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ensure loading historical df matches frequi indices

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This commit is contained in:
Robert Caulk 2022-08-06 07:25:59 +02:00
parent 60d782e5c5
commit ce8fbbf743
2 changed files with 11 additions and 11 deletions
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3
freqtrade/freqai/data_drawer.py
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@ -238,9 +238,10 @@ class FreqaiDataDrawer:
df_concat = pd.concat([pred_df.iloc[:abs(len_diff)], hist_df[pair]],
ignore_index=True, keys=hist_df[pair].keys())
else:
df_concat = hist_df[pair].tail(len(pred_df.index))
df_concat = hist_df[pair].tail(len(pred_df.index)).reset_index(drop=True)
df_concat = df_concat.fillna(0)
self.model_return_values[pair] = df_concat
logger.info(f'Setting initial FreqUI plots from historical data for {pair}.')
else:
for label in dk.label_list:

19
freqtrade/freqai/data_kitchen.py
View File

@ -4,7 +4,7 @@ import logging
import shutil
import sqlite3
from pathlib import Path
from typing import Any, Dict, List, Tuple, Optional
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
import numpy.typing as npt
@ -14,6 +14,7 @@ from sklearn import linear_model
from sklearn.cluster import DBSCAN
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.model_selection import train_test_split
from sklearn.neighbors import NearestNeighbors
from freqtrade.configuration import TimeRange
from freqtrade.data.history.history_utils import refresh_backtest_ohlcv_data
@ -21,8 +22,6 @@ from freqtrade.exceptions import OperationalException
from freqtrade.resolvers import ExchangeResolver
from freqtrade.strategy.interface import IStrategy
from sklearn.neighbors import NearestNeighbors
SECONDS_IN_DAY = 86400
SECONDS_IN_HOUR = 3600
@ -505,7 +504,7 @@ class FreqaiDataKitchen:
point. This metric defines the neighborhood of trained data and is used
for prediction confidence in the Dissimilarity Index
"""
logger.info("computing average mean distance for all training points")
# logger.info("computing average mean distance for all training points")
pairwise = pairwise_distances(
self.data_dictionary["train_features"], n_jobs=self.thread_count)
avg_mean_dist = pairwise.mean(axis=1).mean()
@ -538,7 +537,7 @@ class FreqaiDataKitchen:
if (len(do_predict) - do_predict.sum()) > 0:
logger.info(
f"svm_remove_outliers() tossed {len(do_predict) - do_predict.sum()} predictions"
f"SVM tossed {len(do_predict) - do_predict.sum()} predictions."
)
self.do_predict += do_predict
self.do_predict -= 1
@ -564,8 +563,8 @@ class FreqaiDataKitchen:
]
logger.info(
f"svm_remove_outliers() tossed {len(y_pred) - dropped_points.sum()}"
f" train points from {len(y_pred)}"
f"SVM tossed {len(y_pred) - dropped_points.sum()}"
f" train points from {len(y_pred)} total points."
)
# same for test data
@ -582,8 +581,8 @@ class FreqaiDataKitchen:
]
logger.info(
f"svm_remove_outliers() tossed {len(y_pred) - dropped_points.sum()}"
f" test points from {len(y_pred)}"
f"SVM tossed {len(y_pred) - dropped_points.sum()}"
f" test points from {len(y_pred)} total points."
)
return
@ -621,7 +620,7 @@ class FreqaiDataKitchen:
else:
MinPts = len(self.data_dictionary['train_features'].columns)*2
MinPts = len(self.data_dictionary['train_features'].columns) * 2
# measure pairwise distances to train_features.shape[1]*2 nearest neighbours
neighbors = NearestNeighbors(
n_neighbors=MinPts, n_jobs=self.thread_count)