TuxTune - PDH PDL PDCJust a simple indicator simply to show the previous day High, Low, and Close levels.
Line color, type, width are modifiable
Each line can be turned on/off
Educational
Oscillator [Scalping-Algo]█ POSTING OSCILLATOR
A squeeze momentum indicator that detects volatility compression and shows momentum direction.
█ HOW IT WORKS
This indicator combines Bollinger Bands and Keltner Channels to identify "squeeze" conditions — periods of low volatility that often precede explosive moves.
When Bollinger Bands contract inside Keltner Channels, volatility is compressing. When they expand back out, the squeeze "fires" and price typically makes a strong directional move.
█ HISTOGRAM COLORS
🟦 Bright Cyan — Positive momentum, increasing
🟦 Dark Cyan — Positive momentum, decreasing
🟪 Dark Purple — Negative momentum, increasing
🟪 Bright Magenta — Negative momentum, decreasing
█ SQUEEZE DOTS (ZERO LINE)
🟢 Teal — No squeeze (normal volatility)
⚫ Gray — Low squeeze
🔴 Red — Medium squeeze
🟠 Orange — High squeeze (breakout imminent)
█ HOW TO USE
1. Wait for squeeze dots (gray/red/orange) to appear
2. Watch which direction momentum is building
3. Enter when dots turn teal (squeeze fired)
4. Go long if histogram is cyan, short if magenta
5. Consider exit when colors fade (bright → dark)
█ BEST PRACTICES
• Works best on higher timeframes (1H, 4H, Daily)
• Combine with trend analysis and support/resistance
• Most reliable in trending markets
• Avoid trading against major levels
█ SETTINGS
Length: 20 (default) — Period for all calculations
Adjust based on your timeframe and trading style.
█ ALERTS
Set alerts for:
• Histogram crossing zero
• Squeeze firing (dot color change to teal)
• High squeeze detection (orange dots)
Difference Based Curvature by WizkaThis is my very explorative script which studies the use of "derivatives" in indicating the momentum and the potential reversals. As we know the market data is so noisy and non-stationary (random walk) that mathematical derivatives can not be used. Therefore I use "differences (Diff)" as an analogy to them. The indicator, which I call "Difference-Based Curvature", calculates 10 period differences (ROC10) for three segments (0,10; 10,20; 20,30) and creates of them three degrees of Diff: 1st DIff = ROC(10), 2nd DIff = "dROC" = ROC(0,10) - ROC(10,20), which represents the "curvature" of the price movement. Furthermore, the 3rd Diff = "jROC" is calculated as a change of 2nd diff between consecutive segments. The values of Diffs are plotted as lines, but the interpretation is in the background colors. Dark green indicates strong (accelerating) growth (1. and 2.Diff >0). Light green = slowing increase (2.Diff turns <0). Dark red = strong decrease (1. and 2. Diff <0). Light red = slowing decrease (1.Diff<0, 2.Diff turning >0). Furthermore, red and green arrows are plotted when 3.Diff changes to negative in uptrend or positive in downtrend (hence trying to hint early potential top or bottom formation).
There are a few scale smoothing options, and I mostly use ATR-smoothing on.
It can be noted, that there is a certain resemblance with MACD (or PPO) as can be seen in the chart. This corresponds my intuition of the MACD: 1.Diff vs. MACD-line, 2.DIff vs. sign of Histogram and 3.Diff vs. direction of histogram.
DISCLAIMER: This indicator has not been tested, and use of it only with caution and own responsibility. No decision should be made on one indicator only.
Unfortunately some parameters can only be changed in the script. But it is open.
Have fun experimenting!
Pradip's MACD Divergence ProThis is where the "magic" happens, Pradip. MACD Divergence is one of the most powerful concepts because it acts like an early-warning system. It tells you when the market is "lying"—when the price is moving up or down, but the energy (momentum) behind it is dying.
200 EMA Scalping 1 MinuteOnly Scalping in 1 Minute Super accurate, low faults, Strict rule based management, in Nifty 50
Relative Strength Scatter PlotThis is a modication to the indicator ably coded by LOAMEX but with some minor modifications and uses Australian Stock Exchange indices instead of US. This makes it easier for those to use in other countries becasue it has the template for adding indices and the benchmark.
Refer to the LOAMEX indicator for information or the text in this open source pinescript.
The plot shows the relative strength of various indices to a benchmark index, in this case, the ASX XJO200. Indices or sectors located close to the top right hand quadrant are showing the best out performance and thus make up the best source to create your watchlist.
Similarly, you can put stocks in your portfolio into the indicator and see which ones are closest to the upper right of the plot. Those residing in the bottom left quadrant need to be pruned from your portfolio or watched more carefully with closer stop losses.
HTF Flip Close Levels, Daily Weekly Monthly TASHTF Flip Close Levels (D/W/M) — Support & Resistance Tool
This indicator automatically plots Daily, Weekly, and Monthly support & resistance levels based on higher-timeframe candle close behaviour.
