even_better_sinewave_mod
Description:
Even better sinewave was an indicator developed by John F. Ehlers (see Cycle Analytics for Trader, pg. 159), in which improvement to cycle measurements completely relies on strong normalization of the waveform. The indicator aims to create an artificially predictive indicator by transferring the cyclic data swings into a sine wave. In this indicator, the modified is on the weighted moving average as a smoothing function, instead of using the super smoother, aim to be more adaptive, and the default length is set to 55 bars.
Sinewave
smoothing = (7*hp + 6*hp_1 + 5*hp_2+ 4*hp_3 + 3*hp_4 + 2*hp5 + hp_6) /28
normalize = wave/sqrt(power)
Notes:
sinewave indicator crossing over -0.9 is considered to beginning of the cycle while crossing under 0.9 is considered as an end of the cycle
line color turns to green considered as a confirmation of an uptrend, while turns red as a confirmation of a downtrend
confidence of using indicator will be much in confirmation paired with another indicator such dynamic trendline e.g. moving average
as cited within Ehlers book Cycle Analytic for Traders, the indicator will be useful if the satisfied market cycle mode and the period of the dominant cycle must be estimated with reasonable accuracy
Other Example
Pesquisar nos scripts por "Cycle"
111D SMA / (350D SMA * 2)Indicator: Pi Cycle Ratio
This custom technical indicator calculates a ratio between two moving averages that are used for the PI Cycle Top indicator. The PI Cycle Top indicator triggers when the 111-day simple moving average (111D SMA) crosses up with the 350-day simple moving average (350D SMA *2).
The line value is ratio is calculated as:
Line Value = 111DSMA / (350D SMA × 2)
When the 111D SMA crosses with the 350D SMA triggering the PI Cycle Top, the value of the ratio between the two lines is 1.
This visualizes the ratio between the two moving averages into a single line. This indicator can be used for technical analysis for historical and future moves.
Intellect_city - Halvings Bitcoin CycleWhat is halving?
The halving timer shows when the next Bitcoin halving will occur, as well as the dates of past halvings. This event occurs every 210,000 blocks, which is approximately every 4 years. Halving reduces the emission reward by half. The original Bitcoin reward was 50 BTC per block found.
Why is halving necessary?
Halving allows you to maintain an algorithmically specified emission level. Anyone can verify that no more than 21 million bitcoins can be issued using this algorithm. Moreover, everyone can see how much was issued earlier, at what speed the emission is happening now, and how many bitcoins remain to be mined in the future. Even a sharp increase or decrease in mining capacity will not significantly affect this process. In this case, during the next difficulty recalculation, which occurs every 2014 blocks, the mining difficulty will be recalculated so that blocks are still found approximately once every ten minutes.
How does halving work in Bitcoin blocks?
The miner who collects the block adds a so-called coinbase transaction. This transaction has no entry, only exit with the receipt of emission coins to your address. If the miner's block wins, then the entire network will consider these coins to have been obtained through legitimate means. The maximum reward size is determined by the algorithm; the miner can specify the maximum reward size for the current period or less. If he puts the reward higher than possible, the network will reject such a block and the miner will not receive anything. After each halving, miners have to halve the reward they assign to themselves, otherwise their blocks will be rejected and will not make it to the main branch of the blockchain.
The impact of halving on the price of Bitcoin
It is believed that with constant demand, a halving of supply should double the value of the asset. In practice, the market knows when the halving will occur and prepares for this event in advance. Typically, the Bitcoin rate begins to rise about six months before the halving, and during the halving itself it does not change much. On average for past periods, the upper peak of the rate can be observed more than a year after the halving. It is almost impossible to predict future periods because, in addition to the reduction in emissions, many other factors influence the exchange rate. For example, major hacks or bankruptcies of crypto companies, the situation on the stock market, manipulation of “whales,” or changes in legislative regulation.
---------------------------------------------
Table - Past and future Bitcoin halvings:
---------------------------------------------
Date: Number of blocks: Award:
0 - 03-01-2009 - 0 block - 50 BTC
1 - 28-11-2012 - 210000 block - 25 BTC
2 - 09-07-2016 - 420000 block - 12.5 BTC
3 - 11-05-2020 - 630000 block - 6.25 BTC
4 - 20-04-2024 - 840000 block - 3.125 BTC
5 - 24-03-2028 - 1050000 block - 1.5625 BTC
6 - 26-02-2032 - 1260000 block - 0.78125 BTC
7 - 30-01-2036 - 1470000 block - 0.390625 BTC
8 - 03-01-2040 - 1680000 block - 0.1953125 BTC
9 - 07-12-2043 - 1890000 block - 0.09765625 BTC
10 - 10-11-2047 - 2100000 block - 0.04882813 BTC
11 - 14-10-2051 - 2310000 block - 0.02441406 BTC
12 - 17-09-2055 - 2520000 block - 0.01220703 BTC
13 - 21-08-2059 - 2730000 block - 0.00610352 BTC
14 - 25-07-2063 - 2940000 block - 0.00305176 BTC
15 - 28-06-2067 - 3150000 block - 0.00152588 BTC
16 - 01-06-2071 - 3360000 block - 0.00076294 BTC
17 - 05-05-2075 - 3570000 block - 0.00038147 BTC
18 - 08-04-2079 - 3780000 block - 0.00019073 BTC
19 - 12-03-2083 - 3990000 block - 0.00009537 BTC
20 - 13-02-2087 - 4200000 block - 0.00004768 BTC
21 - 17-01-2091 - 4410000 block - 0.00002384 BTC
22 - 21-12-2094 - 4620000 block - 0.00001192 BTC
23 - 24-11-2098 - 4830000 block - 0.00000596 BTC
24 - 29-10-2102 - 5040000 block - 0.00000298 BTC
25 - 02-10-2106 - 5250000 block - 0.00000149 BTC
26 - 05-09-2110 - 5460000 block - 0.00000075 BTC
27 - 09-08-2114 - 5670000 block - 0.00000037 BTC
28 - 13-07-2118 - 5880000 block - 0.00000019 BTC
29 - 16-06-2122 - 6090000 block - 0.00000009 BTC
30 - 20-05-2126 - 6300000 block - 0.00000005 BTC
31 - 23-04-2130 - 6510000 block - 0.00000002 BTC
32 - 27-03-2134 - 6720000 block - 0.00000001 BTC
Bitcoin Reversal PredictorOverview
This indicator displays two lines that, when they cross, signal a potential reversal in Bitcoin's price trend. Historically, the high or low of a bull market cycle often occurs near the moment these lines intersect. The lines consist of an Exponential Moving Average (EMA) and a logarithmic regression line fitted to all of Bitcoin's historical data.
Inspiration
The inspiration for this indicator came from the PI Cycle Top indicator, which has accurately predicted past bull market peaks. However, I believe the PI Cycle Top indicator may not be as effective in the future. In that indicator, two lines cross to mark the top, but the extent of the cross has been diminishing over time. This was especially noticeable in the 2021 cycle, where the lines barely crossed. Because of this, I created a new indicator that I think will continue to provide reliable reversal signals in the future.
