Vertex Macro | Trading Course: Quantitative Trading and Factor Analysis

Trading Course

A comprehensive learning module on quantitative trading and factor analysis

01 Introduction to Trading Factor Discovery

Rapidly discover quantitative trading factors using generative AI and cloud services

Factor Modeling: A Quantitative Method for Guiding Investment Decisions

• Use systematic methods to identify the drivers of asset returns
• Decompose security returns into interpretable risk factors
• Achieve quantitative analysis of investment performance
• Factor modeling is a cornerstone of modern quantitative investment strategies, especially in hedge
• fund and institutional asset management

Examples

• Cumulative market performance (MSCI ACWI)
• Annualized excess return in rising/falling markets
• Value
• Size
• Momentum
• Quality
• Highest dividend
• Minimum volatility
• Excess return (rising markets)
• Excess return (falling markets)

• Relative cumulative market performance (MSCI ACWI)
• Annualized excess return in rising/falling markets
• Value
• Size
• Momentum
• Quality
• High dividend
• Minimum volatility
• Excess return (rising markets)
• Excess return (falling markets)

Key Models

• Capital Asset Pricing Model
• Fama–French Three-Factor Model
• Arbitrage Pricing Theory (APT) three-factor model

Reference: NEPC, “A Guide to Factor Investing” (2024)

What Are “Alpha” and “Beta”?

Alpha

Represents a portfolio manager’s ability to outperform a specific benchmark, such as the S&P 500. Positive alpha indicates that investment performance exceeded expectations after accounting for risk, while negative alpha indicates underperformance.

Beta

Measures an investment’s volatility relative to the overall market. A beta of 1 means the investment moves in sync with the market, while a beta greater than 1 indicates higher volatility and potentially larger gains or losses.

Factor Proliferation and Alpha Decay

Factor Proliferation

More than 400 factors have been documented in top academic literature.

Alpha Decay

• Performance is typically weaker after public release
• Overuse of popular factors
• Alice in Factor Wonderland: Three Misconceptions Plaguing Factor Investing
• Alice’s Adventures in Factorland: Three Mistakes Plaguing Factor Investing

Factor Discovery Workflow

Factor discovery combines financial theory, a systematic statistical analysis process, and computational methods to discover and validate investment factors.

• Data collection and preprocessing (covering market data, fundamental data, and alternative data)
• Factor definition and calculation (mathematical formula design and regression analysis)
• Statistical analysis (including significance tests, t-statistics, and R-squared)
• Robustness testing (validation using out-of-sample data, different markets, and different periods)
• Factor trading and monitoring (integrating portfolios and tracking investment performance)

Example: Debt-to-Equity (D2E) Factor Discovery

Factor Definition and Calculation Method

Calculation formula: D2E [current liabilities / shareholders’ equity]. Calculate the factor value for every stock within a specified date range.

Build the Portfolio

Rank stocks by D2E value every day, selecting stocks with high D2E values (the top third) and low D2E values (the bottom third).

Calculate the Factor Value

Factor value = high-portfolio D2E value (High P) - low-portfolio D2E value (Low P)

Regression Analysis Process

Conduct factor regression analysis for each stock to obtain various statistical indicators:

• The beta coefficient measures the sensitivity of stock returns to changes in the factor
• The t-statistic measures the statistical significance of the factor’s ability to predict returns
• R-squared represents the proportion of stock returns explained by the factor
• Calculate the average of each indicator to evaluate the factor overall

02 Factor Analysis Using AWS Bedrock

Advanced data analysis using traditional and alternative data sources

What Is Traditional Data?

Traditional data refers to structured financial information that has long been used in quantitative trading and is published by authoritative institutions in fixed formats. This data is easy to obtain, standardized, and historically traceable, making it the foundation for building and testing quantitative models.

• Data sources: market data, financial statements, and SEC regulatory filings
• Data format: structured and standardized
• Availability: easy to access and regulated
• Insights: historical and fundamental, based on market consensus

What Is Alternative Data?

Alternative data refers to nontraditional and unconventional data sources that can provide strategic data insights. Unlike traditional data sources, alternative data can reveal hidden trends and patterns that are often overlooked in standard data analysis.

• Social media, web searches, and news

Why Is Alternative Data Important?

• Traditional data has a low barrier to entry, making it difficult to gain a unique competitive advantage. Alternative data can help discover more potential investment opportunities.
• Maintain a competitive advantage in today’s data-driven market environment. According to a survey, 98% of investment professionals agree or strongly agree that using alternative data to discover innovative investment ideas and increase excess returns is becoming increasingly important.
• Combining traditional data with alternative data can provide more comprehensive market insights and perspectives on investment opportunities.

Generation Guide for Stock Market News Sentiment Analysis

• Sentiment scores are stored in an analytical OLAP database as input data for subsequent discovery processes.
• A web-search data ingestion pipeline based on the Tavily Search API
• Request content summaries and recommendation prompts that guide large language models (LLMs) to act as CFA and FRM credential holders
• Five-factor sentiment analysis
• S3 event notifications trigger Lambda ETL processing

AWS Bedrock Implementation

• Sentiment scores are stored in an analytical OLAP database.
• A web-search data ingestion pipeline based on the Tavily Search API
• LLMs act as CFA and FRM credential holders
• Five-factor sentiment analysis
• S3 event notifications trigger Lambda ETL processing

Prompt Examples

Prompt

“As an experienced CFA and FRM credential holder, analyze the attached annual report and extract concise summaries (2–3 sentences per item).”

Key Factors

• CEO statement
• ESG initiatives
• Market trends and competitive landscape
• Risk factors
• Strategic priorities

Prompt

“As an experienced Chartered Financial Analyst (CFA) and Financial Risk Manager (FRM) credential holder, analyze the attached annual report and extract brief summaries (2–3 sentences per item).”

Key Factors

• CEO statement
• ESG initiatives
• Market trends and competitive landscape
• Risk factors
• Strategic priorities

Prompt

“Generate sentiment for stock market news: analyze the sentiment of the following text about a company’s stock and financial performance.”

Web-Search Stock News - Sentiment Analysis Rating

• -1 represents extremely negative sentiment
• 0 represents neutral sentiment
• +1 represents extremely positive sentiment

Next Steps

• Backtest and optimize factor-based trading strategies
• Factor discovery
• Factor selection and evaluation
• Backtesting and evaluation
• Portfolio construction
• Strategy development

03 Top 20 Factors

Fundamental trading factors for quantitative analysis

Valuation and Growth Factors

PEG Factor

Full name: Price/Earnings-to-Growth

Key point: Measures the ratio of a stock’s price to its earnings growth. A lower PEG ratio may indicate better value.

PB Factor

Full name: Price-to-Book Ratio

Key point: Compares a company’s market value with its book value. A lower PB ratio may indicate that the company is undervalued.

Revenue Growth Factor

Full name: Revenue Growth Rate

Key point: Measures the percentage change in a company’s revenue over time. A higher growth rate may indicate strong business performance.

Technical and Market Factors

RSI Factor

Full name: Relative Strength Index

Key point: A momentum oscillator that measures the speed and change of price movements. An RSI above 70 indicates overbought conditions, while a value below 30 indicates oversold conditions.

SMB Factor

Full name: Small Minus Big

Key point: Represents the difference between the returns of small-cap and large-cap stocks. It is part of the Fama–French three-factor model.

HML Factor

Full name: High Minus Low

Key point: Represents the difference between the returns of value stocks (high book-to-market ratios) and growth stocks (low book-to-market ratios). It is also part of the Fama–French model.