🔹 What this tool does
The script detects HTF momentum flips using closed candles only:
Support is created when:
A red candle is followed by a green candle
The level is drawn at the close of the red candle
Resistance is created when:
A green candle is followed by a red candle
The level is drawn at the close of the green candle
This creates objective, rule-based horizontal levels derived purely from price behavior, not indicators.
🔹 Features
✅ Plots Daily, Weekly, and Monthly levels simultaneously
✅ Works on any timeframe (1m, 5m, 1H, Daily, Weekly, etc.)
✅ Keeps full historical levels, not just the most recent ones
✅ Optional auto-hide tapped levels (when price touches them)
✅ Tap detection:
Wick touch
or Close cross/touch
✅ Levels are always based on HTF candle closes, never wicks
✅ Designed to stay consistent across timeframe changes
🔹 How to use it (IMPORTANT)
This indicator:
❌ Does NOT predict market direction
❌ Does NOT generate buy/sell signals
❌ Does NOT tell you when to enter or exit
It is a context & confluence tool.
You should use these levels together with:
Market structure
Trend analysis
Volume / orderflow / CVD
Your own entry model
Your own risk management
Think of these levels as areas of interest, not automatic trade signals.
🔹 Best use cases
Confluence with:
Local support/resistance
VWAP / Anchored VWAP
Range highs/lows
Liquidity zones
Reversal or continuation patterns
Identifying:
HTF reaction zones
Decision points
Areas where other traders are likely watching
⚠️ Disclaimer
This indicator is a technical analysis tool only.
It is NOT financial advice.
It does NOT guarantee profits.
All trading decisions and risk are your responsibility.
Use it as part of a complete trading system, not as a standalone strategy.
PEGY RatioThe basic metrics that all indicators descend from are for each bar the Open, High, Low, Close and Volume where the Close is often noted as Price. Then the Price/Earnings ratio entered trading. Price/Earnings is often noted as P/E ratio or PE.
The first major formalisation and widespread use of the P/E ratio came in 1934, when Benjamin Graham and David Dodd introduced it in their landmark book "Security Analysis". Their work established the P/E ratio as a core tool in fundamental analysis and value investing.
Graham’s influence was profound: he used the P/E ratio to help investors judge whether a stock was overpriced or underpriced, and his teachings shaped generations of value investors, including Warren Buffett.
The P/E ratio evolved into modern variants like forward P/E and Shiller CAPE.
There’s no single P/E cutoff that definitively marks a “growth” or “income” stock, but investors commonly treat P/E below about 10–15 as value/income oriented and P/E above about 20–25 as growth oriented. It is important to watch the P/E trend. If the P/E is a low value and reducing in value, then the company may be failing, and it is not good to invest in.
P/E is a relative signal, not an absolute rule. A high P/E usually means the market expects above average future earnings growth; a low P/E often signals lower growth expectations, higher current yield, or elevated risk. Benchmarks vary by sector and cycle: what’s “high” for utilities is low for software. Historical market averages (e.g., S&P 500) help frame whether a multiple is elevated or depressed.
The next step was the PEG ratio which was first introduced in 1969 by Mario Farina, who described it in his book "A Beginner’s Guide to Successful Investing in the Stock Market".
The concept later gained widespread popularity thanks to Peter Lynch, who championed it in his 1989 bestseller "One Up on Wall Street", arguing that a “fairly priced” company tends to have a PEG of about 1. Over 1 is overpriced and below is a bargain.
Later the PEGY ratio, a variation of the PEG ratio that added dividend yield into the valuation came into prominence so that mature, dividend paying companies are treated “fairly” . The PEGY ratio emerged in the 1990s as analysts and portfolio managers began adapting the PEG ratio for dividend paying companies. The concept is a natural extension of Peter Lynch’s PEG logic: If growth matters, and dividends matter, combine them into one valuation metric.
PEGY (Price/Earnings Growth% and Dividend Yield) is a straightforward modification of the PEG ratio that adds dividend yield to the growth term so that mature, dividend paying companies aren’t penalized by low growth rates alone. The formula is typically written as:
PEGY=(Price/Earnings)/(Earnings growth %+Dividend yield%)
Peter Lynch (One Up on Wall Street, 1989) is the most cited printed source that describes a dividend adjusted PEG concept and applies it as a practical screening rule for investors. PEGY is in Chapter “Some Fabulous Numbers”.
If earnings are negative, then the PEGY ratio will be negative, and it is best to invest in companies that make money. That is, positive PEGY ratio.
The PEGY ratio can have different ratios depending upon whether historical data is used (Mario Farina preference) or whether forward looking earnings (Peter Lynch preference) is used in the calculations.
Enough for the history lesson. You can quickly go through your watchlist and determine which stocks have a PEGY Ratio from 0 to 1 and eliminate the others. Then whittle down that list to find stocks travelling from bottom left to upper right on the page. Use any other indicators on that reduced list that your tradng plan uses and there you have your list of stocks in which to invest.
