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Bitcoin Monthly Seasonality [Alpha Extract]The Bitcoin Monthly Seasonality indicator analyzes historical Bitcoin price performance across different months of the year, enabling traders to identify seasonal patterns and potential trading opportunities. This tool helps traders: Visualize which months historically perform best and worst for Bitcoin. Track average returns and win rates for each month of the year. Identify seasonal patterns to enhance trading strategies. Compare cumulative or individual monthly performance. 🔶 CALCULATION The indicator processes historical Bitcoin price data to calculate monthly performance metrics Monthly Return Calculation Inputs: Monthly open and close prices. User-defined lookback period (1-15 years). Return Types: Percentage: (monthEndPrice / monthStartPrice - 1) × 100 Price: monthEndPrice - monthStartPrice Statistical Measures Monthly Averages: ◦ Average return for each month calculated from historical data. Win Rate: ◦ Percentage of positive returns for each month. Best/Worst Detection: ◦ Identifies months with highest and lowest average returns. Cumulative Option Standard View: Shows discrete monthly performance. Cumulative View: Shows compounding effect of consecutive months. Example Calculation (Pine Script): monthReturn = returnType == "Percentage" ? (monthEndPrice / monthStartPrice - 1) * 100 : monthEndPrice - monthStartPrice calcWinRate(arr) => winCount = 0 totalCount = array.size(arr) if totalCount > 0 for i = 0 to totalCount - 1 if array.get(arr, i) > 0 winCount += 1 (winCount / totalCount) * 100 else 0.0 🔶 DETAILS Visual Features Monthly Performance Bars: ◦ Color-coded bars (teal for positive, red for negative returns). ◦ Special highlighting for best (yellow) and worst (fuchsia) months. Optional Trend Line: ◦ Shows continuous performance across months. Monthly Axis Labels: ◦ Clear month names for easy reference. Statistics Table: ◦ Comprehensive view of monthly performance metrics. ◦ Color-coded rows based on performance. Interpretation Strong Positive Months: Historically bullish periods for Bitcoin. Strong Negative Months: Historically bearish periods for Bitcoin. Win Rate Analysis: Higher win rates indicate more consistently positive months. Pattern Recognition: Identify recurring seasonal patterns across years. Best/Worst Identification: Quickly spot the historically strongest and weakest months. 🔶 EXAMPLES The indicator helps identify key seasonal patterns Bullish Seasons: Visualize historically strong months where Bitcoin tends to perform well, allowing traders to align long positions with favorable seasonality. Bearish Seasons: Identify historically weak months where Bitcoin tends to underperform, helping traders avoid unfavorable periods or consider short positions. Seasonal Strategy Development: Create trading strategies that capitalize on recurring monthly patterns, such as entering positions in historically strong months and reducing exposure during weak months. Year-to-Year Comparison: Assess how current year performance compares to historical seasonal patterns to identify anomalies or confirmation of trends. 🔶 SETTINGS Customization Options Lookback Period: Adjust the number of years (1-15) used for historical analysis. Return Type: Choose between percentage returns or absolute price changes. Cumulative Option: Toggle between discrete monthly performance or cumulative effect. Visual Style Options: Bar Display: Enable/disable and customize colors for positive/negative bars, Line Display: Enable/disable and customize colors for trend line, Axes Display: Show/hide reference axes. Visual Enhancement: Best/Worst Month Highlighting: Toggle special highlighting of extreme months, Custom highlight colors for best and worst performing months. The Bitcoin Monthly Seasonality indicator provides traders with valuable insights into Bitcoin's historical performance patterns throughout the year, helping to identify potentially favorable and unfavorable trading periods based on seasonal tendencies.
Pine Script® göstergesi
AlphaExtract tarafından
5
Planting & Harvesting SeasonsHello all, as a commodity trader, I use a lot of seasonal patterns in my analysis. Some time ago, I came up with the idea to develop a simple script that visually overlays the typical planting and harvesting periods for key agricultural futures directly on the chart. This script automatically detects the underlying commodity based on the symbol (e.g. ZC, ZW, ZS, CT) and displays color-coded zones for each seasonal window. These zones are based on historical crop calendars and help identify when planting or harvesting typically takes place. The goal is to better align technical setups with fundamental seasonal factors. This is a basic version and meant as a visual aid — not a trading signal in itself. Hope you enjoy it and any feedback is highly appreciated!
Pine Script® göstergesi
DerAndi72 tarafından
[COG]S&P 500 Weekly Seasonality ProjectionS&P 500 Weekly Seasonality Projection This indicator visualizes S&P 500 seasonality patterns based on historical weekly performance data. It projects price movements for up to 26 weeks ahead, highlighting key seasonal periods that have historically affected market performance. Key Features: Projects price movements based on historical S&P 500 weekly seasonality patterns (2005-2024) Highlights six key seasonal periods: Jan-Feb Momentum, March Lows, April-May Strength, Summer Strength, September Dip, and Year-End Rally Customizable forecast length from 1-26 weeks with quick timeframe selection buttons Optional moving average smoothing for more gradual projections Detailed statistics table showing projected price and percentage change Seasonality mini-map showing the full annual pattern with current position Customizable colors and visual elements How to Use: Apply to S&P 500 index or related instruments (daily timeframe or higher recommended) Set your desired forecast length (1-26 weeks) Monitor highlighted seasonal zones that have historically shown consistent patterns Use the projection line as a general guideline for potential price movement Settings: Forecast length: Configure from 1-26 weeks or use quick select buttons (1M, 3M, 6M, 1Y) Visual options: Customize colors, backgrounds, label sizes, and table position Display options: Toggle statistics table, period highlights, labels, and mini-map This indicator is designed as a visual guide to help identify potential seasonal tendencies in the S&P 500. Historical patterns are not guarantees of future performance, but understanding these seasonal biases can provide valuable context for your trading decisions. Note: For optimal visualization, use on Daily timeframe or higher. Intraday timeframes will display a warning message.
Pine Script® göstergesi
CognitiveAlpha tarafından
[COG]Nasdaq Weekly Seasonality ProjectionNasdaq Weekly Seasonality Projection This indicator provides a visualization of Nasdaq seasonality patterns based on historical weekly performance data. It projects price movements for up to 26 weeks ahead, highlighting key seasonal periods that have historically affected tech stocks. Key Features: Projects price movements based on historical Nasdaq weekly seasonality patterns Highlights six key seasonal periods: January Effect, March Lows, April-May Strength, Tech Summer Rally, September Dip, and Q4 Tech Rally Customizable forecast length from 1-26 weeks with quick timeframe selection buttons Optional moving average smoothing for more gradual projections Detailed statistics table showing projected price and percentage change Seasonality mini-map showing the full annual pattern with current position Customizable colors and visual elements How to Use: Apply to Nasdaq indices or tech-focused instruments (daily timeframe or higher recommended) Set your desired forecast length (1-26 weeks) Monitor highlighted seasonal zones that have historically shown consistent patterns Use the projection line as a general guideline for potential price movement Settings: Forecast length: Configure from 1-26 weeks or use quick select buttons (1M, 3M, 6M, 1Y) Visual options: Customize colors, backgrounds, label sizes, and table position Display options: Toggle statistics table, period highlights, labels, and mini-map This indicator is designed as a visual guide to help identify potential seasonal tendencies in Nasdaq and tech stocks. Historical patterns are not guarantees of future performance, but understanding these seasonal biases can provide valuable context for your trading decisions. Note: For optimal visualization, use on Daily timeframe or higher. Intraday timeframes will display a warning message.
