Ai Trading Bot With Python: Machine Learning & Backtest 2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.18 GB
Master AI Trading systems. Build a Python bot proven to scale from 1K to 4K in backtested market cycles.
What you'll learn
Apply backtesting techniques to evaluate trading strategies, turning 1K into 4K
Develop a fully automated trading bot that runs 24/7 on Binance or Kraken
Go straight to the heart of Machine Learning by applying it directly to Bitcoin price prediction
Implement a complete end-to-end Machine Learning pipeline
Build and train deep learning models (Conv1D, LSTM, and hybrid architectures) to predict market movements
Useful tips and tricks in Machine Learning to boost model performance
Algorithmic trading with AI, where decisions are driven by data and backtesting, not emotions
Requirements
Basic Python knowledge (for example: writing simple loops, functions, and classes)
Description
Master AI Trading: Build a Production-Grade Machine Learning BotTake your trading from intuition to automated science. This course provides a comprehensive, step-by-step framework for building a fully autonomous AI Trading Bot-moving from raw market data ingestion to high-performance execution on Binance or Kraken.IMPORTANT: This course treats trading as a Quantitative Science, not a game of chance.The Core Case Study: Engineering a 4x Return We don't just write code; we validate performance. Using a backtested starting capital of 1,000, we demonstrate how to scale a systematic account toward 4,000 using advanced Machine Learning models. This strategy is backed by institutional-grade metrics, ensuring that growth is driven by risk-adjusted logic, not luck.What you'll learn:Ingest & Engineer Financial Data: Automate the collection, cleaning, and scaling of real-time 15-minute Bitcoin data for algorithmic use.Master Quantitative Preprocessing: Apply advanced time-series techniques, including stationarity testing and multi-dimensional feature engineering.Architect Deep Learning Models: Design and train high-performance AI Trading models using Conv1D and LSTM neural networks.Deploy Ensemble Strategies: Combine multiple predictive models to reduce variance and ensure more stable, robust performance in volatile markets.Build an Autonomous Trading Bot: Implement a production-grade Python system that executes real-time trades on major exchanges via API.Validate with Rigorous Backtesting: Evaluate your strategies using historical data to ensure high-probability outcomes before deploying capital.Optimize for Risk-Adjusted Returns: Understand the science of the Sharpe Ratio and drawdowns to turn trading into a systematic enterprise.Who this course is for:Software Engineers & Python Developers: Those looking to transition into Fintech or bridge the gap between backend engineering and quantitative finance.Quantitative Traders & Analysts: Professionals who want to evolve from manual or rule-based trading to autonomous, AI-driven systems.Data Science Professionals: Learners looking for a production-grade, end-to-end project that applies Deep Learning (LSTM/Conv1D) to volatile, real-world time-series data.Finance & Investment Professionals: Individuals seeking to understand the "Black Box" of AI Trading through a transparent, science-first approach.Computer Science Students: Anyone with a Python foundation who wants to build a portfolio-ready Automated Trading System.By the end of this course, you'll have a working trading bot, a deep understanding of the machine learning pipeline for trading, and the confidence to experiment with your own ideas in crypto markets.
Beginners in machine learning who want to apply AI to real-world finance,Aspiring algorithmic traders who want to build their own trading bot from scratch,Python learners who want a practical project that goes beyond theory,Anyone curious about how to use AI in financial markets - from data preprocessing to live trading,Traders who want to move from manual strategies to automated, AI-driven systems
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