Highlight > 0.5% Moves// ------ 1 ------ //
// threshold = input(0.3, title = "threshold%")
// //threshold = 0.3
// pctChange = ((close - open) / open) * 100
// //Define the condition (More than 0.5%)
// isBigMove = pctChange > threshold
// bgcolor(isBigMove ? color.new(color.green, 90) : na)
// barcolor(isBigMove ? color.new(color.green, 60) : na)
// plotshape(isBigMove, style=shape.triangleup, location=location.belowbar, color=color.green, size=size.small)
EURCHF Pro: 1H Trend + Prob + Sessions + Timer + SwingsEURCHF – Table Explanation (Calm & Precision)
EURCHF is a slow and controlled pair.
The table focuses on patience and precision.
🔹 Market Trend (1H)
If the trend is not clear → no trade
EURCHF dislikes choppy markets
👉 The table helps you stay out of bad conditions.
🔹 Session
Best time:
London session only
👉 LOW session = stay out.
🔹 Candle Time Left
Less critical than other pairs.
Still useful for final confirmation
👉 No need to rush.
🔹 Buy / Sell Probability
Best results at 60%+
Fewer trades, higher quality
👉 One clean trade is better than many weak ones.
🔹 RSI / Volume
RSI moves slowly
Weak volume = low continuation
🟢 Result:
A precision-focused table for patient traders.
GBPJPY Pro: 1H Trend + Prob + Sessions + Timer + Swings📊 GBPJPY – Table Explanation (High Volatility Control)
GBPJPY is fast and volatile.
The table is designed to protect you before profit.
🔹 Market Trend (1H)
The most important field for this pair.
Trading against the trend is very risky
👉 Always follow the 1H trend.
🔹 Session
Best trading times:
London
London–New York Overlap
👉 Avoid trading outside these sessions.
🔹 Candle Time Left
Extremely important for GBPJPY.
Entering before candle close can be dangerous
👉 Always wait for confirmation.
🔹 Buy / Sell Probability
50%+ can be acceptable due to strong moves
“READY” status is more important than the number
👉 Quality over quantity.
🔹 RSI / Volume
RSI moves fast
Strong volume often precedes sharp moves
⚠️ Result:
A defensive table that helps avoid late or emotional entries.
USDJPY Pro: 1H Trend + Prob + Sessions + Timer + Swings
📊 USDJPY – Table Explanation (Balanced & Clean)
USDJPY is a well-balanced pair with smooth trends.
The table helps you enter calmly and precisely.
🔹 Market Trend (1H)
Shows the main direction from the 1-Hour timeframe.
BULL → Look for BUY only
BEAR → Look for SELL only
👉 USDJPY respects trend direction very well.
🔹 Session
Displays the current trading session.
London & New York = best volatility
LOW = slow market
👉 Helps you avoid trading during dead hours.
🔹 Candle Time Left
Shows how much time remains before the candle closes.
👉 Very useful for waiting for candle confirmation on USDJPY.
🔹 Buy / Sell Probability
Shows the strength of BUY or SELL setups in %.
55%+ is usually sufficient for this pair
👉 Helps avoid weak or early entries.
🔹 RSI / Volume
Confirms momentum and activity.
Strong volume = better follow-through
✅ Result:
A clean table designed for disciplined, trend-based trading.
OIL (WTI) Pro: 1H Trend + Prob + Sessions + Candle TimerIndicator Features
📈 Multi-Timeframe Trend Detection (1H)
Identifies the main market trend from the 1-Hour timeframe
Displays the trend clearly as Bullish / Bearish / Sideways
Avoids trading against the higher-timeframe direction
🎯 Smart BUY & SELL Signals (On Candles)
Clear BUY and SELL signals directly on the candles
Signals are placed below lows (BUY) and above highs (SELL)
Uses ATR offset so signals are always visible and never hidden inside candles
📊 Separate Buy & Sell Probability
Calculates BUY Probability and SELL Probability independently
Probabilities are shown as percentages
Helps traders decide when to enter and when to wait
🧠 Pullback-Based Logic (No Chasing Price)
Signals are generated only after healthy pullbacks
Prevents entering trades when price is overextended
Displays a “Wait for Pullback” warning during strong trend extensions
Reversal Trend by S B PrasadReversal Trend by S B Prasad (Reversal Pro v3.0)
📝 TradingView Publish Description
Reversal Trend by S B Prasad – Reversal Pro v3.0 is a high-precision, non-repainting reversal detection system designed to identify major market turning points in real time.
This indicator combines:
Adaptive ZigZag logic
ATR + Percentage-based volatility filtering
EMA trend structure
Optional early preview signals
to deliver reliable bullish and bearish reversal signals across all markets and timeframes.
🚀 Key Features
✅ 1. Non-Repainting Confirmed Reversals
Confirmed reversal signals are generated only after price has moved beyond a dynamic volatility-adjusted threshold.
Once plotted, these signals never repaint.
