From 1K To 4K: Build A Bitcoin Trading Bot With Python

A hands-on guide to data, machine learning, and trading bot development with real results.

What you'll learn

Understand how to prepare and engineer features from raw market data to make it suitable for machine learning models

Build and train deep learning models (Conv1D, LSTM, and hybrid architectures) to predict market movements

Apply backtesting techniques to evaluate trading strategies and measure risk/reward performance

Develop a fully automated trading bot that runs 24/7 on Binance Futures

Gain hands-on experience turning a $1,000 backtest into $4,000 equity, learning how to scale strategies responsibly

Requirements

Basic Python knowledge (for example: writing simple loops, functions, and classes)

No prior experience in machine learning or trading required

A computer with an internet connection (Windows, Mac, or Linux)

Description

Do you want to build your own AI-powered Bitcoin trading bot from scratch?This course takes you step by step from raw market data all the way to a fully automated trading system running on real-time data.Starting with 1,000 USD, our goal is to grow the account toward 4,000 USD (Sharpe Ratio: 4.91 annualized, 0.0316 unannualized) using deep learning and systematic trading strategies. Along the way, you’ll gain practical experience in Python, data preprocessing, Conv1D, LSTM, ensembling methods, and backtesting.What you’ll learn:Collect, clean, and scale real 15-minute Bitcoin dataUnderstand stationarity, feature engineering, and time series preprocessingBuild predictive models using Conv1D and LSTMEnsemble multiple models for more stable performanceDesign and implement a live trading bot that runs every 15 minutesBacktest strategies to evaluate performance before going liveWho this course is for:Python developers who want to apply their skills in trading and financeTraders who want to upgrade to systematic, AI-driven approachesData science and machine learning learners looking for a real-world projectAnyone curious about how AI can be applied in cryptocurrency tradingBy 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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Learning approach

Lecture 3 Setup Google Colab

Lecture 4 Understanding the dataset

Lecture 5 Stationary data

Lecture 6 Stationary data transformation

Section 2: Preparing Data for Machine Learning

Lecture 7 Preprocess data into train/test

Section 3: Feedforward Neural Network

Lecture 8 Neural Network fundamentals

Lecture 9 Loss function

Lecture 10 How a Neural Network learns

Lecture 11 Dot product

Lecture 12 Activation function

Section 4: Building & Training Models in PyTorch

Lecture 13 Build the model

Lecture 14 Build the model (part 2)

Lecture 15 Initializations

Lecture 16 Learning rate

Lecture 17 Batch size

Lecture 18 Train and test losses

Lecture 19 Epoch

Lecture 20 Create mini batches

Lecture 21 Training loop

Lecture 22 Test step

Section 5: Backtest (Trading simulation)

Lecture 23 Backtest (part 1)

Lecture 24 Backtest (part 2)

Lecture 25 Add new features (to improve performance)

Lecture 26 Scale the data

Lecture 27 Reproducibility

Lecture 28 Save the training process

Section 6: Advanced Model Architectures

Lecture 29 Convolutional Neural Networks (Conv1D theory)

Lecture 30 Conv1D implementation in PyTorch (code & training)

Lecture 31 LSTM theory

Lecture 32 LSTM implementation in PyTorch (code & training)

Lecture 33 Ensemble method (part 1)

Lecture 34 Ensemble method (part 2)

Section 7: Trading Bot

Lecture 35 Introduction to trading bot

Lecture 36 Loading scalers file

Lecture 37 Loading models file

Lecture 38 bot.py (part 1)

Lecture 39 bot.py (part 2)

Lecture 40 bot.py (part 3)

Lecture 41 Launch the trading bot

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