| # StockLlama |
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| StockLlama is a time series forecasting model based on Llama, enhanced with custom embeddings for improved accuracy. |
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| # Usage: |
| To use the **StockLlama**, follow these steps: |
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| 1. Clone the repository to your local machine. |
| |
| ```bash |
| git clone https://github.com/LegallyCoder/StockLlama |
| ``` |
| 2. Open a terminal or command prompt and navigate to the script's directory. |
| ```bash |
| cd src |
| ``` |
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| 3. Install the required packages using this command: |
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| ```bash |
| pip3 install -r requirements.txt |
| ``` |
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| 4. Open new python file at the script's directory. |
| ```python |
| import yfinance as yf |
| import torch |
| import matplotlib.pyplot as plt |
| import numpy as np |
| from scipy.ndimage import gaussian_filter1d |
| from datetime import datetime, timedelta |
| from modeling_stockllama import StockLlamaForForecasting |
| import pandas as pd |
| |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
| model = StockLlamaForForecasting.from_pretrained("StockLlama/StockLlama").to(device) |
| day = 365 |
| def download_stock_data(stock_symbol): |
| end_date = datetime.today().date() |
| start_date = datetime.today().date() - timedelta(days=day) |
| try: |
| return yf.download(stock_symbol, start=start_date, end=end_date, progress=False) |
| except Exception as e: |
| print(f"Error downloading data for {stock_symbol}: {e}") |
| return None |
| |
| def predict_future_prices(stock_symbol): |
| stock_data = download_stock_data(stock_symbol) |
| if stock_data is not None: |
| subset = stock_data[['Close']].tail(day).reset_index(drop=True) |
| model.eval() |
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| def prepare_data(data): |
| return torch.tensor(data.values, dtype=torch.float32).unsqueeze(0).to(device) |
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| data_tensor = prepare_data(subset) |
| future_predictions = [] |
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| with torch.no_grad(): |
| for _ in range(day): |
| output = model(data_tensor.squeeze(-1)).logits |
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| if len(output.shape) == 3: |
| last_prediction = output[:, -1, :].squeeze(0) |
| elif len(output.shape) == 2: |
| last_prediction = output.squeeze(0) |
| else: |
| raise ValueError("Unexpected model output shape.") |
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| future_predictions.append(last_prediction.item()) |
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| if len(output.shape) == 3: |
| data_tensor = torch.cat((data_tensor[:, 1:, :], output[:, -1, :].unsqueeze(1)), dim=1) |
| elif len(output.shape) == 2: |
| data_tensor = torch.cat((data_tensor[:, 1:], last_prediction.unsqueeze(0).unsqueeze(0)), dim=1) |
| future_predictions = gaussian_filter1d(future_predictions, sigma=1) |
| combined_prices = pd.concat([subset['Close'], pd.Series(future_predictions)], ignore_index=True) |
| historical_dates = stock_data.index[-day:].to_list() |
| prediction_dates = [historical_dates[-1] + timedelta(days=i) for i in range(1, len(future_predictions) + 1)] |
| combined_dates = historical_dates + prediction_dates |
| |
| plt.figure(figsize=(12, 6)) |
| plt.plot(combined_dates[:len(subset)], combined_prices[:len(subset)], label='Historical Prices', linestyle='-') |
| plt.plot(combined_dates[len(subset)-1:], combined_prices[len(subset)-1:], label='Predicted Prices', linestyle='--') |
| plt.xlabel('Date') |
| plt.ylabel('Price') |
| plt.title(f'{stock_symbol} - Combined Historical and Predicted Prices') |
| plt.legend() |
| plt.grid(True) |
| plt.xticks(rotation=45) |
| plt.tight_layout() |
| plt.show() |
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| return future_predictions |
| else: |
| print(f"Data could not be downloaded for {stock_symbol}.") |
| return None |
| |
| stock_symbol = 'AAPL' |
| future_predictions = predict_future_prices(stock_symbol) |
| |
| ``` |
| ## Result |
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| **WARNING:** This model is just a prediction model. I cannot accept any responsibility. |
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| # Training Code: |
| [](https://colab.research.google.com/drive/1a8i6bOKRw9h-gzO4S1GkRa71mZITuMge?usp=sharing) |
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| # Fine-tuning Space: |
| Using ZeroGPU support and LoRA training with any stock market. (You can find stock symbols on Yahoo Finance) |
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| [Hugging Face Space](https://huggingface.co/spaces/Q-bert/StockLlama-TrainOnAnyStock) |
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| For LoRA trained models, You can look [StockLlama](https://huggingface.co/StockLlama) organization. |
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| # For more: |
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| You can reach me on, |
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| [Linkedin](https://www.linkedin.com/in/talha-r%C3%BCzgar-akku%C5%9F-1b5457264/) |
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| [Twitter](https://x.com/TalhaRuzga35606) |
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| [Hugging Face](https://huggingface.co/Q-bert) |
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