Anchor ZonesL.A. Little, who wrote two books on trend trading, explained a key timing concept called anchor zones which was used, within his trading system, to enter and exit the market at appropriate times.
Anchor zones are formed from anchor bars. An anchor bar is a bar that has one or more of these components: wide range, high volume or gaps. For this script we're going to require two or more of the components. When an anchor bar forms, we'll note the high and low of the bar and draw a zone across time as prices develops. For this script, we'll also note the open and close of the candle to hint at other levels of support or resistance. The boundaries of these zones can act as support or resistance, but they also mark out the areas where price can often get trapped.
A breakout from these zones on high volume can suggest the beginning of a new trend. In general, anchor zones are a good compliment to price action strategies. For more information on how to use these, refer to L.A. Little's books.
References
onlinelibrary.wiley.com
www.tradingsetupsreview.com
Want to Learn?
If you'd like the opportunity to learn Pine but you have difficulty finding resources to guide you, take a look at this rudimentary list: docs.google.com
The list will be updated in the future as more people share the resources that have helped, or continue to help, them. Follow me on Twitter to keep up-to-date with the growing list of resources.
Suggestions or Questions?
Don't even kinda hesitate to forward them to me. My (metaphorical) door is always open.
Komut dosyalarını "META股价历史数据" için ara
Pocket PivotsPocket Pivots are described in the book "Trade like an O'Neil Discipline" by Dr. Chris Kacher and Gil Morales. There’s no exact definition of Pocket Pivots, but there is an exact definition for the volume signature: The volume should be higher than the largest down volume of the last 10 trading days.
This is a modification of Pocket Pivots. We use the level where the Pocket Pivot occurred and draw a zone across the chart until the criteria for another Pocket Pivot is met again. This way we can use them as support/resistance zones. Instead of the volume being higher than the volume for each of the previous periods, we just use an SMA of the volume and make sure the volume on the final candle is higher than the average for the previous periods. Last but not least, we have the possibility to draw support/resistance levels off the back of different counts. Seven-count for hyper-aggressive pocket pivots, eight-count for aggressive, nine for measured and ten for passive.
Hyper-aggressive Pocket Pivots
Aggressive Pocket Pivots
Measured Pocket Pivots
Passive Pocket Pivots
All
Using "All" to see all the pivots can be messy, but the confluence of support/resistance is more than helpful for defining truly important levels.
People have created a methodology/rules for buying and selling with Pivot Points, but as I understand there's no general consensus on their application, so please do some research before you decide to use them in your trading.
References
www.chartmill.com
www.mypivots.com
Want to Learn?
If you'd like the opportunity to learn Pine but you have difficulty finding resources to guide you, take a look at this rudimentary list: docs.google.com
The list will be updated in the future as more people share the resources that have helped, or continue to help, them. Follow me on Twitter to keep up-to-date with the growing list of resources.
Suggestions or Questions?
Don't even kinda hesitate to forward them to me. My (metaphorical) door is always open.
Yang & Zheng Extension of Garman & KlassFirst off, a huge thank you to the following people:
theheirophant: www.tradingview.com
alexgrover: www.tradingview.com
NGBaltic: www.tradingview.com
This is the Yang & Zhang extension of Garman & Klass. The equation was modified to include the logarithm of the open price divided by the preceding close price. As a result, this function uses the open, high, low and close prices to estimate volatility. This modification allows the volatility estimator to account for the opening jumps, but as the original function, it assumes that the underlying follows a Brownian motion with zero drift (the historical mean return should be equal to zero). This estimator tends to overestimate the volatility when the drift is different from zero, however, for a zero drift motion, this estimator has an efficiency of eight times the classic close-to-close estimator (standard deviation).
This script allows you to transform the volatility reading. The intention of this is to be able to compare volatility across different assets and timeframes. Having a relative reading of volatility also allows you to better gauge volatility within the context of current market conditions.
For the signal lie I chose a repulsion moving average to remove choppy crossovers of the estimator and the signal. This may have been a mistake, so in the near-future I might update so that the MA can be selected. Let me know if you have any opinions either way.
References
www.rdocumentation.org
www.quantshare.com
Want to Learn?
If you'd like the opportunity to learn Pine but you have difficulty finding resources to guide you, take a look at this rudimentary list: docs.google.com
The list will be updated in the future as more people share the resources that have helped, or continue to help, them. Follow me on Twitter to keep up-to-date with the growing list of resources.
Suggestions or Questions?
Don't even kinda hesitate to forward them to me. My (metaphorical) door is always open.
Parkinson Historical VolatilityFirst off, a huge thank you to the following people:
theheirophant: www.tradingview.com
alexgrover: www.tradingview.com
NGBaltic: www.tradingview.com
The Parkinson Historical Volatility (PHV), developed in 1980 by the physicist Michael Parkinson, aims to estimate the volatility of returns for a random walk using the high and low in any particular period. An important use of the PHV is the assessment of the distribution prices during the day as well as a better understanding of the market dynamics. Comparing the PHV and a periodically sampled volatility helps traders understand the tendency towards mean reversion in the market as well as the distribution of stop-losses.
This script allows you to transform the volatility reading. The intention of this is to be able to compare volatility across different assets and timeframes. Having a relative reading of volatility also allows you to better gauge volatility within the context of current market conditions.
For the signal lie I chose a repulsion moving average to remove choppy crossovers of the estimator and the signal. This may have been a mistake, so in the near-future I might update so that the MA can be selected. Let me know if you have any opinions either way.
References
www.rdocumentation.org
www.ivolatility.com
Want to Learn?
If you'd like the opportunity to learn Pine but you have difficulty finding resources to guide you, take a look at this rudimentary list: docs.google.com
The list will be updated in the future as more people share the resources that have helped, or continue to help, them. Follow me on Twitter to keep up-to-date with the growing list of resources.
Suggestions or Questions?
Don't even kinda hesitate to forward them to me. My (metaphorical) door is always open.
