Book Guide¶

This guide maps ml4t-live to Machine Learning for Trading, Third Edition so you can move between the library docs and the book materials without guessing which notebook or chapter matters.

How To Use This Guide¶

Start with the chapter map if you are reading the book.
Start with the API map if you are coming from the library and want the matching notebook.
Treat the listed code paths as the canonical book-side references in the third_edition materials.

Chapter To Feature Map¶

Book material	What it teaches	`ml4t-live` connection
Chapter 16 strategy simulation	event-driven strategies and parity-friendly design	the strategy surface you carry into `LiveEngine`
Chapter 18 costs	execution frictions and turnover budgets	the live costs you compare against post-deployment
Chapter 19 risk management	kill switches, drawdowns, limits	`LiveRiskConfig`, `SafeBroker`, staged rollout
Chapter 25 live trading systems	brokers, feeds, operational parity, deployment	the core `ml4t-live` library surface
Chapter 26 MLOps governance	shadow mode, challenger rollout, circuit breakers	operational procedures around `ml4t-live`

Notebook And Script Map¶

Book path	Why it matters here
`code/16_strategy_simulation/06_framework_parity.py`	shows why keeping one strategy interface matters before live deployment
`code/25_live_trading/unified_framework_demo.py`	demonstrates the same strategy moving from backtest to live-style execution
`code/25_live_trading/ib_paper_trading_demo.py`	Interactive Brokers connectivity path
`code/25_live_trading/alpaca_paper_trading_demo.py`	Alpaca paper-trading path
`code/25_live_trading/alpaca_crypto_live_demo.py`	Alpaca crypto workflow
`code/25_live_trading/pipeline_verification.py`	parity checks between research and live workflows
`code/25_live_trading/okx_funding_rate_demo.py`	live-style funding-rate deployment with exchange data
`code/25_live_trading/safety_risk_demo.py`	`SafeBroker` limits, shadow mode, and kill-switch behavior
`code/26_mlops_governance/03_safe_model_rollout.py`	shadow-mode and staged-promotion procedures around live deployment
`code/26_mlops_governance/04_circuit_breakers.py`	broader operational safety concepts that complement `SafeBroker`

Case Study Link: Crypto Perpetuals Funding¶

The clearest live-trading case-study bridge in the current book materials is the crypto perpetuals workflow:

Case-study path	Library relevance
`code/case_studies/crypto_perps_funding/03_financial_features.py`	feature definitions that must stay consistent in live inference
`code/case_studies/crypto_perps_funding/14_backtest.py`	the validated backtest side of the strategy
`code/case_studies/crypto_perps_funding/17_risk_management.py`	portfolio and risk assumptions before deployment
`code/25_live_trading/okx_funding_rate_demo.py`	the live-style deployment bridge using exchange funding data

From Book Concepts To Library APIs¶

Book concept	Library API
same strategy in backtest and live	`LiveEngine` plus unchanged `Strategy` subclass
sync strategy calling async infrastructure	`ThreadSafeBrokerWrapper`
explicit deployment risk policy	`LiveRiskConfig`
pre-trade enforcement and kill switch	`SafeBroker`
paper-like live validation without routing orders	`shadow_mode=True` with `VirtualPortfolio`
broker-specific execution path	`IBBroker` or `AlpacaBroker`
live or replay data source	`IBDataFeed`, `AlpacaDataFeed`, `DataBentoFeed`, `CryptoFeed`, `OKXFundingFeed`

What The Book Often Shows Manually¶

The notebooks are pedagogical and frequently expose mechanics directly. The library turns those same ideas into reusable interfaces:

notebook orchestration becomes LiveEngine
ad hoc risk checks become SafeBroker
replay/live feed adapters become DataFeedProtocol implementations
deployment-stage bookkeeping becomes RiskState and VirtualPortfolio

Best Reading Path¶

If you are learning the stack end to end, the most efficient route is:

code/16_strategy_simulation/06_framework_parity.py
Backtest to Live
code/25_live_trading/unified_framework_demo.py
the broker page that matches your venue
code/25_live_trading/safety_risk_demo.py
Risk Controls