Trading with AI and machine learning

Trading performance metrics: In the world of finance and investment, where myriad metrics and measures vie for attention, the Sharpe Ratio stands out as a beacon for assessing risk-adjusted returns.

Feature Engineering for trading: In the intricate world of trading, where milliseconds can mean the difference between profit and loss, the importance of a robust predictive model cannot be overstated.

Avoid overfitting: In the intricate dance of machine learning, where algorithms waltz with vast datasets to produce predictive models, one misstep can lead to a common and deceptive pitfall: overfitting.

Which algorithm to use? In the vast ocean of machine learning, algorithms act as the navigational compass guiding researchers, data scientists, and businesses towards meaningful insights and solutions. From the

Which type of ML? In the dynamic realm of artificial intelligence (AI), machine learning (ML) stands as a pivotal cornerstone, driving innovations across industries, from healthcare to finance. At the

AI for trading In the rapidly evolving landscape of financial markets, machine learning (ML) emerges as a revolutionary force, reshaping the way traders analyze data, devise strategies, and execute trades.

Loss and accuracy: In the context of the LSTM training method, “loss” and “accuracy” serve as metrics to evaluate the performance of the model during training and validation. Let’s break

CNN vs. RNN vs. LSTM Performance after a few training epochs: Related posts: Which Machine Learning Model should I choose For An AI Trading Bot? What is the difference between

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,

Validation Loss The term val_loss stands for “Validation Loss” during the training of a Recurrent Neural Network (RNN) or any other machine learning model. It is a metric that gives

CNN and LSTM models Both Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) are types of neural networks that can be used in trading, but they are generally

Model Evaluation Evaluating the performance of a machine learning model for trading involves multiple aspects, from statistical metrics to out-of-sample testing. Here’s how you can assess if your machine learning

Predicting what? In the context of trading and machine learning, predicting the percent change is generally considered easier and more reliable than predicting the exact close price for several reasons:

What is important? For a scalping trading bot in the Forex market, you’ll want to focus on features that capture short-term price movements and market conditions. Here are some feature

How does feature learning work? Feature learning, also known as representation learning, refers to the automatic identification and extraction of relevant features from raw data. In the context of deep

Machine Learning Models In Forex trading, various deep learning models can be employed to make predictions. Here’s a rundown of some popular ones, along with their pros and cons: Long