Hands-On AI Trading with Python, QuantConnect, and AWS.:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Newark
John Wiley & Sons, Incorporated
2025
|
| Ausgabe: | 1st ed |
| Links: | https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=31889396 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Umfang: | 1 Online-Ressource (410 Seiten) |
| ISBN: | 9781394267668 |
Internformat
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| 245 | 1 | 0 | |a Hands-On AI Trading with Python, QuantConnect, and AWS. |
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| 264 | 4 | |c ©2025 | |
| 300 | |a 1 Online-Ressource (410 Seiten) | ||
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| 505 | 8 | |a Cover -- Title Page -- Copyright -- Contents -- Biographies -- Preface: QuantConnect -- Introduction -- Part I: Foundations of Capital Markets and Quantitative Trading -- Chapter 1: Foundations of Capital Markets -- Market Mechanics -- Market Participants -- Trading Is the "Play -- The Stage and Basic Rules of Trading-The Limit Order Book -- Actors-Liquidity Trader, Market Maker, and Informed Trader -- Liquidity Trader -- Market Maker -- Informed Trader -- AI Actors Wanted -- Data and Data Feeds -- Custom and Alternative Data -- Brokerages and Transaction Costs -- Transaction Costs -- Security Identifiers -- Assets and Derivatives -- US Equities -- US Equity Options -- Index Options -- US Futures -- Cryptocurrency -- Chapter 2: Foundations of Quantitative Trading -- Research Process -- Research -- Backtesting -- Parameter Optimization -- Paper and Live Trading -- Testing and Debugging Tools -- Debuggers -- Logging -- Charting -- Object Store -- Coding Process -- Time and Look-ahead Bias -- Look-ahead Bias -- Market Hours and Scheduling -- Strategy Styles -- Trading Signals -- Allocating Capital -- Regimes and Portfolios of Strategies -- Parameter Sensitivity Testing and Optimization -- 1. Remove -- 2. Replace -- 3. Reduce -- Parameter Sensitivity Testing -- Margin Modeling -- Equities -- Equity Options -- Futures -- Diversification and Asset Selection -- Fundamental Asset Selection -- ETF Constituents Asset Selection -- Dollar-Volume Asset Selection -- Universe Settings -- Indicators and Other Data Transformations -- Automatic Indicators -- Manual Indicators -- Indicator Warm Up -- Storing Objects -- Indicator Events -- Sourcing Ideas -- Hypothesis-driven Testing -- Data Driven Investing -- Quantpedia -- QuantConnect Research and Strategy Explorer -- Part II: Foundations of AI and ML in Algorithmic Trading | |
| 505 | 8 | |a Step-by-step Guide for AI-based Algorithmic Trading -- Chapter 3: Step 1: Problem Definition -- Chapter 4: Step 2: Dataset Preparation -- Data Collection -- Exploratory Data Analysis -- Data Preprocessing -- Handling Missing Data -- Handling Outliers -- Feature Engineering -- Normalization and Standardization of Features -- Transforming Time Series Features to Stationary -- Identification of Cointegrated Time Series with Engle-Granger Test -- Feature Selection -- Correlation Analysis -- Feature Importance Analysis -- Auto-identification of Features -- Dimensionality Reduction/Principal Component Analysis -- Splitting of Dataset into Training, Testing, and Possibly Validation Sets -- How to Split Your Data -- Chapter 5: Step 3: Model Choice, Training, and Application -- Regression -- Linear Regression -- Polynomial Regression -- LASSO Regression -- Ridge Regression -- Markov Switching Dynamic Regression -- Decision Tree Regression -- Support Vector Machines Regression with Wavelet Forecasting -- Classification -- Multiclass Random Forest Model -- Logistic Regression -- Hidden Markov Models -- Gaussian Naive Bayes -- Convolutional Neural Networks -- Ranking -- LGBRanker Ranking -- Clustering -- OPTICS Clustering -- Language Models -- OpenAI Language Model -- Amazon Chronos Model -- FinBERT Model -- Part III: Advanced Applications of AI in Trading and Risk Management -- Getting Started with Source Code -- Chapter 6: Applied Machine Learning -- Example 1-ML Trend Scanning with MLFinlab -- Example 2-Factor Preprocessing Techniques for Regime Detection -- Example 3-Reversion vs. Trending: Strategy Selection by Classification -- Example 4-Alpha by Hidden Markov Models -- Example 5-FX SVM Wavelet Forecasting -- Example 6-Dividend Harvesting Selection of High-Yield Assets -- Example 7-Effect of Positive-Negative Splits | |
