#region imports
from AlgorithmImports import *
#endregion

class FuturesInfo():
    def __init__(self, quantpedia_future:Symbol) -> None:
        self.quantpedia_future:Symbol = quantpedia_future
        self.near_contract:FuturesContract = None
        self.distant_contract:FuturesContract = None
    
    def update_contracts(self, near_contract:FuturesContract, distant_contract:FuturesContract) -> None:
        self.near_contract = near_contract
        self.distant_contract = distant_contract
    
    def is_initialized(self) -> bool:
        return self.near_contract is not None and self.distant_contract is not None
    
# Custom fee model.
class CustomFeeModel():
    def GetOrderFee(self, parameters):
        fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))

# Quantpedia data.
# NOTE: IMPORTANT: Data order must be ascending (datewise)
class QuantpediaFutures(PythonData):
    _last_update_date:Dict[str, datetime.date] = {}

    @staticmethod
    def get_last_update_date() -> Dict[str, datetime.date]:
       return QuantpediaFutures._last_update_date

    def GetSource(self, config:SubscriptionDataConfig, date:datetime, isLiveMode:bool) -> SubscriptionDataSource:
        return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)

    def Reader(self, config:SubscriptionDataConfig, line:str, date:datetime, isLiveMode:bool) -> BaseData:
        data = QuantpediaFutures()
        data.Symbol = config.Symbol
        
        if not line[0].isdigit(): return None
        split = line.split(';')
        
        data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
        data['back_adjusted'] = float(split[1])
        data['spliced'] = float(split[2])
        data.Value = float(split[1])

        # store last update date
        if config.Symbol.Value not in QuantpediaFutures._last_update_date:
            QuantpediaFutures._last_update_date[config.Symbol.Value] = datetime(1,1,1).date()

        if data.Time.date() > QuantpediaFutures._last_update_date[config.Symbol.Value]:
            QuantpediaFutures._last_update_date[config.Symbol.Value] = data.Time.date()

        return data

# https://quantpedia.com/strategies/term-structure-effect-in-commodities/
#
# This simple strategy buys each month the 20% of commodities with the highest roll-returns and shorts the 20% of commodities with the lowest 
# roll-returns and holds the long-short positions for one month. The contracts in each quintile are equally-weighted. 
# The investment universe is all commodity futures contracts.
#
# QC implementation:

#region imports
import numpy as np
from AlgorithmImports import *
import data_tools
#endregion

class TermStructureEffectinCommodities(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2009, 1, 1)
        self.SetCash(100000)

        symbols:Dict[str, str] = {
            'CME_S1': Futures.Grains.Soybeans,
            'CME_W1' : Futures.Grains.Wheat,
            'CME_SM1' : Futures.Grains.SoybeanMeal,
            'CME_C1' : Futures.Grains.Corn,
            'CME_O1' : Futures.Grains.Oats,
            'CME_LC1' : Futures.Meats.LiveCattle,
            'CME_FC1' : Futures.Meats.FeederCattle,
            'CME_LN1' : Futures.Meats.LeanHogs,
            'CME_GC1' : Futures.Metals.Gold,
            'CME_SI1' : Futures.Metals.Silver,
            'CME_PL1' : Futures.Metals.Platinum,

            'CME_HG1' : Futures.Metals.Copper,
            'CME_LB1' : Futures.Forestry.RandomLengthLumber,
            'CME_NG1' : Futures.Energies.NaturalGas,
            'CME_PA1' : Futures.Metals.Palladium,
            'CME_DA1' : Futures.Dairy.ClassIIIMilk,
            
