Identify and Define Market States
Classify Price Patterns: Start by defining what constitutes a “state” in the market. States could be trends (uptrend, downtrend, sideways), volatility levels, or even specific conditions like bullish or bearish.
Use Pattern Recognition: Employ methods like moving averages, support/resistance levels, or statistical indicators (like Bollinger Bands or RSI) to classify these states.
Cluster Analysis: Using clustering algorithms, such as k-means, can help identify clusters or states by grouping similar data patterns.
Apply Geometry and Topological Techniques
Fractal Analysis: Since markets often have fractal-like properties, fractal algorithms can help capture recurring patterns across timeframes.
Topology-Based Analysis: Using techniques like persistent homology or Morse theory, which are used in data topology, can be applied to see if patterns in price data form recognizable structures.
Machine Learning for State Transitions
Use models like Hidden Markov Models (HMM) or LSTM neural networks to predict transitions between states by training on historical data.
Anomaly Detection: Use statistical anomaly detection to identify when the market is entering a rare or transitional state.
Ideal Programming Libraries
For JavaScript and data manipulation, especially since you're learning Ramda for functional programming, you’ll have solid tools for array manipulation and functional workflows.
For JavaScript/Node.js:
Ramda: Great for functional programming, as you’re learning, and ideal for pipeline processing of array-based data, which is essential in sequential market data analysis.
D3.js: Useful for visualizing geometric structures and market trends. It can help create plots of market states and map geometric structures visually.
Technical Indicators Library: Provides prebuilt indicators like moving averages, RSI, and Bollinger Bands. This is a solid foundation for detecting shifts in market states.
For Python (if you decide to bring in a Python backend or precompute data):
Scikit-Learn: Offers clustering algorithms (k-means, DBSCAN) that can identify distinct market states.
TSFresh: Extracts time-series features automatically, which can be a game-changer in identifying state shifts in high-frequency data.
GUDHI or Ripser (for Topological Data Analysis): Both libraries are excellent for applying persistent homology or geometric analysis to detect complex patterns.
TensorFlow or PyTorch: These libraries are ideal for building machine learning models, such as LSTMs or Hidden Markov Models, to predict state transitions.
For Visualization of Market States:
Three.js: If you want to go deeper into the geometry aspect, Three.js allows you to render 3D visualizations of data patterns, which can make geometric structures more tangible.
ECharts or Highcharts: These are highly customizable and support complex visualizations that could help map out states or transition probabilities on charts.
For JavaScript/Node.js: Ramda: Great for functional programming, as you’re learning, and ideal for pipeline processing of array-based data, which is essential in sequential market data analysis. D3.js: Useful for visualizing geometric structures and market trends. It can help create plots of market states and map geometric structures visually. Technical Indicators Library: Provides prebuilt indicators like moving averages, RSI, and Bollinger Bands. This is a solid foundation for detecting shifts in market states. For Python (if you decide to bring in a Python backend or precompute data): Scikit-Learn: Offers clustering algorithms (k-means, DBSCAN) that can identify distinct market states. TSFresh: Extracts time-series features automatically, which can be a game-changer in identifying state shifts in high-frequency data. GUDHI or Ripser (for Topological Data Analysis): Both libraries are excellent for applying persistent homology or geometric analysis to detect complex patterns. TensorFlow or PyTorch: These libraries are ideal for building machine learning models, such as LSTMs or Hidden Markov Models, to predict state transitions. For Visualization of Market States: Three.js: If you want to go deeper into the geometry aspect, Three.js allows you to render 3D visualizations of data patterns, which can make geometric structures more tangible. ECharts or Highcharts: These are highly customizable and support complex visualizations that could help map out states or transition probabilities on charts.