from numpy import floor
from AlgoLib import *
from typing import List, Dict, Tuple
class Weeks52HighEffectinStocks(XXX):
def Initialize(self) -> None:
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
self.SetSecurityInitializer(lambda x: x.SetMarketPrice(self.GetLastKnownPrice(x)))
self.period:int = 12 * 21
# Tranching.
self.holding_period:int = 3
self.managed_queue:List[RebalanceQueueItem] = []
self.industry_count:int = 6
self.leverage:int = 5
self.selection_sorting_key = lambda x:x.MarketCap
# Daily 'high' data.
self.data:Dict[Symbol, SymbolData] = {}
self.symbol:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
self.fundamental_count:int = 500
self.selection_flag:bool = False
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.FundamentalSelectionFunction)
self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)
def OnSecuritiesChanged(self, changes:SecurityChanges) -> None:
for security in changes.AddedSecurities:
security.SetFeeModel(CustomFeeModel())
security.SetLeverage(self.leverage)
def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
# Update the rolling window every day.
for stock in fundamental:
symbol:Symbol = stock.Symbol
if symbol in self.data:
# Store daily price.
self.data[symbol].update(stock.AdjustedPrice)
if not self.selection_flag:
return Universe.Unchanged
selected:List[Fundamental] = [x for x in fundamental if x.HasFundamentalData and x.Market == 'usa' and x.MarketCap != 0 and \
((x.SecurityReference.ExchangeId == "NYS") or (x.SecurityReference.ExchangeId == "NAS") or (x.SecurityReference.ExchangeId == "ASE"))]
if len(selected) > self.fundamental_count:
selected = [x for x in sorted(selected, key=self.selection_sorting_key, reverse=True)[:self.fundamental_count]]
group:Dict[MorningstarIndustryGroupCode, float] = {}
# Warmup price rolling windows.
for stock in selected:
symbol:Symbol = stock.Symbol
if symbol not in self.data:
self.data[symbol] = SymbolData(symbol, self.period)
history:DataFrame = self.History(symbol, self.period, Resolution.Daily)
if history.empty:
self.Log(f"Not enough data for {symbol} yet")
continue
closes:pd.Series = history.loc[symbol].close
for time, close in closes.items():
self.data[symbol].update(close)
if not self.data[symbol].is_ready():
continue
industry_group_code:MorningstarIndustryGroupCode = stock.AssetClassification.MorningstarIndustryGroupCode
if industry_group_code == 0: continue
# Adding stocks in groups.
if industry_group_code not in group:
group[industry_group_code] = []
max_high:float = self.data[symbol].maximum()
price:float = self.data[symbol].get_latest_price()
stock_prilag:float = (stock, price / max_high)
group[industry_group_code].append(stock_prilag)
top_industries:List[MorningstarIndustryGroupCode] = []
low_industries:List[MorningstarIndustryGroupCode] = []
if len(group) != 0:
# Weighted average of ratios calc.
industry_prilag_weighted_avg:Dict[int, float] = {}
for industry_code in group:
total_market_cap:float = sum([stock_prilag_data[0].MarketCap for stock_prilag_data in group[industry_code]])
if total_market_cap == 0: continue
industry_prilag_weighted_avg[industry_code] = sum([stock_prilag_data[1] * (stock_prilag_data[0].MarketCap / total_market_cap) for stock_prilag_data in group[industry_code]])
if len(industry_prilag_weighted_avg) != 0:
# Weighted average industry sorting.
sorted_by_weighted_avg:List = sorted(industry_prilag_weighted_avg.items(), key=lambda x: x[1], reverse = True)
top_industries = [x[0] for x in sorted_by_weighted_avg[:self.industry_count]]
low_industries = [x[0] for x in sorted_by_weighted_avg[-self.industry_count:]]
long:List[Symbol] = []
short:List[Symbol] = []
for industry_code in top_industries:
for stock_prilag_data in group[industry_code]:
symbol:Symbol = stock_prilag_data[0].Symbol
long.append(symbol)
for industry_code in low_industries:
for stock_prilag_data in group[industry_code]:
symbol:Symbol = stock_prilag_data[0].Symbol
short.append(symbol)
long_w:float = self.Portfolio.TotalPortfolioValue / self.holding_period / len(long)
short_w:float = self.Portfolio.TotalPortfolioValue / self.holding_period / len(short)
# symbol/quantity collection
long_symbol_q:List[Tuple[Union[Symbol, int]]] = [(x, floor(long_w / self.data[x].get_latest_price())) for x in long]
short_symbol_q:List[Tuple[Union[Symbol, int]]] = [(x, -floor(short_w / self.data[x].get_latest_price())) for x in short]
self.managed_queue.append(RebalanceQueueItem(long_symbol_q + short_symbol_q))
return long + short
def OnData(self, data:Slice) -> None:
if not self.selection_flag:
return
self.selection_flag = False
remove_item:int = None
# Rebalance portfolio
for item in self.managed_queue:
if item.holding_period == self.holding_period:
# Liquidate
for symbol, quantity in item.symbol_q:
self.MarketOrder(symbol, -quantity)
remove_item = item
# Trade execution
if item.holding_period == 0:
open_symbol_q:List[Tuple[Symbol, int]] = []
for symbol, quantity in item.symbol_q:
if symbol in data and data[symbol]:
self.MarketOrder(symbol, quantity)
open_symbol_q.append((symbol, quantity))
# Only opened orders will be closed
item.symbol_q = open_symbol_q
item.holding_period += 1
# We need to remove closed part of portfolio after loop. Otherwise it will miss one item in self.managed_queue.
if remove_item:
self.managed_queue.remove(remove_item)
def Selection(self) -> None:
self.selection_flag = True
class RebalanceQueueItem():
def __init__(self, symbol_q:Tuple[Symbol, int]) -> None:
# symbol/quantity collections
self.symbol_q:Tuple[Symbol, int] = symbol_q
self.holding_period:int = 0
class SymbolData():
def __init__(self, symbol:Symbol, period:int) -> None:
self.Symbol:Symbol = symbol
self.Price:RollingWindow = RollingWindow[float](period)
def update(self, value:float) -> None:
self.Price.Add(value)
def is_ready(self) -> bool:
return self.Price.IsReady
def maximum(self) -> float:
return max([x for x in self.Price])
def get_latest_price(self) -> float:
return [x for x in self.Price][0]
# Custom fee model.
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))