How It Works
The logarithmic regression line is fitted to the Bitcoin (BTCUSD) chart using two key factors: the 'a' factor (slope) and the 'b' factor (intercept). This results in a steadily decreasing line. The EMA oscillates above and below this regression line. Each time the two lines cross, a vertical colored bar appears, indicating that Bitcoin's price momentum is likely to reverse.
Use Cases
- Price Bottoming:
Bitcoin often bottoms out when the EMA crosses below the logarithmic regression line.
- Price Topping:
In contrast, Bitcoin often peaks when the EMA crosses above the logarithmic regression line.
- Profitable Strategy:
Trading at the crossovers of these lines can be a profitable strategy, as these moments often signal significant price reversals.
Detrended Price Oscillator [NexusSignals]Detrended Price Oscillator (DPO) is a detrended price oscillator, used in technical analysis, strips out price trends in an effort to estimate the length of price cycles from peak to peak or trough to trough.
DPO is not a momentum indicator, instead highlights peaks and troughs in price, which are used to estimate buy and sell points in line with the historical cycle. (cf. to investopedia)
DPO indicator made by NexusSignals components :
a filled area that allow users to see easy the trend of an asset;
a sma moving average on chart (default length is 20)
a 20 sma on oscillator, both ma's are color coded to show uptrend / downtrend
a donchian channel applied to the dpo to show breakouts, breakdowns and resistances/support, reversals
few alerts for price crossing above ma, cross above the 0 dpo line, and for cross above and below the donchian channels top and bottom
How you can use DPO indicator ?
The detrended price oscillator (DPO) can be used for measuring the distance between peaks and troughs in the indicator that may help traders to make future decisions as they can locate the most recent trough and determine when the next one may occur in the meassured distance on oscillator between peaks and troughs.
You can use the indicator to find the potential price reversals, for example when the price of an asset is in a bearish trend and the dpo is bouncing from the donchian channel bottom, that may be a potential swing low for that asset, same thing in a bullish trend when the dpo rejecting at top of donchian channel may be a trend reversal, a pullback or swing high.
When DPO is above the 0 trend is in an uptrend and when dpo is below the zero the asset is possible to move into a downtrend.
Also crosses of DPO above and below the DPO moving average may signalising a trend change.
Ehlers Stochastic Center Of Gravity [CC]The Stochastic Center Of Gravity Indicator was created by John Ehlers (Cybernetic Analysis For Stocks And Futures pgs 79-80), and this is one of the many cycle scripts that I have created but not published yet because, to be honest, I don't use cycle indicators in my everyday trading. Many of you probably do, so I will start publishing my big backlog of cycle-based indicators. These indicators work best with a trend confirmation or some other confirmation indicator to pair with it. The current cycle is the length of the trend, and since most stocks generally change their underlying trend quite often, especially during the day, it makes sense to adjust the length of this indicator to match the stock you are using it on. As you can see, the indicator gives constant buy and sell signals during a trend which is why I recommend using a confirmation indicator.
I have color-coded it to use lighter colors for normal signals and darker colors for strong signals. Buy when the line turns green and sell when it turns red.
Let me know if there are any other scripts you would like to see me publish!
Bitcoin NUPL IndicatorThe Bitcoin NUPL (Net Unrealized Profit/Loss) Indicator is a powerful metric that shows the difference between Bitcoin's market cap and realized cap as a percentage of market cap. This indicator helps identify different market cycle phases, from capitulation to euphoria.
// How It Works
NUPL measures the aggregate profit or loss held by Bitcoin investors, calculated as:
```
NUPL = ((Market Cap - Realized Cap) / Market Cap) * 100
```
// Market Cycle Phases
The indicator automatically color-codes different market phases:
• **Deep Red (< 0%)**: Capitulation Phase - Most coins held at a loss, historically excellent buying opportunities
• **Orange (0-25%)**: Hope & Fear Phase - Early accumulation, price uncertainty and consolidation
• **Yellow (25-50%)**: Optimism & Anxiety Phase - Emerging bull market, increasing confidence
• **Light Green (50-75%)**: Belief & Denial Phase - Strong bull market, high conviction
• **Bright Green (> 75%)**: Euphoria & Greed Phase - Potential market top, historically good profit-taking zone
// Features
• Real-time NUPL calculation with customizable smoothing
• RSI indicator for additional momentum confirmation
• Color-coded background reflecting current market phase
• Reference lines marking key transition zones
• Detailed metrics table showing NUPL value, market sentiment, market cap, realized cap, and RSI
// Strategy Applications
• **Long-term investors**: Use extreme negative NUPL values (deep red) to identify potential bottoms for accumulation
• **Swing traders**: Look for transitions between phases for potential trend changes
• **Risk management**: Consider taking profits when entering the "Euphoria & Greed" phase (bright green)
• **Mean reversion**: Watch for overbought/oversold conditions when NUPL reaches historical extremes
// Settings
• **RSI Length**: Adjusts the period for RSI calculation
• **NUPL Smoothing Length**: Applies moving average smoothing to reduce noise
// Notes
• Premium TradingView subscription required for Glassnode and Coin Metrics data
• Best viewed on daily timeframes for macro analysis
• Historical NUPL extremes have often marked cycle bottoms and tops
• Use in conjunction with other indicators for confirmation
DTT Yearly Volatility Grid [Pro+] (NINE/ANARR)Introduction :
This tool is designed to automate the Digital Time Theory (DTT) framework created by Ivan and Anarr and applies the DTT Yearly Volatility Grid to uncover swing trading opportunities by analyzing Time-based statistical market behavior across the 4H to Daily chart.
Description:
Built upon the proprietary Digital Time Theory (DTT) , this advanced version is tailored for traders seeking multi-day to multi-week moves . It equips swing traders with an edge by analyzing macro Time intervals and volatility behavior across higher Timeframes. Applicable to all major asset classes, including stocks, crypto, forex, and futures , this script breaks down the entire yearly range into Higher-Time Frame Time Models and statistical zones .
This version uses daily intervals to track broader volatility waves, highlight the DTT framework, and pinpoint premium/discount areas across swing cycles. Powered by Time-driven data insights, this tool assists traders in anticipating expansions, understanding long-range Time distortions, and positioning around statistically significant zones in the higher-Time frame narrative.
Key Features:
Time-Based Models and Macro Volatility Awareness:
Automatically populates the chart with DTT Yearly Time Models (4H, Daily), engineered to spotlight macro volatility events across broader market sessions. Helps swing traders identify potential inflection points, reversals, or trend continuation zones.
Average Model Range Probability (AMRP):
Measure the average volatility expected over higher Time-based models. Use AMRP Levels and Projections to assess the range potential of each Yearly Model Time window—vital for monitoring reversals, breakouts, or continuation plays across several sessions or weeks.
Digital Root Candles and HTF Liquidity Draws:
For DTT Yearly Models, the Digital Root Candles are calculated as a specific Daily candle, and can be viewed on the Daily or 4H Timeframe. Analysts can frame premium and discount zones, based on where price is trading in relation to the current or previous model's Digital Roots. These areas also act as anchors for institutional price movement, often serving as bases for accumulation/distribution periods or large impulse moves.
