> ## Documentation Index
> Fetch the complete documentation index at: https://docs.clypt.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# 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](/getting-started/architecture-overview#the-architecture-behind-the-architecture) — applied to quantitative trading for the first time.

```python theme={null}
# 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?

<CardGroup cols={3}>
  <Card title="Builders" icon="code" href="/platform/builder-guide">
    **Strategy developers** who want to go from research to live trading without rewriting code. Use any Python library — PyTorch, XGBoost, HuggingFace — inside composable operators.
  </Card>

  <Card title="Traders" icon="chart-line" href="/platform/trader-guide">
    **Strategy traders** who want verified, independently-tested strategies. Cross-exchange validation ensures strategies aren't overfit to a single venue.
  </Card>
</CardGroup>

## Get Started

<Steps>
  <Step title="Explore">
    ```python theme={null}
    from clyptq import Helper
    Helper.exchanges()          # Available exchanges
    Helper.symbols("binance", "futures", quote="USDT")
    ```
  </Step>

  <Step title="Build">
    ```python theme={null}
    graph = StatefulGraph()
    graph.add_node("sma_fast", SMA(input=close, period=10))
    graph.add_node("sma_slow", SMA(input=close, period=50))
    ```
  </Step>

  <Step title="Run">
    ```python theme={null}
    for result in TradingDriver.from_spec(spec):
        equity_curve.append(result.outputs["equity"])
    ```
  </Step>
</Steps>

<Card title="Full Quickstart Guide" icon="rocket" href="/quickstart">
  Step-by-step: install → build strategy → backtest → analyze results
</Card>

## Documentation Map

<CardGroup cols={2}>
  <Card title="Core Concepts" icon="lightbulb" href="/getting-started/core-concepts">
    TradingSpec, StatefulGraph, FIELD/STATE, TaggedArray, Operators
  </Card>

  <Card title="Architecture" icon="sitemap" href="/getting-started/architecture-overview">
    4-layer architecture and Trading Commerce ecosystem
  </Card>

  <Card title="Engine Fundamentals" icon="gears" href="/engine/field-state">
    FIELD/STATE, TaggedArray, StatefulGraph, operator protocol
  </Card>

  <Card title="Backtesting Accuracy" icon="shield" href="/backtesting/overview">
    Cost models, funding rates, liquidation, lookahead prevention
  </Card>

  <Card title="Operator Reference" icon="book" href="/operators/overview">
    Operators: indicators, signals, transforms, metrics, AI
  </Card>

  <Card title="Tutorials" icon="graduation-cap" href="/tutorials/first-strategy">
    SMA crossover, multi-factor, AI-augmented, cross-exchange arbitrage
  </Card>
</CardGroup>

***

<Warning>
  **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.
</Warning>