| 505 | 8 | |a Example 8-Stop Loss Based on Historical Volatility and Drawdown Recovery -- Example 9-ML Trading Pairs Selection -- Example 10-Stock Selection through Clustering Fundamental Data -- Example 11-Inverse Volatility Rank and Allocate to Future Contracts -- Example 12-Trading Costs Optimization -- Example 13-PCA Statistical Arbitrage Mean Reversion -- Example 14-Temporal CNN Prediction -- Example 15-Gaussian Classifier for Direction Prediction -- Example 16-LLM Summarization of Tiingo News Articles -- Example 17-Head Shoulders Pattern Matching with CNN -- Example 18-Amazon Chronos Model -- Example 19-FinBERT Model -- Chapter 7: Better Hedging with Reinforcement Learning -- Introduction -- A New AI Trading Assistant -- Continuous Hedging Is Not Required -- Machine Learning Comes to the Rescue -- A Simplified but Effective Reinforcement Learning Approach -- Overview of the Reinforcement Learning -- Identification -- Simulation -- Ref inement Training on Actual Market Data -- Testing and Implementation -- Implementation on QuantConnect -- Primary Research Notebook -- The Policy Network -- Model Functions -- Fine-tuning with Market Data -- Results -- Conclusion -- Chapter 8: AI for Risk Management and Optimization -- What Is Corrective AI and Conditional Parameter Optimization? -- Feature Engineering -- Applying Corrective AI to Daily Seasonal Forex Trading -- What Is Conditional Parameter Optimization? -- Applying Conditional Parameter Optimization to an ETF Strategy -- Unconditional vs. Conditional Parameter Optimizations -- Performance Comparisons -- Conditional Portfolio Optimization -- Regime Changes Obliterate Traditional Portfolio Optimization Methods -- Learning to Optimize -- Ranking Is Easier Than Predicting -- The Fama-French Lineage -- Comparison with Conventional Optimization Methods -- Model Tactical Asset Allocation Portfolio | |
| 505 | 8 | |a CPO Software-as-a-Service -- Conclusion -- Definitions of Spread_EMA & -- Spread_VAR -- Chapter 9: Application of Large Language Models and Generative AI in Trading -- Role of Generative AI in Creating Alpha -- Selecting an LLM for Building a Generative AI Application -- Prompt Engineering -- Prompt Engineering in Practice -- Addressing Model "Hallucination -- Question Answering Using a Retrieval Augmented Application in SageMaker Canvas -- RAG Application Costs and Optimization Techniques -- Testing Our Infrastructure -- Summarization -- Useful AI Platforms and Services -- ChatGPT -- Gemini -- Bedrock -- SageMaker -- Q Business -- References -- Subject Index -- Code Index -- EULA. | |
| 700 | 1 | |a Chan, Ernest P. |e Sonstige |4 oth | |
| 700 | 1 | |a Broad, Jared |e Sonstige |4 oth | |
| 700 | 1 | |a Sun, Philip |e Sonstige |4 oth | |
| 700 | 1 | |a Singh, Vivek |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Pik, Jiri |t Hands-On AI Trading with Python, QuantConnect, and AWS |d Newark : John Wiley & Sons, Incorporated,c2025 |z 9781394268436 |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035510545 | |
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Datensatz im Suchindex
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| adam_text | |
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| author | Pik, Jiri |
| author_facet | Pik, Jiri |
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| author_sort | Pik, Jiri |
| author_variant | j p jp |
| building | Verbundindex |
| bvnumber | BV050174665 |