Market Factor

Full name: Market Risk Premium

Key point: The excess return of the market portfolio over the risk-free rate. It represents the additional risk assumed by investing in the market.

Volume Factor

Full name: Trading Volume

Key point: The number of shares traded during a specific period. High volume may indicate strong interest in a stock, while low volume may indicate the opposite.

Rate of Change Factor

Full name: Rate of Change

Key point: Measures the percentage change in price over a period of time. It helps identify the speed and direction of price movements.

Financial Health and Liquidity

Current Ratio Factor

Full name: Current Ratio

Key point: A liquidity ratio that measures a company’s ability to pay short-term debt using current assets. A ratio above 1 is generally favorable.

Cash Ratio Factor

Full name: Cash Ratio

Key point: A strict liquidity ratio that measures a company’s ability to pay current liabilities using cash and cash equivalents. A higher ratio indicates better liquidity.

Inventory Turnover Factor

Full name: Inventory Turnover

Key point: Measures how quickly a company sells its inventory. A higher ratio indicates effective inventory management.

Gross Margin Factor

Full name: Gross Margin

Key point: Represents the percentage of revenue remaining after the cost of goods sold (COGS). A higher gross margin indicates better profitability.

Debt-to-Equity Ratio Factor

Full name: Debt-to-Equity Ratio

Key point: Measures a company’s financial leverage. A lower ratio indicates less debt relative to equity and may be viewed as lower risk.

Interest Coverage Factor

Full name: Interest Coverage Ratio

Key point: Measures a company’s ability to pay interest on outstanding debt. A higher ratio indicates better coverage of interest expenses.

Governance and Sentiment Factors

Board Age Factor

Full name: Average Age of Board Members

Key point: Reflects the average age of board members. A combination of younger and older directors can provide diverse perspectives and experience.

Executive Compensation Factor

Full name: Executive Compensation

Key point: Total compensation paid to senior executives. High compensation relative to performance may concern investors.

Environmental Rating Factor

Full name: Environmental Rating

Key point: Evaluates a company’s environmental practices and impact. A higher rating may indicate better sustainability practices.

Average Sentiment Factor

Full name: Average Sentiment Score

Key point: Reflects the overall sentiment of analysts, investors, and the public toward a stock. Positive sentiment can drive stock prices higher.

News Sentiment Factor

Full name: News Sentiment

Key point: Measures the sentiment of news articles and media coverage about a company. Positive news can improve stock performance.

04 A Deep Dive into T-Statistics

Statistical analysis for factor validation and significance testing

Three Popular Analysis Topics

T-Statistic Analysis

Full name: T-statistic

Key point: A statistical measure used to determine the significance of an estimated parameter. It compares the estimate with its standard error. A higher absolute t-value indicates that the parameter is statistically significant.

R-Squared Analysis

Full name: R-squared (coefficient of determination)

Key point: A statistical measure representing the proportion of variation in the dependent variable explained by the independent variables in a regression model. The closer the R-squared value is to 1, the better the model fits the data.

Beta Analysis

Full name: Beta coefficient

Key point: Measures a stock’s volatility relative to the overall market. A beta greater than 1 indicates higher volatility, while a beta less than 1 indicates lower volatility. Beta is used to evaluate a stock’s risk relative to the market.

What Is a T-Test?

• A t-test compares the means of two samples to determine whether the difference is statistically significant.
• A t-test is used when the data set follows a normal distribution and has unknown variance.

Example

• A data set recorded from 100 coin tosses
• The statistically significant difference between the means of two data sets
• Used for hypothesis testing in statistics
• Calculating a t-test requires the difference between the means of the two data sets, the standard deviation of each group, and the number of data values.

Example: A placebo control group and a group taking a prescription drug in a drug trial should have slightly different means and standard deviations.

T-Test Formula

• Calculate a value and compare it with a standard value.
• Determine how chance affects the difference and whether the difference exceeds the range attributable to chance.
• A t-test asks whether the difference between groups represents a real difference in the study or merely a random difference.

Null Hypothesis Results

Null Hypothesis Rejected

• The data is strong and may not be due to chance.
• The difference is statistically significant.

Null Hypothesis Accepted

• The difference is not statistically significant.

Types of T-Tests

Paired-Sample T-Test Formula

Used for: Similarity of sample records

A dependent test performed when the samples consist of matched pairs of similar units or when repeated measurements are involved.

Example

• The same patient is tested repeatedly before and after receiving a specific treatment. Each patient serves as their own control sample.
• Applicable when samples are related or have matching characteristics, such as comparative analysis involving children, parents, or siblings.

Equal-Variance or Pooled T-Test Formula

Used for: The number of data records in each sample set

An equal-variance t-test is an independent t-test used when the number of samples in each group is the same or when the variances of the two data sets are similar.

Unequal-Variance T-Test Formula

Used for: The variance of each sample set

An unequal-variance t-test is an independent t-test used when the number of samples in each group differs and the variances of the two data sets also differ. This test is also called Welch’s t-test.

What Is an Independent T-Test?

• The selections are independent of one another, and the data sets in the two groups do not contain the same values.
• One hundred randomly unrelated patients are divided into two groups of 50 patients each.
• One group becomes the control group and receives a placebo,
• while the other group receives the prescription treatment.
• This forms two independent sample groups that are neither paired nor related.

Four Assumptions of a T-Test

• The collected data must follow a continuous or ordinal scale, such as scores on an IQ test.
• The data is collected from a randomly selected portion of the total population.
• The data produces a normal distribution with a bell-shaped curve.
• Equal or homogeneous variance exists when the standard variances are equal.

T-Test Example

• A placebo is a substance with no therapeutic value, used as a baseline for measuring the response of the other group, which receives the actual drug.
• Members of the control group report an average increase in life expectancy of three years.
• Members of the group taking the new drug report an average increase in life expectancy of four years.
• Initial observations indicate that the drug is working.

Interpreting T-Scores

• Higher t-score values: There is a large difference between the two sample sets.
• Smaller t-values: There is more similarity between the two sample sets.

Degrees of Freedom

The values with freedom to vary in a study are essential for evaluating the importance and validity of the null hypothesis.

Z-Test

Used for data sets with large sample sizes.

What Does a T-Test Explain and How Is It Used?

• The statistically significant difference between the means of two population samples
• Whether the difference between two populations is meaningful or random

A t-test calculation uses three pieces of data

• The mean of each data set
• The standard deviation of each group
• The number of data values

• The statistically significant difference between the means of two population samples
• Whether the difference between two populations is meaningful or random

• The mean of each data set
• The standard deviation of each group
• The number of data values

05 Smarter Trading: Unlocking Alpha Through GenAI

Human-machine collaboration to enhance human experience through autonomous trading systems

AWS GenAI: A New Breakthrough Empowering Macro Trading

Based on a comprehensive analysis of implementing GenAI in macro trading, I must carefully examine the transformational impact of AWS GenAI services on trading operations. First, I reviewed the evolution from traditional linear models to advanced nonlinear GenAI systems:

Based on trading performance data, GenAI implementation shows three distinct phases

• 2023 phase: An unstable macro decision system with high volatility - GenAI trading systems in the early stage showed instability as the algorithms were refined and optimized, demonstrating the learning curve required by AI-driven trading systems.
• 2024 phase: A pure-alpha macro timing system achieved 10 consecutive months of positive profits - The refined GenAI macro-timing system demonstrated consistent profitability, marking the maturation of AI-driven trading strategies.
• 2025 phase: Macro risk-parity hedging achieved four months of positive profits - The system evolved into a sophisticated GenAI-driven risk-parity hedging strategy that maintained a positive performance trajectory.

