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Seasonality-trading

Seasonality Window Analyzer PROSeasonality Window Analyzer measures how any asset has historically performed during a specific calendar window — defined by a start date and end date (day/month) — across every year of available history. Set your seasonal window once and the indicator automatically scans all historical data, isolating each year's price action within that exact period and recording the return from open to close of the window, the maximum intra-period rise, and the maximum intra-period drawdown. The results table lists every year individually with full color-coding, paginated for readability across long histories, with a persistent statistics panel always visible showing: Average Return, Median, Win Rate, Cumulative Return, Average Rise, Average Drop, Best Year, Worst Year, Sharpe Ratio, and Sortino Ratio. Controls: — Start/End Day & Month: define any calendar window (e.g. 9 Apr → 7 Jun) — Start From Year: restrict the sample to a specific regime — Rows per page / Page: navigate long histories without losing the stats panel Works on any asset and any chart timeframe — data is always read at daily resolution for precision. No repainting. Designed for: macro analysts, seasonal traders, and researchers who want to quantify recurring calendar patterns across equities, commodities, forex, crypto, and indices.
Pine Script®指标
由pcingari提供
Performance Comparison (Zeiierman)█ Overview Performance Comparison (Zeiierman) is a period-mapping comparison engine that shows how the current month, quarter, or year is evolving relative to its historical structure. It takes completed historical periods, compresses each into a normalized timeline, and overlays them on the active period so you can compare paths, pace, expansion, and finish. Instead of only asking where the price is now, the script asks how this period is behaving relative to past periods at the same stage of development. The indicator displays all curves in Percentage Accumulated terms, meaning each period starts at the same zero point and then tracks total return from that period start. This makes it easier to compare period structure on an equal footing, regardless of the asset’s raw price level. █ How It Works ⚪ 1) Period Segmentation The script groups price into repeating time buckets based on the selected Period: Monthly Quarterly Yearly Each new month, quarter, or year starts a fresh period, while completed periods are stored for later comparison. ⚪ 2) Timeline Normalization Because historical periods do not all contain the same number of bars, each is remapped to a shared normalized progress scale from start to end. This allows the script to compare: the beginning of one period to the beginning of another the midpoint of one period to the midpoint of another the final stage of one period to the final stage of another So even if one quarter had more bars than another, both can still be compared on the same visual path. ⚪ 3) Value Mapping The script uses Percentage Accumulated only. Each period begins at 0% and then tracks cumulative return from that period’s starting price: Percentage Accumulated = current price/period starting price − 1 This means all periods are anchored to the same starting point, making relative path comparison much cleaner than raw price comparison. ⚪ 4) Historical Curve Engine Completed periods are collected into comparison buckets across the normalized timeline. From these buckets, the script can draw: Historical paths Median path Average path This creates a period-based structure model rather than a simple price overlay. ⚪ 5) Current Period Tracking The active period is plotted on top of the historical framework, so you can see: whether the current action is stronger or weaker than normal whether it is tracking near the median path whether it is diverging from the average or historical range where the current period sits in time through the timeline bar ⚪ 6) Similarity Table The table compares the current period against past visible periods using four path metrics: MAE: Average distance from the current path. Lower is better. Max Dev: Largest divergence at any point. Lower is better. Dir Match %: How often did both paths move in the same direction? Higher is better. End Diff: Difference at the latest comparable point. Closer to zero is better. This helps identify which historical period most closely resembles the current one. █ Why It Is Useful ⚪ Structural Context The script does not just show whether the price is up or down. It shows whether the current period is unfolding in a way that is typical, weak, extended, delayed, or abnormal relative to history. ⚪ Period-Based Comparison It is especially useful for traders and analysts who think in recurring cycles, such as: monthly structure quarterly seasonality yearly progression █ How to Use ⚪ Historical Comparison Use the historical paths to see how prior periods behaved across the full normalized timeline. ⚪ Median Path Use the median as the most typical historical path. This is often the cleanest benchmark for “normal” behavior. ⚪ Average Path Use the average to measure the broad mean tendency of past periods. ⚪ Current Period Use the current path to judge whether the live period is: leading lagging tracking normally diverging sharply from history ⚪ Similarity Table Use the table to find the closest historical analog to the current period. Low MAE and Max Dev suggest close path similarity. High Dir Match % suggests similar movement behavior. End Diff near zero suggests similar positioning at the current stage. █ Settings Period — groups data into Monthly, Quarterly, or Yearly periods. Completed Periods to Compare — number of finished historical periods used in the comparison engine. Chart Resolution — number of normalized steps used to draw each path. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Pine Script®指标
