Articles liés à Building an Honest AI Trading Agent
Synopsis
Modern markets are close to efficient. Point a machine-learning model at price history, evaluate it honestly, and the up-or-down call slides back toward a coin flip.
Most books about AI and trading never report that, because most never measure it. This one measures everything - and then asks the question that actually matters: what is left to trade once you accept it?
I built a production, multi-asset autonomous trading agent - designed it, deployed it against real broker APIs, and spent the better part of a year trying to break it. Roughly forty directional experiments, each held to leak-free data handling, walk-forward validation, calibration, and statistical tests strict enough to catch a lucky backtest before it catches you. This book is the whole build, end to end, and the whole record of what survived that scrutiny and what did not.
It is also the answer. Something does survive - not a comforting "buy signal," but a different axis of the market entirely, one you can measure, size, and compound with the same rigor used to find it. Getting you there, honestly, is what this book is for.
Inside, you will build:
- Multi-timeframe feature engineering and leak-free labeling (the triple-barrier method) across five asset classes - forex, crypto, commodities, indices, and stocks
- Per-timeframe gradient-boosted models plus a stacking meta-learner, validated by walk-forward optimization across nine rolling windows
- An Observe → Filter → Size → Execute → Monitor → Reflect trading agent, wired to real broker APIs
- A twelve-layer circuit-breaker stack for volatility, drawdown, and portfolio-heat protection, with every position sized by the signature formula: R = P × K × Ω
- The statistical machinery - Deflated Sharpe Ratio, Probability of Backtest Overfitting, forward-AUC - that tells a real edge from a lucky backtest
- Forty documented experiments in the systematic search for direction, and the one axis that came through them intact
Every central claim is backed by a runnable, open companion codebase, so you can reproduce the honest numbers yourself, then point the same leak-free pipeline and statistical toolkit at your own ideas.
This is the book that hands you the instruments, not just the promise: a validated edge to build on, and the discipline to tell your next real discovery from your next false one.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Building an Honest AI Trading Agent (Paperback or Softback)
Edité par
Sergio Saraiva 7/28/2026, 2026
ISBN 10 : 9893402212
ISBN 13 : 9789893402214
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Vendeur : BargainBookStores, Grand Rapids, MI, Etats-Unis
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Paperback or Softback. Etat : New. Building an Honest AI Trading Agent. Book. N° de réf. du vendeur BBS-9789893402214
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Building an Honest AI Trading Agent
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Building an Honest AI Trading Agent (Paperback)
impression à la demandeVendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. Modern markets are close to efficient. Point a machine-learning model at price history, evaluate it honestly, and the up-or-down call slides back toward a coin flip. Most books about AI and trading never report that, because most never measure it. This one measures everything - and then asks the question that actually matters: what is left to trade once you accept it? I built a production, multi-asset autonomous trading agent - designed it, deployed it against real broker APIs, and spent the better part of a year trying to break it. Roughly forty directional experiments, each held to leak-free data handling, walk-forward validation, calibration, and statistical tests strict enough to catch a lucky backtest before it catches you. This book is the whole build, end to end, and the whole record of what survived that scrutiny and what did not. It is also the answer. Something does survive - not a comforting "buy signal," but a different axis of the market entirely, one you can measure, size, and compound with the same rigor used to find it. Getting you there, honestly, is what this book is for. Inside, you will build: Multi-timeframe feature engineering and leak-free labeling (the triple-barrier method) across five asset classes - forex, crypto, commodities, indices, and stocksPer-timeframe gradient-boosted models plus a stacking meta-learner, validated by walk-forward optimization across nine rolling windowsAn Observe Filter Size Execute Monitor Reflect trading agent, wired to real broker APIsA twelve-layer circuit-breaker stack for volatility, drawdown, and portfolio-heat protection, with every position sized by the signature formula: R = P K WThe statistical machinery - Deflated Sharpe Ratio, Probability of Backtest Overfitting, forward-AUC - that tells a real edge from a lucky backtestForty documented experiments in the systematic search for direction, and the one axis that came through them intact Every central claim is backed by a runnable, open companion codebase, so you can reproduce the honest numbers yourself, then point the same leak-free pipeline and statistical toolkit at your own ideas. This is the book that hands you the instruments, not just the promise: a validated edge to build on, and the discipline to tell your next real discovery from your next false one. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9789893402214
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Building an Honest AI Trading Agent
Edité par
Amazon Digital Services LLC - Kdp, 2026
ISBN 10 : 9893402212
ISBN 13 : 9789893402214
Neuf
PAP
Vendeur : PBShop.store US, Wood Dale, IL, Etats-Unis