Extended Visualization:
Track and project prior model ranges (high, low, equilibrium) into the current swing window. This helps visualize macro support/resistance , range expansion, failure zones, and price gravitation levels for longer-term trade planning.
Lookback Periods and Model Count
Utilize adjustable lookback periods to control the number of past DTT Yearly Models displayed—ideal for swing traders and quarterly outlooks. Whether you’re reviewing one yearly model to focus on the present range or several months’ worth of data for backtesting and confluence, this feature keeps charts clean, structured, and aligned with your preferred historical perspective.
By tailoring how many previous Time-based models appear on the chart, traders can better visualize and backtest repeated behaviors, major volatility clusters, and how key levels evolve over Time.
Detailed Data Table:
View statistical AMRP data for multiple DTT Yearly Models in real-Time. The data table helps confirm whether current price movement exceeds, respects, or fails to reach historical volatility ranges—key for analyzing market compression or expansion phases.
Customization Options:
Toggle inner Time interval, calculate AMRP utilizing a custom model lookback, and display styles (solid/dotted lines), including color coordination per drawing. Easily customize your charts and settings to fit your swing trading system or macro analysis.
How Swing Traders Can Use DTT Yearly Volatility Grid Effectively
Identify Swing Premium and Discount Zones:
Use Root Candles and Yearly Time Model AMRP Zones to evaluate where price is positioned in the current Time Model. Using this tool, traders can plan trades with a longer term horizon for a minimum of 1 to 2-weeks or manage entries/exits around market structure shifts and liquidity pools
Expect Macro Volatility Shifts:
Use the HTF models to forecast when and which volatility models are historically known to create larger market impulses . These tools help spot periods of potential exhaustion or breakout, especially near key economic releases, quarterly closes , or macro liquidity zones .
Avoid Low Volatility Consolidations:
AMRP helps you detect when the market is compressing or coiling within a DTT Yearly Model. If price is trading between Digital Root Candles or the AMRP zones, analysts are likely to notice periods of consolidation, and the inability to reach their historical volatility averages.
Usage Guidance:
Add DTT Yearly Volatility Grid (NINE/ANARR) to your TradingView chart.
Make sure to be on the 4H, or Daily Timeframes depending on your asset class and analysis.
Use the DTT Model elements and the Data Table to track expansion zones, premium/discount extremes, and model range behavior.
Terms and Conditions
Our charting tools are products provided for informational and educational purposes only and do not constitute financial, investment, or trading advice. Our charting tools are not designed to predict market movements or provide specific recommendations. Users should be aware that past performance is not indicative of future results and should not be relied upon for making financial decisions. By using our charting tools, the purchaser agrees that the seller and the creator are not responsible for any decisions made based on the information provided by these charting tools. The purchaser assumes full responsibility and liability for any actions taken and the consequences thereof, including any loss of money or investments that may occur as a result of using these products. Hence, by purchasing these charting tools, the customer accepts and acknowledges that the seller and the creator are not liable nor responsible for any unwanted outcome that arises from the development, the sale, or the use of these products. Finally, the purchaser indemnifies the seller from any and all liability. If the purchaser was invited through the Friends and Family Program, they acknowledge that the provided discount code only applies to the first initial purchase of the Toodegrees Premium Suite subscription. The purchaser is therefore responsible for cancelling – or requesting to cancel – their subscription in the event that they do not wish to continue using the product at full retail price. If the purchaser no longer wishes to use the products, they must unsubscribe from the membership service, if applicable. We hold no reimbursement, refund, or chargeback policy. Once these Terms and Conditions are accepted by the Customer, before purchase, no reimbursements, refunds or chargebacks will be provided under any circumstances.
By continuing to use these charting tools, the user acknowledges and agrees to the Terms and Conditions outlined in this legal disclaimer.
Fractal Wave MarkerFractal Wave Marker is an indicator that processes relative extremes of fluctuating prices within 2 periodical aspects. The special labeling system detects and visually marks multi-scale turning points, letting you visualize fractal echoes within unfolding cycles dynamically.
What This Indicator Does
Identifies major and minor swing highs/lows based on adjustable period.
Uses Phi in power exponent to compute a higher-degree swing filter.
Labels of higher degree appear only after confirmed base swings — no phantom levels, no hindsight bias. What you see is what the market has validated.
Swing points unfold in a structured, alternating rhythm . No two consecutive pivots share the same hierarchical degree!
Inspired by the Fractal Market Hypothesis, this script visualizes the principle that market behavior repeats across time scales, revealing structured narrative of "random walk". This inherent sequencing ensures fractal consistency across timeframes. "Fractal echoes" demonstrate how smaller price swings can proportionally mirror larger ones in both structure and timing, allowing traders to anticipate movements by recursive patterns. Cycle Transitions highlight critical inflection points where minor pivots flip polarity such as a series of lower highs progress into higher highs—signaling the birth of a new macro trend. A dense dense clusters of swing points can indicate Liquidity Zones, acting as footprints of institutional accumulation or distribution where price action validates supply and demand imbalances.
Visualization of nested cycles within macro trend anchors - a main feature specifically designed for the chartists who prioritize working with complex wave oscillations their analysis.
HinduTime Choghadiya(Dynamic Day & Night)🕉️ HinduTime Choghadiya (Dynamic Day & Night) — Visualize real-time Choghadiya Muhurat across global timezones with dynamic sunrise/sunset-based day & night cycles. Perfect for astrology-based or Vedic timing strategies.
How to Use:
Add to Chart: Click "Add to chart" from the TradingView script panel.
Select Your Timezone: Use the dropdown to choose your local timezone (e.g., Asia/Kolkata).
Customize Sunrise/Sunset:
Set "Day Start Hour" (typically 6 AM).
Set "Night Start Hour" (typically 6 PM).
Visual Choghadiya Overlay:
Background color represents the current Choghadiya (e.g., Amrit, Shubh, Rog).
Adjusts dynamically by weekday and day/night period.
Use for Timing Entries:
Favorable: Amrit, Shubh, Labh
Neutral: Chal
Avoid: Rog, Kal, Udveg
The Investment Clock Orbital GraphThe Investment Clock Orbital Graph is an advanced visualization tool designed to help traders and investors track economic cycles using a dynamic scatter plot of GDP growth vs. CPI inflation rates.
This indicator is a fusion of two powerful TradingView indicators:
LuxAlgo ’s Relative Strength Scatter Plot – A robust scatter plot for tracking relative strength.
The Investment Clock Indicator – A cycle-based approach to market rotation. This indicator contains more information regarding The Investment Clock.
By combining these approaches, the Investment Clock Orbital Graph enables traders to visualize economic momentum and inflationary trends in a unique, orbital-style scatter plot.
Key Features & Improvements
Orbital Graph Representation – Displays GDP growth and CPI inflation as a dynamic, evolving scatter plot, showing how the economy moves through different phases.