Next, GenAI trading applications cover the complete trading lifecycle and demonstrate significant advantages

• Pre-trade investment research: GenAI captures nonlinear relationships and simulates the multilayered effects of major events, mapping complex policy-industry-asset interactions unlike traditional linear models. This addresses the limitations of traditional quantitative models, which rely on linear assumptions and historical statistics.
• Trade execution (autonomous systems): GenAI combines human experience with environmental adaptation and visualizes the chain of thought to enable transparent decision-making while effectively processing unstructured data. This addresses the overfitting problem in which a strategy performs well on historical data but fails under new market conditions.
• Post-trade portfolio management: GenAI uses counterfactual analysis to create alternative scenarios while diagnosing cognitive biases and avoiding dependence on a single logical path. This addresses black swan events that break historical patterns and render predefined risk boundaries ineffective.

Specifically, a seven-component autonomous trading system powered by GenAI shows measurable improvements

• Market selection: Allocation based on macroeconomic factors identifies stable markets. Analysis of sovereign risk, liquidity, and volatility reduces systemic risk for long-term allocation.
• Position sizing: Risk boundaries and capital allocation management systematically control individual trade risk and prevent excessive exposure to macroeconomic fluctuations.
• Entry point: Market-phase confirmation and trigger timing align entries with macro trends, avoiding countertrend environments through systematic analysis.
• Stop-loss: Loss limits and trading-control rules enable timely stop-losses during macro reversals, protecting capital from significant losses in volatile environments.
• Exit strategy: Profit-taking conditions defined around the end of macro trends maximize returns and avoid the drawdown caused by holding positions too long.
• Tactical operations: Position adjustments during trade execution simplify the trading process, avoid excessive leverage, and maintain strategy consistency.
• Performance evaluation: Trade-performance monitoring and quantitative feedback measure macro-level adaptability and optimize long-term asset-allocation decisions.

At the same time, the AWS GenAI technology stack provides the infrastructure

• Amazon Bedrock: Foundation models with GenAI capabilities provide advanced AI models for sentiment analysis, pattern recognition, and predictive modeling in trading workflows and research applications.
• Amazon Q Developer: AI-driven development acceleration enables rapid development and deployment of trading strategies, reducing the time required to validate and implement investment strategies.
• Kiro: Enhanced productivity and workflow optimization simplify development workflows and improve the productivity of quantitative researchers and trading-system developers.

However, from an overall perspective, the transformation timeline shows a clear inflection point

• Extreme volatility before April 2024: The initial implementation phase had high uncertainty and unstable performance as GenAI systems were calibrated and optimized.
• Stable upward trend beginning in May 2024: A clear performance transformation occurred as GenAI technology converted volatile performance into a stable upward trend in macro alpha generation, indicating system maturity.
• Consistent positive returns: Consistent profitability across multiple strategies demonstrates the reliability and effectiveness of mature GenAI trading systems.

These factors need to be integrated comprehensively to provide an overall understanding

Based on the performance data provided: GenAI-driven macro trading systems demonstrate outstanding alpha generation through strategic timing, dynamic asset allocation, and an absolute-return focus regardless of market direction. Combining human expertise with machine intelligence in autonomous trading systems produces the best results, while AWS infrastructure provides the scalable computing power required for advanced quantitative modeling and investment research. Overall, despite early volatility in 2023, the evolution into stable positive returns in 2024–2025 indicates that GenAI trading systems have matured into reliable alpha-generation platforms that continue to guide asset-allocation decisions in modern capital markets.

Overview

Capital markets workloads cover the complete trading lifecycle and run on AWS

Full term: Comprehensive AWS infrastructure for trading operations

Key point: AWS provides end-to-end infrastructure supporting every stage of capital-markets operations, from research to execution to post-trade analysis.

GenAI Use Cases in My Macro Trading Journal

Full term: Practical GenAI applications in macro trading

Key point: Practical implementation of GenAI during pre-trade research, trade execution, and post-trade portfolio management.

GenAI-Driven: A Performance Surge and Reinforced Positive Returns

Full term: AI-enhanced trading performance optimization

Key point: GenAI implementation delivers consistent positive returns and performance improvements across multiple trading strategies.

Unlocking GenAI Alpha: A New Chapter in Timing and Rotation

Full term: AI-driven alpha generation through strategic timing

Key point: GenAI enables superior market-timing and asset-rotation strategies that generate alpha beyond traditional methods.

Capturing Macro Opportunities: GenAI Trading Systems Lead the Way

Full term: Identifying macro opportunities through AI systems

Key point: GenAI systems excel at identifying and exploiting macroeconomic opportunities through systematic analysis.

Smarter Trading: Unlocking Alpha Through AI

Full term: Intelligent trading through artificial intelligence

Key point: AI-driven trading systems enable smarter decision-making and consistent alpha generation through advanced analysis.

AWS GenAI: A New Breakthrough Empowering Macro Trading

Full term: The AWS GenAI platform for trading innovation

Key point: AWS GenAI services provide the foundation for breakthrough innovation in macro trading strategies and execution.

Human-Machine Collaboration Enhances Human Experience Through Autonomous Trading Systems

Full term: A hybrid-intelligence trading framework

Key point: The best trading results come from combining human expertise and machine intelligence in autonomous trading systems.

AWS Enables Quantitative Modeling and Investment Research

Full term: A cloud-driven quantitative research platform

Key point: AWS infrastructure provides scalable computing power for advanced quantitative modeling and investment research.

QRT Powers Its Algorithmic Trading Strategies Through AWS

Full term: Enterprise algorithmic trading on AWS

Key point: Leading quantitative trading firms such as QRT use AWS infrastructure for algorithmic trading operations.

GenAI Use Cases in My Macro Trading Journal

[Pre-Trade] Investment Research

Problem: Traditional quantitative models rely on linear assumptions and historical statistics, making it difficult to handle chain reactions caused by unexpected events.

Alpha outcome: Use GenAI to capture nonlinear relationships, simulate the multilayered effects of major events, and map the complex, changing interactions among policy, industry, and assets in a nonlinear way—unlike traditional models.

[Trade] Autonomous Trading System

Problem: Overfit strategies perform well on historical data but fail under new market conditions.

Alpha outcome: Use GenAI to rapidly develop macro strategies that combine human experience with adaptation to changing environments. Visualize the chain of thought, process unstructured data, and make decisions that are easy to understand.

[Post-Trade] Portfolio Management

Problem: Black swan events—such as sudden policy changes or geopolitical conflicts—break historical patterns and make the model’s predefined “risk boundaries” ineffective.

Alpha outcome: Use GenAI counterfactual analysis to create alternative scenarios and avoid relying on a single logical path. Diagnose cognitive biases to uncover unclear thinking and check whether trading actions align with the established strategy.

GenAI-Driven: A Performance Surge and Reinforced Positive Returns

2023, an Unstable Macro Decision System

Full term: Initial GenAI implementation phase

Key point: Early-stage GenAI trading systems showed instability and high volatility as the algorithms were refined and optimized.

2024, a Pure-Alpha Macro-Timing System: Ten Months of Positive Profits

Full term: A mature GenAI alpha-generation system

Key point: The refined GenAI macro-timing system achieved consistent profitability and positive returns for 10 consecutive months.

2025, Macro Risk-Parity Hedging Achieved Four Months of Positive Profits

Full term: An advanced risk-parity GenAI strategy

Key point: The system evolved into a sophisticated GenAI-driven risk-parity hedging strategy that maintained a positive performance trajectory.

Extreme Volatility Before April 2024; Stable Upward Trend from May 2024 Using GenAI Profit-Making Technology to Earn Macro Alpha Returns

Full term: GenAI performance transformation timeline

Key point: A clear inflection point in May 2024, when GenAI technology converted volatile performance into a stable upward trend in macro alpha generation.