Pine Script® göstergesi
CognitiveAlpha tarafından
1
Multi-Timeframe VWAP Master ProThe Multi-Timeframe VWAP Suite is a comprehensive and highly customizable indicator designed for traders who rely on Volume-Weighted Average Price (VWAP) across multiple timeframes and periods. This tool provides a complete suite of VWAP calculations, including daily, weekly, monthly, quarterly, yearly, and custom VWAPs, allowing traders to analyze price action and volume trends with precision. Whether you're a day trader, swing trader, or long-term investor, this indicator offers unparalleled flexibility and depth for your trading strategy. Multi-Timeframe VWAPs: Daily, Weekly, Monthly, Quarterly, and Yearly VWAPs: Track VWAP across various timeframes to identify key support and resistance levels. Customizable Timeframes: Use the SMA timeframe input to adjust the period for moving averages and other calculations. Previous Period VWAPs: Previous Daily, Weekly, Monthly, and Quarterly VWAPs: Analyze historical VWAP levels to understand past price behavior and identify potential reversal zones. Previous Year Quarterly VWAPs: Compare current price action to VWAP levels from specific quarters of the previous year. Custom VWAPs: Custom Start Date and Timeframe: Define your own VWAP periods by specifying a start date and timeframe, allowing for tailored analysis. Dynamic Custom VWAP Calculation: Automatically calculates VWAP based on your custom inputs, ensuring flexibility for unique trading strategies. Seasonal and Yearly VWAPs: April, July, and October VWAPs: Analyze seasonal trends by tracking VWAP levels for specific months. Yearly VWAP: Get a broader perspective on long-term price trends with the yearly VWAP. SMA Integration: SMA Overlay: Combine VWAP analysis with a Simple Moving Average (SMA) for additional confirmation of trends and reversals. Customizable SMA Length and Timeframe: Adjust the SMA settings to match your trading style and preferences. User-Friendly Customization: Toggle Visibility and Labels: Easily enable or disable the display of specific VWAPs and their labels to keep your chart clean and focused. Color Customization: Each VWAP line and label is color-coded for easy identification and can be customized to suit your preferences. Dynamic Labeling: Automatic Labels: Labels are dynamically placed on the last bar, providing clear and concise information about each VWAP level. Customizable Label Text: Labels include detailed information, such as the timeframe or custom period, for quick reference. Flexible Timeframe Detection: Automatic Timeframe Detection: The indicator automatically detects new days, weeks, months, and quarters, ensuring accurate VWAP calculations. Support for Intraday and Higher Timeframes: Works seamlessly on all chart timeframes, from 1-minute to monthly charts. Previous Year Quarterly VWAPs: Q1, Q2, Q3, Q4 VWAPs: Compare current price action to VWAP levels from specific quarters of the previous year. User-Selectable Year: Choose the year for which you want to calculate previous quarterly VWAPs. Persistent Monthly VWAPs: Option to Persist Monthly VWAPs Year-Round: Keep monthly VWAP levels visible even after the month ends for ongoing analysis. Comprehensive Analysis: Combines multiple VWAP timeframes and periods into a single tool, eliminating the need for multiple indicators. Customizable and Flexible: Tailor the indicator to your specific trading strategy with customizable timeframes, periods, and settings. Enhanced Decision-Making: Gain deeper insights into price action and volume trends across different timeframes, helping you make more informed trading decisions. Clean and Organized Charts: Toggle visibility and labels to keep your chart clutter-free while still accessing all the information you need. Ideal For: Day Traders: Use daily and intraday VWAPs to identify intraday support and resistance levels. Swing Traders: Analyze weekly and monthly VWAPs to spot medium-term trends and reversals. Long-Term Investors: Leverage quarterly and yearly VWAPs to understand long-term price behavior and key levels. Seasonal Traders: Track April, July, and October VWAPs to capitalize on seasonal trends. The Multi-Timeframe VWAP Suite is a powerful and versatile tool for traders of all styles and timeframes. With its comprehensive suite of VWAP calculations, customizable settings, and user-friendly design, it provides everything you need to analyze price action and volume trends with precision and confidence. Whether you're looking to fine-tune your intraday strategy or gain a broader perspective on long-term trends, this indicator has you covered.
