Methodology & Data Sources

1. What SeasOptima Does

SeasOptima is a statistical analysis platform that identifies recurring seasonal patterns in financial markets. We aggregate historical price data and apply statistical methods to visualize how assets have historically performed during specific time periods. Our platform does not provide investment advice, trading signals, or personalized recommendations. All information presented is purely statistical and educational in nature.

2. Data Sources

All market data used by SeasOptima is sourced from publicly available financial data providers:

• Market Data Providers — We aggregate historical OHLCV (Open, High, Low, Close, Volume) price data from publicly available financial data providers across all asset classes including stocks, indices, commodities, forex, ETFs, and cryptocurrencies.

Data Coverage

We analyze up to 30 years of historical daily price data where available. The exact coverage period varies by symbol depending on data availability. All data is updated daily during market hours.

Data Accuracy

While we strive for accuracy, we rely on third-party data providers and cannot guarantee that all data is error-free. Prices are adjusted for splits and dividends where applicable. Users should verify critical data points independently.

3. Seasonal Pattern Calculation

Seasonal patterns are calculated using the following methodology:

1. Daily Returns: We calculate the daily percentage change for each trading day across all available years of data.
2. Calendar Aggregation: Daily returns are grouped by calendar date (month and day) to identify recurring patterns.
3. Historical Averages: For each calendar period, we compute the average historical return and the percentage of years where the return was positive (win rate).
4. Seasonal Periods: Contiguous periods of consistently positive or negative historical returns are identified and highlighted as seasonal patterns.
5. Statistical Significance: Patterns are evaluated based on the number of years of data, consistency of returns, and win rate to assess their historical reliability.

4. Pattern Consistency Score

The Pattern Consistency Score (PCS) is a proprietary metric that measures how reliably a seasonal pattern has repeated historically:

• Win Rate Component: The percentage of years in which the pattern's expected direction (bullish or bearish) actually occurred.
• Return Consistency: How similar the magnitude of returns has been across different years — lower variance indicates higher consistency.
• Sample Size: More years of data increase the statistical confidence of the pattern.

The PCS is expressed as a score from 0 to 100. Higher scores indicate patterns that have historically repeated more consistently. A high PCS does not predict future performance.

5. Market Regime Analysis

Our market regime detection uses a systematic approach to classify the broader market environment:

• Trend Detection: We use moving averages of major market indices (e.g., S&P 500) to determine whether the market is in a historically bullish, bearish, or neutral phase.
• Regime Classification: Seasonal patterns are analyzed separately for each market regime, allowing users to see how patterns have historically behaved in different market conditions.

Market regime analysis is based on historical classification only and does not predict future market direction.

6. Heat Map & Calendar

Visual tools aggregate historical data as follows:

• Monthly Heat Map: Displays the average historical return for each month across all available years. Color intensity indicates the magnitude of the historical average return.
• Seasonal Calendar: Shows daily historical averages mapped to the current calendar year, allowing users to see which dates have historically shown positive or negative tendencies.

All heat map and calendar values represent historical averages and do not predict future returns.

7. Screener Rankings

The Seasonal Screener filters and ranks symbols based on statistical criteria:

• Filtering: Users can filter by asset class, pattern direction, date range, and minimum win rate.
• Ranking: Results are ranked by historical pattern strength — a combination of average historical return, win rate, and pattern consistency.
• Data Freshness: Screener data is recalculated regularly to incorporate the most recent available data.

Screener rankings reflect historical statistical measures only. A high ranking does not constitute a recommendation to buy or sell any security.