Trading_Strategie_Analysis

In this project, I spearheaded the development and implementation of various trading strategies aimed at optimizing investment performance in financial markets. Leveraging my expertise in data analysis and algorithmic trading, I formulated diverse trading approaches tailored to different market conditions and asset classes.

Dev.Porjects.mp4

Trading Strategies Analysis

Table of Contents

• About the Project
• Features
• Directory Structure
• Setup Instructions
• How to Run
• Tools Description
• Notebook Analysis
• Data Management and Security
• Contributing
• License
• Contact

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About the Project

This project implements and evaluates various trading strategies to optimize performance in financial markets, specifically focusing on cryptocurrency prices. The project is centered on algorithmic trading and uses Python, Jupyter notebooks, and Tableau for strategy analysis, backtesting, and visualization.

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Features

• Comprehensive Data ETL: Automates the extraction and preprocessing of cryptocurrency data from multiple fiat currencies, ensuring well-structured data for analysis.
• Algorithmic Trading Strategies: Implement various algorithms to optimize returns and mitigate risks.
• Backtesting: Assess the historical performance of different strategies using real market data.
• Tableau Visualizations: Visualizations to easily compare the performance of strategies across timeframes and market conditions.

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Directory Structure

Trading_Strategies_Analysis-main/
├── README.md                    # Project documentation
├── Tableau WorkBook/             # Tableau workbook for visualizing strategy results
│   └── Dev Project.twb
├── etl/                          # ETL for cryptocurrency data
│   ├── AUD_btc_prices.csv
│   ├── CAD_btc_prices.csv
│   ├── ETL.ipynb                 # ETL process in Jupyter notebook
│   └── ...                       # Other fiat currency data
├── old strategies/               # Old trading strategies notebooks
│   └── Trading_Strategies_implementation.ipynb
├── preproccessing-data/          # Data preprocessing notebooks
│   └── preprocessed-dataset-for-strategies.ipynb
└── trading algorithms/           # Trading algorithms notebooks
    ├── Trading_Strategy.ipynb
    ├── trading_strategy_3.ipynb
    └── trading_stratergy_2.ipynb

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Setup Instructions

Prerequisites

Ensure the following are installed on your system:

• Python 3.7+
• Jupyter Notebook
• Tableau Desktop (for viewing the visualizations)

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How to Run

1. Running ETL for Cryptocurrency Data

The etl/ETL.ipynb notebook processes cryptocurrency price data from various fiat currencies and loads it for analysis. Run this notebook to prepare data for analysis:

jupyter notebook etl/ETL.ipynb

2. Running Trading Strategies

Explore and evaluate different trading strategies by running the notebooks under trading algorithms/. For example, to run the main trading strategy:

jupyter notebook trading algorithms/Trading_Strategy.ipynb

The backtesting results will be displayed within the notebook, allowing you to see how different strategies perform under various market conditions.

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Tools Description

• Python & Pandas: Used for data manipulation and analysis of cryptocurrency price data.
• Jupyter Notebooks: Interactive notebooks used for backtesting trading strategies.
• Tableau: Used for visualizing the performance of each trading strategy.
• Plotly & Matplotlib: Libraries used for creating visualizations and graphs within the notebooks.

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Data Management & Security

Data Management:

This project ensures effective data handling through:

1. ETL Process: Automated extraction and transformation ensure clean and structured data.
2. Data Preprocessing: Creation of additional features improves the effectiveness of trading models.

Security Considerations:

• Privacy: The dataset contains no personal information, ensuring user privacy is maintained.
• Data Integrity: Preprocessing steps ensure data quality and integrity, a crucial factor in algorithmic trading to avoid skewed results.

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This video presentation offers a detailed overview of the project’s goals, outcomes, and technical components: project presentation

some of the Trading Straegies in Use:

1. seasonalilty startegy:

   

2. RIS:

   

3. Harmonic Startegy(Gately Pattern):

   

4. Moving Average:

   

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Contributing

Contributions to this project are welcome. To contribute:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m 'Added new feature').
4. Push to the branch (git push origin feature-branch).
5. Open a Pull Request.

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