1. Signal Processing Perspective
Many market behaviors can be viewed as time series signals. Applying signal processing techniques can reveal hidden patterns:
- Fourier Transform & Spectral Analysis – Identifies dominant market cycles and frequencies, similar to how sound waves are analyzed.
- Wavelet Transforms – Can detect localized price movements and anomalies, much like wavelet-based edge detection in images.
- Hilbert Transform & Instantaneous Phase Analysis – Helps track momentum shifts in a way similar to how electrical signals are analyzed.
- Autocorrelation & Cross-Correlation – Measures how price movements at different time scales relate to each other.
- Filter-based Techniques (Kalman Filter, Butterworth Filters) – Used to smooth or extract price trends while eliminating noise.
2. Biological Inspiration
Biological systems are known for their adaptive and evolutionary behavior. Markets also evolve dynamically:
- Neural Spiking Models – Price changes could be modeled like neuron firings, helping to capture sudden price movements.
- Homeostasis & Mean-Reversion Analysis – Similar to how organisms maintain equilibrium, price series often return to their mean.
- Biological Growth Models (Gompertz Curve, Logistic Growth) – Can model how price moves in bubbles and crashes.
- Epidemiological Models (SIR Models) – Could be used to model information diffusion in markets.
3. Mathematical & Information Theory
Mathematical structures provide robust ways to extract signals from OHLCV data:
- Entropy & Information Theory (Shannon Entropy, Fisher Information) – Measures how much “surprise” or uncertainty exists in price movements.
- Fractal Geometry & Hurst Exponent – Measures long-term memory effects in markets, indicating trends or mean-reverting behavior.
- Recurrence Quantification Analysis (RQA) – Detects repeating patterns in price dynamics.
- Topological Data Analysis (Persistent Homology) – Studies market structure from a shape-theoretic perspective.
4. Graph Theory & Network Science
Viewing markets as networks enables unique feature extraction:
- Order Book as a Graph – Treating bid/ask price levels as nodes and transactions as edges reveals market microstructure.
- Price Series as a Markov Chain – Helps estimate transition probabilities between different market states.
- Community Detection Algorithms (Louvain, Spectral Clustering) – Identifies correlated asset clusters.
5. Philosophical & Cognitive Sciences
Philosophical and cognitive models of decision-making may provide new perspectives:
- Kantian Causality & Event-Driven Bars – Investigates structural causality in market data, refining event-driven bar techniques.
- Game Theory & Nash Equilibrium Analysis – Models trading as an interactive game with competing rational agents.
- Bayesian Reasoning & Cognitive Bias Models – Captures how traders update beliefs in response to new information.
Application to OHLCV Data: New Event-Driven Bars
Inspired by these concepts, we can extend event-driven bars beyond traditional methods:
- Entropy-Based Bars – Sample bars dynamically based on information content in price movements.
- Neural Spike Bars – Generate bars when price momentum resembles biological neuron firings.
- Network-Connected Bars – Construct bars when significant transactions cluster together in network-space.
- Fractal Bars – Sample bars based on market self-similarity patterns.
Other stuff
1. Quantum Wave Function Analysis (QWFA)
- Treats price movements as quantum wave functions to detect non-classical market states.
- Inspired by quantum superposition and decoherence.
- Uses Schrödinger equation modeling to analyze potential paths of asset prices.
2. Quantum Entanglement Measures in Correlations
- Detects if asset returns are strongly entangled, signaling market-wide structural changes.
- Useful for multi-asset regime shifts.
3. Ising Model for Market Phase Transitions
- Inspired by magnetic spin interactions in physics.
- Can model bull/bear market transitions as phase shifts.
4. Tensor Networks for Feature Compression
- Quantum Tensor Networks (used in Quantum Machine Learning) compress high-dimensional features efficiently.
- Can reduce noise in financial time series before feeding into deep learning models.
F. Chaos Theory & Dynamical Systems
1. Lyapunov Exponents
- Measures how quickly small changes in price propagate.
- High exponent → Unstable (chaotic market), Low exponent → Stable (trending market).
2. Kaplan-Yorke Dimension
- Captures chaotic structure of price series.
- Used in nonlinear forecasting models.
3. Strange Attractors in Market Dynamics
- Detects recurring price states in chaotic environments.
- Helps reduce noise in forecasting by identifying dominant attractor states.
G. Neuroscience & Cognitive Science-Inspired Features
1. Hebbian Learning for Pattern Recognition
- Inspired by synaptic strengthening in neurons.
- Can help autoencoders learn price patterns better.
- Works well with contrastive learning & self-supervised models.
2. Self-Organizing Maps (SOMs) for Regime Clustering
- A type of unsupervised learning that mimics brain’s ability to cluster patterns.
- Could be used to identify distinct market phases.
3. Brain-inspired Spiking Neural Networks (SNNs)
- Uses event-based processing instead of traditional sequential processing.
- Can model high-frequency market events efficiently.
H. Topological Data Analysis (TDA)
1. Persistent Homology for Market Structure
- Uses higher-dimensional topology to capture the shape of financial data.
- Detects hidden loops and voids in market structure.
2. Mapper Algorithm for Regime Detection
- Constructs graphs based on asset return similarities.
- Finds market clusters dynamically.
I. Climate Science & Weather Forecasting Methods
1. Ensemble Forecasting
- Used in meteorology to predict hurricanes.
- Combining multiple models improves robustness.
- Could be applied to financial regime forecasting.
2. Wavelet Coherence for Market Turbulence
- Used in oceanography to detect multi-scale dependencies.
- Can be applied to high-volatility detection.
J. Cybersecurity & Anomaly Detection
1. Hidden Markov Models (HMM) with Cyber Threat Detection Methods
- Used to detect intrusions in networks.
- Can help identify market manipulation & regime shifts.
2. Zero-Day Attack Detection Algorithms
- Used in cybersecurity to detect unknown threats.
- Could be adapted to detect unexpected market events (black swans).