Fibo & Gann Advanced Auto[CongTrader]🔍 Description:
"Fibo & Gann Advanced Auto by CongTrader" is a smart automatic indicator that combines Fibonacci Retracement & Extension levels with Gann Boxes and Fan lines, helping traders identify key support/resistance zones and potential turning points in the market.
This tool automatically detects recent swing highs/lows using pivots and overlays:
📏 Fibonacci Retracement & Extension (0.236 to 1.618)
🟪 Gann Box between 2 latest pivots
📐 Gann Fan Lines (1x1, 2x1, etc.)
🟢 Optional filtered Buy/Sell signals based on wave size and RSI
Designed for discretionary and technical traders who want a visual confirmation of price geometry and market structure.
📘 How to Use:
Apply to any chart & timeframe.
Adjust pivot sensitivity via “Pivot Length” input.
Look for confluence between Fib retracement/extension and Gann box edges for trade entries.
Gann fan lines help visualize trend angles or speed.
Combine with your own strategy for better confirmation (e.g., volume, candlestick pattern).
💡 Tip: Use in higher timeframes (H1, H4, D1) for cleaner and more reliable pivots.
🙏 Thanks:
Created with love and passion for the trading community by CongTrader.
If you find it helpful, please give a like or comment. Feedback is always appreciated!
⚠️ Disclaimer:
This script is for educational and informational purposes only.
It does not constitute financial advice and should not be used as a sole basis for trading decisions.
Always use proper risk management and perform your own analysis before entering any trade.
Trading involves risk, and past performance is not indicative of future results..#fibonacci #gann #gannbox #gannfan #elliottwave #marketstructure
#priceaction #autopivot #congtrader #tradingviewindicator
#technicalanalysis #tradingtools #forextrading #cryptoindicator
#tradingstrategy #tradingsetup #smartmoney #supportresistance
Ketidakstabilan
Marcius Studio® - Trend Detector™Trend Detector™ — is an advanced trend detection indicator that combines statistical Z-Score analysis with a simplified ADF stationarity test .
It is designed to help traders identify strong directional moves while filtering out noise and false signals.
Unlike traditional moving average crossovers or momentum oscillators, this tool evaluates both trend direction and trend strength , giving you a clear visual overview of market conditions.
Important! This indicator is intended for educational and informational purposes . It does not guarantee future performance and should be used together with proper risk management.
Idea
Markets spend 70–80% of the time in consolidation and only 20–30% in trending phases . The key to profitable trading is spotting when a major trend shift begins. Trend Detector™ was built exactly for this purpose — to filter noise and highlight true trend reversals.
How It Works
Calculates the Z-Score of price to detect extreme deviations from the mean.
Applies a simplified ADF t-Statistic test to confirm trend validity.
Uses an ATR-based ribbon for clean visualization of bullish/bearish phases.
Generates Buy/Sell signals when trend switches are confirmed.
Displays an Info Panel with real-time metrics: Z-Score, ADF t-Stat, Trend Strength (0–100), ATR % of price.
Features
Trend Ribbon : visually highlights bullish, bearish, or neutral phases.
Confirmation Filter : avoids false flips by requiring multiple bars of validation.
Strength Score : quantifies how powerful the current trend is.
Signal Markers : “BUY” and “SELL” alerts appear directly on the chart.
Customizable Alerts : get notified when new uptrends or downtrends are detected.
Recommendations
Works well on swing trading timeframes (1H, 4H, Daily).
Use in combination with support/resistance zones or volume profile tools for higher accuracy.
The higher the Trend Strength Score , the more reliable the trend continuation.
Indicator Settings
Analysis Period : number of bars for Z-Score & ADF test.
ATR Length : used for ribbon visualization.
Min Bars to Confirm Trend : filters false trend flips.
Show/Hide options for Ribbon, Signals, and Info Panel.
Example Settings
Timeframe : 1H or 4H
Analysis Period : 20
ATR Length : 14
Min Confirmation Bars : 2–3
Disclaimer
Trading and investing involve risk — always do your own research (DYOR) and seek professional advice. We are not responsible for any financial losses.
Adaptive ATR Stop Loss FinderPlots dynamic ATR-based stop levels with an automatically adjusting multiplier based on volatility. High/low stops and a live table display ATR×multiplier, helping swing and crypto traders protect profits and trail stops efficiently. Adjustable ATR length, smoothing, and colors.
ICT + ORB + VIXFix Confluence Signals (Panel)What it is
ICT + ORB + VIXFix Confluence Signals (Panel) is a signal-only Pine v5 indicator that prints clean BUY/SELL arrows when multiple filters align:
ICT structure: BOS / liquidity sweeps + 3-candle FVG with minimum size by ATR
Trend filter: EMA 50 vs EMA 200
ORB filter: opening-range breakout (custom session + minutes)
VIXFix filter: CM_Williams_VIX_Fix–style volatility spike (incl. inverted top)
Status panel: shows which filters are passing and why a signal didn’t fire
No orders are placed; it’s meant to identify trades and trigger alerts.
How signals are built (picky by design)
A BUY arrow paints when all enabled conditions are true:
Trend: EMA50 > EMA200 (or disabled)
ICT: BOS/sweep and bullish FVG (if enabled)
ORB: price breaks above ORB high after the ORB window closes (if enabled)
VIXFix: recent bottom spike within VIX_Lookback bars (if enabled)
A SELL arrow mirrors the above (downtrend, bearish FVG, break below ORB low, recent top spike).
Tip: Leave “Confirm Close Only” on for cleaner signals and fewer repaint-like artifacts.
Inputs (quick reference)
Use Trend (EMA 50/200): require higher-timeframe trend alignment
Use FVG / Use Sweep/BOS: ICT structure filters
SwingLen: pivot left/right length for swings (structure sensitivity)
MinFVG_ATR: minimum FVG size as a fraction of ATR (e.g., 0.20 = 20% ATR)
Session / ORB_Min / Plot_ORB: first N minutes after session open define the range
VIX_LB, VIX_BBLen, VIX_Mult, VIX_Lookback: VIXFix parameters (spike threshold + validity)
Confirm Close Only: only signal at bar close (recommended)
Show Status Panel: compact checklist explaining current filter states
Alerts
Create alerts on this indicator using “Once per bar close” (recommended) and these built-in conditions:
BUY Signal → message: ICT ORB VIXFix BUY
SELL Signal → message: ICT ORB VIXFix SELL
Recommended starting presets
NQ1! / ES1! (US session), intraday 1–5m
Confirm Close Only: ON
Use Trend: ON
Use FVG + Use Sweep/BOS: ON
SwingLen: 3–5
MinFVG_ATR: 0.20–0.30
Session: 0930–1600 (exchange time)
ORB_Min: 10–15
VIXFix: ON, VIX_Lookback 10–20
If you see too few signals, loosen MinFVG_ATR (e.g., 0.10) or temporarily turn off the VIXFix and ORB filters, then re-enable them.
