Hands on trading python quantconnect par pik jiri (31 résultats)

hardcover. Etat : Acceptable. Connecting readers with great books since 1972. Used textbooks may not include companion materials such as access codes, etc. May have condition issues including wear and notes/highlighting. We ship orders daily and Customer Service is our top priority.

Hardback. Etat : Very Good. Os microrganismos est o em todo o lado, as bact rias e os fungos contaminam o nosso corpo, as nossas casas, locais de trabalho, animais de estima o e todo o ambiente. Felizmente, entre muitos milhares de milh es de bact rias, apenas 1.500 podem ser perigosas para a nossa sa de, causando diferentes doen as como a pneumonia ou infec es cut neas. O verdadeiro problema que o n mero de estirpes bacterianas que desenvolvem resist ncias aos desinfectantes e, especialmente, aos antibi ticos est a aumentar muito rapidamente. Alguns destes microrganismos resistentes s o dif ceis de destruir e podem sobreviver durante mais tempo no ch o e noutras superf cies.…

Hardback. Etat : Very Good. Os microrganismos est o em todo o lado, as bact rias e os fungos contaminam o nosso corpo, as nossas casas, locais de trabalho, animais de estima o e todo o ambiente. Felizmente, entre muitos milhares de milh es de bact rias, apenas 1.500 podem ser perigosas para a nossa sa de, causando diferentes doen as como a pneumonia ou infec es cut neas. O verdadeiro problema que o n mero de estirpes bacterianas que desenvolvem resist ncias aos desinfectantes e, especialmente, aos antibi ticos est a aumentar muito rapidamente. Alguns destes microrganismos resistentes s o dif ceis de destruir e podem sobreviver durante mais tempo no ch o e noutras superf cies.…

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Hardback or Cased Book. Etat : New. Hands-On AI Trading with Python, Quantconnect, and AWS. Book.

HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Hardcover. Etat : new. Hardcover. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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.…

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.…

Etat : new.

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.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New. 2025. 1st Edition. hardcover. . . . . .

Etat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Etat : New. 2025. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

hardcover. Etat : New. Special order direct from the distributor.

Etat : New. In English.

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

Hardcover. Etat : Brand New. 416 pages. 7.10x0.90x10.00 inches. In Stock.

Hardback. Etat : New. New copy - Usually dispatched within 4 working days.

Hardcover. Etat : Brand New. 416 pages. 7.10x0.90x10.00 inches. In Stock.

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

Hardcover. Etat : new. Hardcover. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

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.…

Hardcover. Etat : new. Hardcover. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…