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Three-Body Dynamics Signal Gating Mechanism and Market State Variable Analysis
Quantitative Finance
👤 Quantitative trading researchers, strategy developers, analysts interested in market dynamics and signal gating mechanisms
This paper first introduces a signal gating mechanism based on three-body dynamics, which determines strategy entry and exit timing by estimating market state variables δ (premium), μ (momentum), and σ (volatility) to maximize strategy returns. The author elaborates on the intuitive understanding of these three state variables: the core of δ is the psychological anchoring effect, which can be analyzed through volume distribution; the core of μ is the speed of price changes, measurable by moving averages of log returns; the core of σ is the magnitude of price changes, measurable by the standard deviation of log returns. The article also discusses criteria for judging the effectiveness of estimation methods, i.e., evaluating based on the quality of gating effects, and notes that advanced signal strategies often already include estimates of these state variables but require systematic understanding. Finally, the author suggests that after decoupling signal gating, these state variables can serve as key factors, while the signal strategy itself may only need the simplest form.
- ✨ Proposes a signal gating mechanism based on three-body dynamics, dynamically adjusting strategy entry by estimating δ, μ, and σ
- ✨ Explains in detail the intuitive understanding and estimation methods of market state variables δ, μ, and σ, emphasizing psychological anchoring, price speed, and magnitude
- ✨ Discusses criteria for judging estimation effectiveness, i.e., the improvement in strategy returns due to gating effects
- ✨ Points out that advanced signal strategies already include estimates of market state variables but require systematic understanding
- ✨ Suggests that after decoupling gating, state variables can serve as key factors, simplifying signal strategy design
📅 2026-02-10 · 1,208 words · ~6 min read
Market State Variable Modeling Scheme for Three-Body Gating
Quantitative Finance
👤 Financial quantitative analysts, market researchers, and technical personnel interested in financial market modeling and gating mechanisms
Building on the three-body dynamics hypothesis and gating mechanism concept, this paper systematically outlines the modeling scheme for market state variables δ (premium), μ (momentum), and σ (volatility). The core innovation lies in the definition of δ: through the volume gravitational field model, nonlinear operations (Gaussian kernel functions and gradient calculations) are introduced to maintain its independence from μ and σ. μ is defined as the exponential moving average of returns to extract trend information; σ is defined as the standard deviation of returns to measure volatility amplitude; δ is based on the distribution of volume along the price axis, calculating the regression force when prices deviate from high-volume concentration areas. The article details the specific steps for calculating these three variables from candlestick sequences, including parameter settings and independence arguments, providing a new modeling framework for financial market analysis.
- ✨ δ (premium) is defined through the volume gravitational field model, introducing nonlinear operations to ensure independence from μ (momentum)
- ✨ μ is defined as the exponential moving average of returns, and σ is defined as the standard deviation of returns
- ✨ Specific steps and parameter recommendations for calculating δ, μ, and σ from candlestick sequences
- ✨ Independence arguments for the three variables (δ, μ, σ) are based on nonlinear operations and different information sources
- ✨ Kernel functions (e.g., Gaussian kernel) model psychological anchoring effects, with bandwidth adaptable to volatility
📅 2026-02-10 · 1,581 words · ~8 min read
Prediction Market Arbitrage Project Launch and Technology Selection
Quantitative Finance
👤 Readers interested in prediction markets, high-frequency trading, Rust programming, or technical project development
This article describes the prediction market arbitrage project launched on February 8, 2026, which falls under the high-frequency trading (HFT) category and has extremely high requirements for execution efficiency. The technology selection decision is to use Rust language to build a low-latency trading execution system to cope with the rapid elimination of arbitrage opportunities. The team's current technology stack is limited, and they plan to advance the project through vibe coding, taking this opportunity to deeply learn the Rust ecosystem and toolchain in preparation for future projects. The article also mentions that the team previously had basic Rust experience with Solana smart contracts but not in-depth, and they look forward to embracing challenges through this project.
