Max Daily Movement in %14DMA%-OVED=The average daily movement of a stock over the last 14 trading days, in percentage.
Padrões gráficos
Multi-Timeframe Confluence Predictor by @crypto.erkeDescription
The Multi-Timeframe Confluence Predictor is an advanced technical analysis tool designed to identify high-probability price targets and trend directions by analyzing market data across multiple timeframes. Unlike conventional indicators that rely on a single calculation method, this indicator combines trend analysis, cycle detection, and volume profiling to create a comprehensive prediction system.
This indicator stands out by finding "confluence zones" - areas where multiple prediction methods agree on potential price movements. These zones offer significantly higher probability trading opportunities than any single indicator could provide.
Key Features
Multi-Timeframe Analysis: Combines data from the current timeframe plus three higher timeframes with customizable weights
Adaptive Trend Channels: Uses linear regression with weighted standard deviation to create predictive channels
Cycle Detection: Implements autocorrelation-based cycle finding to identify market rhythms
Volume Profile Integration: Analyzes volume distribution to identify significant price levels
Confluence Zones: Highlights areas where multiple prediction methods agree, with color intensity showing confidence level
Visual Predictions: Shows projected price paths with confidence levels
Alert Conditions: Includes alerts for when price enters high-confluence zones or when cycle patterns change
How It Works
The indicator processes market data through several analytical methods:
Trend Analysis: Linear regression across multiple timeframes identifies the underlying trend direction and strength
Statistical Boundaries: Calculates dynamic standard deviation channels that adapt to changing market volatility
Cycle Detection: Uses autocorrelation to find repeating market patterns without relying on fixed cycle lengths
Volume Analysis: Identifies price levels with significant historical volume to determine potential support/resistance
Confluence Calculation: Combines all analyses to find where multiple methods predict the same outcome
Optimization Tips
Adjust timeframe weights to match your trading style (higher weights for longer timeframes create smoother predictions)
Increase Channel Width Factor for more conservative entries/exits
Decrease Prediction Length for shorter-term trading
Enable/disable Volume Analysis based on the asset being traded (more effective for stocks and major cryptocurrencies)
Cycle Detection works best in ranging or cyclical markets
This indicator combines the power of multi-timeframe analysis, statistical prediction, and volume profiling to give you a comprehensive view of potential price movements. By focusing on areas of confluence, you can significantly improve your trading accuracy and confidence.
Created by @crypto.erke - Follow for more advanced trading indicators and strategies.
STOCK SCHOOL SUPPORT & RESISTANCE ZONEThis indicator automatically detects and plots key Support and Resistance Zones directly on the chart. It helps traders identify potential reversal areas, consolidation regions, and breakout zones. The zones are calculated using recent price action highs and lows, adapting dynamically as new data becomes available.
Key Features:
Automatically plots support and resistance zones as shaded areas
Works across all timeframes and asset classes
Visually distinguishes between support (green zones) and resistance (red zones)
Helps spot potential breakout or bounce areas
Ideal for price action traders, swing traders, and anyone who wants a clear visual of where the market might react.
The LBF modelThe LBF Model is a structural pattern detector that highlights potential reversal zones using a specific sequence of pivot points. It identifies both bearish (LL → LH → LL → HH → LH) and bullish (HH → HL → HH → LL → HL) formations, marking moments where price shows signs of exhaustion and directional shift.
Built purely on price action, the LBF Model avoids indicators and focuses on clean structure. It draws patterns directly on the chart, with customizable sensitivity and colors. Whether used on its own or with other tools, it helps traders spot key turning points with clarity and precision.
