# Pierre Pohly
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import clr
clr.AddReference("System")
clr.AddReference("QuantConnect.Algorithm")
clr.AddReference("QuantConnect.Common")

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *

import tensorflow as tf
tf.config.threading.set_inter_op_parallelism_threads(2)
tf.config.threading.set_intra_op_parallelism_threads(1)

import json
import numpy as np
import pandas as pd
from io import StringIO
from keras.models import Sequential
from keras.layers import Dense, Activation,LSTM,Dropout,Embedding,Flatten
from keras.optimizers import SGD, Adam
from keras.utils.generic_utils import serialize_keras_object
from sklearn.preprocessing import MinMaxScaler

class SimpleEnergyTroll(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2018, 4, 1)   # Set Start Date
        self.SetEndDate(2020, 4, 1)     # Set End Date
        self.SetCash(100000)            # Set Strategy Cash
        
        self.contracts = {}
        self.modelBySymbol = {}
        self.scalersBySymbol = {}

        # In Initialize
        for ticker in ["BTCUSD", "ETHUSD"]: #, "LTCUSD"]: 
            symbol = self.AddCrypto(ticker, Resolution.Hour, Market.GDAX).Symbol
            self.modelBySymbol[symbol] = None
            
            # Read the model saved in the ObjectStore
            #if self.ObjectStore.ContainsKey(f'{symbol}_model'):
            #    modelStr = self.ObjectStore.Read(f'{symbol}_model')
            #    config = json.loads(modelStr)['config']
            #    self.modelBySymbol[symbol] = Sequential.from_config(config)
        
        
        # Look-back period for training set
        self.lookback = 24 * 12
        # Timesteps defines how much features will feed to the network
        self.timesteps = 24

        # Train Neural Network every monday
        self.Train(
            self.DateRules.Every(DayOfWeek.Monday, DayOfWeek.Thursday, DayOfWeek.Wednesday, DayOfWeek.Thursday, DayOfWeek.Friday),
            self.TimeRules.At(2, 0),
            self.NeuralNetworkTraining)

        # Place trades every Weekday, 30 minutes after the market is open
        self.Train(self.DateRules.Every(DayOfWeek.Monday, DayOfWeek.Thursday, DayOfWeek.Wednesday, DayOfWeek.Thursday, DayOfWeek.Friday), \
            #self.TimeRules.At(4, 0), \
            self.TimeRules.Every(TimeSpan.FromMinutes(60)), \
            Action(self.Trade))
        
    # Explore the future contract chain
    #def OnData(self, slice):
        #self.contracts = {}
        #for chain in slice.FutureChains:
        #    contract = list(chain.Value)[0]
        #    self.contracts[contract.Symbol] = contract

    def OnEndOfAlgorithm(self):
        ''' Save the data and the mode using the ObjectStore '''
        for symbol, model in self.modelBySymbol.items():
            modelStr = json.dumps(serialize_keras_object(model))
            self.ObjectStore.Save(f'{symbol}_model', modelStr)
            self.Debug(f'Model for {symbol} sucessfully saved in the ObjectStore')


    def NeuralNetworkTraining(self):
        '''Train the Neural Network and save the model in the ObjectStore'''        
        symbols = list(self.modelBySymbol.keys())
        
        if len(symbols) == 0: 
            self.Debug("no contracts found")
            return 
        
        for symbol in symbols:
            try: 
                # Hourly historical data is used to train the machine learning model
                history = self.History(symbol, (self.lookback + self.timesteps), Resolution.Hour)
                self.Debug(history)
            except: 
                self.Debug("Failed to receive history")
            #history = self.x_scaler.fit_transform(history)

            if 'open' in history and 'close' in history and 'high' in history and 'low' in history: 
                history = np.column_stack((history['open'], history['close'], history['high'], history['low']))
                #history = np.column_stack((history['open']))

            if len(history) < self.lookback: 
                self.Debug("Error while collecting the training data")
                continue
            
            #history = list([i[0] for i in history])

            #self.Debug("Start Training for symbol {0}".format(symbol))
            
            #First convert the data into 3D Array with (x train samples, 60 timesteps, 1 feature)
            x_train = []
            y_train = []
            for i in range(self.timesteps, len(history)): 
                x_train.append(history[i - self.timesteps:i])
                y_train.append([history[i][0]])
            
            x_train, y_train = np.array(x_train), np.array(y_train)
            x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 4))
            y_train = np.reshape(y_train, (y_train.shape[0], 1))
            if np.any(np.isnan(x_train)): 
                self.Debug("Error in Training Data")
                continue
            if np.any(np.isnan(y_train)): 
                self.Debug("Error in Validation Data")
                continue
            
            x_scaler = MinMaxScaler(feature_range=(0, 1))
            y_scaler = MinMaxScaler(feature_range=(0, 1))

            
            x_train = x_scaler.fit_transform(x_train.reshape(-1, x_train.shape[-1])).reshape(x_train.shape)
            #x_train = self.x_scaler.fit_transform(x_train)
            y_train = y_scaler.fit_transform(y_train)
            
