# 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 *

### <summary>
### Basic template algorithm simply initializes the date range and cash. This is a skeleton
### framework you can use for designing an algorithm.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="using quantconnect" />
### <meta name="tag" content="trading and orders" />
class IndicatorSuiteAlgorithm(QCAlgorithm):
 '''Demonstration algorithm of popular indicators and plotting them.'''

 def initialize(self):
 '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

 self._symbol = "SPY"
 self._symbol2 = "GOOG"
 self.custom_symbol = "IBM"
 self.price = 0.0

 self.set_start_date(2013, 1, 1) #Set Start Date
 self.set_end_date(2014, 12, 31) #Set End Date
 self.set_cash(25000) #Set Strategy Cash
 # Find more symbols here: http://quantconnect.com/data

 self.add_equity(self._symbol, Resolution.DAILY)
 self.add_equity(self._symbol2, Resolution.DAILY)
 self.add_data(CustomData, self.custom_symbol, Resolution.DAILY)

 # Set up default Indicators, these indicators are defined on the Value property of incoming data (except ATR and AROON which use the full TradeBar object)
 self.indicators = {
 'BB' : self.bb(self._symbol, 20, 1, MovingAverageType.SIMPLE, Resolution.DAILY),
 'RSI' : self.rsi(self._symbol, 14, MovingAverageType.SIMPLE, Resolution.DAILY),
 'EMA' : self.ema(self._symbol, 14, Resolution.DAILY),
 'SMA' : self.sma(self._symbol, 14, Resolution.DAILY),
 'MACD' : self.macd(self._symbol, 12, 26, 9, MovingAverageType.SIMPLE, Resolution.DAILY),
 'MOM' : self.mom(self._symbol, 20, Resolution.DAILY),
 'MOMP' : self.momp(self._symbol, 20, Resolution.DAILY),
 'STD' : self.std(self._symbol, 20, Resolution.DAILY),
 # by default if the symbol is a tradebar type then it will be the min of the low property
 'MIN' : self.min(self._symbol, 14, Resolution.DAILY),
 # by default if the symbol is a tradebar type then it will be the max of the high property
 'MAX' : self.max(self._symbol, 14, Resolution.DAILY),
 'ATR' : self.atr(self._symbol, 14, MovingAverageType.SIMPLE, Resolution.DAILY),
 'AROON' : self.aroon(self._symbol, 20, Resolution.DAILY),
 'B' : self.b(self._symbol, self._symbol2, 14)
 }

 # Here we're going to define indicators using 'selector' functions. These 'selector' functions will define what data gets sent into the indicator
 # These functions have a signature like the following: decimal Selector(BaseData base_data), and can be defined like: base_data => base_data.value
 # We'll define these 'selector' functions to select the Low value
 #
 # For more information on 'anonymous functions' see: http:#en.wikipedia.org/wiki/Anonymous_function
 # https:#msdn.microsoft.com/en-us/library/bb397687.aspx
 #
 self.selector_indicators = {
 'BB' : self.bb(self._symbol, 20, 1, MovingAverageType.SIMPLE, Resolution.DAILY, Field.LOW),
 'RSI' :self.rsi(self._symbol, 14, MovingAverageType.SIMPLE, Resolution.DAILY, Field.LOW),
 'EMA' :self.ema(self._symbol, 14, Resolution.DAILY, Field.LOW),
 'SMA' :self.sma(self._symbol, 14, Resolution.DAILY, Field.LOW),
 'MACD' : self.macd(self._symbol, 12, 26, 9, MovingAverageType.SIMPLE, Resolution.DAILY, Field.LOW),
 'MOM' : self.mom(self._symbol, 20, Resolution.DAILY, Field.LOW),
 'MOMP' : self.momp(self._symbol, 20, Resolution.DAILY, Field.LOW),
 'STD' : self.std(self._symbol, 20, Resolution.DAILY, Field.LOW),
 'MIN' : self.min(self._symbol, 14, Resolution.DAILY, Field.HIGH),
 'MAX' : self.max(self._symbol, 14, Resolution.DAILY, Field.LOW),
 # ATR and AROON are special in that they accept a TradeBar instance instead of a decimal, we could easily project and/or transform the input TradeBar
 # before it gets sent to the ATR/AROON indicator, here we use a function that will multiply the input trade bar by a factor of two
 'ATR' : self.atr(self._symbol, 14, MovingAverageType.SIMPLE, Resolution.DAILY, self.selector_double__trade_bar),
 'AROON' : self.aroon(self._symbol, 20, Resolution.DAILY, self.selector_double__trade_bar)
 }

 # Custom Data Indicator:
 self.rsi_custom = self.rsi(self.custom_symbol, 14, MovingAverageType.SIMPLE, Resolution.DAILY)
 self.min_custom = self.min(self.custom_symbol, 14, Resolution.DAILY)
 self.max_custom = self.max(self.custom_symbol, 14, Resolution.DAILY)

