Weekly US Futures Reversal Strategy Based on Volume, Open Interest, and Past Returns

from AlgoLib import *
from collections import deque
import numpy as np
import data_tools
#endregion
class ShortTermReversalwithFutures(XXX):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(100000) 
symbols:Dict[str, str] = {
    'CME_S1': Futures.Grains.Soybeans,
    'CME_W1': Futures.Grains.Wheat,
    'CME_BO1': Futures.Grains.SoybeanOil,
    'CME_C1': Futures.Grains.Corn,
    'CME_LC1': Futures.Meats.LiveCattle,
    'CME_FC1': Futures.Meats.FeederCattle,
    'CME_KW2': Futures.Grains.Wheat,
    'ICE_CC1': Futures.Softs.Cocoa,
    'ICE_SB1': Futures.Softs.Sugar11CME,
    
    'CME_GC1': Futures.Metals.Gold,
    'CME_SI1': Futures.Metals.Silver,
    'CME_PL1': Futures.Metals.Platinum,
    'CME_RB1': Futures.Energies.Gasoline,
    'ICE_WT1': Futures.Energies.CrudeOilWTI,
    'ICE_O1': Futures.Energies.HeatingOil,
    'CME_BP1': Futures.Currencies.GBP,
    'CME_EC1': Futures.Currencies.EUR,
    'CME_JY1': Futures.Currencies.JPY,
    'CME_SF1': Futures.Currencies.CHF,
    'CME_ES1': Futures.Indices.SP500EMini,
    'CME_TY1': Futures.Financials.Y10TreasuryNote,
    'CME_FV1': Futures.Financials.Y5TreasuryNote,
}
                
self.period:int = 14
self.futures_info:Dict = {}
min_expiration_days:int = 2
max_expiration_days:int = 360
# daily close, volume and open interest data
self.data:Dict = {}
self.quantile:int = 2
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)
    self.data[data.Symbol] = deque(maxlen=self.period)
    
    # 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
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]
self.futures_info[symbol].update_contracts(near_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)
rebalance_flag:bool = False
ret_volume_oi_data:Dict[Symbol, Tuple[float]] = {}
# 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 futures_info.is_initialized():
            near_c:FuturesContract = futures_info.near_contract
            if self.Securities.ContainsKey(near_c.Symbol):
                # store daily data
                price:float = data[futures_info.quantpedia_future].Value
                vol:int = self.Securities[near_c.Symbol].Volume
                oi:int = self.Securities[near_c.Symbol].OpenInterest
                
                if price != 0 and vol != 0 and oi != 0:
                    self.data[futures_info.quantpedia_future].append((price, vol, oi))
            if rebalance_flag:
                if len(self.data[futures_info.quantpedia_future]) == self.data[futures_info.quantpedia_future].maxlen:
                    # performance
                    prices:List[float] = [x[0] for x in self.data[futures_info.quantpedia_future]]
                    half:List[float] = int(len(prices)/2)
                    prices:List[float] = prices[-half:]
                    ret:float = prices[-1] / prices[0] - 1
                    
                    # volume change
                    volumes:List[int] = [x[1] for x in self.data[futures_info.quantpedia_future]]
                    volumes_t1:List[int] = volumes[-half:]
                    t1_vol_mean:float = np.mean(volumes_t1)
                    t1_vol_total:float = sum(volumes_t1) / t1_vol_mean
                    volumes_t2:List[int] = volumes[:half]
                    t2_vol_mean:float = np.mean(volumes_t2)
                    t2_vol_total:float = sum(volumes_t2) / t2_vol_mean
                    volume_weekly_diff:float = t1_vol_total - t2_vol_total
                    
                    # open interest change
                    interests:List[int] = [x[2] for x in self.data[futures_info.quantpedia_future]]
                    t1_oi:List[int] = interests[-half:]
                    t1_oi_total:float = sum(t1_oi)
                    t2_oi:List[int] = interests[:half]
                    t2_oi_total:float = sum(t2_oi)
                    oi_weekly_diff:float = t1_oi_total - t2_oi_total
                    
                    # store weekly diff data
                    ret_volume_oi_data[futures_info.quantpedia_future] = (ret, volume_weekly_diff, oi_weekly_diff)
if rebalance_flag:
    weight:Dict[Symbol, float] = {}
    if len(ret_volume_oi_data) > self.quantile * 2:
        volume_sorted:List = sorted(ret_volume_oi_data.items(), key = lambda x: x[1][1], reverse = True)
        quantile:int = int(len(volume_sorted) / self.quantile)
        high_volume:List = [x for x in volume_sorted[:quantile]]
        
        open_interest_sorted:List = sorted(ret_volume_oi_data.items(), key = lambda x: x[1][2], reverse = True)
        quantile = int(len(open_interest_sorted) / self.quantile)
        low_oi:List = [x for x in open_interest_sorted[-quantile:]]
        
        filtered:List = [x for x in high_volume if x in low_oi]
        filtered_by_return:List = sorted(filtered, key = lambda x : x[0], reverse = True)
        quantile = int(len(filtered_by_return) / self.quantile)
        long:List[Symbol] = filtered_by_return[-quantile:]
        short:List[Symbol] = filtered_by_return[:quantile]
        if len(long + short) >= 2: 
            # return weighting
            diff:Dict[Symbol, float] = {}
            avg_ret:float = np.average([x[1][0] for x in long + short])

            for symbol, ret_volume_oi in long + short:
                diff[symbol] = ret_volume_oi[0] - avg_ret
            
            total_diff:float = sum([abs(x[1]) for x in diff.items()])
            long_symbols:List[Symbol] = [x[0] for x in long]

            if total_diff != 0:
                for symbol, data in long + short:
                    if symbol in long_symbols:
                        weight[symbol] = diff[symbol] / total_diff
                    else:
                        weight[symbol] = - diff[symbol] / total_diff
    # trade execution
    invested:List[Symbol] = [x.Key for x in self.Portfolio if x.Value.Invested]
    for symbol in invested:
        if symbol not in weight:
            self.Liquidate(symbol)
    for symbol, w in weight.items():
        self.SetHoldings(symbol, w)