Quadrant-Based Market Regimes – Identifies four key economic phases:
1)🔥 Overheating (High Growth, High Inflation)
2)📉 Stagflation (Low Growth, High Inflation)
3)🤒 Recovery (High Growth, Low Inflation)
4)🎈 Reflation (Low Growth, Low Inflation)
Data-Driven Analysis – Utilizes FRED (Federal Reserve Economic Data) for accurate real-world GDP & CPI data.
Trailing Path of Economic Evolution – Tracks historical economic cycles over time to show momentum and cyclical movements.
Customizable Parameters – Set sustainable GDP growth and inflation thresholds, adjust trail length, and fine-tune scatter plot resolution.
Auto-Labeled Quadrants & Revised Accurate Market Guidance – Each quadrant includes newly updated tooltips and annotations (like ETF suggestions) to help traders make informed decisions.
Live Macro Forecasting Tool – Helps traders anticipate future market conditions, rate hikes/cuts, and sector rotations.
How to Use for Trading Decisions
The Investment Clock Orbital Graph helps traders and macro investors by identifying market phases and providing insights into asset class performance during different economic conditions.
📌 Step 1: Identify the Current Quadrant
Locate the most recent point on the orbital graph to see if the economy is in Overheating, Stagflation, Recovery, or Reflation.
📌 Step 2: Forecast Market Trends
The trajectory of the points can predict upcoming economic shifts:
Overheating → Stagflation ➡️ Expect economic slowdowns, bearish stock markets.
Stagflation → Reflation ➡️ Interest rate cuts likely, bonds and defensive stocks perform well.
Reflation → Recovery ➡️ Risk-on rally, technology and cyclicals perform best.
Recovery → Overheating ➡️ Commodities surge, inflation rises, and central banks intervene.
📌 Step 3: Align Trading & Investing Strategies
🔥 Overheating – Favor commodities & energy (Oil, Industrial Stocks, Materials).
📉 Stagflation – Favor defensive assets (Cash, Utilities, Healthcare).
🤒 Recovery – Favor growth stocks (Technology, Consumer Discretionary).
🎈 Reflation – Favor bonds, value stocks, and financials.
📌 Step 4: Monitor Trends Over Time
The indicator visualizes economic movement over multiple months, allowing traders to confirm long-term trends vs. short-term noise.
The Investment Clock Orbital Graph is an essential macro trading tool, providing a real-time visualization of economic conditions. By tracking GDP growth vs. CPI inflation, traders and investors can align their portfolios with major macroeconomic shifts, predict sector rotations, and anticipate central bank policy changes.
Simple Parallel Channel TrackerThis script will automatically draw price channels with two parallel trends lines, the upper trendline and lower trendline. These lines can be changed in terms of appearance at any time.
The Script takes in fractals from local and historic price action points and connects them over a certain period or amount of candles as inputted by the user. It tracks the most recent highs and lows formed and uses this data to determine where the channel begins.
The Script will decide whether to use the most recent high, or low, depending on what comes first.
Why is this useful?
Often, Traders either have no trend lines on their charts, or they draw them incorrectly. Whichever category a trader falls into, there can only be benefits from having Trend lines and Parallel Channels drawn automatically.
Trends naturally occur in all Markets, all the time. These oscillations when tracked allow for a more reliable following of Markets and management of Market cycles.
DTT Weekly Volatility Grid [Pro+] (NINE/ANARR)Introduction:
Automate Digital Time Theory (DTT) Weekly Models with the DTT Weekly Volatility Grid , leveraging the proprietary framework developed by Nine and Anarr. This tool allows to navigate the advanced landscape of Time-based statistical trading for futures, crypto, and forex markets.
Description:
Built on the Digital Time Theory (DTT), this script provides traders with a structured view of time and price interactions, ideal for swing insights. It divides the weekly range into Time models and inner intervals, empowering traders with data-driven insights to anticipate market expansions, detect Time-based distortions, and understand volatility fluctuations at specific Times during the trading week.
Key Features:
Time-Based Weekly Models and Volatility Awareness: The DTT Weekly Time Models automatically map onto your chart, highlighting critical volatility points in weekly sessions. These models help traders recognize potential shifts in the market, ideal for identifying larger, swing-oriented moves.
Average Model Range Probability (AMRP): The AMRP feature calculates the historical probability of reaching previous DTT Weekly Model Ranges. With AMRP and Standard Deviation metrics, traders can evaluate the likelihood of DTT model continuations or breaks, aligning their strategy with higher Timeframe volatility trends.
Root Candles and Liquidity Draws: Visualize Root Candles as liquidity draws, emphasizing premium and discount areas and marking the origin of a Time-based price movement. The tool allows traders to toggle features like opening prices and equilibrium points of each Root Candle. Observing accumulation or distribution zones around these candles provides crucial reference points for strategic swing entries and exits.
Extended Visualization of Weekly Model Ranges: Leverage previous weekly model ranges within the current Time model to observe historical high, low, and equilibrium levels. This feature aids traders in visualizing premium and discount ranges of prior models, pinpointing areas of liquidity and imbalance to watch.
Customization Options: Tailor Time intervals with a variety of line styles (solid, dashed, dotted) and colours to customize each model. Adjust settings to display specific historical weekly models, apply custom labels, and create a personalized view that suits your trading style and focus.
Lookback Periods and Model Count: Select customizable lookback periods to display past models, offering insights into market behaviour over a chosen historical range. This feature enables clean, organized charts and allows analysts to add more models for detailed backtesting and analysis.
Detailed Real-Time Data Table: The live data table provides easy access to AMRP and range data for selected models. This table highlights model targets and anticipated ranges, offering insights into whether previous models have exceeded historical volatility expectations or remained within them.
How Traders Can Use The DTT Weekly Volatility Grid Effectively:
Identifying Premium and Discount Zones: Track weekly ranges using Root Candles and previous model equilibrium levels to assess if prices are trading in premium or discount areas. This information helps framing the broader swing outlook.
Timing Trades Based on Volatility: Recognize potential exhaustion points through AMRP insights or completed model distortions that may signal new expansions. By observing inner intervals and Root Candles, traders can identify periods of high market activity, assisting in Timing weekly entries and exits.
Avoiding Low Volatility Phases: AMRP calculations can indicate periods when price action may slow or become choppy. If price remains within AMRP deviations or near them, traders can adjust risk or step aside, awaiting more favourable conditions for volatility-driven trades as new inner intervals or model roots appear.
Designed for Swing Traders and Higher Timeframes: The Weekly DTT Models are suited for those looking to study higher timeframe trends across futures, forex, and crypto markets. This tool equips traders with volatility-aware, and data-driven insights during extended market cycles.
Usage Guidance:
Add DTT Weekly Volatility Grid (NINE/ANARR) to your TradingView chart.
Customize your preferred time intervals, model history, and visual settings for your session.
Use the data table to track average model ranges and probabilities, ensuring you align your trades with key levels.
Incorporate DTT Weekly Volatility Grid (NINE/ANARR) into your existing strategies to fine-tune your view through based on data-driven insights into volatility and price behaviour.