Pine Script® göstergesi
starseedmaster1 tarafından
2
Window Seasonality IndicatorThis is a time window seasonal returns indicator. That is, it will provide the mean returns for a given time window based on a given number of lookbacks set by the user. The script finds matching time windows, e.g., 1st week of March going back 5 years or 9:00-10:00 window of every day going 50 days, and then calculates an average return for that window close price with respect to the close price in the immediately preceding time window, e.g. last week of February or 8:00-9:00 close price, respectively. There are 4 input options: 1) Historical Periods to Average: Set the number of matching historical windows with which to calculate an average price. The max is 730 lookback windows. Note: for monthly or weekly windows, setting too large a number will cause the script to error out. 2) Use Open Price: calculates the seasonal returns using the open price rather than close price. 3) Show Bands: select from 1 Gaussian standard deviation or a nonparamateric ranked confidence interval. As a rough heuristic, the Gaussian band requires at least 30 lookback periods, and the ranked confidence interval requires 50 or more. 4) Upper Percentile: set the upper cutoff for ranked confidence interval. 5) Lower Percentile: set the lower cutoff for ranked confidence interval. Please be aware, this indicator does not use rigorous statistical methodology and does not imply predictive power. You'll notice the range bands are very wide. Do not trade solely based on this indicator! Certain time windows, such as weekly and monthly, will make more sense applied to commodities, where annual cycles play a role in its supply and demand dynamics. Hourly windows are more useful in looking at equities markets. I like to look at equities with 1-hr windows to see if there is some pattern to overnight behavior or for market open and close.
Pine Script® göstergesi
redbulleconomy tarafından
RShar Seasonal RSISeasonal RSI This indicator, Seasonal RSI, is designed to enhance trading decisions by combining the **Relative Strength Index (RSI)** with insights derived from historical **seasonality patterns**. It not only calculates RSI but also overlays seasonality data for the current week of the year, providing traders with a more contextualized view of market conditions. --- ### **Key Features** #### 1. **Relative Strength Index (RSI) Calculation** - The script calculates the RSI for a user-defined period (`RSI Length`), which is an oscillator used to measure the speed and magnitude of price changes. - RSI values are plotted on the chart, helping traders identify **overbought** and **oversold** conditions. - Thresholds for **Overbought** and **Oversold** levels are customizable, with default values of 70 and 30, respectively. --- #### 2. **Dynamic RSI Coloring Based on Seasonality** - The color of the RSI line dynamically adjusts based on historical **win rates** for the current week of the year: - **Bright Green** for win rates > 65%. - **Green** for win rates between 50-65%. - **Red** for win rates between 35-50%. - **Dark Red** for win rates < 35%. - This feature gives traders a quick visual cue about whether the historical performance of the current week tends to be bullish, neutral, or bearish. --- #### 3. **Overbought and Oversold Level Visualization** - Overbought and oversold levels are displayed as dotted horizontal lines on the RSI chart. - These levels act as visual guides for potential price reversals: - **Overbought (default 70)**: Indicates potential selling pressure. - **Oversold (default 30)**: Indicates potential buying pressure. --- #### 4. **Seasonality Data Integration** - Historical **seasonality data** is used to analyze price performance patterns for each week of the year: - **Win Rate**: The percentage of years in which prices closed higher during the current week. - **Average Weekly Change**: The average price percentage change during the current week over historical data. - This data provides additional context to RSI readings, helping traders align their strategies with seasonal tendencies. --- #### 5. **Information Table Overlay** - A table is displayed in the **top-right corner** of the chart, summarizing seasonality data for the current week: - **Week Win Rate**: Displays the percentage of historical years where prices rose during this week. - **Avg Weekly Change**: Shows the average percentage price change for the current week. Positive values are displayed in green, and negative values are shown in red. - This overlay provides actionable insights without cluttering the chart. --- ### **How It Works** 1. **Seasonality Data**: - A function (`getSeasonalityForWeek`) fetches or uses predefined mock seasonality data for each week of the year. - For each week, it calculates: - The **Win Rate** (percentage of years with positive performance). - The **Mean Change** (average price percentage change). 2. **RSI Plot**: - The RSI line is plotted on the chart. - The line's color is determined by the win rate for the current week, providing a visual representation of historical performance trends. 3. **Threshold Visualization**: - Horizontal lines for overbought and oversold levels are drawn to assist in identifying potential reversal points. 4. **Information Table**: - The table summarizes the current week's seasonality data for quick reference, helping traders make data-driven decisions. --- ### **Use Cases** - **Short-Term Traders**: Use the dynamic RSI colors and seasonality table to align short-term trades with historical patterns of weekly performance. - **Swing Traders**: Identify whether a stock or market is in an overbought/oversold condition while considering the seasonal tendency for the week. - **Contextual Decision-Making**: Combine traditional RSI signals with historical data to reduce false signals and improve timing. --- ### **Limitations** - The script uses **mock seasonality data** in its default state. To make it fully functional, replace the mock data with actual historical performance metrics for your specific ticker or market. - The indicator does not fetch real-time external data due to Pine Script’s limitations, so all seasonality data must be manually updated or hardcoded. --- This indicator provides a powerful way to combine technical analysis with historical trends, offering a unique edge to traders by adding seasonal context to RSI signals.