由Zeiierman提供
3
Quant Seasonality ProQuant Seasonality Pro (QuantSeaz) Quant Seasonality Pro is a data-driven seasonal projection tool that extracts historical day-of-year return patterns and transforms them into a forward-looking price curve. Using log returns, cycle filters, and volatility-based scaling (ATR), it generates a dynamically anchored seasonal roadmap directly on your chart. The indicator allows you to switch between Trading Days (stocks/forex) and Calendar Days (crypto), apply U.S. election cycle filters, and analyze precisly historical data. The projected curve is detrended to isolate true seasonal structure and then scaled to current market volatility for realistic visualization. A built-in statistical dashboard provides: Confidence (%) based on historical win rates Expected Alpha (%) over the selected forward window ATR % (noise level) Viability ratio (Alpha adjusted for risk) This tool is designed for contextual edge — not signal automation. It helps traders align positioning with historical seasonal tendencies while maintaining proper risk management and independent confirmation. Hope you enjoy it
Pine Script®指标
由dinisfranco2006提供
2
Seasonality (100% reliable for the W)Quant Seasonality Pro (QuantSeaz) Quant Seasonality Pro is a data-driven seasonal projection tool that extracts historical day-of-year return patterns and transforms them into a forward-looking price curve. Using log returns, cycle filters, and volatility-based scaling (ATR), it generates a dynamically anchored seasonal roadmap directly on your chart. The indicator allows you to switch between Trading Days (stocks/forex) and Calendar Days (crypto), apply U.S. election cycle filters, and analyze historical data precisly. The projected curve is detrended to isolate true seasonal structure and then scaled to current market volatility for realistic visualization. A built-in statistical dashboard provides: Confidence (%) based on historical win rates Expected Alpha (%) over the selected forward window ATR % (noise level) Viability ratio (Alpha adjusted for risk) This tool is designed for contextual edge — not signal automation. It helps traders align positioning with historical seasonal tendencies while maintaining proper risk management and independent confirmation.
Pine Script®指标
由dinisfranco2006提供
Seasonality (Prev Month Close Expected)Seasonality Indicator This indicator shows how an asset has historically behaved during each calendar month. It highlights the typical price direction and strength for the current month based on long-term seasonal patterns. The projected zone on the chart represents the average historical outcome for the ongoing month, allowing traders to quickly see whether current price action is developing in line with, above, or below its usual seasonal behavior. A heatmap summarizes monthly performance across years, making recurring strong and weak periods easy to identify. Vladimir Popdimitrov
Pine Script®指标
由Market_Atlas提供
Seasonal Strategies V1Seasonal Strategies V1 is a rule-based futures seasonality framework built around predefined calendar windows per asset. The strategy automatically detects the current symbol and activates long or short trading phases strictly based on historically observed seasonal tendencies. All entries and exits are fully time-based — no indicators, no predictions, no discretionary input. Key Features Asset-specific seasonal windows (MMDD-based) Automatic long and short activation Fully time-based entries and exits One position at a time (no pyramiding) Clean chart visualization using subtle background shading No indicators, no filters, no curve fitting Philosophy: This strategy is designed as a structural trading tool, not a forecasting model. It focuses on when a market historically shows seasonal tendencies — not why or how far price might move. Seasonal Strategies V1 intentionally keeps the chart clean and minimal, making it suitable as a baseline framework for research, portfolio-style seasonal approaches, or further extensions in later versions. Intended Use: Futures and commodity markets Seasonality research and testing Systematic, calendar-driven strategies Educational and analytical purposes Disclaimer This script is provided for educational and research purposes only. Past seasonal tendencies do not guarantee future performance. Risk management, position sizing, and portfolio decisions are the responsibility of the user.
