AI-Powered Support and Resistance Detector🚀 AI-Powered Support & Resistance Detector for TradingView 🔥
Are you ready to elevate your trading game to the next level? This advanced AI-driven Pine Script indicator is designed to help traders make smarter and more informed decisions in the fast-paced cryptocurrency market. Built for precision, it works seamlessly on lower timeframes (like 15 and 30 minutes) and provides powerful insights through the following key features:
✨Dynamic Support & Resistance Levels:** Automatically detects key pivot points to plot accurate support and resistance zones on the chart. No need for manual drawing—let the AI handle it!
📊 Trend Prediction with Real-Time Alerts:** Built-in AI logic evaluates price movements and predicts bullish or bearish trends, keeping you ahead of the market.
💡 **Effortless Integration:** Simply copy, paste, and run it on TradingView. The indicator’s clean design ensures minimal noise and actionable signals.
🔧 **Optimized for 4-Hour and Lower Timeframes:** Identify trend reversals and key price zones faster and with unmatched precision.
🎯 **Why Use This Indicator?**
It combines traditional technical analysis concepts with AI-powered trend detection, empowering traders with better market insights. Whether you're a scalper, swing trader, or long-term investor, this tool will help you stay ahead in the volatile crypto market.
---
🔥 **Get Started Now:** Copy the code, paste it into TradingView's Pine Editor, and transform your charting experience forever!
Ready to make better trading decisions with AI on your side? 🚀
Pine实用程序
AI Optimized BTC Trading Strategy v2AI Crypto trading strategy created with chatgpt work on any time frame or pairs
Walk Forward PatternsINTRO
In Euclidean geometry, every mathematical output has a planar projection. 'Walk Forward Patterns' can be considered a practical example of this concept. On the other hand, this indicator might also be viewed as an experiment in 'how playing with Lego as a child contributes to time series analysis' :)
OVERVIEW
This script dynamically generates the necessary optimization and testing ranges for Walk Forward Analysis based on user-defined bar count and length inputs. It performs automatic calculations for each step, offers 8 different window options depending on the inputs, and visualizes the results dynamically. I should also note that most of the window models consist of original patterns I have created.
ADDITIONAL INFO : WHAT IS WALK FORWARD ANALYSIS?
Although it is not the main focus of this indicator, providing a brief definition of Walk Forward Analysis can be helpful in correctly interpreting the results it generates. Walk Forward Analysis (WFA) is a systematic method for optimizing parameters and validating trading strategies. It involves dividing historical data into variable segments, where a strategy is first optimized on an in-sample period and then tested on an out-of-sample period. This process repeats by shifting the windows forward, ensuring that each test evaluates the strategy on unseen data, helping to assess its robustness and adaptability in real market conditions.
ORIGINALITY
There are very few studies on Walk Forward Analysis in TradingView. Even worse, there are no any open-source studies available. Someone has to start somewhere, I suppose. And in my personal opinion, determining the optimization and backtest intervals is the most challenging part of WFA. These intervals serve as a prerequisite for automated parameter optimization. I felt the need to publish this pattern module, which I use in my own WFA models, partly due to this gap on community scripts.
INDICATOR MECHANICS
To use the indicator effectively, you only need to perform four simple tasks:
Specify the total number of bars in your chart in the 'Bar Index' parameter.
Define the optimization (In-Sample Test) length.
Define the testing (Out-Of-Sample Test) length.
Finally, select the window type.
The indicator automatically models everything else (including the number of steps) based on your inputs. And the result; you now have a clear idea of which bars to use for your Walk Forward tests!
A COMMONLY USED WINDOW SELECTION METHOD: ROLLING
A more concrete definition of Walk Forward Analysis, specifically for the widely used Rolling method, can be described as follows:
Parameters that have performed well over a certain period are identified (Optimization: In-Sample).
These parameters are then tested on a shorter, subsequent period (Backtest: Out-of-Sample).
The process is repeated forward in time (At each step, the optimization and backtest periods are shifted by the backtest length).
If the cumulative percentage profit obtained from the backtest results is greater than half of the historical optimization profit, the strategy is considered "successful."
If the strategy is successful, the most recent (untested) optimization values are used for live trading.
OTHER WINDOW OPTIONS
ANCHORED: That's a pattern based on progressively expanding optimization ranges at each step. Backtest ranges move forward in a staircase-like manner.
STATIC: Optimization ranges remain fixed, while backtest ranges are shifted forward.
BLOCKED: Optimization ranges are shifted forward in groups of three blocks. Backtest ranges are also shifted in a staircase manner, even at the cost of creating gaps from the optimization end bars.
TRIANGULAR: Optimization ranges are shifted forward in triangular regions, while backtest ranges move in a staircase pattern.
RATIO: The optimization length increases by 25% of the initial step’s fixed length at each step. In other words, the length grows by 25% of the first step's length incrementally. Backtest ranges always start from the bar where the optimization ends.
FIBONACCI: A variation of the Ratio method, where the optimization shift factor is set to 0.618
RANDOM WALK
Unlike the window models explained above, we can also generate optimization and backtest ranges completely randomly—offering almost unlimited variations! When you select the "Random" option in the "Window" parameter on the indicator interface, random intervals are generated based on various trigonometric calculations. By changing the numerical value in the '🐒' parameter, you can create entirely unique patterns.
WHY THE 🐒 EMOJI?
Two reasons.
First, I think that as humanity, we are a species of tailless primates who become happy when we understand things :). At least evolutionarily. The entire history of civilization is built on the effort to express the universe in a scale we can comprehend. 'Knowledge' is an invention born from this effort, which is why we feel happiness when we 'understand'. Second, I can't think of a better metaphor for randomness than a monkey sitting at a keyboard. See: Monkey Test.
Anyway, I’m rambling :)
NOTES
The indicator generates results for up to 100 steps. As the number of steps increases, the table may extend beyond the screen—don’t forget to zoom out!
FINAL WORDS
I haven’t published a Walk Forward script yet . However, there seem to be examples that can perform parameter optimization in the true sense of the word, producing more realistic results without falling into overfitting in my library. Hopefully, I’ll have the chance to publish one in the coming weeks. Sincerely thanks to Kıvanç Özbilgiç, Robert Pardo, Kevin Davey, Ernest P. Chan for their inspiring publishments.
DISCLAIMER
That's just a script, nothing more. I hope it helps everyone. Do not forget to manage your risk. And trade as safely as possible. Best of luck!
© dg_factor
Nifty 50 Trend Identifier DEEP//@version=5
indicator("Nifty 50 Trend Identifier", overlay=true)
// Inputs
shortMA = ta.sma(close, 7)
longMA = ta.sma(close, 20)
atr = ta.atr(14)
avgVolume = ta.sma(volume, 14)
// Trend Conditions
uptrend = shortMA > longMA and close > longMA and volume > avgVolume
downtrend = shortMA < longMA and close < longMA and volume > avgVolume
sideways = math.abs(shortMA - longMA) / longMA < 0.005
// Plotting
plot(shortMA, color=color.blue, title="Short MA")
plot(longMA, color=color.orange, title="Long MA")
bgcolor(uptrend ? color.new(color.green, 90) : downtrend ? color.new(color.red, 90) : sideways ? color.new(color.gray, 90) : na)
Rsi CustomizedFor the personal strategy made in the time frame of one minute, whenever the trend line indicator reaches the 65 and 35 lines, or especially if it passes through them, we will see a change in the trend, which can be useful for taking scalp positions.
edited by : Masoud.Alz
SMA Scanner with 52-Week HighUpdated the script to include stochastic indicator to the buy indicator
RM MA Crossover StrategyOverview of the Moving Average Crossover Strategy with Price Filter:
Moving Averages:
Fast MA (10-period) and Slow MA (20-period) help track short-term and medium-term price trends.
Buy Signal:
Occurs when the price closes above both MAs, indicating a strong uptrend.
Alternatively, if the Fast MA crosses above the Slow MA, a buy signal is also triggered.
The strategy avoids back-to-back buy signals (only triggered after a previous sell).
Sell Signal:
Occurs when the price closes below both MAs, indicating a potential reversal or downtrend.
Alternatively, if the Fast MA crosses below the Slow MA, a sell signal is also triggered.
The strategy avoids back-to-back sell signals (only triggered after a previous buy).
Signal Alternation:
The script ensures that a buy is only followed by a sell and vice versa to prevent excessive signals.
