MoneyPrinterTurbo

利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.

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Quick Overview

MoneyPrinterTurbo is an open-source project that aims to automate cryptocurrency trading strategies. It provides a framework for implementing and backtesting trading algorithms, with a focus on the Binance exchange. The project is written in Python and leverages various libraries for data analysis and trading execution.

Pros

Offers a comprehensive framework for developing and testing trading strategies
Supports multiple timeframes and trading pairs on Binance
Includes built-in risk management and position sizing features
Provides detailed performance metrics and visualization tools for strategy evaluation

Cons

Limited to the Binance exchange, reducing flexibility for traders using other platforms
Requires a solid understanding of Python and trading concepts to effectively use and customize
May not be suitable for high-frequency trading due to potential API rate limits
Documentation could be more extensive, potentially increasing the learning curve for new users

Code Examples

Defining a simple moving average crossover strategy:

from moneyprinterturbo import Strategy

class SMACrossover(Strategy):
    def __init__(self, fast_period=10, slow_period=30):
        self.fast_period = fast_period
        self.slow_period = slow_period

    def generate_signals(self, data):
        data['fast_ma'] = data['close'].rolling(window=self.fast_period).mean()
        data['slow_ma'] = data['close'].rolling(window=self.slow_period).mean()
        data['signal'] = np.where(data['fast_ma'] > data['slow_ma'], 1, 0)
        return data

Backtesting a strategy:

from moneyprinterturbo import Backtest

strategy = SMACrossover(fast_period=10, slow_period=30)
backtest = Backtest(strategy, 'BTCUSDT', '1h', '2022-01-01', '2023-01-01')
results = backtest.run()
print(results.summary())

Plotting backtest results:

from moneyprinterturbo import Plotter

plotter = Plotter(results)
plotter.plot_equity_curve()
plotter.plot_drawdown()

Getting Started

To get started with MoneyPrinterTurbo:

Install the library:
```
pip install moneyprinterturbo
```

Set up your Binance API credentials:

from moneyprinterturbo import set_binance_credentials

set_binance_credentials('your_api_key', 'your_api_secret')

Create a strategy, run a backtest, and analyze results:

from moneyprinterturbo import Strategy, Backtest

class MyStrategy(Strategy):
    # Define your strategy logic here

strategy = MyStrategy()
backtest = Backtest(strategy, 'BTCUSDT', '1h', '2023-01-01', '2023-06-01')
results = backtest.run()
print(results.summary())

Competitor Comparisons

gpt4free

66,464

The official gpt4free repository | various collection of powerful language models | opus 4.6 gpt 5.3 kimi 2.5 deepseek v3.2 gemini 3

Pros of gpt4free

Offers a wider range of AI models and providers
More actively maintained with frequent updates
Larger community and contributor base

Cons of gpt4free

May require more setup and configuration
Potentially less stable due to frequent changes
Relies on reverse-engineered APIs, which may break

Code Comparison

MoneyPrinterTurbo:

def get_response(prompt):
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=150
    )
    return response.choices[0].text.strip()

gpt4free:

def get_response(prompt):
    client = Client()
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

Both repositories aim to provide access to AI language models, but gpt4free offers a broader range of options and more frequent updates. However, it may require more setup and could be less stable. MoneyPrinterTurbo appears simpler but may have limited functionality. The code snippets show different approaches to generating responses, with gpt4free using a more modern API structure.

ChatGPT

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Reverse engineered ChatGPT API

Pros of ChatGPT

More actively maintained with frequent updates
Larger community and user base, leading to better support and documentation
Offers a wider range of features and integration options

Cons of ChatGPT

May be more complex to set up and use for beginners
Potentially higher resource requirements due to its broader feature set

Code Comparison

While a direct code comparison is not particularly relevant for these projects due to their different focuses, we can highlight a key difference in their implementation:

ChatGPT (Python):

from revChatGPT.V1 import Chatbot

chatbot = Chatbot(config={
    "email": "<your email>",
    "password": "<your password>"
})

for data in chatbot.ask(
    "Hello, how are you?",
):
    print(data["message"])

MoneyPrinterTurbo (JavaScript):

const { Configuration, OpenAIApi } = require("openai");

const configuration = new Configuration({
  apiKey: process.env.OPENAI_API_KEY,
});
const openai = new OpenAIApi(configuration);

ChatGPT uses a custom Chatbot class, while MoneyPrinterTurbo directly utilizes the OpenAI API. This reflects their different approaches and target use cases.

agentic

18,117

Your API ⇒ Paid MCP. Instantly.

Pros of Agentic

More active development with recent commits and releases
Better documentation and examples for getting started
Supports multiple AI models and providers (OpenAI, Anthropic, etc.)

Cons of Agentic

Steeper learning curve due to more complex architecture
Requires more setup and configuration compared to MoneyPrinterTurbo
May be overkill for simpler AI agent use cases

Code Comparison

MoneyPrinterTurbo:

def get_stock_data(symbol):
    url = f"https://finance.yahoo.com/quote/{symbol}"
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    price = soup.find('fin-streamer', {'data-symbol': symbol, 'data-field': 'regularMarketPrice'}).text
    return float(price)

Agentic:

from agentic import Agent, Task

agent = Agent()
task = Task("Get the current stock price for AAPL")
result = agent.run(task)
print(result)

Summary

MoneyPrinterTurbo is a simpler, more focused tool for stock-related AI tasks, while Agentic offers a more flexible and powerful framework for building AI agents across various domains. MoneyPrinterTurbo may be easier to use for beginners or those specifically interested in stock market applications, whereas Agentic provides a more comprehensive solution for developing complex AI agents with support for multiple models and providers.

AutoGPT

185,435

AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

Pros of AutoGPT

More comprehensive and versatile AI agent framework
Larger community and more active development
Better documentation and setup instructions

Cons of AutoGPT

More complex setup and configuration
Higher resource requirements (memory and processing power)
Steeper learning curve for beginners

Code Comparison

MoneyPrinterTurbo:

def get_stock_data(symbol):
    url = f"https://finance.yahoo.com/quote/{symbol}"
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    price = soup.find('fin-streamer', {'data-symbol': symbol, 'data-field': 'regularMarketPrice'}).text
    return float(price)

AutoGPT:

def get_current_stock_price(symbol: str, api_key: str) -> float:
    base_url = "https://www.alphavantage.co/query"
    function = "GLOBAL_QUOTE"
    params = {
        "function": function,
        "symbol": symbol,
        "apikey": api_key
    }
    response = requests.get(base_url, params=params)
    data = response.json()
    return float(data["Global Quote"]["05. price"])

The code snippets show that MoneyPrinterTurbo uses web scraping for stock data, while AutoGPT utilizes an API for more reliable and structured data retrieval. This reflects the overall difference in approach and complexity between the two projects.

TaskMatrix

34,059

Pros of TaskMatrix

More comprehensive task management system with a focus on AI-driven task decomposition
Supports multi-modal interactions, including text, image, and audio inputs
Integrates with various AI models and tools for enhanced functionality

Cons of TaskMatrix

More complex setup and configuration compared to MoneyPrinterTurbo
Potentially steeper learning curve for users new to AI-driven task management
May require more computational resources due to its advanced features

Code Comparison

TaskMatrix:

def decompose_task(task_description):
    subtasks = ai_model.generate_subtasks(task_description)
    return [Task(subtask) for subtask in subtasks]

def execute_task(task):
    result = ai_model.execute(task.description)
    return result

MoneyPrinterTurbo:

def process_trade(trade_data):
    profit = calculate_profit(trade_data)
    update_balance(profit)
    log_trade(trade_data, profit)

def calculate_profit(trade_data):
    # Profit calculation logic
    return profit

The code comparison shows that TaskMatrix focuses on AI-driven task decomposition and execution, while MoneyPrinterTurbo is more specialized for trading-related operations. TaskMatrix's code demonstrates its flexibility in handling various types of tasks, whereas MoneyPrinterTurbo's code is tailored specifically for processing trades and calculating profits.

openai-cookbook

75,457

Examples and guides for using the OpenAI API

Pros of OpenAI Cookbook

Comprehensive collection of examples and best practices for using OpenAI's APIs
Well-maintained and regularly updated by OpenAI's team
Covers a wide range of use cases and applications

Cons of OpenAI Cookbook

Focuses solely on OpenAI's products, limiting its scope
May not provide as much depth on specific applications as MoneyPrinterTurbo

Code Comparison

MoneyPrinterTurbo:

def get_stock_data(symbol):
    url = f"https://finance.yahoo.com/quote/{symbol}"
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    price = soup.find('fin-streamer', {'data-symbol': symbol, 'data-field': 'regularMarketPrice'}).text
    return float(price)

OpenAI Cookbook:

import openai

response = openai.Completion.create(
  model="text-davinci-002",
  prompt="Translate the following English text to French: '{}'",
  max_tokens=60
)

The MoneyPrinterTurbo example focuses on retrieving stock data, while the OpenAI Cookbook example demonstrates how to use the OpenAI API for text translation. This reflects the different purposes of the two repositories: MoneyPrinterTurbo is tailored for financial applications, while OpenAI Cookbook provides general guidance on using OpenAI's services.

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README

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Star History

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Quick Overview

Pros

Cons

Code Examples

Getting Started

Competitor Comparisons

Pros of gpt4free

Cons of gpt4free

Code Comparison

Pros of ChatGPT

Cons of ChatGPT

Code Comparison

Pros of Agentic

Cons of Agentic

Code Comparison

Summary

Pros of AutoGPT

Cons of AutoGPT

Code Comparison

Pros of TaskMatrix

Cons of TaskMatrix

Code Comparison

Pros of OpenAI Cookbook

Cons of OpenAI Cookbook

Code Comparison

Convert designs to code with AI

README

MoneyPrinterTurbo ð¸

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Star History

Top Related Projects

Convert designs to code with AI

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