Pine Script®策略
由DerAndi72提供
Yearly Projection ExplorerThis indicator helps you understand how the current market period has behaved historically by overlaying the same date window from previous years and projecting it forward from today’s price. The script works the following way: Aligns past years to today’s calendar date Normalizes all paths to the last close at the start Projects historical performance X bars forward Displays each year as a separate performance path Calculates and plots the mean (average) projection for quick reference 🔧 How It Works Number of Years: choose how many past years to include (e.g. last 10, 20, or 25 years) Projection Length: choose how many bars (days) ahead to project Each line shows how the market moved during the same period in a specific year Labels show the year and total return at the projection end The mean line highlights the average historical outcome 🧠 Why This Is Useful Identify seasonal tendencies Compare current price action to historical analogs Visualize best / worst historical outcomes Set realistic expectations for short-term moves Add context to discretionary or systematic decisions This tool does not predict the future, but it provides a powerful historical framework to assess what has been typical, rare, or extreme for the current market window. ⚠️ Notes Script works on timenow variable for now, and you might see unexpected periods if today is a day off. Results depend on the selected timeframe and instrument Past performance is not a guarantee of future results Designed for analysis and context, not standalone signals
Pine Script®指标
由QuantNomad提供
Volume Surprise [LuxAlgo]The Volume Surprise tool displays the trading volume alongside the expected volume at that time, allowing users to spot unexpected trading activity on the chart easily. The tool includes an extrapolation of the estimated volume for future periods, allowing forecasting future trading activity. 🔶 USAGE We define Volume Surprise as a situation where the actual trading volume deviates significantly from its expected value at a given time. Being able to determine if trading activity is higher or lower than expected allows us to precisely gauge the interest of market participants in specific trends. A histogram constructed from the difference between the volume and expected volume is provided to easily highlight the difference between the two and may be used as a standalone. The tool can also help quantify the impact of specific market events, such as news about an instrument. For example, an important announcement leading to volume below expectations might be a sign of market participants underestimating the impact of the announcement. Like in the example above, it is possible to observe cases where the volume significantly differs from the expected one, which might be interpreted as an anomaly leading to a correction. 🔹 Detecting Rare Trading Activity Expected volume is defined as the mean (or median if we want to limit the impact of outliers) of the volume grouped at a specific point in time. This value depends on grouping volume based on periods, which can be user-defined. However, it is possible to adjust the indicator to overestimate/underestimate expected volume, allowing for highlighting excessively high or low volume at specific times. In order to do this, select "Percentiles" as the summary method, and change the percentiles value to a value that is close to 100 (overestimate expected volume) or to 0 (underestimate expected volume). In the example above, we are only interested in detecting volume that is excessively high, we use the 95th percentile to do so, effectively highlighting when volume is higher than 95% of the volumes recorded at that time. 🔶 DETAILS 🔹 Choosing the Right Periods Our expected volume value depends on grouping volume based on periods, which can be user-defined. For example, if only the hourly period is selected, volumes are grouped by their respective hours. As such, to get the expected volume for the hour 7 PM, we collect and group the historical volumes that occurred at 7 PM and average them to get our expected value at that time. Users are not limited to selecting a single period, and can group volume using a combination of all the available periods. Do note that when on lower timeframes, only having higher periods will lead to less precise expected values. Enabling periods that are too low might prevent grouping. Finally, enabling a lot of periods will, on the other hand, lead to a lot of groups, preventing the ability to get effective expected values. In order to avoid changing periods by navigating across multiple timeframes, an "Auto Selection" setting is provided. 