Goal:
The strategy seeks to capture sustained trend movements by relying on both MA crossovers and price positioning relative to the moving averages.
This strategy is designed for trend-following, reducing noise from false signals while ensuring clear entries and exits.
Time-Based Vertical Lines (GMT, EST & JST)The Breakfast Breakout is a trading strategy that capitalizes on early morning market volatility, particularly around major financial centers' opening hours. It typically involves identifying a price range formed before a key session opens—such as the London session at 8 AM—and placing breakout trades when price moves beyond this range. Traders often use pending orders to capture momentum in either direction, aiming to profit from the increased liquidity and sharp price movements that occur during this time. This indicator features the addition of the Asia and New York sessions.
Multi-Timeframe RSI Screenermontly RSI >60 AND
weekly RSI > 60 AND
Daily RSI > 40 i need a screen the list of stocks for me in nse
Bank Nifty Trend Identifier//@version=5
indicator("Bank Nifty Trend Identifier", overlay=true)
// Inputs
shortMA = ta.sma(close, 9)
longMA = ta.sma(close, 21)
atr = ta.atr(14)
avgVolume = ta.sma(volume, 14)
// Trend Conditions
uptrend = shortMA > longMA and close > longMA and volume > avgVolume
downtrend = shortMA < longMA and close < longMA and volume > avgVolume
sideways = math.abs(shortMA - longMA) / longMA < 0.005
// Plotting
plot(shortMA, color=color.blue, title="Short MA")
plot(longMA, color=color.orange, title="Long MA")
bgcolor(uptrend ? color.new(color.green, 90) : downtrend ? color.new(color.red, 90) : sideways ? color.new(color.gray, 90) : na)
Moving Averages With Continuous Periods [macp]This script reimagines traditional moving averages by introducing floating-point period calculations, allowing for fractional lengths rather than being constrained to whole numbers. At its core, it provides SMA, WMA, and HMA variants that can work with any decimal length, which proves especially valuable when creating dynamic indicators or fine-tuning existing strategies.
The most significant improvement lies in the Hull Moving Average implementation. By properly handling floating-point mathematics throughout the calculation chain, this version reduces the overshoot tendencies that often plague integer-based HMAs. The result is a more responsive yet controlled indicator that better captures price action without excessive whipsaw.
The visual aspect incorporates a trend gradient system that can adapt to different trading styles. Rather than using fixed coloring, it offers several modes ranging from simple solid colors to more nuanced three-tone gradients that help identify trend transitions. These gradients are normalized against ATR to provide context-aware visual feedback about trend strength.
From a practical standpoint, the floating-point approach eliminates the subtle discontinuities that occur when integer-based moving averages switch periods. This makes the indicator particularly useful in systems where the MA period itself is calculated from market conditions, as it can smoothly transition between different lengths without artificial jumps.
At the heart of this implementation lies the concept of continuous weights rather than discrete summation. Traditional moving averages treat each period as a distinct unit with integer indexing. However, when we move to floating-point periods, we need to consider how fractional periods should behave. This leads us to some interesting mathematical considerations.
Consider the Weighted Moving Average kernel. The weight function is fundamentally a slope: -x + length where x represents the position in the averaging window. The normalization constant is calculated by integrating (in our discrete case, summing) this slope across the window. What makes this implementation special is how it handles the fractional component - when the length isn't a whole number, the final period gets weighted proportionally to its fractional part.
For the Hull Moving Average, the mathematics become particularly intriguing. The standard HMA formula HMA = WMA(2*WMA(price, n/2) - WMA(price, n), sqrt(n)) is preserved, but now each WMA calculation operates in continuous space. This creates a smoother cascade of weights that better preserves the original intent of the Hull design - to reduce lag while maintaining smoothness.
The Simple Moving Average's treatment of fractional periods is perhaps the most elegant. For a length like 9.7, it weights the first 9 periods fully and the 10th period at 0.7 of its value. This creates a natural transition between integer periods that traditional implementations miss entirely.
The Gradient Mathematics
The trend gradient system employs normalized angular calculations to determine color transitions. By taking the arctangent of price changes normalized by ATR, we create a bounded space between 0 and 1 that represents trend intensity. The formula (arctan(Δprice/ATR) + 90°)/180° maps trend angles to this normalized space, allowing for smooth color transitions that respect market volatility context.