| contents | Cover -- Title Page -- Copyright -- Contents -- Biographies -- Preface: QuantConnect -- Introduction -- Part I: Foundations of Capital Markets and Quantitative Trading -- Chapter 1: Foundations of Capital Markets -- Market Mechanics -- Market Participants -- Trading Is the "Play -- The Stage and Basic Rules of Trading-The Limit Order Book -- Actors-Liquidity Trader, Market Maker, and Informed Trader -- Liquidity Trader -- Market Maker -- Informed Trader -- AI Actors Wanted -- Data and Data Feeds -- Custom and Alternative Data -- Brokerages and Transaction Costs -- Transaction Costs -- Security Identifiers -- Assets and Derivatives -- US Equities -- US Equity Options -- Index Options -- US Futures -- Cryptocurrency -- Chapter 2: Foundations of Quantitative Trading -- Research Process -- Research -- Backtesting -- Parameter Optimization -- Paper and Live Trading -- Testing and Debugging Tools -- Debuggers -- Logging -- Charting -- Object Store -- Coding Process -- Time and Look-ahead Bias -- Look-ahead Bias -- Market Hours and Scheduling -- Strategy Styles -- Trading Signals -- Allocating Capital -- Regimes and Portfolios of Strategies -- Parameter Sensitivity Testing and Optimization -- 1. Remove -- 2. Replace -- 3. Reduce -- Parameter Sensitivity Testing -- Margin Modeling -- Equities -- Equity Options -- Futures -- Diversification and Asset Selection -- Fundamental Asset Selection -- ETF Constituents Asset Selection -- Dollar-Volume Asset Selection -- Universe Settings -- Indicators and Other Data Transformations -- Automatic Indicators -- Manual Indicators -- Indicator Warm Up -- Storing Objects -- Indicator Events -- Sourcing Ideas -- Hypothesis-driven Testing -- Data Driven Investing -- Quantpedia -- QuantConnect Research and Strategy Explorer -- Part II: Foundations of AI and ML in Algorithmic Trading Step-by-step Guide for AI-based Algorithmic Trading -- Chapter 3: Step 1: Problem Definition -- Chapter 4: Step 2: Dataset Preparation -- Data Collection -- Exploratory Data Analysis -- Data Preprocessing -- Handling Missing Data -- Handling Outliers -- Feature Engineering -- Normalization and Standardization of Features -- Transforming Time Series Features to Stationary -- Identification of Cointegrated Time Series with Engle-Granger Test -- Feature Selection -- Correlation Analysis -- Feature Importance Analysis -- Auto-identification of Features -- Dimensionality Reduction/Principal Component Analysis -- Splitting of Dataset into Training, Testing, and Possibly Validation Sets -- How to Split Your Data -- Chapter 5: Step 3: Model Choice, Training, and Application -- Regression -- Linear Regression -- Polynomial Regression -- LASSO Regression -- Ridge Regression -- Markov Switching Dynamic Regression -- Decision Tree Regression -- Support Vector Machines Regression with Wavelet Forecasting -- Classification -- Multiclass Random Forest Model -- Logistic Regression -- Hidden Markov Models -- Gaussian Naive Bayes -- Convolutional Neural Networks -- Ranking -- LGBRanker Ranking -- Clustering -- OPTICS Clustering -- Language Models -- OpenAI Language Model -- Amazon Chronos Model -- FinBERT Model -- Part III: Advanced Applications of AI in Trading and Risk Management -- Getting Started with Source Code -- Chapter 6: Applied Machine Learning -- Example 1-ML Trend Scanning with MLFinlab -- Example 2-Factor Preprocessing Techniques for Regime Detection -- Example 3-Reversion vs. Trending: Strategy Selection by Classification -- Example 4-Alpha by Hidden Markov Models -- Example 5-FX SVM Wavelet Forecasting -- Example 6-Dividend Harvesting Selection of High-Yield Assets -- Example 7-Effect of Positive-Negative Splits Example 8-Stop Loss Based on Historical Volatility and Drawdown Recovery -- Example 9-ML Trading Pairs Selection -- Example 10-Stock Selection through Clustering Fundamental Data -- Example 11-Inverse Volatility Rank and Allocate to Future Contracts -- Example 12-Trading Costs Optimization -- Example 13-PCA Statistical Arbitrage Mean Reversion -- Example 14-Temporal CNN Prediction -- Example 15-Gaussian Classifier for Direction Prediction -- Example 16-LLM Summarization of Tiingo News Articles -- Example 17-Head Shoulders Pattern Matching with CNN -- Example 18-Amazon Chronos Model -- Example 19-FinBERT Model -- Chapter 7: Better Hedging with Reinforcement Learning -- Introduction -- A New AI Trading Assistant -- Continuous Hedging Is Not Required -- Machine Learning Comes to the Rescue -- A Simplified but Effective Reinforcement Learning Approach -- Overview of the Reinforcement Learning -- Identification -- Simulation -- Ref inement Training on Actual Market Data -- Testing and Implementation -- Implementation on QuantConnect -- Primary Research Notebook -- The Policy Network -- Model Functions -- Fine-tuning with Market Data -- Results -- Conclusion -- Chapter 8: AI for Risk Management and Optimization -- What Is Corrective AI and Conditional Parameter Optimization? -- Feature Engineering -- Applying Corrective AI to Daily Seasonal Forex Trading -- What Is Conditional Parameter Optimization? -- Applying Conditional Parameter Optimization to an ETF Strategy -- Unconditional vs. Conditional Parameter Optimizations -- Performance Comparisons -- Conditional Portfolio Optimization -- Regime Changes Obliterate Traditional Portfolio Optimization Methods -- Learning to Optimize -- Ranking Is Easier Than Predicting -- The Fama-French Lineage -- Comparison with Conventional Optimization Methods -- Model Tactical Asset Allocation Portfolio CPO Software-as-a-Service -- Conclusion -- Definitions of Spread_EMA & -- Spread_VAR -- Chapter 9: Application of Large Language Models and Generative AI in Trading -- Role of Generative AI in Creating Alpha -- Selecting an LLM for Building a Generative AI Application -- Prompt Engineering -- Prompt Engineering in Practice -- Addressing Model "Hallucination -- Question Answering Using a Retrieval Augmented Application in SageMaker Canvas -- RAG Application Costs and Optimization Techniques -- Testing Our Infrastructure -- Summarization -- Useful AI Platforms and Services -- ChatGPT -- Gemini -- Bedrock -- SageMaker -- Q Business -- References -- Subject Index -- Code Index -- EULA. |
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| edition | 1st ed |
| format | Electronic eBook |
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| id | DE-604.BV050174665 |
| illustrated | Not Illustrated |
| indexdate | 2025-02-19T09:01:37Z |
| institution | BVB |
| isbn | 9781394267668 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035510545 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (410 Seiten) |
| psigel | ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2025 |
| publishDateSearch | 2025 |
| publishDateSort | 2025 |
| publisher | John Wiley & Sons, Incorporated |
| record_format | marc |
| spelling | Pik, Jiri Verfasser aut Hands-On AI Trading with Python, QuantConnect, and AWS. 1st ed Newark John Wiley & Sons, Incorporated 2025 ©2025 1 Online-Ressource (410 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Cover -- Title Page -- Copyright -- Contents -- Biographies -- Preface: QuantConnect -- Introduction -- Part I: Foundations of Capital Markets and Quantitative Trading -- Chapter 1: Foundations of Capital Markets -- Market Mechanics -- Market Participants -- Trading Is the "Play -- The Stage and Basic Rules of Trading-The Limit Order Book -- Actors-Liquidity Trader, Market Maker, and Informed Trader -- Liquidity Trader -- Market Maker -- Informed Trader -- AI Actors Wanted -- Data and Data Feeds -- Custom and Alternative Data -- Brokerages and Transaction Costs -- Transaction Costs -- Security Identifiers -- Assets and Derivatives -- US Equities -- US Equity Options -- Index Options -- US Futures -- Cryptocurrency -- Chapter 2: Foundations of Quantitative Trading -- Research Process -- Research -- Backtesting -- Parameter Optimization -- Paper and Live Trading -- Testing and Debugging Tools -- Debuggers -- Logging -- Charting -- Object Store -- Coding Process -- Time and Look-ahead Bias -- Look-ahead Bias -- Market Hours and Scheduling -- Strategy Styles -- Trading Signals -- Allocating Capital -- Regimes and Portfolios of Strategies -- Parameter Sensitivity Testing and Optimization -- 1. Remove -- 2. Replace -- 3. Reduce -- Parameter Sensitivity Testing -- Margin