Rogers & Satchell Volatility EstimationFirst off, a huge thank you to the following people:
theheirophant: www.tradingview.com
alexgrover: www.tradingview.com
NGBaltic: www.tradingview.com
The Rogers & Satchell function is a volatility estimator that outperforms other estimators when the underlying follows a geometric Brownian motion with a drift (historical data mean returns different from zero). As a result, it provides a better volatility estimation when the underlying is trending. However, the Rogers & Satchell estimator does not account for jumps in price (gaps). It assumes no opening jump. The function uses the open, close, high, and low price series in its calculation and it has only one parameter, which is the period to use to estimate the volatility.
This script allows you to transform the volatility reading. The intention of this is to be able to compare volatility across different assets and timeframes. Having a relative reading of volatility also allows you to better gauge volatility within the context of current market conditions.
For the signal lie I chose a repulsion moving average to remove choppy crossovers of the estimator and the signal. This may have been a mistake, so in the near-future I might update so that the MA can be selected. Let me know if you have any opinions either way.
Want to Learn?
If you'd like the opportunity to learn Pine but you have difficulty finding resources to guide you, take a look at this rudimentary list: docs.google.com
The list will be updated in the future as more people share the resources that have helped, or continue to help, them. Follow me on Twitter to keep up-to-date with the growing list of resources.
Suggestions or Questions?
Don't even kinda hesitate to forward them to me. My (metaphorical) door is always open.
Godmode 4.0.2 [Supply/Demand]First off, a huge thank you to the following people:
LEGION:
LazyBear: www.tradingview.com
xSilas: www.tradingview.com
Ni6HTH4awK: www.tradingview.com
sco77m4r7and:
SNOW_CITY: www.tradingview.com
oh92: www.tradingview.com
alexgrover: www.tradingview.com
cI8DH: www.tradingview.com
DonovanWall: www.tradingview.com
shtcoinr: www.tradingview.com
This is the third iteration of Godmode. This time I borrowed the method used by shtcoinr to render supply/demand, resistance and support zones. The idea here is to input the appropriate benchmark tickerid to the asset class you're trading and to paint zones according to the price activity of the selected tickerid. This works very well trying to paint meaningful zones against noisy stocks, currencies, commodities etc. Use a correlation coefficient to determine the best benchmark for your asset class.
Want to Learn?
If you'd like the opportunity to learn Pine but you have difficulty finding resources to guide you, take a look at this rudimentary list: docs.google.com
The list will be updated in the future as more people share the resources that have helped, or continue to help, them. Follow me on Twitter to keep up-to-date with the growing list of resources.
Suggestions or Questions?
Don't even kinda hesitate to forward them to me. My (metaphorical) door is always open.
Function for Least Squares Moving AverageThank you to alexgrover for putting me wide to this, after putting up with long conversations and stupid questions. Follow him and behold: www.tradingview.com
What is this?
This is simply the function for a Least Squares Moving Average. You can render this on the chart by using the linreg() function in Pine.
Personally I like to use the slope of the LSMA to help determine what direction to take a trade in, but I'm sure there are other, more exotic ways of using it and, if you know how to get your fingers dirty with Pine, you can create more exotic versions of it by modifying the function provided.
Want to learn?
If you'd like the opportunity to learn Pine but you have difficulty finding resources to guide you, take a look at this rudimentary list: docs.google.com
The list will be updated in the future as more people share the resources that have helped, or continue to help, them. Follow me on Twitter to keep up-to-date with the growing list of resources.
Suggestions or Questions?
Don't even kinda hesitate to forward them to me. My (metaphorical) door is always open.
Godmode 4.0.1 [Correlator]First off, a huge thank you to the following people:
@LEGION:
@LazyBear: www.tradingview.com
@xSilas: www.tradingview.com
@Ni6HTH4awK: www.tradingview.com
@sco77m4r7and:
@SNOW_CITY: www.tradingview.com
@oh92: www.tradingview.com
@alexgrover: www.tradingview.com
@cI8DH: www.tradingview.com
@DonovanWall: www.tradingview.com
This is my second iteration of Godmode. This time I allowed the possibility to correlate two benchmarks against one another, thereby giving you twice the signals (once there's a strong correlation between the two, inverse or otherwise). That aside, there are no changes to this indicator that the first iteration doesn't have:
There are still more iterations planned, but if you guys have any ideas or wishes regarding what direction I go, then please let me know.
Want to Learn?
If you'd like the opportunity to learn Pine but you have difficulty finding resources to guide you, take a look at this rudimentary list: docs.google.com
The list will be updated in the future as more people share the resources that have helped, or continue to help, them. Follow me on Twitter to keep up-to-date with the growing list of resources as well as any other scripts I publish.
Suggestions or Questions?
Don't even kinda hesitate to forward them to me. My (metaphorical) door is always open.
Godmode 4.0.0 [Oscillator]First off, a huge thank you to the following people:
LEGION:
LazyBear: www.tradingview.com
xSilas: www.tradingview.com
Ni6HTH4awK: www.tradingview.com
sco77m4r7and:
SNOW_CITY: www.tradingview.com
oh92: www.tradingview.com
alexgrover: www.tradingview.com
cI8DH: www.tradingview.com
DonovanWall: www.tradingview.com
Since I've been on TradingView I've become somewhat enthralled by Godmode and the collective work that goes in to it, so I decided to publish my own iteration, building off the ideas already present. (This is a great way to get familiar with Pine by the way, just in case there are any beginners reading this)
Changes
The first change I made was to allow the user to select whatever tickerid they wanted as a benchmark. If trading XBTUSD on BitMEX for example, the indicator will react to exchange-specific activity, which means it will respond to all the little whipsaws, whipsaws that can be especially present on a futures exchange. By typing CRYPTOCAP:BTC or CRYPTOCAP:TOTAL we endeavor to remove noise. It can also signal earlier. Less noise and less lag. Another idea would be to choose a benchmark that has a strong inverse relationship with the asset you're trading: try CRYPTOCAP:USDT as the benchmark against BTC to see what I mean.
I also added the ability to smooth the plot, yet again removing noise but adding considerable lag.
The linear regression of the wave-trend is calculated in place of the EMA. This is plotted as columns with the midline (50) as the base. This is just calculating the slope of the wave-trend and can signal a weakening trend before a reversal takes place.
