Herman 8-9 am SweepFrom x.com
1. Sweep 8-9am high/low
2. After sweep - 82.66% back to 9am candle open (before 10am)
The rectangle only appears when the 9 a.m. candle closes.
The yellow line only appears if there is a sweep of the High or Low of the rectangle.
The green line only appears if, after the sweep, the price returns to the line before 10 a.m.
If the line is not displayed, there is no sweep before 10 am.
Credits to: @R_Herman_ on X (Twitter)
Thanks and good trading
Statistics
Z-Score Volume with CVD TrendZ-Score Volume & CVD Trend with Exhaustion Signals
This powerful, all-in-one indicator combines statistical volume analysis, Cumulative Volume Delta (CVD), and a custom clustering algorithm to provide a clear and dynamic view of market sentiment. It is designed to help traders identify the prevailing trend and spot potential reversals or trend exhaustion before they happen.
Important Note: This indicator is specifically designed and optimized for use during the Regular Trading Hours (RTH) New York session, which is typically characterized by high volume and volatility. Its signals may be less reliable in low-volume or overnight sessions.
Core Concepts
1. Volume Z-Score
The script first calculates a Z-score for volume, which measures how many standard deviations a bar's volume is from a moving average. This helps to identify statistically significant volume spikes that may signal institutional activity or a major shift in sentiment.
2. Cumulative Volume Delta (CVD)
CVD plots the net difference between buying and selling volume over time. A rising CVD indicates a surplus of buying pressure, while a falling CVD shows a surplus of selling pressure. This provides a clear look at the direction of momentum.
3. Custom Clustering
By combining the Volume Z-score and CVD delta, the script classifies each bar into one of six distinct "clusters." The purpose is to simplify complex data into actionable signals.
High Conviction Bullish: High Z-score volume with strong CVD buying.
High Conviction Bearish: High Z-score volume with strong CVD selling.
Effort vs. Result: High Z-score volume with no clear CVD bias, indicating indecision or a struggle between buyers and sellers.
Quiet Accumulation: Low volume with subtle CVD buying, suggesting passive accumulation.
Quiet Distribution: Low volume with subtle CVD selling, suggesting passive distribution.
Low Conviction/Noise: Low volume and low CVD, representing general market noise.
Trend and Exhaustion Logic
Trend Establishment: The indicator determines the overall trend (Bullish, Bearish, or Neutral) by analyzing the majority of recent clusters over a configurable lookback period.
A Bullish Trend is confirmed when a majority of recent bars are either "High Conviction Bullish" or "Quiet Accumulation."
A Bearish Trend is confirmed when a majority of recent bars are either "High Conviction Bearish" or "Quiet Distribution."
Trend Exhaustion: This is a key feature for identifying potential reversals. The script looks for a divergence between price action and CVD within a confirmed trend.
Bullish Exhaustion Signal: Occurs during a confirmed "Bullish Trend" when you see a bearish divergence (price makes a higher high, but CVD shows negative delta and a close lower than the open). This is a strong sign the uptrend may be running out of steam.
Bearish Exhaustion Signal: Occurs during a confirmed "Bearish Trend" when you see a bullish divergence (price makes a lower low, but CVD shows positive delta and a close higher than the open). This indicates the downtrend may be exhausted.
How to Interpret the Visuals
Volume Bars: Colored to match the cluster they belong to.
Background Color: Shows the overall trend (light green for bullish, light red for bearish).
Circle Markers (bottom): Green circles indicate a bullish trend, and red circles indicate a bearish trend.
Triangles and Circles (top): Represent the specific cluster of each bar.
Trend Exhaustion Markers: Triangles above/below the bar signal potential trend exhaustion.
Info Table: An optional table provides a real-time summary of all key metrics for the current bar.
Settings
Volume EMA Length: Adjusts the moving average used for the Volume Z-score calculation.
Z-Score Look Back: Defines the number of bars to use for the volume and CVD percentile calculation.
Lower/Upper Cluster Percentile: Use these to adjust the sensitivity of the clustering. Tighter ranges (e.g., 25/75) capture more data, while wider ranges (e.g., 10/90) will only signal truly extreme events.
Trend Lookback Bars: Controls how many recent bars are considered when determining the trend.
This script offers a comprehensive and easy-to-read way to integrate volume, momentum, and trend analysis into your trading.
Happy Trading!
ITCRM CCL (aprox BCRA)This script calculates an approximation of the Real Multilateral Exchange Rate Index (ITCRM) with the CCL dollar, replicating the methodology of the Central Bank of Argentina (BCRA) but using the financial exchange rate (AL30C/AL30D) as a base.
Bilateral ARS/currency rates are built for Argentina’s main trading partners (Brazil, USA, Eurozone, China, etc.).
A weighted geometric average is applied according to trade shares.
The index is normalized to base 100 at the start of the series.
⚠️ This is a reference version, not official.
ITCRM CCL (aprox BCRA)This script calculates an approximation of the Real Multilateral Exchange Rate Index (ITCRM) with the CCL dollar, replicating the methodology of the Central Bank of Argentina (BCRA) but using the financial exchange rate (AL30C/AL30D) as a base.
Bilateral ARS/currency rates are built for Argentina’s main trading partners (Brazil, USA, Eurozone, China, etc.).
A weighted geometric average is applied according to trade shares.
The index is normalized to base 100 at the start of the series.
⚠️ This is a reference version, not official.
nATR*ATR Multiplication Indicator - Optimal Selection Tool forThis indicator is specifically designed as an analysis tool for investors using grid bot strategies. It displays both nATR (Normalized Average True Range) and ATR (Average True Range) values on a single chart screen, calculating the multiplication of these two critical volatility measurements.
Primary Purpose of the Indicator:
To facilitate the selection of the most optimal stock and time period for grid bot trading. The nATR*ATR multiplication provides a hybrid measurement that combines both percentage-based return potential (nATR) and absolute volatility magnitude (ATR).
Importance for Grid Bot Strategy:
High nATR: Greater percentage-based return potential
High ATR: Wider price range = Fewer grid levels = More budget allocation per grid
Formula: Price Range/ATR = Theoretical Grid Count
Usage Advantages:
Test different time periods to find the highest multiplication value
Make optimal stock and time frame selections for grid bot setup
Monitor both nATR and ATR values on a single screen
High multiplication values indicate ideal conditions for grid bots
Technical Features:
Adjustable calculation period (1-500 candles)
Visual alert system (high/low multiplication values)
Real-time value tracking table
SMA-based smoothed calculations
This serves as a reliable guide for grid bot investors in optimal timing and stock selection.