What the panel tells you
Trend (UP/DN): EMA relationship
ICT Long/Short: whether structure/FVG requirements pass for each side
ORB Ready / Long/Short: whether the ORB window is complete and which side broke
VIX Long/Short: recent bottom/top spike validity
Signals: BUY/SELL computed this bar
Why none: quick text reason (trend / ict / orb / vix) if no signal
Notes & attribution
VIXFix is a re-implementation of the CM_Williams_VIX_Fix concept (plus inverted top).
ORB logic uses your chosen session; if your market differs, adjust Session or test on SPY/ES1!/NQ1!.
This is an indicator, not financial advice. Always validate on your instruments/timeframes.
Changelog
v1.0 – Initial release: ICT structure + FVG size by ATR, ORB breakout filter, VIXFix spike filter, trend filter, alerts, and status panel.
Tags
ICT, FVG, ORB, VIX, VIXFix, volatility, structure, BOS, sweep, breakout, EMA, futures, indices, NQ, ES, SPY, day trading, confluence, signals
엘리어트 파동 3의 3파//@version=5
indicator("Elliott Wave — 3 of 3 Detector (v5)", overlay=true, timeframe_gaps=true)
// === Inputs ===
pivotLen = input.int(5, "Pivot length (bars each side)", minval=2)
subPivotLen = input.int(3, "Sub-pivot length (internal waves)", minval=2)
retrMin = input.float(0.382, "Wave 2 retracement min", minval=0.1, maxval=0.9, step=0.001)
retrMax = input.float(0.786, "Wave 2 retracement max", minval=0.1, maxval=0.95, step=0.001)
subRetrMin = input.float(0.382, "Subwave 2 retracement min", minval=0.1, maxval=0.9, step=0.001)
subRetrMax = input.float(0.618, "Subwave 2 retracement max", minval=0.1, maxval=0.95, step=0.001)
useRSI = input.bool(true, "Require RSI > threshold")
rsiPeriod = input.int(14, "RSI period", minval=2)
rsiThresh = input.float(55.0, "RSI threshold", minval=40, maxval=70)
useMACD = input.bool(true, "Require MACD histogram > 0")
fastLen = input.int(12, "MACD fast EMA", minval=2)
slowLen = input.int(26, "MACD slow EMA", minval=2)
signalLen = input.int(9, "MACD signal", minval=1)
useEMAFilter = input.bool(true, "Require trend filter (EMA50 > EMA200)")
useVolFilter = input.bool(false, "Require volume > volume MA")
volMaLen = input.int(20, "Volume MA length", minval=1)
plotZigs = input.bool(true, "Plot swing lines")
showLabels = input.bool(true, "Show wave labels")
// === Helpers ===
rsi = ta.rsi(close, rsiPeriod)
macdLine = ta.ema(close, fastLen) - ta.ema(close, slowLen)
signal = ta.ema(macdLine, signalLen)
hist = macdLine - signal
ema50 = ta.ema(close, 50)
ema200 = ta.ema(close, 200)
volMa = ta.sma(volume, volMaLen)
bool momentumOK = (not useRSI or rsi > rsiThresh) and (not useMACD or hist > 0)
bool trendOK = (not useEMAFilter or ema50 > ema200)
bool volOK = (not useVolFilter or volume > volMa)
// === Swing detection (basic pivot-based zigzag) ===
var float pivPrice = array.new_float()
var int pivIndex = array.new_int()
var int pivType = array.new_int() // +1 = swing high, -1 = swing low
ph = ta.pivothigh(high, pivotLen, pivotLen)
pl = ta.pivotlow(low, pivotLen, pivotLen)
f_addPivot(_price, _index, _type) =>
// avoid duplicate consecutive types
if array.size(pivType) > 0 and array.get(pivType, array.size(pivType)-1) == _type
// replace last pivot if same type and is more extreme in same direction
lastPrice = array.get(pivPrice, array.size(pivPrice)-1)
replace = (_type == 1 and _price > lastPrice) or (_type == -1 and _price < lastPrice)
if replace
array.set(pivPrice, array.size(pivPrice)-1, _price)
array.set(pivIndex, array.size(pivIndex)-1, _index)
else
array.push(pivPrice, _price)
array.push(pivIndex, _index)
array.push(pivType, _type)
if not na(ph)
f_addPivot(ph, bar_index - pivotLen, 1)
if not na(pl)
f_addPivot(pl, bar_index - pivotLen, -1)
// Keep arrays from growing unbounded
maxKeep = 200
if array.size(pivPrice) > maxKeep
for _i = 0 to array.size(pivPrice) - maxKeep - 1
array.shift(pivPrice)
array.shift(pivIndex)
array.shift(pivType)
// === Utility to get recent Nth pivot from the end ===
getPrice(n) => array.get(pivPrice, array.size(pivPrice) - 1 - n)
getIndex(n) => array.get(pivIndex, array.size(pivIndex) - 1 - n)
getType(n) => array.get(pivType, array.size(pivType) - 1 - n)
haveAtLeast(n) => array.size(pivPrice) >= n
// === Identify bullish 1-2 structure ===
bool has12 = false
float L0 = na, H1 = na, L2 = na
int L0i = na, H1i = na, L2i = na
if haveAtLeast(3)
// We want last three alternating pivots to be: low (L0), high (H1), low (L2)
t0 = getType(2)
t1 = getType(1)
t2 = getType(0)
if t0 == -1 and t1 == 1 and t2 == -1
L0 := getPrice(2)
H1 := getPrice(1)
L2 := getPrice(0)
L0i := getIndex(2)
H1i := getIndex(1)
L2i := getIndex(0)
// Retracement check for wave 2
wave1 = H1 - L0
retr = wave1 != 0 ? (H1 - L2) / wave1 : na
has12 := wave1 > 0 and not na(retr) and retr >= retrMin and retr <= retrMax
// === Wave 3 start (break above H1) ===
bool wave3Start = has12 and close > H1 and bar_index > H1i
// === Internal subwave 1-2 inside wave 3 using tighter sub-pivots ===
// We'll compute a separate list of sub-pivots since L2 to now, using smaller length
var float spPrice = array.new_float()
var int spIndex = array.new_int()
var int spType = array.new_int()
sph = ta.pivothigh(high, subPivotLen, subPivotLen)
spl = ta.pivotlow(low, subPivotLen, subPivotLen)