Pine Script® göstergesi
RShar_Capital tarafından
Seasonality normalizedThis custom indicator provides an in-depth analysis of historical price performance to identify potential seasonal patterns and correlations. By examining data from the past 10 years, the indicator filters out outlier performances and focuses on the most consistent seasonal trends. Key Features: Intelligent Clustering Algorithm: The indicator employs a custom clustering algorithm to group similar yearly performances together. This approach effectively filters out anomalous years, such as those affected by black swan events like the COVID-19 pandemic, providing a more accurate representation of typical seasonal behavior. Seasonal Correlation Measurement: The indicator calculates the percentage of years exhibiting similar performance patterns for each week. This measurement helps traders assess the strength of seasonal correlations and make informed decisions based on the consistency of historical data. High and Low Seasonality Bands: The indicator plots two distinct bands on the chart, representing the expected range of price movement based on historical highs and lows. These bands offer valuable insight into potential support and resistance levels during specific weeks. Enhanced Visualization: Weeks with high seasonal correlations are prominently highlighted, making it easy for traders to identify periods with the strongest historical patterns. The seasonality bands extend to cover the last and future 3 months, divided into weekly segments, providing a comprehensive view of the current market context. Dynamic Adaptation: The seasonality bands are dynamically tied to the current high and low prices, ensuring that the indicator remains relevant and responsive to the latest market conditions. Under the Hood: The indicator begins by calculating the performance of the asset for each week, going back 10 years. The custom clustering algorithm groups similar performances together, effectively filtering out outlier years. The percentage of years falling into the largest performance cluster is calculated, representing the seasonal correlation for each week. The average performance of the largest cluster is used to plot the high and low seasonality bands, anchored to the current high and low prices. The bands are color-coded based on the strength of the seasonal correlation, with darker colors indicating higher consistency. This indicator is designed to help professional traders identify and capitalize on seasonal patterns in the market. By providing a robust and adaptable framework for analyzing historical performance, the Seasonality Indicator offers valuable insights for making informed trading decisions. We believe this tool will be a valuable addition to your trading arsenal, complementing your existing strategies and enhancing your market analysis capabilities. As a professional trader, your feedback and ideas are invaluable to us. Please share your thoughts, experiences, and suggestions for improvement as you incorporate the Seasonality Indicator into your trading workflow. Together, we can refine this powerful tool to better serve the needs of the trading community.
Pine Script® göstergesi
dsvaryts tarafından
1
Season ChartThis overlay is built on the idea of seasonal charts. It is constructed by taking the percentage change from each close and recording that change for every trading day of any year that is within the sample. We then take the average for each day of all the years. These averages are then cumulated to create the chart as per traditional seasonal chart construction. I have also taken a trimmed mean of the averages to try and dampen the impact one off moves that may have a dramatic effect on the daily averages (for example the crash to $0 in oil in April 2020) however, even removing 10% may not guarantee one off moves won’t affect the average. The construction of the chart is completely dependent on the data provided by TradingView and so it is recommended that if longer sample sizes are used, the user go back to check that the years contained within the sample have a full history. Some data may have large gaps in their history and this can distort the seasonality readings. I have attempted to align the chart with the first trading day of the year, but the start of some months may be out by a day or two as it becomes difficult to track all weeks with differing market holidays closures each year and this in turn varies the total amount of actual trading days in each year as well as leap years. This overlay is designed for the Daily time frame only and will not work on Crypto or any other instrument that trades outside of usual business weekdays. Future updates may include the ability to adapt to Crypto instruments. All feedback and comments welcome!