Quantile Regression Bands [BackQuant]Quantile Regression Bands
Tail-aware trend channeling built from quantiles of real errors, not just standard deviations.
What it does
This indicator fits a simple linear trend over a rolling lookback and then measures how price has actually deviated from that trend during the window. It then places two pairs of bands at user-chosen quantiles of those deviations (inner and outer). Because bands are based on empirical quantiles rather than a symmetric standard deviation, they adapt to skewed and fat-tailed behaviour and often hug price better in trending or asymmetric markets.
Why “quantile” bands instead of Bollinger-style bands?
Bollinger Bands assume a (roughly) symmetric spread around the mean; quantiles don’t—upper and lower bands can sit at different distances if the error distribution is skewed.
Quantiles are robust to outliers; a single shock won’t inflate the bands for many bars.
You can choose tails precisely (e.g., 1%/99% or 5%/95%) to match your risk appetite.
How it works (intuitive)
Center line — a rolling linear regression approximates the local trend.
Residuals — for each bar in the lookback, the indicator looks at the gap between actual price and where the line “expected” price to be.
Quantiles — those gaps are sorted; you select which percentiles become your inner/outer offsets.
Bands — the chosen quantile offsets are added to the current end of the regression line to draw parallel support/resistance rails.
Smoothing — a light EMA can be applied to reduce jitter in the line and bands.
What you see
Center (linear regression) line (optional).
Inner quantile bands (e.g., 25th/75th) with optional translucent fill.
Outer quantile bands (e.g., 1st/99th) with a multi-step gradient to visualise “tail zones.”
Optional bar coloring: bars trend-colored by whether price is rising above or falling below the center line.
Alerts when price crosses the outer bands (upper or lower).
How to read it
Trend & drift — the slope of the center line is your local trend. Persistent closes on the same side of the center line indicate directional drift.
Pullbacks — tags of the inner band often mark routine pullbacks within trend. Reaction back to the center line can be used for continuation entries/partials.
Tails & squeezes — outer-band touches highlight statistically rare excursions for the chosen window. Frequent outer-band activity can signal regime change or volatility expansion.
Asymmetry — if the upper band sits much further from the center than the lower (or vice versa), recent behaviour has been skewed. Trade management can be adjusted accordingly (e.g., wider take-profit upslope than downslope).
A simple trend interpretation can be derived from the bar colouring
Good use-cases
Volatility-aware mean reversion — fade moves into outer bands back toward the center when trend is flat.
Trend participation — buy pullbacks to the inner band above a rising center; flip logic for shorts below a falling center.
Risk framing — set dynamic stops/targets at quantile rails so position sizing respects recent tail behaviour rather than fixed ticks.
Inputs (quick guide)
Source — price input used for the fit (default: close).
Lookback Length — bars in the regression window and residual sample. Longer = smoother, slower bands; shorter = tighter, more reactive.
Inner/Outer Quantiles (τ) — choose your “typical” vs “tail” levels (e.g., 0.25/0.75 inner, 0.01/0.99 outer).
Show toggles — independently toggle center line, inner bands, outer bands, and their fills.
Colors & transparency — customize band and fill appearance; gradient shading highlights the tail zone.
Band Smoothing Length — small EMA on lines to reduce stair-step artefacts without meaningfully changing levels.
Bar Coloring — optional trend tint from the center line’s momentum.
Practical settings
Swing trading — Length 75–150; inner τ = 0.25/0.75, outer τ = 0.05/0.95.
Intraday — Length 50–100 for liquid futures/FX; consider 0.20/0.80 inner and 0.02/0.98 outer in high-vol assets.
Crypto — Because of fat tails, try slightly wider outers (0.01/0.99) and keep smoothing at 2–4 to tame weekend jumps.
Signal ideas
Continuation — in an uptrend, look for pullback into the lower inner band with a close back above the center as a timing cue.
Exhaustion probe — in ranges, first touch of an outer band followed by a rejection candle back inside the inner band often precedes mean-reversion swings.
Regime shift — repeated closes beyond an outer band or a sharp re-tilt in the center line can mark a new trend phase; adjust tactics (stop-following along the opposite inner band).
Alerts included
“Price Crosses Upper Outer Band” — potential overextension or breakout risk.
“Price Crosses Lower Outer Band” — potential capitulation or breakdown risk.
Notes
The fit and quantiles are computed on a fixed rolling window and do not repaint; bands update as the window moves forward.
Quantiles are based on the recent distribution; if conditions change abruptly, expect band widths and skew to adapt over the next few bars.
Parameter choices directly shape behaviour: longer windows favour stability, tighter inner quantiles increase touch frequency, and extreme outer quantiles highlight only the rarest moves.
Final thought
Quantile bands answer a simple question: “How unusual is this move given the current trend and the way price has been missing it lately?” By scoring that question with real, distribution-aware limits rather than one-size-fits-all volatility you get cleaner pullback zones in trends, more honest “extreme” tags in ranges, and a framework for risk that matches the market’s recent personality.
Algorithmic Kalman Filter [CRYPTIK1]Price action is chaos. Markets are driven by high-frequency algorithms, emotional reactions, and raw speculation, creating a constant stream of noise that obscures the true underlying trend. A simple moving average is too slow, too primitive to navigate this environment effectively. It lags, it gets chopped up, and it fails when you need it most.
This script implements an Algorithmic Kalman Filter (AKF), a sophisticated signal processing algorithm adapted from aerospace and robotic guidance systems. Its purpose is singular: to strip away market noise and provide a hyper-adaptive, self-correcting estimate of an asset's true trajectory.
The Concept: An Adaptive Intelligence
Unlike a moving average that mindlessly averages past data, the Kalman Filter operates on a two-step principle: Predict and Update.
Predict: On each new bar, the filter makes a prediction of the true price based on its previous state.
Update: It then measures the error between its prediction and the actual closing price. It uses this error to intelligently correct its estimate, learning from its mistakes in real-time.
The result is a flawlessly smooth line that adapts to volatility. It remains stable during chop and reacts swiftly to new trends, giving you a crystal-clear view of the market's real intention.
How to Wield the Filter: The Core Settings
The power of the AKF lies in its two tuning parameters, which allow you to calibrate the filter's "brain" to any asset or timeframe.
Process Noise (Q) - Responsiveness: This controls how much you expect the true trend to change.