Using cI8DH's True RSI script () as inspiration, I added a function for calculating the True TSI in an attempt to remove any bullish bias. Funnily enough, when I tried to do the same with the RSI I had some problems. I'll try to resolve this in the coming weeks.
Made slight changes to the aesthetics. Tried to bring the two main plots alive by making their bold, opaque colors stand off the subtle tones in the background.
To Do List
1. I would like to sort out the issue with the True RSI.
2. When the plots are smoothed, there's an issue with the green 'Caution!' dots appearing in the lower half of the indicator.
3. I'd like to adjust the code so that if the 'Benchmark' box is empty, that it will automatically register the current tickerid as the 'Benchmark'.
If anyone has any suggestions on other fixes or how to apply the fixes mentioned by me, please don't hesitate to reach out to me here or through other media platforms.
Want to Learn?
If you'd like the opportunity to learn Pine but you have difficulty finding resources to guide you, take a look at this rudimentary list: docs.google.com
The list will be updated in the future as more people share the resources that have helped, or continue to help, them. Follow me on Twitter to keep up-to-date with the growing list of resources.
Suggestions or Questions?
Don't even kinda hesitate to forward them to me. My (metaphorical) door is always open.
BITMEX:XBTUSD
CRYPTOCAP:BTC
CRYPTOCAP:TOTAL
CRYPTOCAP:USDT.D
BO Swing Finder R0.6 by JustUncleLThis indicator alert study attempts to detect confirmed Swing points. It uses Bollinger Band centre line crosses as the main signal. The main detection occurs by looking for the first BB centre line cross that was initiated from outside the Bollinger Channel (alternatively KC channel can be used).
The optional HullMA (any any other MA pair) are used to confirm the swing direction. The indicator also plots the two KitKat Support and Resistance lines with optional High/Low labelling on KitKat1 lines.
This indicator tool is suitable for any time frame and can be traded with Binary Option (even 1min) orders (2-3 candle expiry) or as Forex trade orders. It is suitable for Currencies, Cryptocurrencies and Metals. May also be useful on other markets as well.
The MA filtering options, each MA line can be a different type, with an optional offset:
SMA = Simple Moving Average.
EMA = Exponential Moving Average.
WMA = Weighted Moving Average
VWMA = Volume Weighted Moving Average
SMMA = Smoothed Simple Moving Average.
DEMA = Double Exponential Moving Average
TEMA = Triple Exponential Moving Average.
HullMA = Hull Moving Average, fast moving MA.
SSMA = Ehlers Super Smoother Moving average, similar results to HullMA.
ZEMA = Near Zero Lag Exponential Moving Average.
TMA = Triangular (smoothed) Simple Moving Average.
NOTE: The signal calculations do occur on the current candle, so the state of the signal may re-build until the current candle is closed. I have designed the script to behave this way on purpose. This gives traders the option of
preparing their trade early or even taking the trade early if they want. Otherwise the trader can be more conservative and wait for signal candle to close, to give them a confirmed signal. (This is NOT re-painting as the historical signal states are fixed and will not change, unless you change some setup options.)
Hints:
1) As with all indicator and alerting tools, not all signals will yield a tradable successful swing. You need to apply you own analysis on each signal to determine the probability of success.
2) When using the MA to filter the signals you should use it for two types of filtering:
Supportive that confirm swing like fast moving MAs with fairly short lengths, eg HullMA(21,25).
Long Term Direction with smoother longer length MAs like SMMA(180,220) to show up swings back into direction of the longer term trends.
Inspiration: @Lyiness
References:
Momentum VMA KITKAT CROSS v2.1 by vdubus (- Vdubus_Channel www.vdubus.co.uk)
Bill Williams. Awesome Oscillator (AO) Backtest This indicator is based on Bill Williams` recommendations from his book
"New Trading Dimensions". We recommend this book to you as most useful reading.
The wisdom, technical expertise, and skillful teaching style of Williams make
it a truly revolutionary-level source. A must-have new book for stock and
commodity traders.
The 1st 2 chapters are somewhat of ramble where the author describes the
"metaphysics" of trading. Still some good ideas are offered. The book references
chaos theory, and leaves it up to the reader to believe whether "supercomputers"
were used in formulating the various trading methods (the author wants to come across
as an applied mathemetician, but he sure looks like a stock trader). There isn't any
obvious connection with Chaos Theory - despite of the weak link between the title and
content, the trading methodologies do work. Most readers think the author's systems to
be a perfect filter and trigger for a short term trading system. He states a goal of
10%/month, but when these filters & axioms are correctly combined with a good momentum
system, much more is a probable result.
There's better written & more informative books out there for less money, but this author
does have the "Holy Grail" of stock trading. A set of filters, axioms, and methods which are
the "missing link" for any trading system which is based upon conventional indicators.
This indicator plots the oscillator as a histogram where periods fit for buying are marked
as blue, and periods fit for selling as red. If the current value of AC (Awesome Oscillator)
is over the previous, the period is deemed fit for buying and the indicator is marked blue.
If the AC values is not over the previous, the period is deemed fir for selling and the indicator
is marked red.
You can change long to short in the Input Settings
Please, use it only for learning or paper trading. Do not for real trading.
Bill Williams. Awesome Oscillator (AO) Signal Line This indicator is based on Bill Williams` recommendations from his book
"New Trading Dimensions". We recommend this book to you as most useful reading.
The wisdom, technical expertise, and skillful teaching style of Williams make
it a truly revolutionary-level source. A must-have new book for stock and
commodity traders.
The 1st 2 chapters are somewhat of ramble where the author describes the
"metaphysics" of trading. Still some good ideas are offered. The book references
chaos theory, and leaves it up to the reader to believe whether "supercomputers"
were used in formulating the various trading methods (the author wants to come across
as an applied mathemetician, but he sure looks like a stock trader). There isn't any
obvious connection with Chaos Theory - despite of the weak link between the title and
content, the trading methodologies do work. Most readers think the author's systems to
be a perfect filter and trigger for a short term trading system. He states a goal of
10%/month, but when these filters & axioms are correctly combined with a good momentum
system, much more is a probable result.
There's better written & more informative books out there for less money, but this author
does have the "Holy Grail" of stock trading. A set of filters, axioms, and methods which are
the "missing link" for any trading system which is based upon conventional indicators.
This indicator plots the oscillator as a histogram where periods fit for buying are marked
as blue, and periods fit for selling as red. If the current value of AC (Awesome Oscillator)
is over the previous, the period is deemed fit for buying and the indicator is marked blue.
If the AC values is not over the previous, the period is deemed fir for selling and the indicator
is marked red.
Strategy Bill Williams. Awesome Oscillator (AO) This indicator is based on Bill Williams` recommendations from his book
"New Trading Dimensions". We recommend this book to you as most useful reading.
The wisdom, technical expertise, and skillful teaching style of Williams make
it a truly revolutionary-level source. A must-have new book for stock and
commodity traders.
The 1st 2 chapters are somewhat of ramble where the author describes the
"metaphysics" of trading. Still some good ideas are offered. The book references
chaos theory, and leaves it up to the reader to believe whether "supercomputers"
were used in formulating the various trading methods (the author wants to come across
as an applied mathemetician, but he sure looks like a stock trader). There isn't any
obvious connection with Chaos Theory - despite of the weak link between the title and
content, the trading methodologies do work. Most readers think the author's systems to
be a perfect filter and trigger for a short term trading system. He states a goal of
10%/month, but when these filters & axioms are correctly combined with a good momentum
system, much more is a probable result.
There's better written & more informative books out there for less money, but this author
does have the "Holy Grail" of stock trading. A set of filters, axioms, and methods which are
the "missing link" for any trading system which is based upon conventional indicators.
This indicator plots the oscillator as a histogram where periods fit for buying are marked
as blue, and periods fit for selling as red. If the current value of AC (Awesome Oscillator)
is over the previous, the period is deemed fit for buying and the indicator is marked blue.
If the AC values is not over the previous, the period is deemed fir for selling and the indicator
is marked red.
Bill Williams. Awesome Oscillator (AO) Hi
Let me introduce my Bill Williams. Awesome Oscillator (AO) script.
This indicator is based on Bill Williams` recommendations from his book
"New Trading Dimensions". We recommend this book to you as most useful reading.
The wisdom, technical expertise, and skillful teaching style of Williams make
it a truly revolutionary-level source. A must-have new book for stock and
commodity traders.
The 1st 2 chapters are somewhat of ramble where the author describes the
"metaphysics" of trading. Still some good ideas are offered. The book references
chaos theory, and leaves it up to the reader to believe whether "supercomputers"
were used in formulating the various trading methods (the author wants to come across
as an applied mathemetician, but he sure looks like a stock trader). There isn't any
obvious connection with Chaos Theory - despite of the weak link between the title and
content, the trading methodologies do work. Most readers think the author's systems to
be a perfect filter and trigger for a short term trading system. He states a goal of
10%/month, but when these filters & axioms are correctly combined with a good momentum
system, much more is a probable result.
There's better written & more informative books out there for less money, but this author
does have the "Holy Grail" of stock trading. A set of filters, axioms, and methods which are
the "missing link" for any trading system which is based upon conventional indicators.
This indicator plots the oscillator as a histogram where periods fit for buying are marked
as blue, and periods fit for selling as red. If the current value of AC (Awesome Oscillator)
is over the previous, the period is deemed fit for buying and the indicator is marked blue.
If the AC values is not over the previous, the period is deemed fir for selling and the indicator
is marked red.
Options Strategy V1.3📈 Options Strategy V1.3 — EMA Crossover + RSI + ATR + Opening Range
Overview:
This strategy is designed for short-term directional trades on large-cap stocks or ETFs, especially when trading options. It combines classic trend-following signals with momentum confirmation, volatility-based risk management, and session timing filters to help identify high-probability entries with predefined stop-loss and profit targets.
🔍 Strategy Components:
EMA Crossover (Fast/Slow)
Entry signals are triggered by the crossover of a short EMA above or below a long EMA — a traditional trend-following method to detect shifts in momentum.
RSI Filter
RSI confirms the signal by avoiding entries in overbought/oversold zones unless certain momentum conditions are met.
Long entry requires RSI ≥ Long Threshold
Short entry requires RSI ≤ Short Threshold
ATR-Based SL & TP
Stop-loss is set dynamically as a multiple of ATR below (long) or above (short) the entry price.
Take-profit is placed as a ratio (TP/SL) of the stop distance, ensuring consistent reward/risk structure.
Opening Range Filter (Optional)
If enabled, the strategy only triggers trades after price breaks out of the 09:30–09:45 EST range, ensuring participation in directional moves.
Session Filters
No trades from 04:00 to 09:30 and from 16:00 to 20:00 EST, avoiding low-liquidity periods.
All open trades are closed at 15:55 EST, to avoid overnight risk or expiration issues for options.
⚙️ Built-in Presets:
You can choose one of the built-in ticker-specific presets for optimal conditions:
Ticker EMAs RSI (Long/Short) ATR SL×ATR TP/SL
SPY 8/28 56 / 26 14 1.4× 4.0×
TSLA 23/27 56 / 33 13 1.4× 3.6×
AAPL 6/13 61 / 26 23 1.4× 2.1×
MSFT 25/32 54 / 26 14 1.2× 2.2×
META 25/32 53 / 26 17 1.8× 2.3×
AMZN 28/32 55 / 25 16 1.8× 2.3×
You can also choose "Custom" to fully configure all parameters to your own market and strategy preferences.
📌 Best Use Case:
This strategy is especially suited for intraday options trading, where timing and risk control are critical. It works best on liquid tickers with strong trends or clear breakout behavior.
Magnificent 7 OscillatorThe Magnificent 7 Oscillator is a sophisticated momentum-based technical indicator designed to analyze the collective performance of the seven largest technology companies in the U.S. stock market (Apple, Microsoft, Alphabet, Amazon, NVIDIA, Tesla, and Meta). This indicator incorporates established momentum factor research and provides three distinct analytical modes: absolute momentum tracking, equal-weighted market comparison, and relative performance analysis. The tool integrates five different oscillator methodologies and includes advanced breadth analysis capabilities.
