🚀 AlgoSystem

AlgoGators professional algorithmic backtesting and dashboard visualization library.

🚀 Quick Start

Installation

pip install algosystem

Command Line

# Generate dashboard from CSV
algosystem dashboard strategy.csv

# With benchmark comparison
algosystem dashboard strategy.csv --benchmark sp500

# Launch visual editor
algosystem launch

# Create IP-ready results
algosystem ip strategy.csv --benchmark sp500

Python API

import pandas as pd
from algosystem.api import quick_backtest

# Load strategy data (CSV with date index and price column)
data = pd.read_csv('strategy.csv', index_col=0, parse_dates=True)

# Run backtest and show dashboard
engine = quick_backtest(data)

📊 Dashboard Features

Available Metrics (20+)

• Performance: Total Return, Annualized Return, Volatility
• Risk: Max Drawdown, VaR, CVaR, Skewness
• Ratios: Sharpe, Sortino, Calmar, Information Ratio
• Benchmark: Alpha, Beta, Correlation, Tracking Error

Available Charts (15+)

• Core: Equity Curve, Drawdown, Daily Returns
• Rolling: Sharpe, Sortino, Volatility, Skewness
• Analysis: Monthly Returns, Yearly Returns, Benchmark Comparison

Built-in Benchmarks (40+)

• Indices: S&P 500, NASDAQ, DJIA, Russell 2000
• International: Europe, UK, Japan, China, Emerging Markets
• Sectors: Technology, Healthcare, Financials, Energy
• Assets: Gold, Real Estate, Commodities, Bonds

📖 Documentation

• Installation Guide
• CLI Reference
• Python API
• Dashboard Customization
• Benchmark Integration

🔧 Example Usage

Complete Workflow

from algosystem.api import AlgoSystem

# Load data and benchmark
strategy_data = pd.read_csv('strategy.csv', index_col=0, parse_dates=True)
benchmark_data = AlgoSystem.get_benchmark('sp500')

# Run backtest
engine = AlgoSystem.run_backtest(strategy_data, benchmark_data)

# Print results
AlgoSystem.print_results(engine, detailed=True)

# Generate dashboard
AlgoSystem.generate_dashboard(engine, open_browser=True)

# Export data
AlgoSystem.export_data(engine, 'results.csv')

Engine-Level Control

from algosystem.backtesting import Engine

engine = Engine(
    data=strategy_data,
    benchmark=benchmark_data,
    start_date='2022-01-01',
    end_date='2022-12-31'
)

results = engine.run()
dashboard_path = engine.generate_dashboard()

📋 Data Format

Your CSV should have:

• Date column as index (YYYY-MM-DD)
• Price/value column representing portfolio value

Date,Strategy
2022-01-01,100000.00
2022-01-02,100500.00
2022-01-03,99800.00