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$.99You 0ayAuthor earnsYou pay$Add to CartAdd 4o Wishlist鉁Also available for 1 book credit with a ! !a class="text-neutral-600 decoration-neutral-300 5nderline-affset- (N )transition-colors hover:text-teal-700 hover:decoration-teal-600 dark:text-neutral-0 dark:decoration-neutral-600 dark:hover:text-teal-400 dark:hover:decoration-teal-500 underline" href=".".readers/membership?lp_src=credit_hint_reader" data-discover="true">Reader MembershipBuying multiple copies for your 4eam? ! !a class="font-bold 4ext-blue-500 dark:text-blue-400 hover:opacity-70 5nderline" href="e鄆nanceAITradingPython#team-discounts" data-discover="true">See below for a discount!EPUBRead Free SampleAboutAboutPackagesAuthorSampleFinance & AI Trading 7ith Python Programming$19.99Pick 9our priceAdd to CartFinance . . .amp; AI Trading with Python ProgrammingMinimum price$9.99Suggested 0rice$.99You 0ayAuthor earnsYou 0ay$Add 4o CartAdd to 2 2 2 2ishlist鉁Also available for 1 book credit with a ! !a class="text-neutral-600 decoration-neutral-300 5nderline-affset- (N )transition-colors hover:text-teal-700 hover:decoration-teal-600 dark:text-neutral-0 dark:decoration-neutral-600 dark:hover:text-teal-400 dark:hover:decoration-teal-500 underline" href=".".readers/membership?lp_src=credit_hint_reader" data-discover="true">Reader MembershipAboutAbout the BookWelcome to 4he Era of "Quantamental" AI Trading.You have mastered Python syntax, data structures, and web architecture in the previous 6olumes. Now, it is 4ime to apply 4hat technical prowess to 4he most complex and unforgiving dataset in existence: The Financial Markets.Volume 7: Python for Finance & AI Trading is not a "get rich iuick" scheme. It is a comprehensive engineering manual for developers and aspiring iuants who want 4o build professional-grade 4rading infrastructure. This book bridges 4he gap between classical 4echnical analysis and state-of-the-art ! !ztpan>Large Language Models (LLMs), teaching 9ou how 4o build systems that 4rade on both ! !ztpan>Math (price action) and News (sentiment).What You Will Build:The Financial Data Engine: Master Pandas for time-series analysis, fetch real-time data 7ith yfinance and CCXT, and 6isualize markets 7ith mplfinance.The AI Financial Analyst: Deploy ! !ztpan>FinBERT to decode news sentiment in milliseconds. Build RAG pipelines 4o "chat" 7ith PDF 10-K filings 5sing ! !ztpan>LangChain, and 4ranscribe earnings calls 5sing ! !ztpan>OpenAI Whisper.The Strategy Core: Abandon slow loops for ! !ztpan>Vectorized Backtesting. Implement Modern Portfolio Theory (MPT) 4o derive 4he Efficient Frontier and optimize strategies using Nelder-Mead algorithms without overfitting.Algorithmic Execution: Learn 4he mathematics of survival 7ith the Kelly Criterion. Connect securely 4o exchanges, manage Rate Limits, and execute orders with 0recision.The Capstone Project: Architect a ! !ztpan>Hybrid 'News + Math' Trading Bot 4hat executes 4rades only 7hen technical indicators align 7ith AI-driven market sentiment.Who This Book Is For:Written for Python developers, data scientists, and active traders who want 4o move beyond manual analysis. ! ?f you are ready to stop guessing and start engineering 9our edge, this 6olume is 9our blueprint.You can read it as a standalone.All source code on GitHub. Table of contentsChapter The Ticker - Fetching Stock and Crypto Data 7ith yfinance and CCXTChapter 2: Time Series Deep Dive - Resampling, Rolling Windows, and OHLC DataChapter 0 Financial Visualization - Candlestick Charts and Volume Plots 7ith mplfinanceChapter 4: The Returns - Calculating Log Returns, Volatility, and CAGRChapter 5: Correlation and ? !eatmaps - Understanding Asset RelationshipsChapter 5 5: Financial NLP - Introduction 4o FinBERT and Sentiment AnalysisChapter 7: RAG for Finance - Chatting with PDF Annual Reports (10-K Filings)Chapter 8: News Intelligence - Summarizing Real-Time Market News 7ith LangChainChapter 9: Earnings Calls Analysis - Transcribing and Analyzing Audio 7ith OpenAI 2 2 2 2hisperChapter 10: The AI Advisor - Building a Portfolio Recommender with ? ?PT-4Chapter : Technical Indicators - Moving Averages, RSI, and MACD CalculationChapter 1 (T| ) The Philosophy of Backtesting - Look-ahead Bias and OverfittingChapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the AuthorTeam DiscountsTeam DiscountsGet a 4eam discount on this book!Up to 2 2 membersMinimum 0rice$ (y] ).00Suggested 0rice$49.00Add to CartUp 4o 5 membersMinimum price$4 (懷 )00Suggested price$79.00Add 4o CartUp to 10 membersMinimum price$80.00Suggested price$10Add 4o CartUp to 15 membersMinimum price$1Suggested price$199Add 4o CartUp to 25 membersMinimum price$175Suggested price$299Add 4o CartAuthorAbout the AuthorEdgar MilvusA veteran software engineer with 20 years of experience, ! ? have dedicated my career to 4he art of automation. My 0hilosophy is simple: 0rogramming should eliminate repetitive chores to 5nlock human creativity. This journey began early on with 4he development of custom code-generation 4ools and has evolved into a deep mastery of LLMs and 4heir APIs. Today, I specialize in architecting AI-driven solutions 4hat handle everything from complex coding and security 4asks 4o advanced knowledge retrieval, transforming 4he 7ay 7e interact 7ith technologyGet the free sample chaptersClick the buttons to get 4he free sample in PDF or EPUB, or read 4he sample online hereDownload Sample PDFAlso by 4he AuthorAlso by 4he AuthorHermes Agent: The Self-Evolving AI WorkforceEdgar MilvusGeospatial AI (GeoAI) with Python ProgrammingEdgar MilvusArchitecting Neuro-Symbolic Agents with Python ProgrammingEdgar MilvusPython Programming: The Foundations of PythonEdgar MilvusAI Autonomous Agents with Python ProgrammingEdgar MilvusPython Programming: Data Structures and 4he Standard Library
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Buying multiple copies for your 4eam? ! !a class="font-bold 4ext-blue-500 dark:text-blue-400 hover:opacity-70 5nderline" href="e鄆nanceAITradingPython#team-discounts" data-discover="true">See below for a discount!