This mathematical framework creates a more theoretically sound foundation for moving averages, one that better reflects the continuous nature of price movement in financial markets. The implementation recognizes that time in markets isn't truly discrete - our sampling might be, but the underlying process we're trying to measure is continuous. By allowing for fractional periods, we're creating a better approximation of this continuous reality.
This floating-point moving average implementation offers tangible benefits for traders and analysts who need precise control over their indicators. The ability to fine-tune periods and create smooth transitions makes it particularly valuable for automated systems where moving average lengths are dynamically calculated from market conditions. The Hull Moving Average calculation now accurately reflects its mathematical formula while maintaining responsiveness, making it a practical choice for both systematic and discretionary trading approaches. Whether you're building dynamic indicators, optimizing existing strategies, or simply want more precise control over your moving averages, this implementation provides the mathematical foundation to do so effectively.
Milana Trades - SFP ( with Alert)Swing Failure Pattern (SFP) is a powerful trading tool that helps identify liquidity grabs around swing points and potential price reversals.
How I Use the Indicator
Timeframe Selection:
I primarily focus on higher timeframes (30M and above).
If you're trading within a session, you can use shorter timeframes like 5M or 15M.
Entry Point Identification:
After an SFP forms on a higher timeframe, I switch to a lower timeframe.
On the lower timeframe, I look for confirmation signals indicating a reversal, such as:
iFVG (Imbalance / Fair Value Gap)
MSS (Market Structure Shift)
Breaker Block
Take-Profit Placement:
The ideal target for taking profit is the opposite liquidity zone.
Telegram : milanatrades
Instagram : milana.muslimova_
[AcerX] Leverage, TP & Optimal TP CalculatorHow It Works
Inputs:
Portfolio Allocation (%): The percentage of your portfolio you're willing to risk on the trade.
Stop Loss (%): The stop loss distance below the entry price.
Taker Fee (%) and Maker Fee (%): The fees applied on entry and exit.
Calculations:
The script calculates the required "raw" leverage to risk 1% of your portfolio.
It floors the computed leverage to an integer ("effectiveLeverage").
If the computed leverage is less than 1, it shows an error message (and suggests the maximum allocation for at least 1× leverage).
Otherwise, it calculates the TP levels for target profits of 1.2%, 1.5%, and 2%, and an "Optimal TP" that nets a 1% profit after fees.
Display:
A table is drawn on the top right corner of your chart displaying the effective leverage, the TP levels, and an error message if applicable.
Simply add this script as a new indicator in TradingView, and adjust the inputs as needed.
Happy trading!
DAILY 6$This strategy is designed for XAUUSD (Gold) trading using a $130 account with a goal of making $12 daily while risking $6 per trade. It integrates multiple advanced trading techniques:
1. **Smart Money Concepts (SMC):** Identifies liquidity grabs and market structure shifts to detect potential reversals.
2. **ICT & Order Blocks:** Recognizes key areas where institutions place trades.
3. **Fibonacci Levels:** Uses 0.618 and 0.382 levels for optimal entries and exits.
4. **Fair Value Gaps (FVG):** Finds price inefficiencies that are likely to get filled.
5. **RSI & Volume Analysis:** Confirms momentum with RSI crossovers and high institutional volume.
6. **Wyckoff Accumulation/Distribution:** Ensures entries align with smart money movements.
7. **Session-Based Execution:** Trades only during London & New York sessions, avoiding high-impact news times.
