freqtrade_origin/freqtrade/freqai/prediction_models/RLPredictionModel.py
2022-08-24 13:00:55 +02:00

253 lines
7.9 KiB
Python

import logging
from typing import Any, Dict, Tuple
#from matplotlib.colors import DivergingNorm
from pandas import DataFrame
import pandas as pd
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
import tensorflow as tf
from freqtrade.freqai.prediction_models.BaseTensorFlowModel import BaseTensorFlowModel
from freqtrade.freqai.freqai_interface import IFreqaiModel
from tensorflow.keras.layers import Input, Conv1D, Dense, MaxPooling1D, Flatten, Dropout
from tensorflow.keras.models import Model
import numpy as np
import copy
from keras.layers import *
import random
logger = logging.getLogger(__name__)
# tf.config.run_functions_eagerly(True)
# tf.data.experimental.enable_debug_mode()
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
MAX_EPOCHS = 10
LOOKBACK = 8
class RLPredictionModel_v2(IFreqaiModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), fit().
"""
def fit(self, data_dictionary: Dict, pair) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
train_df = data_dictionary["train_features"]
train_labels = data_dictionary["train_labels"]
test_df = data_dictionary["test_features"]
test_labels = data_dictionary["test_labels"]
n_labels = len(train_labels.columns)
if n_labels > 1:
raise OperationalException(
"Neural Net not yet configured for multi-targets. Please "
" reduce number of targets to 1 in strategy."
)
n_features = len(data_dictionary["train_features"].columns)
BATCH_SIZE = self.freqai_info.get("batch_size", 64)
input_dims = [BATCH_SIZE, self.CONV_WIDTH, n_features]
w1 = WindowGenerator(
input_width=self.CONV_WIDTH,
label_width=1,
shift=1,
train_df=train_df,
val_df=test_df,
train_labels=train_labels,
val_labels=test_labels,
batch_size=BATCH_SIZE,
)
# train_agent()
#pair = self.dd.historical_data[pair]
#gym_env = FreqtradeEnv(data=train_df, prices=0.01, windows_size=100, pair=pair, stake_amount=100)
# sep = '/'
# coin = pair.split(sep, 1)[0]
# # df1 = train_df.filter(regex='price')
# # df2 = df1.filter(regex='raw')
# # df3 = df2.filter(regex=f"{coin}")
# # print(df3)
# price = train_df[f"%-{coin}raw_price_5m"]
# gym_env = RLPrediction_GymAnytrading(signal_features=train_df, prices=price, window_size=100)
# sac = RLPrediction_Agent(gym_env)
# print(sac)
# return 0
return model
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first=True
) -> Tuple[DataFrame, DataFrame]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
:return:
:predictions: np.array of predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
dk.find_features(unfiltered_dataframe)
filtered_dataframe, _ = dk.filter_features(
unfiltered_dataframe, dk.training_features_list, training_filter=False
)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
# optional additional data cleaning/analysis
self.data_cleaning_predict(dk, filtered_dataframe)
if first:
full_df = dk.data_dictionary["prediction_features"]
w1 = WindowGenerator(
input_width=self.CONV_WIDTH,
label_width=1,
shift=1,
test_df=full_df,
batch_size=len(full_df),
)
predictions = self.model.predict(w1.inference)
len_diff = len(dk.do_predict) - len(predictions)
if len_diff > 0:
dk.do_predict = dk.do_predict[len_diff:]
else:
data = dk.data_dictionary["prediction_features"]
data = tf.expand_dims(data, axis=0)
predictions = self.model(data, training=False)
predictions = predictions[:, 0]
pred_df = DataFrame(predictions, columns=dk.label_list)
pred_df = dk.denormalize_labels_from_metadata(pred_df)
return (pred_df, np.ones(len(pred_df)))
def set_initial_historic_predictions(
self, df: DataFrame, model: Any, dk: FreqaiDataKitchen, pair: str
) -> None:
pass
# w1 = WindowGenerator(
# input_width=self.CONV_WIDTH, label_width=1, shift=1, test_df=df, batch_size=len(df)
# )
# trained_predictions = model.predict(w1.inference)
# #trained_predictions = trained_predictions[:, 0, 0]
# trained_predictions = trained_predictions[:, 0]
# n_lost_points = len(df) - len(trained_predictions)
# pred_df = DataFrame(trained_predictions, columns=dk.label_list)
# zeros_df = DataFrame(np.zeros((n_lost_points, len(dk.label_list))), columns=dk.label_list)
# pred_df = pd.concat([zeros_df, pred_df], axis=0)
# pred_df = dk.denormalize_labels_from_metadata(pred_df)
# self.dd.historic_predictions[pair] = DataFrame()
# self.dd.historic_predictions[pair] = copy.deepcopy(pred_df)
class WindowGenerator:
def __init__(
self,
input_width,
label_width,
shift,
train_df=None,
val_df=None,
test_df=None,
train_labels=None,
val_labels=None,
test_labels=None,
batch_size=None,
):
# Store the raw data.
self.train_df = train_df
self.val_df = val_df
self.test_df = test_df
self.train_labels = train_labels
self.val_labels = val_labels
self.test_labels = test_labels
self.batch_size = batch_size
self.input_width = input_width
self.label_width = label_width
self.shift = shift
self.total_window_size = input_width + shift
self.input_slice = slice(0, input_width)
self.input_indices = np.arange(self.total_window_size)[self.input_slice]
def make_dataset(self, data, labels=None):
data = np.array(data, dtype=np.float32)
if labels is not None:
labels = np.array(labels, dtype=np.float32)
ds = tf.keras.preprocessing.timeseries_dataset_from_array(
data=data,
targets=labels,
sequence_length=self.total_window_size,
sequence_stride=1,
sampling_rate=1,
shuffle=False,
batch_size=self.batch_size,
)
return ds
@property
def train(self):
return self.make_dataset(self.train_df, self.train_labels)
@property
def val(self):
return self.make_dataset(self.val_df, self.val_labels)
@property
def test(self):
return self.make_dataset(self.test_df, self.test_labels)
@property
def inference(self):
return self.make_dataset(self.test_df)
@property
def example(self):
"""Get and cache an example batch of `inputs, labels` for plotting."""
result = getattr(self, "_example", None)
if result is None:
# No example batch was found, so get one from the `.train` dataset
result = next(iter(self.train))
# And cache it for next time
self._example = result
return result