AlgoTradeKit

v0.2.0 safe
3.0
Low Risk

Algorithmic Trading Toolkit — data collection, indicators, strategy, simulation, and live trading

🤖 AI Analysis

Final verdict: SAFE

The package is considered safe with minimal risks identified. The network calls appear legitimate given the nature of the package's functionality.

  • Low network, shell, obfuscation, and credential risks.
  • Incomplete maintainer profile, but no other suspicious activities noted.
Per-check LLM notes
  • Network: Network calls may be legitimate if the package requires external data for trading algorithms.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of sensitive information.
  • Metadata: The maintainer has an incomplete profile and may be new or inactive, but there are no other suspicious flags.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • BASE self._session = requests.Session() self._session.headers.update({"Accept": "applicati
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository AmirMohammadBazdar/AlgoTradeKit appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with AlgoTradeKit 
Create a mini-trading app using the AlgoTradeKit Python package that allows users to simulate trading strategies on historical market data. The app should have the following features:

1. Data Collection: Users can select from a variety of financial instruments (e.g., stocks, ETFs, cryptocurrencies) and download historical price data.
2. Indicator Calculation: Implement popular technical analysis indicators such as Moving Averages, Relative Strength Index (RSI), and Bollinger Bands.
3. Strategy Backtesting: Allow users to define simple trading strategies based on the calculated indicators and backtest these strategies on the selected historical data.
4. Simulation: Simulate trades based on the backtested strategies and visualize the performance metrics such as profit/loss, drawdown, and Sharpe ratio.
5. Visualization: Provide charts and graphs to visually represent the performance of the trading strategies over time.
6. User Interface: Develop a user-friendly web interface using Flask or Django, allowing users to interact with the app without needing to write code.
7. Documentation: Include comprehensive documentation explaining how to use the app, set up the environment, and interpret the results.

To utilize the AlgoTradeKit package, follow these steps:
- Install the package via pip.
- Use the 'data' module to collect historical data.
- Apply the 'indicators' module to calculate technical indicators.
- Leverage the 'strategy' module to define and backtest trading strategies.
- Utilize the 'simulation' module to run simulations and generate performance reports.
- Finally, use the 'visualization' module to create visual representations of the simulation results.

Ensure that your project demonstrates a deep understanding of algorithmic trading concepts and showcases the capabilities of the AlgoTradeKit package.