Scalper Signal PRO (EMA + RSI + Stoch)How to use it
Buy Signal:
. EMA 5 crosses above EMA 13
. Price is above EMA 50
. RSI near or just above 30
Sell Signal:
. EMA 5 crosses below EMA 13
. Price is below EMA 50
. RSI near or just below 30
Scalper Signal PRO (EMA + RSI + Stoch)//@version=5
indicator("Scalper Signal PRO (EMA + RSI + Stoch)", overlay=true)
// === INPUTS ===
emaFastLen = input.int(5, "EMA Fast")
emaSlowLen = input.int(13, "EMA Slow")
rsiLen = input.int(14, "RSI Length")
rsiBuy = input.int(30, "RSI Buy Level")
rsiSell = input.int(70, "RSI Sell Level")
kPeriod = input.int(5, "Stoch K")
dPeriod = input.int(3, "Stoch D")
slowing = input.int(3, "Stoch Smoothing")
// === SESSION TIME ===
sessionStart = timestamp ("GMT+8", year, month, dayofmonth, 8, 0)
sessionEnd = timestamp("GMT+8" ,year, month, dayofmonth, 18, 0)
withinSession = time >= sessionStart and time <= sessionEnd
// === LOGIC ===
emaFast = ta.ema(close, emaFastLen)
emaSlow = ta.ema(close, emaSlowLen)
emaBullish = emaFast > emaSlow and ta.crossover(emaFast, emaSlow)
emaBearish = emaFast < emaSlow and ta.crossunder(emaFast, emaSlow)
rsi = ta.rsi(close, rsiLen)
k = ta.sma(ta.stoch(close, high, low, kPeriod), slowing)
d = ta.sma(k, dPeriod)
buyCond = emaBullish and rsi < rsiBuy and k > d and withinSession
sellCond = emaBearish and rsi > rsiSell and k < d and withinSession
// === PLOTS ===
showSignals = input.bool(true, "Show Buy/Sell Signals?")
plotshape(showSignals and buyCond, location=location.belowbar, style=shape.labelup, color=color.green, text="BUY", title="Buy Signal")
plotshape(showSignals and sellCond, location=location.abovebar, style=shape.labeldown, color=color.red, text="SELL", title="Sell Signal")
plot(emaFast, "EMA Fast", color=color.orange)
plot(emaSlow, "EMA Slow", color=color.blue)
// === ALERTS ===
alertcondition(buyCond, title="Buy Alert", message="Scalper PRO Buy Signal")
alertcondition(sellCond, title="Sell Alert", message="Scalper PRO Sell Signal")
// === DASHBOARD ===
var table dash = table.new(position.top_right, 2, 5, frame_color=color.gray, frame_width=1)
bg = color.new(color.black, 85)
table.cell(dash, 0, 0, "Scalper PRO", bgcolor=bg, text_color=color.white, text_size=size.normal)
table.cell(dash, 0, 1, "Trend", bgcolor=bg)
table.cell(dash, 1, 1, emaFast > emaSlow ? "Bullish" : "Bearish", bgcolor=emaFast > emaSlow ? color.green : color.red, text_color=color.white)
table.cell(dash, 0, 2, "RSI", bgcolor=bg)
table.cell(dash, 1, 2, str.tostring(rsi, "#.0"), bgcolor=color.gray, text_color=color.white)
table.cell(dash, 0, 3, "Stoch K/D", bgcolor=bg)
table.cell(dash, 1, 3, str.tostring(k, "#.0") + "/" + str.tostring(d, "#.0"), bgcolor=color.navy, text_color=color.white)
table.cell(dash, 0, 4, "Session", bgcolor=bg)
table.cell(dash, 1, 4, withinSession ? "LIVE" : "OFF", bgcolor=withinSession ? color.green : color.red, text_color=color.white)
Scalper Signal PRO (EMA + RSI + Stoch)//@version=5
indicator("Scalper Signal PRO (EMA + RSI + Stoch)", overlay=true)
// === INPUTS ===
emaFastLen = input.int(5, "EMA Fast")
emaSlowLen = input.int(13, "EMA Slow")
rsiLen = input.int(14, "RSI Length")
rsiBuy = input.int(30, "RSI Buy Level")
rsiSell = input.int(70, "RSI Sell Level")
kPeriod = input.int(5, "Stoch K")
dPeriod = input.int(3, "Stoch D")
slowing = input.int(3, "Stoch Smoothing")
// === SESSION TIME ===
sessionStart = timestamp ("GMT+8", year, month, dayofmonth, 8, 0)
sessionEnd = timestamp("GMT+8" ,year, month, dayofmonth, 18, 0)
withinSession = time >= sessionStart and time <= sessionEnd
// === LOGIC ===
emaFast = ta.ema(close, emaFastLen)
emaSlow = ta.ema(close, emaSlowLen)
emaBullish = emaFast > emaSlow and ta.crossover(emaFast, emaSlow)
emaBearish = emaFast < emaSlow and ta.crossunder(emaFast, emaSlow)
rsi = ta.rsi(close, rsiLen)
k = ta.sma(ta.stoch(close, high, low, kPeriod), slowing)
d = ta.sma(k, dPeriod)
buyCond = emaBullish and rsi < rsiBuy and k > d and withinSession
sellCond = emaBearish and rsi > rsiSell and k < d and withinSession
// === PLOTS ===
showSignals = input.bool(true, "Show Buy/Sell Signals?")
