AlgoDevStudio
Machine Learning Beyond the Hype
Why simple Logistic Regression often beats Deep Learning in market prediction.
"I want an AI that predicts the next candle." We hear this request weekly. It is the wrong goal. Financial markets are non-stationary, noisy, and adversarial. Treating the stock market like an image recognition problem is the fastest way to lose capital.
1. Garbage In, Garbage Out (Feature Engineering)
Feeding raw Open-High-Low-Close (OHLC) data into an LSTM model will fail. The model will essentially learn to predict "Price(t+1) ≈ Price(t)".
The Solution: Stationarity. You must transform your data into stationary features:
- Log Returns instead of Price.
- RSI Slope instead of raw RSI value.
- Distance from MA normalized by Volatility (Z-Score).
2. The Look-Ahead Bias Trap
The most common error in beginner ML trading strategies is data leakage.
Classic Mistake:
df['Target'] = df['Close'].shift(-1)
If you normalize your entire dataset using MinMaxScaling before splitting into Train/Test, you have leaked future information (the global max price) into the past. Your backtest will show 99% accuracy. Your live trade will go to zero.
3. Probability > Prediction
Donot try to predict the exact price. Predict the probability regime.
A Random Forest Classifier that outputs "60% chance of Up Trend" allows you to size your positions dynamically. If confidence is 51%, bet small. If confidence is 80%, bet big. This is where the edge lies—in Risk Management utilizing ML signals, not the signals themselves.
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