Quantsploit

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║                    EXPLOIT THE MARKET                         ║
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Quantitative Analysis Trading Framework with Interactive TUI

Quantsploit is a modular quantitative trading framework inspired by penetration testing tools like Metasploit. It provides an interactive command-line interface for running various financial analysis modules, algorithms, and trading strategies.

Features

• 🖥️ Interactive TUI - Metasploit-style command interface with auto-completion
• 🔧 Modular Architecture - Plugin system for easy extension
• 📊 Technical Analysis - RSI, MACD, SMA, EMA, Bollinger Bands, and more
• 🔍 Market Scanners - Scan multiple stocks for momentum, volume, and patterns
• 📈 Options Analysis - Analyze options chains, Greeks, and opportunities
• 💼 Strategy Backtesting - Test and validate trading strategies
• 📉 Analytics Dashboard - Web-based visualization with interactive charts and comparisons
• 💾 Data Caching - SQLite database for efficient data management
• 📋 Watchlist Management - Track your favorite symbols
• 🎨 Rich Output - Beautiful tables and formatted results

Installation

Prerequisites

• Python 3.8 or higher
• pip

Setup

1. Clone the repository:

git clone https://github.com/yourusername/Quantsploit.git
cd Quantsploit

2. Install dependencies:

pip install -r requirements.txt

3. Install the package:

pip install -e .

Usage

Starting Quantsploit

Run the framework:

python -m quantsploit.main

Or if installed:

quantsploit

Basic Commands

Command	Description	Example
help	Show available commands	help
show modules	List all available modules	show modules
search <query>	Search for modules	search RSI
use <module>	Load a module	use analysis/technical_indicators
show options	Display module options	show options
set <OPTION> <value>	Set module option	set SYMBOL AAPL
run	Execute current module	run
back	Unload current module	back
quote <SYMBOL>	Get real-time quote	quote TSLA
watchlist add <SYMBOL>	Add to watchlist	watchlist add AAPL
exit	Exit Quantsploit	exit

Example Workflows

1. Technical Analysis on a Stock

quantsploit > use analysis/technical_indicators
quantsploit (Technical Indicators) > set SYMBOL AAPL
quantsploit (Technical Indicators) > set PERIOD 6mo
quantsploit (Technical Indicators) > run

This will calculate RSI, MACD, SMA, EMA, and Bollinger Bands for Apple stock over the last 6 months.

2. Scan Multiple Stocks for Momentum

quantsploit > use scanners/price_momentum
quantsploit (Price Momentum Scanner) > set SYMBOLS AAPL,MSFT,GOOGL,TSLA,NVDA
quantsploit (Price Momentum Scanner) > set MIN_GAIN_PCT 3.0
quantsploit (Price Momentum Scanner) > run

3. Analyze Options Chain

quantsploit > use options/options_analyzer
quantsploit (Options Analyzer) > set SYMBOL SPY
quantsploit (Options Analyzer) > set MIN_VOLUME 100
quantsploit (Options Analyzer) > run

4. Backtest a Trading Strategy

quantsploit > use strategies/sma_crossover
quantsploit (SMA Crossover Strategy) > set SYMBOL AAPL
quantsploit (SMA Crossover Strategy) > set PERIOD 1y
quantsploit (SMA Crossover Strategy) > set FAST_PERIOD 10
quantsploit (SMA Crossover Strategy) > set SLOW_PERIOD 30
quantsploit (SMA Crossover Strategy) > set INITIAL_CAPITAL 10000
quantsploit (SMA Crossover Strategy) > run

Available Modules

Analysis Modules

• technical_indicators - Calculate technical indicators (RSI, MACD, SMA, EMA, Bollinger Bands)
• pattern_recognition - Detect candlestick and chart patterns with automated signals
• signal_aggregator - Aggregate multiple strategies for consensus buy/sell signals

Scanner Modules

• price_momentum - Scan multiple stocks for price momentum and volume patterns
• bulk_screener - High-performance parallel screening of large stock universes (SP500, NASDAQ100)
• top_movers - Identify top gainers, momentum leaders, and rank stocks by multiple criteria

Options Modules

• options_analyzer - Analyze options chain, calculate metrics, and identify opportunities

Strategy Modules

• sma_crossover - Simple Moving Average crossover backtesting strategy
• mean_reversion - Statistical mean reversion with z-score and Bollinger Bands analysis
• momentum_signals - Advanced momentum and trend following with multiple confirmations
• multifactor_scoring - Comprehensive multi-factor quantitative scoring system

🔥 Advanced Features

Quantsploit includes cutting-edge quantitative algorithms for serious traders:

• Bulk Analysis - Analyze 100+ stocks in parallel with the Advanced Bulk Screener
• Pattern Recognition - Automated detection of 10+ candlestick and chart patterns
• Mean Reversion - Z-score analysis, percentile ranking, and reversion probability
• Momentum Strategies - Multi-period momentum, acceleration, and relative strength
• Multi-Factor Models - Combine momentum, technical, volatility, and volume factors
• Signal Aggregation - Consensus signals from 5+ strategies with confidence scoring
• Top Movers - Real-time rankings by gainers, momentum, breakouts, and quality

See ADVANCED_STRATEGIES.md for detailed usage guide.

📊 Backtesting Analytics Dashboard

Quantsploit includes a comprehensive web-based dashboard for visualizing and analyzing backtest results:

Features

• Interactive Charts - Visualize performance metrics, returns, and risk across strategies
• Period Analysis - Compare performance across quarters and custom time periods
• Strategy Rankings - Identify top-performing strategies with detailed metrics
• Risk vs Return - Interactive scatter plots and heatmaps
• Multi-Run Comparison - Compare up to 5 different backtest runs
• Export Capabilities - Save charts and generate reports

Quick Start

1. Run a comprehensive backtest:

python run_comprehensive_backtest.py --symbols AAPL,MSFT,GOOGL

2. Launch the dashboard:

./start_dashboard.sh  # Linux/Mac
# or
start_dashboard.bat   # Windows

3. Open your browser to http://localhost:5000

See DASHBOARD.md for complete documentation and usage guide.

Creating Custom Modules

All modules inherit from BaseModule and implement the following structure:

from quantsploit.core.module import BaseModule
from typing import Dict, Any

class MyCustomModule(BaseModule):
    @property
    def name(self) -> str:
        return "My Custom Module"

    @property
    def description(self) -> str:
        return "Description of what this module does"

    @property
    def author(self) -> str:
        return "Your Name"

    @property
    def category(self) -> str:
        return "analysis"  # or scanner, options, strategy

    def _init_options(self):
        super()._init_options()
        self.options.update({
            "MY_OPTION": {
                "value": None,
                "required": True,
                "description": "My custom option"
            }
        })

    def run(self) -> Dict[str, Any]:
        # Your module logic here
        symbol = self.get_option("SYMBOL")

        # Return results
        return {
            "symbol": symbol,
            "result": "some value"
        }

Save your module in the appropriate directory:

• quantsploit/modules/analysis/ - For analysis modules
• quantsploit/modules/scanners/ - For scanner modules
• quantsploit/modules/options/ - For options modules
• quantsploit/modules/strategies/ - For strategy modules

Configuration

Edit config.yaml to customize:

• Database location
• Data caching settings
• Display preferences
• Module paths

Architecture

quantsploit/
├── core/
│   ├── framework.py      # Main framework engine
│   ├── module.py          # Base module class
│   ├── session.py         # Session management
│   └── database.py        # SQLite database
├── modules/
│   ├── analysis/          # Technical analysis modules
│   ├── scanners/          # Market scanner modules
│   ├── options/           # Options analysis modules
│   └── strategies/        # Trading strategy modules
├── ui/
│   ├── console.py         # Interactive TUI
│   ├── commands.py        # Command handlers
│   └── display.py         # Display utilities
└── utils/
    ├── data_fetcher.py    # Market data fetching
    └── helpers.py         # Utility functions

Data Sources

Quantsploit uses:

• yfinance - Yahoo Finance API for market data
• pandas-ta - Technical analysis indicators
• py_vollib - Options pricing and Greeks

Database

The framework uses SQLite to cache:

• Market data (price/volume)
• Analysis results
• Watchlist symbols

Database location: ./quantsploit.db (configurable)

Roadmap

• More technical indicators (Ichimoku, Fibonacci, etc.)
• Advanced options strategies (Iron Condor, Butterfly, etc.)
• Machine learning modules
• Real-time streaming data
• Visualization/charting
• Portfolio management
• Risk analysis modules
• Correlation analysis
• News sentiment analysis
• Backtesting framework enhancements

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add your module or enhancement
4. Submit a pull request

Disclaimer

This framework is for educational and research purposes only. It is not financial advice. Trading stocks and options carries risk. Always do your own research and consult with a financial advisor before making investment decisions.

License

MIT License - See LICENSE file for details

Acknowledgments

• Inspired by Metasploit's modular framework architecture
• Built with Python, pandas, yfinance, and other excellent open-source libraries

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Happy Trading! 📈