plotshape(showSignals and buyCond, location=location.belowbar, style=shape.labelup, color=color.green, text="BUY", title="Buy Signal")
plotshape(showSignals and sellCond, location=location.abovebar, style=shape.labeldown, color=color.red, text="SELL", title="Sell Signal")
plot(emaFast, "EMA Fast", color=color.orange)
plot(emaSlow, "EMA Slow", color=color.blue)
// === ALERTS ===
alertcondition(buyCond, title="Buy Alert", message="Scalper PRO Buy Signal")
alertcondition(sellCond, title="Sell Alert", message="Scalper PRO Sell Signal")
// === DASHBOARD ===
var table dash = table.new(position.top_right, 2, 5, frame_color=color.gray, frame_width=1)
bg = color.new(color.black, 85)
table.cell(dash, 0, 0, "Scalper PRO", bgcolor=bg, text_color=color.white, text_size=size.normal)
table.cell(dash, 0, 1, "Trend", bgcolor=bg)
table.cell(dash, 1, 1, emaFast > emaSlow ? "Bullish" : "Bearish", bgcolor=emaFast > emaSlow ? color.green : color.red, text_color=color.white)
table.cell(dash, 0, 2, "RSI", bgcolor=bg)
table.cell(dash, 1, 2, str.tostring(rsi, "#.0"), bgcolor=color.gray, text_color=color.white)
table.cell(dash, 0, 3, "Stoch K/D", bgcolor=bg)
table.cell(dash, 1, 3, str.tostring(k, "#.0") + "/" + str.tostring(d, "#.0"), bgcolor=color.navy, text_color=color.white)
table.cell(dash, 0, 4, "Session", bgcolor=bg)
table.cell(dash, 1, 4, withinSession ? "LIVE" : "OFF", bgcolor=withinSession ? color.green : color.red, text_color=color.white)
Relative Strength IndexAdd EMA 9 and WMA 45 into regular RSI.
This would help people with free account to add up to three indicators at once.
Thanks
Wyckoff Accumulation Distribution Wyckoff Accumulation & Distribution Indicator (RSI-Based)
This Pine Script is a technical analysis indicator built around the Wyckoff Method, designed to detect accumulation and distribution phases using RSI (Relative Strength Index) and pivot points. It automatically marks key structural turning points on the chart and highlights relevant zones with colored boxes.
What Does It Do?
Draws accumulation and distribution boxes based on RSI behavior.
Automatically detects Wyckoff structural signals:
SC (Selling Climax)
AR (Automatic Rally)
ST (Secondary Test)
BC (Buying Climax)
DAR (Automatic Reaction)
DST (Secondary Test - Distribution)
Identifies trend transitions by detecting sideways RSI movement.
Attempts to detect spring and UTAD-like deviations based on RSI reversals.
Uses RSI extremes in conjunction with pivot points to generate Wyckoff signals.
How Does It Work?
RSI Zone: It identifies sideways markets when RSI stays within ±20 of the 50 level (this range is configurable).
Pivot Points: It detects pivot highs/lows that sync with RSI values (pivotLen is adjustable).
Trend Box Drawing:
When RSI exits the sideways zone, the script draws a gray box between the highest high and lowest low within that range.
If RSI breaks upward, the box becomes green (Accumulation); if downward, it becomes red (Distribution).
Wyckoff Structural Points:
SC/BC: Detected when a pivot occurs with RSI below/above a threshold.
AR/DAR: The next opposite pivot after SC or BC.
ST/DST: The next same-direction pivot after AR or DAR.
How to Use It
Works best on 4H or daily charts for more reliable signals. Shorter timeframes may generate noise.
Primarily used for interpreting RSI structures through the lens of Wyckoff methodology.
Box colors help quickly identify market phase:
Green box: Likely Accumulation
Red box: Likely Distribution
Triangular markers show key signals:
SC, AR, ST: Accumulation points
BC, DAR, DST: Distribution points
Use these signals alongside price action to manually interpret Wyckoff phases.
image.binance.vision
image.binance.vision
What Is the Wyckoff Method?
The Wyckoff Method, developed in the 1930s by Richard Wyckoff, is a market analysis approach that focuses on supply and demand dynamics behind price movements.
Wyckoff’s 5 Phases:
Accumulation: Smart money gradually buying at low prices.
Markup: Price begins trending upwards.
Distribution: Smart money selling to retail traders.
Markdown: Downtrend begins as supply outweighs demand.
Re-accumulation / Re-distribution: Trend-continuation phases with consolidations.
This indicator is specifically designed to detect phase 1 (Accumulation) and phase 3 (Distribution).