f_addSubPivot(_price, _index, _type) =>
if array.size(spType) > 0 and array.get(spType, array.size(spType)-1) == _type
lastPrice = array.get(spPrice, array.size(spPrice)-1)
replace = (_type == 1 and _price > lastPrice) or (_type == -1 and _price < lastPrice)
if replace
array.set(spPrice, array.size(spPrice)-1, _price)
array.set(spIndex, array.size(spIndex)-1, _index)
else
array.push(spPrice, _price)
array.push(spIndex, _index)
array.push(spType, _type)
// Reset sub-pivots after L2 to only track internal wave structure of current move
var int lastL2iTracked = na
if not na(L2i)
if na(lastL2iTracked) or L2i != lastL2iTracked
array.clear(spPrice)
array.clear(spIndex)
array.clear(spType)
lastL2iTracked := L2i
if not na(sph) and (na(L2i) or (bar_index - subPivotLen) >= L2i)
f_addSubPivot(sph, bar_index - subPivotLen, 1)
if not na(spl) and (na(L2i) or (bar_index - subPivotLen) >= L2i)
f_addSubPivot(spl, bar_index - subPivotLen, -1)
// Find subwave 1-2 (sL0 -> sH1 -> sL2) after L2
bool hasSub12 = false
float sL0 = na, sH1 = na, sL2 = na
int sL0i = na, sH1i = na, sL2i = na
if array.size(spPrice) >= 3
st0 = array.get(spType, array.size(spType) - 3)
st1 = array.get(spType, array.size(spType) - 2)
st2 = array.get(spType, array.size(spType) - 1)
if st0 == -1 and st1 == 1 and st2 == -1
sL0 := array.get(spPrice, array.size(spPrice) - 3)
sH1 := array.get(spPrice, array.size(spPrice) - 2)
sL2 := array.get(spPrice, array.size(spPrice) - 1)
sL0i := array.get(spIndex, array.size(spIndex) - 3)
sH1i := array.get(spIndex, array.size(spIndex) - 2)
sL2i := array.get(spIndex, array.size(spIndex) - 1)
// Sub retracement check
sw1 = sH1 - sL0
sRetr = sw1 != 0 ? (sH1 - sL2) / sw1 : na
hasSub12 := sw1 > 0 and not na(sRetr) and sRetr >= subRetrMin and sRetr <= subRetrMax
// === 3 of 3 trigger ===
bool threeOfThree = wave3Start and hasSub12 and close > sH1 and bar_index > sH1i and momentumOK and trendOK and volOK
// === Plotting ===
color upCol = color.new(color.lime, 0)
color dnCol = color.new(color.red, 0)
color neutCol = color.new(color.gray, 60)
plotshape(threeOfThree, title="3 of 3 Buy Signal", style=shape.triangleup, location=location.belowbar, size=size.large, color=upCol, text="3/3")
// Mark the 1-2 and sub 1-2 swings
if showLabels and has12
label.new(H1i, H1, text="1", style=label.style_label_up, color=color.new(color.green, 0))
label.new(L2i, L2, text="2", style=label.style_label_down, color=color.new(color.orange, 0))
if showLabels and hasSub12
label.new(sH1i, sH1, text="(i)", style=label.style_label_up, color=color.new(color.green, 60))
label.new(sL2i, sL2, text="(ii)", style=label.style_label_down, color=color.new(color.orange, 60))
// Draw swing lines
f_plotZigzag() =>
if array.size(pivPrice) >= 2 and plotZigs
for i = 1 to 1
x1 = array.get(pivIndex, array.size(pivIndex) - 2)
y1 = array.get(pivPrice, array.size(pivPrice) - 2)
x2 = array.get(pivIndex, array.size(pivIndex) - 1)
y2 = array.get(pivPrice, array.size(pivPrice) - 1)
line.new(x1, y1, x2, y2, extend=extend.none, color=neutCol, width=1)
f_plotZigzag()
// Visual filters
plot(ema50, color=color.new(color.teal, 0), title="EMA50")
plot(ema200, color=color.new(color.blue, 0), title="EMA200")
bgcolor(threeOfThree ? color.new(color.lime, 85) : na)
// === Alerts ===
alertcondition(threeOfThree, title="3 of 3 long", message="3 of 3 long setup on {{ticker}} {{interval}} — price has broken above subwave (i) high with momentum.")
// === Notes ===
// Heuristic detector:
// 1) Find L0-H1-L2 with L2 retracing 38.2%–78.6% of wave 1.
// 2) Confirm wave 3 start when price breaks above H1.
// 3) Inside wave 3, find sub L0-H1-L2 using tighter sub-pivots with 38.2%–61.8% retracement.
// 4) Trigger 3 of 3 when price breaks above subwave (i) high with momentum/trend/volume filters.
// Tune pivotLen/subPivotLen and thresholds to match your instrument and timeframe.
Bar Risk with Stop Spread – by G.I.N.e TradingThis indicator calculates the potential risk of each individual bar, taking into account the candle’s direction and an adjustable stop spread.
For green (long) candles:
Risk = (Close − Low) + Spread
For red (short) candles:
Risk = (High − Close) + Spread (or (Close − High) if “Original Sign” mode is enabled)
The spread can be specified in price units or ticks, making the tool adaptable to any market instrument.
The result is displayed as a column-style histogram in a separate pane:
Teal bars indicate long-bar risk
Red bars indicate short-bar risk
Optional mode to show both long and short streams separately
Optional label on the last bar showing the most recent calculated risk value
Purpose:
This tool allows traders to quickly assess the “stop distance” risk per bar, including custom spread adjustments, for precise position sizing and money management.
Calm before the StormCalm before the Storm - Bollinger Bands Volatility Indicator
What It Does
This indicator identifies and highlights periods of extremely low market volatility by analyzing Bollinger Bands distance. It uses percentile-based analysis to find the "quietest" market periods and highlights them with a gradient background, operating on the premise that low volatility periods often precede significant price movements.