A higher Q value makes the filter more sensitive and responsive to recent price action. Use this for highly volatile assets or lower timeframes.
A lower Q value makes the filter smoother and more stable, trusting that the underlying trend is slow-moving. Use this for higher timeframes or ranging markets.
Measurement Noise (R) - Smoothness: This controls how much you trust the incoming price data.
A higher R value tells the filter that the price is extremely noisy and to be more skeptical. This results in a much smoother, slower-moving line.
A lower R value tells the filter to trust the price data more, resulting in a line that tracks price more closely.
The interaction between Q and R is what gives the filter its power. The default settings provide a solid baseline, but a true operator will fine-tune these to perfectly match the rhythm of their chosen market.
Tactical Application
The AKF is not just a line; it's a complete framework for viewing the market.
Trend Identification: The primary signal. The filter's color code provides an unambiguous definition of the trend. Teal for an uptrend, Pink for a downtrend. No more guesswork.
Dynamic Support & Resistance: The filter itself acts as a dynamic level. Watch for price to pull back and find support on a rising (Teal) filter in an uptrend, or to be rejected by a falling (Pink) filter in a downtrend.
A Higher-Order Filter: Use the AKF's trend state to filter signals from your primary strategy. For example, only take long signals when the AKF is Teal. This single rule can dramatically reduce noise and eliminate low-probability trades.
This is a professional-grade tool for traders who are serious about gaining a statistical edge. Ditch the lagging averages. Extract the signal from the noise.
Cumulative Returns by Session [BackQuant]Cumulative Returns by Session
What this is
This tool breaks the trading day into three user-defined sessions and tracks how much each session contributes to return, volatility, and volume. It then aggregates results over a rolling window so you can see which session has been pulling its weight, how streaky each session has been, and how sessions relate to one another through a compact correlation heatmap.
We’ve also given the functionality for the user to use a simplified table, just by switching off all settings they are not interested in.
How it works
1) Session segmentation
You define APAC, EU, and US sessions with explicit hours and time zones. The script detects when each session starts and ends on every intraday bar and records its open, intraday high and low, close, and summed volume.
2) Per-session math
At each session end the script computes:
Return — either Percent: (Close−Open)÷Open×100(Close − Open) ÷ Open × 100(Close−Open)÷Open×100 or Points: (Close−Open)(Close − Open)(Close−Open), based on your selection.
Volatility — either Range: (High−Low)÷Open×100(High − Low) ÷ Open × 100(High−Low)÷Open×100 or ATR scaled by price: ATR÷Open×100ATR ÷ Open × 100ATR÷Open×100.
Volume — total volume transacted during that session.
3) Storage and lookback
Each day’s three session stats are stored as a row. You choose how many recent sessions to keep in memory. The script then:
Builds cumulative returns for APAC, EU, US across the lookback.
Computes averages, win rates, and a Sharpe-like ratio avgreturn÷avgvolatilityavg return ÷ avg volatilityavgreturn÷avgvolatility per session.
Tracks streaks of positive or negative sessions to show momentum.
Tracks drawdowns on cumulative returns to show worst runs from peak.
Computes rolling means over a short window for short-term drift.
4) Correlation heatmap
Using the stored arrays of session returns, the script calculates Pearson correlations between APAC–EU, APAC–US, and EU–US, and colors the matrix by strength and sign so you can spot coupling or decoupling at a glance.
What it plots
Three lines: cumulative return for APAC, EU, US over the chosen lookback.
Zero reference line for orientation.
A statistics table with cumulative %, average %, positive session rate, and optional columns for volatility, average volume, max drawdown, current streak, return-to-vol ratio, and rolling average.
A small correlation heatmap table showing APAC, EU, US cross-session correlations.
How to use it
Pick the asset — leave Custom Instrument empty to use the chart symbol, or point to another symbol for cross-asset studies.
Set your sessions and time zones — defaults approximate APAC, EU, and US hours, but you can align them to exchange times or your workflow.
Choose calculation modes — Percent vs Points for return, Range vs ATR for volatility. Points are convenient for futures and fixed-tick assets, Percent is comparable across symbols.
Decide the lookback — more sessions smooths lines and stats; fewer sessions makes the tool more reactive.
Toggle analytics — add volatility, volume, drawdown, streaks, Sharpe-like ratio, rolling averages, and the correlation table as needed.
Why session attribution helps
Different sessions are driven by different flows. Asia often sets the overnight tone, Europe adds liquidity and direction changes, and the US session can dominate range expansion. Separating contributions by session helps you:
Identify which session has been the main driver of net trend.
Measure whether volatility or volume is concentrated in a specific window.
See if one session’s gains are consistently given back in another.
Adapt tactics: fade during a mean-reverting session, press during a trending session.
Reading the tables
Cumulative % — sum of session returns over the lookback. The sign and slope tell you who is carrying the move.
Avg Return % and Positive Sessions % — direction and hit rate. A low average but high hit rate implies many small moves; the reverse implies occasional big swings.
Avg Volatility % — typical intrabars range for that session. Compare with Avg Return to judge efficiency.
Return/Vol Ratio — return per unit of volatility. Higher is better for stability.
Max Drawdown % — worst cumulative give-back within the lookback. A quick way to spot riskiness by session.
Current Streak — consecutive up or down sessions. Useful for mean-reversion or regime awareness.
Rolling Avg % — short-window drift indicator to catch recent turnarounds.
Correlation matrix — green clusters indicate sessions tending to move together; red indicates offsetting behavior.
Settings overview
Basic
Number of Sessions — how many recent days to include.
Custom Instrument — analyze another ticker while staying on your current chart.
Session Configuration and Times
Enable or hide APAC, EU, US rows.
Set hours per session and the specific time zone for each.
Calculation Methods
Return Calculation — Percent or Points.
Volatility Calculation — Range or ATR; ATR Length when applicable.
Advanced Analytics
Correlation, Drawdown, Momentum, Sharpe-like ratio, Rolling Statistics, Rolling Period.
Display Options and Colors
Show Statistics Table and its position.
Toggle columns for Volatility and Volume.
Pick individual colors for each session line and row accents.
Common applications
Session bias mapping — find which window tends to trend in your market and plan exposure accordingly.
Strategy scheduling — allocate attention or risk to the session with the best return-to-vol ratio.
News and macro awareness — see if correlation rises around central bank cycles or major data releases.
