adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

another attempt at fixing datalength bug

Browse Source
This commit is contained in:
robcaulk 2022-05-06 15:10:11 +02:00
parent b08c0888bb
commit f653ace24b
2 changed files with 20 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

29
freqtrade/freqai/data_kitchen.py
View File

@ -39,6 +39,10 @@ class FreqaiDataKitchen:
self.do_predict = np.array([]) self.do_predict = np.array([])
self.target_mean = np.array([]) self.target_mean = np.array([])
self.target_std = np.array([]) self.target_std = np.array([])
self.full_predictions = np.array([])
self.full_do_predict = np.array([])
self.full_target_mean = np.array([])
self.full_target_std = np.array([])
self.model_path = Path() self.model_path = Path()
self.model_filename = "" self.model_filename = ""
@ -313,12 +317,11 @@ class FreqaiDataKitchen:
timerange_backtest.startts = timerange_train.stopts timerange_backtest.startts = timerange_train.stopts
timerange_backtest.stopts = timerange_backtest.startts + bt_period
if timerange_backtest.stopts > config_timerange.stopts: if timerange_backtest.stopts > config_timerange.stopts:
timerange_backtest.stopts = config_timerange.stopts timerange_backtest.stopts = config_timerange.stopts
else:
timerange_backtest.stopts = timerange_backtest.startts + bt_period
start = datetime.datetime.utcfromtimestamp(timerange_backtest.startts) start = datetime.datetime.utcfromtimestamp(timerange_backtest.startts)
stop = datetime.datetime.utcfromtimestamp(timerange_backtest.stopts) stop = datetime.datetime.utcfromtimestamp(timerange_backtest.stopts)
tr_backtesting_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")) tr_backtesting_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d"))
@ -328,7 +331,7 @@ class FreqaiDataKitchen:
if timerange_backtest.stopts == config_timerange.stopts: if timerange_backtest.stopts == config_timerange.stopts:
break break
# print(tr_training_list, tr_backtesting_list) print(tr_training_list, tr_backtesting_list)
return tr_training_list, tr_backtesting_list return tr_training_list, tr_backtesting_list
def slice_dataframe(self, tr: str, df: DataFrame) -> DataFrame: def slice_dataframe(self, tr: str, df: DataFrame) -> DataFrame:
@ -536,10 +539,10 @@ class FreqaiDataKitchen:
ones = np.ones(len_dataframe) ones = np.ones(len_dataframe)
s_mean, s_std = ones * self.data["s_mean"], ones * self.data["s_std"] s_mean, s_std = ones * self.data["s_mean"], ones * self.data["s_std"]
self.predictions = np.append(self.predictions, predictions) self.full_predictions = np.append(self.full_predictions, predictions)
self.do_predict = np.append(self.do_predict, do_predict) self.full_do_predict = np.append(self.full_do_predict, do_predict)
self.target_mean = np.append(self.target_mean, s_mean) self.full_target_mean = np.append(self.full_target_mean, s_mean)
self.target_std = np.append(self.target_std, s_std) self.full_target_std = np.append(self.full_target_std, s_std)
return return
@ -549,11 +552,11 @@ class FreqaiDataKitchen:
when it goes back to the strategy. These rows are not included in the backtest. when it goes back to the strategy. These rows are not included in the backtest.
""" """
filler = np.zeros(len_dataframe - len(self.predictions)) # startup_candle_count filler = np.zeros(len_dataframe - len(self.full_predictions)) # startup_candle_count
self.predictions = np.append(filler, self.predictions) self.full_predictions = np.append(filler, self.full_predictions)
self.do_predict = np.append(filler, self.do_predict) self.full_do_predict = np.append(filler, self.full_do_predict)
self.target_mean = np.append(filler, self.target_mean) self.full_target_mean = np.append(filler, self.full_target_mean)
self.target_std = np.append(filler, self.target_std) self.full_target_std = np.append(filler, self.full_target_std)
return return

5
freqtrade/freqai/freqai_interface.py
View File

@ -84,10 +84,13 @@ class IFreqaiModel(ABC):
preds, do_preds = self.predict(dataframe_backtest) preds, do_preds = self.predict(dataframe_backtest)
self.dh.append_predictions(preds, do_preds, len(dataframe_backtest)) self.dh.append_predictions(preds, do_preds, len(dataframe_backtest))
print('predictions', len(self.dh.full_predictions),
'do_predict', len(self.dh.full_do_predict))
self.dh.fill_predictions(len(dataframe)) self.dh.fill_predictions(len(dataframe))
return self.dh.predictions, self.dh.do_predict, self.dh.target_mean, self.dh.target_std return (self.dh.full_predictions, self.dh.full_do_predict,
self.dh.full_target_mean, self.dh.full_target_std)
def make_labels(self, dataframe: DataFrame) -> DataFrame: def make_labels(self, dataframe: DataFrame) -> DataFrame:
""" """