QTradeX Core

Build, Backtest & Optimize AI-Powered Crypto Trading Bots

Lightning-fast Python framework for algorithmic trading. Deploy on 100+ exchanges with vectorized execution and genetic optimization.

Get Started View on GitHub

Modular, Non-Locked Architecture

Modular architecture - want to use QTradeX's data fetching with a custom backtest engine? Go for it!
Tulip + CCXT Integration
Custom Bot Classes
Fast, Disk-Cached Market Data
Ultra Fast Backtests (even on a Raspberry Pi!)

Bot Development

Extend BaseBot to craft custom strategies with powerful indicators.

Backtesting

Easy-to-navigate CLI & live-coding based testing platform (Just select Autobacktest).

Optimization

Use QPSO, LSGA, or others to fine-tune parameters for maximum performance.

Indicators

Wrapped Tulip indicators for blazing performance and accurate analysis.

Data Sources

Pull candles from 100+ CEXs & DEXs with seamless CCXT integration.

Performance Metrics

Evaluate bots with ROI, Sortino, Win Rate, and dozens more metrics.

Speed

200+ backtests/sec for 3 years of daily candles on a Ryzen 5600x.

Modular Design

Extend BaseBot to craft custom strategies with clean architecture.

ema_cross_bot.py

import qtradex as qx
import numpy as np

class EMACrossBot(qx.BaseBot):
    def __init__(self):
        # Notes:
        # - If you make the tune values integers, the optimizers
        #   will quantize them to the nearest integer.
        # - By putting `_period` at the end of a tune value,
        #   QTradeX core will assume they are periods in days and will scale them
        #   to different candle sizes if the data given isn't daily
        self.tune = {
            "fast_ema_period": 10.0,
            "slow_ema_period": 50.0
        }
        self.clamps = [
            # min, max
            [5,   50 ], # fast_ema
            [20,  100], # slow_ema
        ]

    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=fast * 0.8, selling=fast * 1.2)

    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"),
            )
        )

Quick Start

$pip install qtradex Or, if you want the latest updates: $git clone https://github.com/squidKid-deluxe/QTradeX-Algo-Trading-SDK.git QTradeX $cd QTradeX $pip install -e .

Neural Optimization

Leverage advanced algorithms like QPSO, LSGA, or others to fine-tune parameters for maximum performance.