🔍 2. Adaptive Volatility Threshold
Reversal detection automatically adjusts to market conditions using:
ATR (Average True Range)
Percentage price movement
Absolute minimum reversal distance
This ensures:
Fewer false signals in choppy markets
Faster detection in trending markets
⚙️ 3. Sensitivity Presets + Custom Mode
Choose from built-in presets:
Very High
High
Medium
Low
Very Low
Or use Custom Mode to fine-tune:
ATR Multiplier
Percentage Reversal
Absolute Reversal
ATR Length
📈 4. EMA Trend Filter
Integrated triple-EMA structure (9 / 14 / 21):
Identifies bullish, bearish, and neutral trend states
Helps align reversals with dominant trend direction
Reduces counter-trend false signals
👀 5. Preview Mode (Early Reversal Detection)
Optional preview signals highlight potential upcoming reversals before full confirmation.
Signal Modes:
Confirmed Only
Confirmed + Preview
Preview Only
⚠️ Preview signals are exploratory and may disappear if price invalidates the reversal.
🧠 6. Smart Signal State Engine
Maintains a clean bullish / bearish reversal state:
Bullish reversal → trend flips upward
Bearish reversal → trend flips downward
Automatically resets when structure is invalidated
🔔 7. Built-in Alerts
Alerts available for:
Bullish Reversal
Bearish Reversal
Any Reversal
EMA Buy Signal
EMA Sell Signal
📌 How to Use
▶️ Trend-Following Strategy
Wait for EMA trend alignment
Enter on a confirmed reversal in trend direction
Use recent swing high/low for stop-loss
Trail profits using higher-low / lower-high structure
🔄 Counter-Trend Reversal Strategy
Use higher sensitivity
Look for strong extended moves
Enter on confirmed reversal
Exit at next EMA cross or opposite reversal
⚙️ Recommended Settings
Style Sensitivity Confirmation Bars
Scalping High 0–1
Intraday Medium 0–2
Swing Low 1–3
📎 Best Markets
Crypto
Forex
Indices
Stocks
Commodities
Works on all timeframes (1m → 1D+).
MTF CPRThe Central Pivot Range (CPR) is a technical indicator used to identify key price levels, trend direction, and market volatility.
This script provides a comprehensive MTF CPR engine that tracks Daily, Weekly, and Monthly levels simultaneously. It identifies "Fair Value" through the Central Pivot Range, allowing traders to maintain a clear structural bias across multiple timeframes without switching charts.
Unlike fixed-ratio pivots, these Standard Deviations are projected based on the internal width of each specific CPR. This dynamic calculation ensures that volatility targets (SD levels) are relative to the market's current compression or expansion, providing more accurate exhaustion points.
The indicator offers total control over every timeframe independently. Users can customize the number of SD levels, the specific step-multiplier for each timeframe, and all visual properties including line width, color, and style to ensure maximum chart clarity.
Use it with VWAP for additional confluence.
Predictive Candle and Accuracy CoreThis Predictive Candle – Accuracy Core indicator is designed to project the likely direction and size of the next candle based on market microstructure, volatility, momentum, and volume dynamics. It calculates a delta-based volume imbalance, RSI, EMA distances, ATR, and ADX to assess both the strength and trend of the market. The script applies a market regime filter to allow predictions only when trends are strong and aligned, then computes weighted bullish and bearish scores, normalizes them into probabilities, and self-measures its historical accuracy. Using this, it projects the next candle’s body and wicks, color-coded green or red for bullish or bearish, with a confidence percentage label. The projection adjusts dynamically for volatility, ADX strength, and prediction accuracy, providing traders with a quantitative, adaptive visual cue for potential price movement without repainting.
Volume + ATR Robust Z-Score Suite (MAD)Measure relevant volumes together with high-volatility candles, providing initiative signals based on volume. Mark the relevant candle and use it as support or resistance.
cephxs / New X Opening Gaps [Pro +]NWOG & NDOG - OPENING GAPS
Smart Gap Detection with Intelligent Filtering
Visualizes New Week Opening Gaps (NWOGs) and New Day Opening Gaps (NDOGs) with built-in intelligence to show you only what matters. No more cluttered charts with gaps from 3 months ago that price will never revisit.
THE PROBLEM WITH GAP INDICATORS
Most gap indicators dump every single gap on your chart and call it a day. You end up with 50 boxes cluttering your screen, half of which are miles away from current price and the other half are so tiny they're basically noise.
This one's different and I explain why below.
SMART FILTERING (THE GOOD STUFF)
Two filters work together to keep your chart clean:
Size Filter: Uses ATR-based detection to filter out insignificant gaps, dynamic with less volatile time periods
- Filter None: Show everything (if you really want chaos)
- Filter Insignificant: Hide the micro-gaps that don't matter
- Juicy Gaps Only: Only show gaps worth paying attention to
Distance Filter: Only displays gaps within range of current price
- Really Close: 0.5 ATR - tight focus on immediate levels
- Balanced: 1 ATR - sweet spot for most traders
- Slightly Far: 3 ATR - wider view for swing traders
Cleanup Interval: Controls how quickly out-of-range gaps disappear
- Immediately: Gaps hide/show every bar as price moves
- 5 / 15 / 30 Minutes: Gaps only update visibility at interval boundaries - reduces visual noise during choppy price action
The magic: gaps appear and disappear as price moves toward or away from them. Old gaps that price has left behind fade out, and gaps that become relevant fade back in. Use delayed cleanup intervals if you want gaps to "stick around" a bit longer before disappearing.
