Society Cookbook#

You can also check this cookbook in colab here

1. Concept#

The society module is one of the core modules of CAMEL. By simulating the information exchange process, this module studies the social behaviors among agents.

Currently, the society module aims to enable agents to collaborate autonomously toward completing tasks while keeping consistent with human intentions and requiring minimal human intervention. It includes two frameworks: RolePlaying and BabyAGI, which are used to run the interaction behaviors of agents to achieve objectives.

Taking RolePlaying as an example, this framework was designed in an instruction-following manner. These roles independently undertake the responsibilities of executing and planning tasks, respectively. The dialogue is continuously advanced through a turn-taking mechanism, thereby collaborating to complete tasks. The main concepts include:

Task: a task can be as simple as an idea, initialized by an inception prompt.
AI User: the agent who is expected to provide instructions.
AI Assistant: the agent who is expected to respond with solutions that fulfill the instructions.

2. Types#

2.1 `RolePlaying`#

RolePlaying is a unique cooperative agent framework of CAMEL. Through this framework, agents in CAMEL overcome numerous challenges, such as role flipping, assistant repeats instructions, flake replies, infinite loop of messages, and conversation termination conditions.

When using RolePlaying framework in CAMEL, predefined prompts are used to create unique initial settings for different agents. For example, if the user wants to initialize an assistant agent, the agent will be initialized with the following prompt.

Never forget you are a ASSISTANT_ROLE and I am a USER_ROLE.

This assigns the chosen role to the assistant agent and provides it with information about the user’s role.
Never flip roles! Never instruct me!

This prevents agents from flipping roles. In some cases, we have observed the assistant and the user switching roles, where the assistant suddenly takes control and instructs the user, and the user follows those instructions.
You must decline my instruction honestly if you cannot perform the instruction due to physical, moral, legal reasons or your capability and explain the reasons.

This prohibits the agent from producing harmful, false, illegal, and misleading information.
Unless I say the task is completed, you should always start with: Solution: . should be specific, and provide preferable implementations and examples for task-solving.

This encourages the assistant to always responds in a consistent format, avoiding any deviation from the structure of the conversation, and preventing vague or incomplete responses, which we refer to as flake responses, such as “I will do something”.
Always end your solution with: Next request.

This ensures that the assistant keeps the conversation going by requesting a new instruction to solve.

`RolePlaying` Attributes#

Attribute	Type	Description
assistant_role_name	str	The name of the role played by the assistant.
user_role_name	str	The name of the role played by the user.
critic_role_name	str	The name of the role played by the critic.
task_prompt	str	A prompt for the task to be performed.
with_task_specify	bool	Whether to use a task specify agent.
with_task_planner	bool	Whether to use a task planner agent.
with_critic_in_the_loop	bool	Whether to include a critic in the loop.
critic_criteria	str	Critic criteria for the critic agent.
model	BaseModelBackend	The model backend to use for generating responses.
task_type	TaskType	The type of task to perform.
assistant_agent_kwargs	Dict	Additional arguments to pass to the assistant agent.
user_agent_kwargs	Dict	Additional arguments to pass to the user agent.
task_specify_agent_kwargs	Dict	Additional arguments to pass to the task specify agent.
task_planner_agent_kwargs	Dict	Additional arguments to pass to the task planner agent.
critic_kwargs	Dict	Additional arguments to pass to the critic.
sys_msg_generator_kwargs	Dict	Additional arguments to pass to the system message generator.
extend_sys_msg_meta_dicts	List[Dict]	A list of dicts to extend the system message meta dicts with.
extend_task_specify_meta_dict	Dict	A dict to extend the task specify meta dict with.
output_language	str	The language to be output by the agents.

2.2 `BabyAGI`#

Babyagi is framework from “Task-driven Autonomous Agent”

Installation#

Ensure you have CAMEL AI installed in your Python environment:

[ ]:

%pip install "camel-ai==0.2.1"

Setting Up API Keys#

You’ll need to set up your API keys for OpenAI.

[4]:

import os
from getpass import getpass

# Prompt for the API key securely
openai_api_key = getpass('Enter your API key: ')
os.environ["OPENAI_API_KEY"] = openai_api_key

Enter your API key: ··········

3.1. Using `RolePlaying`#

[5]:

from colorama import Fore

from camel.societies import RolePlaying
from camel.utils import print_text_animated

[7]:

def main(model=None, chat_turn_limit=10) -> None:
# Initial the role-playing session on developing a trading bot task with default model (`GPT_4O_MINI`)
    task_prompt = "Develop a trading bot for the stock market"
    role_play_session = RolePlaying(
        assistant_role_name="Python Programmer",
        assistant_agent_kwargs=dict(model=model),
        user_role_name="Stock Trader",
        user_agent_kwargs=dict(model=model),
        task_prompt=task_prompt,
        with_task_specify=True,
        task_specify_agent_kwargs=dict(model=model),
    )

