Update the documentation

Author: ArtyomVancyan

README.md

@@ -1,15 +1,51 @@
 > [!CAUTION]  
-> This project is intended for educational purposes only and does not constitute financial advice. The stock price predictions generated by this software are not guaranteed to be accurate and should not be relied upon for making investment decisions. The authors of this project are not responsible for any financial losses incurred as a result of using this software.
+> This project is intended for educational purposes only and does not constitute financial advice. The stock price
+> predictions generated by this software are not guaranteed to be accurate and should not be relied upon for making
+> investment decisions. The authors of this project are not responsible for any financial losses incurred as a result of
+> using this software.
 
 # PATTERNITY
 
-This project focuses on predicting stock prices using a deterministic algorithm inspired by the Long Short-Term Memory (LSTM) model of deep learning. Unlike traditional statistical models, this approach leverages the power of pattern recognition to identify and repeat the most similar historical patterns in stock price data, with visualizations inspired by Google Finance day charts.
+This project focuses on predicting stock prices using a deterministic algorithm inspired by the LSTM model of deep
+learning. Unlike traditional statistical models, this approach leverages the power of pattern recognition to identify
+and repeat the most similar historical patterns in stock price data, with visualizations inspired by Google Finance
+charts.
 
-![Screenshot from 2024-07-26 14-58-12](https://github.com/user-attachments/assets/516c5acb-8324-4af1-9a7f-869acd185fb8)
+## Usage
+
+```bash
+python3 -m pip install patternity
+```
+
+Patterity comes with collector functions to get historical data of an instrument. The following example demonstrates how
+to predict and plot the price of Bitcoin. For stocks, you can use the `get_stock` function instead. The `Pattern`
+class (core) is used to predict the price of an instrument and has the following arguments.
+
+- `history` - Historical data of an instrument (required).
+- `window` - The minimum length of the pattern window to be considered. (optional)
+- `progress` - Callback function to track the progress of the prediction. Recommended for large historical data.
+
+```python
+from patternity import Pattern, get_crypto
+from rich.progress import track
+
+if __name__ == "__main__":
+    history = get_crypto("BTCUSDT", depth=200)
+    pattern = Pattern(history, progress=track)
+    pattern.predict()
+    pattern.plot()
+```
+
+If everything is set up correctly, you should see a plot similar to the one below. If nothing was predicted, try on
+larger historical data. Also, you can change the `window` parameter to adjust the minimum length of the pattern to be
+recognized. The higher the `window`, the more accurate the prediction will be and longer it will take to compute.
+
+![](https://github.com/user-attachments/assets/516c5acb-8324-4af1-9a7f-869acd185fb8)
 
 ## Contribute
 
-Any contribution is welcome. Please don't hesitate to open an issue or a discussion if you have any questions not covered by the documentation. Please open a pull request if you have any ideas or suggestions on optimizing the algorithm.
+Any contribution is welcome. Don't hesitate to open an issue or a discussion if you have any questions not covered by
+the documentation. Please open a pull request if you have any ideas or suggestions on optimizing the algorithm.
 
 ## License
 

src/patternity/pattern.py

@@ -6,19 +6,19 @@
 
 
 class Pattern:
-    def __init__(self, history: np.array, min_length: int = 50, progress: Callable[[Iterable], Iterable] = None):
+    def __init__(self, history: np.array, window: int = 50, progress: Callable[[Iterable], Iterable] = None):
         """
         Initialize the pattern detector.
 
         :param history: Historical data to be analyzed. Usually, the closing prices of an asset.
-        :param min_length: Minimum length of the pattern to be considered.
+        :param window: The minimum length of the pattern window to be considered.
         :param progress: Progress bar to be displayed during the analysis.
         """
 
         self.history = history
         self.horizon = len(history)
         self.progress = progress or (lambda x: x)
-        self.min_length = min_length
+        self.window = window
         self.predictions = np.array([])
 
     @staticmethod
@@ -57,7 +57,7 @@ def slices(self) -> tuple[tuple, np.array]:
         """Generates all possible slices of the historical data."""
 
         for i in range(self.horizon):
-            for j in range(i + self.min_length, self.horizon + 1):
+            for j in range(i + self.window, self.horizon + 1):
                 yield (i, j), self.history[i:j]
 
     def patterns(self) -> list[tuple[tuple, np.array]]:
@@ -66,8 +66,8 @@ def patterns(self) -> list[tuple[tuple, np.array]]:
         def valid(x) -> bool:
             return all([
                 self.horizon == x[0][1],
-                len(x[1]) >= self.min_length,
-                len(x[1]) <= self.horizon - self.min_length,
+                len(x[1]) >= self.window,
+                len(x[1]) <= self.horizon - self.window,
             ])
 
         return sorted([x for x in self.slices() if valid(x)], key=lambda x: (len(x[1]), x[0][1]), reverse=True)