trading-strategy-tester 0.1.4

Simulate and evaluate trading strategies

Trading Strategy Tester

A flexible Python package for simulating and evaluating algorithmic trading strategies with modular conditions, strategies, and trade simulations.

Installation

To install the Trading Strategy Tester package, you can use pip:

pip install trading-strategy-tester

If you want to install the latest development version, you can clone the repository and install it locally:

git clone https://github.com/DrDanicka/trading_strategy_tester.git

Documentation

The documentation is available in the docs directory. You can view it online at this link.

Examples

You can find examples of how to use the package in the examples tab of the user documentation. These examples cover various aspects of the package, including strategy creation, execution, and visualization or using technical indicators.

LLM Integration

The project integrates with Large Language Models (LLMs) to help generate trading strategies from natural language descriptions. It is available in 2 types:

1. WEB UI: A web-based interface that allows users to input natural language prompts and visualize the results. You can find can learn more about the web app in this repository.
2. Code interface: You can use the LLM integration directly in your Python code. This can be done via process_prompt(prompt: str, llm_model: LLMModel) function. You can do it like this:

from trading_strategy_tester import process_prompt, LLMModel

trades, graphs, stats, strategy_obj, changes = process_prompt(
    prompt="""Can you generate a long strategy for LULU that goes long when the Open Price is in a negative trend for 77 days
        and price is within a 50% fibonacci level during an uptrend is correct and sells out when the Know Sure Thing varies by
        65.99 percent over 62 days is fulfilled. Set trailing stop-loss at 27.52%. Set the start date as 2013-12-13. Set the 
        interval to 5 days. Set trade commissions to 5%.""",
    llm_model=LLMModel.LLAMA_ALL
)

You can later use the trades, graphs, stats, strategy_obj, and changes variables to visualize the results, analyze the performance, and make further modifications to the strategy.

This project uses Llama 3.2 models via Ollama framework. The models need to be installed first so that the package can use them. Follow these steps:

1. You need to install Ollama on your machine using this link.
2. After that, you have to download the model weights. You can do the following:

git clone https://github.com/DrDanicka/trading_strategy_tester_web_app
cd trading_strategy_tester_web_app
python init_ollama.py

This will download and initialize the Ollama models. After all of this is done, you can delete the trading_strategy_tester_web_app directory if you want to.

Now you can use the process_prompt function to generate trading strategies from natural language prompts.

To learn more about how to write prompts, and LLM integration in general, you can check the LLM Integration documentation.