How It Works
Volatility Measurement: Calculates the distance between Bollinger Bands upper and lower boundaries
Percentile Analysis: Analyzes the lowest X% of volatility periods over a configurable lookback period (default: lowest 40% over 200 bars)
Visual Highlighting: Uses gradient opacity to show volatility levels - the lower the volatility, the more opaque the background highlighting
Adaptive Threshold: Automatically calculates what constitutes "low volatility" based on recent market conditions
Who Should Use It
Primary Users:
Breakout Traders: Looking for consolidation periods that may precede significant moves
Options Traders: Seeking low implied volatility periods before volatility expansion
Swing Traders: Identifying accumulation/distribution phases before trend continuation or reversal
Range Traders: Spotting tight trading ranges for mean reversion strategies
Trading Styles:
Volatility-based strategies
Breakout and momentum trading
Options strategies (volatility plays)
Market timing approaches
When to Use It
Market Conditions:
Consolidation Phases: When price is moving sideways with decreasing volatility
Pre-Announcement Periods: Before earnings, economic data, or major events
Market Transitions: During shifts between trending and ranging markets
Low Volume Periods: When institutional participation is reduced
Strategic Applications:
Entry Timing: Wait for volatility compression before positioning for breakouts
Risk Management: Reduce position sizes during highlighted periods (anticipating volatility expansion)
Options Strategy: Sell premium during low volatility, buy during expansion
Multi-Timeframe Analysis: Combine with higher timeframe trends for confluence
Key Benefits
Objective Volatility Measurement: Removes subjectivity from identifying "quiet" markets
Adaptive Analysis: Automatically adjusts to current market conditions
Visual Clarity: Easy-to-interpret gradient highlighting
Customizable Sensitivity: Adjustable percentile thresholds for different trading styles
Best Used In Combination With:
Trend analysis tools
Support/resistance levels
Volume indicators
Momentum oscillators
This indicator is particularly valuable for traders who understand that periods of low volatility are often followed by periods of high volatility, allowing them to position ahead of potential significant price movements.
Seasonality Monte Carlo Forecaster [BackQuant]Seasonality Monte Carlo Forecaster
Plain-English overview
This tool projects a cone of plausible future prices by combining two ideas that traders already use intuitively: seasonality and uncertainty. It watches how your market typically behaves around this calendar date, turns that seasonal tendency into a small daily “drift,” then runs many randomized price paths forward to estimate where price could land tomorrow, next week, or a month from now. The result is a probability cone with a clear expected path, plus optional overlays that show how past years tended to move from this point on the calendar. It is a planning tool, not a crystal ball: the goal is to quantify ranges and odds so you can size, place stops, set targets, and time entries with more realism.
What Monte Carlo is and why quants rely on it
• Definition . Monte Carlo simulation is a way to answer “what might happen next?” when there is randomness in the system. Instead of producing a single forecast, it generates thousands of alternate futures by repeatedly sampling random shocks and adding them to a model of how prices evolve.
• Why it is used . Markets are noisy. A single point forecast hides risk. Monte Carlo gives a distribution of outcomes so you can reason in probabilities: the median path, the 68% band, the 95% band, tail risks, and the chance of hitting a specific level within a horizon.
• Core strengths in quant finance .
– Path-dependent questions : “What is the probability we touch a stop before a target?” “What is the expected drawdown on the way to my objective?”
– Pricing and risk : Useful for path-dependent options, Value-at-Risk (VaR), expected shortfall (CVaR), stress paths, and scenario analysis when closed-form formulas are unrealistic.
– Planning under uncertainty : Portfolio construction and rebalancing rules can be tested against a cloud of plausible futures rather than a single guess.
• Why it fits trading workflows . It turns gut feel like “seasonality is supportive here” into quantitative ranges: “median path suggests +X% with a 68% band of ±Y%; stop at Z has only ~16% odds of being tagged in N days.”
How this indicator builds its probability cone
1) Seasonal pattern discovery
The script builds two day-of-year maps as new data arrives:
• A return map where each calendar day stores an exponentially smoothed average of that day’s log return (yesterday→today). The smoothing (90% old, 10% new) behaves like an EWMA, letting older seasons matter while adapting to new information.
• A volatility map that tracks the typical absolute return for the same calendar day.
It calculates the day-of-year carefully (with leap-year adjustment) and indexes into a 365-slot seasonal array so “March 18” is compared with past March 18ths. This becomes the seasonal bias that gently nudges simulations up or down on each forecast day.
2) Choice of randomness engine
You can pick how the future shocks are generated:
• Daily mode uses a Gaussian draw with the seasonal bias as the mean and a volatility that comes from realized returns, scaled down to avoid over-fitting. It relies on the Box–Muller transform internally to turn two uniform random numbers into one normal shock.
• Weekly mode uses bootstrap sampling from the seasonal return history (resampling actual historical daily drifts and then blending in a fraction of the seasonal bias). Bootstrapping is robust when the empirical distribution has asymmetry or fatter tails than a normal distribution.
Both modes seed their random draws deterministically per path and day, which makes plots reproducible bar-to-bar and avoids flickering bands.
3) Volatility scaling to current conditions
Markets do not always live in average volatility. The engine computes a simple volatility factor from ATR(20)/price and scales the simulated shocks up or down within sensible bounds (clamped between 0.5× and 2.0×). When the current regime is quiet, the cone narrows; when ranges expand, the cone widens. This prevents the classic mistake of projecting calm markets into a storm or vice versa.
4) Many futures, summarized by percentiles
The model generates a matrix of price paths (capped at 100 runs for performance inside TradingView), each path stepping forward for your selected horizon. For each forecast day it sorts the simulated prices and pulls key percentiles:
• 5th and 95th → approximate 95% band (outer cone).
• 16th and 84th → approximate 68% band (inner cone).
• 50th → the median or “expected path.”
These are drawn as polylines so you can immediately see central tendency and dispersion.
5) A historical overlay (optional)
Turn on the overlay to sketch a dotted path of what a purely seasonal projection would look like for the next ~30 days using only the return map, no randomness. This is not a forecast; it is a visual reminder of the seasonal drift you are biasing toward.
Inputs you control and how to think about them
Monte Carlo Simulation
• Price Series for Calculation . The source series, typically close.
• Enable Probability Forecasts . Master switch for simulation and drawing.
• Simulation Iterations . Requested number of paths to run. Internally capped at 100 to protect performance, which is generally enough to estimate the percentiles for a trading chart. If you need ultra-smooth bands, shorten the horizon.
• Forecast Days Ahead . The length of the cone. Longer horizons dilute seasonal signal and widen uncertainty.
• Probability Bands . Draw all bands, just 95%, just 68%, or a custom level (display logic remains 68/95 internally; the custom number is for labeling and color choice).
• Pattern Resolution . Daily leans on day-of-year effects like “turn-of-month” or holiday patterns. Weekly biases toward day-of-week tendencies and bootstraps from history.
• Volatility Scaling . On by default so the cone respects today’s range context.