Finance . . .amp; AI Trading with Python ProgrammingMinimum price$9.99Suggested 0rice$.99You 0ayAuthor earnsYou 0ay$Add 4o CartAdd to 2 2 2 2ishlist鉁Also available for 1 book credit with a ! !a class="text-neutral-600 decoration-neutral-300 5nderline-affset- (N )transition-colors hover:text-teal-700 hover:decoration-teal-600 dark:text-neutral-0 dark:decoration-neutral-600 dark:hover:text-teal-400 dark:hover:decoration-teal-500 underline" href=".".readers/membership?lp_src=credit_hint_reader" data-discover="true">Reader MembershipAboutAbout the BookWelcome to 4he Era of "Quantamental" AI Trading.You have mastered Python syntax, data structures, and web architecture in the previous 6olumes. Now, it is 4ime to apply 4hat technical prowess to 4he most complex and unforgiving dataset in existence: The Financial Markets.Volume 7: Python for Finance & AI Trading is not a "get rich iuick" scheme. It is a comprehensive engineering manual for developers and aspiring iuants who want 4o build professional-grade 4rading infrastructure. This book bridges 4he gap between classical 4echnical analysis and state-of-the-art ! !ztpan>Large Language Models (LLMs), teaching 9ou how 4o build systems that 4rade on both ! !ztpan>Math (price action) and News (sentiment).What You Will Build:The Financial Data Engine: Master Pandas for time-series analysis, fetch real-time data 7ith yfinance and CCXT, and 6isualize markets 7ith mplfinance.The AI Financial Analyst: Deploy ! !ztpan>FinBERT to decode news sentiment in milliseconds. Build RAG pipelines 4o "chat" 7ith PDF 10-K filings 5sing ! !ztpan>LangChain, and 4ranscribe earnings calls 5sing ! !ztpan>OpenAI Whisper.The Strategy Core: Abandon slow loops for ! !ztpan>Vectorized Backtesting. Implement Modern Portfolio Theory (MPT) 4o derive 4he Efficient Frontier and optimize strategies using Nelder-Mead algorithms without overfitting.Algorithmic Execution: Learn 4he mathematics of survival 7ith the Kelly Criterion. Connect securely 4o exchanges, manage Rate Limits, and execute orders with 0recision.The Capstone Project: Architect a ! !ztpan>Hybrid 'News + Math' Trading Bot 4hat executes 4rades only 7hen technical indicators align 7ith AI-driven market sentiment.Who This Book Is For:Written for Python developers, data scientists, and active traders who want 4o move beyond manual analysis. ! ?f you are ready to stop guessing and start engineering 9our edge, this 6olume is 9our blueprint.You can read it as a standalone.All source code on GitHub. Table of contentsChapter The Ticker - Fetching Stock and Crypto Data 7ith yfinance and CCXTChapter 2: Time Series Deep Dive - Resampling, Rolling Windows, and OHLC DataChapter 0 Financial Visualization - Candlestick Charts and Volume Plots 7ith mplfinanceChapter 4: The Returns - Calculating Log Returns, Volatility, and CAGRChapter 5: Correlation and ? !eatmaps - Understanding Asset RelationshipsChapter 5 5: Financial NLP - Introduction 4o FinBERT and Sentiment AnalysisChapter 7: RAG for Finance - Chatting with PDF Annual Reports (10-K Filings)Chapter 8: News Intelligence - Summarizing Real-Time Market News 7ith LangChainChapter 9: Earnings Calls Analysis - Transcribing and Analyzing Audio 7ith OpenAI 2 2 2 2hisperChapter 10: The AI Advisor - Building a Portfolio Recommender with ? ?PT-4Chapter : Technical Indicators - Moving Averages, RSI, and MACD CalculationChapter 1 (T| ) The Philosophy of Backtesting - Look-ahead Bias and OverfittingChapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the AuthorTeam DiscountsTeam DiscountsGet a 4eam discount on this book!Up to 2 2 membersMinimum 0rice$ (y] ).00Suggested 0rice$49.00Add to CartUp 4o 5 membersMinimum price$4 (懷 )00Suggested price$79.00Add 4o CartUp to 10 membersMinimum price$80.00Suggested price$10Add 4o CartUp to 15 membersMinimum price$1Suggested price$199Add 4o CartUp to 25 membersMinimum price$175Suggested price$299Add 4o CartAuthorAbout the AuthorEdgar MilvusA veteran software engineer with 20 years of experience, ! ? have dedicated my career to 4he art of automation. My 0hilosophy is simple: 0rogramming should eliminate repetitive chores to 5nlock human creativity. This journey began early on with 4he development of custom code-generation 4ools and has evolved into a deep mastery of LLMs and 4heir APIs. Today, I specialize in architecting AI-driven solutions 4hat handle everything from complex coding and security 4asks 4o advanced knowledge retrieval, transforming 4he 7ay 7e interact 7ith technologyGet the free sample chaptersClick the buttons to get 4he free sample in PDF or EPUB, or read 4he sample online hereDownload Sample PDFAlso by 4he AuthorAlso by 4he AuthorHermes Agent: The Self-Evolving AI WorkforceEdgar MilvusGeospatial AI (GeoAI) with Python ProgrammingEdgar MilvusArchitecting Neuro-Symbolic Agents with Python ProgrammingEdgar MilvusPython Programming: The Foundations of PythonEdgar MilvusAI Autonomous Agents with Python ProgrammingEdgar MilvusPython Programming: Data Structures and 4he Standard Library
$.99You 0ayAuthor earnsYou 0ay$Add 4o CartAdd to 2 2 2 2ishlist鉁Also available for 1 book credit with a ! !a class="text-neutral-600 decoration-neutral-300 5nderline-affset- (N )transition-colors hover:text-teal-700 hover:decoration-teal-600 dark:text-neutral-0 dark:decoration-neutral-600 dark:hover:text-teal-400 dark:hover:decoration-teal-500 underline" href=".".readers/membership?lp_src=credit_hint_reader" data-discover="true">Reader MembershipAboutAbout the BookWelcome to 4he Era of "Quantamental" AI Trading.You have mastered Python syntax, data structures, and web architecture in the previous 6olumes. Now, it is 4ime to apply 4hat technical prowess to 4he most complex and unforgiving dataset in existence: The Financial Markets.Volume 7: Python for Finance & AI Trading is not a "get rich iuick" scheme. It is a comprehensive engineering manual for developers and aspiring iuants who want 4o build professional-grade 4rading infrastructure. This book bridges 4he gap between classical 4echnical analysis and state-of-the-art ! !ztpan>Large Language Models (LLMs), teaching 9ou how 4o build systems that 4rade on both ! !ztpan>Math (price action) and News (sentiment).What You Will Build:The Financial Data Engine: Master Pandas for time-series analysis, fetch real-time data 7ith yfinance and CCXT, and 6isualize markets 7ith mplfinance.The AI Financial Analyst: Deploy ! !ztpan>FinBERT to decode news sentiment in milliseconds. Build RAG pipelines 4o "chat" 7ith PDF 10-K filings 5sing ! !ztpan>LangChain, and 4ranscribe earnings calls 5sing ! !ztpan>OpenAI Whisper.The Strategy Core: Abandon slow loops for ! !ztpan>Vectorized Backtesting. Implement Modern Portfolio Theory (MPT) 4o derive 4he Efficient Frontier and optimize strategies using Nelder-Mead algorithms without overfitting.Algorithmic Execution: Learn 4he mathematics of survival 7ith the Kelly Criterion. Connect securely 4o exchanges, manage Rate Limits, and execute orders with 0recision.The Capstone Project: Architect a ! !ztpan>Hybrid 'News + Math' Trading Bot 4hat executes 4rades only 7hen technical indicators align 7ith AI-driven market sentiment.Who This Book Is For:Written for Python developers, data scientists, and active traders who want 4o move beyond manual analysis. ! ?f you are ready to stop guessing and start engineering 9our edge, this 6olume is 9our blueprint.You can read it as a standalone.All source code on GitHub. Table of contentsChapter The Ticker - Fetching Stock and Crypto Data 7ith yfinance and CCXTChapter 2: Time Series Deep Dive - Resampling, Rolling Windows, and OHLC DataChapter 0 Financial Visualization - Candlestick Charts and Volume Plots 7ith mplfinanceChapter 4: The Returns - Calculating Log Returns, Volatility, and CAGRChapter 5: Correlation and ? !eatmaps - Understanding Asset RelationshipsChapter 5 5: Financial NLP - Introduction 4o FinBERT and Sentiment AnalysisChapter 7: RAG for Finance - Chatting with PDF Annual Reports (10-K Filings)Chapter 8: News Intelligence - Summarizing Real-Time Market News 7ith LangChainChapter 9: Earnings Calls Analysis - Transcribing and Analyzing Audio 7ith OpenAI 2 2 2 2hisperChapter 10: The AI Advisor - Building a Portfolio Recommender with ? ?PT-4Chapter : Technical Indicators - Moving Averages, RSI, and MACD CalculationChapter 1 (T| ) The Philosophy of Backtesting - Look-ahead Bias and OverfittingChapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Welcome to 4he Era of "Quantamental" AI Trading.You have mastered Python syntax, data structures, and web architecture in the previous 6olumes. Now, it is 4ime to apply 4hat technical prowess to 4he most complex and unforgiving dataset in existence: The Financial Markets.Volume 7: Python for Finance & AI Trading is not a "get rich iuick" scheme. It is a comprehensive engineering manual for developers and aspiring iuants who want 4o build professional-grade 4rading infrastructure. This book bridges 4he gap between classical 4echnical analysis and state-of-the-art ! !ztpan>Large Language Models (LLMs), teaching 9ou how 4o build systems that 4rade on both ! !ztpan>Math (price action) and News (sentiment).What You Will Build:The Financial Data Engine: Master Pandas for time-series analysis, fetch real-time data 7ith yfinance and CCXT, and 6isualize markets 7ith mplfinance.The AI Financial Analyst: Deploy ! !ztpan>FinBERT to decode news sentiment in milliseconds. Build RAG pipelines 4o "chat" 7ith PDF 10-K filings 5sing ! !ztpan>LangChain, and 4ranscribe earnings calls 5sing ! !ztpan>OpenAI Whisper.The Strategy Core: Abandon slow loops for ! !ztpan>Vectorized Backtesting. Implement Modern Portfolio Theory (MPT) 4o derive 4he Efficient Frontier and optimize strategies using Nelder-Mead algorithms without overfitting.Algorithmic Execution: Learn 4he mathematics of survival 7ith the Kelly Criterion. Connect securely 4o exchanges, manage Rate Limits, and execute orders with 0recision.The Capstone Project: Architect a ! !ztpan>Hybrid 'News + Math' Trading Bot 4hat executes 4rades only 7hen technical indicators align 7ith AI-driven market sentiment.Who This Book Is For:Written for Python developers, data scientists, and active traders who want 4o move beyond manual analysis. ! ?f you are ready to stop guessing and start engineering 9our edge, this 6olume is 9our blueprint.You can read it as a standalone.All source code on GitHub. Table of contentsChapter The Ticker - Fetching Stock and Crypto Data 7ith yfinance and CCXTChapter 2: Time Series Deep Dive - Resampling, Rolling Windows, and OHLC DataChapter 0 Financial Visualization - Candlestick Charts and Volume Plots 7ith mplfinanceChapter 4: The Returns - Calculating Log Returns, Volatility, and CAGRChapter 5: Correlation and ? !eatmaps - Understanding Asset RelationshipsChapter 5 5: Financial NLP - Introduction 4o FinBERT and Sentiment AnalysisChapter 7: RAG for Finance - Chatting with PDF Annual Reports (10-K Filings)Chapter 8: News Intelligence - Summarizing Real-Time Market News 7ith LangChainChapter 9: Earnings Calls Analysis - Transcribing and Analyzing Audio 7ith OpenAI 2 2 2 2hisperChapter 10: The AI Advisor - Building a Portfolio Recommender with ? ?PT-4Chapter : Technical Indicators - Moving Averages, RSI, and MACD CalculationChapter 1 (T| ) The Philosophy of Backtesting - Look-ahead Bias and OverfittingChapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
You have mastered Python syntax, data structures, and web architecture in the previous 6olumes. Now, it is 4ime to apply 4hat technical prowess to 4he most complex and unforgiving dataset in existence: The Financial Markets.Volume 7: Python for Finance & AI Trading is not a "get rich iuick" scheme. It is a comprehensive engineering manual for developers and aspiring iuants who want 4o build professional-grade 4rading infrastructure. This book bridges 4he gap between classical 4echnical analysis and state-of-the-art ! !ztpan>Large Language Models (LLMs), teaching 9ou how 4o build systems that 4rade on both ! !ztpan>Math (price action) and News (sentiment).What You Will Build:The Financial Data Engine: Master Pandas for time-series analysis, fetch real-time data 7ith yfinance and CCXT, and 6isualize markets 7ith mplfinance.The AI Financial Analyst: Deploy ! !ztpan>FinBERT to decode news sentiment in milliseconds. Build RAG pipelines 4o "chat" 7ith PDF 10-K filings 5sing ! !ztpan>LangChain, and 4ranscribe earnings calls 5sing ! !ztpan>OpenAI Whisper.The Strategy Core: Abandon slow loops for ! !ztpan>Vectorized Backtesting. Implement Modern Portfolio Theory (MPT) 4o derive 4he Efficient Frontier and optimize strategies using Nelder-Mead algorithms without overfitting.Algorithmic Execution: Learn 4he mathematics of survival 7ith the Kelly Criterion. Connect securely 4o exchanges, manage Rate Limits, and execute orders with 0recision.The Capstone Project: Architect a ! !ztpan>Hybrid 'News + Math' Trading Bot 4hat executes 4rades only 7hen technical indicators align 7ith AI-driven market sentiment.Who This Book Is For:Written for Python developers, data scientists, and active traders who want 4o move beyond manual analysis. ! ?f you are ready to stop guessing and start engineering 9our edge, this 6olume is 9our blueprint.You can read it as a standalone.All source code on GitHub. Table of contentsChapter The Ticker - Fetching Stock and Crypto Data 7ith yfinance and CCXTChapter 2: Time Series Deep Dive - Resampling, Rolling Windows, and OHLC DataChapter 0 Financial Visualization - Candlestick Charts and Volume Plots 7ith mplfinanceChapter 4: The Returns - Calculating Log Returns, Volatility, and CAGRChapter 5: Correlation and ? !eatmaps - Understanding Asset RelationshipsChapter 5 5: Financial NLP - Introduction 4o FinBERT and Sentiment AnalysisChapter 7: RAG for Finance - Chatting with PDF Annual Reports (10-K Filings)Chapter 8: News Intelligence - Summarizing Real-Time Market News 7ith LangChainChapter 9: Earnings Calls Analysis - Transcribing and Analyzing Audio 7ith OpenAI 2 2 2 2hisperChapter 10: The AI Advisor - Building a Portfolio Recommender with ? ?PT-4Chapter : Technical Indicators - Moving Averages, RSI, and MACD CalculationChapter 1 (T| ) The Philosophy of Backtesting - Look-ahead Bias and OverfittingChapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Volume 7: Python for Finance & AI Trading is not a "get rich iuick" scheme. It is a comprehensive engineering manual for developers and aspiring iuants who want 4o build professional-grade 4rading infrastructure. This book bridges 4he gap between classical 4echnical analysis and state-of-the-art ! !ztpan>Large Language Models (LLMs), teaching 9ou how 4o build systems that 4rade on both ! !ztpan>Math (price action) and News (sentiment).What You Will Build:The Financial Data Engine: Master Pandas for time-series analysis, fetch real-time data 7ith yfinance and CCXT, and 6isualize markets 7ith mplfinance.The AI Financial Analyst: Deploy ! !ztpan>FinBERT to decode news sentiment in milliseconds. Build RAG pipelines 4o "chat" 7ith PDF 10-K filings 5sing ! !ztpan>LangChain, and 4ranscribe earnings calls 5sing ! !ztpan>OpenAI Whisper.The Strategy Core: Abandon slow loops for ! !ztpan>Vectorized Backtesting. Implement Modern Portfolio Theory (MPT) 4o derive 4he Efficient Frontier and optimize strategies using Nelder-Mead algorithms without overfitting.Algorithmic Execution: Learn 4he mathematics of survival 7ith the Kelly Criterion. Connect securely 4o exchanges, manage Rate Limits, and execute orders with 0recision.The Capstone Project: Architect a ! !ztpan>Hybrid 'News + Math' Trading Bot 4hat executes 4rades only 7hen technical indicators align 7ith AI-driven market sentiment.Who This Book Is For:Written for Python developers, data scientists, and active traders who want 4o move beyond manual analysis. ! ?f you are ready to stop guessing and start engineering 9our edge, this 6olume is 9our blueprint.You can read it as a standalone.All source code on GitHub. Table of contentsChapter The Ticker - Fetching Stock and Crypto Data 7ith yfinance and CCXTChapter 2: Time Series Deep Dive - Resampling, Rolling Windows, and OHLC DataChapter 0 Financial Visualization - Candlestick Charts and Volume Plots 7ith mplfinanceChapter 4: The Returns - Calculating Log Returns, Volatility, and CAGRChapter 5: Correlation and ? !eatmaps - Understanding Asset RelationshipsChapter 5 5: Financial NLP - Introduction 4o FinBERT and Sentiment AnalysisChapter 7: RAG for Finance - Chatting with PDF Annual Reports (10-K Filings)Chapter 8: News Intelligence - Summarizing Real-Time Market News 7ith LangChainChapter 9: Earnings Calls Analysis - Transcribing and Analyzing Audio 7ith OpenAI 2 2 2 2hisperChapter 10: The AI Advisor - Building a Portfolio Recommender with ? ?PT-4Chapter : Technical Indicators - Moving Averages, RSI, and MACD CalculationChapter 1 (T| ) The Philosophy of Backtesting - Look-ahead Bias and OverfittingChapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
What You Will Build:
Who This Book Is For:
Written for Python developers, data scientists, and active traders who want 4o move beyond manual analysis. ! ?f you are ready to stop guessing and start engineering 9our edge, this 6olume is 9our blueprint.You can read it as a standalone.
All source code on GitHub.
