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Deep Learning for Trading with LSTM: Smarter Than Signals
Rating: 4.4 out of 5(82 ratings)
8,140 students

Deep Learning for Trading with LSTM: Smarter Than Signals

Learn to Build and Backtest LSTM-Based Trading Strategies Using Technical Indicators and Real Market Data
Last updated 8/2025
English

What you'll learn

  • Understand how AI is transforming algorithmic trading
  • Create predictive trading features from stock data
  • Train LSTM models to predict buy, sell, or hold signals
  • Handle imbalanced financial data using oversampling and focal loss
  • Evaluate trading performance using accuracy, precision, recall, and confusion matrix
  • Visualize predicted trading signals on real stock charts
  • Backtest trading strategies using portfolio simulation
  • Calculate Sharpe Ratio, Drawdown, and Returns for risk analysis

Course content

2 sections28 lectures1h 32m total length
  • Introduction1:59

    After this lecture, students will be able to explain the concept of algorithmic trading and distinguish it from traditional manual trading.

  • AI in Algorithmic Trading1:24

    Students will be able to describe how AI enhances trading algorithms and enables adaptive strategy development.

  • Algorithmic Trading Market Size1:23

    Students will be able to summarize the current and projected market size of algorithmic trading and identify growth factors.

  • Algorithmic Trading- Markets3:07

    Students will be able to list major global companies involved in algorithmic trading and their areas of specialization.

  • How Algorithmic Trading Works2:38

    Students will be able to identify key financial markets using algorithmic trading, including stocks, forex, bonds, and crypto. Students will be able to outline the core components of an algorithmic trading system including data, strategy, risk, and execution.

  • Key Algorithmic Trading Strategies2:31

    Students will be able to describe various trading strategies such as market making, arbitrage, trend following, and pattern recognition.

  • Importance and impact of Algorithmic Trading2:07

    Students will be able to explain the benefits of algorithmic trading, such as speed, accuracy, and reduced emotional bias. Students will be able to evaluate both positive and negative impacts of algorithmic trading on market liquidity and volatility.

  • Traditional Machine Learning Techniques2:13

    Students will be able to describe key ML models like Random Forest, Gradient Boosting, SVM, and KNN in a trading context.

  • Deep Learning1:48

    Students will be able to explain how LSTM and CNNs are used to analyze time-series and visual trading data.

  • Reinforcement Learning (RL)1:46

    Students will be able to summarize the concept of reinforcement learning and how it helps build adaptive trading agents.

  • Genetic Algorithms in Trading2:10

    Students will be able to describe how genetic algorithms simulate evolution to optimize trading strategies.

  • Sentiment Analysis in Algorithmic Trading​3:51

    Students will be able to use NLP concepts to extract sentiment signals from financial text sources like news or tweets.

  • Ensemble Methods in Algorithmic Trading​2:53

    Students will be able to explain how combining multiple models improves prediction accuracy in algorithmic trading.

  • Recap2:45

Requirements

  • Basic knowledge of Python programming
  • Familiarity with Pandas, NumPy, and Matplotlib
  • No prior experience with deep learning or stock trading is required — LSTM and trading concepts are introduced step-by-step
  • Some exposure to machine learning will help in understanding the LSTM-based models more easily

Description

Unlock the power of Artificial Intelligence in the world of trading.

In this hands-on course, you’ll learn how to build, train, and backtest AI-driven algorithmic trading strategies using Python, machine learning, and deep learning tools. Whether you're from finance or tech, this course will help you turn market data into actionable trading signals using LSTM models, sentiment analysis, and advanced evaluation metrics.

You’ll begin with the basics of algorithmic trading, explore the role of AI, and dive deep into tools like Random Forest, Gradient Boosting, CNNs, LSTM, Reinforcement Learning, Genetic Algorithms, and Ensemble Methods. From there, you’ll move into real-world implementation — loading historical stock data, creating predictive features, labeling outcomes, handling class imbalance with focal loss, and evaluating your trading strategy through backtesting and risk metrics like Sharpe Ratio and Drawdown.

This course includes:

  • Real Apple stock data for hands-on practice

  • Feature engineering using technical indicators

  • Custom loss functions like Focal Loss

  • Building an LSTM model from scratch

  • Visualizing trading signals and performance

  • Backtesting with capital growth simulations

By the end, you’ll walk away with a fully functional trading strategy powered by AI — plus the knowledge to apply these techniques across any stock, ETF, or crypto asset.

What You'll Learn

  • Understand how AI is transforming algorithmic trading

  • Create predictive trading features from stock data

  • Train LSTM models to predict buy, sell, or hold signals

  • Handle imbalanced financial data using oversampling and focal loss

  • Evaluate trading performance using accuracy, precision, recall, and confusion matrix

  • Visualize predicted trading signals on real stock charts

  • Backtest trading strategies using portfolio simulation

  • Calculate Sharpe Ratio, Drawdown, and Returns for risk analysis

Who this course is for:

  • Aspiring algorithmic traders looking to build AI-powered strategies
  • Data scientists and ML engineers interested in finance and trading
  • Quantitative analysts and fintech professionals exploring automation
  • Students and researchers in finance, statistics, or computer science
  • Anyone curious about LSTM, NLP, and deep learning for real-time trading