Mia

Here is a structured, comprehensive review of the Smart Stock Scanner system across both Phase 1 (Defensive Improvements) and Phase 2 (Advanced Indicators), including answers to all your listed questions, integration analysis, and actionable recommendations for next steps.

1. Phase 1 (V5.2 – Defensive Improvements)

Defensive Enhancements Review

• MIN_STOCK_MOVE = 1%:

  • Well-implemented for filtering noise; prevents alert spam and minor reversals from triggering.
  • Danger: Could miss signals on low-volatility days or stocks. Recommendation: Make threshold adaptive by stock volatility.

• UHC-before-SPY filter reordering:

  • Correct in prioritizing the most confident signals before global market correlation check.
  • If an event is truly “ultra-high-confidence,” it should not rely on SPY filter.
  • Risk: If UHC definition is too loose, can result in over-triggering during market-wide shocks.

• SPY edge cases:

  • Good coverage for SPY halts, missing bars, and NaN values—protects against broken alerts due to index data issues.
  • Suggestion: Log all SPY-related skips for later review.

• NaN/division protection:

  • Essential for live robustness; ensures algorithm doesn’t halt on strange data spikes or provider errors.
  • Continue to wrap all math in try/excepts or NaN guards.

• EXTREME_VOLATILITY_THRESHOLD (10%):

  • Strong for market-crash days, but static 10% may be too blunt for some stocks.
  • Suggested: Make this dynamic (e.g., 3x 21-day ATR), or different for large-caps vs small-caps.

• Logging for rejections:

  • Critical for QA. Ensure structured logs (time, symbol, reason) and aggregate daily counts to monitor filter efficacy.

Time-of-Day (Power Hour)

• Power Hour Enhancement:
  • Smart idea; amplifies signal weight for increased activity. Effectively prioritizes this window.
• get_time_of_day_period improvement:
  • As long as time boundaries use market timezone, implementation appears sound.

Summary / Recommendations:

• Most defensive upgrades are appropriate and robust.
• Consider slightly more adaptive thresholds for both MIN_STOCK_MOVE and volatility.
• Make all filter-based rejections log to a central error/QA reporting structure.

2. Phase 2 (V6.0 – Advanced Indicators)

Anomaly Detection

• Z-Score Threshold (|Z|≥3.0):

  • A value of 3.0 is statistically valid (captures ~0.3% extremes), suitable for minimizing noise.
  • If too sparse, try 2.5 for scanning. Consider plotting daily alert frequency in backtest.

• 30-day Rolling Window:

  • Decent compromise for calibration, but:
    • For intraday: 30 × (5-min bars) may be more suitable to capture true short-term baseline.
    • Long lookbacks may under-react to regime shifts.
  • Suggest: Consider window tuning in optimization or allow custom per-indicator window.

Technical Indicators

• RSI (14), MACD (12/26/9), BBands (20/2):
  • Defaults are industry standard.
  • For faster alerts or scalps, trial shorter windows (RSI 7, MACD 8/21/5, BBands 20/1.5).
  • For intraday: Confirm signals only from regular trading session.

Multi-Anomaly/Alert Logic

• Multi-anomaly promotion to UHC:

  • Sensible: two of three anomalies constitute strong confirmation.
  • Could add a cool-down per-symbol (e.g., no double UHC in last X minutes).
  • Keep logic flexible for future composite scoring enhancements.

• Technical Summary in Alert:

  • A real plus for actionability—ensures human review/context on every alert.

Summary / Recommendations:

• Z-Score threshold and lookback are reasonable but should be empirically tuned.
• Technical indicators’ values are conventional; possibility to optimize for strategy style.
• Multi-anomaly logic is well designed; consider cool-down.