Pine Script® göstergesi
east-west_trading tarafından
1
Seasonality Widget [LuxAlgo]The Seasonality Widget tool allows users to easily visualize seasonal trends from various data sources. Users can select different levels of granularity as well as different statistics to express seasonal trends. 🔶 USAGE Seasonality allows us to observe general trends occurring at regular intervals. These intervals can be user-selected from the granularity setting and determine how the data is grouped, these include: Hour Day Of Week Day Of Month Month Day Of Year The above seasonal chart shows the BTCUSD seasonal price change for every hour of the day, that is the average price change taken for every specific hour. This allows us to obtain an estimate of the expected price move at specific hours of the day. Users can select when data should start being collected using the "From Date" setting, any data before the selected date will not be included in the calculation of the Seasonality Widget. 🔹 Data To Analyze The Seasonality Widget can return the seasonality for the following data: Price Change Closing price minus the previous closing price. Price Change (%) Closing price minus the previous closing price, divided by the previous closing price, then multiplied by 100. Price Change (Sign) Sign of the price change (-1 for negative change, 1 for positive change), normalized in a range (0, 100). Values above 50 suggest more positive changes on average. Range High price minus low price. Price - SMA Price minus its simple moving average. Users can select the SMA period. Volume Amount of contracts traded. Allow users to see which periods are generally the most /least liquid. Volume - SMA Volume minus its simple moving average. Users can select the SMA period. 🔹 Filter In addition to the "From Date" threshold users can exclude data from specific periods of time, potentially removing outliers in the final results. The period type can be specified in the "Filter Granularity" setting. The exact time to exclude can then be specified in the "Numerical Filter Input" setting, multiple values are supported and should be comma separated. For example, if we want to exclude the entire 2008 period we can simply select "Year" as filter granularity, then input 2008 in the "Numerical Filter Input" setting. Do note that "Sunday" uses the value 1 as a day of the week. 🔶 DETAILS 🔹 Supported Statistics Users can apply different statistics to the grouped data to process. These include: Mean Median Max Min Max-Min Average Using the median allows for obtaining a measure more robust to outliers and potentially more representative of the actual central tendency of the data. Max and Min do not express a general tendency but allow obtaining information on the highest/lowest value of the analyzed data for specific periods. 🔶 SETTINGS Granularity: Periods used to group data. From Data: Starting point where data starts being collected 🔹 Data Analyze: Specific data to be processed by the seasonality widget. SMA Length: Period of the simple moving average used for "Price - SMA" and "Volume - SMA" options in "Analyze". Statistic: Statistic applied to the grouped data. 🔹 Filter Filter Granularity: Period type to exclude in the processed data. Numerical Filter Input: Determines which of the selected hour/day of week/day of month/month/year to exclude depending on the selected Filter Granularity. Only numerical inputs can be provided. Multiple values are supported and must be comma-separated.
Pine Script® göstergesi
LuxAlgo tarafından
8
Seasonal Tendencies - SMC IndicatorsA Seasonal Tendency refers to a historical price action behaviour that tends to repeat during specific times of the year, month over month. It's a roadmap to navigate price action on the daily chart to help determine the medium to long-term bias. Seasonal Tendencies are NOT an exact prediction of future price action but rather serve as a guideline for spotting high-probability opportunities when combined with other elements of SMC Price Action analysis, such as Order Blocks, Fair Value Gaps, etc... The Seasonal Tendencies Indicator has been tested to match what ICT has taught in his lectures. It can be applied to any Market or Asset. However, it's limited by the maximum number of years available on tradingview. Traders can use this Seasonal Tendencies indicator to support their already existing analysis as an added confirmation tool. This indicator should not be used as a main reason to enter a trade idea. The Seasonal Tendencies Indicator can be used in 2 ways: 1) To look for potential points of long-term reversals during specific times of the year. 2) To look for confirmation and align with an existing long-term trend. So how does it work? The Seasonal Tendencies Indicator takes the