Alpaca Stock Trading System

Alpaca Neural Bot v10.0.0 is an advanced AI-powered stock trading bot that uses a CNN-LSTM neural network built with PyTorch to predict market trends and execute trades via the Alpaca API, optimized for GPU acceleration on NVIDIA RTX 5080. It incorporates technical indicators from TA-Lib (such as RSI, MACD, ATR, and ADX), sentiment analysis via Hugging Face Transformers, and robust risk management features including ATR-based stops, trailing stops, drawdown limits, volatility filters, and minimum holding periods. The bot supports comprehensive backtesting with metrics like Sharpe ratio, max drawdown, Monte Carlo simulations, and buy-and-hold benchmarks on historical data from 2015 to the current date (November 09, 2025), alongside live paper trading with email notifications and multiprocessing for efficient training across multiple symbols.

System Overview

• Neural Network Prediction: Employs a CNN-LSTM model (Conv1D layers followed by LSTM) to predict future price directions over LOOK_AHEAD_BARS (7 bars) based on 30-timestep sequences of 23 features from historical and real-time data.

• Model Training: Trains the CNN-LSTM model per symbol using Adam optimizer (learning rate 0.001), BCEWithLogitsLoss, early stopping, and ReduceLROnPlateau scheduler (patience 5, factor 0.5) over customizable epochs with customizable batch size; supports data augmentation via noise addition; in backtest mode, enables an automated retraining cycle (up to X attempts) that retrains until performance criteria are met, then selects and copies the best attempt's models/scalers across all attempts.

• Backtesting: Simulates trades across multiple symbols between a specifyable data, and today. Includes transaction costs, ATR-based stops/profits, min holding periods, performance metrics (Sharpe ratio, max drawdown, win rates, accuracies), Monte Carlo simulations (50,000 runs), buy-and-hold benchmarks.

• Live Trading: Executes market orders during open market hours with email notifications for individual trades and daily summaries.

• Multi-Symbol Support: Handles trading for multiple stocks (e.g., SPY, MSFT, AAPL, AMZN, NVDA, META, GOOGL) with parallel multiprocessing (4 workers) for model training and independent backtesting per symbol.

• GPU Acceleration: Leverages PyTorch with CUDA for accelerated training and inference on RTX 5080, including memory management via torch.cuda.empty_cache() after parallel sessions.

• Sentiment Analysis: Has a framework to utilizes DistilBERT (distilbert-base-uncased-finetuned-sst-2-english) via Hugging Face Transformers for news sentiment scoring, currently defaults to netural. (A simulated place holder)

• Free Tier Compatible: Optimized for Alpaca's free API tier with retry logic (3 attempts, 1-second delay), data caching (24-hour expiry), and rate-limit handling via tenacity.

Disclaimer: It is HIGHLY recommened to use this for educational purposes only. Use paper trading to avoid real financial risk.

Installation Steps

Follow these steps in order to set up the environment.

Note: All commands after step 2 are for WSL/Ubuntu terminal.

Prerequisites

• Drivers: Install the latest nvidia drivers from nvdia app for your GPU.
• Hardware: NVIDIA GPU with at least 16GB VRAM for efficient training. (Confirmed to work properly on RTX 5080 but other 50 series and 40 series card are likely to work).
• Operating System: Windows Subsystem for Linux (WSL2) on Windows 11 or Native Ubuntu 22.04+
• Alpaca Account: Free account with paper trading enabled. Get API keys from Alpaca Dashboard.
• Gmail Account: For email notifications (enable "Less secure app access" or use app password).
• Internet: Stable connection for API calls (VPN is not recommended due to possible rate limits).

Step 1: Set Up WSL (Windows Users Only)

If you're on Windows, enable WSL2 for Linux-based setup. Open PowerShell as Administrator and install Windows Subsystem for Linux:

wsl --install
wsl --install -d Ubuntu
wsl --set-default-version 2
sudo apt update && sudo apt upgrade -y

Restart your PC after successful installation.