# Output initial message with different colors.
    print(
        Fore.GREEN
        + f"AI Assistant sys message:\n{role_play_session.assistant_sys_msg}\n"
    )
    print(
        Fore.BLUE + f"AI User sys message:\n{role_play_session.user_sys_msg}\n"
    )

    print(Fore.YELLOW + f"Original task prompt:\n{task_prompt}\n")
    print(
        Fore.CYAN
        + "Specified task prompt:"
        + f"\n{role_play_session.specified_task_prompt}\n"
    )
    print(Fore.RED + f"Final task prompt:\n{role_play_session.task_prompt}\n")

    n = 0
    input_msg = role_play_session.init_chat()

# Output response step by step with different colors.
# Keep output until detect the terminate content or reach the loop limit.
    while n < chat_turn_limit:
        n += 1
        assistant_response, user_response = role_play_session.step(input_msg)

        if assistant_response.terminated:
            print(
                Fore.GREEN
                + (
                    "AI Assistant terminated. Reason: "
                    f"{assistant_response.info['termination_reasons']}."
                )
            )
            break
        if user_response.terminated:
            print(
                Fore.GREEN
                + (
                    "AI User terminated. "
                    f"Reason: {user_response.info['termination_reasons']}."
                )
            )
            break

        print_text_animated(
            Fore.BLUE + f"AI User:\n\n{user_response.msg.content}\n"
        )
        print_text_animated(
            Fore.GREEN + "AI Assistant:\n\n"
            f"{assistant_response.msg.content}\n"
        )

        if "CAMEL_TASK_DONE" in user_response.msg.content:
            break

        input_msg = assistant_response.msg

if __name__ == "__main__":
    main()

AI Assistant sys message:
BaseMessage(role_name='Python Programmer', role_type=<RoleType.ASSISTANT: 'assistant'>, meta_dict={'task': 'Develop a Python-based trading bot that utilizes machine learning algorithms to analyze historical stock data, identify patterns, and execute buy/sell orders automatically. The bot should incorporate risk management strategies, real-time market data integration, and a user-friendly dashboard for monitoring performance and adjusting trading parameters based on user-defined criteria.', 'assistant_role': 'Python Programmer', 'user_role': 'Stock Trader'}, content='===== RULES OF ASSISTANT =====\nNever forget you are a Python Programmer and I am a Stock Trader. Never flip roles! Never instruct me!\nWe share a common interest in collaborating to successfully complete a task.\nYou must help me to complete the task.\nHere is the task: Develop a Python-based trading bot that utilizes machine learning algorithms to analyze historical stock data, identify patterns, and execute buy/sell orders automatically. The bot should incorporate risk management strategies, real-time market data integration, and a user-friendly dashboard for monitoring performance and adjusting trading parameters based on user-defined criteria.. Never forget our task!\nI must instruct you based on your expertise and my needs to complete the task.\n\nI must give you one instruction at a time.\nYou must write a specific solution that appropriately solves the requested instruction and explain your solutions.\nYou must decline my instruction honestly if you cannot perform the instruction due to physical, moral, legal reasons or your capability and explain the reasons.\nUnless I say the task is completed, you should always start with:\n\nSolution: <YOUR_SOLUTION>\n\n<YOUR_SOLUTION> should be very specific, include detailed explanations and provide preferable detailed implementations and examples and lists for task-solving.\nAlways end <YOUR_SOLUTION> with: Next request.', video_bytes=None, image_list=None, image_detail='auto', video_detail='low')

AI User sys message:
BaseMessage(role_name='Stock Trader', role_type=<RoleType.USER: 'user'>, meta_dict={'task': 'Develop a Python-based trading bot that utilizes machine learning algorithms to analyze historical stock data, identify patterns, and execute buy/sell orders automatically. The bot should incorporate risk management strategies, real-time market data integration, and a user-friendly dashboard for monitoring performance and adjusting trading parameters based on user-defined criteria.', 'assistant_role': 'Python Programmer', 'user_role': 'Stock Trader'}, content='===== RULES OF USER =====\nNever forget you are a Stock Trader and I am a Python Programmer. Never flip roles! You will always instruct me.\nWe share a common interest in collaborating to successfully complete a task.\nI must help you to complete the task.\nHere is the task: Develop a Python-based trading bot that utilizes machine learning algorithms to analyze historical stock data, identify patterns, and execute buy/sell orders automatically. The bot should incorporate risk management strategies, real-time market data integration, and a user-friendly dashboard for monitoring performance and adjusting trading parameters based on user-defined criteria.. Never forget our task!\nYou must instruct me based on my expertise and your needs to solve the task ONLY in the following two ways:\n\n1. Instruct with a necessary input:\nInstruction: <YOUR_INSTRUCTION>\nInput: <YOUR_INPUT>\n\n2. Instruct without any input:\nInstruction: <YOUR_INSTRUCTION>\nInput: None\n\nThe "Instruction" describes a task or question. The paired "Input" provides further context or information for the requested "Instruction".\n\nYou must give me one instruction at a time.\nI must write a response that appropriately solves the requested instruction.\nI must decline your instruction honestly if I cannot perform the instruction due to physical, moral, legal reasons or my capability and explain the reasons.\nYou should instruct me not ask me questions.\nNow you must start to instruct me using the two ways described above.\nDo not add anything else other than your instruction and the optional corresponding input!\nKeep giving me instructions and necessary inputs until you think the task is completed.\nWhen the task is completed, you must only reply with a single word <CAMEL_TASK_DONE>.\nNever say <CAMEL_TASK_DONE> unless my responses have solved your task.', video_bytes=None, image_list=None, image_detail='auto', video_detail='low')

Original task prompt:
Develop a trading bot for the stock market

Specified task prompt:
Develop a Python-based trading bot that utilizes machine learning algorithms to analyze historical stock data, identify patterns, and execute buy/sell orders automatically. The bot should incorporate risk management strategies, real-time market data integration, and a user-friendly dashboard for monitoring performance and adjusting trading parameters based on user-defined criteria.