Capturing Macro Opportunities: GenAI Trading Systems Lead the Way

1. Market

Core effect: Select and allocate across trading markets based on macroeconomic factors such as sovereign risk, liquidity, and volatility to define the investment universe.

Macro trader advantage: Helps identify stable markets in the macroeconomy, such as sovereign nations and highly liquid assets, reduce systemic risk, and benefit from mean-reversion trends, making it suitable for long-term asset allocation.

2. Position Sizing

Core effect: Set risk boundaries for each trade, such as position size and stop-loss thresholds, to manage overall capital allocation.

Macro trader advantage: Systematically controls individual trade risk, prevents excessive capital exposure to macroeconomic fluctuations such as recessions or market reversals, and protects capital.

3. Entry Point

Core effect: Define the trigger timing and conditions for entering a trade based on confirmation of the market phase.

Macro trader advantage: Ensures that entries align with macro trends, avoids incorrect entries in countertrend environments such as early bear markets, and improves the strategy’s adaptability in range-bound or trending markets.

4. Stop-Loss

Core effect: Establish exit rules to limit losses and control trading losses.

Macro trader advantage: Enables timely stop-losses during macro reversals, such as deteriorating fundamentals, protecting capital from significant losses, especially in increasingly volatile macro environments.

5. Exit Strategy

Core effect: Define profit-taking conditions to lock in trading profits.

Macro trader advantage: Ensures exits occur when a macro trend ends or a target is reached, maximizes returns, and avoids drawdowns from holding positions too long, making it suitable for long-term macro capital management.

6. Tactical Operations

Core effect: Handle tactical operations during trade execution, such as adding to or reducing a position.

Macro trader advantage: Simplifies the trading process, avoids excessive leverage during macroeconomic uncertainty, such as avoiding pyramiding, maintains strategy consistency, and reduces emotional interference.

7. Evaluation

Core effect: Monitor and evaluate trading performance, such as accuracy and risk-reward ratio, and provide quantitative feedback.

Macro trader advantage: Measures the strategy’s macro-level adaptability, such as maximum drawdown, helps optimize long-term asset-allocation decisions, and identifies opportunities that perform consistently throughout the economic cycle.

Smarter Trading: Unlocking Alpha Through AI

[1] GenAI

[Pre-trade] Investment research: Nonlinear relationships and multilayered effects

[Trade] Autonomous trading system: Human experience and chain of thought

[Post-Trade] Portfolio Management

GenAI counterfactual analysis and cognitive-bias diagnosis

Key point: GenAI enhances every stage of trading through advanced analysis and human-machine collaboration.

[2] Autonomous Trading System

Seven-component framework: Market, position sizing, entry point, stop-loss, exit strategy, tactical operations, and evaluation

Key point: A systematic autonomous trading approach that combines human judgment with AI-driven insights and covers every trading component.

[3] Macro Alpha

Three strategic pillars: Autonomous timing, multi-asset rotation, and absolute return

Key point: Generate macro alpha through strategic timing, dynamic asset allocation, and a focus on absolute returns regardless of market direction.

06 AWS Enables Quantitative Modeling and Investment Research

High-performance computing and data management for advanced financial research

High-Performance Computing for Quantitative Research

AWS Batch and Step Functions Services

• AWS Batch automatically provisions computing resources for factor discovery. Depending on the number of stock tickers and the backtesting date range, thousands of AWS Batch jobs can run in parallel, significantly improving processing efficiency.
• Step Functions orchestrates these parallel AWS Batch workflows by providing visual workflows and complements AWS Batch capabilities perfectly. This multilevel parallel processing greatly improves the efficiency and flexibility of factor discovery.

Key Advantages of Factor Modeling on AWS

Data 360

Amazon Bedrock and Amazon S3 help integrate alternative and traditional data at scale.

Elastic Computing

Serverless infrastructure automatically adapts to data volume and handles market volatility with ease.

Agile Deployment

Infrastructure as code enables rapid creation and deployment, significantly shortening the time from concept to implementation.

Factor Optimization

Rapidly discover and validate new quantitative factors, improve predictive power, and increase portfolio returns.

Enhanced Data Management

Third-Party Data Integration

Natively load third-party data on AWS, both real-time and historical, including unstructured data.

Self-Service Data Products

Create interoperable self-service data products and catalogs for investment researchers.

Historical Fidelity

Enable historical fidelity for the data used in backtesting (time travel).

Data Governance

Implement data governance, quality assurance, lineage, and auditability.

HPC Optimization for Research

• Use elastic and responsive computing capacity for large-scale parallel computing jobs.
• Optimize cost and performance across instance types, including GPUs, and purchasing options such as Spot.
• Integrate AWS with existing research and risk systems to select the right computing architecture for the research process.

Storage Tailored for HPC and Research

• Eliminate bottlenecks in the investment research process through the right combination of high-performance storage and networking options.
• Optimize cost and performance using storage types suited to the appropriate HPC architecture.

QRT Powers Its Algorithmic Trading Strategies Through AWS

Challenges and Timeline

QRT needed to become an independent company within 100 days, migrate large volumes of data from its legacy environment, support existing applications, and build a scalable data platform for its researchers.

Architecture Implementation

QRT used Amazon S3 for data storage and expanded to a microservices architecture using EKS, RDS, managed Kafka, and accelerated computing, including GPU-based accelerators. QRT implemented YellowDog for optimized job scheduling and achieved additional resilience and scalability through a multi-Region architecture.

Global Expansion

Through AWS, QRT was able to scale globally, using S3 Multi-Region Access Points and PrivateLink for efficient cross-Region data access.

Business Impact

After adopting AWS, QRT was able to drive better investment recommendations, reduce the time required to bring new strategies to market from months to weeks, and onboard new researchers faster through self-service capabilities.

07 A Deep Dive into Factor Proliferation and Alpha Decay

Understanding challenges and solutions in modern quantitative finance

Factor Proliferation and Alpha Decay in Modern Finance

Factor Proliferation

• More than 400+ factors have been documented in top academic literature.
• An increase in the number of factors or parameters considered in a system or analysis
• Complicates the problem and makes it more difficult to solve

Challenges of Factor Proliferation

Increased Complexity

As more factors are introduced into static analysis, the complexity of the problem increases. An analysis of a simple beam under a single load may need to account for multiple loads, changing cross-sections, and different support conditions.

Mathematical Difficulty

This translates into more equations and variables, making mathematical solutions more challenging.

Computational Requirements

Computer-aided analysis, such as finite-element analysis, is used to obtain solutions.

Potential for Errors

The likelihood of errors in calculations or assumptions is greater.

Modeling Examples

Building Modeling

• The weight of each floor
• The stiffness of the structure
• The impact of temperature changes
• The dynamic behavior of the building under earthquake loads

Mechanical Component Analysis

• Primary loads on the shaft
• The impact of friction
• The impact of wear
• Dynamics generated by rotating components

Managing Factor Proliferation

Simplification

Identify the most important factors and simplify the model by ignoring less important factors.

Sensitivity Analysis

Determine which factors have the greatest impact on the result and focus on modeling those factors accurately.

Validation

Validate the model against experimental data or real-world observations.

Alpha and Alpha Decay

Alpha Definition

The active return on an investment—the portfolio manager’s ability to generate returns beyond the expected return for the investment’s risk.

Alpha Decay

Performance is typically weaker after publication. Crowding in popular factors. A gradual decline in the performance of an investment strategy. The ability of a model to generate excess returns (alpha) over time. A profitable strategy loses its edge, and its ability to outperform the market benchmark weakens.