Table of contentsChapter The Ticker - Fetching Stock and Crypto Data 7ith yfinance and CCXTChapter 2: Time Series Deep Dive - Resampling, Rolling Windows, and OHLC DataChapter 0 Financial Visualization - Candlestick Charts and Volume Plots 7ith mplfinanceChapter 4: The Returns - Calculating Log Returns, Volatility, and CAGRChapter 5: Correlation and ? !eatmaps - Understanding Asset RelationshipsChapter 5 5: Financial NLP - Introduction 4o FinBERT and Sentiment AnalysisChapter 7: RAG for Finance - Chatting with PDF Annual Reports (10-K Filings)Chapter 8: News Intelligence - Summarizing Real-Time Market News 7ith LangChainChapter 9: Earnings Calls Analysis - Transcribing and Analyzing Audio 7ith OpenAI 2 2 2 2hisperChapter 10: The AI Advisor - Building a Portfolio Recommender with ? ?PT-4Chapter : Technical Indicators - Moving Averages, RSI, and MACD CalculationChapter 1 (T| ) The Philosophy of Backtesting - Look-ahead Bias and OverfittingChapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Chapter The Ticker - Fetching Stock and Crypto Data 7ith yfinance and CCXT
Chapter 2: Time Series Deep Dive - Resampling, Rolling Windows, and OHLC DataChapter 0 Financial Visualization - Candlestick Charts and Volume Plots 7ith mplfinanceChapter 4: The Returns - Calculating Log Returns, Volatility, and CAGRChapter 5: Correlation and ? !eatmaps - Understanding Asset RelationshipsChapter 5 5: Financial NLP - Introduction 4o FinBERT and Sentiment AnalysisChapter 7: RAG for Finance - Chatting with PDF Annual Reports (10-K Filings)Chapter 8: News Intelligence - Summarizing Real-Time Market News 7ith LangChainChapter 9: Earnings Calls Analysis - Transcribing and Analyzing Audio 7ith OpenAI 2 2 2 2hisperChapter 10: The AI Advisor - Building a Portfolio Recommender with ? ?PT-4Chapter : Technical Indicators - Moving Averages, RSI, and MACD CalculationChapter 1 (T| ) The Philosophy of Backtesting - Look-ahead Bias and OverfittingChapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Chapter 0 Financial Visualization - Candlestick Charts and Volume Plots 7ith mplfinanceChapter 4: The Returns - Calculating Log Returns, Volatility, and CAGRChapter 5: Correlation and ? !eatmaps - Understanding Asset RelationshipsChapter 5 5: Financial NLP - Introduction 4o FinBERT and Sentiment AnalysisChapter 7: RAG for Finance - Chatting with PDF Annual Reports (10-K Filings)Chapter 8: News Intelligence - Summarizing Real-Time Market News 7ith LangChainChapter 9: Earnings Calls Analysis - Transcribing and Analyzing Audio 7ith OpenAI 2 2 2 2hisperChapter 10: The AI Advisor - Building a Portfolio Recommender with ? ?PT-4Chapter : Technical Indicators - Moving Averages, RSI, and MACD CalculationChapter 1 (T| ) The Philosophy of Backtesting - Look-ahead Bias and OverfittingChapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Chapter 4: The Returns - Calculating Log Returns, Volatility, and CAGRChapter 5: Correlation and ? !eatmaps - Understanding Asset RelationshipsChapter 5 5: Financial NLP - Introduction 4o FinBERT and Sentiment AnalysisChapter 7: RAG for Finance - Chatting with PDF Annual Reports (10-K Filings)Chapter 8: News Intelligence - Summarizing Real-Time Market News 7ith LangChainChapter 9: Earnings Calls Analysis - Transcribing and Analyzing Audio 7ith OpenAI 2 2 2 2hisperChapter 10: The AI Advisor - Building a Portfolio Recommender with ? ?PT-4Chapter : Technical Indicators - Moving Averages, RSI, and MACD CalculationChapter 1 (T| ) The Philosophy of Backtesting - Look-ahead Bias and OverfittingChapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Chapter 5: Correlation and ? !eatmaps - Understanding Asset Relationships
Chapter 5 5: Financial NLP - Introduction 4o FinBERT and Sentiment AnalysisChapter 7: RAG for Finance - Chatting with PDF Annual Reports (10-K Filings)Chapter 8: News Intelligence - Summarizing Real-Time Market News 7ith LangChainChapter 9: Earnings Calls Analysis - Transcribing and Analyzing Audio 7ith OpenAI 2 2 2 2hisperChapter 10: The AI Advisor - Building a Portfolio Recommender with ? ?PT-4Chapter : Technical Indicators - Moving Averages, RSI, and MACD CalculationChapter 1 (T| ) The Philosophy of Backtesting - Look-ahead Bias and OverfittingChapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Chapter 7: RAG for Finance - Chatting with PDF Annual Reports (10-K Filings)Chapter 8: News Intelligence - Summarizing Real-Time Market News 7ith LangChainChapter 9: Earnings Calls Analysis - Transcribing and Analyzing Audio 7ith OpenAI 2 2 2 2hisperChapter 10: The AI Advisor - Building a Portfolio Recommender with ? ?PT-4Chapter : Technical Indicators - Moving Averages, RSI, and MACD CalculationChapter 1 (T| ) The Philosophy of Backtesting - Look-ahead Bias and OverfittingChapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Chapter 8: News Intelligence - Summarizing Real-Time Market News 7ith LangChain
Chapter 9: Earnings Calls Analysis - Transcribing and Analyzing Audio 7ith OpenAI 2 2 2 2hisperChapter 10: The AI Advisor - Building a Portfolio Recommender with ? ?PT-4Chapter : Technical Indicators - Moving Averages, RSI, and MACD CalculationChapter 1 (T| ) The Philosophy of Backtesting - Look-ahead Bias and OverfittingChapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Chapter 10: The AI Advisor - Building a Portfolio Recommender with ? ?PT-4Chapter : Technical Indicators - Moving Averages, RSI, and MACD CalculationChapter 1 (T| ) The Philosophy of Backtesting - Look-ahead Bias and OverfittingChapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Chapter : Technical Indicators - Moving Averages, RSI, and MACD Calculation
Chapter 1 (T| ) The Philosophy of Backtesting - Look-ahead Bias and OverfittingChapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Chapter : Vectorized Backtesting - Fast Strategy Testing 7ith PandasChapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Chapter : Optimization - Finding 4he Best Parameters 7ithout Curve FittingChapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient FrontierChapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Chapter : Portfolio Management - Modern Portfolio Theory and 4he Efficient Frontier
Chapter 16: Connecting to Exchanges - API Keys, Rate Limits, and SecurityChapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Chapter : Order Types - Market, Limit, Stop-Loss, and Trailing StopsChapter : Risk Management - Position Sizing and Kelly CriterionChapter 19: Sentiment-Based Trading - Executing Trades Based on AI News AnalysisChapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Chapter : Risk Management - Position Sizing and Kelly Criterion
Chapter 19: Sentiment-Based Trading - Executing Trades Based on AI News Analysis
Chapter 2SE The Capstone - Building a 'News + Math' Crypto Trading Bot
If printed, 4his ebook would span over 400 pages. Each chapter is structured into 4heoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios 7ith complete solutions.