averages of the last 30, 10, and 5 years' prices by default and compares them to the current year's price action (Green Line). However, the number of years chosen for the averages can be modified in the indicator's setting. When looking at the historical price action lines, generally, the price tends to make the lows and highs during specific times of the year. Note that we should not look at the exact dates these lows and highs form, but we take time periods conceptually instead. In the example below, the SP500 5-year average made the low on 14 March, and the SP500 10-year average made the low on 23 March. This gives us the idea that "generally" SP500 makes the low of the year around the 2nd to 3rd week of March every year. So, IF the trader's analysis was pointing out that SP500 is Bullish, then we use the information that we derived from the Seasonal Tendencies Indicator to look for long setups around the 2nd to 3rd week of March for medium to long-term swing trades. The Seasonal Tendencies Indicator can also be useful for day traders as it helps support their daily bias to look for trades within the direction of the higher timeframe trend. How do we measure the strength of the Seasonal Tendencies? When using the Seasonal Tendencies Indicator, it's important to look for periods where the averages converge and get closer to each other. This usually indicates that during those specific periods, there is a high probability for the price to behave in a certain way. So the closer the averages are to each other, the more likely the price would respect the Seasonal Tendencies. Bonus Feature Premium Discount Range As a bonus feature, split the Seasonal Tendencies Indicator's Range into 4 quarters to indicate when the price is at a Premium (above the 50% level in Red) and when the price is at a Discount (below the 50% level in blue). Each Premium and Discount range is also split into 2 halves. Those levels can also be used to identify potential turning points when comparing the Current Year's price positioning in the Yearly Range to historical price action. As you can see from the example below, most major turning points happen at around key price levels.
Pine Script® göstergesi
SMCIndicators tarafından
1
Seasonality Chart [LuxAlgo]The Seasonality Chart script displays seasonal variations of price changes that are best used on the daily timeframe. Users have the option to select the calculation lookback (in years) as well as show the cumulative sum of the seasonal indexes. 🔶 SETTINGS Lookback (Years): Number of years to use for the calculation of the seasonality chart. Cumulative Sum: Displays the cumulative sum of seasonal indexes. Use Percent Change: Uses relative price changes (as a percentage) instead of absolute changes. Linear Regression: Fits a line on the seasonality chart results using the method of least squares. 🔶 USAGE Seasonality refers to the recurrent tendencies in a time series to increase or decrease at specific times of the year. The proposed tool can highlight the seasonal variation of price changes. It is common for certain analysts to use a cumulative sum of these indexes to display the results, highlighting months with the most significant bullish/bearish progressions. The above chart allows us to highlight which months prices tended to have their worst performances over the selected number of years. 🔹 Note Daily price changes are required for the construction of the seasonal chart. Thus, charts using a low timeframe might lack data compared to higher ones. We recommend using the daily timeframe for the best user experience. 🔶 DETAILS To construct our seasonal chart, we obtain the average price changes for specific days on a specific month over a user-set number of years from January to December. These individual averages form "seasonal indexes." This is a common method in classical time series decomposition. Example: To obtain the seasonal index of price changes on January first we record every price change occuring on January first over the years of interest, we then average the result. This operation is done for all days in each month to construct our seasonal chart. Seasonal variations are often highlighted if the underlying time series is affected by seasonal factors. For market prices, it is difficult to assess if there are stable seasonal variations on all securities. The consideration of seasonality by market practitioners has often been highlighted through strategies or observations. One of the most common is expressed by the adage "Sell in May and Go Away" for the US market. We can also mention: January Effect Santa Claus Rally Mark Twain Effect ...etc. These are commonly known as calendar effects and appear from the study of seasonal variations over certain years.