Cross-asset monitoring — set the Custom Instrument to a driver (index future, DXY, yields) to see if your symbol reacts in a particular session.
Notes
This indicator works on intraday charts, since sessions are defined within a day. If you change session clocks or time zones, give the script a few bars to accumulate fresh rows. Percent vs Points and Range vs ATR choices affect comparability across assets, so be consistent when comparing symbols.
Session context is one of the simplest ways to explain a messy tape. By separating the day into three windows and scoring each one on return, volatility, and consistency, this tool shows not just where price ended up but when and how it got there. Use the cumulative lines to spot the steady driver, read the table to judge quality and risk, and glance at the heatmap to learn whether the sessions are amplifying or canceling one another. Adjust the hours to your market and let the data tell you which session deserves your focus.
CME FX Futures Correlation MatrixThis indicator calculates the correlation between major CME FX futures and displays it in a visual table. It shows how closely pairs like EUR/USD, GBP/USD, USD/JPY, USD/CHF, USD/CAD, AUD/USD, and NZD/USD move together or in opposite directions.
The indicator inherits the timeframe of the chart it’s applied to.
Color coding:
Red: strong correlation (absolute value > 80%), both positive and negative
Green: moderate/low correlation
How to launch it
Apply the indicator to a CME chart (e.g., EUR/USD futures).
Set Numbers of Bars Back to the desired lookback period (default 100).
The table appears in the center of the chart, showing correlation percentages between all major FX futures.
Deadband Hysteresis Supertrend [BackQuant]Deadband Hysteresis Supertrend
A two-stage trend tool that first filters price with a deadband baseline, then runs a Supertrend around that baseline with optional flip hysteresis and ATR-based adverse exits.
What this is
A hybrid of two ideas:
Deadband Hysteresis Baseline that only advances when price pulls far enough from the baseline to matter. This suppresses micro noise and gives you a stable centerline.
Supertrend bands wrapped around that baseline instead of raw price. Flips are further gated by an extra margin so side changes are more deliberate.
The goal is fewer whipsaws in chop and clearer regime identification during trends.
How it works (high level)
Deadband step — compute a per-bar “deadband” size from one of four modes: ATR, Percent of price, Ticks, or Points. If price deviates from the baseline by more than this amount, move the baseline forward by a fraction of the excess. If not, hold the line.
Centered Supertrend — build upper and lower bands around the baseline using ATR and a user factor. Track the usual trailing logic that tightens a band while price moves in its favor.
Flip hysteresis — require price to exceed the active band by an extra flip offset × ATR before switching sides. This adds stickiness at the boundary.
Adverse exit — once a side is taken, trigger an exit if price moves against the entry by K × ATR .
If you would like to check out the filter by itself:
What it plots
DBHF baseline (optional) as a smooth centerline.
DBHF Supertrend as the active trailing band.
Candle coloring by trend side for quick read.
Signal markers 𝕃 and 𝕊 at flips plus ✖ on adverse exits.
Inputs that matter
Price Source — series being filtered. Close is typical. HL2 or HLC3 can be steadier.
Deadband mode — ATR, Percent, Ticks, or Points. This defines the “it’s big enough to matter” zone.
ATR Length / Mult (DBHF) — only used when mode = ATR. Larger values widen the do-nothing zone.
Percent / Ticks / Points — alternatives to ATR; pick what fits your market’s convention.
Enter Mult — scales the deadband you must clear before the baseline moves. Increase to filter more noise.
Response — fraction of the excess applied to baseline movement. Higher responds faster; lower is smoother.
Supertrend ATR Period & Factor — traditional band size controls; higher factor widens and flips less often.
Flip Offset ATR — extra ATR buffer required to flip. Useful in choppy regimes.
Adverse Stop K·ATR — per-trade danger brake that forces an exit if price moves K×ATR against entry.
UI — toggle baseline, supertrend, signals, and bar painting; choose long and short colors.
How to read it
Green regime — candles painted long and the Supertrend running below price. Pullbacks toward the baseline that fail to breach the opposite band often resume higher.
Red regime — candles painted short and the Supertrend running above price. Rallies that cannot reclaim the band may roll over.
Frequent side swaps — reduce sensitivity by increasing Enter Mult, using ATR mode, raising the Supertrend factor, or adding Flip Offset ATR.
Use cases
Bias filter — allow entries only in the direction of the current side. Use your preferred triggers inside that bias.
Trailing logic — treat the active band as a dynamic stop. If the side flips or an adverse K·ATR exit prints, reduce or close exposure.
Regime map — on higher timeframes, the combination baseline + band produces a clean up vs down template for allocation decisions.
Tuning guidance
Fast markets — ATR deadband, modest Enter Mult (0.8–1.2), response 0.2–0.35, Supertrend factor 1.7–2.2, small Flip Offset (0.2–0.5 ATR).
Choppy ranges — widen deadband or raise Enter Mult, lower response, and add more Flip Offset so flips require stronger evidence.
Slow trends — longer ATR periods and higher Supertrend factor to keep you on side longer; use a conservative adverse K.
Included alerts
DBHF ST Long — side flips to long.
DBHF ST Short — side flips to short.
Adverse Exit Long / Short — K·ATR stop triggers against the current side.
Strengths
Deadbanded baseline reduces micro whipsaws before Supertrend logic even begins.
Flip hysteresis adds a second layer of confirmation at the boundary.
Optional adverse ATR stop provides a uniform risk cut across assets and regimes.
Clear visuals and minimal parameters to adjust for symbol behavior.
Putting it together
Think of this tool as two decisions layered into one view. The deadband baseline answers “does this move even count,” then the Supertrend wrapped around that baseline answers “if it counts, which side should I be on and where do I flip.” When both parts agree you tend to stay on the correct side of a trend for longer, and when they disagree you get an early warning that conditions are changing.
When the baseline bends and price cannot reclaim the opposite band , momentum is usually continuing. Pullbacks into the baseline that stall before the far band often resolve in trend.
When the baseline flattens and the bands compress , expect indecision. Use the Flip Offset ATR to avoid reacting to the first feint. Wait for a clean band breach with follow through.
When an adverse K·ATR exit prints while the side has not flipped , treat it as a risk event rather than a full regime change. Many users cut size, re-enter only if the side reasserts, and let the next flip confirm a new trend.