- ✨ Prediction market arbitrage project launch, belonging to the high-frequency trading (HFT) category
- ✨ Technology selection adopts Rust language to achieve a low-latency trading execution system
- ✨ Team technology stack is limited, planning to advance the project through vibe coding
- ✨ Take this opportunity to learn the Rust ecosystem and toolchain for future preparation
- ✨ The project has high requirements for execution efficiency to quickly capture arbitrage opportunities
📅 2026-02-08 · 178 words · ~1 min read
Derivation of SDE Equations for Three-Body Dynamics in Capital Markets
Quantitative Finance
👤 Financial modeling researchers, quantitative analysts, economists interested in capital market dynamics
Building on the article 'The Three-Body Dynamics Hypothesis in Capital Markets,' this paper derives a complete system of stochastic differential equations (SDEs) to describe the interactions among momentum capital (M), value capital (V), and liquidity capital (L) in capital markets. The article defines fast variables (such as log premium, momentum, volatility) and slow variables (the volumes of the three types of capital) and extracts 12 formalizable core constraints. Through a detailed analysis of the SDE equations, the article validates these constraints one by one, including positive feedback for M, negative feedback for V, directionless feedback for L, positive and negative feedback loops, payoff matrices, and crowding effects. All constraints are validated, indicating that this SDE system fully implements the qualitative mechanisms from the original article, such as volatility clustering, fat-tailed distributions, and chaotic behavior. The article also conducts phase analysis and statistical property validation, providing a foundation for subsequent numerical simulations, bifurcation analysis, and parameter calibration.
- ✨ Derived a complete SDE system describing the interactions among three types of capital
- ✨ Validated 12 core constraints, including positive/negative feedback and payoff matrices
- ✨ The system explains market characteristics such as volatility clustering and fat-tailed distributions
📅 2026-02-07 · 1,839 words · ~9 min read
EA Project Introduction: AI-Driven Priority Fund for Quantitative Trading
Quantitative Finance
👤 Investors interested in blockchain investments, quantitative trading, and stable returns, particularly those seeking low-risk, principal-protected priority fund participation.
EA (Earnby.AI) is a priority fund project deployed on the BSC chain, settled in USDC, offering stable returns to investors through AI-driven quantitative trading strategies. The project uses a priority/subordinated capital structure, where priority capital enjoys principal protection, and subordinated capital is borne by the project's own funds to assume risks. The management team consists of professionals in quantitative trading and blockchain, including 5 co-founders. The project offers floating returns, currently with an annualized yield of 12%, and investors can redeem at any time. Strategies include directional portfolio strategies and delta-neutral strategies, with historical performance showing a cumulative return of 39.22% and an annualized return of approximately 22%. The project has no management fees, flexible lock-up periods, and aims to provide low-risk, sustainable returns for investors.
- ✨ EA is a priority fund project deployed on the BSC chain, settled in USDC
- ✨ Utilizes AI-driven quantitative trading strategies, including directional portfolio and delta-neutral strategies
- ✨ Capital is divided into priority and subordinated tiers, with priority capital enjoying principal protection
- ✨ The management team consists of 5 professionals in quantitative trading and blockchain
- ✨ Offers floating returns, currently with an annualized yield of 12%, and investors can redeem at any time
📅 2025-11-01 · 1,525 words · ~7 min read
Full Spectrum Analysis: The Optimal Method for Information Monetization
Quantitative Finance
👤 Quantitative traders, investment strategy developers, financial engineers, and advanced investors interested in the Kelly Criterion and leverage optimization.
This article proposes Full Spectrum Analysis (FSA), an investment trading strategy framework optimized based on the Kelly Criterion. It first analyzes the limitations of the traditional Kelly formula in investment applications, such as lack of leverage and short-selling considerations, and liquidation timing issues. Then, FSA constructs a systematic trading decision model by defining outcome spaces, calculating optimal leverage and compound returns. The article elaborates on the mathematical principles of FSA, including the calculation of expected returns and compound returns, as well as the algorithm for solving optimal leverage using Newton's iteration method. Additionally, it introduces historical backtesting methods (e.g., Gross Profit Margin GPM calculation), considerations for live trading modules, and measures to address black swan events. The core advantage of FSA lies in its ability to utilize imperfect probability information to maximize long-term returns by optimizing leverage decisions, reducing the high requirements for information quality.
- ✨ Full Spectrum Analysis (FSA) is based on the Kelly Criterion, optimizing investment leverage to maximize compound growth rate
- ✨ Define outcome spaces, probability distributions, and returns to calculate optimal leverage and compound returns
- ✨ Use Newton's iteration method to solve for optimal leverage, handling feasible regions and convergence issues
- ✨ Introduce Gross Profit Margin (GPM) for historical backtesting to evaluate strategy profitability and capacity
- ✨ Incorporate symmetric black swan event probabilities to limit leverage and prevent abuse and extreme risks
📅 2025-08-10 · 2,810 words · ~13 min read