kairos-engine 0.2.1

Kairos — adaptive crypto trading engine. Curates the best of the open-source ecosystem and adds a learning layer on top.

Kairos

Κairós (καιρός) — ancient Greek for "the opportune moment". In trading, timing is everything.

Kairos is an adaptive crypto trading engine. It curates the best primitives from the open-source ecosystem (ccxt for connectivity, pandas-ta for math, hummingbot patterns for reconnect) and adds a single proprietary layer on top: adaptivity. Adaptive execution, adaptive risk, adaptive parameter tuning, regime-aware everything — and an IngestionActor that surveys the open-source ecosystem weekly and incorporates improvements automatically.

Status

• v0.1.x (shipped): backtesting + paper trading + Binance Spot adapter + 12 indicators + order manager + risk validator + strategy marketplace + analytics + parity module
• v0.2.x (shipped): live runtime + actors + market cache + atomic bracket orders with OCO + reconciliation + Binance live adapter + design hooks for adaptive features
• v0.3+ → v1.0 (roadmap): the differentiators — adaptive execution, behavioral risk, continual tuning, IngestionActor, why-cards, counterfactual shadow, federated learning, signed track records

See docs/vision.md for the full product vision.

Notable history

This package was published as autopilot-engine at v0.1.0 (March 2026). It was renamed to kairos-engine at v0.2.0a0 to reflect its evolution into an adaptive engine with proprietary IP. The legacy autopilot-engine PyPI name remains available with a deprecation alias for 6 months.

Quick start

pip install kairos-engine

from kairos import Engine, Strategy

class MyBot(Strategy):
    def setup(self):
        self.add_ema(8, "fast")
        self.add_ema(21, "slow")
        self.add_rsi(14)

    def on_bar(self, bar):
        if self.fast_ema() > self.slow_ema() and self.rsi() > 50:
            self.buy(15)        # 15% of balance
        elif self.fast_ema() < self.slow_ema():
            self.sell_all()

engine = Engine(exchange="binance", api_key="...", api_secret="...")
engine.add(MyBot, symbol="BTCUSDC", timeframe="1h")
engine.add(MyBot, symbol="ETHUSDC", timeframe="1h")
engine.run()

What makes Kairos different

The pitch in one sentence: the only trading framework that improves itself by learning from the entire open-source ecosystem.

The curation principle

We don't reinvent. We curate the best primitives the open-source community has produced and assemble them into a coherent engine. Examples:

Primitive	Source	License	What we use it for
Exchange transport	ccxt	MIT	200+ exchanges, auth quirks handled
Indicator math	pandas-ta	MIT	Proven implementations of EMA, RSI, ATR, etc.
WebSocket reconnect	Pattern adapted from hummingbot	MIT	Battle-tested backoff + reconnection
Order state machine	Inspired by NautilusTrader	LGPL (study only)	Mature design, clean-room reimplementation
Strategy lifecycle	Inspired by Jesse, Freqtrade	MIT/Apache	Familiar API for the community

Full list at CREDITS.md. All cited in code comments. License-clean.

The differentiation principle

On top of the curated foundation, Kairos adds a single coherent layer: adaptivity.

The 10 differentiators:

1. Adaptive execution — OrderManager learns optimal order type + timing per (instrument × regime × spread)
2. Behavioral risk model — ProtectionActor learns user's true risk tolerance from revealed preferences
3. Continual tuning — Bayesian posterior over strategy parameters, updated per trade
4. Cross-asset signal fusion — regime + factor events consumed by any strategy
5. Regime-aware everything — execution, risk, sizing, UI all modulated by detected regime
6. Why card on every trade — explanation record (indicators, regime, win probability, counterfactual)
7. Counterfactual shadow — always-on parallel sim of alternative strategies → data-driven upgrade recommendations
8. Cryptographically-signed track records — Ed25519-signed performance, not falsifiable
9. Federated cross-user learning — privacy-preserving patterns, compounds with user count
10. IngestionActor — the meta-improvement engine: weekly crawls NT, ccxt, Freqtrade, Jesse, hummingbot, arxiv. LLM-classifies changes. Evaluates against our backtest data. Auto-merges or proposes PRs. This is the moat.

