Machine learning for trading:
Here's a general outline of how you could approach this:
Data Preparation
OHLC Data: Ensure your OHLC (Open, High, Low, Close) dataset, contained within a DataFrame, is thoroughly cleansed and aligned with your desired H1 timeframe.
Feature Engineering: Augment your data with technical indicators as new features. Utilities like TA-Lib or Pandas_TA can be employed to compute these indicators efficiently.
Model Building
Feature Selection: Employ methods such as correlation matrices or tree-based model outputs to pinpoint the most impactful features.
Model Selection: Opt for models that balance predictive power with execution speed, which is pivotal for scalping strategies. You might start with simpler models like Logistic Regression or Decision Trees and compare their performance with more complex ones.
Evaluation
Performance Metrics: Leverage accuracy, precision, recall, and F1-score to gauge your model’s predictive capabilities.
Risk Metrics: Compute the Sharpe ratio to evaluate the risk-adjusted performance of your trading strategy.
Backtesting
Strategy Execution: Apply your predictive model within a backtesting environment to emulate trading activity.
Assessment: Concentrate on both the trading performance metrics and the Sharpe ratio to evaluate the strategy’s effectiveness.
Deployment
Real-time Data Feed: Ensure seamless integration of your model with a live data stream.
Trade Execution: Set up a system using APIs that allows for swift order placement and execution in line with your model’s signals.