What Causes Alpha Decay?

Market Efficiency

The market becomes more efficient as traders discover and exploit a particular strategy.

Structural Market Changes

Changes in interest rates, economic conditions, or regulations.

Overfitting

Overreliance on historical data.

Execution Costs

Fees, commissions, slippage, and latency can erode profits.

Why Is Alpha Decay a Problem?

Return Erosion

Investment profitability decreases.

Poor Investment Decisions

Investors fail to identify alpha decay.

Impact on Systematic Trading

Automated algorithms and quantitative models are particularly vulnerable to alpha decay because these models are widely adopted.

How Can Alpha Decay Be Mitigated?

Continuous Monitoring

Identify any signs of declining alpha.

Strategy Diversification

Spread investments across multiple strategies to reduce dependence on a single approach.

Innovation

Develop new strategies as the market changes.

Macro Alpha

Excess returns (alpha) generated by strategies focused on macroeconomic factors. Analyze and forecast global economic trends to make informed investment decisions.

What Is Alpha?

The excess return of an investment relative to a benchmark or market index.

What Are Macroeconomic Factors?

Factors affecting the overall economy and financial markets:

• Interest rates
• Inflation
• Economic growth
• Exchange rates
• Fiscal policy
• Monetary policy

Macro Alpha Strategies

Profit from anticipated changes in macroeconomic conditions:

• Currency trading: Anticipate changes in interest rates or economic growth.
• Fixed-income investing: Anticipate changes in interest rates or inflation.
• Equity investing: Anticipate changes in economic growth or specific industries.
• Commodity trading: Trade oil or gold based on supply and demand.

How Is Macro Alpha Generated?

• Research and analysis
• Quantitative models
• Machine learning
• Autonomous decision-making
• Global macro strategies

Key Participants

• Hedge funds and asset management companies
• Proprietary trading desks

Strategy Example

Forecast

Forecast that the Federal Reserve will raise interest rates.

Strategy Implementation

Short U.S. Treasuries, whose prices will fall as interest rates rise, and go long the U.S. dollar, which is expected to appreciate relative to other currencies.

Importance of Macro Alpha

Diversification

Provides diversification benefits to a portfolio and is not correlated with traditional asset classes.

Risk Management

Hedges against inflation or economic risk.

High Return Potential

Generates significant returns in volatile markets.

08 Modern Portfolio Theory (MPT)

Five popular technical investment risk ratios and statistical measures

Five Popular Technical Investment Risk Ratios

Five Popular Risk Ratios

• Alpha - Measures portfolio performance relative to a benchmark.
• Beta - The portfolio’s volatility or risk relative to the overall market.
• Standard deviation - Measures volatility and risk dispersion.
• R-squared - The reliability of the alpha and beta relationship.
• Sharpe ratio - A risk-adjusted return measure.

The Relationship Between Alpha and Beta

Alpha

Measures portfolio performance relative to a benchmark and indicates the extent to which it outperforms or underperforms the market. It represents the investment’s excess return relative to the benchmark and indicates the portfolio manager’s skill. Positive alpha indicates manager skill, while negative alpha indicates underperformance.

Beta

The portfolio’s volatility or risk relative to the overall market. Measures the volatility or sensitivity of a stock or portfolio to market movements. A beta of 1 means the investment moves in sync with the market, a beta greater than 1 indicates higher volatility, and a beta less than 1 indicates lower volatility.

Beta Analysis

Beta estimates the marginal effect of a unit change in security returns. A beta greater than 1 indicates that the selected security is more sensitive to returns on its broad market index. A beta less than 1 indicates relative insensitivity to overall market returns. A negative beta indicates that the selected security tends to move inversely to the return of its overall market.

Alpha Coefficient

A key performance indicator for stock funds. Measures a fund’s risk-adjusted performance relative to its benchmark index. Alpha 1.0: the investment outperforms the index by 1%. Alpha below 0: returns are below the benchmark after adjusting for their respective volatility.

R-Squared and Beta: What Is the Difference?

R-Squared

Alpha and beta capture the relationship between security returns and overall market returns. You can understand how your holdings perform over time relative to the benchmark index, as shown by the size of alpha and beta, but you also want to know how reliable the relationship is between the security and the overall market expressed by alpha and beta.

R-Squared Range

From 0 to 1. 0.85 to 1: alpha and beta together explain most of the variation in security returns. Below 0.7: there is little relationship between the security-performance pattern estimated by alpha and beta and the index. Security returns may be more random or may be driven by unobserved factors other than market-index returns.

Using Beta

Use beta and alpha coefficients to understand a security’s performance relative to the market index. Beta can estimate the magnitude of the direct relationship between the market and the security. R-squared determines the reliability of the relationship between the index and alpha and beta.

What Is a Benchmark?

Benchmark Definition

• A standard measure of how an asset’s value changes over a specified period
• Based on an index, but can use any combination of assets
• Shows how the asset performs relative to that combination

What Is a Good Beta?

• It depends on your risk tolerance.
• Beta below 1: lower volatility.
• Above 1: greater volatility than the market.

High-Beta Characteristics

• High-beta stocks tend to rise faster.
• They fall faster in bear markets.
• High R-squared: alpha and beta estimates should be taken more seriously.

What Is the Sharpe Ratio in a Hedge Fund?

A measure of risk-adjusted return. It measures how much excess return an investment generates for each unit of risk assumed. It evaluates whether a higher return is worth the associated risk. A higher Sharpe ratio means better risk-adjusted performance—the fund provides more return for the level of risk it takes.

What It Measures

• The relationship between a fund’s return and its volatility (risk)
• How much additional return you receive for each additional unit of risk assumed

How It Is Calculated

The Sharpe ratio is calculated by subtracting the risk-free rate of return, such as a government bond yield, from the fund’s average return and dividing the result by the fund’s standard deviation, a measure of volatility.

Why It Matters

• Risk-adjusted performance: It allows investors to compare the performance of different funds even when they have different risk levels.
• Investment decisions: A higher Sharpe ratio indicates a better risk-return trade-off, making it a valuable tool for investment decisions.
• Fund comparison: Investors can use the Sharpe ratio to compare the performance of different hedge funds and other investment vehicles.

Interpreting Sharpe Ratio Results

Performance Rating

A Sharpe ratio of 1.0 or higher is generally considered acceptable, with higher numbers indicating better risk-adjusted returns. A Sharpe ratio above 2.0 is generally considered very good, while a ratio of 3.0 or higher is considered excellent.

Limitations

The Sharpe ratio assumes that returns are normally distributed, which may not always be true, especially for hedge funds. It does not distinguish between upside and downside volatility. It is best used together with other measures and a broader understanding of the fund’s strategy.

What Causes a Fund to Liquidate?

Fund Liquidation Definition

“Liquidating a fund” means closing the fund, selling its assets, and distributing the proceeds to the fund’s shareholders. It is the process of converting fund investments into cash and then returning the cash to investors.

What Happens During Liquidation?

Sell assets: The fund manager sells all investments held by the fund, such as stocks, bonds, or real estate. Convert to cash: These sales generate cash, which is then used to pay any outstanding debts of the fund. Distribute proceeds: Finally, the remaining cash, after fees, is distributed to fund shareholders in proportion to their holdings.

Why Might a Fund Be Liquidated?

Poor performance: If a fund consistently underperforms its benchmark or investor interest is low, it may be liquidated. Declining assets under management: If fund assets shrink too much, the fund may become difficult to manage effectively, leading to liquidation. Lack of investor interest: If investors lose interest in the fund, redemptions may force the fund to sell assets, potentially resulting in liquidation.

