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Rehaul organization of return values

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This commit is contained in:
robcaulk
2022-07-02 18:09:38 +02:00
parent 93e1410ed9
commit 106131ff0f
7 changed files with 429 additions and 292 deletions
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26
freqtrade/templates/FreqaiExampleStrategy.py
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@@ -156,6 +156,18 @@ class FreqaiExampleStrategy(IStrategy):
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df['&-s_close'] = (
df["close"]
.shift(-self.freqai_info['feature_parameters']["period"])
.rolling(self.freqai_info['feature_parameters']["period"])
.mean()
/ df["close"]
- 1
)
return df
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -183,20 +195,20 @@ class FreqaiExampleStrategy(IStrategy):
# each training period.
dataframe = self.model.bridge.start(dataframe, metadata, self)
dataframe["target_roi"] = dataframe["target_mean"] + dataframe["target_std"] * 1.25
dataframe["sell_roi"] = dataframe["target_mean"] - dataframe["target_std"] * 1.25
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [df["do_predict"] == 1, df["prediction"] > df["target_roi"]]
enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
if enter_long_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
] = (1, "long")
enter_short_conditions = [df["do_predict"] == 1, df["prediction"] < df["sell_roi"]]
enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]
if enter_short_conditions:
df.loc[
@@ -206,11 +218,11 @@ class FreqaiExampleStrategy(IStrategy):
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
exit_long_conditions = [df["do_predict"] == 1, df["prediction"] < df["sell_roi"] * 0.25]
exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
if exit_long_conditions:
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
exit_short_conditions = [df["do_predict"] == 1, df["prediction"] > df["target_roi"] * 0.25]
exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
if exit_short_conditions:
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
@@ -243,7 +255,7 @@ class FreqaiExampleStrategy(IStrategy):
if ('prediction' + entry_tag not in pair_dict[pair] or
pair_dict[pair]['prediction' + entry_tag] > 0):
with self.model.bridge.lock:
pair_dict[pair]['prediction' + entry_tag] = abs(trade_candle['prediction'])
pair_dict[pair]['prediction' + entry_tag] = abs(trade_candle['&-s_close'])
if not follow_mode:
self.model.bridge.data_drawer.save_drawer_to_disk()
else: