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first step toward cleaning output and enabling multimodel training per pair

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This commit is contained in:
robcaulk
2022-07-01 14:00:30 +02:00
parent 6c7d02cb18
commit 93e1410ed9
3 changed files with 63 additions and 144 deletions
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73
freqtrade/freqai/data_drawer.py
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@@ -1,6 +1,5 @@
import collections
import copy
import json
import logging
import re
@@ -11,6 +10,7 @@ from typing import Any, Dict, Tuple
# import pickle as pk
import numpy as np
import pandas as pd
from pandas import DataFrame
@@ -163,18 +163,13 @@ class FreqaiDataDrawer:
# send pair to end of queue
self.pair_dict[pair]['priority'] = len(self.pair_dict)
def set_initial_return_values(self, pair: str, dh):
def set_initial_return_values(self, pair: str, dh, dataframe: DataFrame) -> None:
self.model_return_values[pair] = {}
self.model_return_values[pair]['predictions'] = dh.full_predictions
self.model_return_values[pair]['do_preds'] = dh.full_do_predict
self.model_return_values[pair]['target_mean'] = dh.full_target_mean
self.model_return_values[pair]['target_std'] = dh.full_target_std
self.model_return_values[pair] = dataframe
self.model_return_values[pair]['target_mean'] = dh.data['target_mean']
self.model_return_values[pair]['target_std'] = dh.data['target_std']
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
self.model_return_values[pair]['DI_values'] = dh.full_DI_values
# if not self.follow_mode:
# self.save_model_return_values_to_disk()
self.model_return_values[pair]['DI_values'] = dh.DI_values
def append_model_predictions(self, pair: str, predictions, do_preds,
target_mean, target_std, dh, len_df) -> None:
@@ -182,7 +177,7 @@ class FreqaiDataDrawer:
# strat seems to feed us variable sized dataframes - and since we are trying to build our
# own return array in the same shape, we need to figure out how the size has changed
# and adapt our stored/returned info accordingly.
length_difference = len(self.model_return_values[pair]['predictions']) - len_df
length_difference = len(self.model_return_values[pair]['prediction']) - len_df
i = 0
if length_difference == 0:
@@ -190,51 +185,29 @@ class FreqaiDataDrawer:
elif length_difference > 0:
i = length_difference + 1
self.model_return_values[pair]['predictions'] = np.append(
self.model_return_values[pair]['predictions'][i:], predictions[-1])
df = self.model_return_values[pair].shift(-i)
df['prediction'].iloc[-1] = predictions[-1]
df['do_predict'].iloc[-1] = do_preds[-1]
df['target_mean'].iloc[-1] = target_mean
df['target_std'].iloc[-1] = target_std
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
self.model_return_values[pair]['DI_values'] = np.append(
self.model_return_values[pair]['DI_values'][i:], dh.DI_values[-1])
self.model_return_values[pair]['do_preds'] = np.append(
self.model_return_values[pair]['do_preds'][i:], do_preds[-1])
self.model_return_values[pair]['target_mean'] = np.append(
self.model_return_values[pair]['target_mean'][i:], target_mean)
self.model_return_values[pair]['target_std'] = np.append(
self.model_return_values[pair]['target_std'][i:], target_std)
df['DI_values'].iloc[-1] = dh.DI_values[-1]
if length_difference < 0:
prepend = np.zeros(abs(length_difference) - 1)
self.model_return_values[pair]['predictions'] = np.insert(
self.model_return_values[pair]['predictions'], 0, prepend)
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
self.model_return_values[pair]['DI_values'] = np.insert(
self.model_return_values[pair]['DI_values'], 0, prepend)
self.model_return_values[pair]['do_preds'] = np.insert(
self.model_return_values[pair]['do_preds'], 0, prepend)
self.model_return_values[pair]['target_mean'] = np.insert(
self.model_return_values[pair]['target_mean'], 0, prepend)
self.model_return_values[pair]['target_std'] = np.insert(
self.model_return_values[pair]['target_std'], 0, prepend)
dh.full_predictions = copy.deepcopy(self.model_return_values[pair]['predictions'])
dh.full_do_predict = copy.deepcopy(self.model_return_values[pair]['do_preds'])
dh.full_target_mean = copy.deepcopy(self.model_return_values[pair]['target_mean'])
dh.full_target_std = copy.deepcopy(self.model_return_values[pair]['target_std'])
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
dh.full_DI_values = copy.deepcopy(self.model_return_values[pair]['DI_values'])
# if not self.follow_mode:
# self.save_model_return_values_to_disk()
prepend_df = pd.DataFrame(np.zeros((abs(length_difference) - 1, len(df.columns))),
columns=df.columns)
df = pd.concat([prepend_df, df], axis=0)
def return_null_values_to_strategy(self, dataframe: DataFrame, dh) -> None:
len_df = len(dataframe)
dh.full_predictions = np.zeros(len_df)
dh.full_do_predict = np.zeros(len_df)
dh.full_target_mean = np.zeros(len_df)
dh.full_target_std = np.zeros(len_df)
dataframe['prediction'] = 0
dataframe['do_predict'] = 0
dataframe['target_mean'] = 0
dataframe['target_std'] = 0
if self.freqai_info.get('feature_parameters', {}).get('DI_threshold', 0) > 0:
dh.full_DI_values = np.zeros(len_df)
dataframe['DI_value'] = 0
def purge_old_models(self) -> None: