📸 See screenshots.md for more visuals
📚 Read the core docs on QTradeX SDK DeepWiki
🤖 Explore the bots at QTradeX AI Agents DeepWiki
💬 Join our Telegram Group for discussion & support

QTradeX is a lightning-fast Python framework for designing, backtesting, and deploying algorithmic trading bots, built for crypto markets with support for 100+ exchanges, AI-driven optimization, and blazing-fast vectorized execution.

Like what we're doing? Give us a ⭐!

Whether you're exploring a simple EMA crossover or engineering a strategy with 20+ indicators and genetic optimization, QTradeX gives you:

✅ Modular Architecture
✅ Tulip + CCXT Integration
✅ Custom Bot Classes
✅ Fast, Disk-Cached Market Data
✅ Near-Instant Backtests (even on Raspberry Pi!)

• 🧠 Bot Development: Extend BaseBot to craft custom strategies
• 🔁 Backtesting: Plug-and-play CLI & code-based testing
• 🧬 Optimization: Use QPSO or LSGA to fine-tune parameters
• 📊 Indicators: Wrapped Tulip indicators for blazing performance
• 🌐 Data Sources: Pull candles from 100+ CEXs/DEXs with CCXT
• 📈 Performance Metrics: Evaluate bots with ROI, Sortino, Win Rate
• 🤖 Speed: Up to 50+ backtests/sec on low-end hardware

 qtradex/ ├── core/ # Bot logic and backtesting ├── indicators/ # Technical indicators ├── optimizers/ # QPSO and LSGA ├── plot/ # Trade/metric visualization ├── private/ # Execution & paper wallets ├── public/ # Data feeds and utils ├── common/ # JSON RPC, BitShares nodes └── setup.py # Install script 

git clone https://github.com/squidKid-deluxe/QTradeX-Algo-Trading-SDK.git QTradeX cd QTradeX pip install -e .

import qtradex as qx import numpy as np class EMACrossBot(qx.BaseBot): def __init__(self): self.tune = {"fast_ema": 10, "slow_ema": 50} def indicators(self, data): return { "fast_ema": qx.ti.ema(data["close"], self.tune["fast_ema"]), "slow_ema": qx.ti.ema(data["close"], self.tune["slow_ema"]), } def strategy(self, tick_info, indicators): fast = indicators["fast_ema"] slow = indicators["slow_ema"] if fast > slow: return qx.Buy() elif fast < slow: return qx.Sell() return qx.Thresholds(buying=0.5 * fast, selling=2 * fast) def plot(self, *args): qx.plot( self.info, *args, ( # key name label color axis idx axis name ("fast_ema", "EMA 1", "white", 0, "EMA Cross"), ("slow_ema", "EMA 2", "cyan", 0, "EMA Cross"), ) ) # Load data and run data = qx.Data( exchange="kucoin", asset="BTC", currency="USDT", begin="2020-01-01", end="2023-01-01" ) bot = EMACrossBot() qx.dispatch(bot, data)

🔗 See more bots in QTradeX AI Agents

• 📈 More indicators (non-Tulip sources)
• 🏦 TradFi Connectors: Stocks, Forex, and Comex support

Want to help out? Check out the Issues list for forseeable improvements and bugs.

• 🧠 QTradeX Algo Trading Strategies
• 📘 Tulipy Docs
• 🌍 CCXT Docs

WTFPL — Do what you want. Just be awesome about it 😎

✨ Ready to start? Clone the repo, run your first bot, and tune away. Once tuned - LET THE EXECUTIONS BEGIN!