# 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>
### In this algorithm we show how you can easily use the universe selection feature to fetch symbols
### to be traded using the BaseData custom data system in combination with the AddUniverse{T} method.
### AddUniverse{T} requires a function that will return the symbols to be traded.
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="custom universes" />
class DropboxBaseDataUniverseSelectionAlgorithm(QCAlgorithm):

 def initialize(self) -> None:
 self.universe_settings.resolution = Resolution.DAILY

 # Order margin value has to have a minimum of 0.5% of Portfolio value, allows filtering out small trades and reduce fees.
 # Commented so regression algorithm is more sensitive
 #self.settings.minimum_order_margin_portfolio_percentage = 0.005

 self.set_start_date(2017, 7, 6)
 self.set_end_date(2018, 7, 4)

 universe = self.add_universe(StockDataSource, self.stock_data_source)

 historical_selection_data = self.history(universe, 3)
 if len(historical_selection_data) != 3:
 raise ValueError(f"Unexpected universe data count {len(historical_selection_data)}")

 for universe_data in historical_selection_data["symbols"]:
 if len(universe_data) != 5:
 raise ValueError(f"Unexpected universe data receieved")

 self._changes = None

 def stock_data_source(self, data: list[DynamicData]) -> list[Symbol]:
 list = []
 for item in data:
 for symbol in item["Symbols"]:
 list.append(symbol)
 return list

 def on_data(self, slice: Slice) -> None:
 if slice.bars.count == 0:
 return
 if not self._changes:
 return

 # start fresh
 self.liquidate()

 percentage = 1 / slice.bars.count
 for trade_bar in slice.bars.values():
 self.set_holdings(trade_bar.symbol, percentage)

 # reset changes
 self._changes = None

 def on_securities_changed(self, changes: SecurityChanges) -> None:
 self._changes = changes

class StockDataSource(PythonData):

 def get_source(self, config: SubscriptionDataConfig, date: datetime, is_live_mode: bool) -> SubscriptionDataSource:
 url = "https://www.dropbox.com/s/2l73mu97gcehmh7/daily-stock-picker-live.csv?dl=1" if is_live_mode else \
 "https://www.dropbox.com/s/ae1couew5ir3z9y/daily-stock-picker-backtest.csv?dl=1"
 return SubscriptionDataSource(url, SubscriptionTransportMedium.REMOTE_FILE)

 def reader(self, config: SubscriptionDataConfig, line: str, date: datetime, is_live_mode: bool) -> DynamicData:
 if not (line.strip() and line[0].isdigit()):
 return None

 stocks = StockDataSource()
 stocks.symbol = config.symbol

 csv = line.split(',')
 if is_live_mode:
 stocks.time = date
 stocks["Symbols"] = csv
 else:
 stocks.time = datetime.strptime(csv[0], "%Y%m%d")
 stocks["Symbols"] = csv[1:]
 return stocks