6M Screener
Demo data
Screening for 6–12 months

Four screening stages, from 1,200 stocks to a few candidates

Every stock must pass every stage. A high score elsewhere cannot override a failed gate. The final reward-to-risk ratio must reach 3:1.

Illustrative counts
Screening process

Select a stage

Select an indicator to see its definition, formula, scoring scale and source.

Each score is 0–100. Gates are independent of ranking weights.

Example

A stock through the screening process

Eight invented companies with simulated metrics. Select one and run the screening.

Demo A

Trade setup

Result

Ranking and position size

Only companies passing all four gates qualify. At most 1% of the portfolio is risked per trade.

Weights are normalized to 100%. Position size = portfolio × risk budget ÷ (entry − stop). Green: 1%; yellow: 0.5%; red: 0%.

Weights change ranking only; the gates determine eligibility.

#CompanyFundamentalsTechnicalsRiskCRVTotalShares (€10,000 portfolio)
Context

Model limitations

A filter, not a forecast

Passing all gates does not guarantee a price rise.

Weights need backtesting

Research-based weights require historical validation without survivorship bias before real use.

A simplified market regime

Four signals cannot replace economic analysis; they help filter adverse markets.

Reward/risk is an entry filter

A 3:1 ratio does not establish the actual probability of reaching the target.

Sources
  1. Jegadeesh, N. & Titman, S. (1993). Returns to Buying Winners and Selling Losers. Journal of Finance, 48(1).
  2. Bernard, V. & Thomas, J. (1989). Post-Earnings-Announcement Drift. Journal of Accounting Research, 27.
  3. Chan, L., Jegadeesh, N. & Lakonishok, J. (1996). Momentum Strategies. Journal of Finance, 51(5).
  4. Novy-Marx, R. (2013). The Other Side of Value: The Gross Profitability Premium. Journal of Financial Economics, 108(1).
  5. Asness, C., Frazzini, A. & Pedersen, L. (2019). Quality Minus Junk. Review of Accounting Studies, 24.
  6. Fama, E. & French, K. (1992). The Cross-Section of Expected Stock Returns. Journal of Finance, 47(2).
  7. Campbell, J., Hilscher, J. & Szilagyi, J. (2008). In Search of Distress Risk. Journal of Finance, 63(6).
  8. George, T. & Hwang, C. (2004). The 52-Week High and Momentum Investing. Journal of Finance, 59(5).
  9. Lee, C. & Swaminathan, B. (2000). Price Momentum and Trading Volume. Journal of Finance, 55(5).
  10. Faber, M. (2007). A Quantitative Approach to Tactical Asset Allocation. Journal of Wealth Management, 9(4).
  11. Ang, A., Hodrick, R., Xing, Y. & Zhang, X. (2006). The Cross-Section of Volatility and Expected Returns. Journal of Finance, 61(1).
  12. Frazzini, A. & Pedersen, L. (2014). Betting Against Beta. Journal of Financial Economics, 111(1).
  13. Amihud, Y. (2002). Illiquidity and Stock Returns. Journal of Financial Markets, 5(1).
  14. Estrella, A. & Mishkin, F. (1998). Predicting U.S. Recessions: Financial Variables as Leading Indicators. Review of Economics and Statistics, 80(1).
  15. Daniel, K. & Moskowitz, T. (2016). Momentum Crashes. Journal of Financial Economics, 122(2).
  16. Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.