StockTuna v0.2.0 Pre-Alpha

Developed by Justin Bodnar
Website: justinbodnar.com
Email: contact@justinbodnar.com

About StockTuna

StockTuna is a Python library currently in pre-alpha development, designed for processing stock market and securities historical data. It integrates with the Alpaca API to provide users with real-time and historical market data, facilitating the development of sophisticated algorithmic trading strategies. Visit the official website at stocktuna.com for more information and resources.

For a full list of StockTuna methods and code examples, visit the Documentation Overview

Features of StockTuna

• Paper and Live Trading Modes: Two subclasses (PaperTuna and LiveTuna) make it easy to switch between paper trading and live trading.
• Alpaca API Integration: Direct access to real-time and historical market data via Alpaca.
• Indicator Functions: Includes functions for calculating Simple Moving Average (SMA), Exponential Moving Average and Relative Strength Index (RSI).
• Predefined Market Indices: A cannedtuna.py file contains hardcoded lists of stock symbols for major market indices, such as the Dow Jones, NASDAQ 100, NYSE FANG, and S&P 500.

Available Classes and Functions

• PaperTuna: Initializes a PaperTuna object for paper trading using Alpaca's paper trading API.
• LiveTuna: Initializes a LiveTuna object for live trading using Alpaca's live trading API.
• StockTuna: The core class that handles API connection and provides utility functions for data analysis.
  • get_api_connection(base_url): Establishes a connection to the Alpaca API using provided credentials.
  • sma(bars, period): Calculates the Simple Moving Average (SMA) over a specified period, returning a list of values.
  • sma_chart(bars, periods, symbol): calculates and graphs a stock price against the SMAs for all input SMA periods. periods is an arbitrarily large list of integers representing all SMAs to be graphed. Chart is saved in ./examples/charts/.
  • ema(bars, period): Calculates the Exponential Moving Average (EMA) over a specified period, returning a list of values.
  • ema_chart(bars, periods, symbol): calculates and graphs a stock price against the EMAs for all input EMA periods. periods is an arbitrarily large list of integers representing all EMAs to be graphed. Chart is saved in ./examples/charts/.
  • rsi(bars, period): Calculates the Relative Strength Index (RSI) for the given period, and returns a list of the values.

Example Code

Within ./examples/, moving_average_crossover_strategy.py, exponential_moving_average_crossover_stragey.py, and relative_strength_index_strategy.py demonstrate the use of PaperTuna to fetch historical stock data, calculate the specified indicator, and identify buy/sell signals based on the data. Using hardcoded stock symbols or stock indices from cannedtuna.py, the script backtests the strategy with one year of historical data. It simulates trades using a $100,000 starting balance, tracks gains and losses, and calculates final portfolio performance, providing insight into the strategy’s effectiveness over time. Simply update the symbol variable for the stock to test, change the hardcoded indicator variables short_period and long_period, and run the script.

Also within ./examples/, the files moving_average_crossover_strategy_bruteforce.py, and exponential_moving_average_crossover_strategy_bruteforce.py demonstrate the use of the SMA and EMA functions to find the best parameters for use in these strategies. Hardcoded short_period_range and long_period_range variables can be modified to change the search space, while the index variable can be used for testing against a specific stock, a list of stocks, or a hardcoded index in CannedTuna. Examples of each can be found within these files. Simply update these variables, and run the script.

Example Results

Within ./results/, moving_average_crossover_strategy.md, and exponential_moving_average_crossover_stragey.md, the results of the bruteforcing scripts run against hardcode market indices from CannedTuna. These show the best SMA and EMA parameters for each stock market index found in CannedTuna.

Get Started

Follow these steps to set up and start using StockTuna:

1. Clone the Repository:

   git clone https://github.com/yourusername/StockTuna.git
   cd StockTuna

2. Install Dependencies: Install the required dependencies by running:

   pip install .

3. Set Up API Credentials: StockTuna requires an Alpaca API key and secret to access market data. You have two options:

   • Option 1: Directly provide credentials in your code. Example:

     from stocktuna.stocktuna import PaperTuna
     tuna = PaperTuna(alpaca_key='your_alpaca_key', alpaca_secret='your_alpaca_secret', verbosity=2)

   • Option 2: Use a local api_auth.py file:
     • Rename examples/api_auth_template.py to api_auth.py.
     • Add your Alpaca API credentials to the file.

4. Run the Example: Navigate to the examples/ directory and run moving_average_crossover_strategy.py to see how to use StockTuna to fetch data and generate SMA plots:

   cd examples
   python3 moving_average_crossover_strategy.py

   This script demonstrates how to use the library to pull historical stock data, calculate moving averages, and visualize them along with buy and sell signals. The default example uses the PaperTuna subclass for paper trading.

Prerequisites

• Python 3.7 or higher.
• An Alpaca API account (sign up at alpaca.markets).

To Do

• Expand Indicator Functions: Need more mathematical functions like SMA, EMA, etc.

About the Author

Justin Bodnar is the developer behind StockTuna. For more information about his work or to get in touch, visit justinbodnar.com or send an email to contact@justinbodnar.com.