It plots clear **buy/sell signals** and **entry/exit markers** to guide decision-making, helping traders capitalize on Gold’s price movements efficiently. 🚀
NTS Forex (NewStrategy) //@version=5
indicator('NTS Forex (NewStrategy) ',overlay = true)
//---------------------- Engulfing ------------------------------
//barcolor(open > close ? close > open ? close >= open ? close >= open ? close - open > open - close ? color.yellow :na :na : na : na : na)
//-----------------------------------------------------------------
var timeF = timeframe.period
getResolution(tf) =>
switch tf
'1' => '3'
'3' => '5'
'5' => '15'
'15'=> '15'
'30'=> '45'
'45'=> '60'
'60'=> '120'
'120'=> '180'
'180'=> '240'
'240'=> 'D'
'D'=> 'W'
'W'=> 'M'
var atrP = 10
var atrF = 5
res = getResolution(timeF)
trailType = input.string('modified', 'Trailtype', options= )
var int ATRPer = 0
var int ATRFac = 0
if timeF == '45'
ATRPer := 12
ATRFac := 12
else if timeF == '15'
ATRPer := 9
ATRFac := 9
else
ATRPer := 7
ATRFac := 7
manualFts = input.bool(false, title = 'use manual FTS')
ATRPeriod = manualFts ? input.int(15, title = 'ATRPeriod') : ATRPer
ATRFactor = manualFts ? input.int(15, title = 'ATRFactor') : ATRFac
var testTable = table.new(position=position.bottom_left, columns=2, rows=4, bgcolor=color.new(color.gray,50), border_width=2, border_color=color.black)
if barstate.islast
table.cell(testTable, 0, 0, 'Chart Time',text_color = color.red,text_size = size.small)
table.cell(testTable, 1, 0, timeF,text_color = color.green,text_size = size.small)
table.cell(testTable, 0, 1, 'NTS Time',text_color = color.red,text_size = size.small)
table.cell(testTable, 1, 1, res,text_color = color.green,text_size = size.small)
table.cell(testTable, 0, 2, 'ATRPeriod',text_color = color.red,text_size = size.small)
table.cell(testTable, 1, 2, str.tostring(ATRPeriod),text_color = color.green,text_size = size.small)
table.cell(testTable, 0, 3, 'ATRFactor',text_color = color.red,text_size = size.small)
table.cell(testTable, 1, 3, str.tostring(ATRFactor),text_color = color.green,text_size = size.small)
norm_o = request.security(ticker.new(syminfo.prefix, syminfo.ticker), res, open)
norm_h = request.security(ticker.new(syminfo.prefix, syminfo.ticker), res, high)
norm_l = request.security(ticker.new(syminfo.prefix, syminfo.ticker), res, low)
norm_c = request.security(ticker.new(syminfo.prefix, syminfo.ticker), res, close)
Wild_ma(_src, _malength) =>
_wild = 0.0
_wild := nz(_wild ) + (_src - nz(_wild )) / _malength
_wild
/////////// TRUE RANGE CALCULATIONS /////////////////
HiLo = math.min(norm_h - norm_l, 1.5 * nz(ta.sma(norm_h - norm_l, ATRPeriod)))
HRef = norm_l <= norm_h ? norm_h - norm_c : norm_h - norm_c - 0.5 * (norm_l - norm_h )
LRef = norm_h >= norm_l ? norm_c - norm_l : norm_c - norm_l - 0.5 * (norm_l - norm_h)
trueRange = trailType == 'modified' ? math.max(HiLo, HRef, LRef) : math.max(norm_h - norm_l, math.abs(norm_h - norm_c ), math.abs(norm_l - norm_c ))
loss = ATRFactor * Wild_ma(trueRange, ATRPeriod)
Up = norm_c - loss
Dn = norm_c + loss
TrendUp = Up
TrendDown = Dn
Trend = 1
TrendUp := norm_c > TrendUp ? math.max(Up, TrendUp ) : Up
TrendDown := norm_c < TrendDown ? math.min(Dn, TrendDown ) : Dn
Trend := norm_c > TrendDown ? 1 : norm_c < TrendUp ? -1 : nz(Trend , 1)
trail = Trend == 1 ? TrendUp : TrendDown
ex = 0.0
ex := ta.crossover(Trend, 0) ? norm_h : ta.crossunder(Trend, 0) ? norm_l : Trend == 1 ? math.max(ex , norm_h) : Trend == -1 ? math.min(ex , norm_l) : ex
state = Trend == 1 ? 'long' : 'short'
fib1Level = 61.8
fib2Level = 78.6
fib3Level = 88.6
f1 = ex + (trail - ex) * fib1Level / 100
f2 = ex + (trail - ex) * fib2Level / 100
f3 = ex + (trail - ex) * fib3Level / 100
l100 = trail + 0
Fib1 = plot(f1, 'Fib 1', style=plot.style_line, color=color.new(#ff7811, 0))
Fib2 = plot(f2, 'Fib 2', style=plot.style_line, color=color.new(color.gray, 0))
Fib3 = plot(f3, 'Fib 3', style=plot.style_line, color=color.new(#00ff3c, 0))
L100 = plot(l100, 'l100', style=plot.style_line, color=color.rgb(120, 123, 134))
fill(Fib3, L100, color=state == 'long' ? color.new(#00eaff, 12) : state == 'short' ? #ff5280e9 : na)
changcDir = ta.cross(f3,l100)
alertcondition(changcDir, title="Alert: FTS Direction Change", message="FTS has changed direction!")