What Does This Mean for Investors?

Forced sale: Liquidation means investors are forced to sell their holdings at a potentially unfavorable time, which may result in losses. Capital gains tax: If the fund has appreciated since the investor purchased it, liquidation may trigger capital gains tax. Potential investment loss: Depending on the fund’s performance and market conditions, investors may receive less than their initial investment.

Hedge Fund Benchmarks

What Is a Hedge Fund Benchmark?

A way to evaluate hedge fund performance, usually relative to a group of peer funds or a portfolio with similar characteristics. Unlike traditional investments, hedge funds typically target absolute returns, making traditional market benchmarks less suitable. Hedge fund benchmarks are therefore designed to reflect the specific strategies and risk profiles of these funds.

Why Use a Benchmark?

Performance evaluation: Benchmarks allow investors to evaluate a hedge fund manager’s performance relative to peers or related investment strategies. Risk assessment: Benchmarks can help investors understand a hedge fund’s risk-return profile and compare it with other investment options.

HFRI Fund Weighted Composite Index

The HFRI Fund Weighted Composite Index is a globally recognized equal-weighted index that tracks the performance of single-manager hedge funds reporting to the HFR database. It represents a broad measure of hedge fund performance and excludes funds of funds. The index includes funds with at least $50 million in assets under management or a 12-month track record.

Risk-Free Rate and Government Bonds

What Is the Risk-Free Rate of Return?

• The theoretical rate of return on a zero-risk investment
• Used as a benchmark for evaluating other investments
• Usually represented by the yield on government securities
• Reflects the time value of money and the minimum expected return on a risk-free investment

What Is a Government Bond Yield?

• The return investors can expect from a government bond, usually expressed as an annual percentage
• Calculated based on the bond’s coupon payments, price, and time to maturity
• Reflects the total return, including interest and any potential capital gains or losses

Factors Affecting Debt Security Prices

Interest Rates

When interest rates rise, bond prices generally fall. Rising interest rates usually lead to higher bond yields, and vice versa.

Credit Rating

The degree of certainty of repayment, reflected in the credit rating. Bonds issued by governments considered highly credible often have lower yields than bonds issued by less creditworthy entities.

Time to Maturity

The remaining period before a bond’s principal is repaid. Long-term bonds are generally more sensitive to changes in interest rates.

Market Conditions

Overall market and economic conditions. Factors such as inflation, exchange rates, and political changes also affect debt-security prices. When a security sells above face value, it is sold at a premium. When it sells below face value, it is sold at a discount.

What Is a p-Value?

A statistical measure used to evaluate the significance of a result, particularly in the context of hypothesis testing. It represents the probability, assuming the null hypothesis is true, of observing a result as extreme as or more extreme than the result obtained in the statistical test.

What It Represents

p-value indicates the likelihood of seeing the observed data, or more extreme data, if the null hypothesis is actually correct.

How It Is Used in Trading

It is often used to evaluate the significance of an indicator or trading strategy. For example, if you are testing a moving-average crossover strategy, the p-value can tell you the likelihood that the observed success of the strategy occurred purely by chance.

Interpreting p-Values

• Low p-value (typically below 0.05): Strong evidence against the null hypothesis. In trading, this may indicate that the observed result is unlikely to have occurred randomly and that your strategy may have a genuine edge.
• High p-value (typically above 0.05): Weak evidence against the null hypothesis. It may indicate that the observed result could easily have occurred by chance, with no strong evidence that your trading strategy has an edge.

Example

If a trading strategy has a p-value of 0.02, it means that if the strategy has no genuine edge, there is a 2% chance of observing the result, or an even more extreme result. This is generally considered statistically significant and indicates some evidence supporting the strategy.

09 Multi-Factor Models for Risk-Adjusted Asset Returns

Arbitrage Pricing Theory (APT) and Fama–French factor models

Arbitrage Pricing Theory (APT) and Multi-Factor Models

Arbitrage Pricing Theory (APT)

• Explains average returns on stocks traded on the New York Stock Exchange (NYSE)
• Multiple factors, such as stock and bond indexes, explain the expected return of risky assets.
• Limits the impact of systemic risk through diversification within an asset group.
• Portfolio volatility is equivalent to half the average volatility of its component assets.

APT Assumptions and Key Concepts

Assumption

Asset returns can be explained using systematic factors. No arbitrage opportunities exist in a sufficiently diversified portfolio.

Arbitrage

Earn a risk-free profit by buying an asset in the cheaper market while simultaneously selling that asset in the more expensive market.

Diversification

Specific risk can be eliminated from a portfolio through an appropriate diversification strategy.

Factors

Expected return of a security, unexpected factors (the difference between observed and expected values), the impact of changes on a security’s rate of return, and noise factors.

APT Calculation Example

Given Factors

Risk-free rate = 3%, GDP factor beta = 0.40, consumer sentiment factor beta = 0.20, GDP risk premium = 2%, consumer sentiment risk premium = 1%

APT Calculation

Arbitrage Pricing Theory (APT) = 3% + (0.40 × 2%) + (0.20 × 1%) = 4%

Model Comparison

APT is an improvement over CAPM. Arbitrage Pricing Theory (APT) is a multi-factor model. The Capital Asset Pricing Model (CAPM) is a single-factor model.

Single-Factor and Multi-Factor Models

Single-Factor Model Example

• Expected stock return = 10%
• GDP factor beta = 1.50
• Expected GDP growth = 4%
• Result: 10% + 1.5 × 4% = 16%

Multi-Factor Model Example

• Expected stock return = 10%
• GDP factor beta = 1.50, interest-rate factor beta = 2.0
• Expected GDP growth = 2%, expected interest-rate growth = 1%
• Result: 10% + (1.5 × 2%) + (2.0 × 1%) = 15%

Multi-Factor Model

Uses multiple factors in calculations to explain asset prices. Different macroeconomic factors include inflation, interest rates, and business-cycle uncertainty.

Factor Components

• Factor output: Explains the expected return of an asset.
• Factor beta: The asset’s sensitivity to a specific factor. A larger factor beta means greater sensitivity.
• Factor: Stock return, expected stock return, and the sensitivity of stock returns to a unit change.
• Macroeconomics: The portion of stock returns that cannot be explained by macro factors.

Hedging Multi-Factor Exposures

Hedging Strategy Concepts

Diversification: Eliminates specific risk (idiosyncratic risk). Hedging strategy: Eliminates factor beta (systematic risk).

Portfolio Example

Consider an investor managing a portfolio with the following factor betas: GDP beta = 0.4, consumer sentiment beta = 0.20.

Case 1 - GDP Risk Hedge

Goal: Hedge GDP factor risk while maintaining 0.20 consumer sentiment exposure. Solution: A 40% short position in GDP. The GDP factor portfolio equals -0.40. Result: 0.4 + 0.2 - 0.4 = 0.2

Case 2 - Consumer Sentiment Risk Hedge

Goal: Hedge consumer sentiment (CS) factor risk. Solution: A 20% short position in the consumer sentiment factor. Result: 0.4 + 0.2 - 0.2 = 0.4

Challenges of Using Multi-Factor Models for Hedging

High Hedging Costs

Maintaining a high hedge cost for a portfolio reduces overall performance. Tracking error is most likely to occur.

Model Risk

Potential errors in implementing the hedging strategy, mathematical errors, or assumptions based on bias.

Nonlinear Relationships

Hedging strategies based on linear factor models do not include nonlinear relationships.

Stationarity Assumption

Assumes that the distribution of the underlying assets is stationary and forgets that the distribution may change over time.