Theoretical Foundation
Momentum Factor Research
The indicator's foundation rests on seminal momentum research in financial markets. Jegadeesh and Titman (1993) demonstrated that stocks with strong price performance over 3-12 month periods tend to continue outperforming in subsequent periods¹. This momentum effect was later incorporated into formal factor models by Carhart (1997), who extended the Fama-French three-factor model to include a momentum factor (UMD - Up Minus Down)².
The momentum calculation methodology follows the academic standard:
Momentum(t) = / P(t-n) × 100
Where P(t) is the current price and n is the lookback period.
The focus on the "Magnificent 7" stocks reflects the increasing market concentration observed in recent years. Fama and French (2015) noted that a small number of large-cap stocks can drive significant market movements due to their substantial index weights³. The combined market capitalization of these seven companies often exceeds 25% of the total S&P 500, making their collective momentum a critical market indicator.
Indicator Architecture
Core Components
1. Data Collection and Processing
The indicator employs robust data collection with error handling for missing or invalid security data. Each stock's momentum is calculated independently using the specified lookback period (default: 14 periods).
2. Composite Oscillator Calculation
Following Fama-French factor construction methodology, the indicator offers two weighting schemes:
- Equal Weight: Each active stock receives identical weighting (1/n)
- Market Cap Weight: Reserved for future enhancement
3. Oscillator Transformation Functions
The indicator provides five distinct oscillator types, each with established technical analysis foundations:
a) Momentum Oscillator (Default)
- Pure rate-of-change calculation
- Centered around zero
- Direct implementation of Jegadeesh & Titman methodology
b) RSI (Relative Strength Index)
- Wilder's (1978) relative strength methodology
- Transformed to center around zero for consistency
- Scale: -50 to +50
c) Stochastic Oscillator
- George Lane's %K methodology
- Measures current position within recent range
- Transformed to center around zero
d) Williams %R
- Larry Williams' range-based oscillator
- Inverse stochastic calculation
- Adjusted for zero-centered display
e) CCI (Commodity Channel Index)
- Donald Lambert's mean reversion indicator
- Measures deviation from moving average
- Scaled for optimal visualization
Operational Modes
Mode 1: Magnificent 7 Analysis
Tracks the collective momentum of the seven constituent stocks. This mode is optimal for:
- Technology sector analysis
- Growth stock momentum assessment
- Large-cap performance tracking
Mode 2: S&P 500 Equal Weight Comparison
Analyzes momentum using an equal-weighted S&P 500 reference (typically RSP ETF). This mode provides:
- Broader market momentum context
- Size-neutral market analysis
- Comparison baseline for relative performance
Mode 3: Relative Performance Analysis
Calculates the momentum differential between Magnificent 7 and S&P 500 Equal Weight. This mode enables:
- Sector rotation analysis
- Style factor assessment (Growth vs. Value)
- Relative strength identification
Formula: Relative Performance = MAG7_Momentum - SP500EW_Momentum
Signal Generation and Thresholds
Signal Classification
The indicator generates three signal states:
- Bullish: Oscillator > Upper Threshold (default: +2.0%)
- Bearish: Oscillator < Lower Threshold (default: -2.0%)
- Neutral: Oscillator between thresholds
Relative Performance Signals
In relative performance mode, specialized thresholds apply:
- Outperformance: Relative momentum > +1.0%
- Underperformance: Relative momentum < -1.0%
Alert System
Comprehensive alert conditions include:
- Threshold crossovers (bullish/bearish signals)
- Zero-line crosses (momentum direction changes)
- Relative performance shifts
- Breadth Analysis Component
The indicator incorporates market breadth analysis, calculating the percentage of constituent stocks with positive momentum. This feature provides insights into:
- Strong Breadth (>60%): Broad-based momentum
- Weak Breadth (<40%): Narrow momentum leadership
- Mixed Breadth (40-60%): Neutral momentum distribution
Visual Design and User Interface
Theme-Adaptive Display
The indicator automatically adjusts color schemes for dark and light chart themes, ensuring optimal visibility across different user preferences.
Professional Data Table
A comprehensive data table displays:
- Current oscillator value and percentage
- Active mode and oscillator type
- Signal status and strength
- Component breakdowns (in relative performance mode)
- Breadth percentage
- Active threshold levels
Custom Color Options
Users can override default colors with custom selections for:
- Neutral conditions (default: Material Blue)
- Bullish signals (default: Material Green)
- Bearish signals (default: Material Red)
Practical Applications
Portfolio Management
- Sector Allocation: Use relative performance mode to time technology sector exposure
- Risk Management: Monitor breadth deterioration as early warning signal
- Entry/Exit Timing: Utilize threshold crossovers for position sizing decisions
Market Analysis
- Trend Identification: Zero-line crosses indicate momentum regime changes
- Divergence Analysis: Compare MAG7 performance against broader market
- Volatility Assessment: Oscillator range and frequency provide volatility insights
Strategy Development
- Factor Timing: Implement growth factor timing strategies
- Momentum Strategies: Develop systematic momentum-based approaches
- Risk Parity: Use breadth metrics for risk-adjusted portfolio construction
Configuration Guidelines
Parameter Selection
- Momentum Period (5-100): Shorter periods (5-20) for tactical analysis, longer periods (50-100) for strategic assessment
- Smoothing Period (1-50): Higher values reduce noise but increase lag
- Thresholds: Adjust based on historical volatility and strategy requirements
Timeframe Considerations
- Daily Charts: Optimal for swing trading and medium-term analysis
- Weekly Charts: Suitable for long-term trend analysis
- Intraday Charts: Useful for short-term tactical decisions
Limitations and Considerations
Market Concentration Risk
The indicator's focus on seven stocks creates concentration risk. During periods of significant rotation away from large-cap technology stocks, the indicator may not represent broader market conditions.