Final task prompt:
Develop a Python-based trading bot that utilizes machine learning algorithms to analyze historical stock data, identify patterns, and execute buy/sell orders automatically. The bot should incorporate risk management strategies, real-time market data integration, and a user-friendly dashboard for monitoring performance and adjusting trading parameters based on user-defined criteria.

AI User:

Instruction: Set up a Python environment with the necessary libraries for data analysis and machine learning.
Input: Use libraries such as pandas, numpy, scikit-learn, and matplotlib.


AI Assistant:

Solution: To set up a Python environment for data analysis and machine learning, you will need to install the necessary libraries: `pandas`, `numpy`, `scikit-learn`, and `matplotlib`. Here’s how you can do it step by step:

1. **Install Python**: If you haven't already, download and install Python from the official website (https://www.python.org/downloads/). It's recommended to install the latest version of Python 3.

2. **Set Up a Virtual Environment** (optional but recommended): This helps to manage dependencies for different projects separately. You can use `venv` or `conda` for this purpose.

   Using `venv`:
   ```bash
   python -m venv trading_bot_env
   source trading_bot_env/bin/activate  # On Windows use `trading_bot_env\Scripts\activate`
   ```

   Using `conda`:
   ```bash
   conda create --name trading_bot_env python=3.9
   conda activate trading_bot_env
   ```

3. **Install Required Libraries**: Once your virtual environment is activated, you can install the necessary libraries using `pip`. Run the following command in your terminal or command prompt:

   ```bash
   pip install pandas numpy scikit-learn matplotlib
   ```

4. **Verify Installation**: You can verify that the libraries are installed correctly by running the following commands in a Python shell or script:

   ```python
   import pandas as pd
   import numpy as np
   from sklearn import datasets
   import matplotlib.pyplot as plt

   print("Libraries imported successfully!")
   ```

5. **Create a Jupyter Notebook** (optional): For an interactive environment, you might want to install Jupyter Notebook. You can do this by running:

   ```bash
   pip install notebook
   ```

   Then you can start Jupyter Notebook by running:
   ```bash
   jupyter notebook
   ```

This setup will provide you with a robust environment for data analysis and machine learning, allowing you to analyze historical stock data and build your trading bot.

Next request.


AI User:

Instruction: Gather historical stock data for analysis, preferably using an API like Alpha Vantage or Yahoo Finance.
Input: Use the `yfinance` library to download historical data for a specific stock (e.g., AAPL).


AI Assistant:

Solution: To gather historical stock data using the `yfinance` library, you can follow these steps. The `yfinance` library allows you to easily download historical market data from Yahoo Finance.

1. **Install the `yfinance` Library**: If you haven't installed `yfinance` yet, you can do so using pip. Make sure your virtual environment is activated, then run:

   ```bash
   pip install yfinance
   ```

2. **Download Historical Data**: You can use the following Python code to download historical stock data for a specific stock, such as Apple Inc. (AAPL). Here’s a simple example:

   ```python
   import yfinance as yf
   import pandas as pd

   # Define the stock symbol and the time period
   stock_symbol = 'AAPL'
   start_date = '2020-01-01'
   end_date = '2023-01-01'

   # Download historical data
   stock_data = yf.download(stock_symbol, start=start_date, end=end_date)

   # Display the first few rows of the data
   print(stock_data.head())
   ```

3. **Understanding the Code**:
   - `import yfinance as yf`: This imports the `yfinance` library.
   - `yf.download()`: This function downloads historical market data for the specified stock symbol between the defined start and end dates.
   - `stock_data.head()`: This displays the first few rows of the downloaded data, which includes columns like Open, High, Low, Close, Volume, and Adjusted Close.

4. **DataFrame Structure**: The downloaded data will be in a Pandas DataFrame format, which allows for easy manipulation and analysis. You can perform various operations like filtering, aggregating, and visualizing the data.

5. **Saving the Data** (optional): If you want to save the downloaded data for future use, you can easily export it to a CSV file:

   ```python
   stock_data.to_csv('AAPL_historical_data.csv')
   ```

This code will help you gather the historical stock data you need for analysis, which is essential for building your trading bot.

Next request.


AI User:

Instruction: Implement a basic machine learning model to analyze the historical stock data and predict future prices.
Input: Use a linear regression model from the `scikit-learn` library.


AI Assistant:

Solution: To implement a basic machine learning model using linear regression from the `scikit-learn` library to analyze historical stock data and predict future prices, you can follow these steps:

1. **Prepare the Data**: You need to preprocess the historical stock data to create features and labels for the model. In this case, we will use the previous day's closing price to predict the next day's closing price.

