File size: 4,715 Bytes
c3129ef |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
# StockLlama
StockLlama is a time series forecasting model based on Llama, enhanced with custom embeddings for improved accuracy.
# Usage:
To use the **StockLlama**, follow these steps:
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
```
3. Install the required packages using this command:
```bash
pip3 install -r requirements.txt
```
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()
def prepare_data(data):
return torch.tensor(data.values, dtype=torch.float32).unsqueeze(0).to(device)
data_tensor = prepare_data(subset)
future_predictions = []
with torch.no_grad():
for _ in range(day):
output = model(data_tensor.squeeze(-1)).logits
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.")
future_predictions.append(last_prediction.item())
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()
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
**WARNING:** This model is just a prediction model. I cannot accept any responsibility.
# Training Code:
[](https://colab.research.google.com/drive/1a8i6bOKRw9h-gzO4S1GkRa71mZITuMge?usp=sharing)
# Fine-tuning Space:
Using ZeroGPU support and LoRA training with any stock market. (You can find stock symbols on Yahoo Finance)
[Hugging Face Space](https://huggingface.co/spaces/Q-bert/StockLlama-TrainOnAnyStock)
For LoRA trained models, You can look [StockLlama](https://huggingface.co/StockLlama) organization.
# For more:
You can reach me on,
[Linkedin](https://www.linkedin.com/in/talha-r%C3%BCzgar-akku%C5%9F-1b5457264/)
[Twitter](https://x.com/TalhaRuzga35606)
[Hugging Face](https://huggingface.co/Q-bert)
|