GAP TYPES EXPLAINED
New Week Opening Gaps (NWOGs):
The gap between Friday's close and Monday's open. These form over the weekend when markets are closed and often act as significant support/resistance.
Two classifications:
Void Gaps: Gap direction aligns with Friday's candle direction (continuation)
Overlap Gaps: Gap direction conflicts with Friday's candle (potential reversal)
New Day Opening Gaps (NDOGs):
The gap between one day's close and the next day's open. Smaller but frequent - useful for intraday traders looking for fill targets.
FEATURES
Automatic Week/Day Detection: Handles forex (17:00 ET open) and futures (18:00 ET open) correctly
DST-Aware: Uses New York timezone with automatic daylight saving adjustments
50% Equilibrium Line: Marks the midpoint of each gap - key level for entries
Days Ago Labels: Shows how old each gap is at a glance
Extension Modes: Choose between live-extending boxes or fixed-width boxes
Separate Color Schemes: Different colors for void vs overlap NWOGs, bullish vs bearish NDOGs
INPUTS
NWOG Display
Show NWOGs: Master toggle
Extension Mode: "Extend Live" or "Extend to Week Close"
Maximum NWOGs: Limit displayed gaps (1-50)
Show Void/Overlap Gaps: Toggle each type independently
Show NWOG Labels: Toggle gap labels
NDOG Display
Show NDOGs: Master toggle
Extension Mode: "Extend Live" or "Extend to Day Close"
Maximum NDOGs: Limit displayed gaps (1-50)
Show NDOG Labels: Toggle gap labels
Filter Settings
Size Filter: Filter None / Filter Insignificant / Juicy Gaps Only
Only Show Near Price: Enable/disable distance filtering
Distance Filter: Really Close / Balanced / Slightly Far
Cleanup Interval: Immediately / 5 Minutes / 15 Minutes / 30 Minutes - controls how often gaps update visibility
ATR Period: Period for ATR calculation (default: 14)
Right Edge Offset: How many bars ahead boxes extend
Styling
Box Transparency: Fill and border opacity
Midline Style: Solid / Dotted / Dashed
Label Style: Simple ("NWOG, 5d ago") or Descriptive ("NWOG (Void Bull), 5d ago")
Label Size: Tiny / Small / Normal / Large
RECOMMENDED SETTINGS
For intraday (1m-15m):
Size Filter: Filter Insignificant
Distance Filter: Really Close or Balanced
Show NDOGs: On
Maximum NDOGs: 5-10
For swing trading (1H-4H):
Size Filter: Juicy Gaps Only
Distance Filter: Balanced or Slightly Far
Show NWOGs: On
Maximum NWOGs: 10-20
TIMEFRAME NOTES
Works on daily timeframe and below. Above daily, the indicator disables itself since NWOG/NDOG gap detection requires daily open/close data.
ASSET SUPPORT
Automatically handles different market open times:
Forex: Week opens Sunday 17:00 ET, closes Friday 17:00 ET
Futures: Week opens Sunday 18:00 ET, closes Friday 16:15 ET
Stocks/Other: Uses session-based detection
FAQ
Why do gaps appear and disappear?
That's the distance filter working. As price moves, gaps that were far away become relevant and appear. Gaps that price leaves behind disappear. This keeps your chart focused on actionable levels.
What's the difference between void and overlap gaps?
Void gaps continue Friday's direction (trend continuation). Overlap gaps conflict with Friday's direction (potential reversal setup). Different traders prefer different types.
Why can't I see any gaps?
Check your filter settings. "Juicy Gaps Only" with "Really Close" distance filter is very selective. Try "Filter Insignificant" with "Balanced" for more gaps.
DISCLAIMER
This indicator is for educational purposes only. Opening gaps are one tool among many - they don't guarantee fills or reversals. Always use proper risk management and never trade based on a single indicator. Past gap fills don't guarantee future performance. Do your own analysis.
CHANGELOG
Pro +: Added smart size/distance filtering, void/overlap classification, NDOG support, DST-aware timezone handling
Base: Initial NWOG visualization
Made with ❤️ by fstarlabs
Asset Drift ModelThis Asset Drift Model is a statistical tool designed to detect whether an asset exhibits a systematic directional tendency in its historical returns. Unlike traditional momentum indicators that react to price movements, this indicator performs a formal hypothesis test to determine if the observed drift is statistically significant, economically meaningful, and structurally stable across time. The result is a classification that helps traders understand whether historical evidence supports a directional bias in the asset.