Final thoughts
Deadband Hysteresis Supertrend is best read as a regime lens. The baseline defines your tolerance for noise, the bands define your trailing structure, and the flip offset plus adverse ATR stop define how forgiving or strict you want to be at the boundary. On strong trends it helps you hold through shallow shakeouts. In choppy conditions it encourages patience until price does something meaningful. Start with settings that reflect the cadence of your market, observe how often flips occur, then nudge the deadband and flip offset until the tool spends most of its time describing the move you care about rather than the noise in between.
Fixed Range Volume Profile"Distribution of transaction volume by price group (transaction volume by price block)"
Instructions for use (Professional Manual)
1. a basic concept
By vertical axis (price), shows the cumulative trading volume traded in the segment.
The longer the block, the more transactions took place in that price range.
Colors distinguish between buying/selling strength (green = buying advantage, red = selling advantage).
2. Key components
POC (Point of Control)
→ Longest block (most traded price segment, "key selling point").
VAH / VAL (Value Area High/Low)
→ Top/bottom segments where approximately 70% of the total volume is formed.
→ Role of "Major Support/Resistance".
High Capacity Node (HVN)
→ Significantly higher trading volumes → strong support/resistance.
Low Volume Node (LVN)
→ Low volume section → areas where prices are easily passed.
3. practical application
Find Support/Resistance
The thickest block (POC) is used as a place where prices often rebound/resist.
a trading entry/liquidation strategy
Buy if the price is supported near HVN,
When breaking through the LVN, fast movement (gap movement) can be expected.
break/goal setting
Finger = Under the LVN,
Target = Next HVN.
Judgment of trends
When the block distribution is concentrated above, "Increase to Collection Section"
If you're driven below, you're "in a downtrend to a variance section."
4. Precautions
The volume distribution is "past data based" and is not an indicator of the future.
Rather than using it alone, it is more effective to combine with Fibonacci, trend lines, and candle patterns.
In particular, in the volatile market, the LVN breakthrough → may signal a surge/fall.
In summary, this block indicator is "a map showing the most market participants at any price point".
In other words, it is useful for finding support/resistance as a tool for analyzing sales and establishing the basis for trading strategies.
Crypto OI AgregatedCrypto OI Aggregated — Open Interest Aggregator for Crypto Exchanges
General Description
The indicator is designed for comprehensive analysis of Open Interest (OI) across major cryptocurrency exchanges. It consolidates data from multiple platforms, visualizes it as candlestick charts or deltas, and builds tables with breakdowns by exchange and contract type. This allows traders to quickly understand where market interest is concentrated and how the market structure is shifting.
Unlike standard tools that only show data from a single exchange, this indicator provides a full market overview and makes it easy to compare dynamics across different platforms.
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Key Features
• Aggregation of OI data from exchanges: Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit (feel free to leave a comment if you’d like me to add other exchanges that provide open interest data)
• Support for contract types: USDT.P, USD.P, USDC.P, USD.PM
• Automatic normalization of various OI data formats from different providers
• Display modes:
• OI candlestick chart (total aggregated OI)
• OI Delta (change in OI per bar)
• Full table with detailed data by exchange and contract type
• Short summary table with totals in USD and base assets
• Support for USD or COIN denomination
• Convenient formatting for large numbers
• Customizable colors
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How to Use the Indicator
1. Select Exchanges
In the settings, enable or disable specific exchanges. It is recommended to activate only the ones you need for analysis — this will make the indicator faster.
2. Choose Data Type
• OI — aggregated open interest from selected exchanges.
• OI delta — delta (change in OI compared to the previous bar).
3. Denomination
• USD — values are converted into USD equivalents.
• COIN — values are shown in the base asset (BTC, ETH, etc.).
4. Reading the Chart
• OI candlesticks show the overall OI dynamics.
• Delta histogram highlights how much OI has grown or decreased per bar.
• Colors are fully customizable.
5. Tables
• Enabled via the Show table option.
• Full Table → Rows = exchanges, Columns = contract types. Cells contain OI values in either USD or the base asset, depending on settings. Quickly shows where the main interest is concentrated.
• Short Table → Displays only the total OI values in USD and the base asset.
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Important Notes
• For better readability of large values, two custom formatting functions were implemented. They work similarly to format.volume, but with improved digit grouping and adjustable decimal precision. In the tables, the top row is formatted using format.volume, while the bottom row uses the improved formatting functions for clearer representation.
str(d, n, s) =>
str.substring(d, 0, str.length(d) - n) + '.' + str.substring(d, str.length(d) - n, str.length(d) - (n - 2)) + s
format(_r) =>
d = str.tostring(math.round(_r))
str.length(d) > 9 ? str(d, 9, " B") : str.length(d) > 6 ? str(d, 6, " M") : str.length(d) > 3 ? str(d, 3, " K") : d
⸻
Conclusion: Crypto OI Aggregated is a convenient and powerful tool for cryptocurrency derivatives traders. It enables tracking of OI dynamics across multiple exchanges simultaneously, detecting imbalances between contracts, and identifying signals that are not visible when analyzing a single exchange.
dr.forexy strategy 1“Dear friends, please do not use this strategy on your own! This setup works best on the 5-minute timeframe. I hope it brings you great profits.”
Adaptive FoS LibraryThis library provides Adaptive Functions that I use in my scripts. For calculations, I use the max_bars_back function with a fixed length of 200 bars to prevent errors when a script tries to access data beyond its available history. This is a key difference from most other adaptive libraries — if you don’t need it, you don’t have to use it.
Some of the adaptive length functions are normalized. In addition to the adaptive length functions, this library includes various methods for calculating moving averages, normalized differences between fast and slow MA's, as well as several normalized oscillators.
High Probability Order Blocks [AlgoAlpha]🟠 OVERVIEW
This script detects and visualizes high-probability order blocks by combining a volatility-based z-score trigger with a statistical survival model inspired by Kaplan-Meier estimation. It builds and manages bullish and bearish order blocks dynamically on the chart, displays live survival probabilities per block, and plots optional rejection signals. What makes this tool unique is its use of historical mitigation behavior to estimate and plot how likely each zone is to persist, offering traders a probabilistic perspective on order block strength—something rarely seen in retail indicators.
🟠 CONCEPTS
Order blocks are regions of strong institutional interest, often marked by large imbalances between buying and selling. This script identifies those areas using z-score thresholds on directional distance (up or down candles), detecting statistically significant moves that signal potential smart money footprints. A bullish block is drawn when a strong up-move (zUp > 4) follows a down candle, and vice versa for bearish blocks. Over time, each block is evaluated: if price “mitigates” it (i.e., closes cleanly past the opposite side and confirmed with a 1 bar delay), it’s considered resolved and logged. These resolved blocks then inform a Kaplan-Meier-like survival curve, estimating the likelihood that future blocks of a given age will remain unbroken. The indicator then draws a probability curve for each side (bull/bear), updating it in real time.