🔹 Group Length The length setting allows controlling the maximum size of a volume group. Using higher lengths will provide an expected value on more historical data, further highlighting recurring patterns. 🔹 Recommended Assets Obtaining the expected volume for a specific period (time of the day, day of the week, quarter, etc) is most effective when on assets showing higher signs of periodicity in their trading activity. This is visible on stocks, futures, and forex pairs, which tend to have a defined, recognizable interval with usually higher trading activity. Assets such as cryptocurrencies will usually not have a clearly defined periodic trading activity, which lowers the validity of forecasts produced by the tool, as well as any conclusions originating from the volume to expected volume comparisons. 🔶 SETTINGS Length: Maximum number of records in a volume group for a specific period. Older values are discarded. Smooth: Period of a SMA used to smooth volume. The smoothing affects the expected value. 🔹 Periods Auto Selection: Automatically choose a practical combination of periods based on the chart timeframe. Custom periods can be used if disabling "Auto Selection". Available periods include: - Minutes - Hours - Days (can be: Day of Week, Day of Month, Day of Year) - Months - Quarters 🔹 Summary Method: Method used to obtain the expected value. Options include Mean (default) or Percentile. Percentile: Percentile number used if "Method" is set to "Percentile". A value of 50 will effectively use a median for the expected value. 🔹 Forecast Forecast Window: Number of bars ahead for which the expected volume is predicted. Style: Style settings of the forecast.
Pine Script®指标
由LuxAlgo提供
10
Seasonal Pattern DecoderSeasonal Pattern Decoder The Seasonal Pattern Decoder is a powerful tool designed for traders and analysts who want to uncover and leverage seasonal tendencies in financial markets. Instead of cluttering your chart with complex visuals, this indicator presents a clean, intuitive table that summarizes historical monthly performance, allowing you to spot recurring patterns at a glance. How It Works The indicator fetches historical monthly data for any symbol and calculates the percentage return for each month over a specified number of years. It then organizes this data into a comprehensive table, providing a clear, year-by-year and month-by-month breakdown of performance. Key Features Historical Performance Table: Displays monthly returns for up to a user-defined number of years, making it easy to compare performance across different periods. Color-Coded Heatmap: Each cell is colored based on the performance of the month. Strong positive returns are shaded in green, while strong negative returns are shaded in red, allowing for immediate visual analysis of monthly strength or weakness. Annual Summary: A "Σ" column shows the total percentage return for each full calendar year. AVG Row: Calculates and displays the average return for each month across all the years shown in the table. WR Row: Shows the "Win Rate" for each month, which is the percentage of time that month had a positive return. This is crucial for identifying high-probability seasonal trends. How to Use Add the "Seasonal Pattern Decoder" indicator to your chart. Note that it works best on Daily, Weekly, or Monthly timeframes. A warning message will be displayed on intraday charts. In the indicator settings, adjust the "Lookback Period" to control how many years of historical data you want to analyze. Use the "Show Years Descending" option to sort the table from the most recent year to the oldest. The "Heat Range" setting allows you to adjust the sensitivity of the color-coding to fit the volatility of the asset you are analyzing. This tool is ideal for confirming trading biases, developing seasonal strategies, or simply gaining a deeper understanding of an asset's typical behavior throughout the year. ## Disclaimer This indicator is designed as a technical analysis tool and should be used in conjunction with other forms of analysis and proper risk management. Past performance does not guarantee future results, and traders should thoroughly test any strategy before implementing it with real capital.
