Simon Moisy
1e4cb87da3
- Introduced `check_symbols.py` to load and filter ETH perpetual markets from the OKX exchange using CCXT. - Updated the backtester to normalize signals to a 5-tuple format, incorporating size management for trades. - Enhanced portfolio functions to support variable size and leverage adjustments based on initial capital. - Added a new method in `CryptoQuantClient` for chunked historical data fetching to avoid API limits. - Improved market symbol normalization in `market.py` to handle different formats. - Updated regime strategy parameters based on recent research findings for optimal performance.
109 lines
2.7 KiB
Python
109 lines
2.7 KiB
Python
"""
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Portfolio simulation utilities for backtesting.
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Handles long-only and long/short portfolio creation using VectorBT.
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"""
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import pandas as pd
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import vectorbt as vbt
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from engine.logging_config import get_logger
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logger = get_logger(__name__)
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def run_long_only_portfolio(
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close: pd.Series,
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entries: pd.DataFrame,
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exits: pd.DataFrame,
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init_cash: float,
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fees: float,
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slippage: float,
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freq: str,
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sl_stop: float | None,
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tp_stop: float | None,
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sl_trail: bool,
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leverage: int
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) -> vbt.Portfolio:
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"""
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Run a long-only portfolio simulation.
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Args:
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close: Close price series
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entries: Entry signals
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exits: Exit signals
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init_cash: Initial capital
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fees: Transaction fee percentage
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slippage: Slippage percentage
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freq: Data frequency string
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sl_stop: Stop loss percentage
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tp_stop: Take profit percentage
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sl_trail: Enable trailing stop loss
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leverage: Leverage multiplier
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Returns:
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VectorBT Portfolio object
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"""
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effective_cash = init_cash * leverage
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return vbt.Portfolio.from_signals(
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close=close,
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entries=entries,
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exits=exits,
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init_cash=effective_cash,
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fees=fees,
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slippage=slippage,
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freq=freq,
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sl_stop=sl_stop,
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tp_stop=tp_stop,
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sl_trail=sl_trail,
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size=1.0,
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size_type='percent',
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)
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def run_long_short_portfolio(
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close: pd.Series,
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long_entries: pd.DataFrame,
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long_exits: pd.DataFrame,
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short_entries: pd.DataFrame,
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short_exits: pd.DataFrame,
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init_cash: float,
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fees: float,
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slippage: float,
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freq: str,
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sl_stop: float | None,
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tp_stop: float | None,
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sl_trail: bool,
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leverage: int,
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size: pd.Series | float = 1.0,
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size_type: str = 'value' # Changed to 'value' to support reversals/sizing
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) -> vbt.Portfolio:
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"""
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Run a portfolio supporting both long and short positions.
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Uses VectorBT's native support for short_entries/short_exits
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to simulate a single unified portfolio.
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"""
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effective_cash = init_cash * leverage
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# If size is passed as value (USD), we don't scale it by leverage here
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# The backtester has already scaled it by init_cash.
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# If using 'value', vbt treats it as "Amount of CASH to use for the trade"
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return vbt.Portfolio.from_signals(
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close=close,
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entries=long_entries,
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exits=long_exits,
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short_entries=short_entries,
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short_exits=short_exits,
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init_cash=effective_cash,
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fees=fees,
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slippage=slippage,
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freq=freq,
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sl_stop=sl_stop,
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tp_stop=tp_stop,
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sl_trail=sl_trail,
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size=size,
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size_type=size_type,
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)
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