Earnings Day - Price Predictor [DunesIsland]

It's designed to analyze and visualize historical stock price movements on earnings report days, focusing on percentage changes.
Here's a breakdown of what it does, step by step:

Key Inputs and Setup

• User Input: There's a single input for "Lookback Years" (default: 10), which determines how far back in time (approximately) the indicator analyzes earnings data. It uses a rough calculation of milliseconds in that period to filter historical data.
  
• Data Fetching: It uses TradingView's request.earnings function to pull actual earnings per share (EPS) data for the current ticker. Earnings days are identified where EPS data exists on a bar but not on the previous one (to avoid duplicates).
  
• Price Change Calculation: For each detected earnings day, it computes the percentage price movement as (close - close[1]) / close[1] * 100, representing the change from the previous close to the current close on that day.
  

Processing and Calculations (on the Last Bar)

• Lookback Filter: It calculates a cutoff timestamp for the lookback period and processes only earnings events within that window.
  

Overall Averages:

• Separates positive (≥0%) and negative (<0%) percentage changes.
  

Seasonality (Next Quarter Prediction):

• Identifies the most recent earnings quarter (latest_q).
  
• Predicts the "next" quarter (e.g., if latest is Q4, next is Q1;
  
• Again, separates positive and negative changes, computing their respective averages.
  

Visual Outputs

• Lookback: How far to fetch the data in years.
• Average Change (Green): Showing the average of all positive changes.
• Average Change (Red): Showing the average of all negative changes.
• Seasonality Change (Green): Showing the average of positive changes for the predicted next quarter.
• Seasonality Change (Red): Showing the average of negative changes for the predicted next quarter.

Purpose and Usage

This indicator helps traders assess a stock's historical reaction to earnings announcements. The overall averages give a broad sense of typical gains/losses, while the seasonality focuses on quarter-specific trends to "predict" potential movement for the upcoming earnings (based on past same-quarter performance). It's best used on daily charts for stocks with reliable earnings data. Note that quarter inference is calendar-based and may not perfectly match fiscal calendars for all companies—it's an approximation.