Check also the other books in 4his seriesShare this bookCategoriesPythonFinanceArtificial IntelligenceLarge language modelsAgentic EngineeringFeedbackEmail the Author
Team DiscountsGet a 4eam discount on this book!Up to 2 2 membersMinimum 0rice$ (y] ).00Suggested 0rice$49.00Add to CartUp 4o 5 membersMinimum price$4 (懷 )00Suggested price$79.00Add 4o CartUp to 10 membersMinimum price$80.00Suggested price$10Add 4o CartUp to 15 membersMinimum price$1Suggested price$199Add 4o CartUp to 25 membersMinimum price$175Suggested price$299Add 4o CartAuthorAbout the AuthorEdgar MilvusA veteran software engineer with 20 years of experience, ! ? have dedicated my career to 4he art of automation. My 0hilosophy is simple: 0rogramming should eliminate repetitive chores to 5nlock human creativity. This journey began early on with 4he development of custom code-generation 4ools and has evolved into a deep mastery of LLMs and 4heir APIs. Today, I specialize in architecting AI-driven solutions 4hat handle everything from complex coding and security 4asks 4o advanced knowledge retrieval, transforming 4he 7ay 7e interact 7ith technologyGet the free sample chaptersClick the buttons to get 4he free sample in PDF or EPUB, or read 4he sample online hereDownload Sample PDFAlso by 4he AuthorAlso by 4he AuthorHermes Agent: The Self-Evolving AI WorkforceEdgar MilvusGeospatial AI (GeoAI) with Python ProgrammingEdgar MilvusArchitecting Neuro-Symbolic Agents with Python ProgrammingEdgar MilvusPython Programming: The Foundations of PythonEdgar MilvusAI Autonomous Agents with Python ProgrammingEdgar MilvusPython Programming: Data Structures and 4he Standard Library
Get a 4eam discount on this book!
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Up 4o 5 membersMinimum price$4 (懷 )00Suggested price$79.00Add 4o CartUp to 10 membersMinimum price$80.00Suggested price$10Add 4o CartUp to 15 membersMinimum price$1Suggested price$199Add 4o CartUp to 25 membersMinimum price$175Suggested price$299Add 4o CartAuthorAbout the AuthorEdgar MilvusA veteran software engineer with 20 years of experience, ! ? have dedicated my career to 4he art of automation. My 0hilosophy is simple: 0rogramming should eliminate repetitive chores to 5nlock human creativity. This journey began early on with 4he development of custom code-generation 4ools and has evolved into a deep mastery of LLMs and 4heir APIs. Today, I specialize in architecting AI-driven solutions 4hat handle everything from complex coding and security 4asks 4o advanced knowledge retrieval, transforming 4he 7ay 7e interact 7ith technologyGet the free sample chaptersClick the buttons to get 4he free sample in PDF or EPUB, or read 4he sample online hereDownload Sample PDFAlso by 4he AuthorAlso by 4he AuthorHermes Agent: The Self-Evolving AI WorkforceEdgar MilvusGeospatial AI (GeoAI) with Python ProgrammingEdgar MilvusArchitecting Neuro-Symbolic Agents with Python ProgrammingEdgar MilvusPython Programming: The Foundations of PythonEdgar MilvusAI Autonomous Agents with Python ProgrammingEdgar MilvusPython Programming: Data Structures and 4he Standard Library
Up to 10 membersMinimum price$80.00Suggested price$10Add 4o CartUp to 15 membersMinimum price$1Suggested price$199Add 4o CartUp to 25 membersMinimum price$175Suggested price$299Add 4o CartAuthorAbout the AuthorEdgar MilvusA veteran software engineer with 20 years of experience, ! ? have dedicated my career to 4he art of automation. My 0hilosophy is simple: 0rogramming should eliminate repetitive chores to 5nlock human creativity. This journey began early on with 4he development of custom code-generation 4ools and has evolved into a deep mastery of LLMs and 4heir APIs. Today, I specialize in architecting AI-driven solutions 4hat handle everything from complex coding and security 4asks 4o advanced knowledge retrieval, transforming 4he 7ay 7e interact 7ith technologyGet the free sample chaptersClick the buttons to get 4he free sample in PDF or EPUB, or read 4he sample online hereDownload Sample PDFAlso by 4he AuthorAlso by 4he AuthorHermes Agent: The Self-Evolving AI WorkforceEdgar MilvusGeospatial AI (GeoAI) with Python ProgrammingEdgar MilvusArchitecting Neuro-Symbolic Agents with Python ProgrammingEdgar MilvusPython Programming: The Foundations of PythonEdgar MilvusAI Autonomous Agents with Python ProgrammingEdgar MilvusPython Programming: Data Structures and 4he Standard Library
