• EMA 50/200 with EMA-100 anchor (always visible, white)
• Cross Up/Down dots (style-configurable)
• First Pullback marker (toggle) with ATR tolerance & window
• Solid bias bar coloring (Nevox or classic)
• Optional bias fill between Fast/Slow
• Minimal 1-cell HUD (OFF by default)
• Ready-made alerts with clean prefixes
⯁ SETTINGS (quick)
Visual: Classic colors toggle; Bias Fill (ON); Fill Transparency (85); Bar Color (solid, ON; auto-disabled when Classic is ON).
Core: Source = Close; EMA Fast = 50; EMA Slow = 200.
Pullback: Show marker (ON); Target EMA = EMA 100; Tolerance × ATR = 0.5; Max Bars After Cross = 40; ATR Length = 14.
HUD: Mini HUD OFF; Position selector.
Status Line: OFF by default (optional EMA values).
⯁ ALERTS (built-in)
• Cross Up (Fast above Slow) — confirmed at bar close
• Cross Down (Fast below Slow) — confirmed at bar close
• First Pullback LONG — first return to target after long cross
• First Pullback SHORT — first return to target after short cross
Prefix: EMA and message includes {{ticker}} {{interval}} @ {{close}}.
Suggested: set TradingView alerts to Once Per Bar Close.
⯁ HOW TO USE
• Read trend quickly: 50 above 200 with a rising 100 = healthy long bias.
• Use the First Pullback to time entries after a cross (default target = EMA 100).
• Tune Tolerance × ATR by symbol/TF; 0.3–0.7 is a good start.
• Keep charts clean: bias fill + barcolor ON; switch to Classic for green/red if preferred.
⯁ WHY IT’S DIFFERENT
It preserves the classic 50/200 logic but adds a consistent EMA-100 anchor, a single, one-shot pullback detector, and clean bias bars — all in a lightweight overlay with no repaint tricks.
⯁ DISCLAIMER
Backtest and paper-trade before using live. Not financial advice. Performance depends on market, timeframe, and parameters.
Adaptive Machine Learning Trading System [PhenLabs]📊Adaptive ML Trading System
Version: PineScript™v6
📌Description
The Adaptive ML Trading System is a sophisticated machine learning indicator that combines ensemble modeling with advanced technical analysis. This system uses XGBoost, Random Forest, and Neural Network algorithms to generate high-confidence trading signals while incorporating robust risk management features. Traders benefit from objective, data-driven decision-making that adapts to changing market conditions.
🚀Points of Innovation
• Machine Learning Ensemble - Three integrated models (XGBoost, Random Forest, Neural Network)
• Confidence-Based Trading - Only executes trades when ML confidence exceeds threshold
• Dynamic Risk Management - ATR-based stop loss and max drawdown protection
• Adaptive Position Sizing - Volatility-adjusted position sizing with confidence weighting
• Real-Time Performance Metrics - Live tracking of win rate, Sharpe ratio, and performance
• Multi-Timeframe Feature Analysis - Adaptive lookback periods for different market regimes
🔧Core Components
• ML Ensemble Engine - Weighted combination of XGBoost, Random Forest, and Neural Network outputs
• Feature Normalization System - Advanced preprocessing with custom tanh/sigmoid activation
• Risk Management Module - Dynamic position sizing and drawdown protection
• Performance Dashboard - Real-time metrics and risk status monitoring
• Alert System - Comprehensive alert conditions for entries, exits, and risk events
🔥Key Features
• High-confidence ML signals with customizable confidence thresholds
• Multiple trading modes (Conservative, Balanced, Aggressive) for different risk profiles
• Integrated stop loss and risk management with ATR-based calculations
• Real-time performance metrics including win rate and Sharpe ratio
• Comprehensive alert system with entry, exit, and risk management notifications
• Visual confidence bands and threshold indicators for easy signal interpretation
🎨Visualization
• ML Signal Line - Primary signal output ranging from -1 to +1
• Confidence Bands - Visual representation of model confidence levels
• Threshold Lines - Customizable buy/sell threshold levels
• Position Histogram - Current market position visualization
• Performance Tables - Real-time metrics display in customizable positions
📖Usage Guidelines
Model Configuration
• Confidence Threshold: Default 0.55, Range 0.5-0.95 - Minimum confidence for signals
• Model Sensitivity: Default 0.9, Range 0.1-2.0 - Adjusts signal sensitivity
