Add QtradeX

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QtradeX

New AI Tuned Open Source
Botscript Deployment Platform
Crypto, Defi, Stocks, Forex Comex

LET THE EXECUTIONS BEGIN!

SDK

https://github.com/squidKid-deluxe/QTradeX-Algo-Trading-SDK

BOTS

https://github.com/squidKid-deluxe/QTradeX-AI-Agents

DOCS

https://deepwiki.com/squidKid-deluxe/QTradeX-Algo-Trading-SDK
https://deepwiki.com/squidKid-deluxe/QTradeX-AI-Agents

AMA

https://t.me/qtradex_sdk

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Key Features QtradeX SDK

Based on the codebase, QTradeX stands out with several key features that make it a powerful algorithmic trading framework:

1. Advanced Optimization Algorithms

   QTradeX offers multiple sophisticated optimization algorithms for fine-tuning trading strategies:

   • Quantum Particle Swarm Optimization (QPSO): An enhanced optimization technique that combines quantum mechanics principles with particle swarm optimization, featuring N-Dimensional Coordinate Gradient Ascent, Cyclic Simulated Annealing, and Neuroplastic Synaptic Learning.
   • Local Search Genetic Algorithm (LSGA): A genetic algorithm approach for parameter optimization.
   • Iterative Parametric Space Expansion (IPSE): A systematic approach to exploring parameter spaces.

2. Comprehensive Performance Metrics

   QTradeX provides extensive performance evaluation metrics beyond simple ROI:

   • Advanced Financial Metrics: Includes Sharpe Ratio, Sortino Ratio, Maximum Drawdown, Calmar Ratio, Omega Ratio, Beta, Alpha, Information Ratio, and more.
   • Trade-Specific Metrics: Evaluates Win Rate, Profit Factor, and Payoff Ratio to provide detailed insights into strategy performance.

3. Exceptional Speed and Efficiency

   QTradeX is designed for high-performance backtesting:

   • Fully Vectorized Calculations: All indicators are calculated once and cached as vectors for maximum efficiency.
   • Data Caching: Exchange API data is collected once and stored on disk to minimize redundant API calls.
   • Back Propagation: Achieves 50+ backtests per second even on modest hardware like a Raspberry Pi.
   • Instant Backtest: Can process 1000 candles with 10 indicators in less than 0.1 seconds.

4. Versatile Exchange Integration

   QTradeX offers broad market access:

   • DEX and CEX Support: Works with both Bitshares Decentralized Exchange and 100+ Centralized Exchanges through CCXT integration.
   • Comprehensive Exchange API: Includes methods for order creation, cancellation, and management across multiple exchanges.

5. Flexible Trading Modes

   QTradeX provides multiple execution modes to suit different needs:

   • Backtesting: Simulate trades with historical data to evaluate strategy performance.
   • Paper Trading: Test strategies with live data but without executing real trades.
   • Live Trading: Execute actual trades on exchanges using the same strategy code.
   • Fill Testing: Evaluate order execution quality on real exchanges.

6. Modular Architecture

   QTradeX is built with a clean, modular design:

   • Component-Based Design: Separates core functionality, indicators, optimization, and visualization into distinct modules.
   • Extensible Bot Framework: The BaseBot class provides a foundation for creating custom trading strategies.
   • Well-Defined Interfaces: Components interact through clear interfaces, making the system easy to extend and customize.

7. Data Integration Capabilities

   QTradeX offers robust data handling:

   • Multiple Data Sources: Can fetch market data from various exchanges and sources.
   • Composite Data Handling: Supports creating implied price data through intermediary assets.
   • Flexible Time Frames: Works with different candle sizes and automatically adjusts parameters accordingly.

Key Features QtradeX AI Agents

The QTradeX-AI-Agents repository stands out as a comprehensive collection of algorithmic trading bots for cryptocurrency trading. Based on the codebase, here are the key features that make this repository distinctive:

1. Diverse Trading Strategies

   The repository contains a wide variety of trading strategies, each implemented as a separate bot with its own approach to market analysis:

   • Moving Average Based strategies like EmaCross and DirectionalMovement that use crossovers to identify trends.
   • Oscillator Based strategies like Aroon and AroonMfiVwap that identify overbought/oversold conditions.
   • Volatility Based strategies like BlackHoleStrategy and Cthulhu that detect price compression and breakouts.
   • Multi-Indicator strategies like ClassicalCryptoBot, CryptoMasterBot, and Confluence that combine multiple technical indicators.
   • Trend Detection strategies like ExtinctionEvent and Forty96 that distinguish between trending and ranging markets.

2. Consistent Architecture

   All bots follow a consistent implementation pattern based on the qtradex (often abbreviated as qx) framework:

   • Standardized Bot Structure: Each bot implements the same core methods: __init__, indicators, strategy, plot, and fitness.
   • Parameter Tuning System: All bots use a tune dictionary for configurable parameters and a clamps dictionary for parameter constraints, enabling optimization.
   • Consistent Execution Flow: The bots share a common execution pattern through the qx.dispatch system.

3. Advanced Technical Indicators

   The repository leverages a rich set of technical indicators through the qx.ti namespace:

   • Trend Indicators: EMA, SMA, ADX, Directional Movement, PSAR.
   • Momentum Oscillators: RSI, MACD, Stochastic, Aroon.
   • Volatility Indicators: Bollinger Bands, Standard Deviation, ATR.
   • Price Transformations: VWAP, Fisher Transform.

4. Sophisticated Analysis Techniques

   Several bots implement advanced analysis methods beyond basic indicators:

   • Fourier Transform Analysis: The mac_dr_si.py bot uses FFT with low-pass filtering to remove market noise.
   • Slope-Based Trend Detection: The ma_sabres.py bot uses derivatives to calculate the slope of moving averages.
   • Market Regime Detection: Several bots can distinguish between trending and range-bound markets.

5. Optimization and Performance Evaluation

   The repository includes a robust system for strategy optimization and performance assessment:

   • Automated Tuning: The repository contains a tunes directory with optimized parameters for each strategy.
   • Performance Metrics: Strategies are evaluated using metrics like ROI, Sortino ratio, and win rate.
   • Visualization Tools: Each bot implements a plot method for visualizing indicators and trading signals.

6. Production-Ready Implementations

   The repository includes several strategies that are specifically noted as "known good strategies" ready for production use:

   • cthulhu.py, extinction_event.py, iching.py, forty96.py, harmonica.py, ma_sabres.py, and parabolic_ten.py.