Extra Notes
Repainting is minimal, as pivots are confirmed using historical candles.
Labels use plotshape for a clean, minimalist visual style.
Other Wyckoff events (like SOS, LPS, UT, UTAD) could be added in future updates.
This script does not generate buy/sell signals; it is meant for structural interpretation.
MM Day Trader Levels Signal IndicatorVisual elements (CALL/PUT labels and markers) are now prioritized at the top of the chart for improved readability and immediate trade signal clarity.
⚡ High-Frequency Pro Strategy | Enhanced Filtersfind the supply ondemand for Gold and the best areat to import
[JPMM]SuperTrend- Overview
This indicator is built on a combination of indicators: SuperTrend, RSI, RVI, ADX, EMA50.
It works on the principle of finding accumulation zones combined with momentum.
- How It Works
The indicator helps to find overbought/oversold zones and then gives buy/sell signals according to the concept of accumulation/distribution.
- How to Use
You can buy or sell when you see a BUY/SELL signal.
Along with that, there will be additional signals from SuperTrend and EMA50. It will help you have more effective perspectives to make decisions while trading.
- Settings
SuperTrend Factor: default is 7
- A large factor (5 or more) is suitable for long-term trading, helping to filter out noise from small price fluctuations.
- A small factor (below 5) is suitable for short-term trading, reacting more quickly to price changes.
SuperTrend ATR Length: default is 17
- ATR Length is a parameter that determines the number of candles used to calculate the Average True Range (ATR).
RSI OverBought: default is 60
- When RSI crosses the RSI OverBought level, the market may be in an overbought state, signaling that the price may be about to reverse down.
RSI OverSold: default is 40
- When RSI falls below the RSI OverSold level, the market may be in an oversold state, signaling that the price may be about to reverse up.
- Disclaimer:
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Signals2TradeSignals2Trade is a powerful indicator that combines a daily one-trade strategy with smart money liquidity zones. It automatically detects the first breakout of the day, sets entry, stop-loss, and take-profit based on your desired risk-reward ratio, and visually marks entry and exit points. Additionally, it identifies key supply and demand areas using pivot levels and highlights them as dynamic smart money blocks on the chart. Ideal for day traders, SMC traders, and anyone looking for structured setups without spending hours on analysis.
NIKHIL ROY INDICATOR + Reversal Trap Entry//@version=5
indicator("NIKHIL ROY INDICATOR + Reversal Trap Entry", overlay=true)
// === SETTINGS ===
res_tf = "15"
lookback = 50
// === PREVIOUS 15min CANDLE ===
prevHigh = request.security(syminfo.tickerid, res_tf, high )
prevLow = request.security(syminfo.tickerid, res_tf, low )
prevOpen = request.security(syminfo.tickerid, res_tf, open )
prevClose = request.security(syminfo.tickerid, res_tf, close )
// Draw previous 15m candle (body and wick)
plot(prevHigh, "Prev High", color=color.gray, linewidth=1)
plot(prevLow, "Prev Low", color=color.gray, linewidth=1)
bgcolor((timeframe.isintraday and timeframe.period == "15") ? na : color.new(color.gray, 90))
// === SUPPORT & RESISTANCE ===
var float support = na
var float resistance = na
pivotLow = ta.pivotlow(low, 5, 5)
pivotHigh = ta.pivothigh(high, 5, 5)
if not na(pivotLow)
support := pivotLow
if not na(pivotHigh)
resistance := pivotHigh
plot(support, "Support", color=color.green, linewidth=2, style=plot.style_linebr)
plot(resistance, "Resistance", color=color.red, linewidth=2, style=plot.style_linebr)
// === SWING BREAK DETECTION ===
swingHighBreak = high > resistance and high <= resistance
swingLowBreak = low < support and low >= support
// === BREAKOUT FAILURE ===
breakoutFailure = swingHighBreak and close < resistance
breakdownFailure = swingLowBreak and close > support
// === RETEST AFTER WEAKNESS ===
retestSell = close < resistance and high > resistance and close < open
retestBuy = close > support and low < support and close > open
// === SIGNALS ===
// SELL on resistance retest + weakness
sellSignal = retestSell
// SELL if breakout fails
trapSell = breakoutFailure
// BUY on support retest + strength
buySignal = retestBuy
// BUY if breakdown fails
trapBuy = breakdownFailure
// === PLOT SIGNALS ===
plotshape(sellSignal, title="Sell Weakness", location=location.abovebar, color=color.red, style=shape.arrowdown, size=size.large, text="SELL")
plotshape(trapSell, title="Trap Sell", location=location.abovebar, color=color.red, style=shape.arrowdown, size=size.large, text="TRAP SELL")
plotshape(buySignal, title="Buy Strength", location=location.belowbar, color=color.green, style=shape.arrowup, size=size.large, text="BUY")
plotshape(trapBuy, title="Trap Buy", location=location.belowbar, color=color.green, style=shape.arrowup, size=size.large, text="TRAP BUY")
AI-Powered Trading Bot DemoAI-Powered Trading Bot by Polygon Solutions Inc.
