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README.md

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+---
+license: mit
+language:
+- en
+tags:
+- td3
+- stock prediction
+- reinforcement learning
+---
+
+# TD3 Model for AAPL Stock Prediction
+## Model Description
+
+This model is a TD3 (Twin Delayed Deep Deterministic Policy Gradient) algorithm applied for stock price prediction, specifically trained on AAPL (Apple Inc.) stock data. The TD3 model is a reinforcement learning agent that interacts with a stock market environment and is trained to predict and maximize the return from trading AAPL stock.
+
+### Key Features
+- Asset: AAPL (Apple Inc.) Stock
+- Model Type: TD3 (Twin Delayed DDPG)
+- Action Space: Continuous (Buy, Sell, Hold decisions)
+- Reward: Modeled on cumulative returns
+- Training Data: Historical stock prices and related financial indicators for AAPL stock
+- Environment: Custom stock trading environment simulating price movement and portfolio management
+- Framework: PyTorch
+
+## Quick Run
+To use this model for stock prediction and trading, install the required dependencies and load the model via Hugging Face. Here is an example code snippet:
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+import torch.nn as nn
+import numpy as np
+
+# Download the model
+model_path = hf_hub_download(repo_id="siddheshtv/td3-stock-aapl", filename="td3_stock_prediction_model_AAPL_full.pth")
+
+# Load the model
+checkpoint = torch.load(model_path)
+
+# Recreate the Actor class
+class Actor(nn.Module):
+    def __init__(self, state_dim, action_dim, max_action):
+        super(Actor, self).__init__()
+        self.net = nn.Sequential(
+            nn.Linear(state_dim, 400),
+            nn.ReLU(),
+            nn.Dropout(0.2),
+            nn.Linear(400, 300),
+            nn.ReLU(),
+            nn.Dropout(0.2),
+            nn.Linear(300, action_dim),
+            nn.Tanh()
+        )
+        self.max_action = max_action
+
+    def forward(self, state):
+        return self.max_action * self.net(state)
+
+# Instantiate the model
+model = Actor(checkpoint['state_dim'], checkpoint['action_dim'], checkpoint['max_action'])
+model.load_state_dict(checkpoint['model_state_dict'])
+model.eval()  # Set the model to evaluation mode
+
+# Function to select action
+def select_action(state):
+    with torch.no_grad():
+        state = torch.FloatTensor(state.reshape(1, -1))
+        return model(state).cpu().data.numpy().flatten()
+
+# Example usage
+state = np.random.rand(checkpoint['state_dim'])  # Replace with actual state data
+action = select_action(state)
+print(f"Predicted action: {action}")
+```
+
+## Citation
+
+```
+@misc{siddheshtv-td3,
+  title={TD3 Model for AAPL Stock Prediction},
+  author={Siddhesh Kulthe},
+  year={2024},
+  howpublished={\url{https://huggingface.co/siddheshtv/td3-stock-aapl}},
+  note={TD3 model for predicting stock price movements of AAPL using reinforcement learning},
+}
+```