feat: Create README file

README.md

@@ -1 +1,98 @@
-# README
+# Stock Investment Simulator
+
+---
+
+## Introduction
+
+This project provides tools for simulating stock investments using historical data. It features a Python-based environment for fetching stock data, simulating investments, and visualizing investment outcomes.
+
+## Features
+
+- Data extraction from **Yahoo Finance API**.
+- Simulation of stock investments based on historical data.
+- Graphical representation of investment performance.
+- Ability to process a list of stock tickers from a **JSON** file.
+
+---
+
+## Installation and Setup
+
+### Prerequisites
+- Python 3.8 or higher
+- Pip (Python package manager)
+
+### Setting Up a Virtual Environment
+It's recommended to use a virtual environment to avoid conflicts with other Python projects or system-wide packages. To set up a virtual environment, navigate to the project's root directory and run:
+
+```bash
+python -m venv venv
+```
+
+To activate the virtual environment:
+
+- On Windows:
+```bash
+.\venv\Scripts\activate
+```
+
+- On macOS and Linux:
+```bash
+source venv/bin/activate
+```
+
+### Installing Dependencies
+
+With the virtual environment activated, install the required packages using:
+
+```bash
+pip install -r requirements.txt
+```
+
+---
+
+## Usage
+
+**Before starting to use the project, make sure that the paths within `src/settings/config.py` are created**
+
+### Data Processor
+
+The `data_processor.py` script is used to fetch and save historical stock data. The stock tickers are read from a JSON file named `stocks.json`.
+
+Create a `stocks.json` file in the project root with the following format:
+
+```json
+{
+    "data": [
+        "AAPL",
+        "MSFT",
+        "GOOGL",
+        ...
+    ]
+}
+```
+
+Run the `data_processor.py` script:
+
+```bash
+python data_processor.py
+```
+
+---
+
+## Investment Simulation
+
+Use `main.py` to simulate investments based on the fetched data.
+
+1. Edit `main.py` to specify the stock ticker, purchase date, and sale date.
+
+2. Run the script:
+
+```bash
+python main.py
+```
+
+---
+
+## License
+
+This project is open-sourced and available to everyone under the [MIT License](LICENSE).

main.py

@@ -3,16 +3,14 @@
 from datetime import timedelta
 from src.settings import config
 
+
 def simulate_investment(data, buy_date, sell_date):
-    # Convertendo strings de data para objetos datetime
     buy_date = pd.to_datetime(buy_date)
     sell_date = pd.to_datetime(sell_date)
 
-    # Calculando o intervalo de datas para o gráfico
     start_date = buy_date - timedelta(days=90)
     end_date = sell_date + timedelta(days=90)
 
-    # Limitando os dados ao intervalo de interesse
     plot_data = data.loc[start_date:end_date]
 
     if buy_date not in plot_data.index or sell_date not in plot_data.index:
@@ -23,11 +21,11 @@ def simulate_investment(data, buy_date, sell_date):
     return_on_investment = (sell_price - buy_price) / buy_price * 100
 
     plt.figure(figsize=(10, 6))
-    plt.plot(plot_data.index, plot_data['Close'], label='Preço da Ação')
+    plt.plot(plot_data.index, plot_data['Close'], label='Stock Price')
     plt.scatter([buy_date, sell_date], [buy_price, sell_price], color='red')
-    plt.title(f'Investimento em Ações: Compra em {buy_date.date()}, Venda em {sell_date.date()}')
-    plt.xlabel('Data')
-    plt.ylabel('Preço da Ação')
+    plt.title(f'Investment in Stocks: Purchase in {buy_date.date()}, Sell ​​in {sell_date.date()}')
+    plt.xlabel('Date')
+    plt.ylabel('Stock Price')
     plt.legend()
     plt.grid(True)
     plt.show()
@@ -36,16 +34,19 @@ def simulate_investment(data, buy_date, sell_date):
 
 
 def main():
+    buy_date = '2020-06-01'
+    sell_date = '2020-12-01'
     ticker = "AAPL"
+
     data = pd.read_csv(f"{config.APP_PATH_ASSETS_CSV_FOLDER}/{ticker.lower()}.csv")
 
     data['Date'] = data['Date'].apply(lambda x: pd.to_datetime(x).strftime('%Y-%m-%d'))
     data.set_index('Date', inplace=True)
     data.index = pd.to_datetime(data.index)
 
-    result = simulate_investment(data, '2020-06-01', '2020-12-01')
+    result = simulate_investment(data, buy_date, sell_date)
     print('Return (%):', result)
 
 
 if __name__ == "__main__":
-    main()
+    main()