Plotting & UI
• Probability Cone . Plots the outer and inner percentile envelopes.
• Expected Path . Plots the median line through the cone.
• Historical Overlay . Dotted seasonal-only projection for context.
• Band Transparency/Colors . Customize primary (outer) and secondary (inner) band colors and the mean path color. Use higher transparency for cleaner charts.
What appears on your chart
• A cone starting at the most recent bar, fanning outward. The outer lines are the ~95% band; the inner lines are the ~68% band.
• A median path (default blue) running through the center of the cone.
• An info panel on the final historical bar that summarizes simulation count, forecast days, number of seasonal patterns learned, the current day-of-year, expected percentage return to the median, and the approximate 95% half-range in percent.
• Optional historical seasonal path drawn as dotted segments for the next 30 bars.
How to use it in trading
1) Position sizing and stop logic
The cone translates “volatility plus seasonality” into distances.
• Put stops outside the inner band if you want only ~16% odds of a stop-out due to noise before your thesis can play.
• Size positions so that a test of the inner band is survivable and a test of the outer band is rare but acceptable.
• If your target sits inside the 68% band at your horizon, the payoff is likely modest; outside the 68% but inside the 95% can justify “one-good-push” trades; beyond the 95% band is a low-probability flyer—consider scaling plans or optionality.
2) Entry timing with seasonal bias
When the median path slopes up from this calendar date and the cone is relatively narrow, a pullback toward the lower inner band can be a high-quality entry with a tight invalidation. If the median slopes down, fade rallies toward the upper band or step aside if it clashes with your system.
3) Target selection
Project your time horizon to N bars ahead, then pick targets around the median or the opposite inner band depending on your style. You can also anchor dynamic take-profits to the moving median as new bars arrive.
4) Scenario planning & “what-ifs”
Before events, glance at the cone: if the 95% band already spans a huge range, trade smaller, expect whips, and avoid placing stops at obvious band edges. If the cone is unusually tight, consider breakout tactics and be ready to add if volatility expands beyond the inner band with follow-through.
5) Options and vol tactics
• When the cone is tight : Prefer long gamma structures (debit spreads) only if you expect a regime shift; otherwise premium selling may dominate.
• When the cone is wide : Debit structures benefit from range; credit spreads need wider wings or smaller size. Align with your separate IV metrics.
Reading the probability cone like a pro
• Cone slope = seasonal drift. Upward slope means the calendar has historically favored positive drift from this date, downward slope the opposite.
• Cone width = regime volatility. A widening fan tells you that uncertainty grows fast; a narrow cone says the market typically stays contained.
• Mean vs. price gap . If spot trades well above the median path and the upper band, mean-reversion risk is high. If spot presses the lower inner band in an up-sloping cone, you are in the “buy fear” zone.
• Touches and pierces . Touching the inner band is common noise; piercing it with momentum signals potential regime change; the outer band should be rare and often brings snap-backs unless there is a structural catalyst.
Methodological notes (what the code actually does)
• Log returns are used for additivity and better statistical behavior: sim_ret is applied via exp(sim_ret) to evolve price.
• Seasonal arrays are updated online with EWMA (90/10) so the model keeps learning as each bar arrives.
• Leap years are handled; indexing still normalizes into a 365-slot map so the seasonal pattern remains stable.
• Gaussian engine (Daily mode) centers shocks on the seasonal bias with a conservative standard deviation.
• Bootstrap engine (Weekly mode) resamples from observed seasonal returns and adds a fraction of the bias, which captures skew and fat tails better.
• Volatility adjustment multiplies each daily shock by a factor derived from ATR(20)/price, clamped between 0.5 and 2.0 to avoid extreme cones.
• Performance guardrails : simulations are capped at 100 paths; the probability cone uses polylines (no heavy fills) and only draws on the last confirmed bar to keep charts responsive.
• Prerequisite data : at least ~30 seasonal entries are required before the model will draw a cone; otherwise it waits for more history.
Strengths and limitations
• Strengths :
– Probabilistic thinking replaces single-point guessing.
– Seasonality adds a small but meaningful directional bias that many markets exhibit.
– Volatility scaling adapts to the current regime so the cone stays realistic.
• Limitations :
– Seasonality can break around structural changes, policy shifts, or one-off events.
– The number of paths is performance-limited; percentile estimates are good for trading, not for academic precision.
– The model assumes tomorrow’s randomness resembles recent randomness; if regime shifts violently, the cone will lag until the EWMA adapts.
– Holidays and missing sessions can thin the seasonal sample for some assets; be cautious with very short histories.
Tuning guide
• Horizon : 10–20 bars for tactical trades; 30+ for swing planning when you care more about broad ranges than precise targets.
• Iterations : The default 100 is enough for stable 5/16/50/84/95 percentiles. If you crave smoother lines, shorten the horizon or run on higher timeframes.
• Daily vs. Weekly : Daily for equities and crypto where month-end and turn-of-month effects matter; Weekly for futures and FX where day-of-week behavior is strong.
• Volatility scaling : Keep it on. Turn off only when you intentionally want a “pure seasonality” cone unaffected by current turbulence.
Workflow examples
• Swing continuation : Cone slopes up, price pulls into the lower inner band, your system fires. Enter near the band, stop just outside the outer line for the next 3–5 bars, target near the median or the opposite inner band.
• Fade extremes : Cone is flat or down, price gaps to the upper outer band on news, then stalls. Favor mean-reversion toward the median, size small if volatility scaling is elevated.
• Event play : Before CPI or earnings on a proxy index, check cone width. If the inner band is already wide, cut size or prefer options structures that benefit from range.
Good habits
• Pair the cone with your entry engine (breakout, pullback, order flow). Let Monte Carlo do range math; let your system do signal quality.
• Do not anchor blindly to the median; recalc after each bar. When the cone’s slope flips or width jumps, the plan should adapt.
• Validate seasonality for your symbol and timeframe; not every market has strong calendar effects.
Summary
The Seasonality Monte Carlo Forecaster wraps institutional risk planning into a single overlay: a data-driven seasonal drift, realistic volatility scaling, and a probabilistic cone that answers “where could we be, with what odds?” within your trading horizon. Use it to place stops where randomness is less likely to take you out, to set targets aligned with realistic travel, and to size positions with confidence born from distributions rather than hunches. It will not predict the future, but it will keep your decisions anchored to probabilities—the language markets actually speak.
PLAIN VAMSThe PLAIN VAMS (Volatility-Adjusted Momentum Score) is a visual tool designed to help traders identify momentum shifts relative to prevailing volatility conditions. Unlike traditional momentum indicators, VAMS adapts dynamically to price fluctuations by comparing current price levels to volatility-based boundaries derived from customizable moving averages.
