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US Stock Picker & Algo Lab

US Stock Picker & Algo Lab

Discover, backtest and validate US stock trading strategies, pick from any universe, and generate an IBKR paper-trading deployment script. No API keys, free daily data, institutional anti-overfit validation.
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在 Capafy 上執行
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DeepSeek V4 Pro

US Stock Picker & Algo Lab

Stop guessing which stocks to hold. This agent ranks any US universe, finds strategies that survive out-of-sample testing, and deploys to IBKR paper trading — with institutional anti-overfit discipline and zero API keys required.

No CSV upload. No subscriptions. No broker credentials in the cloud. Free daily market data.


Watch it work

Product demo: ranked universe, verified validation gates, and honest NOISE verdict on overfit scans

A plain-language ask flows through to a ranked, validated, deployable result — real market data, no API keys.


The real report

Self-contained HTML deliverables, generated on free daily market data. English-primary, dark institutional look.


What you actually see — Stock Picker

Rank your universe, validated against buying everything.

The complete low-volatility stock picker report: universe ranking, holdings table, validation gates

The verdict — 5/5 hard gates PASS for the verified low-volatility screen, shown honestly.

Low-volatility stock picker header: verified 35.8% vs 28.2% benchmark, 5/5 PASS, OOS positive, random control z=3.40
  • Picker CAGR 35.8% vs 28.2% equal-weight benchmark — excess +7.6 pp
  • 5/5 validation gates PASS for the verified 63-day low-volatility screen, including random-control permutation (z = 3.40)
  • Per-rebalance holdings table with transaction-cost and ticker-back-mapping disclosures

What you actually see — Strategy Discovery

Sweep 8 classic families over 56 configs — and get the truth about whether any survive.

The complete strategy discovery report: leaderboard, honesty gates, NOISE verdict

When the edge is noise, it says so plainly.

Strategy discovery header: top candidates ranked, all FAIL Deflated Sharpe, honest NOISE verdict
  • Full leaderboard of all 8 families ranked by Sharpe
  • Deflated Sharpe corrects for how many configs you tried
  • When nothing survives, it says NOISE — never sells you a curve-fit

What you get

1. Stock picking (headline) — ranks any US universe by momentum (12/6/3-month, 12-1), low volatility, or mean reversion. Holds the top N equal-weight, rotates on a schedule, validated against a buy-everything benchmark.

2. Strategy discovery — sweeps 8 classic families (SMA/EMA/MACD cross, RSI/Bollinger reversion, Donchian breakout, momentum, vol-regime trend) over parameter grids to find what works for your tickers.

3. Anti-overfit validation — every candidate runs through Deflated Sharpe, walk-forward, out-of-sample split, and a random-control permutation test. Most real scans surface noise — this tool says so plainly instead of selling you a curve-fit.

4. IBKR paper deployment — generates a ready-to-run ib_insync script for your IBKR Paper account. You review it, you run it locally. No live orders from the cloud, no hosted credentials.


The validation gates

Gate Threshold
Excess return > 0
Beats benchmark CAGR > equal-weight
OOS positive OOS CAGR > 0
Worst year > -50%
Random control z > 1.5

PASS requires all hard gates. Any failure is surfaced as INSUFFICIENT — never masked, never spun.


The honest edge

Most "strategy finder" tools hand you the flashiest equity curve. This one tells you the likelihood it's overfit. That honesty is the product — and it's what survives real markets.


Deployment boundary

The skill generates a reviewable ib_insync script for IBKR Paper (port 7497). You run it locally on your own machine. The cloud never executes live orders and never holds your broker credentials.


Example requests

  • "Which of my watchlist should I hold next month?"
  • "Is this moving-average crossover real edge or overfit noise?"
  • "Pick the top 5 of these ETFs by low volatility."
  • "Generate an IBKR paper-trading script for the best momentum config."

Who it's for

  • Retail traders building a disciplined, rules-based approach to US equities
  • Watchlist managers who want evidence before adding names
  • Quant-curious investors who want institutional validation without the quant degree
  • Anyone burned by backtests that "look great" but die on paper

Requirements

  • Python 3.9+, pandas, numpy on the machine where you run the generated deployment script
  • scipy optional (pure-stdlib fallback included)
  • The discovery/picking itself runs entirely on Capafy with free public data

For informational and educational purposes only. Not financial, investment, or trading advice. Backtested results do not guarantee future performance; all trading involves risk of loss. Consult a qualified financial professional before trading.