Mean Variance Carry Trade Strategy

1from AlgorithmImports import *
2import pandas as pd
3from collections import deque
4import data_tools
5class MeanVarianceCarryTradeStrategy(QCAlgorithm):
6def Initialize(self):
7self.SetStartDate(2009, 1, 1)
8self.SetCash(100000)
9
10self.tickers:dict[str, str] = {
11    'CME_AD1' : Futures.Currencies.AUD, # Australian Dollar Futures, Continuous Contract #1
12    'CME_BP1' : Futures.Currencies.GBP, # British Pound Futures, Continuous Contract #1
13    'CME_CD1' : Futures.Currencies.CAD, # Canadian Dollar Futures, Continuous Contract #1
14    'CME_EC1' : Futures.Currencies.EUR, # Euro FX Futures, Continuous Contract #1
15    'CME_JY1' : Futures.Currencies.JPY, # Japanese Yen Futures, Continuous Contract #1
16    'CME_MP1' : Futures.Currencies.MXN, # Mexican Peso Futures, Continuous Contract #1
17    'CME_NE1' : Futures.Currencies.NZD, # New Zealand Dollar Futures, Continuous Contract #1
18    'CME_SF1' : Futures.Currencies.CHF, # Swiss Franc Futures, Continuous Contract #1
19}
20self.period:int = 250
21self.max_missing_days:int = 5
22self.min_expiration_days:int = 0
23self.max_expiration_days:int = 360
24
25self.futures_data:dict[str, FuturesData] = {}
26for qp_ticker, qc_ticker in self.tickers.items():
27    # quantpedia #1 Contract
28    data = self.AddData(data_tools.QuantpediaFutures, qp_ticker, Resolution.Daily)
29    data.SetFeeModel(data_tools.CustomFeeModel())
30    data.SetLeverage(5)
31    quantpedia_future_symbol:Symbol = data.Symbol
32    # QC futures
33    future:Future = self.AddFuture(qc_ticker, Resolution.Daily, dataNormalizationMode=DataNormalizationMode.Raw)
34    future.SetFilter(timedelta(days=self.min_expiration_days), timedelta(days=self.max_expiration_days))
35    self.futures_data[future.Symbol.Value] = data_tools.FuturesData(quantpedia_future_symbol, self.period)
36
37self.recent_month:int = -1
38def FindAndUpdateContracts(self, futures_chain, ticker) -> None:
39near_contract:FuturesContract = None
40dist_contract:FuturesContract = None
41if ticker in futures_chain:
42    contracts:list[:FuturesContract] = [contract for contract in futures_chain[ticker] if contract.Expiry.date() > self.Time.date()]
43    if len(contracts) >= 2:
44        contracts:list[:FuturesContract] = sorted(contracts, key=lambda x: x.Expiry, reverse=False)
45        near_contract = contracts[0]
46        dist_contract = contracts[1]
47self.futures_data[ticker].update_contracts(near_contract, dist_contract)
48
49def OnData(self, data):
50# daily update QC futures data 
51if data.FutureChains.Count > 0:
52    for ticker, future_obj in self.futures_data.items():
53        # check if near contract is expired or is not initialized
54        if not future_obj.is_initialized() or \
55            (future_obj.is_initialized() and future_obj.near_contract.Expiry.date() == self.Time.date()):
56            self.FindAndUpdateContracts(data.FutureChains, ticker)
57        # update QC futures rolling return
58        if future_obj.is_initialized():
59            near_c:FuturesContract = future_obj.near_contract
60            dist_c:FuturesContract = future_obj.distant_contract
61            if near_c.Symbol in data and data[near_c.Symbol] and dist_c.Symbol in data and data[dist_c.Symbol]:
62                raw_price1:float = data[near_c.Symbol].Value * self.Securities[ticker].SymbolProperties.PriceMagnifier
63                raw_price2:float = data[dist_c.Symbol].Value * self.Securities[ticker].SymbolProperties.PriceMagnifier
64                if raw_price1 != 0 and raw_price2 != 0:
65                    daily_return:float = raw_price1 / raw_price2 - 1 
66                    future_obj.update_roll_return(daily_return)
67# check if quantpedia data still coming
68for _, future_obj in self.futures_data.items():
69    quantpedia_future:Symbol = future_obj.quantpedia_future
70    # if quantpedia_future in data and data[quantpedia_future]:
71    #     future_obj.update_quantpedia_last_update(self.Time.date())
72# rebalance monthly
73if self.recent_month != self.Time.month:
74    self.recent_month = self.Time.month
75    self.Rebalance()
76def Rebalance(self):
77custom_data_last_update_date: Dict[Symbol, datetime.date] = data_tools.QuantpediaFutures.get_last_update_date()
78self.Liquidate()
79ready_futures:dict[Symbol, list] = {}
80
81# filter ready futures and reset futures, which do not recieve new data
82for _, future_obj in self.futures_data.items():
83    data_ready_flag = future_obj.is_ready()
84    if self.securities[future_obj.quantpedia_future].get_last_data() and self.time.date() > custom_data_last_update_date[future_obj.quantpedia_future]:
85        self.liquidate()
86        return
87    if data_ready_flag:
88        ready_futures[future_obj.quantpedia_future] = future_obj.reverse_roll_return()
89    elif data_ready_flag:
90        future_obj.reset_data()
91
92ready_futures_length:int = len(ready_futures)
93if ready_futures_length == 0: return
94df:pd.DataFrame = pd.DataFrame(ready_futures, columns=ready_futures.keys())
95optimization:data_tools.PortfolioOptimization = data_tools.PortfolioOptimization(df, 0, ready_futures_length)
96opt_weight:list[float] = optimization.opt_portfolio()
97if sum(opt_weight) == 0: return
98for index in range(ready_futures_length):
99    weight:float = opt_weight[index]
100    if weight >= 0.001:
101        self.SetHoldings(df.columns[index], weight)