Data Preprocessing Failures That Sabotage Deep Learning Price Models
Common Preprocessing Mistakes That Destroy Model Performance
Check your feature scaling approach before training begins. Most freelancers normalize prices without considering whether returns or percentage changes make more sense for financial time series. Using raw price values creates non-stationary data that confuses recurrent networks.
Examine your handling of missing values in market data feeds. Forward-filling gaps during weekends or holidays introduces look-ahead bias. Your model learns patterns that cannot exist in real-time deployment.
Verify your train-test split preserves temporal order. Random shuffling of time series data leaks future information into training sets. Split chronologically and maintain a realistic gap between training end and validation start.
Inspect outlier treatment in your pipeline. Removing legitimate price spikes eliminates the exact events your model needs to predict. Document which anomalies represent data errors versus genuine market behavior.
Review your feature engineering for data snooping. Creating indicators that reference future data points produces impressive backtests that fail in production. Each feature must use only information available at prediction time.
Test your preprocessing on held-out periods. Parameter choices that work for 2020 data may fail spectacularly on 2023 volatility patterns.
model = Sequential([ LSTM(128, return_sequences=True), Dropout(0.2), Dense(1, activation='linear') ])