Modeling -- Equities -- Equity Options -- Futures -- Diversification and Asset Selection -- Fundamental Asset Selection -- ETF Constituents Asset Selection -- Dollar-Volume Asset Selection -- Universe Settings -- Indicators and Other Data Transformations -- Automatic Indicators -- Manual Indicators -- Indicator Warm Up -- Storing Objects -- Indicator Events -- Sourcing Ideas -- Hypothesis-driven Testing -- Data Driven Investing -- Quantpedia -- QuantConnect Research and Strategy Explorer -- Part II: Foundations of AI and ML in Algorithmic Trading Step-by-step Guide for AI-based Algorithmic Trading -- Chapter 3: Step 1: Problem Definition -- Chapter 4: Step 2: Dataset Preparation -- Data Collection -- Exploratory Data Analysis -- Data Preprocessing -- Handling Missing Data -- Handling Outliers -- Feature Engineering -- Normalization and Standardization of Features -- Transforming Time Series Features to Stationary -- Identification of Cointegrated Time Series with Engle-Granger Test -- Feature Selection -- Correlation Analysis -- Feature Importance Analysis -- Auto-identification of Features -- Dimensionality Reduction/Principal Component Analysis -- Splitting of Dataset into Training, Testing, and Possibly Validation Sets -- How to Split Your Data -- Chapter 5: Step 3: Model Choice, Training, and Application -- Regression -- Linear Regression -- Polynomial Regression -- LASSO Regression -- Ridge Regression -- Markov Switching Dynamic Regression -- Decision Tree Regression -- Support Vector Machines Regression with Wavelet Forecasting -- Classification -- Multiclass Random Forest Model -- Logistic Regression -- Hidden Markov Models -- Gaussian Naive Bayes -- Convolutional Neural Networks -- Ranking -- LGBRanker Ranking -- Clustering -- OPTICS Clustering -- Language Models -- OpenAI Language Model -- Amazon Chronos Model -- FinBERT Model -- Part III: Advanced Applications of AI in Trading and Risk Management -- Getting Started with Source Code -- Chapter 6: Applied Machine Learning -- Example 1-ML Trend Scanning with MLFinlab -- Example 2-Factor Preprocessing Techniques for Regime Detection -- Example 3-Reversion vs. Trending: Strategy Selection by Classification -- Example 4-Alpha by Hidden Markov Models -- Example 5-FX SVM Wavelet Forecasting -- Example 6-Dividend Harvesting Selection of High-Yield Assets -- Example 7-Effect of Positive-Negative Splits Example 8-Stop Loss Based on Historical Volatility and Drawdown Recovery -- Example 9-ML Trading Pairs Selection -- Example 10-Stock Selection through Clustering Fundamental Data -- Example 11-Inverse Volatility Rank and Allocate to Future Contracts -- Example 12-Trading Costs Optimization -- Example 13-PCA Statistical Arbitrage Mean Reversion -- Example 14-Temporal CNN Prediction -- Example 15-Gaussian Classifier for Direction Prediction -- Example 16-LLM Summarization of Tiingo News Articles -- Example 17-Head Shoulders Pattern Matching with CNN -- Example 18-Amazon Chronos Model -- Example 19-FinBERT Model -- Chapter 7: Better Hedging with Reinforcement Learning -- Introduction -- A New AI Trading Assistant -- Continuous Hedging Is Not Required -- Machine Learning Comes to the Rescue -- A Simplified but Effective Reinforcement Learning Approach -- Overview of the Reinforcement Learning -- Identification -- Simulation -- Ref inement Training on Actual Market Data -- Testing and Implementation -- Implementation on QuantConnect -- Primary Research Notebook -- The Policy Network -- Model Functions -- Fine-tuning with Market Data -- Results -- Conclusion -- Chapter 8: AI for Risk Management and Optimization -- What Is Corrective AI and Conditional Parameter Optimization? -- Feature Engineering -- Applying Corrective AI to Daily Seasonal Forex Trading -- What Is Conditional Parameter Optimization? -- Applying Conditional Parameter Optimization to an ETF Strategy -- Unconditional vs. Conditional Parameter Optimizations -- Performance Comparisons -- Conditional Portfolio Optimization -- Regime Changes Obliterate Traditional Portfolio Optimization