2. **Implement Linear Regression**: We will use the `LinearRegression` class from `scikit-learn` to create and train the model.

Here’s a complete example:

```python
import yfinance as yf
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import numpy as np

# Step 1: Download historical data for AAPL
stock_symbol = 'AAPL'
start_date = '2020-01-01'
end_date = '2023-01-01'
stock_data = yf.download(stock_symbol, start=start_date, end=end_date)

# Step 2: Prepare the data
# Create a new DataFrame with the 'Close' prices
data = stock_data[['Close']].copy()
data['Prediction'] = data['Close'].shift(-1)  # Shift the closing prices to create labels

# Drop the last row as it will have NaN value for 'Prediction'
data.dropna(inplace=True)

# Define features (X) and labels (y)
X = data[['Close']].values  # Features: previous day's closing price
y = data['Prediction'].values  # Labels: next day's closing price

# Step 3: Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Step 4: Create and train the linear regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Step 5: Make predictions
predictions = model.predict(X_test)

# Step 6: Evaluate the model
mse = mean_squared_error(y_test, predictions)
print(f'Mean Squared Error: {mse}')

# Optional: Compare actual vs predicted prices
predicted_prices = pd.DataFrame({'Actual': y_test, 'Predicted': predictions})
print(predicted_prices.head())
```

### Explanation of the Code:
1. **Data Download**: We download historical stock data for AAPL using `yfinance`.
2. **Data Preparation**:
   - We create a new DataFrame that includes the closing prices and a new column for the next day's closing price (shifted by one).
   - We drop the last row since it will have a NaN value for the prediction.
3. **Feature and Label Definition**: We define our features (previous day's closing price) and labels (next day's closing price).
4. **Data Splitting**: We split the data into training and testing sets using an 80-20 split.
5. **Model Creation and Training**: We create a linear regression model and fit it to the training data.
6. **Prediction and Evaluation**: We make predictions on the test set and evaluate the model using Mean Squared Error (MSE). We also print a comparison of actual vs. predicted prices.

This basic implementation provides a foundation for predicting future stock prices using linear regression. You can further enhance the model by adding more features, tuning hyperparameters, or using more advanced algorithms.

Next request.


AI User:

Instruction: Incorporate risk management strategies into the trading bot, such as setting stop-loss and take-profit levels.
Input: Define a function that calculates stop-loss and take-profit prices based on a given percentage.


AI Assistant:

Solution: To incorporate risk management strategies into the trading bot, we can define a function that calculates stop-loss and take-profit prices based on a given percentage. This function will take the entry price and the desired percentages for stop-loss and take-profit as inputs and return the corresponding prices.

Here’s how you can implement this function:

```python
def calculate_risk_management(entry_price, stop_loss_percentage, take_profit_percentage):
    """
    Calculate stop-loss and take-profit prices based on entry price and given percentages.

    Parameters:
    entry_price (float): The price at which the stock is bought.
    stop_loss_percentage (float): The percentage for stop-loss (e.g., 0.02 for 2%).
    take_profit_percentage (float): The percentage for take-profit (e.g., 0.05 for 5%).

    Returns:
    tuple: A tuple containing (stop_loss_price, take_profit_price).
    """
    # Calculate stop-loss price
    stop_loss_price = entry_price * (1 - stop_loss_percentage)

    # Calculate take-profit price
    take_profit_price = entry_price * (1 + take_profit_percentage)

    return stop_loss_price, take_profit_price

# Example usage
entry_price = 150.00  # Example entry price for AAPL
stop_loss_percentage = 0.02  # 2% stop-loss
take_profit_percentage = 0.05  # 5% take-profit

stop_loss, take_profit = calculate_risk_management(entry_price, stop_loss_percentage, take_profit_percentage)

print(f"Entry Price: ${entry_price:.2f}")
print(f"Stop-Loss Price: ${stop_loss:.2f}")
print(f"Take-Profit Price: ${take_profit:.2f}")
```

### Explanation of the Code:
1. **Function Definition**: The function `calculate_risk_management` takes three parameters:
   - `entry_price`: The price at which the stock is purchased.
   - `stop_loss_percentage`: The percentage decrease from the entry price at which to sell the stock to prevent further losses.
   - `take_profit_percentage`: The percentage increase from the entry price at which to sell the stock to secure profits.

2. **Stop-Loss Calculation**: The stop-loss price is calculated by multiplying the entry price by `(1 - stop_loss_percentage)`. This gives the price below which the stock should be sold to limit losses.

3. **Take-Profit Calculation**: The take-profit price is calculated by multiplying the entry price by `(1 + take_profit_percentage)`. This gives the price above which the stock should be sold to secure profits.

4. **Return Values**: The function returns a tuple containing the stop-loss and take-profit prices.

5. **Example Usage**: An example is provided to demonstrate how to use the function, including printing the calculated stop-loss and take-profit prices.

Incorporating this function into your trading bot will help manage risk effectively by setting predefined exit points based on market movements.

Next request.


