# Clypt

## Docs

- [ClyptQ](https://docs.clypt.ai/index.md): The Quantitative Trading Engine that makes Research = Backtest = Live
- [Quickstart](https://docs.clypt.ai/quickstart.md): Build and backtest your first strategy in 5 minutes
- [Core Concepts](https://docs.clypt.ai/getting-started/core-concepts.md): Five building blocks you'll encounter throughout ClyptQ
- [Architecture Overview](https://docs.clypt.ai/getting-started/architecture-overview.md): The 4-layer architecture of ClyptQ and the Trading Commerce ecosystem
- [Builder Guide](https://docs.clypt.ai/platform/builder-guide.md): Strategy development and marketplace listing guide for strategy creators
- [Trader Guide](https://docs.clypt.ai/platform/trader-guide.md): Strategy evaluation and deployment guide for traders
- [Strategy Marketplace](https://docs.clypt.ai/platform/marketplace.md): How the Clypt strategy marketplace works for builders and traders
- [Jupyter Notebook Workflow](https://docs.clypt.ai/platform/jupyter-workflow.md): Research, backtest, paper trade, and go live — all from Jupyter cells
- [Supported Exchanges](https://docs.clypt.ai/platform/supported-exchanges.md): Exchange support matrix with market types, fees, limits, and capabilities
- [Compliance](https://docs.clypt.ai/platform/compliance.md): Regulatory compliance, per-country exchange rules, and KYC requirements
- [FIELD & STATE](https://docs.clypt.ai/engine/field-state.md): Two data namespaces that power every ClyptQ strategy — market data in, portfolio state back
- [TaggedArray](https://docs.clypt.ai/engine/tagged-array.md): The 4-field data structure that powers all data flow in ClyptQ
- [Operators](https://docs.clypt.ai/engine/operators.md): Stateless computation units — the building blocks of every ClyptQ strategy
- [StatefulGraph](https://docs.clypt.ai/engine/stateful-graph.md): DAG-based execution engine where operators are stateless and the graph is stateful
- [Lookback Buffers](https://docs.clypt.ai/engine/lookback-buffers.md): Pre-allocated circular buffers that manage historical data for operators
- [Operator Protocol](https://docs.clypt.ai/engine/operator-protocol.md): The standardized interface for all computation in ClyptQ — 14 roles, custom operators, self-referencing patterns, and ML/DL integration
- [TradingSpec](https://docs.clypt.ai/engine/trading-spec.md): The unified configuration hierarchy that defines everything about a trading system
- [Execution Pipeline](https://docs.clypt.ai/engine/execution-pipeline.md): From graph output to filled orders — the Intention → Delta → Order → Fill pipeline
- [Data System](https://docs.clypt.ai/engine/data-system.md): Data sources, observation specs, storage, and the warmup pipeline
- [Data Sources](https://docs.clypt.ai/data/overview.md): Supported data types, collection pipeline, and storage architecture
- [Onchain Data](https://docs.clypt.ai/data/onchain.md): RPC-based and DeFiLlama onchain data collection across 16 chains and 20 DeFiLlama ecosystems
- [FRED Macro Data](https://docs.clypt.ai/data/fred.md): Federal Reserve Economic Data — interest rates, inflation, liquidity, employment, and more as alpha inputs
- [Backtesting Accuracy](https://docs.clypt.ai/backtesting/overview.md): Why ClyptQ backtests produce fundamentally more accurate results than vectorized frameworks
- [Backtest Constraints](https://docs.clypt.ai/backtesting/constraints.md): Control which execution constraints are active during backtesting with BacktestConstraintsSpec
- [Lookahead Bias Prevention](https://docs.clypt.ai/backtesting/lookahead-bias-prevention.md): How ClyptQ prevents lookahead bias by design, not by discipline