Methods -- Learning to Optimize -- Ranking Is Easier Than Predicting -- The Fama-French Lineage -- Comparison with Conventional Optimization Methods -- Model Tactical Asset Allocation Portfolio CPO Software-as-a-Service -- Conclusion -- Definitions of Spread_EMA & -- Spread_VAR -- Chapter 9: Application of Large Language Models and Generative AI in Trading -- Role of Generative AI in Creating Alpha -- Selecting an LLM for Building a Generative AI Application -- Prompt Engineering -- Prompt Engineering in Practice -- Addressing Model "Hallucination -- Question Answering Using a Retrieval Augmented Application in SageMaker Canvas -- RAG Application Costs and Optimization Techniques -- Testing Our Infrastructure -- Summarization -- Useful AI Platforms and Services -- ChatGPT -- Gemini -- Bedrock -- SageMaker -- Q Business -- References -- Subject Index -- Code Index -- EULA. Chan, Ernest P. Sonstige oth Broad, Jared Sonstige oth Sun, Philip Sonstige oth Singh, Vivek Sonstige oth Erscheint auch als Druck-Ausgabe Pik, Jiri Hands-On AI Trading with Python, QuantConnect, and AWS Newark : John Wiley & Sons, Incorporated,c2025 9781394268436 |
| spellingShingle | Pik, Jiri Hands-On AI Trading with Python, QuantConnect, and AWS. Cover -- Title Page -- Copyright -- Contents -- Biographies -- Preface: QuantConnect -- Introduction -- Part I: Foundations of Capital Markets and Quantitative Trading -- Chapter 1: Foundations of Capital Markets -- Market Mechanics -- Market Participants -- Trading Is the "Play -- The Stage and Basic Rules of Trading-The Limit Order Book -- Actors-Liquidity Trader, Market Maker, and Informed Trader -- Liquidity Trader -- Market Maker -- Informed Trader -- AI Actors Wanted -- Data and Data Feeds -- Custom and Alternative Data -- Brokerages and Transaction Costs -- Transaction Costs -- Security Identifiers -- Assets and Derivatives -- US Equities -- US Equity Options -- Index Options -- US Futures -- Cryptocurrency -- Chapter 2: Foundations of Quantitative Trading -- Research Process -- Research -- Backtesting -- Parameter Optimization -- Paper and Live Trading -- Testing and Debugging Tools -- Debuggers -- Logging -- Charting -- Object Store -- Coding Process -- Time and Look-ahead Bias -- Look-ahead Bias -- Market Hours and Scheduling -- Strategy Styles -- Trading Signals -- Allocating Capital -- Regimes and Portfolios of Strategies -- Parameter Sensitivity Testing and Optimization -- 1. Remove -- 2. Replace -- 3. Reduce -- Parameter Sensitivity Testing -- Margin Modeling -- Equities -- Equity Options -- Futures -- Diversification and Asset Selection -- Fundamental Asset Selection -- ETF Constituents Asset Selection -- Dollar-Volume Asset Selection -- Universe Settings -- Indicators and Other Data Transformations -- Automatic Indicators -- Manual Indicators -- Indicator Warm Up -- Storing Objects -- Indicator Events -- Sourcing Ideas -- Hypothesis-driven Testing -- Data Driven Investing -- Quantpedia -- QuantConnect Research and Strategy Explorer -- Part II: Foundations of AI and ML in Algorithmic Trading Step-by-step Guide for AI-based Algorithmic Trading -- Chapter 3: Step 1: Problem Definition -- Chapter 4: Step 2: Dataset Preparation -- Data Collection -- Exploratory Data Analysis -- Data Preprocessing -- Handling Missing Data -- Handling Outliers -- Feature Engineering -- Normalization and Standardization of Features -- Transforming Time Series Features to Stationary -- Identification of Cointegrated Time Series with Engle-Granger Test -- Feature Selection -- Correlation Analysis -- Feature Importance Analysis -- Auto-identification of Features -- Dimensionality Reduction/Principal Component Analysis -- Splitting of Dataset into Training, Testing, and Possibly Validation Sets -- How to Split Your Data -- Chapter 5: Step 3: Model Choice, Training, and Application -- Regression -- Linear Regression -- Polynomial Regression -- LASSO Regression -- Ridge Regression -- Markov Switching Dynamic Regression -- Decision Tree Regression -- Support Vector Machines Regression with