Pine Script®指标
由dchunt-stack提供
3
Daily Seasonality Strength + Prediction TableDaily Seasonality Strength + Prediction Table Return Estimates: This indicator uses historical price data to calculate average returns for each day (of the week or month) and uses these to predict the next day’s return. Seasonality Strength: It measures seasonality strength by comparing predicted returns with actual returns, using the inverse of MSE (higher values mean stronger seasonality). supports up to 10 assets This script is for informational and educational purposes only. It does not constitute financial, investment, or trading advice. I am not a financial advisor. Any decisions you make based on this indicator are your own responsibility. Always do your own research and consult with a qualified financial professional before making any investment decisions. Past performance is no guarantee of future results. The value of the instruments may fluctuate and is not guaranteed
Pine Script®指标
由Hutmacher42提供
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®指标
由AlphaExtract提供
5
[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®指标
由CognitiveAlpha提供
[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®指标
由CognitiveAlpha提供
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®指标
由starseedmaster1提供
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®指标
由redbulleconomy提供
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®指标
由RShar_New2025提供
Unlock the Power of Seasonality: Monthly Performance StrategyThe Monthly Performance Strategy leverages the power of seasonality—those cyclical patterns that emerge in financial markets at specific times of the year. From tax deadlines to industry-specific events and global holidays, historical data shows that certain months can offer strong opportunities for trading. This strategy was designed to help traders capture those opportunities and take advantage of recurring market patterns through an automated and highly customizable approach. The Inspiration Behind the Strategy: This strategy began with the idea that market performance is often influenced by seasonal factors. Historically, certain months outperform others due to a variety of reasons, like earnings reports, holiday shopping, or fiscal year-end events. By identifying these periods, traders can better time their market entries and exits, giving them an advantage over those who solely rely on technical indicators or news events. The Monthly Performance Strategy was built to take this concept and automate it. Instead of manually analyzing market data for each month, this strategy enables you to select which months you want to focus on and then executes trades based on predefined rules, saving you time and optimizing the performance of your trades. Key Features: Customizable Month Selection: The strategy allows traders to choose specific months to test or trade on. You can select any combination of months—for example, January, July, and December—to focus on based on historical trends. Whether you’re targeting the historically strong months like December (often driven by the 'Santa Rally') or analyzing quieter months for low volatility trades, this strategy gives you full control. Automated Monthly Entries and Exits: The strategy automatically enters a long position on the first day of your selected month(s) and exits the trade at the beginning of the next month. This makes it perfect for traders who want to benefit from seasonal patterns without manually monitoring the market. It ensures precision in entering and exiting trades based on pre-set timeframes. Re-entry on Stop Loss or Take Profit: One of the standout features of this strategy is its ability to re-enter a trade if a position hits the stop loss (SL) or take profit (TP) level during the selected month. If your trade reaches either a SL or TP before the month ends, the strategy will automatically re-enter a new trade the next trading day. This feature ensures that you capture multiple trading opportunities within the same month, instead of exiting entirely after a successful or unsuccessful trade. Essentially, it keeps your capital working for you throughout the entire month, not just when conditions align perfectly at the beginning. Built-in Risk Management: Risk management is a vital part of this strategy. It incorporates an Average True Range (ATR)-based stop loss and take profit system. The ATR helps set dynamic levels based on the market’s volatility, ensuring that your stops and targets adjust to changing market conditions. This not only helps limit potential losses but also maximizes profit potential by adapting to market behavior. Historical Performance Testing: You can backtest this strategy on any period by setting the start year. This allows traders to analyze past market data and optimize their strategy based on historical performance. You can fine-tune which months to trade based on years of data, helping you identify trends and patterns that provide the best trading results. Versatility Across Asset Classes: While this strategy can be particularly effective for stock market indices and sector rotation, it’s versatile enough to apply to other asset classes like forex, commodities, and even cryptocurrencies. Each asset class may exhibit different seasonal behaviors, allowing you to explore opportunities across various markets with this strategy. How It Works: The trader selects which months to test or trade, for example, January, April, and October. The