🟠 FEATURES
Live label inside each block showing survival probability or “N.E.D.” if insufficient data.
Kaplan-Meier survival curves drawn directly on the chart to show estimated strength decay.
Rejection markers (▲ ▼) if price bounces cleanly off an active order block.
Alerts for zone creation and rejection signals, supporting rule-based trading workflows.
🟠 USAGE
Read the label inside each block for Age | Survival% (or N.E.D. if there aren’t enough samples yet); higher survival % suggests blocks of that age have historically lasted longer.
Use the right-side survival curves to gauge how probability decays with age for bull vs bear blocks, and align entries with the side showing stronger survival at current age.
Treat ▲ (bullish rejection) and ▼ (bearish rejection) as optional confluence when price tests a boundary and fails to break.
Turn on alerts for “Bullish Zone Created,” “Bearish Zone Created,” and rejection signals so you don’t need to watch constantly.
If your chart gets crowded, enable Prevent Overlap ; tune Max Box Age to your timeframe; and adjust KM Training Window / Minimum Samples to trade off responsiveness vs stability.
Stop Loss vs Take Profit Probability and EVThis stop loss and take profit calculator uses a Monte Carlo simulation to calculate the probability of hitting your Stop Loss or Take Profit levels across different time horizons (expressed in bars).
It provides data-driven insights to optimize your risk management and position sizing by showing Expected Value for each scenario.
As a quant, I love using statistical data to help my decisions and get better EV from my trades.
🔬 How It's Calculated
Monte Carlo Simulation: Runs 1,000-10,000 price simulations using a random walk model
Volatility Analysis: Combines ATR-based and Historical Volatility for accurate price movement modeling
Expected Value: Calculates profit/loss expectation using formula: (TP_Probability × Reward) - (SL_Probability × Risk)
Time Horizons: Tests multiple timeframes (1, 5, 10, 20, 50 bars) to find optimal holding periods
Risk/Reward Ratios: Automatically calculates and displays R:R ratios for quick assessment
💡 Use Cases
Position Sizing - Determine optimal risk per trade based on Expected Value
Time Horizon Optimization - Find the best holding period for your strategy
Stop Loss Placement - Validate SL levels using probability analysis
Take Profit Optimization - Set TP levels with statistical backing
Strategy Backtesting - Compare different R:R setups before entering trades
Risk Management - Avoid trades with negative Expected Value
Swing vs Day Trading - Choose timeframes with highest success probability
🎯 How to Use
Setup Trade: Enter your entry price, stop loss, and take profit levels
You can add or remove time horizons denominated in bars. Say you are looking at 1h candles, adding a 24-bar time horizon means you are looking into 24 hours
Choose Direction: Select Long or Short position
Review Table
Analyze Expected Value: Focus on positive EV scenarios (green background)
Optimize Timing: Select time horizons with best risk/reward profile
Adjust Parameters: Modify volatility calculation method and simulation count if needed
Examples
Here's how you can read the tables.
Example 1:
In this chart, we are analyzing the TP and SL probabilities as well as the EV (expected value) for a stock. I want to check what the likelihood is that my SL and TP get triggered over the next 5 days. The stock market is open for 6.5 hours per day, which is 13 bars in this 30-minute bar chart. 26 bars is 2 days, 39 bars is 3 days and so on.
Although this trade is more likely to trigger my SL than my TP, in some of the time horizons we have a positive expected value because of the risk/reward of our trade (i.e. distance of the SL and TP from the price) and the probability of hitting SL and TP.
Example 2:
In this example, we have applied the indicator to gold. Because the TP is much closer to the price, the probability of hitting the TP is much higher.
We can also observe that the expected Value in the shorter time frames is better than in the longer ones. This can give us some clues to set up our trade. If we know that the EV is positive, we can allocate more to that specific trade.
Enjoy, and please let me know your feedback! 😊🥂
ETFs Sector PerformanceDisplays a table of the Top 8 performing ETFs over a selected period (1M / 2M / 3M / 6M) to quickly identify industry strength.
Pre-Set Universe (39 ETFs)
ITA — iShares U.S. Aerospace & Defense ETF
DBA — Invesco DB Agriculture Fund
BOTZ — Global X Robotics & Artificial Intelligence ETF
JETS — U.S. Global Jets ETF
XLB — Materials Select Sector SPDR Fund
XBI — SPDR S&P Biotech ETF
PKB — Invesco Dynamic Building & Construction ETF
ICLN — iShares Global Clean Energy ETF
SKYY — First Trust Cloud Computing ETF
DBC — Invesco DB Commodity Index Tracking Fund
XLY — Consumer Discretionary Select Sector SPDR Fund
XLP — Consumer Staples Select Sector SPDR Fund
BLOK — Amplify Transformational Data Sharing ETF
KARS — KraneShares Electric Vehicles & Future Mobility ETF
XLE — Energy Select Sector SPDR Fund
ESPO — VanEck Video Gaming and eSports ETF
XLF — Financial Select Sector SPDR Fund
PBJ — Invesco Dynamic Food & Beverage ETF
ITB — iShares U.S. Home Construction ETF
XLI — Industrial Select Sector SPDR Fund
PAVE — Global X U.S. Infrastructure Development ETF
PEJ — Invesco Dynamic Leisure & Entertainment ETF
LIT — Global X Lithium & Battery Tech ETF
IHI — iShares U.S. Medical Devices ETF
XME — SPDR S&P Metals & Mining ETF
FCG — First Trust Natural Gas ETF
URA — Global X Uranium ETF
PPH — VanEck Pharmaceutical ETF
QTUM — Defiance Quantum Computing & Machine Learning ETF
IYR — iShares U.S. Real Estate ETF
XRT — SPDR S&P Retail ETF
SOXX — iShares Semiconductor ETF
BOAT — SonicShares Global Shipping ETF
IGV — iShares Expanded Tech-Software Sector ETF
TAN — Invesco Solar ETF
SLX — VanEck Steel ETF
IYZ — iShares U.S. Telecommunications ETF
IYT — iShares U.S. Transportation ETF
XLU — Utilities Select Sector SPDR Fund
Theil-Sen Line Filter [BackQuant]Theil-Sen Line Filter
A robust, median-slope baseline that tracks price while resisting outliers. Designed for the chart pane as a clean, adaptive reference line with optional candle coloring and slope-flip alerts.