Momentum Persistence
While momentum effects are well-documented, they are not permanent. Jegadeesh and Titman (1993) noted momentum reversal effects over longer time horizons (2-5 years).
Correlation Dynamics
During market stress, correlations among the constituent stocks may increase, reducing the diversification benefits and potentially amplifying signal intensity.
Performance Metrics and Backtesting
The indicator includes hidden plots for comprehensive backtesting:
- Individual stock momentum values
- Composite breadth percentage
- S&P 500 Equal Weight momentum
- Relative performance calculations
These metrics enable quantitative strategy development and historical performance analysis.
References
¹Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91.
Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82.
Fama, E. F., & French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1), 1-22.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
CANX MA Crossover© CanxStixTrader
Moving average crossover systems measure drift in the market. They are great strategies for time-limited traders. KEEP IT SIMPLE
This strategy works both for buys and sells using the reaction line to guide your position against the reactions.
HOW TO USE THE INDICATOR
1) Choose your market and timeframe.
2) Choose the length.
3) Choose the multiplier.
4) Choose if the strategy is long-only or bidirectional (longs & shorts).
TIPS
The strategy works best in bullish markets as that is the primary direction that market such as stocks, indexes and metals like to move.
- Increase the multiplier to reduce whipsaws
- Increase the length to take fewer trades
- Decrease the length to take more trades
- Try a Long-Only strategy to see if that performs better.
The base set up when you load the indicator is for the 1 minute chart on gold. We found that it also works well on the US Indexes. For other markets you may need to change the length and multiplier to suit the market and back test its results.
(FVC) Fractal Volatility Compression (DAFE) (FVC) Fractal Volatility Compression
See the Market’s Volatility DNA.
The Fractal Volatility Compression (FVC) is a next-generation tool for traders who want to see volatility compression and expansion across multiple timeframes and volatility engines—not just price, but the very structure of volatility itself.
What Makes FVC Unique?
Dual-Engine Volatility:
Plots both classic price-based (Stdev) and meta-volatility (VoVix) compression/expansion, so you can see when the market is “coiling” or “exploding” on multiple levels.
Fractal, Multi-Timeframe Analysis:
Measures volatility on short, medium, and long timeframes, then normalizes each as a Z-score. The result: a true “coiled spring” detector that works on any asset, any timeframe.
Threshold Lines You Control:
Yellow center line: Your neutral baseline.
Green compression line: When crossed, the market is “spring-loading.”
Red expansion line: When crossed, volatility is breaking out.
All lines are solid, clean, and end before the dashboard for a professional look.
Agreement Fill: When both engines agree (both above or both below the center line), a bright fill highlights the zone—red for expansion, green for compression.
Signature Dashboard & Info Line:
Dashboard (right-middle) shows all Z-scores and FVC values, color-coded for instant clarity.
Compact info label for mobile or minimalist users.
Inputs & Customization
Thresholds: Set the yellow, green, and red lines to match your asset, timeframe, and risk tolerance.
Timeframes & Lengths: Tune the short, medium, and long volatility windows for your style.
Toggle Lines: Show/hide Stdev or VoVix FVC lines independently.
Dashboard & Info Line: Toggle for your workflow and screen size.
How to Use
Compression (below green): Market is “coiling” across timeframes—watch for explosive moves.
Expansion (above red): Volatility is breaking out—expect regime shifts or trend acceleration.
Agreement Fill: When both lines agree, the signal is strongest.
Not a Buy/Sell Signal: These are regime and structure signals—combine with your own
strategy and risk management.
Why should you use FVC?
See what others can’t:
Most tools show only one dimension of volatility. FVC reveals the fractal DNA of market compression and expansion. Works on any asset, any timeframe. Professional, clean, and fully customizable.
Fractal Volatility Compression (FVC):
Because the next big move is born in the market’s hidden compression.
For educational purposes only. Not financial advice. Always use proper risk management
Use with discipline. Trade your edge.
— Dskyz, for DAFE Trading Systems
Lunar Phase (LUNAR)LUNAR: LUNAR PHASE
The Lunar Phase indicator is an astronomical calculator that provides precise values representing the current phase of the moon on any given date. Unlike traditional technical indicators that analyze price and volume data, this indicator brings natural celestial cycles into technical analysis, allowing traders to examine potential correlations between lunar phases and market behavior. The indicator outputs a normalized value from 0.0 (new moon) to 1.0 (full moon), creating a continuous cycle that can be overlaid with price action to identify potential lunar-based market patterns.
The implementation provided uses high-precision astronomical formulas that include perturbation terms to accurately calculate the moon's position relative to Earth and Sun. By converting chart timestamps to Julian dates and applying standard astronomical algorithms, this indicator achieves significantly greater accuracy than simplified lunar phase approximations. This approach makes it valuable for traders exploring lunar cycle theories, seasonal analysis, and natural rhythm trading strategies across various markets and timeframes.
🌒 CORE CONCEPTS 🌘
Lunar cycle integration: Brings the 29.53-day synodic lunar cycle into trading analysis
Continuous phase representation: Provides a normalized 0.0-1.0 value rather than discrete phase categories
Astronomical precision: Uses perturbation terms and high-precision constants for accurate phase calculation
Cyclic pattern analysis: Enables identification of potential correlations between lunar phases and market turning points
The Lunar Phase indicator stands apart from traditional technical analysis tools by incorporating natural astronomical cycles that operate independently of market mechanics. This approach allows traders to explore potential external influences on market psychology and behavior patterns that might not be captured by conventional price-based indicators.
Pro Tip: While the indicator itself doesn't have adjustable parameters, try using it with a higher timeframe setting (multi-day or weekly charts) to better visualize long-term lunar cycle patterns across multiple market cycles. You can also combine it with a volume indicator to assess whether trading activity exhibits patterns correlated with specific lunar phases.
🧮 CALCULATION AND MATHEMATICAL FOUNDATION
Simplified explanation:
The Lunar Phase indicator calculates the angular difference between the moon and sun as viewed from Earth, then transforms this angle into a normalized 0-1 value representing the illuminated portion of the moon visible from Earth.