Wavelet Forecasting -- Classification -- Multiclass Random Forest Model -- Logistic Regression -- Hidden Markov Models -- Gaussian Naive Bayes -- Convolutional Neural Networks -- Ranking -- LGBRanker Ranking -- Clustering -- OPTICS Clustering -- Language Models -- OpenAI Language Model -- Amazon Chronos Model -- FinBERT Model -- Part III: Advanced Applications of AI in Trading and Risk Management -- Getting Started with Source Code -- Chapter 6: Applied Machine Learning -- Example 1-ML Trend Scanning with MLFinlab -- Example 2-Factor Preprocessing Techniques for Regime Detection -- Example 3-Reversion vs. Trending: Strategy Selection by Classification -- Example 4-Alpha by Hidden Markov Models -- Example 5-FX SVM Wavelet Forecasting -- Example 6-Dividend Harvesting Selection of High-Yield Assets -- Example 7-Effect of Positive-Negative Splits Example 8-Stop Loss Based on Historical Volatility and Drawdown Recovery -- Example 9-ML Trading Pairs Selection -- Example 10-Stock Selection through Clustering Fundamental Data -- Example 11-Inverse Volatility Rank and Allocate to Future Contracts -- Example 12-Trading Costs Optimization -- Example 13-PCA Statistical Arbitrage Mean Reversion -- Example 14-Temporal CNN Prediction -- Example 15-Gaussian Classifier for Direction Prediction -- Example 16-LLM Summarization of Tiingo News Articles -- Example 17-Head Shoulders Pattern Matching with CNN -- Example 18-Amazon Chronos Model -- Example 19-FinBERT Model -- Chapter 7: Better Hedging with Reinforcement Learning -- Introduction -- A New AI Trading Assistant -- Continuous Hedging Is Not Required -- Machine Learning Comes to the Rescue -- A Simplified but Effective Reinforcement Learning Approach -- Overview of the Reinforcement Learning -- Identification -- Simulation -- Ref inement Training on Actual Market Data -- Testing and Implementation -- Implementation on QuantConnect -- Primary Research Notebook -- The Policy Network -- Model Functions -- Fine-tuning with Market Data -- Results -- Conclusion -- Chapter 8: AI for Risk Management and Optimization -- What Is Corrective AI and Conditional Parameter Optimization? -- Feature Engineering -- Applying Corrective AI to Daily Seasonal Forex Trading -- What Is Conditional Parameter Optimization? -- Applying Conditional Parameter Optimization to an ETF Strategy -- Unconditional vs. Conditional Parameter Optimizations -- Performance Comparisons -- Conditional Portfolio Optimization -- Regime Changes Obliterate Traditional Portfolio Optimization Methods -- Learning to Optimize -- Ranking Is Easier Than Predicting -- The Fama-French Lineage -- Comparison with Conventional Optimization Methods -- Model Tactical Asset Allocation Portfolio CPO Software-as-a-Service -- Conclusion -- Definitions of Spread_EMA & -- Spread_VAR -- Chapter 9: Application of Large Language Models and Generative AI in Trading -- Role of Generative AI in Creating Alpha -- Selecting an LLM for Building a Generative AI Application -- Prompt Engineering -- Prompt Engineering in Practice -- Addressing Model "Hallucination -- Question Answering Using a Retrieval Augmented Application in SageMaker Canvas -- RAG Application Costs and Optimization Techniques -- Testing Our Infrastructure -- Summarization -- Useful AI Platforms and Services -- ChatGPT -- Gemini -- Bedrock -- SageMaker -- Q Business -- References -- Subject Index -- Code Index -- EULA. |
| title | Hands-On AI Trading with Python, QuantConnect, and AWS. |
| title_auth | Hands-On AI Trading with Python, QuantConnect, and AWS. |
| title_exact_search | Hands-On AI Trading with Python, QuantConnect, and AWS. |
| title_full | Hands-On AI Trading with Python, QuantConnect, and AWS. |
| title_fullStr | Hands-On AI Trading with Python, QuantConnect, and AWS. |
| title_full_unstemmed | Hands-On AI Trading with Python, QuantConnect, and AWS. |
| title_short | Hands-On AI Trading with Python, QuantConnect, and AWS. |
| title_sort | hands on ai trading with python quantconnect and aws |
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