 # in addition to defining indicators on a single security, you can all define 'composite' indicators.
 # these are indicators that require multiple inputs. the most common of which is a ratio.
 # suppose we seek the ratio of BTC to SPY, we could write the following:
 spy_close = Identity(self._symbol)
 ibm_close = Identity(self.custom_symbol)

 # this will create a new indicator whose value is IBM/SPY
 self.ratio = IndicatorExtensions.over(ibm_close, spy_close)

 # we can also easily plot our indicators each time they update using th PlotIndicator function
 self.plot_indicator("Ratio", self.ratio)

 # The following methods will add multiple charts to the algorithm output.
 # Those chatrs names will be used later to plot different series in a particular chart.
 # For more information on Lean Charting see: https://www.quantconnect.com/docs#Charting
 Chart('BB')
 Chart('STD')
 Chart('ATR')
 Chart('AROON')
 Chart('MACD')
 Chart('Averages')
 # Here we make use of the Schelude method to update the plots once per day at market close.
 self.schedule.on(self.date_rules.every_day(), self.time_rules.before_market_close(self._symbol), self.update_plots)

 def on_data(self, data: Slice):
 '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.

 Arguments:
 data: Slice object keyed by symbol containing the stock data
 '''

 if (#not data.bars.contains_key(self._symbol) or
 not self.indicators['BB'].is_ready or
 not self.indicators['RSI'].is_ready):
 return

 if not data.bars.contains_key(self._symbol):
 return

 self.price = data[self._symbol].close

 if not self.portfolio.hold_stock:
 quantity = int(self.portfolio.cash / self.price)
 self.order(self._symbol, quantity)
 self.debug('Purchased SPY on ' + self.time.strftime('%Y-%m-%d'))

 def update_plots(self):
 if not self.indicators['BB'].is_ready or not self.indicators['STD'].is_ready:
 return

 # Plots can also be created just with this one line command.
 self.plot('RSI', self.indicators['RSI'])
 # Custom data indicator
 self.plot('RSI-FB', self.rsi_custom)

 # Here we make use of the chats decalred in the Initialize method, plotting multiple series
 # in each chart.
 self.plot('STD', 'STD', self.indicators['STD'].current.value)

 self.plot('BB', 'Price', self.price)
 self.plot('BB', 'BollingerUpperBand', self.indicators['BB'].upper_band.current.value)
 self.plot('BB', 'BollingerMiddleBand', self.indicators['BB'].middle_band.current.value)
 self.plot('BB', 'BollingerLowerBand', self.indicators['BB'].lower_band.current.value)


 self.plot('AROON', 'Aroon', self.indicators['AROON'].current.value)
 self.plot('AROON', 'AroonUp', self.indicators['AROON'].aroon_up.current.value)
 self.plot('AROON', 'AroonDown', self.indicators['AROON'].aroon_down.current.value)

 # The following Plot method calls are commented out because of the 10 series limit for backtests
 #self.plot('ATR', 'ATR', self.indicators['ATR'].current.value)
 #self.plot('ATR', 'ATRDoubleBar', self.selector_indicators['ATR'].current.value)
 #self.plot('Averages', 'SMA', self.indicators['SMA'].current.value)
 #self.plot('Averages', 'EMA', self.indicators['EMA'].current.value)
 #self.plot('MOM', self.indicators['MOM'].current.value)
 #self.plot('MOMP', self.indicators['MOMP'].current.value)
 #self.plot('MACD', 'MACD', self.indicators['MACD'].current.value)
 #self.plot('MACD', 'MACDSignal', self.indicators['MACD'].signal.current.value)

 def selector_double__trade_bar(self, bar):
 trade_bar = TradeBar()
 trade_bar.close = 2 * bar.close
 trade_bar.data_type = bar.data_type
 trade_bar.high = 2 * bar.high
 trade_bar.low = 2 * bar.low
 trade_bar.open = 2 * bar.open
 trade_bar.symbol = bar.symbol
 trade_bar.time = bar.time
 trade_bar.value = 2 * bar.value
 trade_bar.period = bar.period
 return trade_bar


class CustomData(PythonData):
 def get_source(self, config, date, is_live):
 zip_file_name = LeanData.generate_zip_file_name(config.Symbol, date, config.Resolution, config.TickType)
 source = Globals.data_folder + "/equity/usa/daily/" + zip_file_name
 return SubscriptionDataSource(source)

 def reader(self, config, line, date, is_live):
 if line == None:
 return None

 custom_data = CustomData()
 custom_data.symbol = config.symbol

 csv = line.split(",")
 custom_data.time = datetime.strptime(csv[0], '%Y%m%d %H:%M')
 custom_data.end_time = custom_data.time + timedelta(days=1)
 custom_data.value = float(csv[1]) / 10000.0
 return custom_data