Key Features:
- Volatility-Adjusted Zones: Prices are evaluated against upper and lower dynamic boundaries, signaling potential overbought or oversold momentum conditions.
Two Modes:
- PLAIN VAMS (default): Uses a longer lookback period for smoother, trend-following behavior.
- RAW VAMS: A shorter lookback for high-sensitivity, intraday or scalping setups.
Customizable Moving Averages:
Choose from multiple MA types (EMA, SMA, WMA, etc.) to match your strategy preferences.
Visual Clarity:
- Color-coded candles for quick signal recognition.
- Optional background shading for immediate context.
- Boundary lines to define momentum thresholds.
How It Works:
The script calculates a moving average (based on user-selected type and period) and applies an upper and lower multiplier to create dynamic price boundaries. When price closes beyond these bands, it suggests a strong directional momentum move. The indicator is fully customizable to adapt to your trading style and timeframe.
Use Cases:
- Identify potential breakouts or trend continuations.
- Filter entries/exits based on momentum strength.
- Combine with other tools for confirmation in your strategy.
This indicator does not repaint or use future-looking data. It’s designed for discretionary and systematic traders looking for an adaptive way to visualize momentum relative to market volatility.
Live NY Session Movement (points)//@version=5
indicator("Live NY Session Movement (points)", overlay=true)
// --- Inputs ---
nySession = input.session("0830-1700", "NY Session (local NY time)")
nyTimezone = input.string("America/New_York", "Session Timezone")
showShade = input.bool(true, "Shade NY Session")
// --- In-session detection (per-bar) ---
inNy = not na(time(timeframe.period, nySession, nyTimezone))
// --- Track session H/L and live movement ---
var float sessHigh = na
var float sessLow = na
var label liveLab = na
var bool wasIn = false
// session edge flags
justStarted = inNy and not wasIn
justEnded = not inNy and wasIn
wasIn := inNy
if justStarted
sessHigh := high
sessLow := low
if inNy
sessHigh := na(sessHigh) ? high : math.max(sessHigh, high)
sessLow := na(sessLow) ? low : math.min(sessLow, low)
movePts = sessHigh - sessLow
// create once, then update in place each bar
if na(liveLab)
liveLab := label.new(bar_index, high, "NY Move: " + str.tostring(movePts, format.mintick), style=label.style_label_down, textcolor=color.white, color=color.new(color.blue, 0), size=size.small)
label.set_x(liveLab, bar_index)
label.set_y(liveLab, high)
label.set_text(liveLab, "NY Move: " + str.tostring(movePts, format.mintick))
else
// clean up at end of session
sessHigh := na
sessLow := na
if not na(liveLab)
label.delete(liveLab)
liveLab := na
// Optional: shade the session so you can see it clearly
bgcolor(showShade and inNy ? color.new(color.blue, 92) : na)
Traders Reality Rate Spike Monitor 0.1 betaTraders Reality Rate Spike Monitor
## **Early Warning System for Interest Rate-Driven Market Crashes**
Based on critical market analysis revealing the dangerous correlation between interest rate spikes and major market selloffs, this indicator provides **three-tier alerts** for US 10-Year Treasury yield acceleration.
### **📊 Key Market Intelligence:**
**Historical Precedent:** The 2018 market crash occurred when unrealized bank losses hit $256 billion with interest rates at just 2.5%. **Current unrealized losses have reached $560 billion** - more than double the 2018 levels - while rates sit at 4.5%.
**Critical Vulnerabilities:**
- **$559 billion in tech sector debt** maturing through 2025
- **65% of investment-grade debt** rated BBB (vulnerable to adverse conditions)
- **$9.5 trillion in total debt** requiring refinancing
- Every 1% rate increase costs the economy **$360 billion annually**
### **🚨 Alert System:**
**📊 WATCH (20+ basis points/3 days):** Early positioning signal
**⚠️ WARNING (30+ basis points/3 days):** Prepare for volatility
**🚨 CRITICAL (40+ basis points/3 days):** Historical crash threshold
### **💡 Why This Matters:**
Interest rate spikes historically trigger major market corrections:
- **2018:** 70 basis points spike → 20% S&P 500 crash
- **2025:** Similar pattern led to massive selloffs
- **Current risk:** 2x higher unrealized losses than 2018
### **⚡ Features:**
✅ **Zero chart clutter** - invisible until alerts trigger
✅ **Dynamic calculation** - automatically adjusts to current yield levels
✅ **Multi-timeframe compatibility** - works on any chart timeframe
✅ **Professional alerts** - with actual basis point calculations
### **🎯 Use Case:**
Perfect for traders and investors who understand that **debt refinancing pressure** and **unrealized bank losses** create systemic risks that manifest through interest rate volatility. When rates spike rapidly, leveraged positions unwind and markets crash.
**"Every point costs us $360 billion a year. Think of that."** - This indicator helps you see those critical rate movements before the market does.
---
**Disclaimer:** This indicator is for educational purposes. Past performance does not guarantee future results. Always manage risk appropriately.
---
This description positions your indicator as a **serious professional tool** based on real market analysis rather than just another technical indicator! 🚀
Marcius Studio® - Cross-Asset Correlator™Cross-Asset Correlator™ — a pair-trading strategy that identifies correlation breakdowns between two assets and captures profit opportunities from market inefficiencies.
The strategy enters trades when the correlation drops below a set threshold and closes positions once correlation recovers.
The main concept is to exploit temporary divergence between two assets by going long the stronger one and short the weaker one, aiming to profit when their correlation reverts.
Important : This script illustrates asset correlation concepts for educational purposes only. It's not for live trading—requires adjustments and offers no performance guarantees. Always apply risk management.
TradingView Limitation
By default, TradingView’s built-in Strategy interface does not support backtesting with two different assets .
To overcome this, the script is implemented as an indicator with a fully custom backtesting engine that calculates PnL, trades, and performance statistics directly on the chart.
Idea
Markets move in clusters : altcoins follow BTC, memecoins track Solana, L2 projects mirror Ethereum. But correlations aren’t perfect—temporary divergences create pricing inefficiencies.
The logic:
When an asset lags or overshoots its usual correlation, it’s a mispricing opportunity.
Trade the reversion: buy undervalued divergence, sell overextended convergence.
The market eventually corrects, but the inefficiency window allows profit before realignment.
OKX Signal Bot Integration
This script includes a built-in interface for OKX Signal Bot .
