SharpEducation

Educational notebooks for the Sharp Research YouTube channel — covering technical analysis, regression, logistic regression, machine learning, and economic data strategies, all in Python.

Getting Started

1. Clone the repo and open a terminal in the project folder

2. Create a virtual environment

python -m venv myenv

3. Activate it

Windows:

myenv\Scripts\activate

Mac/Linux:

source myenv/bin/activate

4. Install dependencies

pip install -r requirements.txt

You're ready to go. Open any notebook and adjust the global variables at the top to test different strategies and parameters.

Repository Structure

| Folder | Content | |---|---| | Introduction/ | Moving averages and basic strategy building | | TA/ | Technical indicators — MACD, RSI, MFI, Bollinger Bands, and more | | Regression/ | Linear and multi-variable regression analysis | | Logistic_Regression/ | Logistic regression models and evaluation | | ML/ | Train/test splits and overfitting | | Economic/ | FRED and interest rate strategies | | Advanced/ | Risk/reward and additional concepts |

Requirements

Python 3.8+. All dependencies are listed in requirements.txt.