Input Stock Data
Paste your historical stock data below or upload a file. The data must be tab-separated.
Requires at least 10 data points. The column order must be: Timestamp High Low Open Close Volume.
Tip: You can copy this data from financial websites like Yahoo Finance or MSN Money. Just navigate to the "Historical Data" tab for a stock and copy the table.
The Local HMM Engine
This tool uses a classical statistical method called a Hidden Markov Model (HMM) to analyze stock price movements. The entire calculation happens in your browser—no data is sent to a server. The process involves four main steps:
In simple terms: Think of the market as having hidden "moods" (like 'optimistic', 'cautious', or 'panicked'). We can't see these moods directly, but we can see their effects: the daily price movements (e.g., 'big jump up', 'small dip down'). The HMM learns two key things from the historical data: 1) How likely the market is to switch from one mood to another, and 2) Which price movements are typical for each mood. To make a prediction, it looks at today's price movement, makes its best guess about the market's current mood, and then predicts the next mood and the price action that will likely follow.
- Vector Quantization: The engine converts each data point into a vector based on its price changes. It then groups these vectors into a smaller set of distinct "market patterns" using k-means clustering.
- Model Training: Using the Baum-Welch algorithm, the HMM learns the probabilities of transitioning between different unobservable "hidden market states" (e.g., 'bullish', 'bearish') and which visible market patterns each state produces.
- Robustness with Laplace Smoothing: As a final touch, the model applies Laplace's Rule of Succession (or add-one smoothing) during training. This ensures that even rare market patterns are given a small, non-zero probability of occurring, which makes the model more robust and prevents overly confident, and potentially incorrect, predictions based on sparse data.
- Prediction: Based on the most recent data, the engine calculates the most probable state for the next period and predicts the resulting price movement, leading to a Buy, Sell, or Hold signal.
Important Information
For Informational Purposes Only
This tool is for educational purposes only. The predictions are based on a mathematical model applied to past data and are not guaranteed to be accurate. This is not financial advice. Always consult a qualified financial professional before making investment decisions.