It can generate structured JSON alerts (ENTER / EXIT, long / short) and directly manage trades on OKX exchange .
This allows seamless automation of correlation-based strategies without manual order execution.
Note : The OKX Signal Bot (for demo use only) assists with alerts & trade management but does not ensure profits. You are fully responsible for your trades—always apply risk management.
Strategy Parameters
Symbol 1 / Symbol 2 : trading instruments to be analyzed.
SMA Period : smoothing period for price averages.
Correlation Period : number of bars used to calculate correlation coefficient.
Upper Correlation Threshold : level above which trades are closed.
Lower Correlation Threshold : level below which new trades are opened.
percentage_investment (%) : allocation per entry signal (used for OKX integration).
Example Settings OKX:FARTCOINUSDT.P / OKX:PENGUUSDT.P
Timeframe : 1H
SMA Period : 60
Correlation Period : 25
Upper Threshold : 0.9
Lower Threshold : 0.1
percentage_investment : 10%
How the Code Works
Retrieves closing prices of two selected assets.
Calculates correlation coefficient and moving averages.
When correlation breaks below the lower threshold, the script opens a pair trade (long/short depending on SMA relation).
When correlation recovers above the upper threshold, all open trades are closed.
Real-time alerts are generated in JSON format for OKX bots (ENTER/EXIT signals).
Built-in backtesting engine tracks PnL, trades, and statistics (7d / 30d / total).
Visual labels mark entries, exits, and PnL results directly on the chart.
Disclaimer
Trading involves risk — always do your own research (DYOR) and seek professional financial advice. We are not responsible for any potential financial losses.
All-in-One EMA & BBThis script combines Bollinger Bands and multiple EMAs into one powerful tool. It includes:
1) Bollinger Bands with customizable MA type and colors.
2) EMA 21 on Daily and Weekly timeframes.
3) EMA 21, 50, 100, 200 on current chart timeframe.
4) Toggle options for each indicator for a clean, flexible view.
Ideal for traders seeking multi-timeframe trend analysis and volatility insights.
Monthly Expected Move (IV + Realized)What it does
Overlays 1-month expected move bands on price using both forward-looking options data and backward-looking realized movement:
IV30 band — from your pasted 30-day implied vol (%)
Straddle band — from your pasted ATM ~30-DTE call+put total
HV band — from Historical Volatility computed on-chart
ATR band — from ATR% extrapolated to ~1 trading month
Use it to quickly answer: “How much could this stock move in ~1 month?” and “Is the market now pricing more/less movement than we’ve actually been getting?”
Inputs (quick)
Implied (forward-looking)
Use IV30 (%) — paste annualized IV30 from your options platform.
Use ATM 30-DTE Straddle — paste Call+Put total (per share) at the ATM strike, ~30 DTE.
Realized (backward-looking)
HV lookback (days) — default 21 (≈1 trading month).
ATR length — default 14.
Note: TradingView can’t fetch option data automatically. Paste the IV30 % or the straddle total you read from your broker (use Mark/mid prices).
How it’s calculated
IV band (±%) = IV30 × √(21/252) (annualized → ~1-month).
Straddle band (±%) = (ATM Call + Put) / Spot to that expiry (≈30 DTE).
HV band (±%) = stdev(log returns, N) × √252 × √(21/252).
ATR band (±%) = (ATR(len)/Close) × √21.
All bands are plotted as upper/lower envelopes around price, plus an on-chart readout of each ±% for quick scanning.
How to use it (at a glance)
IV/Straddle bands wider than HV/ATR → market expects bigger movement than recent actuals (possible catalyst/expansion).
All bands narrow → likely a low-mover; look elsewhere if you want action.
HV > IV → realized swings exceed current pricing (mean-reversion or vol bleed often follows).
Pro tips
For ATM straddle: pick the expiry closest to ~30 DTE, use the ATM strike (closest to spot), and add Call Mark + Put Mark (per share). If the exact ATM strike isn’t quoted, average the two neighboring strikes.
The simple straddle/spot heuristic can read slightly below the IV-derived 1σ; that’s normal.
Keep the chart on daily timeframe—the math assumes trading-day conventions (~252/yr, ~21/mo).
Volume Profile Grid [Alpha Extract]A sophisticated volume distribution analysis system that transforms market activity into institutional-grade visual profiles, revealing hidden support/resistance zones and market participant behavior. Utilizing advanced price level segmentation, bullish/bearish volume separation, and dynamic range analysis, the Volume Profile Grid delivers comprehensive market structure insights with Point of Control (POC) identification, Value Area boundaries, and volume delta analysis. The system features intelligent visualization modes, real-time sentiment analysis, and flexible range selection to provide traders with clear, actionable volume-based market context.
🔶 Dynamic Range Analysis Engine
Implements dual-mode range selection with visible chart analysis and fixed period lookback, automatically adjusting to current market view or analyzing specified historical periods. The system intelligently calculates optimal bar counts while maintaining performance through configurable maximum limits, ensuring responsive profile generation across all timeframes with institutional-grade precision.
// Dynamic period calculation with intelligent caching
get_analysis_period() =>
if i_use_visible_range
chart_start_time = chart.left_visible_bar_time
current_time = last_bar_time
time_span = current_time - chart_start_time
tf_seconds = timeframe.in_seconds()
estimated_bars = time_span / (tf_seconds * 1000)
range_bars = math.floor(estimated_bars)
final_bars = math.min(range_bars, i_max_visible_bars)
math.max(final_bars, 50) // Minimum threshold
else
math.max(i_periods, 50)
🔶 Advanced Bull/Bear Volume Separation
Employs sophisticated candle classification algorithms to separate bullish and bearish volume at each price level, with weighted distribution based on bar intersection ratios. The system analyzes open/close relationships to determine volume direction, applying proportional allocation for doji patterns and ensuring accurate representation of buying versus selling pressure across the entire price spectrum.
🔶 Multi-Mode Volume Visualization
Features three distinct display modes for bull/bear volume representation: Split mode creates mirrored profiles from a central axis, Side by Side mode displays sequential bull/bear segments, and Stacked mode separates volumes vertically. Each mode offers unique insights into market participant behavior with customizable width, thickness, and color parameters for optimal visual clarity.