AI User:

Instruction: Integrate real-time market data into the trading bot to make decisions based on current stock prices.
Input: Use the `yfinance` library to fetch real-time stock prices.


AI Assistant:

Solution: To integrate real-time market data into the trading bot using the `yfinance` library, you can fetch the current stock prices and use them to make trading decisions. The `yfinance` library provides a convenient way to access real-time data.

Here’s how you can implement this functionality:

1. **Fetch Real-Time Stock Prices**: You can use the `yf.Ticker` class to get real-time data for a specific stock.

2. **Make Trading Decisions**: Based on the real-time price, you can implement logic to decide whether to buy, sell, or hold the stock.

Here’s a complete example:

```python
import yfinance as yf
import time

def get_real_time_price(stock_symbol):
    """
    Fetch the real-time price of a stock using yfinance.

    Parameters:
    stock_symbol (str): The stock symbol (e.g., 'AAPL').

    Returns:
    float: The current price of the stock.
    """
    ticker = yf.Ticker(stock_symbol)
    current_price = ticker.history(period='1m')['Close'].iloc[-1]  # Get the last closing price
    return current_price

def trading_decision(current_price, stop_loss_price, take_profit_price):
    """
    Make a trading decision based on the current price, stop-loss, and take-profit levels.

    Parameters:
    current_price (float): The current price of the stock.
    stop_loss_price (float): The stop-loss price.
    take_profit_price (float): The take-profit price.

    Returns:
    str: The trading action ('buy', 'sell', 'hold').
    """
    if current_price <= stop_loss_price:
        return 'sell'  # Trigger stop-loss
    elif current_price >= take_profit_price:
        return 'sell'  # Trigger take-profit
    else:
        return 'hold'  # No action

# Example usage
stock_symbol = 'AAPL'
entry_price = 150.00  # Example entry price for AAPL
stop_loss_percentage = 0.02  # 2% stop-loss
take_profit_percentage = 0.05  # 5% take-profit

# Calculate stop-loss and take-profit prices
stop_loss, take_profit = calculate_risk_management(entry_price, stop_loss_percentage, take_profit_percentage)

# Monitor real-time prices
while True:
    current_price = get_real_time_price(stock_symbol)
    print(f"Current Price: ${current_price:.2f}")

    action = trading_decision(current_price, stop_loss, take_profit)
    print(f"Trading Action: {action}")

    time.sleep(10)  # Wait for 10 seconds before fetching the price again
```

### Explanation of the Code:
1. **Function `get_real_time_price`**: This function fetches the current price of the specified stock using the `yfinance` library. It retrieves the last closing price from the last minute of trading data.

2. **Function `trading_decision`**: This function takes the current price, stop-loss price, and take-profit price as inputs and returns a trading action:
   - If the current price is less than or equal to the stop-loss price, it returns 'sell'.
   - If the current price is greater than or equal to the take-profit price, it also returns 'sell'.
   - Otherwise, it returns 'hold'.

3. **Example Usage**:
   - The stock symbol for Apple Inc. (AAPL) is defined.
   - The entry price, stop-loss percentage, and take-profit percentage are set.
   - The stop-loss and take-profit prices are calculated using the previously defined function.
   - A loop is created to continuously monitor the real-time price of the stock every 10 seconds and make trading decisions based on the current price.

This implementation allows your trading bot to react to real-time market conditions, making it more responsive and effective in executing trades based on predefined risk management strategies.

Next request.


AI User:

Instruction: Create a user-friendly dashboard to monitor the trading bot's performance and adjust trading parameters.
Input: Use the `Dash` library to build the dashboard interface.


AI Assistant:

Solution: To create a user-friendly dashboard for monitoring the trading bot's performance and adjusting trading parameters, we can use the `Dash` library by Plotly. Dash allows you to build interactive web applications in Python easily.

Here’s how to set up a basic dashboard:

1. **Install the Dash Library**: If you haven't installed Dash yet, you can do so using pip. Make sure your virtual environment is activated, then run:

