Broad philip (27 résultats)

Etat : Very Good. Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee.

Pamphlet. Etat : Very Good. Vol 70, No 3, Sept, 1936, pp. 333-349, 8 Color Plts, BW Photos, Extracted from orig vol, begins with title page, stapled & trimmed , thus appears like a pamphlet, else VG.

Etat : Good. 1st Edition. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Hardcover. Etat : Very Good. No Jacket. Former library book; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Etat : Good. 1st Edition. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Hardcover. Etat : Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Hardcover. Etat : Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

hardcover. Etat : Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

hardcover. Etat : Very Good. 1st Edition. Used - Very Good.

Softcover. Etat : Good with no dust jacket. Sound binding. Clean, off-white pages. Wrappers are lightly soiled with handling wear. Contents: Simpich, Indiana journey. Culver, Hoosier haunts and holidays. Broughton, A modern dragon hunt on Komodo. Broad, Within the halls of Cambridge. Wakeman and Edwards, Where the winding Cam mirrors Cambridge spires. Roberts, Sojourning in the Italy of today. Many advertisements. 10.0" tall.…

Etat : new.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New.

Hardback. Etat : New. Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance Hands-On AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt. Unlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks. The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used: Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab.Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM.Predict market volatility regimes and allocate funds accordingly.Predict daily returns of tech stocks using classifiers.Forecast Forex pairs' future prices using Support Vector Machines and wavelets.Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs.Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications.Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization.Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch.AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation. Written by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with Hands-On AI Trading with Python, QuantConnect, and AWS.…

Hardback. Etat : New. Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance Hands-On AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt. Unlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks. The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used: Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab.Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM.Predict market volatility regimes and allocate funds accordingly.Predict daily returns of tech stocks using classifiers.Forecast Forex pairs' future prices using Support Vector Machines and wavelets.Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs.Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications.Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization.Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch.AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation. Written by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with Hands-On AI Trading with Python, QuantConnect, and AWS.…

hardcover. Etat : New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

magazine. Etat : Very Good. No Dust Jacket. very good condition. Vintage paper items are not returnable article has many illustrations/photos in b/w and color; article has many illustrations/photos in b/w and color; NOVI008920; 17 pages.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New. Satisfaction Guaranteed or your money back.

Etat : New. In English.

Etat : New. 1st edition NO-PA16APR2015-KAP.

Hardcover -. Etat : Good. Hardcover dj/ pub. 1988/ Gd.+ condition/ 294 pages The issue of Star Wars and the research, development, testing and deployment of missile-defense systems in space has been a central issue. And it wil l continue to be for years to come, perhaps gene rations. (B4866). Book.

Etat : New. Brand new! Please provide a physical shipping address.

Hardback. Etat : New. Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance Hands-On AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt. Unlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks. The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used: Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab.Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM.Predict market volatility regimes and allocate funds accordingly.Predict daily returns of tech stocks using classifiers.Forecast Forex pairs' future prices using Support Vector Machines and wavelets.Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs.Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications.Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization.Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch.AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation. Written by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with Hands-On AI Trading with Python, QuantConnect, and AWS.…

Hardback. Etat : New. Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance Hands-On AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt. Unlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks. The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used: Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab.Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM.Predict market volatility regimes and allocate funds accordingly.Predict daily returns of tech stocks using classifiers.Forecast Forex pairs' future prices using Support Vector Machines and wavelets.Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs.Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications.Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization.Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch.AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation. Written by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with Hands-On AI Trading with Python, QuantConnect, and AWS.…