pyalgoture 0.5.1

A comprehensive algorithmic trading platform for cryptocurrency and stock markets with backtesting and real-time trading capabilities

PyAlgoture

A comprehensive algorithmic trading platform for cryptocurrency and stock markets with backtesting and real-time trading capabilities.

Developed by Algoture

Features

• Backtesting: Test your trading strategies on historical data
• Real-time Trading: Execute strategies in live markets
• Multiple Exchanges: Support for Binance, Bybit, Bitget, OKX, and more
• Data Feeds: Crypto and stock data feed support
• Portfolio Management: Advanced portfolio management tools
• Risk Management: Built-in risk management agents
• AI Agents: LLM-powered trading agents for sentiment analysis, valuation, and more
• Technical Indicators: Comprehensive technical analysis tools

Installation

pip install pyalgoture

Quick Start

Create a Strategy

from pyalgoture import Strategy, BackTest, BacktestBroker, CryptoDataFeed
from datetime import datetime

class MyStrategy(Strategy):
    def on_init(self):
        # Initialize indicators and parameters
        pass
    
    def on_bar(self, bar):
        # Your trading logic here
        pass

# Create a data feed
datafeed = CryptoDataFeed(
    code="BTCUSDT",
    exchange="binance",
    asset_type="SPOT",
    start_date=datetime(2024, 1, 1),
    end_date=datetime(2024, 12, 31),
    interval="1d"
)

# Create a broker
broker = BacktestBroker(
    balance=10000,
    trade_at="close",
    slippage=0.001
)

# Run backtest
backtest = BackTest(
    feeds=[datafeed],
    StrategyClass=MyStrategy,
    broker=broker
)

backtest.start()

Main Classes

Core Classes

• Strategy - Base class for implementing trading strategies
• Orchestrator - Main orchestrator for coordinating data feeds, strategies, and brokers
• Context - Stores shared data and common functions for trading operations
• Broker - Base broker class for handling trading operations
• DataFeed - Base class for market data feeds

Orchestrators

• BackTest - Backtesting orchestrator for testing strategies on historical data
• RealTime - Real-time trading orchestrator for live market execution
• BackTestRapid - Fast backtesting orchestrator

Brokers

• BacktestBroker - Broker for backtesting scenarios
• RealtimeBroker - Broker for live trading

Data Feeds

• CryptoDataFeed - Cryptocurrency market data feed
• StockDataFeed - Stock market data feed
• CustomDataFeed - Custom data feed implementation

Exchange Clients

Binance:

• BinanceClient - REST client for Binance
• BinanceDataWebsocket - WebSocket client for market data
• BinanceTradeWebsocket - WebSocket client for trading

Bybit:

• BybitClient - REST client for Bybit
• BybitDataWebsocket - WebSocket client for market data
• BybitTradeWebsocket - WebSocket client for trading

Bitget:

• BitgetClient - REST client for Bitget
• BitgetDataWebsocket - WebSocket client for market data
• BitgetTradeWebsocket - WebSocket client for trading

OKX:

• OKXClient - REST client for OKX
• OKXDataWebsocket - WebSocket client for market data
• OKXTradeWebsocket - WebSocket client for trading

Utilities

• Indicator - Technical indicator calculations
• BarGenerator - Generate OHLCV bars from tick data
• BarManager - Manage and aggregate bar data
• Patterns - Pattern recognition indicators
• EventEngine - Event-driven architecture engine
• FundTracker - Track fund performance and NAV
• PortfolioManager - Manage portfolio allocations
• SignalMQ - Message queue for trading signals
• NotificationSender - Send notifications for trading events

Agent Classes (AI Trading Agents)

• Agent - Main agent orchestrator
• BaseAgent - Base class for all trading agents
• MarketDataAgent - Market data analysis agent
• SentimentAgent - Sentiment analysis agent
• ValuationAgent - Asset valuation agent
• RiskManagementAgent - Risk management agent
• PortfolioManagementAgent - Portfolio management agent
• PricePatternAgent - Price pattern recognition agent
• QuantAgent - Quantitative analysis agent
• StatArbAgent - Statistical arbitrage agent
• LiquidityManagementAgent - Liquidity management agent

Alpha Research (Machine Learning)

• AlphaDataset - Dataset for alpha research
• AlphaModel - Base class for alpha models
• AlphaStrategy - Strategy class for alpha research
• AlphaLab - Lab environment for alpha research
• LgbModel - LightGBM model implementation
• MlpModel - Multi-layer perceptron model
• LassoModel - Lasso regression model
• DoubleEnsembleModel - Double ensemble model

Data Objects

• OrderData - Order information
• TradeData - Trade execution information
• PositionData - Position information
• AccountData - Account information
• BarData - OHLCV bar data
• TickData - Tick data
• InstrumentData - Instrument information

Enums

• Side - Buy/Sell side
• OrderType - Order types (Limit, Market, etc.)
• Status - Order/trade status
• Exchange - Supported exchanges
• AssetType - Asset types (Spot, Future, Option, etc.)
• PositionSide - Position sides (Long, Short)
• TimeInForce - Order time in force

Database Classes

• FilesystemDatabase - File-based database
• MysqlDatabase - MySQL database (lazy import)
• DuckdbDatabase - DuckDB database (lazy import)
• SqliteDatabase - SQLite database (lazy import)

Statistics

• Statistic - Statistical analysis and performance metrics

Documentation

For detailed documentation and API reference, please visit the project repository.

Requirements

• Python >= 3.8
• See pyproject.toml for full list of dependencies

License

MIT License - see LICENSE file for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

For issues and questions, please open an issue on GitHub.

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Algoture - Advanced Algorithmic Trading Solutions