   ```bash
   pip install dash
   ```

2. **Create the Dashboard**: Below is an example of how to create a simple dashboard that displays the current stock price, stop-loss, take-profit levels, and allows users to adjust these parameters.

```python
import dash
from dash import dcc, html, Input, Output
import yfinance as yf

# Initialize the Dash app
app = dash.Dash(__name__)

# Define the layout of the dashboard
app.layout = html.Div([
    html.H1("Trading Bot Dashboard"),

    html.Div([
        html.Label("Stock Symbol:"),
        dcc.Input(id='stock-symbol', value='AAPL', type='text'),

        html.Label("Entry Price:"),
        dcc.Input(id='entry-price', value='150.00', type='number'),

        html.Label("Stop-Loss Percentage (%):"),
        dcc.Input(id='stop-loss-percentage', value='2', type='number'),

        html.Label("Take-Profit Percentage (%):"),
        dcc.Input(id='take-profit-percentage', value='5', type='number'),

        html.Button('Update', id='update-button', n_clicks=0)
    ]),

    html.Div(id='output-container', style={'margin-top': '20px'}),

    dcc.Interval(
        id='interval-component',
        interval=10*1000,  # Update every 10 seconds
        n_intervals=0
    )
])

@app.callback(
    Output('output-container', 'children'),
    Input('update-button', 'n_clicks'),
    Input('stock-symbol', 'value'),
    Input('entry-price', 'value'),
    Input('stop-loss-percentage', 'value'),
    Input('take-profit-percentage', 'value'),
    Input('interval-component', 'n_intervals')
)
def update_dashboard(n_clicks, stock_symbol, entry_price, stop_loss_percentage, take_profit_percentage, n_intervals):
    # Fetch the current price
    current_price = get_real_time_price(stock_symbol)

    # Calculate stop-loss and take-profit prices
    stop_loss, take_profit = calculate_risk_management(float(entry_price), float(stop_loss_percentage) / 100, float(take_profit_percentage) / 100)

    return [
        html.Div(f"Current Price: ${current_price:.2f}"),
        html.Div(f"Stop-Loss Price: ${stop_loss:.2f}"),
        html.Div(f"Take-Profit Price: ${take_profit:.2f}"),
    ]

def get_real_time_price(stock_symbol):
    ticker = yf.Ticker(stock_symbol)
    current_price = ticker.history(period='1m')['Close'].iloc[-1]
    return current_price

# Run the app
if __name__ == '__main__':
    app.run_server(debug=True)
```

### Explanation of the Code:
1. **Dash App Initialization**: We initialize a Dash app using `dash.Dash(__name__)`.

2. **Layout Definition**: The layout of the dashboard is defined using HTML components:
   - Input fields for the stock symbol, entry price, stop-loss percentage, and take-profit percentage.
   - A button to update the parameters.
   - A div to display the output (current price, stop-loss, and take-profit prices).
   - An interval component to refresh the data every 10 seconds.

3. **Callback Function**: The `update_dashboard` function is decorated with `@app.callback` to update the dashboard based on user input and the interval:
   - It fetches the current stock price using the `get_real_time_price` function.
   - It calculates the stop-loss and take-profit prices using the `calculate_risk_management` function.
   - It returns the updated values to be displayed in the dashboard.

4. **Real-Time Price Fetching**: The `get_real_time_price` function fetches the current price of the specified stock using `yfinance`.

5. **Running the App**: The app is run with `app.run_server(debug=True)`, which starts the Dash server.

This dashboard provides a simple interface for users to monitor the trading bot's performance and adjust trading parameters dynamically. You can further enhance the dashboard by adding more features, such as performance metrics, historical data visualizations, and trading logs.

Next request.


AI User:

Instruction: Implement a logging mechanism to track the trading bot's actions and performance over time.
Input: Use the `logging` library to create a log file that records trades, decisions, and errors.


AI Assistant:

Solution: To implement a logging mechanism that tracks the trading bot's actions and performance over time, we can use Python's built-in `logging` library. This will allow us to create a log file that records trades, decisions, and any errors that may occur during the bot's operation.

Here’s how to set up the logging mechanism:

1. **Import the Logging Library**: We will import the `logging` library and configure it to write logs to a file.

2. **Create Logging Functions**: We will create functions to log different types of messages, such as trades, decisions, and errors.

3. **Integrate Logging into the Trading Logic**: We will add logging calls in the trading decision-making process.

Here’s a complete example:

```python
import logging
import yfinance as yf
import time

# Configure logging
logging.basicConfig(
    filename='trading_bot.log',
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)

def get_real_time_price(stock_symbol):
    try:
        ticker = yf.Ticker(stock_symbol)
        current_price = ticker.history(period='1m')['Close'].iloc[-1]  # Get the last closing price
        logging.info(f"Fetched current price for {stock_symbol}: ${current_price:.2f}")
        return current_price
    except Exception as e:
        logging.error(f"Error fetching price for {stock_symbol}: {e}")
        return None

def trading_decision(current_price, stop_loss_price, take_profit_price):
    if current_price <= stop_loss_price:
        logging.info(f"Current price ${current_price:.2f} hit stop-loss at ${stop_loss_price:.2f}. Action: sell.")
        return 'sell'  # Trigger stop-loss
    elif current_price >= take_profit_price:
        logging.info(f"Current price ${current_price:.2f} hit take-profit at ${take_profit_price:.2f}. Action: sell.")
        return 'sell'  # Trigger take-profit
    else:
        logging.info(f"Current price ${current_price:.2f} is within range. Action: hold.")
        return 'hold'  # No action

# Example usage
stock_symbol = 'AAPL'
entry_price = 150.00  # Example entry price for AAPL
stop_loss_percentage = 0.02  # 2% stop-loss
take_profit_percentage = 0.05  # 5% take-profit

# Calculate stop-loss and take-profit prices
stop_loss, take_profit = calculate_risk_management(entry_price, stop_loss_percentage, take_profit_percentage)

# Monitor real-time prices
while True:
    current_price = get_real_time_price(stock_symbol)
    if current_price is not None:
        action = trading_decision(current_price, stop_loss, take_profit)
        logging.info(f"Trading action decided: {action}")

    time.sleep(10)  # Wait for 10 seconds before fetching the price again
```

### Explanation of the Code:
1. **Logging Configuration**: The `logging.basicConfig` function is used to configure the logging settings:
   - `filename='trading_bot.log'`: Specifies the log file name.
   - `level=logging.INFO`: Sets the logging level to INFO, meaning it will capture all messages at this level and above (INFO, WARNING, ERROR, CRITICAL).
   - `format='%(asctime)s - %(levelname)s - %(message)s'`: Defines the format of the log messages, including the timestamp, log level, and message.