Terms and Conditions
Our charting tools are products provided for informational and educational purposes only and do not constitute financial, investment, or trading advice. Our charting tools are not designed to predict market movements or provide specific recommendations. Users should be aware that past performance is not indicative of future results and should not be relied upon for making financial decisions. By using our charting tools, the purchaser agrees that the seller and the creator are not responsible for any decisions made based on the information provided by these charting tools. The purchaser assumes full responsibility and liability for any actions taken and the consequences thereof, including any loss of money or investments that may occur as a result of using these products. Hence, by purchasing these charting tools, the customer accepts and acknowledges that the seller and the creator are not liable nor responsible for any unwanted outcome that arises from the development, the sale, or the use of these products. Finally, the purchaser indemnifies the seller from any and all liability. If the purchaser was invited through the Friends and Family Program, they acknowledge that the provided discount code only applies to the first initial purchase of the Toodegrees Premium Suite subscription. The purchaser is therefore responsible for cancelling – or requesting to cancel – their subscription in the event that they do not wish to continue using the product at full retail price. If the purchaser no longer wishes to use the products, they must unsubscribe from the membership service, if applicable. We hold no reimbursement, refund, or chargeback policy. Once these Terms and Conditions are accepted by the Customer, before purchase, no reimbursements, refunds or chargebacks will be provided under any circumstances.
By continuing to use these charting tools, the user acknowledges and agrees to the Terms and Conditions outlined in this legal disclaimer.
Abdozo - Highlight First DaysAbdozo - Highlight First Days Indicator
This Pine Script indicator helps traders easily identify key timeframes by highlighting the first trading day of the week and the first day of the month. It provides visual markers directly on your chart, helping you stay aware of potential market trends and turning points.
Features:
- Highlight First Day of the Week (Monday): Automatically marks Mondays to help you track weekly market cycles.
- Highlight First Day of the Month: Spot the start of each month with ease to analyze monthly performance and trends.
Fourier For Loop [BackQuant]Fourier For Loop
PLEASE Read the following, as understanding an indicator's functionality is essential before integrating it into a trading strategy. Knowing the core logic behind each tool allows for a sound and strategic approach to trading.
Introducing BackQuant's Fourier For Loop (FFL) — a cutting-edge trading indicator that combines Fourier transforms with a for-loop scoring mechanism. This innovative approach leverages mathematical precision to extract trends and reversals in the market, helping traders make informed decisions. Let's break down the components, rationale, and potential use-cases of this indicator.
Understanding Fourier Transform in Trading
The Fourier Transform decomposes price movements into their frequency components, allowing for a detailed analysis of cyclical behavior in the market. By transforming the price data from the time domain into the frequency domain, this indicator identifies underlying patterns that traditional methods may overlook.
In this script, Fourier transforms are applied to the specified calculation source (defaulted to HLC3). The transformation yields magnitude values that can be used to score market movements over a defined range. This scoring process helps uncover long and short signals based on relative strength and trend direction.
Why Use Fourier Transforms?
Fourier Transforms excel in identifying recurring cycles and smoothing noisy data, making them ideal for fast-paced markets where price movements may be erratic. They also provide a unique perspective on market volatility, offering traders additional insights beyond standard indicators.
Calculation Logic: For-Loop Scoring Mechanism
The For Loop Scoring mechanism compares the magnitude of each transformed point in the series, summing the results to generate a score. This score forms the backbone of the signal generation system.
Long Signals: Generated when the score surpasses the defined long threshold (default set at 40). This indicates a strong bullish trend, signaling potential upward momentum.
Short Signals: Triggered when the score crosses under the short threshold (default set at -10). This suggests a bearish trend or potential downside risk.'
Thresholds & Customization
The indicator offers customizable settings to fit various trading styles:
Calculation Periods: Control how many periods the Fourier transform covers.
Long/Short Thresholds: Adjust the sensitivity of the signals to match different timeframes or risk preferences.
Visualization Options: Traders can visualize the thresholds, change the color of bars based on trend direction, and even color the background for enhanced clarity.
Trading Applications
This Fourier For Loop indicator is designed to be versatile across various market conditions and timeframes. Some of its key use-cases include:
Cycle Detection: Fourier transforms help identify recurring patterns or cycles, giving traders a head-start on market direction.
Trend Following: The for-loop scoring system helps confirm the strength of trends, allowing traders to enter positions with greater confidence.
Risk Management: With clearly defined long and short signals, traders can manage their positions effectively, minimizing exposure to false signals.
Final Note
Incorporating this indicator into your trading strategy adds a layer of mathematical precision to traditional technical analysis. Be sure to adjust the calculation start/end points and thresholds to match your specific trading style, and remember that no indicator guarantees success. Always backtest thoroughly and integrate the Fourier For Loop into a balanced trading system.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future .
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
B4Signals Ichimoku Premium Addon Kyushu Sushi RollKyushu Ashi, one of the Ichimoku strategies, was initially presented in the Ichimoku Kinko Hyo Weekly book by Goichi Hosoda.
<< Historical Context >>
During his time contributing market analysis to the Miyako Newspaper, traders faced the laborious task of manually recording daily open, close, high, and low price levels, alongside the five price values of Ichimoku (Tenkan sen, Kijun sen, Chiko span, Senko span A, Senko span B). With the absence of personal computers, this process was notably cumbersome. In response to traders' requests for a simplified analysis method, Goichi Hosoda introduced the Kyushu Ashi technique.
<< About Kyushu Ashi >>
Derived from Japanese, where "Kyu" denotes nine, "Shu" refers to week, and "Ashi" translates to candles, Kyushu Ashi aims to identify market reversals and trend continuations by leveraging Kihon Suchi time cycles. However, as stated in the original book, trading solely with Kyushu Ashi is not advisable; it is recommended to combine it with the Ichimoku's five lines for a comprehensive trading approach.
Our indicator enhances the Kyushu Ashi strategy by incorporating the original signals in a simplified manner showing up or down carrets in the legs. Additionally, it colors candles with no signals, enabling traders to spot areas of consolidation, continuation, and trend reversal effectively. Drawing inspiration from Fisher's Sushi Roll indicator, our tool identifies these trend changes and adds a trend cloud for the prevailing trend. This comprehensive approach maximizes the effectiveness of the Kyushu Ashi strategy and assists traders in making informed trading decisions.
This is an advanced version with the following features:
- Shows Kyushu Legs
- Shows Kyushu Leg signals
- Draws trend cloud based on Kyushu Legs
- Sends alerts when no Kyushu signal is present, alerting trader of consolidation
- Colors No Kyushu signal candles
- Draws dynamic consolidation zones for breakout identification
Market Health MonitorThe Market Health Monitor is a comprehensive tool designed to assess and visualize the economic health of a market, providing traders with vital insights into both current and future market conditions. This script integrates a range of critical economic indicators, including unemployment rates, inflation, Federal Reserve funds rates, consumer confidence, and housing market indices, to form a robust understanding of the overall economic landscape.
Drawing on a variety of data sources, the Market Health Monitor employs moving averages over periods of 3, 12, 36, and 120 months, corresponding to quarterly, annual, three-year, and ten-year economic cycles. This selection of timeframes is specifically chosen to capture the nuances of economic movements across different phases, providing a balanced view that is sensitive to both immediate changes and long-term trends.