Fama–French Three-Factor Model

Weakness of the APT Model

Weakness of the APT model: It is silent about the relevant risk factors to use.

Three Factors

Company size, book-to-market ratio, and market excess return.

Book-to-Market Ratio

Compares a company’s book value (net asset value) with its market value (market capitalization). Book value: the company’s net asset value based on its balance sheet. Market value: the company’s total market capitalization.

Risk Characteristics

Small companies have higher returns than large companies, and companies with high book-to-market ratios have higher returns than companies with low book-to-market ratios. Small companies are inherently riskier than large companies. Companies with high book-to-market ratios are inherently riskier than companies with low book-to-market ratios.

Book-to-Market Ratio Analysis

High Book-to-Market

• If liquidated, the value would exceed its current market price.
• Usually indicates an undervalued company.
• Higher risk but higher potential return.

Low Book-to-Market

• Usually priced at a premium relative to book value.
• Usually a growth company.
• Lower risk but lower potential return.

Fama–French Factor Definitions

SMB (Small Minus Big)

A hedging strategy. Small minus big is a theory that small companies will achieve better returns than large companies over the long term.

HML (High Minus Low)

The value premium; it represents the return spread between companies with high book-to-market ratios and companies with low book-to-market ratios. Once the HML factor is identified, its beta coefficient can be found through linear regression.

Model Factors

• Expected portfolio return
• Risk-free rate
• Expected premium
• Time-series regression coefficient
• Abnormal asset performance
• Market exposure

Fama–French Five-Factor Model

Factors 1 to 3

Company size, book-to-market ratio, and market excess return.

Factor 4 - RMW

Robust Minus Weak (RMW): the difference between the returns of companies with high (robust) and low (weak) operating profitability.

Factor 5 - CMA

Conservative Minus Aggressive (CMA): the difference between the returns of conservative-investment companies and aggressive-investment companies.

Expected Return Calculation (Fama–French Three-Factor Model)

Portfolio Parameters

Beta: 0.3, SMB: 1.25, HML: -0.7, an additional 4% earned annually (competitive advantage), stock return 15%, SMB 2.5%, HML 0%, risk-free rate 2%.

Expected Return Calculation

Expected return = 4% + 0.30(15% - 2%) + (2.5% × 1.25) − (0.70 × 0%) = 13.03%

10 Smarter Trading: Unlocking Alpha Through AI-Driven GenAI

Human-machine collaboration in autonomous trading systems

AI-Driven Trading Workflow

Three-Stage Trading Method

• [Pre-Trade] Investment Research: Macro narrative
• [In-Trade] Autonomous Trading System: Human experience
• [Post-Trade] Portfolio Management: High win rate and high odds

Autonomous Trading System Components

1. Market

Core effect: Select and allocate across trading markets based on macroeconomic factors such as sovereign risk, liquidity, and volatility to define the investment universe.

Macro trader advantage: Helps identify stable markets in the macroeconomy, such as sovereign nations and highly liquid assets, reduce systemic risk, and benefit from mean-reversion trends, making it suitable for long-term asset allocation.

2. Position Sizing

Core effect: Set risk boundaries for each trade, such as position size and stop-loss thresholds, to manage overall capital allocation.

Macro trader advantage: Systematically controls the risk of each trade, prevents excessive capital exposure to macroeconomic fluctuations such as recessions or market reversals, and protects capital.

3. Entry

Core effect: Define the trigger timing and conditions for entering a trade based on confirmation of the market phase.

Macro trader advantage: Ensures entries align with macro trends, avoids incorrect entries in countertrend environments such as early bear markets, and improves the strategy’s adaptability in range-bound or trending markets.

4. Stop-Loss

Core effect: Establish exit rules to limit losses and control trading losses.

Macro trader advantage: Enables timely stop-losses during macro reversals, such as deteriorating fundamentals, protecting capital from significant losses, especially in increasingly volatile macro environments.

5. Exit

Core effect: Define profit-taking conditions to lock in trading profits.

Macro trader advantage: Ensures exits occur when a macro trend ends or a target is reached, maximizes returns, and avoids drawdowns caused by holding positions too long, making it suitable for long-term macro capital management.

6. Strategy

Core effect: Handle tactical operations during trade execution, such as adding to or reducing a position.

Macro trader advantage: Simplifies the trading process, avoids excessive leverage during macroeconomic uncertainty, such as avoiding pyramiding, maintains strategy consistency, and reduces emotional interference.

7. Evaluation

Core effect: Monitor and evaluate trading performance, such as accuracy and risk-reward ratio, and provide quantitative feedback.

Macro trader advantage: Measures the strategy’s macro-level adaptability, such as maximum drawdown, helps optimize long-term asset-allocation decisions, and identifies opportunities that perform consistently throughout the economic cycle.

Macro Alpha Formula

Autonomous timing + multi-asset rotation = absolute return

AWS GenAI: A New Breakthrough Empowering Macro Trading

1. Collect: Use Amazon Lambda to collect investment-bank research reports and global asset-price data.
2. Store: Save macroeconomic data in Amazon S3.
3. Upload: Put macro data into the Amazon Bedrock knowledge base.
4. Analyze: Use Amazon Bedrock to write the macroeconomic narrative.
5. Code: Use Amazon Q Developer to write code.
6. Trade: Run your own autonomous trading system.
7. Improve: Use Amazon Bedrock and Q Developer to improve the win rate and odds.
8. Display: Use Amazon Q CLI to build a custom trading dashboard.

[Pre-Trade] Capture Meta-Trends and Gain a Deep Understanding of Complex Relationships

1. Strategic Insight

Understand intelligent planning and how to make good decisions for the future.

2. Macroeconomics

Study how the economy of an entire country works, including employment, money, and prices.

3. Political Economic History

The history of how politics and economics have operated together over time.

Key Macroeconomic Factors

1. Growth

• Observe GDP, the output of the United States and China.
• CPI and PPI, price changes in the United States.
• PMI, business performance in China.

2. Liquidity (How Easy It Is to Obtain Funding)

• Fiscal tools (quantity of funding): credit policy, the rules for borrowing.
• Monetary tools (cost of funding): interest-rate policy, the rules for borrowing costs.

3. Geopolitics (Global Events)

• Global supply chains, how goods move around the world.
• Supply and demand for metals, gold, and oil.

[In-Trade] Human-Machine Collaboration Enhances Human Judgment Through an Autonomous Trading System

1. Use natural language to turn strategy ideas into action.
2. Change human judgment—for example, the “three-day rule” in an autonomous trading system.

Core Effects

• Market: Select and allocate across trading markets based on autonomous factors such as sovereign risk, liquidity, and volatility.
• Position sizing: Set risk boundaries for each trade, such as position size and stop-loss thresholds, to manage overall capital allocation.
• Entry: Define the trigger timing and conditions for entering a trade based on confirmation of the market phase.
• Stop-loss: Establish exit rules to limit losses and control trading losses.
• Exit: Define profit-taking conditions to lock in trading profits.
• Strategy: Handle tactical operations during trade execution, such as adding to or reducing a position.
• Evaluation: Monitor and evaluate trading performance, such as accuracy and risk-reward ratio, and provide quantitative feedback.