The core question the indicator answers is simple: Has this asset shown a reliable tendency to move in one direction over the past three years, and is that tendency strong enough to matter?
What is drift and why does it matter
In financial economics, drift refers to the expected rate of return of an asset over time. The concept originates from the geometric Brownian motion model, which describes asset prices as following a random walk with an added drift component (Black and Scholes, 1973). If drift is zero, price movements are purely random. If drift is positive, the asset tends to appreciate over time. If negative, it tends to depreciate.
The existence of drift has profound implications for trading strategy. Eugene Fama's Efficient Market Hypothesis (Fama, 1970) suggests that in efficient markets, risk-adjusted drift should be minimal because prices already reflect all available information. However, decades of empirical research have documented persistent anomalies. Jegadeesh and Titman (1993) demonstrated that stocks with positive past returns continue to outperform, a phenomenon known as momentum. DeBondt and Thaler (1985) found evidence of long-term mean reversion. These findings suggest that drift is not constant and can vary across assets and time periods.
For practitioners, understanding drift is fundamental. A positive drift implies that long positions have a statistical edge over time. A negative drift suggests short positions may be advantageous. No detectable drift means the asset behaves more like a random walk, where directional strategies have no inherent advantage.
How professionals use drift analysis
Institutional investors and hedge funds have long incorporated drift analysis into their systematic strategies. Quantitative funds typically estimate drift as part of their alpha generation process, using it to tilt portfolios toward assets with favorable expected returns (Grinold and Kahn, 2000).
The challenge lies not in calculating drift but in determining whether observed drift is genuine or merely statistical noise. A naive approach might conclude that any positive average return indicates positive drift. However, financial returns are noisy, and short samples can produce misleading estimates. This is why professional quants rely on formal statistical inference.
The standard approach involves testing the null hypothesis that expected returns equal zero against the alternative that they differ from zero. The test statistic is typically a t-ratio: the sample mean divided by its standard error. However, financial returns often exhibit serial correlation and heteroskedasticity, which invalidate simple standard errors. To address this, practitioners use heteroskedasticity and autocorrelation consistent standard errors, commonly known as HAC or Newey-West standard errors (Newey and West, 1987).
Beyond statistical significance, professional investors also consider economic significance. A statistically significant drift of 0.5 percent annually may not justify trading costs. Conversely, a large drift that fails to reach statistical significance due to high volatility may still inform portfolio construction. The most robust conclusions require both statistical and economic thresholds to be met.
Methodology
The Asset Drift Model implements a rigorous inference framework designed to minimize false positives while detecting genuine drift.
Return calculation
The indicator uses logarithmic returns over non-overlapping 60-day periods. Non-overlapping returns are essential because overlapping returns introduce artificial autocorrelation that biases variance estimates (Richardson and Stock, 1989). Using 60-day horizons rather than daily returns reduces noise and captures medium-term drift relevant for position traders.
The sample window spans 756 trading days, approximately three years of data. This provides 12 independent observations for the full sample and 6 observations per half-sample for structural stability testing.
Statistical inference
The indicator calculates the t-statistic for the null hypothesis that mean returns equal zero. To account for potential residual autocorrelation, it applies a simplified HAC correction with one lag, appropriate for non-overlapping returns where autocorrelation is minimal by construction.
Statistical significance requires the absolute t-statistic to exceed 2.0, corresponding to approximately 95 percent confidence. This threshold follows conventional practice in financial econometrics (Campbell, Lo, and MacKinlay, 1997).
Power analysis
A critical but often overlooked aspect of hypothesis testing is statistical power: the probability of detecting drift when it exists. With small samples, even substantial drift may fail to reach significance due to high standard errors. The indicator calculates the minimum detectable effect at 95 percent confidence and requires observed drift to exceed this threshold. This prevents classifying assets as having no drift when the test simply lacks power to detect it.
Robustness checks
The indicator applies multiple robustness checks before classifying drift as genuine.
First, the sign test examines whether the proportion of positive returns differs significantly from 50 percent. This non-parametric test is robust to distributional assumptions and verifies that the mean is not driven by outliers.
Second, mean-median agreement ensures that the mean and median returns share the same sign. Divergence indicates skewness that could distort inference.
Third, structural stability splits the sample into two halves and requires consistent signs of both means and t-statistics across sub-periods. This addresses the concern that drift may be an artifact of a specific regime rather than a persistent characteristic (Andrews, 1993).
Fourth, the variance ratio test detects mean-reverting behavior. Lo and MacKinlay (1988) showed that if returns follow a random walk, the variance of multi-period returns should scale linearly with the horizon. A variance ratio significantly below one indicates mean reversion, which contradicts persistent drift. The indicator blocks drift classification when significant mean reversion is detected.
Classification system
Based on these tests, the indicator classifies assets into three categories.
Strong evidence indicates that all criteria are met: statistical significance, economic significance (at least 3 percent annualized drift), adequate power, and all robustness checks pass. This classification suggests the asset has exhibited reliable directional tendency that is both statistically robust and economically meaningful.