The trading strategy works exceptionally well with equities and cryptocurrencies, incorporating risk management protocols. It is a fully operational trading bot with no human intervention. I am using a stock for the demo.
Buy: green highlighted background
Sell: red highlighted background
Anchored Darvas Box## ANCHORED DARVAS BOX
---
### OVERVIEW
**Anchored Darvas Box** lets you drop a single timestamp on your chart and build a Darvas-style consolidation zone forward from that exact candle. The indicator freezes the first user-defined number of bars to establish the range, verifies that price respects that range for another user-defined number of bars, then waits for the first decisive breakout. The resulting rectangle captures every tick of the accumulation phase and the exact moment of expansion—no manual drawing, complete timestamp precision.
---
### HISTORICAL BACKGROUND
Nicolas Darvas’s 1950s box theory tracked institutional accumulation by hand-drawing rectangles around tight price ranges. A trade was triggered only when price escaped the rectangle.
The anchored version preserves Darvas’s logic but pins the entire sequence to a user-chosen candle: perfect for analysing a market open, an earnings release, FOMC minute, or any other catalytic bar.
---
### ALGORITHM DETAIL
1. **ANCHOR BAR**
*You provide a timestamp via the settings panel.* The script waits until the chart reaches that bar and records its index as **startBar**.
2. **RANGE DEFINITION — BARS 1-7**
• `rangeHigh` = highest high of bars 1-7 plus optional tolerance.
• `rangeLow` = lowest low of bars 1-7 minus optional tolerance.
3. **RANGE VALIDATION — BARS 8-14**
• Price must stay inside ` `.
• Any violation aborts the test; no box is created.
4. **ARMED STATE**
• If bars 8-14 hold the range, two live guide-lines appear:
– **Green** at `rangeHigh`
– **Red** at `rangeLow`
• The script is now “armed,” waiting indefinitely for the first true breakout.
5. **BREAKOUT & BOX CREATION**
• **Up breakout** =`high > rangeHigh` → rectangle drawn in **green**.
• **Down breakout**=`low < rangeLow` → rectangle drawn in **red**.
• Box extends from **startBar** to the breakout bar and never updates again.
• Optional labels print the dollar and percentage height of the box at its left edge.
6. **OPTIONAL COOLDOWN**
• After the box is painted the script can stay silent for a user-defined number of bars, letting you study the fallout without another range immediately arming on top of it.
---
### INPUT PARAMETERS
• **ANCHOR TIME** – Precise yyyy-mm-dd HH:MM:SS that seeds the sequence.
• **BARS TO DEFINE RANGE** – Default 7; affects both definition and validation windows.
• **OPTIONAL TOLERANCE** – Absolute price buffer to ignore micro-wicks.
• **COOLDOWN BARS AFTER BREAKOUT** – Pause length before the indicator is allowed to re-anchor (set to zero to disable).
• **SHOW BOX DISTANCE LABELS** – Toggle to print Δ\$ and Δ% on every completed box.
---
### USER WORKFLOW
1. Add the indicator, open settings, and set **ANCHOR TIME** to the candle you care about (e.g., “2025-04-23 09:30:00” for NYSE open).
2. Watch live as the script:
– Paints the seven-bar range.
– Draws validation lines.
– Locks in the box on breakout.
3. Use the box boundaries as structural stops, targets, or context for further trades.
---
### PRACTICAL APPLICATIONS
• **OPENING RANGE BREAKOUTS** – Anchor at the first second of the session; capture the initial 7-bar range and trade the first clean break.
• **EVENT STUDIES** – Anchor at a news candle to measure immediate post-event volatility.
• **VOLUME PROFILE FUSION** – Combine the anchored box with VPVR to see if the breakout occurs at a high-volume node or a low-liquidity pocket.
• **RISK DISCIPLINE** – Stop-loss can sit just inside the opposite edge of the anchored range, enforcing objective risk.