Troubleshooting:

• If WSL installation fails, enable "Virtual Machine Platform" in Windows Features (Control Panel > Programs > Turn Windows features on/off).
• Check WSL version: wsl --list --verbose. Ensure Ubuntu is on version 2.

Step 2: Install pyenv for Python Management

**From now on the rest of the steps will be completed in wsl**

We use Pyenv because it allows isolated Python environments.

Install dependencies for Pyenv:

Use this command:

sudo apt install -y build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev python3-openssl git

Install pyenv:

curl https://pyenv.run | bash

Add to shell profile (add to ~/.bashrc or ~/.zshrc). Open the file with the nano text editor with this command:

nano ~/.bashrc

Then add the new paths to the end of the file.

export PATH="$HOME/.pyenv/bin:$PATH"

eval "$(pyenv init --path)"

eval "$(pyenv virtualenv-init -)"

Press Cntrl + O then Enter to save. Then use Cntrl + X to exit the file. Now back in the command line refresh the file with:

source ~/.bashrc

Verify Pyenv was setup correcty with:

pyenv --version

Expected output: pyenv 2.6.7 (Latest as of 8/24/25)

Troubleshooting:

• If "Unable to locate package llvm" use:

    sudo add-apt-repository universe
    sudo apt update
  

  then re-attempt the command:

  sudo apt install -y make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev python3-openssl git
  

• If pyenv not found, restart terminal or run exec $SHELL.

• Error with dependencies: Re-run sudo apt install command.

Step 3: Set Up Python Environment

Use Python 3.10.12 (compatible with dependencies). Install Python 3.10:

pyenv install 3.10.12

Create virtual environment:

pyenv virtualenv 3.10.12 pytorch_env

Activate the virtual envoriment (You will also need this command everytime you want to run the program after exiting:

pyenv activate pytorch_env

Upgrade pip:

pip install --upgrade pip

Troubleshooting

• If you get pyenv activate' requires Pyenv and Pyenv-Virtualenv to be loaded into your shell. Check your shell configuration and Pyenv and Pyenv-Virtualenv installation instructions. When using the command pyenv activate pytorch_env Run each line individually:

    echo 'eval "$(pyenv init -)"' >> ~/.bashrc
    eval "$(pyenv init -)"
    source ~/.bashrc
  

  then reattempt pyenv activate pytorch_env

• If build fails, ensure all dependencies from Step 1 are installed.

• Syntax Errors: Make sure all the paths are written properly with no mistakes.

Step 4: Install CUDA Toolkit in WSL

Open your WSL terminal (e.g., Ubuntu): Run wsl in Command Prompt or search for "Ubuntu" in the Start menu. Update the package list:

sudo apt update && sudo apt upgrade -y

Install the CUDA toolkit for WSL-Ubuntu: Add the NVIDIA CUDA repository: Follow the instructions at https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0.

Select "WSL-Ubuntu" > "2.0" > "x86_64"

Then copy and run the provided commands:

    wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-keyring_1.1-1_all.deb
    sudo dpkg -i cuda-keyring_1.1-1_all.deb
    sudo apt-get update
    sudo apt-get install cuda-toolkit-12-6  # Replace 12-6 with the latest version, e.g., 12-5 if needed

Add new paths to bash by running these commmands one by one:

    echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrc
    echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
    echo 'export PATH=/usr/lib/wsl/lib:$PATH' >> ~/.bashrc
    source ~/.bashrc

Verify CUDA installation: Run nvcc --version in WSL. It should display the CUDA version.

Step 5: Install cuDNN in WSL

Download cuDNN from the NVIDIA Developer website: https://developer.nvidia.com/rdp/cudnn-download.

• You made need to sign up for an NVIDIA Developer account if you don't have one (free).
• Select cuDNN for CUDA 12.x (matching your toolkit version) and "Linux" (x86_64).
• Select Linux > x86_64 > Ubuntu > 24.04 (Yours may varry) > deb (local) > FULL
• Download the deb file (e.g., cudnn-local-repo-ubuntu2404-9.12.0_1.0-1_amd64.deb). In WSL, extract and install cuDNN: Copy the downloaded file to WSL (e.g., via /mnt/c/Users/YourUsername/Downloads/).