3. Phase Integration & Compatibility

Compatibility & Potential Conflicts

• Phase 1 defense with Phase 2 indicators:
  • Generally compatible, assuming data feeding is unified and all indicators are initialized.
  • Edge case: Extreme volatility filter (Phase 1) could skip days when strong anomaly alerts (Phase 2) actually appear. Need exception logic for these rare cases.
  • MIN_STOCK_MOVE (1%) may override valid signals if price moves are smooth/low volatility, despite indicator-based anomalies.

Performance & Scalability

• Processing cost:

  • Z-Score and technical indicator calculations are light for single stocks, but for 1000+ stocks, rolling window stats and indicators may require careful batch updates.
  • Recommend: Batch history pulls and minimize per-bar calculations in OnData; use scheduled events for periodic updates.
  • RollingWindow in QC is memory-efficient, but system RAM must be considered at large scale.

• OnData bottlenecks:

  • OnData must not block; offload heavy stats to scheduled or async tasks.

Logic Flow

• Order of checks:

  • Current order is logical: MIN_STOCK_MOVE → UHC/Anomaly → SPY Filter → Alert.
  • For some anomaly-based signals, it may be worth running anomaly detection before MIN_STOCK_MOVE, since tiny-but-meaningful moves on low price/volatility stocks could be overlooked.

• Alert filtering:

  • If too many filters in sequence, may over-screen and miss rare but important events.
  • Recommend: Log the number and reasons for all rejected signals daily.

Alert Quality

• Expected reduction in false positives:
  • Defensive + anomaly logic should substantially lower noise compared to V1.
  • Risks of over-filtering—genuine opportunities (esp. in low-vol stocks) may be lost.
  • Alert info should include enough data (summary, % move, technicals, rationale) for trader action.

4. Final Evaluation & Recommendations

Overall System Assessment

• Rating: 7.5/10
  • Well-architected, but needs adaptive thresholds and robustness testing at scale.

Top 3 Strengths

1. Robust redundancy and layered filtering for “safe” operation, especially in production/live.
2. Modern, multi-factor anomaly detection with responsive, context-rich alerts.
3. Clear, actionable alert summaries and confidence scoring.

Top 3 Weaknesses

1. Static filter thresholds may lag behind regime changes (volatility, liquidity shifts).
2. Potential OnData performance limitations at large scale without batching/optimization.
3. No native news/sentiment, custom slippage, or advanced out-of-hours control.

Testing Readiness

• Backtesting readiness:

  • Nearly there—needs targeted regression for:
    • Edge cases (SPY halts, market-wide events, holiday sessions, tiny-move anomalies)
    • Large universe stress test for performance/memory
    • Tracking all filter-rejected events for review

• Critical issues:

  • Confirm cool-down logic to avoid duplicate alerts; persist state if possible.
  • Ensure all data/indicators are warm before use (handles startup/mid-day starts).
  • Add slippage/cost modeling for realistic PnL.

Recommendations

• Add adaptive/min-max thresholds (per-stock, per-market regime).
• Optimize indicator calculations (batch updates, scheduled events).
• Enhance logging for every rejection/filter pass.
• Persist cool-down state across interruptions if live trading is considered.
• Consider parameter optimization and auto-tuning based on recent data.
• From DREAM_FEATURES_EXPLAINED.md: If not present, prioritize news/sentiment and adaptive position sizing.

Next Steps

• Do NOT proceed to live trading yet. Recommend:
  • Complete above improvements and backtesting.
  • Track: Alert accuracy/false positive rate, time-to-alert, memory/CPU usage, filter-rejection counts, live PnL volatility, signal turnover.
  • Move to paper after a stable 30-day live backtest without data/rejection bugs.
  • Only consider live after sustained performance, high QA pass, and zero duplicate/corrupted alerts.

Summary:

• The system is intelligently designed with strong multi-phase improvements.
• A few key improvements in adaptivity, performance, and logging are required before live/paper trading.
• Go/no-go: NO-GO for live, READY for focused backtesting following the recommendations above.

If you’d like, I can clarify any of the points or help prioritize next tasks!