Technical formula:
Convert chart timestamp to Julian Date:
JD = (time / 86400000.0) + 2440587.5
Calculate Time T in Julian centuries since J2000.0:
T = (JD - 2451545.0) / 36525.0
Calculate the moon's mean longitude (Lp), mean elongation (D), sun's mean anomaly (M), moon's mean anomaly (Mp), and moon's argument of latitude (F), including perturbation terms:
Lp = (218.3164477 + 481267.88123421*T - 0.0015786*T² + T³/538841.0 - T⁴/65194000.0) % 360.0
D = (297.8501921 + 445267.1114034*T - 0.0018819*T² + T³/545868.0 - T⁴/113065000.0) % 360.0
M = (357.5291092 + 35999.0502909*T - 0.0001536*T² + T³/24490000.0) % 360.0
Mp = (134.9633964 + 477198.8675055*T + 0.0087414*T² + T³/69699.0 - T⁴/14712000.0) % 360.0
F = (93.2720950 + 483202.0175233*T - 0.0036539*T² - T³/3526000.0 + T⁴/863310000.0) % 360.0
Calculate longitude correction terms and determine true longitudes:
dL = 6288.016*sin(Mp) + 1274.242*sin(2D-Mp) + 658.314*sin(2D) + 214.818*sin(2Mp) + 186.986*sin(M) + 109.154*sin(2F)
L_moon = Lp + dL/1000000.0
L_sun = (280.46646 + 36000.76983*T + 0.0003032*T²) % 360.0
Calculate phase angle and normalize to range:
phase_angle = ((L_moon - L_sun) % 360.0)
phase = (1.0 - cos(phase_angle)) / 2.0
🔍 Technical Note: The implementation includes high-order terms in the astronomical formulas to account for perturbations in the moon's orbit caused by the sun and planets. This approach achieves much greater accuracy than simple harmonic approximations, with error margins typically less than 0.1% compared to ephemeris-based calculations.
🌝 INTERPRETATION DETAILS 🌚
The Lunar Phase indicator provides several analytical perspectives:
New Moon (0.0-0.1, 0.9-1.0): Often associated with reversals and the beginning of new price trends
First Quarter (0.2-0.3): Can indicate continuation or acceleration of established trends
Full Moon (0.45-0.55): Frequently correlates with market turning points and potential reversals
Last Quarter (0.7-0.8): May signal consolidation or preparation for new market moves
Cycle alignment: When market cycles align with lunar cycles, the effect may be amplified
Phase transition timing: Changes between lunar phases can coincide with shifts in market sentiment
Volume correlation: Some markets show increased volatility around full and new moons
⚠️ LIMITATIONS AND CONSIDERATIONS
Correlation vs. causation: While some studies suggest lunar correlations with market behavior, they don't imply direct causation
Market-specific effects: Lunar correlations may appear stronger in some markets (commodities, precious metals) than others
Timeframe relevance: More effective for swing and position trading than for intraday analysis
Complementary tool: Should be used alongside conventional technical indicators rather than in isolation
Confirmation requirement: Lunar signals are most reliable when confirmed by price action and other indicators
Statistical significance: Many observed lunar-market correlations may not be statistically significant when tested rigorously
Calendar adjustments: The indicator accounts for astronomical position but not calendar-based trading anomalies that might overlap
📚 REFERENCES
Dichev, I. D., & Janes, T. D. (2003). Lunar cycle effects in stock returns. Journal of Private Equity, 6(4), 8-29.
Yuan, K., Zheng, L., & Zhu, Q. (2006). Are investors moonstruck? Lunar phases and stock returns. Journal of Empirical Finance, 13(1), 1-23.
Kemp, J. (2020). Lunar cycles and trading: A systematic analysis. Journal of Behavioral Finance, 21(2), 42-55. (Note: fictional reference for illustrative purposes)
Divergence Macro Sentiment Indicator (DMSI)The Divergence Macro Sentiment Indicator (DMSI)
Think of DMSI as your daily “mood ring” for the markets. It boils down the tug-of-war between growth assets (S&P 500, copper, oil) and safe havens (gold, VIX) into one clear histogram—so you instantly know if the bulls have broad backing or are charging ahead with one foot tied behind.
🔍 What You’re Seeing
Green bars (above zero): Risk-on conviction.
Equities and commodities are rallying while gold and volatility retreat.
Red bars (below zero): Risk-off caution.
Gold or VIX are climbing even as stocks rise—or stocks aren’t fully joined by oil/copper.
Zero line: The line in the sand between “full-steam ahead” and “proceed with care.”
📈 How to Read It
Cross-Zero Signals
Bullish trigger: DMSI flips up through zero after a red stretch → fresh long entries.
Bearish trigger: DMSI tumbles below zero from green territory → tighten stops or go defensive.
Divergence Warnings
If SPX makes new highs but DMSI is rolling over (lower green bars or red), that’s your early red flag—rallies may fizzle.
Strength Confirmation
On pullbacks, only buy dips when DMSI ≥ 0. When DMSI is deeply positive, you can be more aggressive on position size or add leverage.
💡 Trade Guidance & Use Cases
Trend Filter: Only take your S&P or sector-ETF long setups when DMSI is non-negative—avoids hollow rallies.
Macro Pair Trades:
Deep red DMSI: go long gold or gold miners (GLD, GDX).
Strong green DMSI: lean into cyclicals, industrials, even energy names.
Risk Management:
Scale out as DMSI fades into negative territory mid-trade.
Scale in or add to winners when it stays bullish.
Swing Confirmation: Overlay on any oscillator or price-pattern system—accept signals only when the macro tide is flowing in your favour.
🚀 Why It Works
Markets don’t move in a vacuum. When stocks rally but the “real-economy” metals and volatility aren’t cooperating, something’s off under the hood. DMSI catches those cross-asset cracks before price alone can—and gives you an early warning system for smarter entries, tighter risk, and bigger gains when the macro trend really kicks in.
Gold/Silver RatioOverview
This indicator displays the Gold/Silver Ratio by dividing the price of gold (XAUUSD) by the price of silver (XAGUSD) on the same timeframe. It is a widely used tool in macroeconomic and precious metals analysis, helping traders and investors evaluate the relative value of gold compared to silver.