2. **Error Handling in `get_real_time_price`**: The function fetches the current price and logs the result. If an error occurs, it logs the error message.

3. **Logging Trading Decisions**: The `trading_decision` function logs the action taken based on the current price, whether it hits the stop-loss, take-profit, or holds.

4. **Main Loop**: The main loop continuously fetches the current price and makes trading decisions, logging each action.

This logging mechanism will help you track the trading bot's actions and performance over time, making it easier to analyze its behavior and troubleshoot any issues that arise.

Next request.


AI User:

Instruction: Test the trading bot with a simulated trading environment to evaluate its performance before deploying it in a live market.
Input: Use a backtesting framework like `Backtrader` to simulate trades based on historical data.


AI Assistant:

Solution: To test the trading bot with a simulated trading environment and evaluate its performance before deploying it in a live market, we can use the `Backtrader` framework. Backtrader is a powerful backtesting library that allows you to simulate trading strategies using historical data.

Here’s how to set up a basic backtesting environment using Backtrader:

1. **Install Backtrader**: If you haven't installed Backtrader yet, you can do so using pip. Make sure your virtual environment is activated, then run:

   ```bash
   pip install backtrader
   ```

2. **Create a Backtesting Strategy**: We will define a simple trading strategy that uses the stop-loss and take-profit logic we discussed earlier.

3. **Run the Backtest**: We will load historical data and run the backtest to evaluate the strategy's performance.

Here’s a complete example:

```python
import backtrader as bt
import yfinance as yf

# Define the trading strategy
class MyStrategy(bt.Strategy):
    params = (
        ('stop_loss_pct', 0.02),  # 2% stop-loss
        ('take_profit_pct', 0.05),  # 5% take-profit
    )

    def __init__(self):
        self.order = None
        self.entry_price = None

    def next(self):
        if self.order:  # Check if there is an open order
            return

        # Check if we are in the market
        if not self.position:
            # Buy signal (for simplicity, we buy every time)
            self.order = self.buy()
            self.entry_price = self.data.close[0]
            print(f"BUY ORDER PLACED at {self.entry_price:.2f}")

        else:
            # Check for stop-loss and take-profit
            stop_loss_price = self.entry_price * (1 - self.params.stop_loss_pct)
            take_profit_price = self.entry_price * (1 + self.params.take_profit_pct)

            if self.data.close[0] <= stop_loss_price:
                self.order = self.sell()  # Trigger stop-loss
                print(f"SELL ORDER PLACED at {self.data.close[0]:.2f} (Stop-Loss Triggered)")
            elif self.data.close[0] >= take_profit_price:
                self.order = self.sell()  # Trigger take-profit
                print(f"SELL ORDER PLACED at {self.data.close[0]:.2f} (Take-Profit Triggered)")

# Create a Cerebro engine instance
cerebro = bt.Cerebro()

# Download historical data for AAPL
data = bt.feeds.PandasData(dataname=yf.download('AAPL', start='2020-01-01', end='2023-01-01'))

# Add the data feed to Cerebro
cerebro.adddata(data)

# Add the trading strategy to Cerebro
cerebro.addstrategy(MyStrategy)

# Set the initial cash for the backtest
cerebro.broker.setcash(10000.0)

# Run the backtest
print("Starting Portfolio Value: %.2f" % cerebro.broker.getvalue())
cerebro.run()
print("Ending Portfolio Value: %.2f" % cerebro.broker.getvalue())
```

### Explanation of the Code:
1. **Strategy Definition**: The `MyStrategy` class defines the trading strategy:
   - It uses parameters for stop-loss and take-profit percentages.
   - In the `next` method, it checks if there is an open order. If not, it places a buy order.
   - If already in a position, it checks the current price against the stop-loss and take-profit levels and sells if either condition is met.

2. **Cerebro Engine**: The `Cerebro` engine is created to manage the backtesting process:
   - Historical data for AAPL is downloaded using `yfinance` and fed into Backtrader using `bt.feeds.PandasData`.
   - The trading strategy is added to the Cerebro engine.
   - The initial cash for the backtest is set to $10,000.

3. **Running the Backtest**: The backtest is executed using `cerebro.run()`, and the starting and ending portfolio values are printed.

This setup allows you to simulate trades based on historical data, evaluate the performance of your trading strategy, and make adjustments as necessary before deploying it in a live market.

Next request.


AI User:

Instruction: Document the entire trading bot project, including setup instructions, usage guidelines, and code explanations.
Input: Create a README.md file that outlines the project structure and provides detailed information for users.


