Şimdi başlat

Komut dosyalarını "Cycle" için ara

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.
Pine Script® göstergesi
BarefootJoey tarafından
6
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.
Pine Script® göstergesi
WillSebastian tarafından
1
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.
Pine Script® göstergesi
Abdozo tarafından
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
Pine Script® göstergesi
BackQuant tarafından
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.
Pine Script® göstergesi
mezor13 tarafından
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.
Pine Script® göstergesi
DarklyEnergized tarafından
3
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!
Pine Script® göstergesi
TradeAutomation tarafından
6
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.
Pine Script® göstergesi
Alliegaytor tarafından
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.
Pine Script® göstergesi
jtomasfg tarafından
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! ⌛
Pine Script® göstergesi
BarefootJoey tarafından
4
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"
Pine Script® göstergesi
fikira tarafından
4
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
Pine Script® göstergesi
fikira tarafından
9
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
Pine Script® göstergesi
INTELLECT_city tarafından
90cycle @joshuuu90 minute cycle is a concept about certain time windows of the day. This indicator has two different options. One uses the 90 minute cycle times mentioned by traderdaye, the other uses the cls operational times split up into 90 minutes session. e.g. we can often see a fake move happening in the 90 minute window between 2.30am and 4am ny time. The indicator draws vertical lines at the start/end of each session and the user is able to only display certain sessions (asia, london, new york am and pm) For the traderdayes option, the indicator also counts the windows from 1 to 4 and calls them q1,q2,q3,q4 (q-quarter) ⚠️ Open Source ⚠️ Coders and TV users are authorized to copy this code base, but a paid distribution is prohibited. A mention to the original author is expected, and appreciated. ⚠️ Terms and Conditions ⚠️ This financial tool is for educational purposes only and not financial advice. Users assume responsibility for decisions made based on the tool's information. Past performance doesn't guarantee future results. By using this tool, users agree to these terms.
Pine Script® göstergesi
joshuuu tarafından
15
inverse_fisher_transform_adaptive_stochastic█ Description The indicator is the implementation of inverse fisher transform an indicator transform of the adaptive stochastic (dominant cycle), as in the Cycle Analytics for Trader pg. 198 (John F. Ehlers). Indicator transformation in brief means reshaping the indicator to be more interpretable. The inverse fisher transform is achieved by compressing values near the extremes many extraneous and irrelevant wiggles are removed from the indicator, as cited. █ Inverse Fisher Transform input = 2*(adaptive_stoc - .5) output = e(2*k*input) -1 / e(2*k*input) +1 █ Feature: iFish i.e. output value trigger i.e. previous 1 bar of iFish * 0.90 if iFish crosses above the trigger, consider a buy indicated with the green line while, iFish crosses below the trigger, consider a sell indicate by the red line in addition iFish needs to be greater than the previous iFish
Pine Script® göstergesi
palitoj_endthen tarafından
BTC Pi MultipleThe Pi Multiple is a function of 350 and 111-day moving average. When both intersect and the 111-day MA crosses above, it has historically coincided with a cycle top with a 3-day margin. With the Pi Multiple, this intersection is visible when the line crosses zero upwards. The indicator is called the Pi Multiple because 350/111 is close to Pi. It is based on the Pi Cycle Top Indicator developed by Philip Swift and has been modified for better readability by David Bertho.
Pine Script® göstergesi
cryptoonchain tarafından
2
QG-Particle OscillatorThis is an advanced oscillator based on auxiliary particle filter. It separates signal from noise and uses smoothing algorithm similar to JMA. The main oscillator line is a smoothed and detrended version of the price series similar to detrended oscillator line. The purple/aqua lines are a prediction based on an additional adaptive smoothing technique and current volatility. The prediction is smoothed twice and is supposed to represent the true signal without any noise, thus the prediction should always be less than the raw detrend line. However, certain volatile conditions will cause the prediction to cross above/below the detrend line. When this happens the likelihood of a reversal or pullback is extremely high. There are 3 dots on the zero line- Red, Green and Yellow. The yellow dots warn of an eminent pullback 2 bars before it actually occurs. This is a non-repainting indicator. One can also use this indicator to trade CCI signals, similar to zero line rejection in existing trend. The indicator has 2 settings- Period and Phase. The phase represents cycle phase and Period represents oscillator period. Credits: This indicator has been originally published for Ninjatrader and this is conversion into pinescript.
