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How to build and test complete trading strategies in Python. Full code walkthroughs posted on the Sharp Research education page

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# SharpEducation

Educational notebooks for the [Sharp Research YouTube channel](https://www.youtube.com/@sharp_research) — 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**
```bash
python -m venv myenv
```

**3. Activate it**

Windows:
```bash
myenv\Scripts\activate
```
Mac/Linux:
```bash
source myenv/bin/activate
```

**4. Install dependencies**
```bash
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`.

SharpEducation

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