l1 = state == "long" and ta.crossunder(norm_c, f1 )
l2 = state == "long" and ta.crossunder(norm_c, f2 )
l3 = state == "long" and ta.crossunder(norm_c, f3 )
s1 = state == "short" and ta.crossover(norm_c, f1 )
s2 = state == "short" and ta.crossover(norm_c, f2 )
s3 = state == "short" and ta.crossover(norm_c, f3 )
atr = ta.sma(trueRange, 14)
//////////// FIB ALERTS /////////////////////
alertcondition(l1, title = "cross over Fib1", message = "Price crossed below Fib1 level in long trend")
alertcondition(l2, title = "cross over Fib2", message = "Price crossed below Fib2 level in long trend")
alertcondition(l3, title = "cross over Fib3", message = "Price crossed below Fib3 level in long trend")
alertcondition(s1, title = "cross under Fib1", message = "Price crossed above Fib1 level in short trend")
alertcondition(s2, title = "cross under Fib2", message = "Price crossed above Fib2 level in short trend")
alertcondition(s3, title = "cross under Fib3", message = "Price crossed above Fib3 level in short trend")
alertcondition(fixnan(f1)!=fixnan(f1 ), title = "Stop Line Change", message = "Stop Line Change")
MgboIntroduction:
Crafted with TTrades, the Fractal Model empowers traders with a refined approach to Algorithmic Price Delivery. Specifically designed for those aiming to capitalize on expansive moves, this model anticipates momentum shifts, swing formations, orderflow continuations, as well as helping analysts highlight key areas to anticipate price deliveries.
Description:
> The Fractal Model° is rooted in the cyclical nature of price movements, where price alternates between large and small ranges. Expansion occurs when price moves consistently in one direction with momentum. By combining higher Timeframe closures with the confirmation of the change in state of delivery (CISD) on the lower Timeframe, the model reveals moments when expansion is poised to occur.> Thanks to TTrades' extensive research and years of studying these price behaviours, the Fractal Model° is a powerful, adaptive tool that seamlessly adjusts to any asset, market condition, or Timeframe, translating complex price action insights into an intuitive and responsive system.
The TTrades Fractal Model remains stable and non-repainting, offering traders reliable, unchanged levels within the given Time period. This tool is meticulously designed to support analysts focus on price action and dynamically adapt with each new Time period.
Key Features:
Custom History: Control the depth of your historical view by selecting the number of previous setups you’d like to analyze on your chart, from the current setup only (0) to a history of up to 40 setups. This feature allows you to tailor the chart to your specific charting style, whether you prefer to see past setups or the current view only.>Fractal Timeframe Pairings: This indicator enables users to observe and analyze lower Timeframe (LTF) movements within the structure of a higher Timeframe (HTF) candle. By examining LTF price action inside each HTF candle, analysts can gain insight into micro trends, structure shifts, and key entry points that may not be visible on the higher Timeframe alone. This approach provides a layered perspective, allowing analysts to closely monitoring how the LTF movements unfold within the overarching HTF context.
For a more dynamic and hands-off user experience, the Automatic feature autonomously adjusts the higher Timeframe pairing based the current chart Timeframe, ensuring accurate alignment with the Fractal Model, according to TTrades and his studies
Bias Selection: This feature allows analysts complete control over bias and setup detection, allowing one to view bullish or bearish formations exclusively, or opt for a neutral bias to monitor both directions. Easily toggle the bias filter on Fractal Model to align with your higher Timeframe market draw.
Time-Based Vertical Lines (GMT 8-9 AM)The Breakfast Breakout is a trading strategy that capitalizes on early morning market volatility, particularly around major financial centers' opening hours. It typically involves identifying a price range formed before a key session opens—such as the London session at 8 AM—and placing breakout trades when price moves beyond this range. Traders often use pending orders to capture momentum in either direction, aiming to profit from the increased liquidity and sharp price movements that occur during this time.