- [Cost Models](https://docs.clypt.ai/backtesting/cost-models.md): Exchange-specific fee structures, slippage modeling, and CCXT auto-fetch
- [Funding Rate Simulation](https://docs.clypt.ai/backtesting/funding-rates.md): How ClyptQ simulates crypto futures funding rates in backtest
- [Liquidation Logic](https://docs.clypt.ai/backtesting/liquidation-logic.md): Exchange-specific margin-based liquidation simulation in backtest
- [Exchange Specifics](https://docs.clypt.ai/backtesting/exchange-specifics.md): Per-exchange details for Binance, Bybit, Gateio, OKX, Coinbase, Kraken, and Aster
- [Why ClyptQ?](https://docs.clypt.ai/competitive/overview.md): What makes ClyptQ different from every other quant trading platform
- [Research = Backtest = Live](https://docs.clypt.ai/competitive/code-parity.md): The code parity guarantee that only hedge funds had — until now
- [ClyptQ vs QuantConnect](https://docs.clypt.ai/competitive/vs-quantconnect.md): How ClyptQ improves on QuantConnect's pioneering cloud-based quant trading platform
- [ClyptQ vs QuantRocket](https://docs.clypt.ai/competitive/vs-quantrocket.md): How ClyptQ compares to QuantRocket's Docker-based quant trading pipeline
- [ClyptQ vs Nautilus Trader](https://docs.clypt.ai/competitive/vs-nautilus.md): Performance vs accessibility — why you don't have to choose
- [ClyptQ vs Vectorized Frameworks](https://docs.clypt.ai/competitive/vs-vectorized.md): Why tick-by-tick backtesting beats vectorized approaches like Freqtrade, Zipline, and Backtrader
- [AI-Powered Trading Logic](https://docs.clypt.ai/competitive/semantic-operators.md): LLM scoring, web search, and sentiment analysis as first-class operators
- [Your First Strategy](https://docs.clypt.ai/tutorials/first-strategy.md): Build a SMA crossover strategy from scratch with full explanations
- [Multi-Factor Strategy](https://docs.clypt.ai/tutorials/multi-factor.md): Combine multiple alphas, transforms, and filters into a portfolio-optimized strategy
- [AI-Augmented Strategy](https://docs.clypt.ai/tutorials/ai-augmented.md): Combine traditional indicators with LLM scoring and web search using the Gate pattern
- [Cross-Exchange Arbitrage](https://docs.clypt.ai/tutorials/cross-exchange-arb.md): Build a multi-venue arbitrage strategy with VenueAwareSizing and TotalEquityCalculator
- [Backtest to Live](https://docs.clypt.ai/tutorials/backtest-to-live.md): Deploy the same strategy from backtest through paper to live trading
- [Community Examples](https://docs.clypt.ai/tutorials/community.md): Open-source strategy examples contributed by the ClyptQ community
- [Operator Reference](https://docs.clypt.ai/operators/overview.md): Complete catalog of prebuilt operators across 14 roles
- [Moving Averages](https://docs.clypt.ai/operators/indicators/moving-averages.md): SMA, EMA, DEMA, TEMA, WMA, TRIMA, T3, KAMA, MAMA, MA
- [Momentum Indicators](https://docs.clypt.ai/operators/indicators/momentum.md): RSI, MACD, MOM, ROC, CMO, APO, PPO, STOCH, STOCHRSI, WILLR, ULTOSC, BOP, TRIX, CCI
- [Trend Indicators](https://docs.clypt.ai/operators/indicators/trend.md): ADX, DI, AROON, SAR, ICHIMOKU
- [Volatility Indicators](https://docs.clypt.ai/operators/indicators/volatility.md): ATR, NATR, BBANDS, STDDEV
- [Volume Indicators](https://docs.clypt.ai/operators/indicators/volume.md): OBV, AD, ADOSC, MFI, VWAP, CCI
- [Statistics and Math](https://docs.clypt.ai/operators/indicators/statistics.md): LINEARREG, BETA, CORREL, VAR, TSF, MINMAX, Hilbert Transform
- [Candlestick Patterns](https://docs.clypt.ai/operators/indicators/patterns.md): 48+ CDL pattern recognition operators
