Season — Indicators and Strategies

[COG]S&P 500 Weekly Seasonality Projection S&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.

[COG]Nasdaq Weekly Seasonality Projection Nasdaq 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.

SASDv2r Sensitive Altcoin Season Detector V2 This Pine Script™ code, titled "SASDv2r" (Sensitive Altcoin Season Detector version 2 revised), is designed for cryptocurrency trading analysis on the TradingView platform and tailored for those interested in tracking when altcoins might be outperforming Bitcoin, potentially indicating a market shift towards altcoins. Feel free to use and modify. If you made it better, please let me know. Intention was to help the community with a tool for retail traders have no access to advanced, MV indicators. Solution uses classic TA only. Use it witl TOTAL3/BTC indicator. Please check: it gave signal just before last alt season % rose more than 250%. Market Cap Data Fetching: The script fetches market capitalization data for Bitcoin, Ethereum, and all other altcoins (excluding Bitcoin and Ethereum) using request.security function. Altcoin to Bitcoin Ratio: It calculates the ratio of total market cap of altcoins to Bitcoin's market cap (altToBtcRatio), which is central to identifying an "altcoin season." Moving Averages: Several moving averages are computed for different time frames (50-day SMA, 200-day SMA, 20-day SMA, and 10-day EMA) to analyze trends in the altcoin to Bitcoin ratio. Momentum Indicators: The script uses RSI (Relative Strength Index) and MACD (Moving Average Convergence Divergence) to gauge momentum and potential reversal points in the market. Custom Indicators: It includes Volume Weighted Moving Average (VWMA) and a custom momentum indicator (altMomentum and altMomentumAvg) to provide additional insights into market movements. Volatility Measurement: Bollinger Bands are calculated to assess volatility in the altcoin to Bitcoin ratio, which helps identify periods of high or low market activity. Visual Analysis: Various plots are added to the chart for visual interpretation, including the altcoin to Bitcoin ratio, different moving averages, and Bollinger Bands. Alt Season Detection: The script defines conditions for detecting when an "altcoin season" might be starting, based on crossovers of moving averages, RSI levels, MACD signals, and other custom criteria. Performance Tracking: After signaling an alt season, the script evaluates the performance over the next 30 days by checking if there's been an increase in the altcoin to Bitcoin ratio, adding labels for positive or negative trends.(this one is in progress). Logic still gives false signals and aim is to identify failed signals. Visual Signals: Labels are placed on the chart to visually indicate the beginning of a potential alt season or the performance outcome after a signal, aiding traders in making informed decisions.

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.

[SM] Bitcoin cycles bull market An indicator to determine the seasonality / cyclicality of bitcoin for long trades. Application - For traders: Identification of zones with lower risk of entering long positions - For swing traders and investors: customizable calendar of entries into long position Indicator structure 1. Vertical zones (green and red) of time ranges. Only for historical bars. The range width is adjustable in the indicator settings. 2. Table (in the form of a calendar) for determining the time of entering a trade in the future. The table is not editable. It displays the result of the configured zones on the historical bars. General settings - choose the color of the Tradingview theme (light or dark) Table settings - Turn table display on / off - Set the number of months to be displayed in the table Settings of vertical zones (green and red) Each cycle (1 month summer, 1 month autumn, ...) has four dates - start date of the green zone (day and month) - date of the end of the green zone - start date of the red zone - date of the end of the red zone

AlexD Market annual seasonality The indicator displays the percentage of bullish days with a given date over several years. This allows you to determine the days of the year when the price usually goes up or down. Indicator has a built-in "simple moving average" shifted back by half a period, due to which the delay of this smoothing is removed.

season This script is meant to help verify the existence of a seasonal effect in asset returns, using a Z-test. There are three steps: 1. Think of a way to identify a season. The available methods are: by month, by week of the year, by day of the month, by day of the week, by hour of the day, and by minute of the hour. 2. Set the chart to the unit of your season. For example, if you want to check whether a crop commodity's harvest season has a seasonal implication, select "month". If you want to investigate the exchange's opening or close, select "hour". 3. Using the inputs, select the unit (e.g. "month", "dayofweek", "hour", etc.) and the range that identifies the season. The example natural gas chart has set "start" to 8 and "end" to 12 for September through December. The test logic is as follows: The "season" you select has a fixed length; for example, months eight through twelve has a length of four. This length is used to compute a sample mean, which is the mean return of all September-December periods in the chart. It is also used to calculate the mean/stdev of every other four-month period in the chart history. The latter is considered the "population." Using a Z-test, the script scores the difference between the sample returns and the population returns, and displays the results at two levels of significance (P = 0.05 and P = 0.01). The null hypothesis is "there is no difference between the seasonal periods and the population of ordinary periods". If the Z-score is sufficiently large or small, we can reject the null hypothesis and say that there is a seasonal effect at the given level of confidence. The output table will show green for a rejection of the null hypothesis (meaning there is a seasonal effect) or red of acceptance (there is no seasonal effect). The seasonal periods that you have defined will be highlighted on the chart, so you can make sure they are correct. Additionally, the output table shows the mean, median, standard deviation, and top and bottom percentiles for both the seasonal and population samples. Many news sites, twitter feeds, influences, etc. enjoy posting statistics about past returns, like "the stock market has gone up on this day 85 out of the past 100 years" and so on. Unfortunately, these posts don't tell you that many of these statistics are meaningless, as even totally random price fluctuations will cause many such interesting figures to occur. This script provides a limited means of testing some such seasonal effects so you can see if they are probably just random, or if they may have some meaning. Note that Tradingview seems to use 1-based indexing for daily or higher timeframes, and 0-based indexing for intraday timeframes: Months: 1-12 Weeks: 1-52 Days (of month): 1-31 Days (of week): 1-7 Hours (of day): 0-23 Minutes (of hour): 0-59

How To Set Trade Dates Example how to backtest specific date(s) which can be useful for testing seasonality strategy ideas such as "Sell in May", etc. If using Daily period with wild card dates it will not trade on 1st day of month. If market is closed will trade on next open day. Works only with Daily or lower timeframes. When using on Daily timeframe set dates 1 day earlier to execute on desired dates. Entering 0 in any of the date fields acts a wild card. Example -> Entry Year: 0 is "Every Year", Exit Month: 0 is "Every Month", etc. NOTICE: This is an example script and not meant to be used as an actual strategy. By using this script or any portion thereof, you acknowledge that you have read and understood that this is for research purposes only and I am not responsible for any financial losses you may incur by using this script!