Key Features:
Economic Indicators Integration: The script synthesizes crucial economic data such as unemployment rates, inflation levels, and housing market trends, offering a multi-dimensional perspective on market health.
Adaptability to Market Conditions: The inclusion of both short-term and long-term moving averages allows the Market Health Monitor to adapt to varying market conditions, making it a versatile tool for different trading strategies.
Oscillator Thresholds for Recession and Growth: The script sets specific thresholds that, when crossed, indicate either potential economic downturns (recessions) or periods of growth (expansions), allowing traders to anticipate and react to changing market conditions proactively.
Color-Coded Visualization: The Market Health Monitor employs a color-coding system for ease of interpretation:
-- A red background signals unhealthy economic conditions, cautioning traders about potential risks.
-- A bright red background indicates a confirmed recession, as declared by the NBER, signaling a critical time for traders to reassess risk exposure.
-- A green background suggests a healthy market with expected economic expansion, pointing towards growth-oriented opportunities.
Comprehensive Market Analysis: By combining various economic indicators, the script offers a holistic view of the market, enabling traders to make well-informed decisions based on a thorough understanding of the economic environment.
Key Criteria and Parameters:
Economic Indicators:
Labor Market: The unemployment rate is a critical indicator of economic health.
High or rising unemployment indicates reduced consumer spending and economic stress.
Inflation: Key for understanding monetary policy and consumer purchasing power.
Persistent high inflation can lead to economic instability, while deflation can signal weak
demand.
Monetary Policy: Reflected by the Federal Reserve funds rate.
Changes in the rate can influence economic activity, borrowing costs, and investor
sentiment.
Consumer Confidence: A predictor of consumer spending and economic activity.
Reflects the public’s perception of the economy
Housing Market: The housing market often leads the economy into recession and recovery.
Weakness here can signal broader economic problems.
Market Data:
Stock Market Indices: Reflect overall investor sentiment and economic
expectations. No gains in a stock market could potentially indicate that economy is
slowing down.
Credit Conditions: Indicated by the tightness of bank lending, signaling risk
perception.
Commodity Insight:
Crude Oil Prices: A proxy for global economic activity.
Indicator Timeframe:
A default monthly timeframe is chosen to align with the release frequency of many economic indicators, offering a balanced view between timely data and avoiding too much noise from short-term fluctuations. Surely, it can be chosen by trader / analyst.
The Market Health Monitor is more than just a trading tool—it's a comprehensive economic guide. It's designed for traders who value an in-depth understanding of the economic climate. By offering insights into both current conditions and future trends, it encourages traders to navigate the markets with confidence, whether through turbulent times or in periods of growth. This tool doesn't just help you follow the market—it helps you understand it.
Dark Energy Divergence OscillatorThe Dark Energy Divergence Oscillator (DEDO)
What makes The Universe grow at an accelerating pace?
Dark Energy.
What makes The Economy grow at an accelerating pace?
Debt.
Debt is the Dark Energy of The Economy.
I pronounce DEDO "Deed-oh", but variations are fine with me.
Note: The Pine Script version of DEDO is improved from the original formula, which used a constant all-time high calculation in the normalization factor. This was technically not as accurate for calculating liquidity pressure in historical data because it meant that historical prices were being tested against future liquidity factors. Now using Pine, the functions can be normalized for the bar at the time of calculation, so the liquidity factors are normalized per candle, not across the entire series, which feels like an improvement to me.
Thought Process:
It's all about the liquidity. What I started with is a correlation between major stock indices such as SPX and WRESBAL , a balance sheet metric on FRED
After September 2008, when QE was initiated, many asset valuations started to follow more closely with liquidity factors. This led me to create a function that could combine asset prices and liquidity in WRESBAL , in order to calculate their divergence and chart the signal in TradingView.
The original formula:
First, we don't want "non-QE" data. we only want data for the market affected by QE .
So, find SPX on the day of pre-QE: 1255.08 and subtract that from the 2022 top 4818.62 = 3563.54
With this post-QE SPX range, now you can normalize the price level simply by dividing by the range = ( SPX -1255.08)/3563.54)
Normalization produces values from 0 to 1 so that they can be compared with other normalized figures.
In order to test the 0 to 1 normalized SPX range measure against the liquidity number, WRESBAL , it's the same idea: normalize it using the max as the denominator and you get a 0 to 1 liquidity index:
( WRESBAL /4276000000000)
Subtract one from the other to get the divergence:
(( WRESBAL /4276000000000)-(( SPX -1255.08)/3563.54))*10
x10 to reduce decimal places, but this option is configurable in DEDO's input settings tab.
Positive values indicate there's ample liquidity to hold up price or even create bullish momentum in some cases. Negative values mean price levels are potentially extended beyond what liquidity levels can support.
Note: many viewers of the charts on social media wanted the values to go down in alignment with price moving down, so inverting the chart is what I do with Option + I. I like the fact that negative values represent a deficit in liquidity to hold up price but that's just me.
Now with Pine Script and some help from other liquidity focused accounts on TradingView , I was able to derive a script that includes central bank liquidity and Reverse Repo liquidity drain, all in one algorithm, with adjustable settings.
Central bank assets included in this version:
-JPY (Japan)
-CNY (China)
-UK (British Pound)
-SNB (Swiss National Bank)
-ECB (European Central Bank )
Central Bank assets can be adjusted to an allocation % so that the formula is adjusted for the market cap of the asset.
A handy table in the lower right corner displays useful information about the asset market cap, and percentage it represents in the liquidity pool.
Reverse repo soak is also an optional addition in the Input settings using the RRPONTSYD value from FRED. This value is subtracted from global liquidity used to determine divergence since it is swept away from markets when residing in the Fed's reverse repo facility.
There is an option to draw a line at the Zero bound. This provides a convenience so that the line doesn't keep having to be redrawn on every chart. The normalized equation produces a value that should oscillate around zero, as price/valuation grows past liquidity support, falls under it, and repeats in cycles.
S&P 500 Quandl Data & RatiosTradingView has a little-known integration that allows you to pull in 3rd party data-sets from Nasdaq Data Link, also known as Quandl. Today, I am open-sourcing for the community an indicator that uses the Quandl integration to pull in historical data and ratios on the S&P500. I originally coded this to study macro P/E ratios during peaks and troughs of boom/bust cycles.
The indicator pulls in each of the following datasets, as defined and provided by Quandl. The user can select which datasets to pull in using the indicator settings:
Dividend Yield : S&P 500 dividend yield (12 month dividend per share)/price. Yields following June 2022 (including the current yield) are estimated based on 12 month dividends through June 2022, as reported by S&P. Sources: Standard & Poor's for current S&P 500 Dividend Yield. Robert Shiller and his book Irrational Exuberance for historic S&P 500 Dividend Yields.
Price Earning Ratio : Price to earnings ratio, based on trailing twelve month as reported earnings. Current PE is estimated from latest reported earnings and current market price. Source: Robert Shiller and his book Irrational Exuberance for historic S&P 500 PE Ratio.
