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β€Žbacktesting/backtesting.pyβ€Ž

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@@ -681,7 +681,7 @@ def pl(self):
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@property
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def pl_pct(self):
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"""Trade profit (positive) or loss (negative) in percent."""
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"""Trade profit (positive) or loss (negative) in percent relative to trade entry price."""
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price = self.__exit_price or self.__broker.last_price
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gross_pl_pct = copysign(1, self.__size) * (price / self.__entry_price - 1)
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β€Ždoc/alternatives.mdβ€Ž

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@@ -38,8 +38,6 @@ kindly have a look at some similar alternative Python backtesting frameworks:
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A unique new method for designing trading and investing systems_.
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- [QTPyLib](https://github.com/ranaroussi/qtpylib) -
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a versatile, event-driven algorithmic trading library.
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- [Gemini](https://github.com/anfederico/Gemini) -
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a backtester namely focusing on cryptocurrency markets.
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- [Quantdom](https://github.com/constverum/Quantdom) -
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a Qt-based framework that lets you focus on modeling financial strategies,
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portfolio management, and analyzing backtests.
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a library to construct, backtest, analyze, and evaluate portfolios
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and their benchmarks, with comprehensive documentation illustrating
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all underlying methodologies and statistics.
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- [Gemini](https://github.com/anfederico/Gemini) -
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a backtester namely focusing on cryptocurrency markets.

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