- [AlphaOperator DSL](https://docs.clypt.ai/operators/signals/alpha-operator.md): Base class for all alpha signals — numba-accelerated time-series and cross-sectional helpers
- [Alpha Signals](https://docs.clypt.ai/operators/signals/alphas.md): 21 alpha signals across momentum, mean reversion, volatility, volume, and liquidity categories
- [Cross-Sectional Factors](https://docs.clypt.ai/operators/signals/factors.md): 8 risk factors for portfolio construction
- [Alpha 101](https://docs.clypt.ai/operators/signals/alpha-101.md): 101 Formulaic Alphas — systematic alpha generation from Kakushadze (2016)
- [Alpha 191](https://docs.clypt.ai/operators/signals/alpha-191.md): 191 Formulaic Alphas — systematic alpha generation from Guotai Junan (2017)
- [Scalers](https://docs.clypt.ai/operators/transforms/scalers.md): ZScore, Rank, MinMaxScale, L1Norm, L2Norm, Softmax, Clip, Winsorize
- [Neutralizers](https://docs.clypt.ai/operators/transforms/neutralizers.md): Demean, Neutralize, GroupNeutralize, BetaNeutralize, FactorNeutralize, BarraNeutralizer
- [Portfolio Optimizers](https://docs.clypt.ai/operators/transforms/optimizers.md): MeanVarianceOptimizer, RiskParityOptimizer, EqualWeight, ClipWeights, MaxPositions
- [Position Operators](https://docs.clypt.ai/operators/transforms/position.md): WeightsToPositions, TurnoverConstraint, LotRounder, PositionLimits
- [Universe Filters](https://docs.clypt.ai/operators/universe/filters.md): VolumeFilter, VolatilityFilter, PriceFilter, LiquidityFilter, DataAvailabilityFilter
- [Universe Scores](https://docs.clypt.ai/operators/universe/scores.md): VolumeScore, LiquidityScore, VolatilityScore
- [Performance Metrics](https://docs.clypt.ai/operators/metrics.md): Rolling and accumulated performance metrics for strategy evaluation
- [Balance Operators](https://docs.clypt.ai/operators/balance.md): Cash balance, equity calculation, margin monitoring, and position queries
- [Order Operators](https://docs.clypt.ai/operators/order.md): TargetPositionIntention, FuturesTargetPositionIntention, DynamicUniverseIntention, ArbitrageIntention
- [Semantic Operators](https://docs.clypt.ai/operators/semantic.md): SentimentParser, WebSearchOperator, LLMScorer
- [Control Operators](https://docs.clypt.ai/operators/control.md): Conditional gates for controlling execution flow
- [Utility Operators](https://docs.clypt.ai/operators/utility.md): Identity, Resample, FieldMerge, SymbolSelect, SymbolDrop, Constant, IntervalGate
- [Roadmap Overview](https://docs.clypt.ai/roadmap/overview.md): High-level timeline and priorities for ClyptQ platform development
- [Multi-Asset Expansion](https://docs.clypt.ai/roadmap/us-stocks.md): Roadmap for expanding beyond crypto to stocks, forex, ETFs, commodities, and more
- [Data Marketplace](https://docs.clypt.ai/roadmap/data-marketplace.md): Vendor-licensed data integrations for news, fundamentals, on-chain, and alternative data
- [Rust Core Engine](https://docs.clypt.ai/roadmap/rust-conversion.md): Migrating the compute engine to Rust for PyTorch-level performance with Python research ergonomics
- [Trading Competitions](https://docs.clypt.ai/roadmap/competitions.md): Hedge fund-style trading competitions on the Clypt platform
- [Prediction Markets](https://docs.clypt.ai/roadmap/prediction-markets.md): Expanding Clypt into prediction market trading and strategy development
- [B2B Quant Solutions](https://docs.clypt.ai/roadmap/b2b-quant.md): Enterprise quant infrastructure — Quant DB, LLM agent teams, and white-label engine
- [Glossary](https://docs.clypt.ai/glossary.md): Quick reference for ClyptQ terminology

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