Learn to Build and Backtest LSTM-Based Trading Strategies Using Technical Indicators and Real Market Data
β±οΈ Length: 1.5 total hours
β 4.43/5 rating
π₯ 7,104 students
π August 2025 update
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- Course Overview
- Deep Learning for Trading with LSTM: Smarter Than Signals provides a high-level, practical immersion into the world of quantitative finance where artificial intelligence replaces static, legacy-based trading rules with dynamic predictive modeling.
- This curriculum is specifically engineered to bridge the gap between traditional technical analysis and modern recurrent neural networks, focusing on the Long Short-Term Memory (LSTM) architecture which is uniquely suited for time-series forecasting due to its internal memory cells.
- Unlike standard trading courses that rely on lagging indicators, this course demonstrates how to ingest raw historical price data and technical overlays to train a model that recognizes complex, non-linear patterns that the human eye or standard math formulas often overlook.
- Students will explore the end-to-end pipeline of an algorithmic trading system, beginning with automated data acquisition and ending with a rigorous backtesting environment to validate the efficacy of the neural network’s predictions.
- The course focuses on the philosophy of “smarter signals,” teaching learners how to use Deep Learning to filter out market noise and focus on the underlying structural dependencies within financial time-series data.
- Through a series of condensed, high-impact modules, the training moves from theoretical neural network concepts to the hands-on implementation of Long-Short strategies using real-world stock and cryptocurrency market data.
- Requirements / Prerequisites
- A foundational understanding of Python Programming is essential, specifically familiarity with data structures like lists, dictionaries, and basic control flow logic.
- Learners should have a basic grasp of Data Analysis libraries such as Pandas for data manipulation and NumPy for numerical operations, as these are the backbone of financial data preprocessing.
- General knowledge of Financial Markets, including an understanding of what OHLC (Open, High, Low, Close) data represents and how basic technical indicators like Moving Averages work.
- Access to a modern development environment, such as Jupyter Notebooks or Google Colab, is required to execute the deep learning models and visualize the training performance in real-time.
- While a deep mathematical background is not strictly necessary, a comfort level with basic Statistics and Algebra will help in understanding how loss functions and weights are optimized during the training phase.
- No prior experience with TensorFlow or Keras is required, as the course provides a guided approach to building the layers of the neural network from scratch.
- Skills Covered / Tools Used
- Time-Series Preprocessing: Master the art of normalizing and scaling financial data using Scikit-Learn’s MinMaxScaler to ensure the LSTM model converges efficiently.
- Neural Network Architecture: Learn to construct Keras Sequential models, specifically focusing on LSTM layers, Dropout layers for regularization, and Dense layers for final price prediction output.
- Feature Engineering: Integrate technical indicators such as the Relative Strength Index (RSI) and Bollinger Bands as additional input features to give the model a multi-dimensional view of market momentum.
- API Data Integration: Utilize the yfinance library to programmatically pull years of historical market data directly into your Python environment for training and testing purposes.
- Model Evaluation: Implement performance metrics such as Mean Squared Error (MSE) and Directional Accuracy to quantify how well the AI is predicting future price movements.
- Vectorized Backtesting: Apply the modelβs predictions to historical data to calculate cumulative returns, Sharpe ratios, and drawdowns, ensuring the strategy is viable before risking actual capital.
- Benefits / Outcomes
- Shift from Lagging to Leading: Gain the ability to transform traditional lagging indicators into predictive features that allow for more proactive entry and exit points in the market.
- Automated Strategy Development: Develop a reusable framework for algorithmic trading that can be applied to various asset classes, including equities, forex, and digital assets.
- Advanced Problem Solving: Learn to mitigate common deep learning pitfalls such as overfitting and “look-ahead bias,” which are critical for maintaining the integrity of a trading bot.
- Enhanced Portfolio Diversification: By incorporating non-correlated AI-driven strategies into a broader portfolio, students can achieve better risk-adjusted returns compared to traditional buy-and-hold methods.
- Future-Proof Skillset: Position yourself at the forefront of the FinTech revolution by mastering the specific subset of machine learning that is currently dominating institutional quantitative hedge funds.
- Confidence in Data: Move away from emotional trading by relying on a systematic, data-driven approach that makes decisions based on statistical probability rather than intuition or “gut feeling.”
- PROS
- High Signal-to-Noise Ratio: The course is highly optimized at 1.5 hours, delivering pure technical implementation without unnecessary fluff or long-winded theoretical lectures.
- Up-to-Date Content: With an August 2025 update, the code snippets and library dependencies are verified to work with the latest versions of TensorFlow and Python.
- Real-World Application: Unlike theoretical AI courses, this focuses strictly on financial market data, making the projects immediately relevant to traders and analysts.
- Accessible Complexity: The instructor breaks down complex Recurrent Neural Network (RNN) concepts into digestible steps that are easy to follow even for those new to deep learning.
- CONS
- Hardware Limitations: Deep learning models, especially LSTMs, can be computationally intensive; students with older hardware may experience slow training times unless they utilize cloud-based solutions like Google Colab.
Learning Tracks: English,Finance & Accounting,Investing & Trading
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