TradingBuddy

TradingBuddy is a comprehensive trading automation and backtesting platform designed to help traders develop, test, and implement trading strategies.

Features

• Backtesting Engine: Test trading strategies against historical data to evaluate performance
• Strategy Development: Create custom trading strategies with flexible parameters
• Automation Framework: Design and implement rule-based trading automations
• Data Management: MongoDB integration for storing and retrieving market data

Project Structure

• /automation/: Core automation components including decision trees and evaluators
• /backtester/: Complete backtesting framework with strategy tools and performance analysis
• /experiment/: Django app for running backtests and analyzing results
• /project/: Django project configuration files
• /candlestick/: Candlestick pattern analysis tools
• /results/: Storage for backtesting results

Getting Started

Installation

1. Clone the repository
2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install dependencies:

   pip install django djangorestframework pandas numpy pymongo
   

4. Run the Django development server:

   python manage.py runserver
   

Usage

Creating a Trading Strategy

Extend the Strategy base class in backtester/core/strategy.py:

from backtester.core.strategy import Strategy

class MyTradingStrategy(Strategy):
    def init(self):
        super().init()
        # Initialize indicators and strategy parameters
        
    def next(self):
        super().next()
        # Define trading logic based on market data
        self.automation_evaluator.next()

Running a Backtest

from backtester.core.backtesting import Backtest
from my_strategy import MyTradingStrategy

# Load market data
data = pd.DataFrame(...)  # Your OHLCV data

# Create and run backtest
bt = Backtest(data, MyTradingStrategy, cash=100_000, exclusive_orders=False)
stats = bt.run()

# Analyze results
print(stats)

Working with Automations

The automation framework allows you to create rule-based trading decisions:

automation_flow = {
    "root": {
        "type": "decision",
        "name": "Today is Thursday",
        "criteria": {
            "type": "any",
            "criteria": [
                {
                    "input": {"day": 4},
                    "type": "day_of_week"
                }
            ]
        },
        "outcomes": [
            # Define trading actions based on conditions
        ]
    }
}

API Endpoints

• GET /api/home/: Basic API health check
• POST /api/test-strategy/: Run backtests with custom automation flows

Data Management

The project uses MongoDB for storing and retrieving market data. Configure your connection in database.py.