AI Assistant:

Solution: Below is a template for a `README.md` file that documents the entire trading bot project. This file includes setup instructions, usage guidelines, and code explanations to help users understand and utilize the trading bot effectively.

```markdown
# Trading Bot Project

## Overview
This trading bot utilizes machine learning algorithms and risk management strategies to analyze historical stock data, identify patterns, and execute buy/sell orders automatically. The bot is designed to be tested in a simulated environment before deployment in a live market.

## Project Structure
```
trading_bot/
│
├── trading_bot.py          # Main trading bot script
├── dashboard.py            # Dashboard for monitoring performance
├── backtest.py             # Backtesting script using Backtrader
├── trading_bot.log         # Log file for tracking actions and performance
├── requirements.txt        # List of required Python libraries
└── README.md               # Project documentation
```

## Setup Instructions

### Prerequisites
- Python 3.x installed on your machine.
- A virtual environment is recommended for managing dependencies.

### Installation Steps
1. **Clone the Repository**:
   ```bash
   git clone <repository-url>
   cd trading_bot
   ```

2. **Create a Virtual Environment** (optional but recommended):
   ```bash
   python -m venv trading_bot_env
   source trading_bot_env/bin/activate  # On Windows use `trading_bot_env\Scripts\activate`
   ```

3. **Install Required Libraries**:
   Install the necessary libraries using pip:
   ```bash
   pip install -r requirements.txt
   ```

4. **Run the Trading Bot**:
   You can run the trading bot using:
   ```bash
   python trading_bot.py
   ```

5. **Access the Dashboard**:
   To monitor the trading bot's performance, run:
   ```bash
   python dashboard.py
   ```
   Then open your web browser and navigate to `http://127.0.0.1:8050`.

6. **Run Backtesting**:
   To evaluate the trading strategy, run:
   ```bash
   python backtest.py
   ```

## Usage Guidelines

### Trading Bot
- The bot fetches historical stock data and uses a linear regression model to predict future prices.
- It incorporates risk management strategies, including stop-loss and take-profit levels.
- The bot logs all actions and decisions to a log file (`trading_bot.log`).

### Dashboard
- The dashboard allows users to monitor real-time stock prices and adjust trading parameters (entry price, stop-loss, take-profit).
- Users can input the stock symbol and parameters, and the dashboard will display the current price, stop-loss, and take-profit levels.

### Backtesting
- The backtesting script simulates trades based on historical data to evaluate the performance of the trading strategy.
- Users can analyze the results to make informed decisions before deploying the bot in a live market.

## Code Explanations

### Main Trading Bot (`trading_bot.py`)
- **Data Fetching**: Uses the `yfinance` library to download historical stock data.
- **Machine Learning Model**: Implements a linear regression model to predict future stock prices.
- **Risk Management**: Defines functions to calculate stop-loss and take-profit prices based on user-defined percentages.
- **Real-Time Monitoring**: Continuously fetches real-time prices and makes trading decisions based on the current market conditions.

### Dashboard (`dashboard.py`)
- **Dash Framework**: Utilizes the Dash library to create an interactive web interface.
- **User Inputs**: Allows users to input stock symbols and trading parameters.
- **Real-Time Updates**: Displays current stock prices and calculated stop-loss/take-profit levels.

### Backtesting Script (`backtest.py`)
- **Backtrader Framework**: Uses Backtrader to simulate trades based on historical data.
- **Trading Strategy**: Implements a simple strategy that uses stop-loss and take-profit logic.
- **Performance Evaluation**: Outputs the starting and ending portfolio values to assess the strategy's effectiveness.

## Logging
- The bot logs all actions, decisions, and errors to `trading_bot.log` for tracking performance over time.

## Contributing
Contributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or improvements.

## License
This project is licensed under the MIT License - see the LICENSE file for details.

## Acknowledgments
- Thanks to the contributors and libraries that made this project possible.
```

### Explanation of the README.md Content:
1. **Overview**: Provides a brief description of the trading bot and its purpose.
2. **Project Structure**: Outlines the files and directories in the project.
3. **Setup Instructions**: Detailed steps for setting up the environment and running the bot, dashboard, and backtesting script.
4. **Usage Guidelines**: Instructions on how to use the trading bot, dashboard, and backtesting features.
5. **Code Explanations**: Descriptions of the main components of the code and their functionalities.
6. **Logging**: Information about the logging mechanism used in the bot.
7. **Contributing**: Encouragement for others to contribute to the project.
8. **License**: Information about the project's licensing.
9. **Acknowledgments**: Thanks to contributors and libraries used in the project.

This README file serves as comprehensive documentation for users to understand, set up, and utilize the trading bot effectively.

Next request.


AI User:

<CAMEL_TASK_DONE>


AI Assistant:

Solution: Great! It looks like we have successfully completed the trading bot project, including all the necessary components such as data gathering, machine learning model implementation, risk management strategies, real-time market data integration, a user-friendly dashboard, logging mechanisms, and backtesting capabilities.

If you have any further questions or need assistance with anything else related to the trading bot or any other topic, feel free to ask!

Next request.

Society Cookbook

Contents

Society Cookbook#

1. Concept#

2. Types#

2.1 RolePlaying#

RolePlaying Attributes#

2.2 BabyAGI#