Weak evidence indicates statistical significance without economic significance. The drift is detectable but small, typically below 3 percent annually. Such assets may still have directional tendency but the magnitude may not justify concentrated positioning.
No evidence indicates insufficient statistical support for drift. This does not prove the asset is driftless; it means the available data cannot distinguish drift from random variation. The indicator provides the specific reason for rejection, such as failed power analysis, inconsistent sub-samples, or detected mean reversion.
Dashboard explanation
The dashboard displays all relevant statistics for transparency.
Classification shows the current drift assessment: Positive Drift, Negative Drift, Positive (weak), Negative (weak), or No Drift.
Evidence indicates the strength of evidence: Strong, Weak, or None, with the specific reason for rejection if applicable.
Inference shows whether the sample is sufficient for analysis. Blocked indicates fewer than 10 observations. Heuristic indicates 10 to 19 observations, where asymptotic approximations are less reliable. Allowed indicates 20 or more observations with reliable inference.
The t-statistics for full sample and both half-samples show the test statistics and sample sizes. Double asterisks denote significance at the 5 percent level.
Power displays OK if observed drift exceeds the minimum detectable effect, or shows the MDE threshold if power is insufficient.
Sign Test shows the z-statistic for the proportion test. An asterisk indicates significance at 10 percent.
Mean equals Median indicates agreement between central tendency measures.
Struct(m) shows structural stability of means across half-samples, including the standardized level deviation.
Struct(t) shows whether t-statistics have consistent signs across half-samples.
VR Test shows the variance ratio and its z-statistic. An asterisk indicates the ratio differs significantly from one.
Econ. Sig. indicates whether drift exceeds the 3 percent annual threshold.
Drift (ann.) shows the annualized drift estimate.
Regime indicates whether the asset exhibits mean-reverting behavior based on the variance ratio test.
Practical applications for traders
For discretionary traders, the indicator provides a quantitative foundation for directional bias decisions. Rather than relying on intuition or simple price trends, traders can assess whether historical evidence supports their directional thesis.
For systematic traders, the indicator can serve as a regime filter. Trend-following strategies may perform better on assets with detectable positive drift, while mean-reversion strategies may suit assets where drift is absent or the variance ratio indicates mean reversion.
For portfolio construction, drift analysis helps identify assets where long-only exposure has historical justification versus assets requiring more balanced or tactical positioning.
Limitations
This indicator performs retrospective analysis and does not predict future returns. Past drift does not guarantee future drift. Markets evolve, regimes change, and historical patterns may not persist.
The three-year sample window captures medium-term tendencies but may miss shorter regime changes or longer structural shifts. The 60-day return horizon suits position traders but may not reflect intraday or weekly dynamics.
Small samples yield heuristic rather than statistically robust results. The indicator flags such cases but users should interpret them with appropriate caution.
References
Andrews, D.W.K. (1993) Tests for parameter instability and structural change with unknown change point. Econometrica, 61(4).
Black, F. and Scholes, M. (1973) The pricing of options and corporate liabilities. Journal of Political Economy, 81(3).
Campbell, J.Y., Lo, A.W. and MacKinlay, A.C. (1997) The econometrics of financial markets. Princeton: Princeton University Press.
DeBondt, W.F.M. and Thaler, R. (1985) Does the stock market overreact? Journal of Finance, 40(3).
Fama, E.F. (1970) Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25(2).
Grinold, R.C. and Kahn, R.N. (2000) Active portfolio management. 2nd ed. New York: McGraw-Hill.
Jegadeesh, N. and Titman, S. (1993) Returns to buying winners and selling losers. Journal of Finance, 48(1).
Lo, A.W. and MacKinlay, A.C. (1988) Stock market prices do not follow random walks. Review of Financial Studies, 1(1).
Newey, W.K. and West, K.D. (1987) A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3).
Richardson, M. and Stock, J.H. (1989) Drawing inferences from statistics based on multiyear asset returns. Journal of Financial Economics, 25(2).
ANTS MVP Indicator David Ryan's Institutional Accumulation🚀 ANTS MVP Indicator – David Ryan's Legendary Accumulation Signal
Discover stocks under heavy **institutional buying** before they explode — just like 3-time U.S. Investing Champion David Ryan used to crush the markets!
This is a faithful, open-source recreation of the famous **ANTS (Momentum-Volume-Price)** pattern popularized by David Ryan (protégé of William O'Neil / IBD / CAN SLIM fame). It scans for the classic 15-day "MVP" setup that often appears in early stages of massive winners.
Key Features:
• Colored "Ants" diamonds show signal strength:
- Gray: Momentum only (12+ up days in 15)
- Yellow: Momentum + Volume surge (≥20% avg volume increase)
- Blue: Momentum + Price gain (≥20% rise)
- Green: FULL MVP (all three!) – the strongest institutional demand signal!
