SharpEducation
Unverified ML strategy on Multi by n84d. BotFinder score 18 out of 100.
How to build and test complete trading strategies in Python. Full code walkthroughs posted on the Sharp Research education page
Source: github
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SharpEducation
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 3. Activate it Windows: Mac/Linux: 4. Install dependencies 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 | | LogisticRegression/ | 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.
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