What this is
A trend filter that estimates the underlying slope of price using a Theil-Sen style median of past slopes, then advances a baseline by a controlled fraction of that slope each bar. The result is a smooth line that reacts to real directional change while staying calm through noise, gaps, and single-bar shocks.
Why Theil-Sen
Classical moving averages are sensitive to outliers and shape changes. Ordinary least squares is sensitive to large residuals. The Theil-Sen idea replaces a single fragile estimate with the median of many simple slopes, which is statistically robust and less influenced by a few extreme bars. That makes the baseline steadier in choppy conditions and cleaner around regime turns.
What it plots
Filtered baseline that advances by a fraction of the robust slope each bar.
Optional candle coloring by baseline slope sign for quick trend read.
Alerts when the baseline slope turns up or down.
How it behaves (high level)
Looks back over a fixed window and forms many “current vs past” bar-to-bar slopes.
Takes the median of those slopes to get a robust estimate for the bar.
Optionally caps the magnitude of that per-bar slope so a single volatile bar cannot yank the line.
Moves the baseline forward by a user-controlled fraction of the estimated slope. Lower fractions are smoother. Higher fractions are more responsive.
Inputs and what they do
Price Source — the series the filter tracks. Typical is close; HL2 or HLC3 can be smoother.
Window Length — how many bars to consider for slopes. Larger windows are steadier and slower. Smaller windows are quicker and noisier.
Response — fraction of the estimated slope applied each bar. 1.00 follows the robust slope closely; values below 1.00 dampen moves.
Slope Cap Mode — optional guardrail on each bar’s slope:
None — no cap.
ATR — cap scales with recent true range.
Percent — cap scales with price level.
Points — fixed absolute cap in price points.
ATR Length / Mult, Cap Percent, Cap Points — tune the chosen cap mode’s size.
UI Settings — show or hide the line, paint candles by slope, choose long and short colors.
How to read it
Up-slope baseline and green candles indicate a rising robust trend. Pullbacks that do not flip the slope often resolve in trend direction.
Down-slope baseline and red candles indicate a falling robust trend. Bounces against the slope are lower-probability until proven otherwise.
Flat or frequent flips suggest a range. Increase window length or decrease response if you want fewer whipsaws in sideways markets.
Use cases
Bias filter — only take longs when slope is up, shorts when slope is down. It is a simple way to gate faster setups.
Stop or trail reference — use the line as a trailing guide. If price closes beyond the line and the slope flips, consider reducing exposure.
Regime detector — widen the window on higher timeframes to define major up vs down regimes for asset rotation or risk toggles.
Noise control — enable a cap mode in very volatile symbols to retain the line’s continuity through event bars.
Tuning guidance
Quick swing trading — shorter window, higher response, optionally add a percent cap to keep it stable on large moves.
Position trading — longer window, moderate response. ATR cap tends to scale well across cycles.
Low-liquidity or gappy charts — prefer longer window and a points or ATR cap. That reduces jumpiness around discontinuities.
Alerts included
Theil-Sen Up Slope — baseline’s one-bar change crosses above zero.
Theil-Sen Down Slope — baseline’s one-bar change crosses below zero.
Strengths
Robust to outliers through median-based slope estimation.
Continuously advances with price rather than re-anchoring, which reduces lag at turns.
User-selectable slope caps to tame shock bars without over-smoothing everything.
Minimal visuals with optional candle painting for fast regime recognition.
Notes
This is a filter, not a trading system. It does not account for execution, spreads, or gaps. Pair it with entry logic, risk management, and higher-timeframe context if you plan to use it for decisions.
Supertrend [TradingConToto]Supertrend — ADX/DI + EMA Gap + Breakout (with Mobile UI)
What makes it original
Supertrend combines trend strength (ADX/DI), multi-timeframe bias (EMA63 and EMA 200D equivalent), a structural filter based on the distance between EMA2400 and EMA4800 expressed in ATR units, and a momentum confirmation through a previous high breakout.
This is not a random mashup — it’s a sequence of filters designed to reduce trades in ranging markets and prioritize mature trends:
Direction: +DI > -DI (trend led by buyers).
Strength: ADX > mean(ADX) (avoids weak, choppy phases).
Short-term bias: Close > EMA63.
Long-term bias: Close > EMA4800 ≈ EMA200 daily on H1.
Momentum: Close > High (immediate breakout).
Structure: (EMA2400 − EMA4800) > k·ATR (ensures separation in ATR units, filters out flat phases).
Entries & exits
Entry: when all six conditions are met and no open position exists.
Exit: if +DI < -DI or Close < EMA63.
Visuals: EMA63 is painted green while in position and red otherwise, with a supertrend-style band; “BUY” labels appear below the green band and “SELL” labels above the red band.
UI: includes a compact table (mobile-friendly) showing the state of each condition.
Default parameters used in this publication
Initial capital: 10,000
Position size: 10% of equity (≤10% per trade is considered sustainable).
Commission: 0.01% per side (adjust to your broker/market).
Slippage: 1 tick
Pyramiding: 0 (only one position at a time)
Adjust commission/slippage to match your market. For US equities, commissions are often per share; for spot crypto, 0.10–0.20% total is common. I publish with 0.01% per side as a conservative example to avoid overestimating results.
Recommended backtest dataset
Timeframe: H1
Multi-cycle window (e.g. 2015–today)
Symbols with high liquidity (e.g. NASDAQ-100 large caps, or BTC/ETH spot) to generate 100+ trades. Avoid cherry-picked short windows.
Why each filter matters
+DI > -DI + ADX > mean: reduce counter-trend trades and weak signals.
Close > EMA63 + Close > EMA4800: enforce trend alignment in short and long horizons.
Breakout High : requires immediate momentum, avoids early entries.
EMA gap in ATR units: blocks flat or compressed structures where EMA200D aligns with price.
Limitations
The breakout filter may skip healthy pullbacks; the design prioritizes continuation over perfect entry price.
No fixed trailing stop/TP; exits depend on trend degradation via DI/EMA63.
Results vary with real costs (commissions, slippage, funding). Adjust defaults to your broker.
How to use
Apply it on a clean chart (no other indicators when publishing).
Keep in mind the default parameters above; if you change them, mention it in your notes and use the same values in the Strategy Tester.