Pine Script® göstergesi
LuxAlgo tarafından
7
ILM Seasonality Monthly - Day of Month - Tabular FormUse this indicator on Daily Timeframe This indicator displays the seasonality data for any instrument (index/stock/ futures /currency) in a tabular data by day of the month (Day 1..31). User can change the start of the year and month for analysis from the inputs. Year is represented in rows and Day of the month (Day) is represented in cols. This indicator uses Daily Data feed to calculate the % change Summary data for Day of month is displayed at the bottom of the table
Pine Script® göstergesi
ILuvMarkets tarafından
9
SFC Smart Money Manipulation - Time, Advanced Market StructureThis indicator shows the market structure in more advanced way and different time cycles. Markets moves in cycles and swings. The indicator will help to determine these cycles and swings by time and price. These are the two columns of the market understanding. The third one is volume/ momentum, but it will not be discussed here. Advanced Market Structure According to ICT and Larry Williams Market Structure is not only Highs and Lows. They present more advanced understanding of the MS: -Short Term Highs/ Lows -Intermediate Term Highs/ Lows -Long Term Highs/ Lows Rules of how to determine the Swing Points according to Larry Williams: "A market has made a short-term low when we have a day (or bar if you are using different time periods) that has a higher low on both sides. By the same token a short-term high will be a day (or bar) that has lower bars on both sides of it." "A short-term high with lower short-term highs on both sides is an intermediate- term high. By the same token, a short-term low with higher short-term lows on both sides is an intermediate-term low." "An intermediate-term high with lower intermediate-term highs on both sides of it is just naturally a long-term high by our definition, thanks to understanding market structure. An intermediate-term low with higher intermediate-term lows on both sides of it is just naturally a long-term low by our definition, thanks to understanding market structure." If the Highs and Lows are labeled properly there is high probability to predict the next High or Low. In this way the trader will know how the current trend is changing and what kind of retracement is coming - deep or shallow. Timing Market moves in time cycles. There is a theory that the swings are equal by time and length. This is not always the case, but very very often. Indicator time features: - Swing Trading days - how many time market needed to form a swing. Only Long term(main) Swings are measured. This will help trader to label T-formations. " T Formations is cyclically related for formations that can be drawn to project the time frame of likely turning points. Basically T-formations are based on the concept that the time distance between the starting low/high of the cyclical wave and its peak is likely to be subsequently repeated between that peak and the final low/high of that cycle." - Seasonality - theoretically an asset should go up or down in particular yearly quarter. Practically the direction not always match to quarters. Thats why the indicator shows the theoretical seasonal direction and historical real direction. Seasonal direction is automatically displayed or XAUUSD, XAGUSD, EURUSD, AUDUSD, GBPUSD. There is a ways to set the seasonality manually. - Earnings Season - This time is very important for Stocks and Indices. Most of the time the assets are in bullish trend during the Earnings Seasons. - Monthly separator - Shows the monthly time cycle - Gold bullish months - There are studies on Gold market. They shows that Gold is very bullish in particular months. These are displayed. The indicator works only on Daily Time Frame.
Pine Script® göstergesi
SerpentForexClub tarafından
1
Month/Month Percentage % Change, Historical; Seasonal TendencyTable of monthly % changes in Average Price over the last 10 years (or the 10 yrs prior to input year). Useful for gauging seasonal tendencies of an asset; backtesting monthly volatility and bullish/bearish tendency. ~~User Inputs~~ Choose measure of average: sma(close), sma(ohlc4), vwap(close), vwma(close). Show last 10yrs, with 10yr average % change, or to just show single year. Chose input year; with the indicator auto calculating the prior 10 years. Choose color for labels and size for labels; choose +Ve value color and -Ve value color. Set 'Daily bars in month': 21 for Forex/Commodities/Indices; 30 for Crypto. Set precision: decimal places ~~notes~~ -designed for use on Daily timeframe (tradingview is buggy on monthly timeframe calculations, and less precise on weekly timeframe calculations). -where Current month of year has not occurred yet, will print 9yr average. -calculates the average change of displayed month compared to the previous month: i.e. Jan22 value represents whole of Jan22 compared to whole of Dec21. -table displays on the chart over the input year; so for ES, with 2010 selected; shows values from 2001-2010, displaying across 2010-2011 on the chart. -plots on seperate right hand side scale, so can be shrunk and dragged vertically. -thanks to @gabx11 for the suggestion which inspired me to write this
Pine Script® göstergesi
twingall tarafından
10
RSI - S&P Sector ETFsThe script displays RSI of each S&P SPDR Sector ETF XLB - Materials XLC - Communications XLE - Energy XLF - Financials XLI - Industrials XLK - Technology XLP - Consumer Staples XLRE - Real Estate XLU - Utilities XLV - Healthcare XLY - Consumer Discretionary It is meant to identify changes in sector rotation, compare oversold/overbought signals of each sector, and/or any price momentum trading strategy applicable to a trader.
Pine Script® göstergesi
All_Verklempt tarafından
2