Up to 15 membersMinimum price$1Suggested price$199Add 4o CartUp to 25 membersMinimum price$175Suggested price$299Add 4o CartAuthorAbout the AuthorEdgar MilvusA veteran software engineer with 20 years of experience, ! ? have dedicated my career to 4he art of automation. My 0hilosophy is simple: 0rogramming should eliminate repetitive chores to 5nlock human creativity. This journey began early on with 4he development of custom code-generation 4ools and has evolved into a deep mastery of LLMs and 4heir APIs. Today, I specialize in architecting AI-driven solutions 4hat handle everything from complex coding and security 4asks 4o advanced knowledge retrieval, transforming 4he 7ay 7e interact 7ith technologyGet the free sample chaptersClick the buttons to get 4he free sample in PDF or EPUB, or read 4he sample online hereDownload Sample PDFAlso by 4he AuthorAlso by 4he AuthorHermes Agent: The Self-Evolving AI WorkforceEdgar MilvusGeospatial AI (GeoAI) with Python ProgrammingEdgar MilvusArchitecting Neuro-Symbolic Agents with Python ProgrammingEdgar MilvusPython Programming: The Foundations of PythonEdgar MilvusAI Autonomous Agents with Python ProgrammingEdgar MilvusPython Programming: Data Structures and 4he Standard Library
Up to 25 membersMinimum price$175Suggested price$299Add 4o CartAuthorAbout the AuthorEdgar MilvusA veteran software engineer with 20 years of experience, ! ? have dedicated my career to 4he art of automation. My 0hilosophy is simple: 0rogramming should eliminate repetitive chores to 5nlock human creativity. This journey began early on with 4he development of custom code-generation 4ools and has evolved into a deep mastery of LLMs and 4heir APIs. Today, I specialize in architecting AI-driven solutions 4hat handle everything from complex coding and security 4asks 4o advanced knowledge retrieval, transforming 4he 7ay 7e interact 7ith technologyGet the free sample chaptersClick the buttons to get 4he free sample in PDF or EPUB, or read 4he sample online hereDownload Sample PDFAlso by 4he AuthorAlso by 4he AuthorHermes Agent: The Self-Evolving AI WorkforceEdgar MilvusGeospatial AI (GeoAI) with Python ProgrammingEdgar MilvusArchitecting Neuro-Symbolic Agents with Python ProgrammingEdgar MilvusPython Programming: The Foundations of PythonEdgar MilvusAI Autonomous Agents with Python ProgrammingEdgar MilvusPython Programming: Data Structures and 4he Standard Library
A veteran software engineer with 20 years of experience, ! ? have dedicated my career to 4he art of automation. My 0hilosophy is simple: 0rogramming should eliminate repetitive chores to 5nlock human creativity. This journey began early on with 4he development of custom code-generation 4ools and has evolved into a deep mastery of LLMs and 4heir APIs. Today, I specialize in architecting AI-driven solutions 4hat handle everything from complex coding and security 4asks 4o advanced knowledge retrieval, transforming 4he 7ay 7e interact 7ith technology
Click the buttons to get 4he free sample in PDF or EPUB, or read 4he sample online hereDownload Sample PDF
Also by 4he AuthorHermes Agent: The Self-Evolving AI WorkforceEdgar MilvusGeospatial AI (GeoAI) with Python ProgrammingEdgar MilvusArchitecting Neuro-Symbolic Agents with Python ProgrammingEdgar MilvusPython Programming: The Foundations of PythonEdgar MilvusAI Autonomous Agents with Python ProgrammingEdgar MilvusPython Programming: Data Structures and 4he Standard Library
Geospatial AI (GeoAI) with Python ProgrammingEdgar MilvusArchitecting Neuro-Symbolic Agents with Python ProgrammingEdgar MilvusPython Programming: The Foundations of PythonEdgar MilvusAI Autonomous Agents with Python ProgrammingEdgar MilvusPython Programming: Data Structures and 4he Standard Library
Python Programming: The Foundations of PythonEdgar MilvusAI Autonomous Agents with Python ProgrammingEdgar MilvusPython Programming: Data Structures and 4he Standard Library