Update README.md

README.md

@@ -22,8 +22,11 @@ Data Source: Benzinga Time-Series Data
 
 **Features:** MACD, MACD-Hist, RSI, SlowK, SlowD <br>
 
+> [!NOTE]
 **We choose a classification approach. Why?** <br>
-Trading decisions are categorical: you either buy, sell, or hold. Since we want an approach that is (1) easy to interpret and (2) quick to implement (for quicker testing and given limited timeframe), classification was the way to go! <br><br> Perfect for high frequency trading! ✨
+Trading decisions are categorical: you either buy, sell, or hold. Since we want an approach that is (1) easy to interpret and (2) quick to implement (for quicker testing and given limited timeframe), classification was the way to go.
+
+Perfect for high frequency trading! ✨
 
 ---
 **Method:** Create a label called “signal”  that represents whether we should buy or sell a stock based on its price over the next 2 days
@@ -47,11 +50,29 @@ Trading decisions are categorical: you either buy, sell, or hold. Since we want
 - **Threshold_weak** = 3%
 - **Min_diff** = 1%
 
+---
 
-## Signal Sample of NVDA
+Here’s a glimpse of our algorithm applied to approximately 180 days of NVDA data:
 ![nvda](https://github.com/user-attachments/assets/3bb16aa9-dcc1-4daf-b1d4-e4dc3017dbb6)
 
-
+## Model Selection
+| Model | Reasoning |
+| ----- | ------ |
+| Random Forest Classifer (RF) | • Less prone to overfitting due to using multiple decision trees instead of one <br> • Resistant to noise and outliers |       
+| Gradient Boosting Classifer (GB) | • Tends to achieve higher accuracy compared to Random Forest <br> • Excellant capturing complex relationships between features and the target variable |
+
+**Hyperparameter Tuning**
+>[!WARNING]
+> There was an error in how we configured and ran RandomizedSearchCV, which affected the hyperparameter sampling process. As a result, the reported results may be skewed and should not be considered fully reliable. We recommend rerunning the search with corrected parameters to ensure accuracy.
+
+| Tuning Method | Accuracy Score |
+| ----- | ------ |
+| RF - None | **0.67** |
+| RF - GridSearchCV | 0.54 |
+| RF - RandomizedSearchCV | 0.54 |
+| GB - None | 0.55 |
+| GB - GridSearchCV | 0.60 |
+| GB - RandomizedSearchCV | 0.59 |
 
 ## NoSQL Database 
 Since our dataset updates daily, we used MongoDB to store our data on the cloud (better accessibility ✅) The database was organized into 3 categorical collections:
@@ -65,15 +86,15 @@ Since our dataset updates daily, we used MongoDB to store our data on the cloud
 ├── Market Logs/
 ```
 
-**Historical Data:** Stores historical stock data for the last 6 months, with 5-minute candlestick intervals (this is what the model is trained on)
+- **Historical Data:** Stores historical stock data for the last 6 months, with 5-minute candlestick intervals (this is what the model is trained on)
 
-**EMA-78 Data:** Stores 78 rows of past data needed to compute the technical indictator during real-time excutation
+- **EMA-78 Data:** Stores 78 rows of past data needed to compute the technical indictator during real-time excutation
 
-**Market Logs:** Stores logs of model executation (buy or sell). The schema is as follows:
-- Timestamp 
-- Trade Type 
-- Monetary Metrics (e.g., trader’s funds and stock prices)
-- Volume
+- **Market Logs:** Stores logs of model executation (buy or sell). The schema is as follows:
+  - Timestamp 
+  - Trade Type 
+  - Monetary Metrics (e.g., trader’s funds and stock prices)
+  - Volume
 
 
 ## Installing Dependencies
@@ -91,6 +112,6 @@ To install the necessary dependencies for this project, follow these steps:
    ```
 
 ## Credits and Acknowledgements 
-Special thanks to Swathi Senthil, George Abu Daoud, Bharath Venkataraman, and Boshen Parthasarathy for the feedback and mentorship.
+Special thanks to Swathi Senthil, George Abu Daoud, Bharath Venkataraman, and Boshen Parthasarathy for the mentorship and feedback.
 
 **Tools/Libraries:** Google Colab, VSCode, Scikit-learn, MongoDB, Benzinga