ClyptQ

The Quantitative Trading Engine that makes Research = Backtest = Live

What Is ClyptQ?

ClyptQ is a production-grade quantitative trading engine where the same code runs identically in research, backtesting, and live trading. No rewrites. No adapters. No “production mode.” Its architecture draws from the same fundamental principle that makes every computer work — the separation of stateless computation from stateful memory — applied to quantitative trading for the first time.

# This SAME code runs in backtest, paper, and live
spec = TradingSpec(
    data=data_config,
    strategy=TradingStrategySpec(graph=graph),
    execution=TradingExecutionSpec(
        accounts=[account],
    ),
    mode="backtest",    # ← Change to "paper" or "live"
)

driver = TradingDriver.from_spec(spec)
for result in driver:
    print(f"{result.timestamp} | equity: {result.outputs['equity']}")

Who Is ClyptQ For?

Builders

Strategy developers who want to go from research to live trading without rewriting code. Use any Python library — PyTorch, XGBoost, HuggingFace — inside composable operators.

Traders

Strategy traders who want verified, independently-tested strategies. Cross-exchange validation ensures strategies aren’t overfit to a single venue.

Get Started

1. Explore

   from clyptq import Helper
   Helper.exchanges()          # Available exchanges
   Helper.symbols("binance", "futures", quote="USDT")
   
2. Build

   graph = StatefulGraph()
   graph.add_node("sma_fast", SMA(input=close, period=10))
   graph.add_node("sma_slow", SMA(input=close, period=50))
   
3. Run

   for result in TradingDriver.from_spec(spec):
       equity_curve.append(result.outputs["equity"])
   

Full Quickstart Guide

Step-by-step: install → build strategy → backtest → analyze results

Documentation Map

Core Concepts

TradingSpec, StatefulGraph, FIELD/STATE, TaggedArray, Operators

Architecture

4-layer architecture and Trading Commerce ecosystem

Engine Fundamentals

FIELD/STATE, TaggedArray, StatefulGraph, operator protocol

Backtesting Accuracy

Cost models, funding rates, liquidation, lookahead prevention

Operator Reference

Operators: indicators, signals, transforms, metrics, AI

Tutorials

SMA crossover, multi-factor, AI-augmented, cross-exchange arbitrage

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Risk Disclaimer — Trading carries substantial risk. Past performance does not guarantee future results. All strategies are provided for informational purposes. Never invest more than you can afford to lose.