The Stock Tuna Project: a framework for the meaningful processing of historical stock market data

To Do

The list of features to implement.

Mission

The mission of StockTuna is to create a Python3 library that enables the straightforward implementation of automated trading strategies. This library aims to support algorithm development, backtesting, and statistical analysis of results.

Specific Goals:

Provide an easy way for Python3 developers to develop, test, and implement automated trading strategies.
Further my knowledge of algorithmic trading.
Develop my skills in Python3 library development.
Utilize ChatGPT for efficient code generation and experimentation.

Consider getting involved! You can help by forking the project repo, or donating enough money that I can resign from my job.

Code Examples

Example files to test indicators:

Bruteforcing the best indicator hyperparameters for a stock, list of stocks, or an index

To Do

✅Create bruteforce script for Simple Moving Average (SMA)
✅Create a bruteforce script for Exponential Moving Average (EMA)
Combine SMA and RSI into basic strategy file for backtesting.
✅Expand hardcoded data found in cannedtuna.py
✅Make backtesting examples more realistic via comparing to a simple buy/hold strateg in indicator methods
Make backtesting examples more realistic via slippage
Make backtesting examples more realistic via position sizing
Make backtesting examples more realistic via segmenting tests into bull/bear markets
Create pip3 package vie PyPi
Create indicator functions.
- ✅Simple Moving Average (SMA)
- ✅Test SMA
- ✅Relative Strength Index (RSI)
- ✅Test RSI
- ✅Exponential Moving Average (EMA)
- ✅Test EMA
- Bollinger Bands
- Moving Average Convergence Divergence (MACD)
- Average True Range (ATR)
- Stochastic Oscillator
- Parabolic SAR
- Ichimoku Cloud
- Fibonacci Retracement
- VWAP (Volume Weighted Average Price)
- ADX (Average Directional Index)