CAPE/Shiller PE Ratio : Shiller PE ratio for the S&P 500. Price earnings ratio is based on average inflation-adjusted earnings from the previous 10 years, known as the Cyclically Adjusted PE Ratio (CAPE Ratio), Shiller PE Ratio, or PE 10 FAQ. Data courtesy of Robert Shiller from his book, Irrational Exuberance.
Earnings Yield : S&P 500 Earnings Yield. Earnings Yield = trailing 12 month earnings divided by index price (or inverse PE) Yields following March, 2022 (including current yield) are estimated based on 12 month earnings through March, 2022 the latest reported by S&P. Source: Standard & Poor's
Price Book Ratio : S&P 500 price to book value ratio. Current price to book ratio is estimated based on current market price and S&P 500 book value as of March, 2022 the latest reported by S&P. Source: Standard & Poor's
Price Sales Ratio : S&P 500 Price to Sales Ratio (P/S or Price to Revenue). Current price to sales ratio is estimated based on current market price and 12 month sales ending March, 2022 the latest reported by S&P. Source: Standard & Poor's
Inflation Adjusted SP500 : Inflation adjusted SP500. Other than the current price, all prices are monthly average closing prices. Sources: Standard & Poor's Robert Shiller and his book Irrational Exuberance for historic S&P 500 prices, and historic CPIs.
Revenue Per Share : Trailing twelve month S&P 500 Sales Per Share (S&P 500 Revenue Per Share) non-inflation adjusted current dollars. Source: Standard & Poor's
Earnings Per Share : S&P 500 Earnings Per Share. 12-month real earnings per share inflation adjusted, constant August, 2022 dollars. Sources: Standard & Poor's for current S&P 500 Earnings. Robert Shiller and his book Irrational Exuberance for historic S&P 500 Earnings.
Disclaimer: This is not financial advice. Open-source scripts I publish in the community are largely meant to spark ideas that can be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
ln(close/20 sma) adjusted for time (BTC)(This indicator was designed for the BTC index chart)
Designed for Bitcoin. Plots the log of the close/20W SMA with a linear offset m*t, where m is the gradient I've chosen and t is the candle index. Anything above 1 is a mania phase/market cycle top. If it peaks around 0.92 and rolls over, it could be a local/market cycle top.
This will obviously not work at all in the long term as Bitcoin will not continue following the trend line on the log plot (you can even see it start to deviate in the Jan-Feb 2021 peaks where the indicator went to 1.15).
It identifies the 2011, 2013 (both of them), 2017 tops as being just above 1. It also identifies the 2019 local peak and 2021 market cycle top at ~0.94.
Feel free to change the gradient or even add a function to curve the straight line eventually. I made this for fun, feel free to use it as you wish.
Modern Economic Eras DashboardOverview
This script provides a historical macroeconomic visualization of U.S. markets, highlighting long-term structural "eras" such as the Bretton Woods period, the inflationary 1970s, and the post-2020 "Age of Disorder." It overlays key economic indicators sourced from FRED (Federal Reserve Economic Data) and displays notable market crashes, all in a clean and rescaled format for easy comparison.
Data Sources & Indicators
All data is loaded monthly from official FRED series and rescaled to improve readability:
🔵 Real GDP (FRED:GDP): Total output of the U.S. economy.
🔴 Inflation Index (FRED:CPIAUCSL): Consumer price index as a proxy for inflation.
⚪ Debt to GDP (FRED:GFDGDPA188S): Federal debt as % of GDP.
🟣 Labor Force Participation (FRED:CIVPART): % of population in the labor force.
🟠 Oil Prices (FRED:DCOILWTICO): Monthly WTI crude oil prices.
🟡 10Y Real Yield (FRED:DFII10): Inflation-adjusted yield on 10-year Treasuries.
🔵 Symbol Price: Optionally overlays the charted asset’s price, rescaled.
Historical Crashes
The dashboard highlights 10 major U.S. market crashes, including 1929, 2000, and 2008, with labeled time spans for quick context.
Era Classification
Six macroeconomic eras based on Deutsche Bank’s Long-Term Asset Return Study (2020) are shaded with background color. Each era reflects dominant economic regimes—globalization, wars, monetary systems, inflationary cycles, and current geopolitical disorder.
Best Use Cases
✅ Long-term macro investors studying structural market behavior
✅ Educators and analysts explaining economic transitions
✅ Portfolio managers aligning strategy with macroeconomic phases
✅ Traders using history for cycle timing and risk assessment
Technical Notes
Designed for monthly timeframe, though it works on weekly.
Uses close price and standard request.security calls for consistency.
Max labels/lines configured for broader history (from 1860s to present).
All plotted series are rescaled manually for better visibility.
Originality
This indicator is original and not derived from built-in or boilerplate code. It combines multiple economic dimensions and market history into one interactive chart, helping users frame today's markets in a broader structural context.
The Mayan CalendarThis indicator displays the current date in the Mayan Calendar, based on real-time UTC time. It calculates and presents:
🌀 Long Count (Baktun.Katun.Tun.Uinal.Kin) – A linear count of days since the Mayan epoch (August 11, 3114 BCE).
🔮 Tzolk'in Date – A 260-day sacred cycle combining a number (1–13) and one of 20 day names (e.g., 4 Ajaw).
🌾 Haab' Date – A 365-day civil cycle divided into 18 months of 20 days + 5 "nameless" days (Wayeb').
The calculations follow Smithsonian standards and align with the Maya Calendar Converter from the National Museum of the American Indian:
👉 maya.nmai.si.edu
The results are shown in a table overlay on your chart's top-right corner. This indicator is great for symbolic traders, astro enthusiasts, or anyone interested in ancient timekeeping systems woven into financial timeframes. Enjoy, time travelers! ⌛
Log Regression OscillatorThe Log Regression Oscillator transforms the logarithmic regression curves into an easy-to-interpret oscillator that displays potential cycle tops/bottoms.
🔶 USAGE
Calculating the logarithmic regression of long-term swings can help show future tops/bottoms. The relationship between previous swing points is calculated and projected further. The calculated levels are directly associated with swing points, which means every swing point will change the calculation. Importantly, all levels will be updated through all bars when a new swing is detected.
The "Log Regression Oscillator" transforms the calculated levels, where the top level is regarded as 100 and the bottom level as 0. The price values are displayed in between and calculated as a ratio between the top and bottom, resulting in a clear view of where the price is situated.
The main picture contains the Logarithmic Regression Alternative on the chart to compare with this published script.
Included are the levels 30 and 70. In the example of Bitcoin, previous cycles showed a similar pattern: the bullish parabolic was halfway when the oscillator passed the 30-level, and the top was very near when passing the 70-level.
🔹 Proactive
A "Proactive" option is included, which ensures immediate calculations of tentative unconfirmed swings.
Instead of waiting 300 bars for confirmation, the "Proactive" mode will display a gray-white dot (not confirmed swing) and add the unconfirmed Swing value to the calculation.
The above example shows that the "Calculated Values" of the potential future top and bottom are adjusted, including the provisional swing.
When the swing is confirmed, the calculations are again adjusted, showing a red dot (confirmed top swing) or a green dot (confirmed bottom swing).