Pine Script® göstergesi
QuantG tarafından
Lyapunov Market Instability (LMI)Lyapunov Market Instability (LMI) What is Lyapunov Market Instability? Lyapunov Market Instability (LMI) is a revolutionary indicator that brings chaos theory from theoretical physics into practical trading. By calculating Lyapunov exponents—a measure of how rapidly nearby trajectories diverge in phase space—LMI quantifies market sensitivity to initial conditions. This isn't another oscillator or trend indicator; it's a mathematical lens that reveals whether markets are in chaotic (trending) or stable (ranging) regimes. Inspired by the meditative color field paintings of Mark Rothko, this indicator transforms complex chaos mathematics into an intuitive visual experience. The elegant simplicity of the visualization belies the sophisticated theory underneath—just as Rothko's seemingly simple color blocks contain profound depth. Theoretical Foundation (Chaos Theory & Lyapunov Exponents) In dynamical systems, the Lyapunov exponent (λ) measures the rate of separation of infinitesimally close trajectories: λ > 0: System is chaotic—small changes lead to dramatically different outcomes (butterfly effect) λ < 0: System is stable—trajectories converge, perturbations die out λ ≈ 0: Edge of chaos—transition between regimes Phase Space Reconstruction Using Takens' embedding theorem , we reconstruct market dynamics in higher dimensions: Time-delay embedding: Create vectors from price at different lags Nearest neighbor search: Find historically similar market states Trajectory evolution: Track how these similar states diverged over time Divergence rate: Calculate average exponential separation Market Application Chaotic markets (λ > threshold): Strong trends emerge, momentum dominates, use breakout strategies Stable markets (λ < threshold): Mean reversion dominates, fade extremes, range-bound strategies work Transition zones: Market regime about to change, reduce position size, wait for confirmation How LMI Works 1. Phase Space Construction Each point in time is embedded as a vector using historical prices at specific delays (τ). This reveals the market's hidden attractor structure. 2. Lyapunov Calculation For each current state, we: - Find similar historical states within epsilon (ε) distance - Track how these initially similar states evolved - Measure exponential divergence rate - Average across multiple trajectories for robustness 3. Signal Generation Chaos signals: When λ crosses above threshold, market enters trending regime Stability signals: When λ crosses below threshold, market enters ranging regime Divergence detection: Price/Lyapunov divergences signal potential reversals 4. Rothko Visualization Color fields: Background zones represent market states with Rothko-inspired palettes Glowing line: Lyapunov exponent with intensity reflecting market state Minimalist design: Focus on essential information without clutter Inputs: 📐 Lyapunov Parameters Embedding Dimension (default: 3) Dimensions for phase space reconstruction 2-3: Simple dynamics (crypto/forex) - captures basic momentum patterns 4-5: Complex dynamics (stocks/indices) - captures intricate market structures Higher dimensions need exponentially more data but reveal deeper patterns Time Delay τ (default: 1) Lag between phase space coordinates 1: High-frequency (1m-15m charts) - captures rapid market shifts 2-3: Medium frequency (1H-4H) - balances noise and signal 4-5: Low frequency (Daily+) - focuses on major regime changes Match to your timeframe's natural cycle Initial Separation ε (default: 0.001) Neighborhood size for finding similar states 0.0001-0.0005: Highly liquid markets (major forex pairs) 0.0005-0.002: Normal markets (large-cap stocks) 0.002-0.01: Volatile markets (crypto, small-caps) Smaller = more sensitive to chaos onset Evolution Steps (default: 10) How far to track trajectory divergence 5-10: Fast signals for scalping - quick regime detection 10-20: Balanced for day trading - reliable signals 20-30: Slow signals for swing trading - major regime shifts only Nearest Neighbors (default: 5) Phase space points for averaging 3-4: Noisy/fast markets - adapts quickly 5-6: Balanced (recommended) - smooth yet responsive 7-10: Smooth/slow markets - very stable signals 📊 Signal Parameters Chaos Threshold (default: 0.05) Lyapunov value above which market is chaotic 0.01-0.03: Sensitive - more chaos signals, earlier detection 0.05: Balanced - optimal for most markets 0.1-0.2: Conservative - only strong trends trigger Stability Threshold (default: -0.05) Lyapunov value below which market is stable -0.01 to -0.03: Sensitive - quick stability detection -0.05: Balanced - reliable ranging signals -0.1 to -0.2: Conservative - only deep stability Signal Smoothing (default: 3) EMA period for noise reduction 1-2: Raw signals for experienced traders 3-5: Balanced - recommended for most 6-10: Very smooth for position traders 🎨 Rothko Visualization Rothko Classic: Deep reds for chaos, midnight blues for stability Orange/Red: Warm sunset tones throughout Blue/Black: Cool, meditative ocean depths Purple/Grey: Subtle, sophisticated palette Visual Options: Market Zones : Background fields showing regime areas Transitions: Arrows marking regime changes Divergences: Labels for price/Lyapunov divergences Dashboard: Real-time state and trading signals Guide: Educational panel explaining the theory Visual Logic & Interpretation Main Elements Lyapunov Line: The heart of the indicator Above chaos threshold: Market is trending, follow momentum Below stability threshold: Market is ranging, fade extremes Between thresholds: Transition zone, reduce risk Background Zones: Rothko-inspired color fields Red zone: Chaotic regime (trending) Gray zone: Transition (uncertain) Blue zone: Stable regime (ranging) Transition Markers: Up triangle: Entering chaos - start trend following Down triangle: Entering stability - start mean reversion Divergence Signals: Bullish: Price makes low but Lyapunov rising (stability breaking down) Bearish: Price makes high but Lyapunov falling (chaos dissipating) Dashboard Information Market State: Current regime (Chaotic/Stable/Transitioning) Trading Bias: Specific strategy recommendation Lyapunov λ: Raw value for precision Signal Strength: Confidence in current regime Last Change: Bars since last regime shift Action: Clear trading directive Trading Strategies In Chaotic Regime (λ > threshold) Follow trends aggressively: Breakouts have high success rate Use momentum strategies: Moving average crossovers work well Wider stops: Expect larger swings Pyramid into winners: Trends tend to persist In Stable Regime (λ < threshold) Fade extremes: Mean reversion dominates Use oscillators: RSI, Stochastic work well Tighter stops: Smaller expected moves Scale out at targets: Trends don't persist In Transition Zone Reduce position size: Uncertainty is high Wait for confirmation: Let regime establish Use options: Volatility strategies may work Monitor closely: Quick changes possible Advanced Techniques - Multi-Timeframe Analysis - Higher timeframe LMI for regime context - Lower timeframe for entry timing - Alignment = highest probability trades - Divergence Trading - Most powerful at regime boundaries - Combine with support/resistance - Use for early reversal detection - Volatility Correlation - Chaos often precedes volatility expansion - Stability often precedes volatility contraction - Use for options strategies Originality & Innovation LMI represents a genuine breakthrough in applying chaos theory to markets: True Lyapunov Calculation: Not a simplified proxy but actual phase space reconstruction and divergence measurement Rothko Aesthetic: Transforms complex math into meditative visual experience Regime Detection: Identifies market state changes before price makes them obvious Practical Application: Clear, actionable signals from theoretical physics This is not a combination of existing indicators or a visual makeover of standard tools. It's a fundamental rethinking of how we measure and visualize market dynamics. Best Practices Start with defaults: Parameters are optimized for broad market conditions Match to your timeframe: Adjust tau and evolution steps Confirm with price action: LMI shows regime, not direction Use appropriate strategies: Chaos = trend, Stability = reversion Respect transitions: Reduce risk during regime changes Alerts Available Chaos Entry: Market entering chaotic regime - prepare for trends Stability Entry: Market entering stable regime - prepare for ranges Bullish Divergence: Potential bottom forming Bearish Divergence: Potential top forming Chart Information Script Name: Lyapunov Market Instability (LMI) Recommended Use: All markets, all timeframes Best Performance: Liquid markets with clear regimes Academic References Takens, F. (1981). "Detecting strange attractors in turbulence" Wolf, A. et al. (1985). "Determining Lyapunov exponents from a time series" Rosenstein, M. et al. (1993). "A practical method for calculating largest Lyapunov exponents" Note: After completing this indicator, I discovered @loxx's 2022 "Lyapunov Hodrick-Prescott Oscillator w/ DSL". While both explore Lyapunov exponents, they represent independent implementations with different methodologies and applications. This indicator uses phase space reconstruction for regime detection, while his combines Lyapunov concepts with HP filtering. Disclaimer This indicator is for research and educational purposes only. It does not constitute financial advice or provide direct buy/sell signals. Chaos theory reveals market character, not future prices. Always use proper risk management and combine with your own analysis. Past performance does not guarantee future results. See markets through the lens of chaos. Trade the regime, not the noise. Bringing theoretical physics to practical trading through the meditative aesthetics of Mark Rothko Trade with insight. Trade with anticipation. — Dskyz , for DAFE Trading Systems
Pine Script® göstergesi
DskyzInvestments tarafından
