Built a pre-market ML system that predicts SPY intraday direction before the open

neo-futurism · 2026-03-12T00:12:17+00:00

=== SUMMARY === - The author, u/neo-futurism, has developed a Machine Learning (ML) system to predict the intraday direction of the SPY ETF using only pre-market data (4:00 AM to 9:30 AM ET). - The core thesis is that pre-market price action and volume patterns contain significant predictive power for the subsequent regular trading session, a concept supported by some academic research. - The author uses the model's output (direction and confidence) to frame their trading day but has implemented a "blank card" system to hide low-confidence signals to avoid personal cognitive biases like anchoring. - **Quality assessment:** This is well-researched DD. The author discusses their methodology (ML classifiers, feature engineering), validation techniques (Combinatorial Purged Cross-Validation), and acknowledges the psychological pitfalls of using such a system, indicating a sophisticated and self-aware approach. === SENTIMENT === NEUTRAL === TRADE IDEAS === TICKER - DIRECTION | confidence: 0.75 | sentiment: +0.70 Speaker: u/neo-futurism Thesis: 1. THE FACT: An ML model trained on 5 years of SPY pre-market data (price, volume) has shown predictive power for the intraday direction, validated with robust cross-validation methods. 2. THE BRIDGE: If the model outputs a high-confidence "UP" signal before the market opens, it suggests a statistical edge that the day's price action will be bullish. 3. THE VERDICT: On days where the pre-market model generates a high-confidence bullish signal, a long position in SPY at or near the market open is warranted to capture the predicted upward directional bias. 4. RISKS: The model's accuracy is not 100% and can fail, especially on "chop" days or during unexpected news events. The 15-day sample size is small, and the model's edge could decay over time. Timeframe: short-term Key Points: - ML model predicts intraday direction from pre-market data. - High-confidence signals provide a directional bias for the day. - System uses price action

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Summary

The author, u/neo-futurism, has developed a Machine Learning (ML) system to predict the intraday direction of the SPY ETF using only pre-market data (4:00 AM to 9:30 AM ET).
The core thesis is that pre-market price action and volume patterns contain significant predictive power for the subsequent regular trading session, a concept supported by some academic research.
The author uses the model's output (direction and confidence) to frame their trading day but has implemented a "blank card" system to hide low-confidence signals to avoid personal cognitive biases like anchoring.
Quality assessment: This is well-researched DD. The author discusses their methodology (ML classifiers, feature engineering), validation techniques (Combinatorial Purged Cross-Validation), and acknowledges the psychological pitfalls of using such a system, indicating a sophisticated and self-aware approach.

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Comments 148

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Been quietly working on this for a few weeks which started after seeing a thread where someone claimed a single pre-market candle predicts next day's direction. Sounded like a bait. And it probably was.  
  
But I couldn't stop thinking about it not because I believed it but cuz I realized even a simple signal like that could create a directional bias in my own head before I'd even looked at a chart.

The core idea is that the day's bias is largely set before 9:30. What surprised me is there's actual academic backing for it, I wasn't expecting that going in. Pre-market price action, volume patterns, and some other features do carry predictive power. It's not random but it's definitely farther than a coin flip if you model it properly and validate it hard. After training a ML model on 5 years of SPY data the results were interesting enough to build a real system around.

Every morning before the open, it pulls pre-market data, builds features from the 4:00 to 9:30 AM window only, and scores three ML classifiers across different time horizons. Direction and confidence, displayed on a local dashboard. I also layered in options walls and GEX as a separate system for a full upcoming session context.  
  
The ironic part is that once I started using it, the model started warping my own decisions even when confidence was low. I'd see a directional signal and it would anchor me, then I'd fight my own read, override good setups, and lose money. Classic case of trusting the machine more than myself due to my personal agorithmic bias!

So the fix was hiding direction entirely below a certain confidence threshold. No number, label, nothing. If it doesn't meet the bar I just get a blank card. 

Validation is done with [CPCV](https://towardsai.net/p/l/the-combinatorial-purged-cross-validation-method) as backtesting financial time series with standard k-fold is not the best method imo.

So far, recent 15 day scorecard and today's live output below, all out of sample. Apart from today's chop day, morning and day models are good so far but still not reading too much into it. It has only been useful for framing the session. Few bad bias days aside it's been a net positive for my process.

Curious if anyone else is doing pre-market feature engineering and what's actually working for them

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