            
            #self.Debug(x_train.shape)
            
            #self.Debug(y_train.shape)
            #self.Debug(y_train)
            # build a neural network from the 1st layer to the last layer
            '''
            model = Sequential()

            model.add(Dense(10, input_dim = 1))
            model.add(Activation('relu'))
            model.add(Dense(1))

            sgd = SGD(lr = 0.01)   # learning rate = 0.01

            # choose loss function and optimizing method
            model.compile(loss='mse', optimizer=sgd)
            '''
            if symbol in self.modelBySymbol and self.modelBySymbol[symbol] is not None: 
                model = self.modelBySymbol[symbol]
                iterations = 1
            else: 
                #If Model not exist for symbol then create one
                opt_cells = 5
                model = Sequential()
                
                model.add(LSTM(units = opt_cells, return_sequences = True, input_shape = (x_train.shape[1], 4)))
                
                model.add(Dropout(0.2))
                
                model.add(LSTM(units = opt_cells, return_sequences = True))
                model.add(Dropout(0.2))
                
                model.add(LSTM(units = opt_cells, return_sequences = True))
                model.add(Dropout(0.2))
                
                model.add(LSTM(units = opt_cells, return_sequences = False))
                model.add(Dropout(0.2))
                
                model.add(Dense(1, activation='linear'))
        
                adam = Adam(lr=0.001, clipnorm=1.0)
                model.compile(loss='mean_squared_error', optimizer=adam, metrics=['accuracy'])
                
                iterations = 50
            # pick an iteration number large enough for convergence 
            for step in range(iterations):
                # training the model
                #cost = model.train_on_batch(predictor, predictand)
                hist = model.fit(x_train, y_train,  epochs = 1) #verbose=0,
                acc = list(hist.history['accuracy'])[-1]
                loss = list(hist.history['loss'])[-1]

            
            self.scalersBySymbol[symbol] = (x_scaler, y_scaler)
            self.modelBySymbol[symbol] = model
            self.Debug("End Training for symbol {0} with accuracy {1}".format(symbol, acc))

    def Trade(self):
        '''
        Predict the price using the trained model and out-of-sample data
        Enter or exit positions based on relationship of the open price of the current bar and the prices defined by the machine learning model.
        Liquidate if the open price is below the sell price and buy if the open price is above the buy price 
        '''
        target = 1 / len(self.Securities)

        for symbol, model in self.modelBySymbol.items():
            if model is None: 
                continue
            # Get the out-of-sample history
            try: 
                history = self.History(symbol, (self.lookback + self.timesteps), Resolution.Hour)
            except: 
                self.Debug("Failed to receive history")
            #history = self.x_scaler.fit_transform(history)
            
            if 'open' in history and 'close' in history and 'high' in history and 'low' in history: 
                history = np.column_stack((history['open'], history['close'], history['high'], history['low']))
                #history = np.column_stack((history['open']))
            if len(history) < self.lookback: 
                self.Debug("Error while collecting the testing data")
                continue
            
            #history = self.x_scaler.fit_transform(history)
            
            #history = list([i[0] for i in history])
            #if not 'open' in history:
            #    continue
            #history = history['open'].to_list()
                        
            #if len(history) < self.lookback: 
            #    self.Debug("Error while collecting the testing data")
            #    continue
            #First convert the data into 3D Array with (x train samples, 60 timesteps, 1 feature)
            x_test = []
            for i in range(self.timesteps, len(history)): 
                x_test.append(history[i - self.timesteps:i])
            
            x_test = np.array(x_test)
            x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 4))
            
            if np.any(np.isnan(x_test)): 
                self.Debug("Error in Testing Data")
                continue
            
            if symbol in self.scalersBySymbol and self.scalersBySymbol[symbol] is not None: 
                x_scaler = self.scalersBySymbol[symbol][0]
                y_scaler = self.scalersBySymbol[symbol][1]
            
            x_test = x_scaler.transform(x_test.reshape(-1, x_test.shape[-1])).reshape(x_test.shape)
            
            #x_test = self.x_scaler.fit_transform(x_test)
            
            #self.Debug(x_test.shape)
            # Get the final predicted price
            #self.Debug(model.predict(history))
            predictions = model.predict(x_test)
            #self.Debug(predictions)
            prediction = y_scaler.inverse_transform(predictions)[0][-1]
            historyStd = np.std(history)
            
            self.Debug("Prediction for symbol {0}: {1}".format(symbol, prediction))

            holding = self.Portfolio[symbol]
            openPrice = self.Securities[symbol].Open;
            
            self.Debug("Open Price for symbol {0}: {1}".format(symbol, openPrice))


            # Follow the trend
            if holding.Invested:
                #self.Debug("if {0} < {1}".format(openPrice, prediction - historyStd))
                if openPrice < prediction - historyStd:
                    self.Liquidate(symbol)
            else:
                self.Debug("if {0} > {1}".format(openPrice, prediction + historyStd))
                if openPrice > prediction + historyStd:
                    self.SetHoldings(symbol, target)