🔹 Dashboard
When less than two swings are available (top/bottom), this will be shown in the dashboard.
The user can lower the "Threshold" value or switch to a lower timeframe.
🔹 Notes
Logarithmic regression is typically used to model situations where growth or decay accelerates rapidly at first and then slows over time, meaning some symbols/tickers will fit better than others.
Since the logarithmic regression depends on swing values, each new value will change the calculation. A well-fitted model could not fit anymore in the future.
Users have to check the validity of swings; for example, if the direction of swings is downwards, then the dataset is not fitted for logarithmic regression.
In the example above, the "Threshold" is lowered. However, the calculated levels are unreliable due to the swings, which do not fit the model well.
Here, the combination of downward bottom swings and price accelerates slower at first and faster recently, resulting in a non-fit for the logarithmic regression model.
Note the price value (white line) is bound to a limit of 150 (upwards) and -150 (down)
In short, logarithmic regression is best used when there are enough tops/bottoms, and all tops are around 100, and all bottoms around 0.
Also, note that this indicator has been developed for a daily (or higher) timeframe chart.
🔶 DETAILS
In mathematics, the dot product or scalar product is an algebraic operation that takes two equal-length sequences of numbers (arrays) and returns a single number, the sum of the products of the corresponding entries of the two sequences of numbers.
The usual way is to loop through both arrays and sum the products.
In this case, the two arrays are transformed into a matrix, wherein in one matrix, a single column is filled with the first array values, and in the second matrix, a single row is filled with the second array values.
After this, the function matrix.mult() returns a new matrix resulting from the product between the matrices m1 and m2.
Then, the matrix.eigenvalues() function transforms this matrix into an array, where the array.sum() function finally returns the sum of the array's elements, which is the dot product.
dot(x, y)=>
if x.size() > 1 and y.size() > 1
m1 = matrix.new()
m2 = matrix.new()
m1.add_col(m1.columns(), y)
m2.add_row(m2.rows (), x)
m1.mult (m2)
.eigenvalues()
.sum()
🔶 SETTINGS
Threshold: Period used for the swing detection, with higher values returning longer-term Swing Levels.
Proactive: Tentative Swings are included with this setting enabled.
Style: Color Settings
Dashboard: Toggle, "Location" and "Text Size"
Logarithmic Regression AlternativeLogarithmic regression is typically used to model situations where growth or decay accelerates rapidly at first and then slows over time. Bitcoin is a good example.
𝑦 = 𝑎 + 𝑏 * ln(𝑥)
With this logarithmic regression (log reg) formula 𝑦 (price) is calculated with constants 𝑎 and 𝑏, where 𝑥 is the bar_index .
Instead of using the sum of log x/y values, together with the dot product of log x/y and the sum of the square of log x-values, to calculate a and b, I wanted to see if it was possible to calculate a and b differently.
In this script, the log reg is calculated with several different assumed a & b values, after which the log reg level is compared to each Swing. The log reg, where all swings on average are closest to the level, produces the final 𝑎 & 𝑏 values used to display the levels.
🔶 USAGE
The script shows the calculated logarithmic regression value from historical swings, provided there are enough swings, the price pattern fits the log reg model, and previous swings are close to the calculated Top/Bottom levels.
When the price approaches one of the calculated Top or Bottom levels, these levels could act as potential cycle Top or Bottom.
Since the logarithmic regression depends on swing values, each new value will change the calculation. A well-fitted model could not fit anymore in the future.
Swings are based on Weekly bars. A Top Swing, for example, with Swing setting 30, is the highest value in 60 weeks. Thirty bars at the left and right of the Swing will be lower than the Top Swing. This means that a confirmation is triggered 30 weeks after the Swing. The period will be automatically multiplied by 7 on the daily chart, where 30 becomes 210 bars.
Please note that the goal of this script is not to show swings rapidly; it is meant to show the potential next cycle's Top/Bottom levels.
🔹 Multiple Levels
The script includes the option to display 3 Top/Bottom levels, which uses different values for the swing calculations.
Top: 'high', 'maximum open/close' or 'close'
Bottom: 'low', 'minimum open/close' or 'close'
These levels can be adjusted up/down with a percentage.
Lastly, an "Average" is included for each set, which will only be visible when "AVG" is enabled, together with both Top and Bottom levels.
🔹 Notes
Users have to check the validity of swings; the above example only uses 1 Top Swing for its calculations, making the Top level unreliable.
Here, 1 of the Bottom Swings is pretty far from the bottom level, changing the swing settings can give a more reliable bottom level where all swings are close to that level.
Note the display was set at "Logarithmic", it can just as well be shown as "Regular"
In the example below, the price evolution does not fit the logarithmic regression model, where growth should accelerate rapidly at first and then slows over time.
Please note that this script can only be used on a daily timeframe or higher; using it at a lower timeframe will show a warning. Also, it doesn't work with bar-replay.
🔶 DETAILS
The code gathers data from historical swings. At the last bar, all swings are calculated with different a and b values. The a and b values which results in the smallest difference between all swings and Top/Bottom levels become the final a and b values.
The ranges of a and b are between -20.000 to +20.000, which means a and b will have the values -20.000, -19.999, -19.998, -19.997, -19.996, ... -> +20.000.
As you can imagine, the number of calculations is enormous. Therefore, the calculation is split into parts, first very roughly and then very fine.
The first calculations are done between -20 and +20 (-20, -19, -18, ...), resulting in, for example, 4.
The next set of calculations is performed only around the previous result, in this case between 3 (4-1) and 5 (4+1), resulting in, for example, 3.9. The next set goes even more in detail, for example, between 3.8 (3.9-0.1) and 4.0 (3.9 + 0.1), and so on.
1) -20 -> +20 , then loop with step 1 (result (example): 4 )
2) 4 - 1 -> 4 +1 , then loop with step 0.1 (result (example): 3.9 )
3) 3.9 - 0.1 -> 3.9 +0.1 , then loop with step 0.01 (result (example): 3.93 )
4) 3.93 - 0.01 -> 3.93 +0.01, then loop with step 0.001 (result (example): 3.928)
This ensures complicated calculations with less effort.
These calculations are done at the last bar, where the levels are displayed, which means you can see different results when a new swing is found.
Also, note that this indicator has been developed for a daily (or higher) timeframe chart.
🔶 SETTINGS
Three sets
High/Low
• color setting
• Swing Length settings for 'High' & 'Low'
• % adjustment for 'High' & 'Low'
• AVG: shows average (when both 'High' and 'Low' are enabled)
Max/Min (maximum open/close, minimum open/close)
• color setting
• Swing Length settings for 'Max' & 'Min'
• % adjustment for 'Max' & 'Min'
• AVG: shows average (when both 'Max' and 'Min' are enabled)
Close H/Close L (close Top/Bottom level)
• color setting
• Swing Length settings for 'Close H' & 'Close L'
• % adjustment for 'Close H' & 'Close L'
• AVG: shows average (when both 'Close H' and 'Close L' are enabled)
Show Dashboard, including Top/Bottom levels of the desired source and calculated a and b values.
Show Swings + Dot size