            'CME_RB1' : Futures.Energies.Gasoline,
            'ICE_WT1' : Futures.Energies.CrudeOilWTI,
            'ICE_CC1' : Futures.Softs.Cocoa,
            'ICE_O1' : Futures.Energies.HeatingOil,
            'ICE_SB1' : Futures.Softs.Sugar11CME,
        }
    
        self.futures_info:Dict[str, data_tools.FuturesInfo] = {}
        self.quantile:int = 5
        min_expiration_days:int = 2
        max_expiration_days:int = 360
        
        for qp_symbol, qc_future in symbols.items():
            # QP futures
            data:Security = self.AddData(data_tools.QuantpediaFutures, qp_symbol, Resolution.Daily)
            data.SetFeeModel(data_tools.CustomFeeModel())
            data.SetLeverage(5)
            
            # QC futures
            future:Future = self.AddFuture(qc_future, Resolution.Daily)
            future.SetFilter(timedelta(days=min_expiration_days), timedelta(days=max_expiration_days))
            self.futures_info[future.Symbol.Value] = data_tools.FuturesInfo(data.Symbol)
        
        self.recent_month:int = -1
        self.Settings.MinimumOrderMarginPortfolioPercentage = 0.

    def find_and_update_contracts(self, futures_chain, symbol) -> None:
        near_contract:FuturesContract = None
        dist_contract:FuturesContract = None

        if symbol in futures_chain:
            contracts:List = [contract for contract in futures_chain[symbol] if contract.Expiry.date() > self.Time.date()]

            if len(contracts) >= 2:
                contracts:List = sorted(contracts, key=lambda x: x.Expiry, reverse=False)
                near_contract = contracts[0]
                dist_contract = contracts[1]

        self.futures_info[symbol].update_contracts(near_contract, dist_contract)

    def OnData(self, data: Slice) -> None:
        if data.FutureChains.Count > 0:
            for symbol, futures_info in self.futures_info.items():
                # check if near contract is expired or is not initialized
                if not futures_info.is_initialized() or \
                    (futures_info.is_initialized() and futures_info.near_contract.Expiry.date() <= self.Time.date()):
                    self.find_and_update_contracts(data.FutureChains, symbol)
        
        roll_return:Dict[Symbol, float] = {}
        rebalance_flag:bool = False

        # roll return calculation
        for symbol, futures_info in self.futures_info.items():
            # futures data is present in the algorithm
            if futures_info.quantpedia_future in data and data[futures_info.quantpedia_future]:
                # new month rebalance
                if self.Time.month != self.recent_month and not self.IsWarmingUp:
                    self.recent_month = self.Time.month
                    rebalance_flag = True

                if rebalance_flag:
                    if futures_info.is_initialized():
                        near_c:FuturesContract = futures_info.near_contract
                        dist_c:FuturesContract = futures_info.distant_contract
                        if self.Securities.ContainsKey(near_c.Symbol) and self.Securities.ContainsKey(dist_c.Symbol):
                            raw_price1:float = self.Securities[near_c.Symbol].Close
                            raw_price2:float = self.Securities[dist_c.Symbol].Close

                            if raw_price1 != 0 and raw_price2 != 0:
                                roll_return[futures_info.quantpedia_future] = raw_price1 / raw_price2 - 1

        if rebalance_flag:
            weights:Dict[Symbol, float] = {}
            
            long:List[Symbol] = []
            short:List[Symbol] = []
            if len(roll_return) >= self.quantile:

                # roll return sorting
                sorted_by_roll:List[Tuple] = sorted(roll_return.items(), key = lambda x: x[1], reverse=True)
                quantile:int = int(len(sorted_by_roll) / self.quantile)
                long = [x[0] for x in sorted_by_roll[:quantile]]
                short = [x[0] for x in sorted_by_roll[-quantile:]]

            # trade execution
            invested:List[Symbol] = [x.Key for x in self.Portfolio if x.Value.Invested]
            for symbol in invested:
                if symbol not in long + short:
                    self.Liquidate(symbol)

            for i, portfolio in enumerate([long, short]):
                for symbol in portfolio:
                    if symbol in data and data[symbol]:
                        self.SetHoldings(symbol, ((-1) ** i) / len(portfolio))