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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur L2-9789893402214
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Building an Honest AI Trading Agent
Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur L2-9789893402214
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Building an Honest AI Trading Agent
Edité par
Amazon Digital Services LLC - Kdp Jul 2026, 2026
ISBN 10 : 9893402212
ISBN 13 : 9789893402214
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware - Modern markets are close to efficient. Point a machine-learning model at price history, evaluate it honestly, and the up-or-down call slides back toward a coin flip. Most books about AI and trading never report that, because most never measure it. This one measures everything - and then asks the question that actually matters: what is left to trade once you accept it I built a production, multi-asset autonomous trading agent - designed it, deployed it against real broker APIs, and spent the better part of a year trying to break it. Roughly forty directional experiments, each held to leak-free data handling, walk-forward validation, calibration, and statistical tests strict enough to catch a lucky backtest before it catches you. This book is the whole build, end to end, and the whole record of what survived that scrutiny and what did not. It is also the answer. Something does survive - not a comforting 'buy signal,' but a different axis of the market entirely, one you can measure, size, and compound with the same rigor used to find it. Getting you there, honestly, is what this book is for. Inside, you will build: - Multi-timeframe feature engineering and leak-free labeling (the triple-barrier method) across five asset classes - forex, crypto, commodities, indices, and stocks- Per-timeframe gradient-boosted models plus a stacking meta-learner, validated by walk-forward optimization across nine rolling windows- An Observe ? Filter ? Size ? Execute ? Monitor ? Reflect trading agent, wired to real broker APIs- A twelve-layer circuit-breaker stack for volatility, drawdown, and portfolio-heat protection, with every position sized by the signature formula: R = P × K × O- The statistical machinery - Deflated Sharpe Ratio, Probability of Backtest Overfitting, forward-AUC - that tells a real edge from a lucky backtest- Forty documented experiments in the systematic search for direction, and the one axis that came through them intact Every central claim is backed by a runnable, open companion codebase, so you can reproduce the honest numbers yourself, then point the same leak-free pipeline and statistical toolkit at your own ideas. This is the book that hands you the instruments, not just the promise: a validated edge to build on, and the discipline to tell your next real discovery from your next false one. N° de réf. du vendeur 9789893402214
Acheter neuf
EUR 36,43
Expédition à EUR 35
Expédition depuis Allemagne vers Etats-Unis
Expédition depuis Allemagne vers Etats-Unis
Quantité disponible : 2 disponible(s)
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Building an Honest AI Trading Agent (Paperback)
impression à la demandeVendeur : CitiRetail, Stevenage, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. Modern markets are close to efficient. Point a machine-learning model at price history, evaluate it honestly, and the up-or-down call slides back toward a coin flip. Most books about AI and trading never report that, because most never measure it. This one measures everything - and then asks the question that actually matters: what is left to trade once you accept it? I built a production, multi-asset autonomous trading agent - designed it, deployed it against real broker APIs, and spent the better part of a year trying to break it. Roughly forty directional experiments, each held to leak-free data handling, walk-forward validation, calibration, and statistical tests strict enough to catch a lucky backtest before it catches you. This book is the whole build, end to end, and the whole record of what survived that scrutiny and what did not. It is also the answer. Something does survive - not a comforting "buy signal," but a different axis of the market entirely, one you can measure, size, and compound with the same rigor used to find it. Getting you there, honestly, is what this book is for. Inside, you will build: Multi-timeframe feature engineering and leak-free labeling (the triple-barrier method) across five asset classes - forex, crypto, commodities, indices, and stocksPer-timeframe gradient-boosted models plus a stacking meta-learner, validated by walk-forward optimization across nine rolling windowsAn Observe Filter Size Execute Monitor Reflect trading agent, wired to real broker APIsA twelve-layer circuit-breaker stack for volatility, drawdown, and portfolio-heat protection, with every position sized by the signature formula: R = P K WThe statistical machinery - Deflated Sharpe Ratio, Probability of Backtest Overfitting, forward-AUC - that tells a real edge from a lucky backtestForty documented experiments in the systematic search for direction, and the one axis that came through them intact Every central claim is backed by a runnable, open companion codebase, so you can reproduce the honest numbers yourself, then point the same leak-free pipeline and statistical toolkit at your own ideas. This is the book that hands you the instruments, not just the promise: a validated edge to build on, and the discipline to tell your next real discovery from your next false one. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9789893402214
Acheter neuf
EUR 31,56
Expédition à EUR 43,62
Expédition depuis Royaume-Uni vers Etats-Unis
Expédition depuis Royaume-Uni vers Etats-Unis
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