• Ensemble Mode: Conservative/Balanced/Aggressive - Trading style preference
• Signal Threshold: Default 0.55, Range 0.3-0.9 - ML signal threshold for entries
Risk Management
• Position Size %: Default 10%, Range 1-50% - Portfolio percentage per trade
• Max Drawdown %: Default 15%, Range 5-30% - Maximum allowed drawdown
• Stop Loss ATR: Default 2.0, Range 0.5-5.0 - Stop loss in ATR multiples
• Dynamic Sizing: Default true - Volatility-based position adjustment
Display Settings
• Show Signals: Default true - Display entry/exit signals
• Show Threshold Signals: Default true - Display ±0.6 threshold crosses
• Show Confidence Bands: Default true - Display ML confidence levels
• Performance Dashboard: Default true - Show metrics table
✅Best Use Cases
• Swing trading with 1-5 day holding periods
• Trend-following strategies in established trends
• Volatility breakout trading during high-confidence periods
• Risk-adjusted position sizing for portfolio management
• Multi-timeframe confirmation for existing strategies
⚠️Limitations
• Requires sufficient historical data for accurate ML predictions
• May experience low confidence periods in choppy markets
• Performance varies across different asset classes and timeframes
• Not suitable for very short-term scalping strategies
• Requires understanding of basic risk management principles
💡What Makes This Unique
• True machine learning ensemble with multiple model types
• Confidence-based trading rather than simple signal generation
• Integrated risk management with dynamic position sizing
• Real-time performance tracking and metrics
• Adaptive parameters that adjust to market conditions
🔬How It Works
Feature Calculation: Computes 20+ technical features from price/volume data
Feature Normalization: Applies custom normalization for ML compatibility
Ensemble Prediction: Combines XGBoost, Random Forest, and Neural Network outputs
Signal Generation: Produces confidence-weighted trading signals
Risk Management: Applies position sizing and stop loss rules
Execution: Generates alerts and visual signals based on thresholds
💡Note:
This indicator works best on daily and 4-hour timeframes for most assets. Ensure you understand the risk management settings before live trading. The system includes automatic risk-off modes that halt trading during excessive drawdown periods.
Market Sentiment Trend Gauge [LevelUp]Market Sentiment Trend Gauge simplifies technical analysis by mathematically combining momentum, trend direction, volatility position, and comparison against a market benchmark, into a single trend score from -100 to +100. Displayed in a separate pane below your chart, it resolves conflicting signals from RSI, moving averages, Bollinger Bands, and market correlations, providing clear insights into trend direction, strength, and relative performance.
THE PROBLEM MARKET SENTIMENT TREND GAUGE (MSTG) SOLVES
Traditional indicators often produce conflicting signals, such as RSI showing overbought while prices rise or moving averages indicating an uptrend despite market underperformance. MSTG creates a weighted composite score to answer: "What's the overall bias for this asset?"
KEY COMPONENTS AND WEIGHTINGS
The trend score combines
▪ Momentum (25%): Normalized 14-period RSI, capped at ±100.
▪ Trend Direction (35%): 10/21-period EMA relationships,
▪ Volatility Position (20%): Price position, 20-period Bollinger Bands, capped at ±100.
▪ Market Comparison (20%): Daily performance vs. SPY benchmark, capped at ±100.
Final score = Weighted sum, smoothed with 5-period EMA.
INTERPRETING THE MSTG CHART
Trend Score Ranges and Colors
▪ Bright Green (>+30): Strong bullish; ideal for long entries.
▪ Light Green (+10 to +30): Weak bullish; cautiously favorable.
▪ Gray (-10 to +10): Neutral; avoid directional trades.
▪ Light Red (-10 to -30): Weak bearish; exercise caution.
▪ Bright Red (<-30): Strong bearish; high-risk for longs, consider shorts.
Reference Lines
▪ Zero Line (Gray): Separates bullish/bearish; crossovers signal trend changes.
▪ ±30 Lines (Dotted, Green/Red): Thresholds for strong trends.
▪ ±60 Lines (Dashed, Green/Red): Extreme strength zones (not overbought/oversold); manage risk (tighten stops, partial profits) but trends may persist.
Background Colors
▪ Green Tint (>+20): Bullish environment; favorable for longs.
