c9ae507bb7
- Introduced a comprehensive framework for incremental trading strategies, including modules for strategy execution, backtesting, and data processing. - Added key components such as `IncTrader`, `IncBacktester`, and various trading strategies (e.g., `MetaTrendStrategy`, `BBRSStrategy`, `RandomStrategy`) to facilitate real-time trading and backtesting. - Implemented a robust backtesting framework with configuration management, parallel execution, and result analysis capabilities. - Developed an incremental indicators framework to support real-time data processing with constant memory usage. - Enhanced documentation to provide clear usage examples and architecture overview, ensuring maintainability and ease of understanding for future development. - Ensured compatibility with existing strategies and maintained a focus on performance and scalability throughout the implementation.
35 lines
1.0 KiB
Python
35 lines
1.0 KiB
Python
"""
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Incremental Trading Execution
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This module provides trading execution and position management for incremental strategies.
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It handles real-time trade execution, risk management, and performance tracking.
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Components:
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- IncTrader: Main trader class for strategy execution
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- PositionManager: Position state and trade execution management
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- TradeRecord: Data structure for completed trades
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- MarketFees: Fee calculation utilities
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Example:
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from IncrementalTrader.trader import IncTrader, PositionManager
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from IncrementalTrader.strategies import MetaTrendStrategy
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strategy = MetaTrendStrategy("metatrend")
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trader = IncTrader(strategy, initial_usd=10000)
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# Process data stream
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for timestamp, ohlcv in data_stream:
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trader.process_data_point(timestamp, ohlcv)
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results = trader.get_results()
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"""
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from .trader import IncTrader
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from .position import PositionManager, TradeRecord, MarketFees
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__all__ = [
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"IncTrader",
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"PositionManager",
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"TradeRecord",
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"MarketFees",
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] |