Quant AI Alpha Pick

Quant AI Alpha Pick produces systematic quantitative alpha candidates using factor models + AI overlay.

Find it at: /quant-ai

Available on Pro and Elite plans.

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1. What it is

Quant AI Alpha Pick is Alphio's institutional-style quant strategy for US equities. The system maintains a diversified basket of roughly 30–50 stocks, selected and rebalanced by a multi-factor quantitative model with an AI overlay on top.

The product is designed for mid-term investing — holding periods of several weeks to months rather than days. Turnover is intentionally low: positions only change when the underlying signals materially change, not on a fixed schedule.

The page is organized as two tabs:

• Portfolio — latest trades, current positions and sector breakdown, previous winners.
• Performance — backtest curve and statistics.

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2. Signal anatomy

Quant AI Alpha Pick doesn't expose per-trade entry / target / stop the way the swing products do. Instead, every position carries:

Field	What it means
Symbol	The US stock ticker held in the basket.
Cost	Entry price for the position.
Price	Current market price.
P&L %	Unrealized return on the position.
Position Weight	Share of total basket allocated to this name.
Entry Date	When the position was opened.
Sector	Industry / sector classification used for the allocation pie chart.

You also get a Latest Trades view (the most recent additions / exits) and a Previous Winners view (recently realized top performers) for context.

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3. Strategy logic

Quant AI Alpha Pick combines multiple components:

• Multi-factor alpha signals — institutional-grade factors (value, momentum, quality, etc.) blended into a composite alpha score.
• AI overlay — the AI re-weights and prunes the factor outputs to adapt to regime and to filter out names where factor signals look fragile.
• Risk filters — sector, liquidity, and idiosyncratic risk filters applied at the portfolio level.
• Low-turnover rebalance — positions are added / removed when signal conditions materially change, not on a fixed cadence; avoids over-trading and keeps execution costs low.

The goal is to capture alpha (excess return vs. the S&P 500) systematically, by holding a diversified basket of the model's highest-conviction names through normal mid-term horizons.

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4. How to use

1. Read the portfolio. Open /quant-ai to see what the strategy is currently holding (30–50 names), the sector mix, and what it most recently traded.
2. Review Performance. Flip to the Performance tab to see the cumulative return curve vs. the S&P 500 and the running stats.
3. Copy-trade the strategy. Use the Copy Trade button at the top of the page to mirror the basket into your connected broker account via Copy Trading. The wizard locks the signal type to Quant AI; pick fixed or proportional sizing and an allocation amount. Every rebalance is mirrored automatically.
4. Hold mid-term. Unlike the day-trading and swing products, Quant AI is intentionally low-turnover. Resist the urge to override the model with discretionary trades on the same account.

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5. Backtest / performance

The Performance tab shows Quant AI Alpha Pick's cumulative return curve plotted alongside the S&P 500 for transparency, plus running stats (cumulative return, win rate, drawdown, etc.). The Previous Winners section on the Portfolio tab lists recently realized top performers as additional context.

warning

Exact backtested return / alpha / Sharpe numbers update continuously as new trades close and the model is retuned. Always validate against the in-app Performance tab; do not quote a fixed figure from this page.

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6. Required tier

Quant AI Alpha Pick is a paid product.

Available on Pro and Elite plans.

warning

Quant AI Alpha Pick is likely Elite-only — it sits in the higher entitlement bundle alongside SwingMax Portfolio. Verify with the product team and confirm against Subscription tiers before relying on access.

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7. Risk disclosure

Quant AI Alpha Pick is informational. The strategy's signals and basket composition are AI / model outputs and are not investment advice. Past performance and backtested results do not guarantee future returns; factor strategies can experience extended periods of underperformance. The strategy holds long equity positions and is exposed to market, sector, and factor-crowding risk. Copy-trading the strategy commits your allocation amount to mirrored positions in your broker account — you are responsible for managing inherited positions if you stop the copy. Always verify trades with your broker.