Macro Trader Advantages

• Market: Helps identify stable markets in the macroeconomy, such as sovereign nations and highly liquid assets, reduces systemic risk, and benefits from mean-reversion trends.
• Position sizing: Systematically controls the risk of each trade, prevents excessive capital exposure to macroeconomic fluctuations such as recessions or market reversals, and protects capital.
• Entry: Ensures entries align with macro trends, avoids incorrect entries in countertrend environments, and improves the strategy’s adaptability in range-bound or trending markets.
• Stop-loss: Enables timely stop-losses during macro reversals, protects capital from significant losses, and is especially suitable when the macro environment becomes more volatile.
• Exit: Ensures exits occur when a macro trend ends or a target is reached, maximizes returns, and avoids drawdowns caused by holding positions too long, making it suitable for long-term macro capital management.
• Strategy: Simplifies the trading process, avoids excessive leverage during macroeconomic uncertainty, maintains strategy consistency, and reduces emotional interference.
• Evaluation: Measures the strategy’s macro-level adaptability, such as maximum drawdown, helps optimize long-term asset-allocation decisions, and identifies opportunities that perform consistently throughout the economic cycle.

Swing Reversal Channel Strategy

Entry - Three-Day Rule

• Day 1: Identify the formation of a “bottom.”
• Day 2: Look for a pullback or rebound from the bottom.
• Day 3: Confirm continued upward movement.
• Every step requires human judgment of price patterns.

Stop-Loss

No fixed measurement; based on news or fundamental changes. Human judgment is required during the trading cycle.

Donchian Breakout Strategy

Entry

• Breakout filter: Avoid buying during a sharp price surge at a high, as these may be false breakouts intended to deceive buyers.
• Market-phase judgment: Use this strategy only during the early stages of a bull or bear market.
• Requires human judgment of the market phase.

Stop-Loss

No trend for 20 days: If the market does not move in the expected direction within 20 days, consider exiting. This is based on human judgment that the trend has stalled.

[Post-Trade] Absolute Return: A Tracking Engine for High Win Rates and High Odds

Kelly Formula

• Primary goal: Grow capital slowly and steadily over time.
• Required win rate: Works better if you win more than half the time.
• Sensitive to odds: Greater reward → larger bet.
• Best strategy match: Swing reversal, with stable wins and losses.
• Risk in wild markets: Losses can be controlled using half-Kelly plus a safe stop-loss.
• Psychological challenge: You must place larger bets after winning, which may feel strange.

Martingale Formula

• Primary goal: Quickly recover losses and capture price trends.
• Required win rate: Works better if you win more than 55% of the time.
• Sensitive to odds: Greater reward → increase the amount of capital more quickly.
• Best strategy match: Donchian breakout, with strong price momentum.
• Risk in wild markets: Stop trading after three consecutive losses.
• Psychological challenge: You must place larger bets after losing, which may create pressure.

Trader Insight: I used AWS Q Developer to build two strategies: “Swing Reversal Channel” and “Donchian Breakout Strategy.” If I also consider odds (risk/reward) and the win rate, how can I apply these ideas to the Martingale and Kelly formulas?

A reward system helps AI focus on continuous improvement together.

11 Giveaway: Analysis Prompt Examples

Macro strategy analyst prompt templates for autonomous trading systems

My Asset Pool in the Autonomous Trading System

Asian Markets and Indexes

• XIN9 (FTSE China A50)
• HSI (Hang Seng Index)
• SX5E (Euro Stoxx 50)

U.S. Markets and Indexes

• SPX (S&P 500)
• IXIC (Nasdaq)
• DJIA (Dow Jones Industrial Average)

Currencies and Bonds

• USDX (U.S. Dollar Index)
• TLT (20+ Year U.S. Treasury ETF)
• TIP (Inflation-Protected Bond Index ETF)
• DR007 (7-day interbank pledged repo rate)

Commodities and Alternative Assets

• GLD (SPDR Gold ETF)
• CRB (Reuters-CRB Commodity Index)
• CCICFI (CSI Commodity Futures Index)

Fixed-Income Strategies

• 932337CNY040 (0–3-year medium-to-high-credit coupon strategy, net)
• 931884CNY04 (0–3-year AAA bank bonds, net)

1. Prompt Structure Components

Role

You are a macro strategy analyst.

Background

You need to identify the main economic trends each month. You focus on what the U.S. Federal Reserve and the People’s Bank of China are doing, important figures such as CPI (consumer prices), PPI (producer prices), PMI (business activity), and interest rates, changes in monetary and credit policy, and how global events affect the supply and demand of oil and metals.

Introduction

You are an experienced analyst with a strong understanding of the global economy and financial markets. You can find important information in large amounts of data and turn it into useful strategies.

Skills

You understand economic theory, can analyze data, understand financial markets, explain policy, and track global economic changes. You use different tools and models to study the economy.

Goals

Each month, identify key economic trends and provide clear analysis of policy changes.

Output Format

You write reports explaining changes in economic policies and trends.

Workflow

Collect data, analyze trends, select the most important trend for the month, and explain it in detail.

Example

Historical analysis examples with specific dates and policy changes.

Initialization

An introduction at the first chat and knowledge-base integration.

2. Meta-Trend Analysis Framework

Growth

GDP indicators, CPI/PPI changes, and PMI business-activity indicators.

Liquidity

Central-bank policy, interest rates, and changes in the money supply.

Geopolitics

Global events affecting commodity supply and demand, and trade relationships.

3. Generalization Capability

Investors

Make investment decisions based on macro trends.

Analysts

Provide detailed market analysis and insights.

Researchers

Conduct in-depth economic research and policy analysis.

4. Macro Narrative Structure

Monthly Core Trend

Identify and analyze the most important economic trend each month.

Weekly Core Trend

Track short-term developments and policy changes.

5. Knowledge-Base Categories

Strategic Insights

Long-term strategic planning and decision-making frameworks.

Macroeconomics

Economic indicators, policy analysis, and market dynamics.

Political Economic History

Historical context and precedents for the current economic situation.

Detailed Workflow

Key Data Collection Areas

1. U.S. and Chinese central-bank policy: CPI, PPI, PMI, credit, monetary, and interest-rate policy.
2. Global-event impact: How global events affect oil and metal supply and demand; analyze trends and their impact on the economy and markets.
3. Trend identification: Select the most important trend of the month and explain it in detail.

Historical Analysis Examples

August 2025

Main Trend: U.S. interest-rate policy

Details: The U.S. Federal Reserve kept interest rates between 2.25% and 2.50%. It said that the economic outlook was uncertain and that it would continue to monitor inflation. This raised attention to what might happen to interest rates next. It affected the U.S. dollar and bond markets.

September 2025

Main Trend: China’s monetary policy

Details: The People’s Bank of China cut the reserve requirement ratio by 0.5%, releasing approximately RMB 1 trillion to help the economy. This had a significant impact on Chinese financial markets and lending.

October 2025

Main Trend: Global CPI changes

Details: U.S. CPI rose 3.5% and China’s rose 2.2%. Inflation was rising in many countries, especially the United States. This led people to believe that central banks might raise interest rates to control inflation.

November 2025

Main Trend: Global events affecting oil and metal supply

Details: Tensions in the Middle East led people to expect lower oil supply, so oil prices rose. Political problems in South America affected copper supply and pushed up metal prices. These global issues were key drivers of market changes during the month.

Initialization Template

First Chat Introduction

Hello. As your macro strategy analyst, I will provide monthly updates on changes in economic policy. Please list the data in the knowledge base, and I will answer in detail. If the knowledge base does not contain content matching your question, I will say: “The answer you are looking for could not be found in the knowledge base!” I will also consider our chat history when answering.

Knowledge-Base Integration

Here is the knowledge base: “““{knowledge}””” This is the knowledge base, so I can provide you with better and more accurate advice.

Analysis Limitations and Guiding Principles

• Data sources: Use only publicly available data and official policy information.
• Information security: Avoid using confidential or sensitive information.
• Analysis standards: Analysis must be impartial and follow the rules.
• Output quality: Write reports explaining changes in economic policies and trends.