▪ Red Tint (<-20): Bearish environment; caution for longs.
▪ Light Gray Tint (-20 to +20): Neutral/range-bound; wait for signals.
Extreme Readings vs. Traditional Signals
MSTG ±60 indicates maximum alignment of all factors, not reversals (unlike RSI >70/<30). Use for risk management, not automatic exits. Strong trends can sustain extremes; breakdowns occur below +30 or above -30.
INFORMATION TABLE INTERPRETATION
Trend Score Symbols
▲▲ >+30 strong bullish
▲ +10 to +30
● -10 to +10 neutral
▼ -30 to -10
▼▼ <-30 strong bearish
Colors: Green (positive), White (neutral), Red (negative).
Momentum Score
+40 to +100 strong bullish
0 to +40 moderate bullish
-40 to 0 moderate bearish
-100 to -40 strong bearish
Market vs. Stock
▪ Green: Stock outperforming market
▪ Red: Stock underperforming market
Example Interpretations:
-0.45% / +1.23% (Green): Market down, stock up = Strong relative strength
+2.10% / +1.50% (Red): Both rising, but stock lagging = Relative weakness
-1.20% / -0.80% (Green): Both falling, but stock declining less = Defensive strength
UNDERSTANDING EXTREME READINGS VS TRADITIONAL OVERBOUGHT/OVERSOLD
⚠️ Critical distinctions
Traditional Overbought/Oversold Signals:
▪ Single indicator (like RSI >70 or <30) showing momentum excess
▪ Often suggests immediate reversal or pullback expected
▪ Based on "price moved too far, too fast" concept
MSTG Extreme Readings (±60):
▪ Composite alignment of 4 different factors (momentum, trend, volatility, relative strength)
▪ Indicates maximum strength in current direction
▪ NOT a reversal signal - means "all systems extremely bullish/bearish"
Key Differences:
▪ RSI >70: "Price got ahead of itself, expect pullback"
▪ MSTG >+60: "Everything is extremely bullish right now"
▪ Strong trends can maintain extreme MSTG readings during major moves
▪ Breakdowns happen when MSTG falls below +30, not at +60
Proper Usage of Extreme Readings:
▪ Risk Management: Tighten stops, take partial profits
▪ Position Sizing: Reduce new position sizes at extremes
▪ Trend Continuation: Watch for sustained extreme readings in strong markets
▪ Exit Signals: Look for breakdown below +30, not reversal from +60
TRADING WITH MSTG
Quick Assessment
1. Check trend symbol for direction.
2. Confirm momentum strength.
3. Note relative performance color.
Examples:
▲▲ 55.2 (Green), Momentum +28.4, Outperforming: Strong buy setup.
▼ -18.6 (Red), Momentum -43.2, Underperforming: Defensive positioning.
Entry Conditions
▪ Long: stock outperforming market
- Score >+30 (bright green)
- Sustained green background
- ▲▲ symbol,
▪ Short: stock underperforming market
- Score <-30 (bright red)
- Sustained red background
- ▼▼ symbol
Avoid Trading When:
▪ Gray zone (-10 to +10).
▪ Rapid color changes or frequent zero-line crosses (choppy market).
▪ Gray background (range-bound).
Risk Management:
▪ Stop Loss: Exit on zero-line crossover against position.
▪ Take Profit: Partial at ±60 for risk control.
▪ Position Sizing: Larger when signals align; smaller in extremes or mixed conditions.
KEY ADVANTAGES
▪ Unified View: Weighted composite reduces noise and conflicts.
▪ Visual Clarity: 5-color system with gradients for rapid recognition.
▪ Market Context: Relative strength vs. SPY identifies leaders/laggards.
▪ Flexibility: Works across timeframes (1-min to weekly); customizable table.
▪ Noise Reduction: EMA smoothing minimizes false signals.
EXAMPLES
Strong Bull: Trend Score 71.9, Momentum Score 76.9
Neutral: Trend Score 0.1, Momentum Score -9.2
Strong Bear: Trend Score -51.7, Momentum Score -51.5
PERFORMANCE AND LIMITATIONS
Strengths: Trend identification, noise reduction, relative performance versus market.
Limitations: Lags at turning points, less effective in extreme volatility or non-trending markets.
Recommendations: View on multiple timeframes, combine with price action and fundamentals.