strategy will automatically open a long position on the first trading day of each selected month. If the trade hits either the take profit or stop loss within the month, the strategy will close the current position and re-enter a new trade on the next trading day, provided the month has not yet ended. This ensures that the strategy continues to capture any potential gains throughout the month, rather than stopping after one successful trade. At the start of the next month, the position is closed, and if the next month is also selected, a new trade is initiated following the same process. Risk Management and Dynamic Adjustments: Incorporating risk management with this strategy is as easy as turning on the ATR-based system. The strategy will automatically calculate stop loss and take profit levels based on the market’s current volatility, adjusting dynamically to the conditions. This ensures that the risk is controlled while allowing for flexibility in capturing profits during both high and low volatility periods. Maximizing the Seasonal Edge: By automating entries and exits based on specific months and combining that with dynamic risk management, the Ultimate Monthly Performance Strategy takes advantage of seasonal patterns without requiring constant monitoring. The added re-entry feature after hitting a stop loss or take profit ensures that you are always in the game, maximizing your chances to capture profitable trades during favorable seasonal periods. Who Can Benefit from This Strategy? This strategy is perfect for traders who: Want to exploit the predictable, recurring patterns that occur during specific months of the year. Prefer a hands-off, automated trading approach that allows them to focus on other aspects of their portfolio or life. Seek to manage risk effectively with ATR-based stop losses and take profits that adjust to market conditions. Appreciate the ability to re-enter trades when a take profit or stop loss is hit within the month, ensuring that they don't miss out on multiple opportunities during a favorable period. In summary, the Ultimate Monthly Performance Strategy provides traders with a comprehensive tool to capitalize on seasonal trends, optimize their trading opportunities throughout the year, and manage risk effectively. The built-in re-entry system ensures you continue to benefit from the market even after hitting targets within the same month, making it a robust strategy for traders looking to maximize their edge in any market. Risk Disclaimer: Trading financial markets involves significant risk and may not be suitable for all investors. The Monthly Performance Strategy is designed to help traders identify seasonal trends, but past performance does not guarantee future results. It is important to carefully consider your risk tolerance, financial situation, and trading goals before using any strategy. Always use appropriate risk management and consult with a professional financial advisor if necessary. The use of this strategy does not eliminate the risk of losses, and traders should be prepared for the possibility of losing their entire investment. Be sure to test the strategy on a demo account before applying it in live markets.
Pine Script®策略
由exlux提供
2
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®指标
由east-west_trading提供
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®指标
由LuxAlgo提供
8
Turn of the Month Strategy [Honestcowboy]The end of month effect is a well known trading strategy in the stock market. Quite simply, most stocks go up at the end of the month. What's even better is that this effect spills over to the next phew days of the next month. In this script we backtest this theory which should work especially well on SP500 pair. By default the strategy buys 2 days before the end of each month and exits the position 3 days into the next month. The strategy is a long only strategy and is extremely simple. The SP500 is one of the #1 assets people use for long term investing due to it's "9.8%" annualised return. However as a trader you want the best deal possible. This strategy is only inside the market for about 25% of the time while delivering a similar return per exposure with a lower drawdown. Here are some hypothesis why turn of the month effect happens in the stock markets: Increased inflow from savings accounts to stocks at end of month Rebalancing of portfolios by fund managers at end of month The timing of monthly cash flows received by pension funds, which are reinvested in the stock market. The script also has some inputs to define how many days before end of the month you want to buy the asset and how long you want to hold it into the next month. It is not possible to buy the asset exactly on this day every month as the market closes on the weekend. I've added some logic where it will check if that day is a friday, saturdady or sunday. If that is the case it will send the buy signal on the end of thursday, this way we enter on the friday and don't lose that months trading opportunity. The backtest below uses 4% exposure per trade as to show the equity curve more clearly and because of publishing rules. However, most fund managers and investors use 100% exposure. This way you actually risk money to earn money. Feel free to adjust the settings to your risk profile to get a clearer picture of risks and rewards before implementing in your portfolio.
Pine Script®策略
由Honestcowboy提供
2
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®指标
由LuxAlgo提供
8