TradeWave Methodology
TradeWave is a quantitative engine that analyzes decades of market history to find recurring seasonal patterns and measure their risk and return. This page explains how we build those patterns, which statistics we show and what the main limitations are.
1. What TradeWave does
TradeWave ingests long term daily price and volume data for stocks, indices, exchange traded funds and other liquid instruments. The engine scans these histories for recurring calendar based windows where price has shown a consistent tendency to move higher or lower.
Seasonal patterns in TradeWave are fully rules based. Given an instrument, a start date, a window length and a lookback period, the result is deterministic. There is no machine learning, curve fitting or discretionary manual adjustment in the pattern calculations.
Key principle
TradeWave separates calculation from narrative. The engine produces historical statistics based on hard market data. Any written commentary or news style reporting that references these results is created afterwards from the numbers.
2. Data sources and universe
TradeWave uses end of day data from established market data vendors and exchange feeds. Coverage includes:
- Major United States equities listed on NYSE and Nasdaq
- Key global stock indices and index tracking exchange traded funds
- Selected futures, commodities, foreign exchange pairs and volatility indices
Price histories are adjusted for stock splits, reverse splits and cash dividends when applicable so that historical returns reflect actual investor experience.
Specific vendor names and licensing arrangements are not listed here, but the engine operates only on data that is time aligned and quality checked for continuity.
3. Data processing and cleaning
Before any seasonal pattern is calculated, TradeWave applies several data hygiene steps:
- Correction of obvious pricing anomalies that conflict with exchange data
- Adjustment for corporate actions such as splits and spin offs
- Alignment of trading calendars to handle holidays and unexpected closures
- Exclusion of instruments with insufficient history for the requested lookback
These steps ensure the pattern reflects a consistent view of actual traded prices.
4. How seasonal patterns are defined
Every TradeWave pattern is defined by four core parameters:
- Instrument
- Pattern start date
- Window length (number of trading days)
- Lookback period (number of historical years)
For each year in the lookback, the engine simulates entering at the official daily close on the start date and exiting at the close after the specified number of trading days.
Long patterns interpret rising prices as gains; short patterns invert the return series so that downward moves correspond to positive returns.
5. Performance statistics displayed
Percent Profitable
The percentage of years that finished with a positive return in the selected direction.
Average Profit
The average of all winning years.
Average Profit - All
The average net return across all years, including losses.
Sharpe Ratio
A risk adjusted measure based on the mean and standard deviation of yearly returns.
TradeWave Ratio (TWR)
A metric that reflects how far price typically travels in the trade direction within the window, incorporating both the final return and the maximum favorable excursion.
6. Intraperiod risk and excursion metrics
Maximum Favorable Excursion (MFE)
The best point-to-peak intraperiod gain measured during the window.
Maximum Adverse Excursion (MAE)
The worst drawdown against the position inside the window.
MFE and MAE provide context on how smooth or volatile a pattern has been year by year.
7. Interpretation and limitations
Seasonal patterns summarize historical behavior, not guaranteed outcomes. Market conditions, company fundamentals and macro environments evolve. A pattern that was strong over a given lookback may weaken or fail in future years.
- Extreme outliers can affect averages
- Market structure can change over multi-decade periods
- Returns do not include transaction costs or slippage
- Drawdowns (MAE) may be significant even in winning years
TradeWave outputs are informational and not investment advice.
8. How this methodology is used
The TradeWave engine provides the underlying statistics for any articles, reports or analysis tools that reference seasonal market behavior. Whether the data appears on TradeWave itself or in future publishing platforms, all numbers originate from the deterministic calculation process described on this page.
This page serves as the authoritative reference for how seasonal metrics are calculated throughout the TradeWave ecosystem.