Run each command sequentially (in pyenv:

cd /mnt/c/Users/YOUR USER/Downloads
sudo dpkg -i cudnn-local-repo-ubuntu2404-9.12.0_1.0-1_amd64.deb
sudo cp /var/cudnn-local-repo-ubuntu2404-9.12.0/cudnn-local-*-keyring.gpg /usr/share/keyrings/
sudo apt update
sudo apt install -y libcudnn9-cuda-12 libcudnn9-dev-cuda-12 libcudnn9-static-cuda-12
echo 'export LD_LIBRARY_PATH=/usr/lib/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc

Troubleshooting

• Run:

  wget https://developer.download.nvidia.com/compute/cudnn/9.12.0/local_installers/cudnn-local-repo-ubuntu2404-9.12.0_1.0-1_amd64.deb
  sudo dpkg -i cudnn-local-repo-ubuntu2404-9.12.0_1.0-1_amd64.deb
  sudo cp /var/cudnn-local-repo-ubuntu2404-9.12.0/cudnn-*-keyring.gpg /usr/share/keyrings/
  sudo apt-get update
  sudo apt-get -y install cudnn
  

• If CUDA is not detected: Check nvidia-smi in WSL for GPU info.

• Errors with versions: Ensure CUDA toolkit, cuDNN, and PyTorch match (e.g., all for CUDA 12.x).

• For detailed guides: Refer to NVIDIA's CUDA on WSL user guide (linked above) or PyTorch installation docs at https://pytorch.org/get-started/locally/.

Step 6: Install PyTorch with confirm CUDA Support

For RTX 50 & 40 series GPU acceleration we will PyTorch for training the AI model (use cu128 for CUDA 12.8; adjust if your version differs):

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128 

Verify PyTorch, CUDA, and GPU detection:

python -c "import torch; print(torch.__version__); print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0))"

Expected: Shows PyTorch version (e.g., 2.4.1+cu128), True, and "NVIDIA GeForce RTX _ _ _ _".

Troubleshooting

• False for CUDA: Ensure NVIDIA drivers are installed.
• Wrong device: Verify nvidia-smi shows RTX 5080.
• Installation error: Check PyTorch index URL for CUDA version compatibility.

Step 7: Install TA-Lib

TA-Lib requires building from source for technical indicators.

Install dependencies:

sudo apt install -y build-essential wget

Download and build TA-Lib: Remove any previous build artifacts (optional, to clean)

rm -rf ~/ta-lib ta-lib-0.4.0-src.tar.gz

Re-download and build with /usr/local prefix

wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar -xzf ta-lib-0.4.0-src.tar.gz
cd ta-lib
./configure --prefix=/usr/local
make
sudo make install
cd ~
rm -rf ta-lib ta-lib-0.4.0-src.tar.gz

Set environment variables for pip to find TA-Lib

export TA_INCLUDE_PATH=/usr/local/include
export TA_LIBRARY_PATH=/usr/local/lib

Install the Python wrapper pip install TA-Lib==0.4.32

Install Python wrapper:

pip install TA-Lib==0.4.32

Troubleshooting

• Build error: Ensure build-essential is installed. If configure fails, check for missing libraries (e.g., sudo apt install libncurses5-dev). Another option it to try and download a prebuilt wheel from the Ta-Lib repo.
• Import error: Verify with python -c "import talib; print(talib.version)".

