Model Algorithms
Haiyue
4min
Comparison Table of Common Probability Models for Financial Trend Modeling
| Model | Core Idea | Advantages | Disadvantages | Typical Application Scenarios |
|---|---|---|---|---|
| Markov Chain (MC) | System’s future state depends only on current state | Simple and intuitive, easy to compute | Can only model adjacent dependencies, cannot handle observation noise | Simple price trend (up/down) prediction |
| Hidden Markov Model (HMM) | States are hidden, can only be inferred through observations | Suitable for trend recognition, can handle noise | Strong assumptions (e.g., Gaussian distribution), limited features | Market trend recognition (bull/bear/sideways) |
| Gaussian Mixture Model (GMM) | Data is mixed from multiple Gaussian distributions | Can identify different market patterns, more flexible than single normal distribution | No temporal dependencies, needs to be combined with HMM | Return distribution modeling, market state clustering |
| Bayesian Network (BN) | Uses directed graph to represent conditional dependencies | Can model causal relationships, multi-factor analysis | Complex structure learning, large data requirements | Multi-factor market modeling (interest rates, inflation, volume) |
| Dynamic Bayesian Network (DBN) | Extends HMM, can handle multivariate time series | More flexible, can handle multi-market data | High computational complexity | Joint modeling of price, volume, volatility |
| Kalman Filter (KF) | Linear Gaussian state space model | Computationally efficient, suitable for real-time estimation | Only suitable for linear, Gaussian assumptions | Trend smoothing, price prediction |
| Particle Filter (PF) | Nonlinear, non-Gaussian state estimation | Flexible, suitable for complex markets | High computational overhead, slow convergence | Real-time state estimation in high-frequency trading |
| Conditional Random Field (CRF) | Directly models conditional probability P(Y | X) | Can utilize more context and features | Complex training, requires large amounts of data |
| Copula Model | Separates marginal distribution and correlation modeling | Can flexibly model asset correlations | Complex parameter estimation | Multi-asset correlation analysis (stocks + cryptocurrency) |
How to Choose a Model
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