# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# 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.

from AlgorithmImports import *

from keras.models import *
from tensorflow import keras
from keras import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import SGD

class KerasNeuralNetworkAlgorithm(QCAlgorithm):

 def initialize(self) -> None:
 self.set_start_date(2019, 1, 1) # Set Start Date
 self.set_end_date(2020, 4, 1) # Set End Date
 self.set_cash(100000) # Set Strategy Cash

 self._model_by_symbol = {}

 for ticker in ["SPY", "QQQ", "TLT"]:
 symbol = self.add_equity(ticker).symbol

 # Read the model saved in the ObjectStore
 for kvp in self.object_store:
 key = f'{symbol}_model'
 if not (key == kvp.key and kvp.value):
 continue
 file_path = self.object_store.get_file_path(kvp.key)
 self._model_by_symbol[symbol] = keras.models.load_model(file_path)
 self.debug(f'Model for {symbol} sucessfully retrieved. File {file_path}. Size {len(kvp.value)}. Weights {self._model_by_symbol[symbol].get_weights()}')

 # Look-back period for training set
 self._lookback = 30

 # Train Neural Network every monday
 self.train(
 self.date_rules.every(DayOfWeek.MONDAY),
 self.time_rules.after_market_open("SPY"),
 self.neural_network_training)

 # Place trades on Monday, 30 minutes after the market is open
 self.schedule.on(
 self.date_rules.every_day("SPY"),
 self.time_rules.after_market_open("SPY", 30),
 self.trade)

 def on_end_of_algorithm(self) -> None:
 ''' Save the data and the mode using the ObjectStore '''
 for symbol, model in self._model_by_symbol.items():
 key = f'{symbol}_model.keras'
 file = self.object_store.get_file_path(key)
 model.save(file)
 self.object_store.save(key)
 self.debug(f'Model for {symbol} sucessfully saved in the ObjectStore')

 def neural_network_training(self) -> None:
 '''Train the Neural Network and save the model in the ObjectStore'''
 symbols = self.securities.keys()

 # Daily historical data is used to train the machine learning model
 history = self.history(symbols, self._lookback + 1, Resolution.DAILY)
 history = history.open.unstack(0)

 for symbol in symbols:
 if symbol not in history:
 continue

 predictor = history[symbol][:-1]
 predictand = history[symbol][1:]

 # 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(learning_rate = 0.01) # learning rate = 0.01

 # choose loss function and optimizing method
 model.compile(loss='mse', optimizer=sgd)

 # pick an iteration number large enough for convergence
 for step in range(200):
 # training the model
 cost = model.train_on_batch(predictor, predictand)

 self._model_by_symbol[symbol] = model

 def trade(self) -> None:
 '''
 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._model_by_symbol.items():
 if symbol not in self.current_slice.bars:
 continue
 
 # Get the out-of-sample history
 history = self.history(symbol, self._lookback, Resolution.DAILY)
 history = history.open.unstack(0)[symbol]

 # Get the final predicted price
 prediction = model.predict(history)[0][-1]
 history_std = np.std(history)

 holding = self.portfolio[symbol]
 open_price = self.current_slice.bars[symbol].open

 # Follow the trend
 if holding.invested:
 if open_price < prediction - history_std:
 self.liquidate(symbol)
 else:
 if open_price > prediction + history_std:
 self.set_holdings(symbol, target)