Step 8: Install Other Dependencies

Install the remaining packages with exact versions for compatibility. Install dependencies:

pip install alpaca-py==0.28.0 transformers==4.45.2 pandas==2.2.3 numpy==1.26.4 scikit-learn==1.5.2 tenacity==9.0.0 tqdm==4.66.5 colorama==0.4.6 protobuf==5.28.3

Verify imports:

python -c "import torch, numpy, pandas, alpaca, transformers, sklearn, talib, tenacity, tqdm, colorama; print('All dependencies imported successfully')"

Troubleshooting:

Conflicts: Use --no-deps for problematic packages (e.g., pip install alpaca-py==0.28.0 --no-deps).
Protobuf error: Ensure protobuf==5.28.3 (for Transformers compatibility).
Import error: Reinstall the package (e.g., pip install --force-reinstall transformers==4.45.2).

Step 9: Configure API Keys and Email

• Replace placeholders in CONFIG (lines 147-148, 153-156) with your values:
• ALPACA_API_KEY and ALPACA_SECRET_KEY from Alpaca.
• EMAIL_SENDER, EMAIL_PASSWORD, EMAIL_RECEIVER for Gmail.
• Save the script.

Step 10: Running the Script

Note: If you do not want to train your own models / scalars, You can download already trained models & scalars, put them in a folder, amd put the address into the 'Config': Activate virtual environment:

pyenv activate pytorch_env

Run backtest with force-train

python /mnt/c/Users/aipla/Downloads/alpaca_neural_bot_v6.7.py --backtest --force-train

Run live trading:

python /mnt/c/Users/aipla/Downloads/alpaca_neural_bot_v6.7.py

Cool Facts about the hardware

• The raw compute happening on the RTX 5080 during training is equivalent to running ~85,000 PlayStation 5 consoles all at full tilt doing nothing but matrix multiplications.
• If each floating-point operation were a grain of sand, in one second the GPU would process enough sand to fill an Olympic swimming pool … every 45 seconds.
• In one second, this program flips more transistors states (1s/0s) than there are stars in the observable universe (~2 × 10¹² galaxies × ~10¹¹ stars each) … 4,000 times over.
• The CPU is moving enough data through RAM to stream ~23 000 simultaneous 4K Netflix videos.
• In total, the computer switches 28 quintillion (28,000,000,000,000,000,000) transistor state changes per second

Troubleshooting

• Rate Limit Error ("too many requests"): Add time.sleep(1) after bars = client.get_stock_bars(request).df in fetch_data (line 287). Increase to 2 seconds if persists.
• Missing Headers (longintrepr.h): Ensure apt-get install -y python3.12-dev ran successfully. Verify with find /usr/include -name longintrepr.h.
• Wheel Not Supported: Use Python 3.10-compatible wheels. For aiohttp, try pip install aiohttp==3.9.5 if 3.8.1 fails.
• CUDA Not Available: Check nvidia-smi. Reinstall drivers/CUDA. Verify with python -c "import torch; print(torch.cuda.is_available())".
• Dependency Conflicts: Use --no-deps for alpaca-py (e.g., pip install alpaca-py==0.28.0 --no-deps). Uninstall conflicting packages (e.g., pip uninstall aiohttp).
• TA-Lib Import Error: Rebuild TA-Lib and reinstall wrapper. Verify with python -c "import talib; print(talib.__version__)".
• API Key Error: Ensure keys are valid. Test with python -c "from alpaca.trading.client import TradingClient; client = TradingClient('YOUR_KEY', 'YOUR_SECRET', paper=True); print(client.get_account())".
• Model Training Hangs: Reduce batch size in CONFIG (line 75) to 16 if GPU memory is low. Monitor with nvidia-smi.
• Backtest IndexError: Clear cache with rm -rf ./cache and re-run to fetch data up to today.
• Email Failure: Ensure Gmail app password is used (not regular password). Test SMTP with a simple script. Ensure all dependency files are installed: pip install --upgrade torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128 pip install --upgrade alpaca-py transformers pandas numpy scikit-learn ta-lib tenacity tqdm colorama protobuf==5.28.3

If issues persist, check trades.log for details or open an issue on the GitHub repo.

License GNU Lesser General Public License v2.1. See LICENSE for details.

Author: Vladimir Makarov

Most recent change: 11/24/2025

GitHub: vmakarov28/Alpaca-Stock-Trading-Bot