// Bull/Bear volume calculation with weighted distribution
for bar_offset = 0 to actual_periods - 1
bar_high = high
bar_low = low
bar_volume = volume
// Calculate intersection weight
weight = math.min(bar_high, next_level) - math.max(bar_low, current_level)
weight := weight / (bar_high - bar_low)
weighted_volume = bar_volume * weight
// Classify volume direction
if bar_close > bar_open
level_bull_volume += weighted_volume
else if bar_close < bar_open
level_bear_volume += weighted_volume
else // Doji handling
level_bull_volume += weighted_volume * 0.5
level_bear_volume += weighted_volume * 0.5
🔶 Point of Control & Value Area Detection
Implements institutional-standard POC identification by locating the price level with maximum volume accumulation, providing critical support/resistance zones. The Value Area calculation uses sophisticated sorting algorithms to identify the price range containing 70% of trading volume, revealing the market's accepted value zone where institutional participants concentrate their activity.
🔶 Volume Delta Analysis System
Incorporates real-time volume delta calculation with configurable dominance thresholds to identify significant bull/bear imbalances. The system visually highlights price levels where buying or selling pressure exceeds threshold percentages, providing immediate insight into directional volume flow and potential reversal zones through color-coded delta indicators.
// Value Area calculation using 70% volume accumulation
total_volume_sum = array.sum(total_volumes)
target_volume = total_volume_sum * 0.70
// Sort volumes to find highest activity zones
for i = 0 to array.size(sorted_volumes) - 2
for j = i + 1 to array.size(sorted_volumes) - 1
if array.get(sorted_volumes, j) > array.get(sorted_volumes, i)
// Swap and track indices for value area boundaries
// Accumulate until 70% threshold reached
for i = 0 to array.size(sorted_indices) - 1
accumulated_volume += vol
array.push(va_levels, array.get(volume_levels, idx))
if accumulated_volume >= target_volume
break
❓How It Works
🔶 Weighted Volume Distribution
Implements proportional volume allocation based on the percentage of each bar that intersects with price levels. When a bar spans multiple levels, volume is distributed proportionally based on the intersection ratio, ensuring precise representation of trading activity across the entire price spectrum without double-counting or volume loss.
🔶 Real-Time Profile Generation
Profiles regenerate on each bar close when in visible range mode, automatically adapting to chart zoom and scroll actions. The system maintains optimal performance through intelligent caching mechanisms and selective line updates, ensuring smooth operation even with maximum resolution settings and extended analysis periods.
🔶 Market Sentiment Analysis
Features comprehensive volume analysis table displaying total volume metrics, bullish/bearish percentages, and overall market sentiment classification. The system calculates volume dominance ratios in real-time, providing immediate insight into whether buyers or sellers control the current price structure with percentage-based sentiment thresholds.
🔶 Visual Profile Mapping
Provides multi-layered visual feedback through colored volume bars, POC line highlighting, Value Area boundaries, and optional delta indicators. The system supports profile mirroring for alternative perspectives, line extension for future reference, and customizable label positioning with detailed price information at critical levels.
Why Choose Volume Profile Grid
The Volume Profile Grid represents the evolution of volume analysis tools, combining traditional volume profile concepts with modern visualization techniques and intelligent analysis algorithms. By integrating dynamic range selection, sophisticated bull/bear separation, and multi-mode visualization with POC/Value Area detection, it provides traders with institutional-quality market structure analysis that adapts to any trading style. The comprehensive delta analysis and sentiment monitoring system eliminates guesswork while the flexible visualization options ensure optimal clarity across all market conditions, making it an essential tool for traders seeking to understand true market dynamics through volume-based price discovery.
VIX > 20/25 HighlightThis indicator tracks the CBOE Volatility Index (VIX) and highlights when volatility exceeds critical thresholds.
Plots the VIX with dashed reference lines at 20 and 25.
Background turns orange when the VIX is above 20.
Background turns bright red when the VIX is above 25.
Includes alert conditions to notify you when the VIX crosses above 20 or 25.
Use this tool to quickly visualize periods of elevated market stress and manage risk accordingly.
Candle Body Size AlertThis indicator monitors the body size of each candle (close minus open, ignoring wicks) and compares it to a user-defined threshold measured in ticks. If the candle body exceeds the threshold, the indicator triggers an alert condition at the close of the candle.
Features:
1. Adjustable threshold in ticks (default: 4000)
2. Adjustable timeframe (or use chart timeframe)
3. Alerts only at candle close (no intrabar signals)
Use Case:
Designed for traders who want to be notified when unusually large candles form, helping to identify strong momentum moves or volatility spikes.
Average True Range %The ATR% oscillator measures market volatility as a percentage of the closing price, smooths it using a chosen method (RMA, SMA, EMA, or WMA), and compares it to the threshold levels of 0.95% and 1.20%.
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Opening Range Breakout🚀 ORB – Optimized for Peak Performance 🚀
Catch the morning breakout moves with zero guesswork!
This plug-and-play Opening Range Breakout strategy is fully optimized ; no settings to tweak, no parameters to adjust.
✅ Pre-tuned for U.S. market open on 5-minute charts.
✅ Built-in risk management with stop loss, take profit, and one trade per day.
✅ Auto exit before market close to lock in gains and avoid late-day whipsaws.
Perfect for day traders who want to focus on execution, not experimentation.
Just load it, trade it, and let the strategy do the heavy lifting.
⚠ Disclaimer : Educational use only. Always backtest and paper trade before using with real capital.
Key Features
• No Guesswork – Pre-set with the best-performing configuration.
• Opening Range Breakout Logic – Identifies the early range of the market and trades strong breakouts.
• Strict Risk Management – Stop loss and take profit levels are automatically calculated from the range size.
• One Trade Per Day – Prevents overtrading and keeps the focus on quality setups.
• End-of-Day Auto Exit – Closes all open trades at 3:30 PM EST to avoid late-session volatility.
How It Works
1. Opening Range Calculation: At market open (9:30 AM EST), the strategy monitors opening range.
2. Breakout Entry: Monitors the breakouts with moment.
3. Risk & Profit Targets: Stop loss and take profit are calculated automatically based on the range size. Risk-to-reward ratio is set for balanced performance.
4. Trade Control: Only one trade per day (either long or short). All trades are force-closed at 3:30 PM EST.
Scalper ProCreated by 77
version 0.9 (Pre-release version)
Overview
The Scalper Pro Algo is a specialized day trading indicator optimized for the various timeframe, tailored for both stock and cryptocurrency markets. It delivers precise buy and sell signals, highlights dynamic overbought and oversold zones, and flags potential reversal points to support active traders.
At its core, the indicator blends a Kalman-filtered Super trend algorithm with VWMA (Volume-Weighted Moving Average) bands. This fusion enables trend-following and mean-reversion strategies by identifying high-probability entry and exit points. The Kalman filtering helps reduce market noise and minimize false signals, offering traders clearer, more dependable guidance for scalping and short-term trades.