Nifty Support-Resistance Breakout (Nomaan Shaikh) The Nifty Support-Resistance Breakout Indicator is a powerful tool designed for traders to identify key support and resistance levels on the Nifty index chart and detect potential breakout opportunities. This indicator automates the process of plotting dynamic support and resistance levels based on historical price action and provides clear visual signals when a breakout occurs. It is ideal for traders who rely on technical analysis to make informed trading decisions.
RT + TL v12This is a script to populate gamma/vanna/charm levels from option greeks heatmaps in Vexly. Heavy levels indicate levels with highest confluence. TLU and TLL are upper and lower levels. The levels are posted everyday on their discord server.
Range Trader another tool in Vexly, the levels from which can be plotted as well. Just copy the headline from range trader into "RT Range" and the modes into "RT mode"
Example:
RT Mode will take this string as input and plot the levels:
upper_mode
6049.037
mp_mode
6035.407
lower_mode
6021.776
RT Range will take this string as input and plot the levels:
6044.893 - 6017.652
Hope this helps
QoQ Economic & Financial Indicator ChangesA straightforward indicator for analyzing quarter-over-quarter (QoQ) percentage changes in economic and financial data series. Perfect for visualizing dynamic changes in:
Economic Indicators (GDP, House Price Indices, Employment Figures)
Company Financial Metrics (Revenue, EPS, Operating Margins)
Balance Sheet Items (Assets, Liabilities, Equity)
Cash Flow Statement Components
Other Quarterly Economic & Financial Data
Features:
Automatically calculates QoQ percentage changes
Color-coded visualization (green for positive, red for negative changes)
Displays exact percentage values
Includes adjustable scale factor for different data series
Zero line reference for easy trend identification
EUR/USD vs USD/CHF SpreadA typical Pine Script for spread trading would include:
Fetching Data: Getting the real-time price of EUR/USD and USD/CHF.
Calculating the Synthetic EUR/CHF Price: Since EUR/USD * USD/CHF ≈ EUR/CHF, we use this relation to analyze deviations.
Computing the Spread: Taking the difference between EUR/USD and the synthetic EUR/CHF price.
Z-Score Normalization: Measuring how far the spread deviates from the mean (Mean Reversion).
Overlay and Visuals: Plotting the spread and key levels to visualize trading signals.
TOTAL3/BTC This Pine Script™ code, named "TOTAL3/BTC with Arrow," is designed for cryptocurrency analysis on TradingView.
This script essentially provides a visual tool for traders to gauge when altcoins might be gaining or losing ground relative to Bitcoin through moving average analysis and color-coded trend indication.
Intention was to help the community with a script based on classic TA only.
Use it with SASDv2r indicator.
Feel free to make it better. If you did so, please let me know.
Main elements:
Data Fetching: It retrieves market cap data for all cryptocurrencies excluding Bitcoin and Ethereum (TOTAL3) and for Bitcoin (BTC).
Ratio Calculation: The script calculates the ratio of TOTAL3 to BTC market caps, which indicates how altcoins (excluding ETH) are performing relative to Bitcoin.
Plotting the Ratio: This ratio is plotted on the chart with a blue line, allowing traders to see the relative performance visually.
Moving Averages: Two Simple Moving Averages (SMA) are calculated for this ratio, one for 20 periods (ma20) and another for 50 periods (ma50), though these are not plotted in the current version of the code.
Reference Lines: Horizontal lines are added at ratios of 0.3 and 0.8 to serve as visual equilibrium points or thresholds for analysis.
Complex Moving Average: The script uses constants (len, len2, cc, smoothe) from another script, suggesting it's adapting or simplifying another's logic for multi-timeframe analysis.
Average Calculation: Two SMAs (avg and avg2) are computed using the constants defined, focusing on different lengths for trend analysis.
Direction Determination: It checks if the moving average is trending up or down by comparing the current value with its value smoothe bars earlier.
Color Coding: The color of the plotted moving average changes based on its direction (lime for up, red for down, aqua if no clear direction), aiding in quick visual interpretation of trends.
Plotting: Finally, the script plots this multi-timeframe moving average with a dynamic color to reflect the current market trend of the TOTAL3/BTC ratio, with a thicker line for visibility.