• Toggle to show ONLY green ants for cleaner charts
• Position ants above or below bars
• Built-in alert for NEW green ants (copy the alert condition or use alert() triggers)
• Optional background highlight + label on the last bar for quick spotting
Why ANTS Works:
- Flags consistent up-days + volume explosion + solid price advance
- Often clusters before major breakouts (cup-with-handle, flat bases, etc.)
- Used by pros to find leaders early (think NVDA, TSLA, CELH runs)
- Great for daily charts + combining with RS Rating, earnings growth, and market uptrends
How to Use:
1. Add to daily stock charts
2. Watch for GREEN ants (full MVP) in bases or near pivots
3. Wait for volume breakout above resistance for entry
4. Set alerts for "GREEN ANTS MVP detected!" to catch them live
Fully open code – feel free to tweak thresholds (lookback, % gains, etc.)!
Inspired by public descriptions from IBD, Deepvue, and Ryan's teachings.
If this helps you spot winners, drop a ❤️ like, comment your biggest ANTS catch, and follow for more CAN SLIM-style tools!
Questions? Want screener tweaks or strategy version? Comment below!
#ANTS #DavidRyan #MVPPattern #InstitutionalAccumulation #CANSLIM #TradingView #MomentumTrading #StockScanner The time it takes for a stock to rise significantly after a green ANTS (full MVP) signal appears varies widely — there is no fixed or guaranteed timeframe. The ANTS indicator (developed by David Ryan) flags strong institutional accumulation over a rolling ~3-week (15-day) period, but the actual price breakout or major advance often comes later, after further consolidation or a proper setup.
Typical Timings from Real-World Usage and Examples
Short-term (days to weeks): Sometimes the green ants appear during or right at the start of a breakout — price can rise 10–30%+ in the following 1–4 weeks if momentum continues and volume supports it (e.g., Rocket Lab (RKLB) showed ANTS strength ahead of a powerful breakout in examples from IBD).
Medium-term (weeks to months): More commonly, green ants signal early accumulation while the stock is still building or tightening in a base (e.g., cup-with-handle, flat base, high tight flag, or pullback to 10/21 EMA). The big move (often 50–200%+) happens after the stock forms a proper buy point (pivot breakout on high volume), which can take 2–12 weeks after the first green ants.
Longer-term leaders: In historical CAN SLIM winners, ANTS often appeared during the stealth accumulation phase (before the stock became obvious), with the major multi-month/year run starting 1–6 months later once the market confirmed an uptrend and the stock broke out.
Key points from David Ryan/IBD sources:
ANTS is a demand confirmation tool, not a precise timing signal.
Many stocks with green ants are extended when the signal fires — wait for a pullback/consolidation before expecting the next leg up.
In strong bull markets, clusters of green ants over several bars increase the odds of an imminent or near-term move.
If no breakout follows within ~1–3 months (and market weakens), the signal may fizzle — cut losses or move on.
Bottom line: Expect 0–3 months for meaningful upside in good setups, but always wait for a classic buy point (breakout above resistance on volume) rather than buying the ants alone. Backtest examples (e.g., via TradingView replay on past leaders like NVDA, TSLA, or CELH during their runs) to see the lag in action.
Optimized SMC - OB & FVG MTFOB & FVG on different timeframes
Optimized version that can show HTF PDAs on LTF
US Recessions - ShadingThis indicator shades the chart background during every U.S. recession as dated by the National Bureau of Economic Research (NBER). Recessions are defined using NBER’s business cycle peak-to-trough months, and the script shades from the peak month through the trough month (inclusive) using monthly boundaries.
What it does
* Applies a shaded overlay on your chart **only during recession periods**.
* Works on any symbol and any timeframe (crypto, equities, FX, commodities, bonds, indices).
* Includes options to:
- Toggle shading on/off
- Choose your preferred shading colour
- Adjust transparency for readability
Why this overlay is important for analysing any asset class
Even if you trade or invest in assets that aren’t directly tied to U.S. GDP (like crypto or commodities), U.S. recessions often coincide with major shifts in:
-Risk appetite (risk-on vs risk-off behaviour)
-Liquidity conditions (credit availability, financial stress)
-Interest-rate expectations and central bank response
-Earnings expectations and corporate defaults
-Volatility regimes (large, sustained changes in volatility)
Having recession shading directly on the price chart helps you quickly see whether price action is happening in a historically “normal” expansion environment, or in a macro regime where behaviour can change dramatically. This is particularly useful in a deeper analysis like comparing GOLD to SPX. This chart makes it clear how in recessions the S&P bleeds against Gold therefor making the concept more visual and better for understanding.
Of course this is just an example of how it can be used, there are plenty of other factors which can be overlayed like unemployment and interest rates for an even better understanding.
Please DM majordistribution.inc on Instagram for any info - FREE - NO Course
7 Custom Moving Averages (SMA / EMA / HMA)Key Features
✅ 7 Moving Averages at Once
✅ You can choose the type of each moving average (SMA / EMA / HMA)
✅ Each moving average has its own length and color
✅ Direct overlay on the price chart
✅ Pine Script v6 (latest)