---
### ADVANCED CUSTOMISATION IDEAS
• **MULTIPLE ANCHORS** – Clone the indicator and anchor several boxes (e.g., London open, New York open).
• **DYNAMIC WINDOW** – Switch the 7-bar fixed length to a volatility-scaled length (ATR percentile).
• **STRATEGY WRAPPER** – Turn the indicator into a `strategy{}` script and back-test anchored boxes on decades of data.
---
### FINAL THOUGHTS
Anchored Darvas Boxes give you Darvas’s timeless range-break methodology anchored to any candle of interest—perfect for dissecting openings, economic releases, or your own bespoke “important” bars with laboratory precision.
Auto Darvas Boxes## AUTO DARVAS BOXES
---
### OVERVIEW
**Auto Darvas Boxes** is a fully-automated, event-driven implementation of Nicolas Darvas’s 1950s box methodology.
The script tracks consolidation zones in real time, verifies that price truly “respects” those zones for a fixed validation window, then waits for the first decisive range violation to mark a directional breakout.
Every box is plotted end-to-end—from the first candle of the sideways range to the exact candle that ruptures it—giving you an on-chart, visually precise record of accumulation or distribution and the expansion that follows.
---
### HISTORICAL BACKGROUND
* Nicolas Darvas was a professional ballroom dancer who traded U.S. equities by telegram while touring the world.
* Without live news or Level II, he relied exclusively on **price** to infer institutional intent.
* His core insight: true market-moving entities leave footprints in the form of tight ranges; once their buying (or selling) is complete, price erupts out of the “box.”
* Darvas’s original procedure was manual—he kept notebooks, drew rectangles around highs and lows, and entered only when price punched out of the roof of a valid box.
* This indicator distills that logic into a rolling, self-resetting state machine so you never miss a box or breakout on any timeframe.
---
### ALGORITHM DETAIL (FOUR-STATE MACHINE)
**STATE 0 – RANGE DEFINITION**
• Examine the last *N* candles (default 7).
• Record `rangeHigh = highest(high, N) + tolerance`.
• Record `rangeLow = lowest(low, N) – tolerance`.
• Remember the index of the earliest bar in this window (`startBar`).
• Immediately transition to STATE 1.
**STATE 1 – RANGE VALIDATION**
• Observe the next *N* candles (again default 7).
• If **any** candle prints `high > rangeHigh` or `low < rangeLow`, the validation fails and the engine resets to STATE 0 **beginning at the violating candle**—no halfway boxes, no overlap.
• If all *N* candles remain inside the range, the box becomes **armed** and we transition to STATE 2.
**STATE 2 – ARMED (LIVE VISUAL FEEDBACK)**
• Draw a **green horizontal line** at `rangeHigh`.
• Draw a **red horizontal line** at `rangeLow`.
• Lines are extended in real time so the user can see the “live” Darvas ceiling and floor.
• Engine waits indefinitely for a breakout candle:
– **Up-Breakout** if `high > rangeHigh`.
– **Down-Breakout** if `low < rangeLow`.
**STATE 3 – BREAKOUT & COOLDOWN**
• Upon breakout the script:
1. Deletes the live range lines.
2. Draws a **filled rectangle (box)** from `startBar` to the breakout bar.
◦ **Green fill** when price exits above the ceiling.
◦ **Red fill** when price exits below the floor.
3. Optionally prints two labels at the left edge of the box:
◦ Dollar distance = `rangeHigh − rangeLow`.
◦ Percentage distance = `(rangeHigh − rangeLow) / rangeLow × 100 %`.
• After painting, the script waits a **user-defined cooldown** (default = 7 bars) before reverting to STATE 0. The cooldown guarantees separation between consecutive tests and prevents overlapping rectangles.
---
### INPUT PARAMETERS (ALL ADJUSTABLE FROM THE SETTINGS PANEL)
* **BARS TO DEFINE RANGE** – Number of candles used for both the definition and validation windows. Classic Darvas logic uses 7 but feel free to raise it on higher timeframes or volatile instruments.
* **OPTIONAL TOLERANCE** – Absolute price buffer added above the ceiling and below the floor. Use a small tolerance to ignore single-tick spikes or data-feed noise.
* **COOLDOWN BARS AFTER BREAKOUT** – How long the engine pauses before hunting for the next consolidation. Setting this equal to the range length produces non-overlapping, evenly spaced boxes.
* **SHOW BOX DISTANCE LABELS** – Toggle on/off. When on, each completed box displays its vertical size in both dollars and percentage, anchored at the box’s left edge.
