Price & return moving average, trend analysis
product_id trend_momentum_factor_us · 4800+ stocks · daily refresh · since 2003-01-01 · 2 data columns · API/CSV
curl "https://www.kavout.com/api?product_id=trend_momentum_factor_us&api_key=YourAPIKey"
symbol / trade_date / field to filter · docs →GET · JSON · CSVTrend momentum takes a rolling average of the price and return used in price momentum, looking to identify patterns of momentum among equities.
The trend momentums are constructed with a cross-section regression approach that provides a unified framework incorporating information used in the three major price patterns: - Short-term reversal effects - Momentum effects - Long-term reversal effects
Stocks that have high forecasted expected returns from the cross-section regression yield higher future returns on average, and stocks that have low forecasted expected returns tend to yield lower future returns on average.
Data through 2026-10-26
Data through 2026-10-26
Trend momentum factor scores are available for all the tradable stock equities on the US stock exchanges, including major securities markets such as NYSE, Nasdaq and Dow Jones Industrial.
| Sector | Count | Type | Start date |
|---|---|---|---|
| Finance | 1,133 | Equities | 2003-01-01 |
| Health Technology | 775 | Equities | 2003-01-01 |
| Technology Services | 392 | Equities | 2003-01-01 |
| Electronic Technology | 387 | Equities | 2003-01-01 |
| Producer Manufacturing | 304 | Equities | 2003-01-01 |
| Consumer Services | 286 | Equities | 2003-01-01 |
| Non-Energy Minerals | 237 | Equities | 2003-01-01 |
| Energy Minerals | 223 | Equities | 2003-01-01 |
| Commercial Services | 223 | Equities | 2003-01-01 |
| Industrial Services | 194 | Equities | 2003-01-01 |
Trend momentum factors are calculated daily for thousands of stocks.
This data is available via API and CSV download.
Trend momentum utilizes moving average prices and returns respectively of various time lengths 3 days, 5 days, 10 days, 20 days, 50 days, 100 days and 200 days to capture information which is justified by a proposed general equilibrium model.
Stocks that have high forecasted expected returns from the cross-section regression yield higher future returns on average, and stocks that have low forecasted expected returns tend to yield lower future returns on average.
In order to calculate trends, trend momentum focuses on the moving averages of prices and returns. Also different from price momentum, trend momentum looks at more time cadences.
We applied trend momentum metrics to a universe of the largest 500 US equities by market capitalization and ranked each stock on its overall trend momentum. We then divided the universe into 10 quantiles of stocks, with the top quantile (Q_10) representing the highest trend momentum stocks and the bottom quantile (Q_1) representing the lowest trend momentum stocks.