Merge branch 'freqtrade:develop' into strategy_utils
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@@ -248,13 +248,13 @@ FreqAI also provides a built in episodic summary logger called `self.tensorboard
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"""
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def calculate_reward(self, action: int) -> float:
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if not self._is_valid(action):
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self.tensorboard_log("is_valid")
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self.tensorboard_log("invalid")
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return -2
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```
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!!! Note
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The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)` would add 0.23 to `float_metric`. In this case you can also disable incrementing using `inc=False` parameter.
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The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)`. In this case the metric values are not incremented.
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### Choosing a base environment
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@@ -1,6 +1,6 @@
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markdown==3.3.7
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mkdocs==1.4.2
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mkdocs-material==9.1.1
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mkdocs-material==9.1.2
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mdx_truly_sane_lists==1.3
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pymdown-extensions==9.10
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jinja2==3.1.2
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@@ -1040,11 +1040,10 @@ from datetime import timedelta, datetime, timezone
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# Within populate indicators (or populate_buy):
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if self.config['runmode'].value in ('live', 'dry_run'):
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# fetch closed trades for the last 2 days
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trades = Trade.get_trades([Trade.pair == metadata['pair'],
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Trade.open_date > datetime.utcnow() - timedelta(days=2),
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Trade.is_open.is_(False),
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]).all()
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# fetch closed trades for the last 2 days
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trades = Trade.get_trades_proxy(
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pair=metadata['pair'], is_open=False,
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open_date=datetime.now(timezone.utc) - timedelta(days=2))
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# Analyze the conditions you'd like to lock the pair .... will probably be different for every strategy
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sumprofit = sum(trade.close_profit for trade in trades)
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if sumprofit < 0:
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