Ensure your dataset produces 100+ trades for statistical validity.
Retail Sentiment Indicator - Multi-Asset CFD & Fear/Greed IndexRetail Sentiment Indicator - Multi-Asset CFD & Fear/Greed Index
Overview
The Retail Sentiment Indicator provides real-time sentiment data for major financial instruments including stocks, forex, commodities, and cryptocurrencies. This indicator displays retail trader positioning and market sentiment using CFD data and fear/greed indices.
Methodology and Scale Calculation
This indicator operates on a **-50 to +50 scale** with zero representing perfect market equilibrium.
Scale Interpretation:
- **Zero (0)**: Market balance - exactly 50% of investors buying, 50% selling
- **Positive values**: Majority buying pressure
- Example: If 63% of investors are buying, the indicator shows +13 (63 - 50 = +13)
- **Negative values**: Majority selling pressure
- Example: If 92% of investors are selling, the indicator shows -42 (50 - 92 = -42)
BTC Fear & Greed Index Scaling:
The original `BTC FEAR&GREED` index is natively scaled from 0-100 by its creator. In our indicator, this data has been rescaled to also fit the -50 to +50 range for consistency with other sentiment data sources.
This unified scaling approach allows for direct comparison across all instruments and data sources within the indicator.
-Important Data Source Selection-
Bitcoin (BTC) Data Sources
When viewing Bitcoin charts, the indicator offers **two different data sources**:
1. **Default Auto-Mode**: `BTCUSD Retail CFD` - Retail CFD traders sentiment data (automatically loaded).
2. **Manual Selection**: `BTC FEAR&GREED` - Fear & Greed Index from website: alternative dot me
**To access BTC Fear & Greed Index**: Input settings -> disable checkbox "Auto-load Sentiment Data" -> manually select "BTC FEAR&GREED" from the dropdown menu.
US Stock Market Data Sources
For US stocks and indices (S&P 500, NASDAQ, Dow Jones), there are **two data source options**:
1. **Default Auto-Mode**: Individual retail CFD sentiment data for each instrument
2. **Manual Selection**: `SNN FEAR&GREED` - SNN's Fear & Greed Index covering the overall US market sentiment. SNN was used as the name to avoid any potential trademark infringement.
**To access SNN Fear & Greed Index**: When viewing US market charts, disable in input settings checkbox "Auto-load Sentiment Data" and manually select "SNN FEAR&GREED" from the dropdown menu.
This distinction allows traders to choose between **instrument-specific retail sentiment** (auto-mode) or **broader market sentiment indices** (manual selection).
Features
- **Auto-Detection**: Automatically loads sentiment data based on the current chart symbol
- **Manual Selection**: Choose from 40+ supported instruments when auto-detection is unavailable
- **Multiple Data Sources**: Combines retail CFD sentiment with Fear & Greed indices
- **Visual Zones**: Clear greed/fear zones with color-coded backgrounds
- **Real-time Updates**: Live sentiment data from merged data sources
Supported Instruments
Major Indices
- S&P 500, NASDAQ, Dow Jones 30, DAX
Forex Pairs
- Major pairs: EURUSD, GBPUSD, USDJPY, USDCHF, USDCAD
- Cross pairs: EURJPY, GBPJPY, AUDUSD, NZDUSD, and 20+ others
Commodities
- Precious metals: Gold (XAUUSD), Silver (XAGUSD)
- Energy: WTI Oil
- Agricultural: Wheat, Coffee
- Industrial: Copper
Cryptocurrencies
- Bitcoin (BTC) sentiment data
- BTC & SNN Fear & Greed indices
How to Use
1. **Auto Mode** (Default): Enable "Auto-load Sentiment Data" to automatically display sentiment for the current chart symbol
2. **Manual Mode**: Disable auto-load and select from the dropdown menu for specific instruments
3. **Interpretation**:
- Values above 0 (green) indicate retail greed/bullish sentiment
- Values below 0 (red) indicate retail fear/bearish sentiment
- Fear & Greed indices use 0-100 scale (50 is neutral)
Data Sources
This indicator uses curated sentiment data from retail CFD providers and established fear/greed indices. Data is updated regularly and sourced from reputable financial data providers.
Trading Strategy & Market Philosophy
Contrarian Trading Approach
The primary purpose of this indicator is based on the fundamental market principle that **the majority of retail investors are often wrong**, and markets typically move opposite to the positions held by the majority of market participants.
Key Strategy Guidelines:
- **Contrarian Signal**: When the majority of users are positioned on one side of the market, there is statistically greater market advantage in taking positions in the opposite direction
- **Trend Exhaustion Signal**: An interesting observed phenomenon occurs when, during a long-lasting trend where the majority of investors have consistently been on the wrong side, the Sentiment indicator suddenly shows that the majority has flipped and opened positions in the direction of that long-running trend. This is often a signal of fuel exhaustion for further movement in that direction
Interpretation Examples
- High greed readings (majority bullish) → Consider bearish opportunities
- High fear readings (majority bearish) → Consider bullish opportunities
- Sudden sentiment flip during established trends → Potential trend reversal signal
Technical Notes
- Built with PineScript v6
- Dynamic symbol detection with fallback options
- Optimized for performance with minimal resource usage
- Color-coded visualization with customizable zones
Data Sources & Expansion
Acknowledgments
We extend our gratitude to **TradingView** for enabling the use of custom data feeds based on GitHub repositories, making this comprehensive sentiment analysis possible.
Data Expansion Opportunities
As the operator of this indicator, I am **open to suggestions for new data sources** that could be integrated and published. If you have ideas for additional instruments or sentiment data:
How to Submit Suggestions:
1. Send a **private message** with your proposal
2. Include: **instrument/data type**, **source**, and **brief description**
3. If technically feasible, we will work to import and publish the data
Data Infrastructure Status
Current Data Upload Process:
Please note that sentiment data uploads may occasionally experience minor interruptions. However, this should not pose significant issues as sentiment data typically changes gradually rather than rapidly.
Infrastructure Development:
We are actively working on establishing permanent cloud-based infrastructure to ensure continuous, automated data collection and upload processes. This will provide more reliable and consistent data availability in the future.
Disclaimer
This indicator is for educational and informational purposes only. Sentiment data should be used as part of a comprehensive trading strategy and not as the sole basis for trading decisions. Past performance does not guarantee future results. The contrarian approach described is a market theory and may not always produce profitable results.