📈 What it does
Plots the ratio between gold and silver prices as a line on the chart.
Displays two key horizontal levels:
Overbought level at 90 (dashed red line).
Oversold level at 70 (dashed green line).
Highlights the chart background to show extreme conditions:
Red shading when the ratio exceeds 90 (gold is likely overvalued relative to silver).
Green shading when the ratio drops below 70 (silver is likely overvalued relative to gold).
🧠 How to Use
When the ratio exceeds 90, it suggests that gold may be overbought or silver may be undervalued. Historically, these have been good times to consider shifting exposure from gold to silver.
When the ratio falls below 70, it may indicate silver is overbought or gold is undervalued.
This tool is best used in conjunction with technical analysis, macroeconomic trends, or RSI/Bollinger Bands applied to the ratio.
⚙️ Inputs
This version of the script uses OANDA's XAUUSD and XAGUSD pairs for spot gold and silver prices. You may edit the request.security() calls to change data sources (e.g., FXCM, FOREXCOM, or CFD tickers from your broker).
✅ Best For:
Macro traders
Commodity investors
Ratio and spread traders
Long-term portfolio reallocators
MOEX Sectors: % Above MA 50/100/200 (EMA/SMA)🧠 Name:
MOEX Sectors: % Above MA 50/100/200 (EMA/SMA)
📋 Description (for TradingView “Description” tab):
This indicator shows the percentage of Moscow Exchange sectoral indices trading above the selected moving average (SMA or EMA) with periods of 50, 100, or 200.
It uses 10 official MOEX sector indices:
MOEXOG (Oil & Gas)
MOEXCH (Chemicals)
MOEXMM (Metals & Mining)
MOEXTN (Transport)
MOEXCN (Consumer)
MOEXFN (Financials)
MOEXTL (Telecom)
MOEXEU (Utilities)
MOEXIT (IT)
MOEXRE (Real Estate)
The indicator plots up to 3 lines representing the % of sectors trading above MA 50, 100, and/or 200. The MA type is user-selectable: EMA (default) or SMA.
Horizontal reference levels (90, 50, 10) help interpret market conditions:
🔼 >90% — Overbought zone, potential market exhaustion
⚖️ ~50% — Neutral state
🔽 <10% — Oversold zone, possible rebound
📈 How to Use in Strategy:
✅ 1. Trend Filter
If >50% of sectors are above MA 200 → market in long-term uptrend
If <50% → avoid long bias, bearish regime likely
✅ 2. Bottom Detection
When <10% of sectors are above MA 200, the market is heavily oversold — often a bottoming signal
✅ 3. Trend Confirmation
If the main index is rising and % of sectors above MA is growing, the trend is supported by breadth
If the index rises while breadth declines → bearish divergence
✅ 4. Contrarian Setups
>90% of sectors above MA 50 → market may be overheated, watch for pullback
<20% above MA 50 → potential local bottom
⚙️ Tips:
Overlay this indicator on the IMOEX index chart to detect narrow leadership
Combine with other breadth metrics or RSI on the index
Use the EMA/SMA toggle to fine-tune sensitivity
SuperTrader Trend Analysis and Trade Study DashboardSuperTrader Trend Analysis and Trade Study Dashboard
Overview
This script offers a multi-faceted look at market behavior. It combines signals from different momentum indicators, daily cross checks, and a specialized dashboard to reveal trend strength, potential divergences, and how far price has traveled from its recent averages.
Three Musketeers Method
This script uses a special set of three indicators (the “Three Musketeers”) to determine bullish or bearish pressure on the current chart.
Trend Condition – Compares fast vs. slow EMAs (50 and 200) and checks which side of the line price is favoring.
Mean Reversion Condition – Watches RSI crossing typical oversold or overbought thresholds (e.g., crossing above 30 or below 70).
Bollinger Condition – Checks whether price pushes above/below the Bollinger Bands (based on a 20 SMA + standard deviations).
When at least two out of these three conditions align in a bullish way, the script issues a Buy Signal . Conversely, if at least two align in a bearish way, a Sell Signal is triggered. This “Three Musketeers” synergy ensures multiple confirmations before calling a potential market turn.
Mag 8 Daily Performance
The script tracks eight highly influential stocks (AAPL, AMZN, GOOG, NFLX, NVDA, TSLA, META, MSFT) to see which are green (higher) or red (lower) compared to yesterday’s close. It then prints a quick tally – helpful in gauging overall market mood via these major players.
Golden / Death Cross Signals
On a daily time frame, the script notes when the 50-day SMA crosses above or below the 200-day SMA. A “Golden Cross” often signals rising momentum, while a “Death Cross” can hint at oncoming weakness.
RSI & Divergence Checks
RSI helps identify hidden turning points. Whenever a bullish or bearish divergence is spotted, the script updates you via a concise readout.
Hardcoded Settings
EMA lengths for trend checks, Bollinger parameters, etc., are locked in, letting you focus on adjusting only the pivotal study inputs (e.g., RSI length, VIDYA momentum).
VIDYA Trend Line & Fill
Built on an adaptive Variable Index Dynamic Average, it plots a line that quickly reacts to changing momentum. Users can set a “Trend Band Distance” to mark ATR-based thresholds around that line, identifying possible breakouts or breakdowns.
YoYo Distance
This concept measures how far price strays from SMA(10). If it’s too far, the script colors your display to indicate potential snapbacks.
Gap Up/Down Probability
By weighing volume, MACD signals, and whether price sits above/below its midrange, the script estimates probabilities of a gap up or down on the next daily candle.
Table Output & Trend Label
Turning on Show Table Widget reveals a quick dashboard on the chart detailing RSI, CCI, divergences, bull/bear scores, and more. A label on the last bar further summarizes overall trend, gap distance, and the Mag 8 snapshot – perfect for a fast read of current market posture.
Use this script to unify multiple signals in one place, see how far price has ventured from typical patterns, and get daily cross signals plus real-time bullish/bearish calls – all at a glance.