§1-9 are user-facing features. §10 is the structural differentiator: a framework that gets smarter every week without manual releases.

Features (v0.1, what works today)

Core

• Async event-driven engine with deterministic execution (paper / backtest)
• Multi-strategy on multiple pairs simultaneously
• 12 built-in indicators: EMA, SMA, RSI, ATR, Bollinger, MACD, Stochastic, VWAP, OBV, Donchian, ADX, HMA
• Pre-trade risk validation (balance, lot size, notional, rate limits)
• Real-time position tracking with PnL calculation

Orders

• Market, Limit, Stop-Market, Stop-Limit
• Bracket orders (entry + SL + TP)
• Post-only, reduce-only execution flags

Exchanges

• Binance Spot (REST + WebSocket)
• Paper exchange for backtesting / simulation

Backtest

• Tick-level replay with parquet data catalog
• Sharpe, Sortino, profit factor analytics
• Walkforward parameter sweeps

Strategy marketplace

• Registry + builder for composable strategies
• JSON-config → Strategy class

Parity module

• kairos.parity.match_fills(baseline, candidate) — compare two engine outputs (fills) with configurable tolerances, produce PASS/WARN/FAIL verdict
• 21 unit tests, pure Python, no I/O
• Use case: validate a new engine version against a baseline before cutover

Roadmap

• v0.2.x — Production runtime (3-4 weeks)
  • Live event loop with adapter registration
  • Actor base class + event routing
  • MarketCache with warmup gating
  • OrderManager extensions (brackets, OCO, reconciler)
  • Binance live adapter with user-data WebSocket
• v0.3 — Learning milestone (3-4 weeks after v0.2)
  • Adaptive execution (§1)
  • Continual tuning (§3)
  • Why card on every fill (§6)
• v0.4 — Meta-improvement milestone (4-6 weeks)
  • IngestionActor v1 (§10)
  • Counterfactual shadow (§7)
• v0.5 — Personalization — behavioral risk + cross-asset fusion + federated v1
• v0.6 — Marketplace trust — signed track records + marketplace integration
• v1.0 — GA, commercial launch

Roughly 6-9 months from today to v1.0 at a steady pace. Quality > speed.

Reference customer

Trading Autopilot is the flagship SaaS that runs on Kairos. After v3 of Trading Autopilot ships on Kairos v0.2 (~13 weeks of migration work), every Kairos differentiator that ships will be deployed to real users running real trades.

Documentation

• Vision — manifesto, differentiators, moat, glossary
• Architecture — runtime, actors, cache, execution
• Getting Started — first strategy in 10 lines
• Strategy Guide — patterns, examples, anti-patterns
• Indicators — full list + parameters
• Backtesting — data catalog, metrics, walkforward
• Exchange Setup — Binance API key configuration
• CREDITS.md — adopted primitives + license attribution

Contributing

See CONTRIBUTING.md. Note: Kairos is moving toward a more proprietary licensing model from v1.0 onwards. v0.1.x and v0.2.x stay MIT. Contributions to v0.1.x / v0.2.x are welcome under MIT terms; contributions to v0.3+ work will be subject to the final license terms (TBD).

License

v0.1.x and v0.2.x: MIT (see LICENSE).

The Kairos team is reviewing the license model for v1.0+. The current intent is source-available with commercial-use restrictions (similar to Business Source License or Elastic License). The MIT-licensed v0.1/v0.2 code remains MIT forever — license changes are not retroactive.

If you're considering Kairos for commercial use, open an issue or reach out so we can keep you informed.

Citations

If Kairos contributes to academic or commercial work:

@software{kairos2026,
  title  = {Kairos: an adaptive crypto trading engine},
  author = {Trading Autopilot Team},
  year   = {2026},
  url    = {https://github.com/Vekkris76/kairos}
}