TASC 2025.06 Cybernetic Oscillator█ OVERVIEW This script implements the Cybernetic Oscillator introduced by John F. Ehlers in his article "The Cybernetic Oscillator For More Flexibility, Making A Better Oscillator" from the June 2025 edition of the TASC Traders' Tips . It cascades two-pole highpass and lowpass filters, then scales the result by its root mean square (RMS) to create a flexible normalized oscillator that responds to a customizable frequency range for different trading styles. █ CONCEPTS Oscillators are indicators widely used by technical traders. These indicators swing above and below a center value, emphasizing cyclic movements within a frequency range. In his article, Ehlers explains that all oscillators share a common characteristic: their calculations involve computing differences . The reliance on differences is what causes these indicators to oscillate about a central point. The difference between two data points in a series acts as a highpass filter — it allows high frequencies (short wavelengths) to pass through while significantly attenuating low frequencies (long wavelengths). Ehlers demonstrates that a simple difference calculation attenuates lower-frequency cycles at a rate of 6 dB per octave. However, the difference also significantly amplifies cycles near the shortest observable wavelength, making the result appear noisier than the original series. To mitigate the effects of noise in a differenced series, oscillators typically smooth the series with a lowpass filter, such as a moving average. Ehlers highlights an underlying issue with smoothing differenced data to create oscillators. He postulates that market data statistically follows a pink spectrum , where the amplitudes of cyclic components in the data are approximately directly proportional to the underlying periods. Specifically, he suggests that cyclic amplitude increases by 6 dB per octave of wavelength. Because some conventional oscillators, such as RSI, use differencing calculations that attenuate cycles by only 6 dB per octave, and market cycles increase in amplitude by 6 dB per octave, such calculations do not have a tangible net effect on larger wavelengths in the analyzed data. The influence of larger wavelengths can be especially problematic when using these oscillators for mean reversion or swing signals. For instance, an expected reversion to the mean might be erroneous because oscillator's mean might significantly deviate from its center over time. To address the issues with conventional oscillator responses, Ehlers created a new indicator dubbed the Cybernetic Oscillator. It uses a simple combination of highpass and lowpass filters to emphasize a specific range of frequencies in the market data, then normalizes the result based on RMS. The process is as follows: Apply a two-pole highpass filter to the data. This filter's critical period defines the longest wavelength in the oscillator's passband. Apply a two-pole SuperSmoother (lowpass filter) to the highpass-filtered data. This filter's critical period defines the shortest wavelength in the passband. Scale the resulting waveform by its RMS. If the filtered waveform follows a normal distribution, the scaled result represents amplitude in standard deviations. The oscillator's two-pole filters attenuate cycles outside the desired frequency range by 12 dB per octave. This rate outweighs the apparent rate of amplitude increase for successively longer market cycles (6 dB per octave). Therefore, the Cybernetic Oscillator provides a more robust isolation of cyclic content than conventional oscillators. Best of all, traders can set the periods of the highpass and lowpass filters separately, enabling fine-tuning of the frequency range for different trading styles. █ USAGE The "Highpass period" input in the "Settings/Inputs" tab specifies the longest wavelength in the oscillator's passband, and the "Lowpass period" input defines the shortest wavelength. The oscillator becomes more responsive to rapid movements with a smaller lowpass period. Conversely, it becomes more sensitive to trends with a larger highpass period. Ehlers recommends setting the smallest period to a value above 8 to avoid aliasing. The highpass period must not be smaller than the lowpass period. Otherwise, it causes a runtime error. The "RMS length" input determines the number of bars in the RMS calculation that the indicator uses to normalize the filtered result. This indicator also features two distinct display styles, which users can toggle with the "Display style" input. With the "Trend" style enabled, the indicator plots the oscillator with one of two colors based on whether its value is above or below zero. With the "Threshold" style enabled, it plots the oscillator as a gray line and highlights overbought and oversold areas based on the user-specified threshold. Below, we show two instances of the script with different settings on an equities chart. The first uses the "Threshold" style with default settings to pass cycles between 20 and 30 bars for mean reversion signals. The second uses a larger highpass period of 250 bars and the "Trend" style to visualize trends based on cycles spanning less than one year:
Pine Script® göstergesi
PineCodersTASC tarafından
12
Bitcoin as % Global M2 signalThis script provides signal system: Buy signal: each time the YoY of the Global M2 rises more than 2.5% while the distance between the bitcoin price as a percentage of the Global M2 is below its yearly SMA. Sell signal: the distance between the bitcoin price as a percentage of the Global M2 and its yearly SMA is > 0.7 This is a very simple system, but it seems to work pretty well to ride the bitcoin price cycle wave. The parameters are hard coded but they can be easily changed to test different levels for both the buy and sell signals.
Pine Script® göstergesi
UnderstandingBitcoinPrice tarafından
2