---
### REAL-TIME VISUALISATION
* During the **armed** phase you see two extended, colour-coded guide-lines showing the exact high/low that must hold.
* When the breakout finally occurs, those lines vanish and the rectangle instantly appears, coloured to match the breakout direction.
* This immediate visual feedback turns any chart into a live Darvas tape—no manual drawing, no lag.
---
### PRACTICAL USE-CASES & BEST-PRACTICE WORKFLOWS
* **INTRADAY MOMENTUM** – Drop the script on 1- to 15-minute charts to catch tight coils before they explode. The coloured box marks the precise origin of the expansion; stops can sit just inside the opposite side of the box.
* **SWING & POSITION TRADING** – On 4-hour or daily charts, boxes often correspond to accumulation bases or volatility squeezes. Waiting for the box-validated breakout filters many false signals.
* **MEAN-REVERSION OR “FADE” STRATEGIES** – If a breakout immediately fails and price re-enters the box, you may have trapped momentum traders; fading that failure can be lucrative.
* **RISK MANAGEMENT** – Box extremes provide objective, structure-based stop levels rather than arbitrary ATR multiples.
* **BACK-TEST RESEARCH** – Because each box is plotted from first range candle to breakout candle, you can programmatically measure hold time, range height, and post-breakout expectancy for any asset.
---
### CUSTOMISATION IDEAS FOR POWER USERS
* **VOLATILITY-ADAPTIVE WINDOW** – Replace the fixed 7-bar length with a dynamic value tied to ATR percentile so the consolidation window stretches or compresses with volatility.
* **MULTI-TIMEFRAME LOGIC** – Only arm a 5-minute box if the 1-hour trend is aligned.
* **STRATEGY WRAPPER** – Convert the indicator to a full `strategy{}` script, automate entries on breakouts, and benchmark performance across assets.
* **ALERTS** – Create TradingView alerts on both up-breakout and down-breakout conditions; route them to webhook for broker automation.
---
### FINAL THOUGHTS
**Auto Darvas Boxes** packages one of the market’s oldest yet still potent price-action frameworks into a modern, self-resetting indicator. Whether you trade equities, futures, crypto, or forex, the script highlights genuine contraction-expansion sequences—Darvas’s original “boxes”—with zero manual effort, letting you focus solely on execution and risk.
Weekly & Daily Opening Ranges [WOR + DOR]Shows PWC/PWH/WOL/WOH etc.
This indicator was based on YAMAGUCCI framework for price action trading
Smart Multi-Signal System PROInput Parameters The script allows users to customize settings via TradingView’s input panel:lookback (default: 20): The number of bars to look back to identify pivot highs/lows for supply/demand zones. Affects the sensitivity of zone detection.risk_reward (default: 5.0): The risk-reward ratio for calculating take-profit levels relative to stop-loss. For example, a 5.0 ratio means the take-profit target is 5 times the stop-loss distance.rsi_period (default: 14): The period for calculating the RSI, used as a momentum filter.rsi_overbought (default: 70): RSI level above which a market is considered overbought (used for sell signals).rsi_oversold (default: 30): RSI level below which a market is considered oversold (used for buy signals
12 Hour Heikin AshiThis is a Pine Script (version 6) indicator that creates 12-hour Heikin Ashi candles. Heikin Ashi candles smooth out price data to help identify trends by using modified formulas for open, high, low, and close prices. We’ll use a higher timeframe aggregation approach to calculate the Heikin Ashi values based on 12-hour periods.
COT Index / smart money indexBernd Skorupinski’s Smart money index / COT index
How I Use Bernd Skorupinski’s OTC Tools to Spot High-Probability Trades –
Example on Gold
When it comes to trading commodities like Gold and Crude Oil, I follow a process rooted in institutional data — specifically, the Commitments of Traders (COT) report. Through the Online Trading Campus tools developed by Bernd Skorupinski, I combine insights from the Valuation Tool, Seasonality Forecasting Tool, and COT Analysis for a refined edge.
👉 Get LIFETIME access to ALL of Bernds indicators for just 99 EUR (limited time offer) – available here: linktr.ee/Thomasloophole
📈 Step 1: Technical Demand Zone
The first thing I do is analyze price action. In this Gold trade, I spotted price approaching a key demand level — a zone where price has historically reacted due to large institutional buying.
🟢 Chart Insight: Price was reaching a long-term demand zone dating back several years.
🧠 Step 2: COT Index – Institutional Extreme
Next, I bring up the COT Index, specifically using the CAMPUS COT Index indicator. What I saw here was powerful:
The commercials (smart money) were at a 312-week buying extreme — meaning they haven’t been buying this aggressively in over 6 years.
