All stock perdection for Feb 20

Screening Filters

1. Market Cap ≥ $5,000,000,000 (market_cap: {min: 5000000000})

   • Purpose: Focus on larger, more established companies when generating performance predictions.
   • Rationale:
     • Large-cap stocks tend to have more stable financials and more analyst/quant model coverage, which usually makes performance predictions more robust.
     • For a broad “performance prediction” question, it’s more meaningful to look at major companies rather than tiny, highly erratic microcaps where any prediction is mostly noise.

2. Monthly Average Dollar Volume ≥ $1,000,000 (monthly_average_dollar_volume: {min: 1000000})

   • Purpose: Ensure the stocks selected are actively traded and liquid.
   • Rationale:
     • High trading volume means prices better reflect information and are less prone to random spikes or gaps, making statistical or model-based forecasts more reliable.
     • It also ensures that the stocks are realistically tradable for most investors who might want to act on these predictions.

3. Listed on Major U.S. Exchanges (list_exchange: ['XNYS', 'XNAS', 'XASE'])

   • Purpose: Limit the universe to U.S.-listed securities on NYSE, NASDAQ, or NYSE American.
   • Rationale:
     • These exchanges have stricter listing standards, better reporting, and more consistent data—important for building and trusting performance models.
     • When someone asks about “performance prediction for all stocks,” the most practical interpretation is usually within a major, well-covered market like U.S. large caps.

4. Index Component: S&P 500 or Nasdaq 100 (is_index_component: ['GSPC', 'NDX'])

   • Purpose: Focus the predictions on the most important benchmark stocks in the U.S. market.
   • Rationale:
     • S&P 500 (GSPC) and Nasdaq 100 (NDX) contain many of the largest, most followed companies. These are the core of “the market” for most investors.
     • Performance prediction models are typically most mature and accurate for these names because of rich historical data and constant analyst attention.
     • For a broad question about future performance, highlighting predictions for key benchmark constituents gives the most relevant, actionable subset rather than an unmanageable universe of all listed stocks.

5. Probability of 1‑Month Price Rise ≥ 75% (one_month_rise_prob: {min: 75})

   • Purpose: Select only stocks that the model assigns a relatively high probability of rising over the specific one‑month horizon (here, interpreted as February 2026).
   • Rationale:
     • Your question is forward-looking (“performance prediction”), so this filter directly uses a probabilistic forecast: only stocks where the model sees at least a 75% chance of a positive return for that month.
     • It doesn’t guarantee gains, but it tilts the list toward names where the statistical edge is meaningfully in favor of a rise, rather than just random.

6. Predicted 1‑Month Return ≥ +8% (one_month_predict_return: {min: 8})

   • Purpose: Ensure that the predicted magnitude of the move is meaningfully positive, not just slightly above zero.
   • Rationale:
     • A stock with a 75% chance of gaining only 1–2% may not be interesting once you factor in risk and costs.
     • By requiring at least an +8% model‑predicted return for the month, the screen focuses on stocks with both a high probability of rising and a strong expected upside during February 2026.

Why Results Match Your Question

• Your question asks for performance predictions for February 2026. The filters use 1‑month ahead prediction metrics (rise probability and expected return), which is the natural way to quantify “performance prediction” for that specific month.
• Instead of showing every stock (many of which are illiquid, obscure, or poorly modeled), the screen zeroes in on:
  • Large, liquid U.S. names on major exchanges,
  • That are part of flagship indices (S&P 500 / Nasdaq 100),
  • And that have strongly positive model forecasts for that February 2026 window (high probability of rising and sizable predicted upside).

So, the filters translate your broad “performance prediction” request into a focused list of major stocks where the model indicates a favorable outlook for that specific month.