This is a major institutional footprint. Historically, when commercials are at or above 100%, price often follows shortly after with strong moves.
📊 Step 3: COT Net Positions
To strengthen my case, I checked the COT Net Positions. Here's where it got interesting:
Commercials hadn’t held this many net-long positions since 15 years ago.
📉 That’s rare. When something like this aligns with price at a demand level — it’s a green light.
🔁 Step 4: COT Index Divergence
For final confirmation, I look for divergence between commercials and retailers.
✅ In this case, commercials were buying heavily
❌ Retail traders were shorting heavily
This opposite behavior (smart money vs. dumb money) is my cue to prepare a trade.
🎯 Step 5: Entry, Target & Risk
Once all conditions align:
✅ Demand level confirmed
✅ 312-week commercial extreme
✅ 15-year net position high
✅ Retailers on the other side
…I set up the trade.
📌 Stop-loss: Just below demand
🎯 Target: Previous major high
📈 RRR (Risk/Reward): 1:14.8
That means for every 1% risked, I stood to gain nearly 15%. And yes — this trade hit full target.
📢 Why This Matters
This is not about guessing. It’s about:
Following institutional footprints
Using retail sentiment as confirmation
Aligning multiple data points (price, time, net positions)
Bernd Skorupinski’s tools make this process repeatable.
👉 Get LIFETIME access to ALL of Bernds indicators for just 99 EUR (limited time offer) – available here: linktr.ee/Thomasloophole
Bernd Skorupinski strategy
Online Trading Campus indicators
COT Index Trading
Smart money index
Trading Gold using COT
Institutional order flow strategy
OTC - COT Net positions 2.0 Bernd Skorupinski’s COT net position.
The COT net position indicator is used in combination with the COT index indicator
How I Use Bernd Skorupinski’s OTC Tools to Spot High-Probability Trades –
Example on Gold
When it comes to trading commodities like Gold and Crude Oil, I follow a process rooted in institutional data — specifically, the Commitments of Traders (COT) report. Through the Online Trading Campus tools developed by Bernd Skorupinski, I combine insights from the Valuation Tool, Seasonality Forecasting Tool, and COT Analysis for a refined edge.
👉 Get LIFETIME access to ALL of Bernds indicators for just 99 EUR (limited time offer) – available here: linktr.ee/Thomasloophole
📈 Step 1: Technical Demand Zone
The first thing I do is analyze price action. In this Gold trade, I spotted price approaching a key demand level — a zone where price has historically reacted due to large institutional buying.
🟢 Chart Insight: Price was reaching a long-term demand zone dating back several years.
🧠 Step 2: COT Index – Institutional Extreme
Next, I bring up the COT Index, specifically using the CAMPUS COT Index indicator. What I saw here was powerful:
The commercials (smart money) were at a 312-week buying extreme — meaning they haven’t been buying this aggressively in over 6 years.
This is a major institutional footprint. Historically, when commercials are at or above 100%, price often follows shortly after with strong moves.
📊 Step 3: COT Net Positions
To strengthen my case, I checked the COT Net Positions. Here's where it got interesting:
Commercials hadn’t held this many net-long positions since 15 years ago.
📉 That’s rare. When something like this aligns with price at a demand level — it’s a green light.
🔁 Step 4: COT Index Divergence
For final confirmation, I look for divergence between commercials and retailers.
✅ In this case, commercials were buying heavily
❌ Retail traders were shorting heavily
This opposite behavior (smart money vs. dumb money) is my cue to prepare a trade.
🎯 Step 5: Entry, Target & Risk
Once all conditions align:
✅ Demand level confirmed
✅ 312-week commercial extreme
✅ 15-year net position high
✅ Retailers on the other side
…I set up the trade.
📌 Stop-loss: Just below demand
🎯 Target: Previous major high
📈 RRR (Risk/Reward): 1:14.8
That means for every 1% risked, I stood to gain nearly 15%. And yes — this trade hit full target.
📢 Why This Matters
This is not about guessing. It’s about:
Following institutional footprints
Using retail sentiment as confirmation
Aligning multiple data points (price, time, net positions)
Bernd Skorupinski’s tools make this process repeatable.
👉 Get LIFETIME access to ALL of Bernds indicators for just 99 EUR (limited time offer) – available here: linktr.ee/Thomasloophole
Bernd Skorupinski strategy
Online Trading Campus indicators
COT Index Trading
Smart money index
Trading Gold using COT
Institutional order flow strategy
